CROSS-REFERENCE TO RELATED APPLICATIONS This application claims benefit of priority from U.S. Provisional Application Serial

No. 61/119,996, filed on December 4, 2008.

STATEMENT AS TO FEDERALLY SPONSORED RESEARCH

This invention was made with government support under grant no. CAl 14810 awarded by the National Institutes of Health. The government has certain rights in the invention.

TECHNICAL FIELD

This document relates to materials and methods for determining gene expression in cells, and for diagnosing prostate cancer and assessing prognosis of prostate cancer patients.

BACKGROUND

Prostate cancer is the most common malignancy in men and is the cause of considerable morbidity and mortality (Howe et al. (2001) /. Natl. Cancer Inst. 93:824-842). It may be useful to identify genes that could be reliable early diagnostic and prognostic markers and therapeutic targets for prostate cancer, as well as other diseases and disorders.

SUMMARY

This document is based in part on the discovery that RNA expression changes can be identified that can distinguish normal prostate stroma from tumor-adjacent stroma in the absence of tumor cells, and that such expression changes can be used to signal the "presence of tumor." A linear regression method for the identification of cell-type specific expression of RNA from array data of prostate tumor-enriched samples was previously developed and validated (see, U.S. Publication No. 20060292572 and Stuart et al. (2004) Proc. Natl. Acad. ScL USA 101 :615-620, both incorporated herein by reference in their entirety). As described herein, the approach was extended to evaluate differential expression data obtained from normal volunteer prostate biopsy samples with tumor-adjacent stroma. Over a thousand gene expression changes were observed. A subset of stroma-specific genes were used to derive a classifier of 131 probe sets that accurately identified tumor or nontumor status of a large number of independent test cases. These observations indicate that tumor-adjacent stroma exhibits a larger number of gene expression changes and that subset may be selected to reliably identify tumor in the absence of tumor cells. The classifier may be useful in the diagnosis of stroma-rich biopsies of clinical cases with equivocal pathology readings. The present disclosure includes, inter alia, the following: (1) extensive cross- validation of RNA biomarkers for prostate cancer relapse, across multiple datasets; (2) a "bi- modal" method for generating classifiers and testing them on samples that have mixed tissue; and (3) two methods for identifying genes in "reactive-stroma" that can be used as markers for the presence of cancer even when the sample does not include tumor but instead has regions of reactive stroma, near tumor.

In one aspect, this document features an in vitro method for identifying a subject as having or not having prostate cancer, comprising: (a) providing a prostate tissue sample from the subject; (b) measuring the level of expression for prostate cancer signature genes in the sample; (c) comparing the measured expression levels to reference expression levels for the prostate cancer signature genes; and (d) if the measured expression levels are significantly greater or less than the reference expression levels, identifying the subject as having prostate cancer, and if the measured expression levels are not significantly greater or less than the reference expression levels, identifying the subject as not having prostate cancer. The prostate tissue sample may not include tumor cells, or the prostate tissue sample may include tumor cells and stromal cells. The prostate cancer signature genes can be selected from the genes listed in Table 3 or Table 4 herein. The method can include determining whether measured expression levels for ten or more prostate cancer signature genes are significantly greater or less than reference expression levels for the ten or more prostate cancer signature genes, and classifying the subject as having prostate cancer that is likely to relapse if the measured expression levels are significantly greater or less than the reference expression levels, or classifying the subject as having prostate cancer not likely to relapse if the measured expression levels are not significantly greater or less than the reference expression levels. The ten or more prostate cancer signature genes can be selected from the genes listed in Table 3 or Table 4 herein. The method can include determining whether measured expression levels for twenty or more prostate cancer signature genes are significantly greater or less than reference expression levels for the twenty or more prostate cancer signature genes, and classifying the subject as having prostate cancer that is likely to relapse if the measured expression levels are significantly greater or less than the reference expression levels, or classifying the subject as having prostate cancer not likely to relapse if the measured expression levels are not significantly greater or less than the reference expression levels. The twenty or more prostate cancer signature genes can be selected from the genes listed in Table 3 or Table 4 herein. In another aspect, this document features a method for determining the prognosis of a subject diagnosed as having prostate cancer, comprising: (a) providing a prostate tissue sample from the subject; (b) measuring the level of expression for prostate cancer signature genes in the sample; (c) comparing the measured expression levels to reference expression levels for the prostate cancer signature genes; and (d) if the measured expression levels are not significantly greater or less than the reference expression levels, identifying the subject as having a relatively better prognosis than if the measured expression levels are significantly greater or less than the reference expression levels, or if the measured expression levels are significantly greater or less than the reference expression levels, identifying the subject as having a relatively worse prognosis than if the measured expression levels are not significantly greater or less than the reference expression levels. The prostate tissue sample may not include tumor cells, or the prostate tissue sample may include tumor cells and stromal cells. The prostate cancer signature genes can be selected from the genes listed in Table 8A or 8B herein.

In another aspect, this document features a method for identifying a subject as having or not having prostate cancer, comprising: (a) providing a prostate tissue sample from the subject, wherein the sample comprises prostate stromal cells; (b) measuring expression levels for one or more genes in the stromal cells, wherein the one or more genes are prostate cancer signature genes; (c) comparing the measured expression levels to reference expression levels for the one or more genes, wherein the reference expression levels are determined in stromal cells from non-cancerous prostate tissue; and (d) if the measured expression levels are significantly greater or less than the reference expression levels, identifying the subject as having prostate cancer, and if the measured expression levels are not significantly greater or less than the reference expression levels, identifying the subject as not having prostate cancer. The prostate tissue sample may not include tumor cells, or the prostate tissue sample may include tumor cells and stromal cells. The prostate cancer signature genes can be selected from the genes listed in Table 3 or Table 4 herein.

In another aspect, this document features a method for determining a prognosis for a subject diagnosed as having prostate cancer, comprising: (a) providing a prostate tissue sample from the subject, wherein the sample comprises prostate stromal cells; (b) measuring expression levels for one or more genes in the stromal cells, wherein the one or more genes are prostate cancer signature genes; (c) comparing the measured expression levels to reference expression levels for the one or more genes, wherein the reference expression levels are determined in stromal cells from non-cancerous prostate tissue; and (d) if the measured expression levels are not significantly greater or less than the reference expression levels, identifying the subject as having a relatively better prognosis than if the measured expression levels are significantly greater or less than the reference expression levels, or if the measured expression levels are significantly greater or less than the reference expression levels, identifying the subject as having a relatively worse prognosis than if the measured expression levels are not significantly greater or less than the reference expression levels. The prostate tissue sample may not include tumor cells, or the prostate tissue sample may include tumor cells and stromal cells. The prostate cancer signature genes can be selected from the genes listed in Table 3 or Table 4 herein.

In still another aspect, this document features a method for identifying a subject as having or not having prostate cancer, comprising: (a) providing a prostate tissue sample from the subject; (b) measuring expression levels for one or more prostate cell-type predictor genes in the sample; (c) determining the percentages of tissue types in the sample based on the measured expression levels; (d) measuring expression levels for one more prostate cancer signature genes in the sample; (e) determining a classifier based on the percentages of tissue types and the measured expression levels; and (f) if the classifier falls into a predetermined range of prostate cancer classifiers, identifying the subject as having prostate cancer, or if the classifier does not fall into the predetermined range, identifying the subject as not having prostate cancer. Steps (b) and (d) can be carried out simultaneously. This document also features a method for determining a prognosis for a subject diagnosed with and treated for prostate cancer, comprising: (a) providing a prostate tissue sample from the subject; (b) measuring expression levels for one or more prostate tissue predictor genes in the sample; (c) determining the percentages of tissue types in the sample based on the measured expression levels; (d) measuring expression levels for one more prostate cancer signature genes in the sample; (e) determining a classifier based on the percentages of tissue types and the measured expression levels; and (f) if the classifier falls into a predetermined range of prostate cancer relapse classifiers, identifying the subject as being likely to relapse, or if the classifier does not fall into the predetermined range, identifying the subject as not being likely to relapse. Steps (b) and (d) are carried out simultaneously.

In yet another aspect, this document features a method for identifying the proportion of two or more tissue types in a tissue sample, comprising: (a) using a set of other samples of known tissue proportions from a similar anatomical location as the tissue sample in an animal or plant, wherein at least two of the other samples do not contain the same relative content of each of the two or more cell types; (b) measuring overall levels of one or more gene expression or protein analytes in each of the other samples; (c) determining the regression relationship between the relative proportion of each tissue type and the measured overall levels of each gene expression or protein analyte in the other samples; (d) selecting one or more analytes that correlate with tissue proportions in the other samples; (e) measuring overall levels of one or more of the analytes in step (d) in the tissue sample; (f) matching the level of each analyte in the tissue sample with the level of the analyte in step (d) to determine the predicted proportion of each tissue type in the tissue sample; and (g) selecting among predicted tissue proportions for the tissue sample obtained in step (f) using either the median or average proportions of all the estimates. The tissue sample can contain cancer cells (e.g., prostate cancer cells).

In another aspect, this document features a method for comparing the levels of two or more analytes predicted by one or more methods to be associated with a change in a biological phenomenon in two sets of data each containing more than one measured sample, comprising: (a) selecting only analytes that are assayed in both sets of data; (b) ranking the analytes in each set of data using a comparative method such as the highest probability or lowest false discovery rate associated with the change in the biological phenomenon; (c) comparing a set of analytes in each ranked list in step (b) with each other, selecting those that occur in both lists, and determining the number of analytes that occur in both lists and show a change in level associated with the biological phenomenon that is in the same direction; and (d) calculating a concordance score based on the probability that the number of comparisons would show the observed number of change in the same direction, at random. In step (a), the length of each list can be varied to determine the maximum concordance score for the two ranked lists.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used to practice the invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. IA a graph plotting the incidence numbers of 339 probe sets obtained by 105- fold permutation procedure for gene selection, as described in Example 1 herein. The dashed horizontal line marks the incidence number = 50. All probe sets with an incidence of >50 were selected for training using PAM using all 15 normal biopsy and the 13 original minimum tumor-bearing stroma cases. FIGS. IB- IE are a series of histograms plotting tumor percentage for Datasets 1-4, respectively. The tumor percentage data of FIGS. IB and 1C were provided by SPECS pathologists, while the tumor percentage data of FIGS. ID and IE were estimated using CellPred. Asterisks in FIG. IB indicate misclassified tumor-bearing cases in Dataset 1. FIG. 2A is a Venn diagram of genes identified by differential expression analysis, "b," "t" and "a" in the plot represent normal biopsies, tumor-adjacent stroma, and rapid autopsies, respectively. FIG. 2B is a scatter plot showing differential expression of 160 probe sets in stroma cells and tumor cells. FIG. 2C is a PCA plot for a training set based on 131 selected diagnostic probe sets.

FIGS. 3A-3D are a series of scatter plots of predicted tissue percentages and pathologist estimated tissue percentages as described in Example 2 herein. X-axes: predicted tissue percentages; y-axes: pathologist estimated tissue percentages. FIG. 3A - Prediction of dataset 2 tumor percentages using models developed from dataset 1. FIG. 3B - Prediction of dataset 2 stroma percentages using models developed from dataset 1. FIG. 3C - Prediction of dataset 1 tumor percentages using models developed from dataset 2. FIG. 3D - Prediction of dataset 1 stroma percentages using models developed from dataset 2.

FIG. 4 is a series of graphs plotting predicted tissue percentages for dataset 3, as described in Example 2 herein. FIGS. 4A and 4B are histograms of predicted tumor percentages, and FIG. 4C is a plot of percentages of tumor+stroma for each individual sample.

FIG. 5 is a series of scatter plots of the differential intensity of specific genes identified as being differentially expressed between relapse and non-relapse cases found among datasets 1, 2, and 3, as described in Example 2 herein. X-axes: relapse vs. non-relapse intensity changes in dataset 1. Y-axes: relapse vs. non-relapse changes in dataset 3 (FIGS. 5 A and 5B) or dataset 2 (FIG. 5C). FIG. 5A - Tumor specific genes correlating with relapse common to datasets 1 and 3. FIG. 5B - Stroma specific genes correlating with relapse common to datasets 1 and 3. FIG. 5C - Tumor specific genes correlating with relapse common to datasets 1 and 2. FIG. 6 is a pair of graphs plotting average prediction error rates for in silico tissue component prediction discrepancies compared to pathologists' estimates using 10-fold cross validation. Solid circles: dataset 1; empty circles: dataset 2; empty squares: dataset 3; empty diamonds: dataset 4. X-axes: number of genes used in the prediction model. Y-axes: average prediction error rates (%). FIG. 6A shows prediction error rates for tumor components, and FIG. 6B shows prediction error rates for stroma components. FIG. 7 is a pair of graphs showing tissue component predictions on publicly available datasets. FIG. 7A is a histogram plot of the in silico predicted tumor components (%) of 219 arrays that were generated from samples prepared as tumor-enriched prostate cancer samples. X-axis: in silico predicted tumor cell percentages (%). Y-axis: frequency of samples. FIG. 7B is a box -plot showing the differences of tumor tissue components in non-recurrence and recurrence groups of prostate cancer samples for dataset 5. X-axis: sample groups, NR: non- recurrence group; REC: recurrence group. Y-axis: tumor cell percentages (%).

FIG. 8 is a series of scatter plots showing predicted tissue percentages and pathologist estimated tissue percentages. X-axis: predicted tissue percentages; y-axis: pathologist estimated tissue percentages. FIG. 8A - Prediction of dataset 2 tumor percentages using models developed from dataset 1. The Pearson correlation coefficient is 0.74. FIG. 8B - Prediction of dataset 2 stroma percentages using models developed from dataset 1. The Pearson correlation coefficient is 0.70. FIG. 8C - Prediction of dataset 2 BPH percentages using models developed from dataset 1. The Pearson correlation coefficient is 0.45. FIG. 8D - Prediction of dataset 1 tumor percentages using models developed from dataset 2. The

Pearson Correlation Coefficient is 0.87. FIG. 8E - Prediction of dataset 1 stroma percentages using models developed from dataset 2. The Pearson Correlation Coefficient is 0.78. FIG. 8F - Prediction of dataset 1 BPH percentages using models developed from dataset 2. The Pearson Correlation Coefficient is 0.57. FIG. 9 is a pair of graphs plotting correlation of the amount of differential gene expression, termed gamma, between disease recurrence and disease free cases for a 91 patient case set measured on Ul 33 A GeneChips compared to an independent 86 patient case set measured on the Ul 33 A plus2 platform. Genes are identified as specific to differential expression by tumor epithelial cells, "gamma T," left panel, or stroma cells, "gamma S," right panel.

FIG. 10 is a graph plotting correlation between the quantification of stain concentration between a trained human expert and the proposed unsupervised method. Circles represent individual scores for a given tissue sample (a total of 97 samples). The line is result of unsupervised spectral unmixing for concentration estimation. The unsupervised approach is within 3% of the linear regression of the manually labeled data. FIG. 11 is a flow diagram of the automated acquisition and visualization demonstrated on a colon cancer tissue microarray. The only inputs required are the scan area (x, y, dx, dy) and the number of cores. After these steps are completed, the images are ready for diagnosis/scoring. The image in "b" is a single field of view from a 20xobjective and "c" is a montage of images acquired at 2Ox.

FIG. 12 is a graph plotting genes identified when different sample sizes were used (circles). The squares represent the overlap between the longest gene list (666 genes at sample size = 120) and other gene lists. The other points (s and t) illustrate the overlap between each gene lists and the tumor/stroma genes identified with MLR. FIGS. 13A and 13B are graphs representing relapse associated genes identified for tumor cells, while FIGS. 13C-13F show relapse associated genes identified for stroma cells. The circles indicate the numbers of genes identified when different sample sizes were used. The squares represent the overlap between the reference gene list and other gene lists. The other points illustrate the overlap between each gene lists and the tumor/stroma genes identified with MLR.

FIG. 14 is a graph plotting results by averaging 100 randomly selected samples when different sample sizes were used for differential expression analysis. The squares, circles, and diamonds represent specificity, sensitivity and false discovery rate, respectively.

DETAILED DESCRIPTION

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as is commonly understood by one of skill in the art to which the invention(s) belong. All patents, patent applications, published applications and publications, GENBANK " sequences, websites and other published materials referred to throughout the entire disclosure herein, unless noted otherwise, are incorporated by reference in their entirety. In the event that there is a plurality of definitions for terms herein, those in this section prevail. Where reference is made to a URL or other such identifier or address, it understood that such identifiers particular information on the internet can change, equivalent information can be found by searching the internet. Reference thereto evidences the availability and public dissemination of such information. Differential expression includes to both quantitative as well as qualitative differences in the extend of the genes' expression depending on differential development and/or tumor growth. Differentially expressed genes can represent marker genes, and/or target genes. The expression pattern of a differentially expressed gene disclosed herein can be utilized as part of a prognostic or diagnostic evaluation of a subject. The expression pattern of a differentially expressed gene can be used to identify the presence of a particular cell type in a sample. A differentially expressed gene disclosed herein can be used in methods for identifying reagents and compounds and uses of these reagents and compounds for the treatment of a subject as well as methods of treatment. The terms "biological activity," "bioactivity," "activity," and "biological function" can be used interchangeably, and can refer to an effector or antigenic function that is directly or indirectly performed by a polypeptide (whether in its native or denatured conformation), or by any fragment thereof in vivo or in vitro. Biological activities include, without limitation, binding to polypeptides, binding to other proteins or molecules, enzymatic activity, signal transduction, activity as a DNA binding protein, as a transcription regulator, and ability to bind damaged DNA. A bioactivity can be modulated by directly affecting the subject polypeptide. Alternatively, a bioactivity can be altered by modulating the level of the polypeptide, such as by modulating expression of the corresponding gene.

The term "gene expression analyte" refers to a biological molecule whose presence or concentration can be detected and correlated with gene expression. For example, a gene expression analyte can be a mRNA of a particular gene, or a fragment thereof (including, e.g., by-products of mRNA splicing and nucleolytic cleavage fragments), a protein of a particular gene or a fragment thereof (including, e.g., post-translationally modified proteins or by-products therefrom, and proteolytic fragments), and other biological molecules such as a carbohydrate, lipid or small molecule, whose presence or absence corresponds to the expression of a particular gene.

A gene expression level is to the amount of biological macromolecule produced from a gene. For example, expression levels of a particular gene can refer to the amount of protein produced from that particular gene, or can refer to the amount of mRNA produced from that particular gene. Gene expression levels can refer to an absolute (e.g., molar or gram- quantity) levels or relative (e.g., the amount relative to a standard, reference, calibration, or to another gene expression level). Typically, gene expression levels used herein are relative expression levels. As used herein in regard to determining the relationship between cell content and expression levels, gene expression levels can be considered in terms of any manner of describing gene expression known in the art. For example, regression methods that consider gene expression levels can consider the measurement of the level of a gene expression analyte, or the level calculated or estimated according to the measurement of the level of a gene expression analyte.

A marker gene is a differentially expressed gene which expression pattern can serve as part of a phenotype-indicating method, such as a predictive method, prognostic or diagnostic method, or other cell-type distinguishing evaluation, or which, alternatively, can be used in methods for identifying compounds useful for the treatment or prevention of diseases or disorders, or for identifying compounds that modulate the activity of one or more gene products.

A phenotype indicated by methods provided herein can be a diagnostic indication, a prognostic indication, or an indication of the presence of a particular cell type in a subject. Diagnostic indications include indication of a disease or a disorder in the subject, such as presence of tumor or neoplastic disease, inflammatory disease, autoimmune disease, and any other diseases known in the art that can be identified according to the presence or absence of particular cells or by the gene expression of cells. In another embodiment, prognostic indications refers to the likely or expected outcome of a disease or disorder, including, but not limited to, the likelihood of survival of the subject, likelihood of relapse, aggressiveness of the disease or disorder, indolence of the disease or disorder, and likelihood of success of a particular treatment regimen.

The phrase "gene expression levels that correspond to levels of gene expression analytes" refers to the relationship between an analyte that indicates the expression of a gene, and the actual level of expression of the gene. Typically the level of a gene expression analyte is measured in experimental methods used to determine gene expression levels. As understood by one skilled in the art, the measured gene expression levels can represent gene expression at a variety of levels of detail (e.g., the absolute amount of a gene expressed, the relative amount of gene expressed, or an indication of increased or decreased levels of expression). The level of detail at which the levels of gene expression analytes can indicate levels of gene expression can be based on a variety of factors that include the number of controls used, the number of calibration experiments or reference levels determined, and other factors known in the art. In some methods provided herein, increase in the levels of a gene expression analyte can indicate increase in the levels of the gene expressed, and a 5 decrease in the levels of a gene expression analyte can indicate decrease in the levels of the gene expressed.

A regression relationship between relative content of a cell type and measured overall levels of a gene expression analyte is a quantitative relationship between cell type and level of gene expression analyte that is determined according to the methods provided herein based o on the amount of cell type present in two or more samples and experimentally measured levels of gene expression analyte. In one embodiment, the regression relationship is determined by determining the regression of overall levels of each gene expression analyte on determined cell proportions. In one embodiment, the regression relationship is determined by linear regression, where the overall expression level or the expression analyte5 levle is treated as directly proportional to (e.g., linear in) cell percent either for each cell type in turn or all at once and the slopes of these linear relationships can be expressed as beta values.

As used herein, a heterogeneous sample is to a sample that contains more than one cell type. For example, a heterogeneous sample can contain stromal cells and tumor cells.0 Typically, as used herein, the different cell types present in a sample are present in greater than about 0.1%, 0.2%, 0.3%, 0.5%, 0.7%, 1%, 2%, 3%, 4% or 5% or greater than 0.1%, 0.2%, 0.3%, 0.5%, 0.7%, 1%, 2%, 3%, 4% or 5%. As is understood in the art, cell samples, such as tissue samples from a subject, can contain minute amounts of a variety of cell types (e.g., nerve, blood, vascular cells). However, cell types that are not present in the sample in5 amounts greater than about 0.1%, 0.2%, 0.3%, 0.5%, 0.7%, 1%, 2%, 3%, 4% or 5% or greater than 0.1%, 0.2%, 0.3%, 0.5%, 0.7%, 1%, 2%, 3%, 4% or 5%, are not typically considered components of the heterogeneous cell sample, as used herein.

Related cell samples can be samples that contain one or more cell types in common. Related cell samples can be samples from the same tissue type or from the same organ.0 Related cell samples can be from the same or different sources (e.g., same or different individuals or cell cultures, or a combination thereof). As provided herein, in the case of three or more different cell samples, it is not required that all samples contain a common cell type, but if a first sample does not contain any cell types that are present in the other samples, the first sample is not related to the other samples.

Tumor cells are cells with cytological and adherence properties consisting of nuclear and cyoplasmic features and patterns of cell-to-cell association that are known to pathologists skilled in the art as sufficient for the diagnosis as cancers of various types. In some embodiments, tumor cells have abnormal growth properties, such as neoplastic growth properties.

The "cells associated with tumor" refers to cells that, while not necessarily malignant, are present in tumorous tissues or organs or particular locations of tissues or organs, and are not present, or are present at insignificant levels, in normal tissues or organs, or in particular locations of tissues or organs.

Benign prostatic hyperplastic (BPH) cells are cells of the epithelial lining of hyperplastic prostate glands. Dilated cystic glands cells are cells of the epithelial lining of dilated (atrophic) cystic prostate glands.

Stromal cells include connective tissue cells and smooth muscle cells forming the stroma of an organ. Exemplary stromal cells are cells of the stroma of the prostate gland.

A reference refers to a value or set of related values for one or more variables. In one example, a reference gene expression level refers to a gene expression level in a particular cell type. Reference expression levels can be determined according to the methods provided herein, or by determining gene expression levels of a cell type in a homogenous sample. Reference levels can be in absolute or relative amounts, as is known in the art. In certain embodiments, a reference expression level can be indicative of the presence of a particular cell type. For example, in certain embodiments, only one particular cell type may have high levels of expression of a particular gene, and, thus, observation of a cell type with high measured expression levels can match expression levels of that particular cell type, and thereby indicate the presence of that particular cell type in the sample. In another embodiment, a reference expression level can be indicative of the absence of a particular cell type. As provided herein, two or more references can be considered in determining whether or not a particular cell type is present in a sample, and also can be considered in determining the relative amount of a particular cell type that is present in the sample. A modified t statistic is a numerical representation of the ability of a particular gene product or indicator thereof to indicate the presence or absence of a particular cell type in a sample. A modified t statistic incorporating goodness of fit and effect size can be formulated according to known methods (see, e.g., Tusher (2001) Proc. Natl. Acad. ScL USA 98:5116- 5121), where σ _β is the standard error of the coefficient, and k is a small constant, as follows: t = β(k + σ _β )

The relative content of a cell type or cell proportion is the amount of a cell mixture that is populated by a particular cell type. Typically, heterogeneous cell mixtures contain two or more cell types, and, therefore, no single cell type makes up 100% of the mixture. Relative content can be expressed in any of a variety of forms known in the art; For example, relative content can be expressed as a percentage of the total amount of cells in a mixture, or can be expressed relative to the amount of a particular cell type. As used herein, percent cell or percent cell composition is the percent of all cells that a particular cell type accounts for in a heterologous cell mixture, such as a microscopic section sampling a tissue. An array or matrix is an arrangement of addressable locations or addresses on a device. The locations can be arranged in two dimensional arrays, three dimensional arrays, or other matrix formats. The number of locations can range from several to at least hundreds of thousands. Most importantly, each location represents a totally independent reaction site. Arrays include but are not limited to nucleic acid arrays, protein arrays and antibody arrays. A nucleic acid array refers to an array containing nucleic acid probes, such as oligonucleotides, polynucleotides or larger portions of genes. The nucleic acid on the array can be single stranded. Arrays wherein the probes are oligonucleotides are referred to as oligonucleotide arrays or oligonucleotide chips. A microarray, herein also refers to a biochip or biological chip, an array of regions having a density of discrete regions of at least about 100/cm , and can be at least about 1000/cm . The regions in a microarray have typical dimensions, e.g., diameters, in the range of between about 10-250 μm, and are separated from other regions in the array by about the same distance. A protein array refers to an array containing polypeptide probes or protein probes which can be in native form or denatured. An antibody array refers to an array containing antibodies which include but are not limited to monoclonal antibodies (e.g., from a mouse), chimeric antibodies, humanized antibodies or phage antibodies and single chain antibodies as well as fragments from antibodies. An agonist is an agent that mimics or upregulates (e.g., potentiates or supplements) the bioactivity of a protein. An agonist can be a wild-type protein or derivative thereof having at least one bioactivity of the wild-type protein. An agonist can also be a compound that upregulates expression of a gene or which increases at least one bioactivity of a protein. An agonist can also be a compound which increases the interaction of a polypeptide with another molecule, e.g., a target peptide or nucleic acid.

The terms "polynucleotide" and "nucleic acid molecule" refer to nucleotides of any length, either ribonucleotides or deoxyribonucleotides. This term refers only to the primary structure of the molecule. Thus, this term includes double- and single-stranded DNA and RNA. It also includes known types of modifications, for example, labels which are known in the art, methylation, caps, substitution of one or more of the naturally occurring nucleotides with an analog, internucleotide modifications such as, for example, those with uncharged linkages (e.g., phosphorothioates and phosphorodithioates), those containing pendant moieties, such as, for example, proteins (including, e.g., nucleases, toxins, antibodies, signal peptides, and poly-L-lysine), those with intercalators (e.g., acridine and psoralen), those containing chelators (e.g., metals and radioactive metals), those containing alkylators, those with modified linkages (e.g., alpha anomeric nucleic acids), and those containing nucleotide analogs (e.g., peptide nucleic acids), as well as unmodified forms of the polynucleotide. A polynucleotide derived from a designated sequence typically is a polynucleotide sequence which is comprised of a sequence of approximately at least about 6 nucleotides, at least about 8 nucleotides, at least about 10-12 nucleotides, or at least about 15-20 nucleotides corresponding to a region of the designated nucleotide sequence. Corresponding polynucleotides are homologous to or complementary to a designated sequence. Typically, the sequence of the region from which the polynucleotide is derived is homologous to or complementary to a sequence that is unique to a gene provided herein.

Recombinant polypeptides are polypeptides made using recombinant techniques, i.e., through the expression of a recombinant nucleic acid. A recombinant polypeptide can be distinguished from naturally occurring polypeptide by at least one or more characteristics. For example, the polypeptide may be isolated or purified away from some or all of the proteins and compounds with which it is normally associated in its wild type host, and thus may be substantially pure. For example, an isolated polypeptide is unaccompanied by at least some of the material with which it is normally associated in its natural state, constituting at least about 0.5%, or at least about 5% by weight of the total protein in a given sample. A substantially pure polypeptide comprises at least about 50-75% by weight of the total protein, at least about 80%, or at least about 90%. The definition includes the production of a polypeptide from one organism in a different organism or host cell. Alternatively, the polypeptide may be made at a significantly higher concentration than is normally seen, through the use of an inducible promoter or high expression promoter, such that the protein is made at increased concentration levels. Alternatively, the polypeptide may be in a form not normally found in nature, as in the addition of an epitope tag or amino acid substitutions, insertions and deletions, as discussed below.

The terms "disease" and "disorder" refer to a pathological condition in an organism resulting from, e.g., infection or genetic defect, and characterized by identifiable symptoms.

The "percent sequence identity" between a particular nucleic acid or amino acid sequence and a sequence referenced by a particular sequence identification number is determined as follows. First, a nucleic acid or amino acid sequence is compared to the sequence set forth in a particular sequence identification number using the BLAST 2 Sequences (B12seq) program from the stand-alone version of BLASTZ containing BLASTN version 2.0.14 and BLASTP version 2.0.14. This stand-alone version of BLASTZ can be obtained from Fish & Richardson's web site (world wide web at fr.com/blast) or the United States government's National Center for Biotechnology Information web site (world wide web at ncbi.nlm.nih.gov). Instructions explaining how to use the B12seq program can be found in the readme file accompanying BLASTZ. B12seq performs a comparison between two sequences using either the BLASTN or BLASTP algorithm. BLASTN is used to compare nucleic acid sequences, while BLASTP is used to compare amino acid sequences. To compare two nucleic acid sequences, the options are set as follows: -i is set to a file containing the first nucleic acid sequence to be compared (e.g., C:\seql.txt); -j is set to a file containing the second nucleic acid sequence to be compared (e.g., C:\seq2.txt); -p is set to blastn; -o is set to any desired file name (e.g., C:\output.txt); -q is set to -1; -r is set to 2; and all other options are left at their default setting. For example, the following command can be used to generate an output file containing a comparison between two sequences: C:\B12seq -i c:\seql.txt -j c:\seq2.txt -p blastn -o c:\output.txt -q -1 -r 2. To compare two amino acid sequences, the options of B12seq are set as follows: -i is set to a file containing the first amino acid sequence to be compared (e.g., C:\seql.txt); -j is set to a file containing the second amino acid sequence to be compared (e.g., C:\seq2.txt); -p is set to blastp; -o is set to any desired file name (e.g., C:\output.txt); and all other options are left at their default setting. For example, the following command can be used to generate an output file containing a comparison between two amino acid sequences: C:\B12seq -i c:\seql.txt -j c:\seq2.txt -p blastp -o c:\output.txt. If the two compared sequences share homology, then the designated output file will present those regions of homology as aligned sequences. If the two compared sequences do not share homology, then the designated output file will not present aligned sequences.

Once aligned, the number of matches is determined by counting the number of positions where an identical nucleotide or amino acid residue is presented in both sequences. The percent sequence identity is determined by dividing the number of matches either by the length of the sequence set forth in the identified sequence, or by an articulated length (e.g., 100 consecutive nucleotides or amino acid residues from a sequence set forth in an identified sequence), followed by multiplying the resulting value by 100. For example, a nucleic acid sequence that has 1166 matches when aligned with a 1200 bp sequence is 97.1 percent identical to the 1200 bp sequence (i.e., 1166÷1200*100=97.1). It is noted that the percent sequence identity value is rounded to the nearest tenth. For example, 75.11, 75.12, 75.13, and 75.14 is rounded down to 75.1, while 75.15, 75.16, 75.17, 75.18, and 75.19 is rounded up to 75.2. It is also noted that the length value will always be an integer. In another example, a target sequence containing a 20-nucleotide region that aligns with 20 consecutive nucleotides from an identified sequence as follows contains a region that shares 75 percent sequence identity to that identified sequence (i.e., 15÷20* 100=75). Polypeptides that at least 90% identical have percent identities from 90 to 100 relative to the reference polypeptides. Identity at a level of 90% or more can be indicative of the fact that, for a polynucleotide length of 100 amino acids no more than 10% (i.e., 10 out of 100) amino acids in the test polypeptide differ from those of the reference polypeptides. Similar comparisons can be made between test and reference polynucleotides. Such differences can be represented as point mutations randomly distributed over the entire length of an amino acid sequence or they can be clustered in one or more locations of varying length up to the maximum allowable, e.g., 10/100 amino acid difference (approximately 90% identity). Differences are defined as nucleic acid or amino acid substitutions, or deletions. At the level of homologies or identities above about 85-90%, the result should be independent of the program and gap parameters set; such high levels of identity can be assessed readily, often without relying on software.

A primer refers to an oligonucleotide containing two or more deoxyribonucleotides or ribonucleotides, typically more than three, from which synthesis of a primer extension product can be initiated. Experimental conditions conducive to synthesis include the presence of nucleoside triphosphates and an agent for polymerization and extension, such as DNA polymerase, and a suitable buffer, temperature and pH.

Animals can include any animal, such as, but are not limited to, goats, cows, deer, sheep, rodents, pigs and humans. Non-human animals, exclude humans as the contemplated animal. The SPs provided herein are from any source, animal, plant, prokaryotic and fungal. Genetic therapy can involve the transfer of heterologous nucleic acid, such as DNA, into certain cells, target cells, of a mammal, particularly a human, with a disorder or conditions for which such therapy is sought. The nucleic acid, such as DNA, is introduced into the selected target cells in a manner such that the heterologous nucleic acid, such as DNA, is expressed and a therapeutic product encoded thereby is produced. Alternatively, the heterologous nucleic acid, such as DNA, can in some manner mediate expression of DNA that encodes the therapeutic product, or it can encode a product, such as a peptide or RNA that in some manner mediates, directly or indirectly, expression of a therapeutic product. Genetic therapy can also be used to deliver nucleic acid encoding a gene product that replaces a defective gene or supplements a gene product produced by the mammal or the cell in which it is introduced. The introduced nucleic acid can encode a therapeutic compound, such as a growth factor inhibitor thereof, or a tumor necrosis factor or inhibitor thereof, such as a receptor therefor, that is not normally produced in the mammalian host or that is not produced in therapeutically effective amounts or at a therapeutically useful time. The heterologous nucleic acid, such as DNA, encoding the therapeutic product can be modified prior to introduction into the cells of the afflicted host in order to enhance or otherwise alter the product or expression thereof. Genetic therapy can also involve delivery of an inhibitor or repressor or other modulator of gene expression. A heterologous nucleic acid is nucleic acid that encodes RNA or RNA and proteins that are not normally produced in vivo by the cell in which it is expressed or that mediates or encodes mediators that alter expression of endogenous nucleic acid, such as DNA, by affecting transcription, translation, or other regulatable biochemical processes. Heterologous nucleic acid, such as DNA, can also be referred to as foreign nucleic acid, such as DNA. Any nucleic acid, such as DNA, that one of skill in the art would recognize or consider as heterologous or foreign to the cell in which is expressed is herein encompassed by heterologous nucleic acid; heterologous nucleic acid includes exogenously added nucleic acid that is also expressed endogenously. Examples of heterologous nucleic acid include, but are not limited to, nucleic acid that encodes traceable marker proteins, such as a protein that confers drug resistance, nucleic acid that encodes therapeutically effective substances, such as anti-cancer agents, enzymes and hormones, and nucleic acid, such as DNA, that encodes other types of proteins, such as antibodies. Antibodies that are encoded by heterologous nucleic acid can be secreted or expressed on the surface of the cell in which the heterologous nucleic acid has been introduced. Heterologous nucleic acid is generally not endogenous to the cell into which it is introduced, but has been obtained from another cell or prepared synthetically. Generally, although not necessarily, such nucleic acid encodes RNA and proteins that are not normally produced by the cell in which it is now expressed.

A therapeutically effective product for gene therapy can be a product encoded by heterologous nucleic acid, typically DNA, that, upon introduction of the nucleic acid into a host, a product is expressed that ameliorates or eliminates the symptoms, manifestations of an inherited or acquired disease or that cures the disease. Also included are biologically active nucleic acid molecules, such as RNAi and antisense.

Disease or disorder treatment or compound can include any therapeutic regimen and/or agent that, when used alone or in combination with other treatments or compounds, can alleviate, reduce, ameliorate, prevent, or place or maintain in a state of remission of clinical symptoms or diagnostic markers associated with the disease or disorder.

Nucleic acids include DNA, RNA and analogs thereof, including peptide nucleic acids (PNA) and mixtures thereof. Nucleic acids can be single or double-stranded. When referring to probes or primers, optionally labeled, with a detectable label, such as a fluorescent or radiolabel, single-stranded molecules are contemplated. Such molecules are typically of a length such that their target is statistically unique or of low copy number (typically less than 5, generally less than 3) for probing or priming a library. Generally a probe or primer contains at least 14, 16 or 30 contiguous of sequence complementary to or identical a gene of interest. Probes and primers can be 10, 20, 30, 50, 100 or more nucleic acids long.

Operative linkage of heterologous nucleic acids to regulatory and effector sequences of nucleotides, such as promoters, enhancers, transcriptional and translational stop sites, and other signal sequences refers to the relationship between such nucleic acid, such as DNA, and such sequences of nucleotides. Thus, operatively linked or operationally associated refers to the functional relationship of nucleic acid, such as DNA, with regulatory and effector sequences of nucleotides, such as promoters, enhancers, transcriptional and translational stop sites, and other signal sequences. For example, operative linkage of DNA to a promoter refers to the physical and functional relationship between the DNA and the promoter such that the transcription of such DNA is initiated from the promoter by an RNA polymerase that specifically recognizes, binds to and transcribes the DNA. In order to optimize expression and/or in vitro transcription, it can be necessary to remove, add or alter 5' untranslated portions of the clones to eliminate extra, potential inappropriate alternative translation initiation (i.e., start) codons or other sequences that can interfere with or reduce expression, either at the level of transcription or translation. Alternatively, consensus ribosome binding sites {see, e.g., Kozak (1991) /. Biol Chem. 266:19867-19870) can be inserted immediately 5' of the start codon and can enhance expression. The desirability of (or need for) such modification can be empirically determined.

A sequence complementary to at least a portion of an RNA, with reference to antisense oligonucleotides, means a sequence having sufficient complementarity to be able to hybridize with the RNA, generally under moderate or high stringency conditions, forming a stable duplex; in the case of double-stranded antisense nucleic acids, a single strand of the duplex DNA (or dsRNA) can thus be tested, or triplex formation can be assayed. The ability to hybridize depends on the degree of complementarily and the length of the antisense nucleic acid. Generally, the longer the hybridizing nucleic acid, the more base mismatches with a gene encoding RNA it can contain and still form a stable duplex (or triplex, as the case can be). One skilled in the art can ascertain a tolerable degree of mismatch by use of standard procedures to determine the melting point of the hybridized complex.

Antisense polynucleotides are synthetic sequences of nucleotide bases complementary to mRNA or the sense strand of double-stranded DNA. Admixture of sense and antisense polynucleotides under appropriate conditions leads to the binding of the two molecules, or hybridization. When these polynucleotides bind to (hybridize with) mRNA, inhibition of protein synthesis (translation) occurs. When these polynucleotides bind to double-stranded DNA, inhibition of RNA synthesis (transcription) occurs. The resulting inhibition of translation and/or transcription leads to an inhibition of the synthesis of the protein encoded by the sense strand. Antisense nucleic acid molecules typically contain a sufficient number of nucleotides to specifically bind to a target nucleic acid, generally at least 5 contiguous nucleotides, often at least 14 or 16 or 30 contiguous nucleotides or modified nucleotides complementary to the coding portion of a nucleic acid molecule that encodes a gene of interest. An antibody is an immunoglobulin, whether natural or partially or wholly synthetically produced, including any derivative thereof that retains the specific binding ability the antibody. Hence antibody includes any protein having a binding domain that is homologous or substantially homologous to an immunoglobulin binding domain. Antibodies include members of any immunoglobulin groups, including, but not limited to, IgG, IgM, IgA, IgD, IgY and IgE.

An antibody fragment is any derivative of an antibody that is less than full-length, retaining at least a portion of the full-length antibody's specific binding ability. Examples of antibody fragments include, but are not limited to, Fab, Fab', F(ab)2, single-chain Fvs (scFV), FV, dsFV diabody and Fd fragments. The fragment can include multiple chains linked together, such as by disulfide bridges. An antibody fragment generally contains at least about 50 amino acids and typically at least 200 amino acids.

An Fv antibody fragment is composed of one variable heavy domain (VH) and one variable light domain linked by noncovalent interactions. A dsFV is an Fv with an engineered intermolecular disulfide bond, which stabilizes the VH-VL pair. An F(ab)2 fragment is an antibody fragment that results from digestion of an immunoglobulin with pepsin at pH 4.0-4.5; it can be recombinantly expressed to produce the equivalent fragment. Fab fragments are antibody fragments that result from digestion of an immunoglobulin with papain; they can be recombinantly expressed to produce the equivalent fragment. scFVs refer to antibody fragments that contain a variable light chain (VL) and variable heavy chain (VH) covalently connected by a polypeptide linker in any order. The linker is of a length such that the two variable domains are bridged without substantial interference. Included linkers are (Gly-Ser)n residues with some GIu or Lys residues dispersed throughout to increase solubility.

Humanized antibodies are antibodies that are modified to include human sequences of amino acids so that administration to a human does not provoke an immune response. Methods for preparation of such antibodies are known. For example, to produce such antibodies, the encoding nucleic acid in the hybridoma or other prokaryotic or eukaryotic cell, such as an E. coli or a CHO cell, that expresses the monoclonal antibody is altered by recombinant nucleic acid techniques to express an antibody in which the amino acid composition of the non-variable region is based on human antibodies. Computer programs have been designed to identify such non-variable regions.

Diabodies are dimeric scFV; diabodies typically have shorter peptide linkers than scFvs, and they generally dimerize.

The phrase "production by recombinant means by using recombinant DNA methods" refers to the use of the well known methods of molecular biology for expressing proteins encoded by cloned DNA.

An "effective amount" of a compound for treating a particular disease is an amount that is sufficient to ameliorate, or in some manner reduce the symptoms associated with the disease. Such amount can be administered as a single dosage or can be administered according to a regimen, whereby it is effective. The amount can cure the disease but, typically, is administered in order to ameliorate the symptoms of the disease. Repeated administration can be required to achieve the desired amelioration of symptoms.

A compound that modulates the activity of a gene product either decreases or increases or otherwise alters the activity of the protein or, in some manner up- or down- regulates or otherwise alters expression of the nucleic acid in a cell. Pharmaceutically acceptable salts, esters or other derivatives of the conjugates include any salts, esters or derivatives that can be readily prepared by those of skill in this art using known methods for such derivatization and that produce compounds that can be administered to animals or humans without substantial toxic effects and that either are 5 pharmaceutically active or are prodrugs.

A drug or compound identified by the screening methods provided herein refers to any compound that is a candidate for use as a therapeutic or as a lead compound for the design of a therapeutic. Such compounds can be small molecules, including small organic molecules, peptides, peptide mimetics, antisense molecules or dsRNA, such as RNAi, o antibodies, fragments of antibodies, recombinant antibodies and other such compounds that can serve as drug candidates or lead compounds.

A non-malignant cell adjacent to a malignant cell in a subject is a cell that has a normal morphology (e.g., is not classified as neoplastic or malignant by a pathologist, cell sorter, or other cell classification method), but, while the cell was present intact in the5 subject, the cell was adjacent to a malignant cell or malignant cells. As provided herein, cells of a particular type (e.g., stroma) adjacent to a malignant cell or malignant cells can display an expression pattern that differs from cells of the same type that are not adjacent to a malignant cell or malignant cells. In accordance with the methods provided herein, cells that are adjacent to malignant cells can be distinguished from cells of the same type that are0 adjacent to non-malignant cells, according to their differential gene expression. As used herein regarding the location of cells, adjacent refers to a first cell and a second cell being sufficiently proximal such that the first cell influences the gene expression of the second cell. For example, adjacent cells can include cells that are in direct contact with each other, adjacent cell can include cells within 500 microns, 300 microns, 200 microns 100 microns or5 50 microns, of each other.

A tumor is a collection of malignant cells. Malignant as applied to a cell refers to a cell that grows in an uncontrolled fashion. In some embodiments, a malignant cell can be anaplastic. In some embodiments, a malignant cell can be capable of metastasizing.

Hybridization stringency for, which can be used to determine percentage mismatch is0 as follows:

1) high stringency: O.lx SSPE, 0.1% SDS, 65 °C. 2) medium stringency: 0.2x SSPE, 0.1% SDS, 50°C.

3) low stringency: 1.Ox SSPE, 0.1% SDS, 50°C.

A vector (or plasmid) refers to discrete elements that can be used to introduce heterologous nucleic acid into cells for either expression or replication thereof. Vectors 5 typically remain episomal, but can be designed to effect integration of a gene or portion thereof into a chromosome of the genome. Also contemplated are vectors that are artificial chromosomes, such as yeast artificial chromosomes and mammalian artificial chromosomes. Selection and use of such vehicles are well known to those of skill in the art. An expression vector includes vectors capable of expressing DNA that is operatively linked with regulatory o sequences, such as promoter regions, that are capable of effecting expression of such DNA fragments. Thus, an expression vector refers to a recombinant DNA or RNA construct, such as a plasmid, a phage, recombinant virus or other vector that, upon introduction into an appropriate host cell, results in expression of the cloned DNA. Appropriate expression vectors are well known to those of skill in the art and include those that are replicable in 5 eukaryotic cells and/or prokaryotic cells and those that remain episomal or those that integrate into the host cell genome.

Disease prognosis refers to a forecast of the probable outcome of a disease or of a probable outcome resultant from a disease. Non-limiting examples of disease prognoses include likely relapse of disease, likely aggressiveness of disease, likely indolence of disease,0 likelihood of survival of the subject, likelihood of success in treating a disease, condition in which a particular treatment regimen is likely to be more effective than another treatment regimen, and combinations thereof.

Aggressiveness of a tumor or malignant cell is the capacity of one or more cells to attain a position in the body away from the tissue or organ of origin, attach to another portion5 of the body, and multiply. Experimentally, aggressiveness can be described in one or more manners, including, but not limited to, post-diagnosis survival of subject, relapse of tumor, and metastasis of tumor. Thus, in the disclosures provided herein, data indicative of time length of survival, relapse, non-relapse, time length for metastasis, or non-metastasis, are indicative of the aggressiveness of a tumor or a malignant cell. When survival is considered,0 one skilled in the art will recognize that aggressiveness is inversely related to the length of time of survival of the subject. When time length for metastasis is considered, one skilled in the art will recognize that aggressiveness is directly related to the length of time of survival of a subject. As used herein, indolence refers to non-aggressiveness of a tumor or malignant cell; thus, the more aggressive a tumor or cell, the less indolent, and vice versa. As an example of a cell attaining a position in the body away from the tissue or organ of origin, a malignant prostate cell can attain an extra-prostatic position, and thus have one characteristic of an aggressive malignant cell. Attachment of cells can be, for example, on the lymph node or bone marrow of a subject, or other sites known in the art.

A composition refers to any mixture. It can be a solution, a suspension, liquid, powder, a paste, aqueous, non-aqueous or any combination thereof. A fluid is composition that can flow. Fluids thus encompass compositions that are in the form of semi-solids, pastes, solutions, aqueous mixtures, gels, lotions, creams and other such compositions.

Cell-type-associated patterns of gene expression Primary tissues are composed of many (e.g., two or more) types of cells.

Identification of genes expressed in a specific cell type present within a tissue in other methods can require physical separation of that cell type and the cell type's subsequent assay. Although it is possible to physically separate cells according to type, by methods such as laser capture microdissection, centrifugation, FACS, and the like, this is time consuming and costly and in certain embodiments impractical to perform. Known expression profiling assays (either RNA or protein) of primary tissues or other specimens containing multiple cell types either (1) do not take into account that multiple cell types are present or (2) physically separate the component cell types before performing the assay. Other analyses have been performed without regard to the presence of multiple cell types, thereby identifying markers indicative of a shift in the relative proportion of various cell types present in a sample, but not representative of a specific cell type. Previous analytic approaches cannot discern interactions between different types of cells.

Provided herein are methods, compositions and kits based on the development of a model, where the level of each gene product assayed can be correlated to a specific cell type. This approach for determination of cell-type-specific gene expression obviates the need for physical separation of cells from tissues or other specimens with heterogeneous cell content. Furthermore, this method permits determination of the interaction between the different types of cells contained in such heterogeneous mixtures, which would otherwise have been difficult or impossible had the cells been first physically separated and then assayed. Using the approaches provided herein, a number of biomarkers can be identified related to various diseases and disorders. Exemplified herein is the identification of biomarkers for prostate cancer and benign prostatic hypertophy. Such biomarkers can be used in diagnosis and prognosis and treatment decisions.

The methods, compositions, combinations and kits provided herein employ a regression-based approach for identification of cell-type-specific patterns of gene expression in samples containing more than one type of cell. In one example, the methods, compositions, combinations and kits provided herein employ a regression-based approach for identification of cell-type-specific patterns of gene expression in cancer. These methods, compositions, combinations and kits provided herein can be used in the identification of genes that are differentially expressed in malignant versus non-malignant cells and further identify tumor-dependent changes in gene expression of non-malignant cells associated with malignant cells relative to non-malignant cells not associated with malignant cells. The methods, compositions, combinations and kits provided herein also can be used in correlating a phenotype with gene expression in one or more cell types. For example such a method can include determining the relative content of each cell type in two or more related heterogeneous cell samples, wherein at least two of the samples do not contain the same relative content of each cell type, measuring overall levels of one or more gene expression analytes in each sample, determining the regression relationship between the relative content of each cell type and the measured overall levels, and calculating the level of each of the one or more analytes in each cell type according to the regression relationship, where gene expression levels correspond to the calculated levels of analytes. In another example such a method can include determining the relative content of each cell type in two or more related heterogeneous cell samples, wherein at least two of the samples do not contain the same relative content of each cell type, measuring overall levels of two or more gene expression analytes in each sample, determining the regression relationship between the relative content of each cell type and the measured overall levels, and calculating the level of each of the two or more analytes in each cell type according to the regression relationship, where gene expression levels correspond to the calculated levels of analytes. Such methods can further include identifying genes differentially expressed in at least one cell type relative to at least one other cell type. In such methods, the analyte can be a nucleic acid molecule and a protein. The methods provided herein can be used for determining cell-type-specific gene expression in any heterogeneous cell population. The methods provided herein can find application in samples known to contain a variety of cell types, such as brain tissue samples and muscle tissue samples. The methods provided herein also can find application in samples in which separation of cell type can represent a tedious or time consuming operation, which is no longer required under the methods provided herein. Samples used in the present methods can be any of a variety of samples, including, but not limited to, blood, cells from blood (including, but not limited to, non-blood cells such as epithelial cells in blood), plasma, serum, spinal fluid, lymph fluid, skin, sputum, alimentary and genitourinary samples (including, but not limited to, urine, semen, seminal fluid, prostate aspirate, prostatic fluid, and fluid from the seminal vesicles), saliva, milk, tissue specimens (including, but not limited to, prostate tissue specimens), tumors, organs, and also samples of in vitro cell culture constituents.

In certain embodiments, the methods provided herein can be used to differentiate true markers of tumor cells, hyperplastic cells, and stromal cells of cancer. As exemplified herein, least squares regression using individual cell-type proportions can be used to produce clear predictions of cell-specific expression for a large number of genes. In an example provided herein applied to prostate cancer, many of these predictions are accepted on the basis of prior knowledge of prostate gene expression and biology, which provide confidence in the method. These are illustrated by numerous genes predicted to be preferentially expressed by stromal cells that are characteristic of connective tissue and only poorly expressed or absent in epithelial cells.

In some embodiments, the methods provided herein allow segregation of molecular tumor and nontumor markers into more discrete and informative groups. Thus, genes identified as tumor-associated can be further categorized into tumor versus stroma (epithelial versus mesenchymal) and tumor versus hyperplastic (perhaps reflecting true differences between the malignant cell and its hyperplastic counterpart). The methods provided herein can be used to distinguish tumor and non-tumor markers in a variety of cancers, including, without limitation, cancers classified by site such as cancer of the oral cavity and pharynx (lip, tongue, salivary gland, floor of mouth, gum and other mouth, nasopharynx, tonsil, oropharynx, hypopharynx, other oral/pharynx); cancers of the digestive system (esophagus; stomach; small intestine; colon and rectum; anus, anal canal, and anorectum; liver; intrahepatic bile duct; gallbladder; other biliary; pancreas; retroperitoneum; peritoneum, omentum, and mesentery; other digestive); cancers of the respiratory system (nasal cavity, middle ear, and sinuses; larynx; lung and bronchus; pleura; trachea, mediastinum, and other respiratory); cancers of the mesothelioma; bones and joints; and soft tissue, including heart; skin cancers, including melanomas and other non-epithelial skin cancers; Kaposi's sarcoma and breast cancer; cancer of the female genital system (cervix uteri; corpus uteri; uterus, nos; ovary; vagina; vulva; and other female genital); cancers of the male genital system (prostate gland; testis; penis; and other male genital); cancers of the urinary system (urinary bladder; kidney and renal pelvis; ureter; and other urinary); cancers of the eye and orbit; cancers of the brain and nervous system (brain; and other nervous system); cancers of the endocrine system (thyroid gland and other endocrine, including thymus); lymphomas (Hodgkin's disease and non-Hodgkin's lymphoma), multiple myeloma, and leukemias (lymphocytic leukemia; myeloid leukemia; monocytic leukemia; and other leukemias); and cancers classified by histological type, such as Neoplasm, malignant; carcinoma, NOS; carcinoma, undifferentiated, NOS; giant and spindle cell carcinoma; small cell carcinoma, NOS; papillary carcinoma, NOS; squamous cell carcinoma, NOS; lymphoepithelial carcinoma; basal cell carcinoma, NOS; pilomatrix carcinoma; transitional cell carcinoma, NOS; papillary transitional cell carcinoma; adenocarcinoma, NOS; gastrinoma, malignant; cholangiocarcinoma; hepatocellular carcinoma, NOS ; combined hepatocellular carcinoma and cholangiocarcinoma; trabecular adenocarcinoma; adenoid cystic carcinoma; adenocarcinoma in adenomatous polyp; adenocarcinoma, familial polyposis coli; solid carcinoma, NOS; carcinoid tumor, malignant; bronchiolo-alveolar adenocarcinoma; papillary adenocarcinoma, NOS; ccarcinoma; acidophil carcinoma; oxyphilic adenocarcinoma; basophil carcinoma; clear cell adenocarcinoma, NOS; granular cell carcinoma; follicular adenocarcinoma, NOS; papillary and follicular adenocarcinoma; nonencapsulating sclerosing carcinoma; adrenal cortical carcinoma; endometroid carcinoma; skin appendage carcinoma; apocrine adenocarcinoma; sebaceous adenocarcinoma; ceruminous adenocarcinoma; mucoepidermoid carcinoma; cystadenocarcinoma, NOS; papillary cystadenocarcinoma, NOS; papillary serous cystadenocarcinoma; mucinous cystadenocarcinoma, NOS; mucinous adenocarcinoma; signet ring cell carcinoma; infiltrating duct carcinoma; medullary carcinoma, NOS; lobular carcinoma; inflammatory carcinoma; Paget's disease, mammary; acinar cell carcinoma; adenosquamous carcinoma; adenocarcinoma with squamous metaplasia; thymoma, malignant; ovarian stromal tumor, malignant; thecoma, malignant; granulosa cell tumor, malignant; androblastoma, malignant; Sertoli cell carcinoma; Leydig cell tumor, malignant; lipid cell tumor, malignant; paraganglioma, malignant; extra- mammary paraganglioma, malignant; pheochromocytoma; glomangiosarcoma; malignant melanoma, NOS; amelanotic melanoma; superficial spreading melanoma; malignant melanoma in giant pigmented nevus; epithelioid cell melanoma; blue nevus, malignant; sarcoma, NOS; fibrosarcoma, NOS; fibrous histiocytoma, malignant; myxosarcoma; liposarcoma, NOS; leiomyosarcoma, NOS; rhabdomyosarcoma, NOS; embryonal rhabdomyosarcoma; alveolar rhabdomyosarcoma; stromal sarcoma, NOS; mixed tumor, malignant, NOS; Mullerian mixed tumor; nephroblastoma; hepatoblastoma; carcinosarcoma, NOS; mesenchymoma, malignant; Brenner tumor, malignant; phyllodes tumor, malignant; synovial sarcoma, NOS; mesothelioma, malignant; dysgerminoma; embryonal carcinoma, NOS; teratoma, malignant, NOS; struma ovarii, malignant; choriocarcinoma; mesonephroma, malignant; hemangiosarcoma; hemangioendothelioma, malignant; Kaposi's sarcoma; hemangiopericytoma, malignant; lymphangiosarcoma; osteosarcoma, NOS; juxtacortical osteosarcoma; chondrosarcoma, NOS; chondroblastoma, malignant; mesenchymal chondrosarcoma; giant cell tumor of bone; Ewing's sarcoma; odontogenic tumor, malignant; ameloblastic odontosarcoma; ameloblastoma, malignant; ameloblastic fibrosarcoma; pinealoma, malignant; chordoma; glioma, malignant; ependymoma, NOS; astrocytoma,

NOS; protoplasmic astrocytoma; fibrillary astrocytoma; astroblastoma; glioblastoma, NOS; oligodendroglioma, NOS; oligodendroblastoma; primitive neuroectodermal; cerebellar sarcoma, NOS; ganglioneuroblastoma; neuroblastoma, NOS; retinoblastoma, NOS; olfactory neurogenic tumor; meningioma, malignant; neurofibrosarcoma; neurilemmoma, malignant; granular cell tumor, malignant; malignant lymphoma, NOS; Hodgkin's disease, NOS; Hodgkin's; paragranuloma, NOS; malignant lymphoma, small lymphocytic; malignant lymphoma, large cell, diffuse; malignant lymphoma, follicular, NOS; mycosis fungoides; other specified non-Hodgkin's lymphomas; malignant histiocytosis; multiple myeloma; mast cell sarcoma; immunoproliferative small intestinal disease; leukemia, NOS; lymphoid leukemia, NOS; plasma cell leukemia; erythroleukemia; lymphosarcoma cell leukemia; myeloid leukemia, NOS; basophilic leukemia; eosinophilic leukemia; monocytic leukemia, NOS; mast cell leukemia; megakaryoblastic leukemia; myeloid sarcoma; and hairy cell leukemia.

In an example comparing the results of a prostate tissue analysis using the methods provided herein to the results of previous methods, the vast majority of markers associated with normal prostate tissues in previous microarray-based studies relate to cells of the stroma. This result is not surprising given that normal samples can be composed of a relatively greater proportion of stromal cells.

In the example of prostate analysis, the strongest single discriminator between benign prostate hyperplasia (BPH) cells and tumor cells was CKl 5, a result confirmed by immunohistochemistry. CKl 5 has previously received little attention in this context, but BPH markers play an important role in the diagnosis of ambiguous clinical cases.

Transcripts whose expression levels have high covariance with cross-products of tissue proportions suggest that expression in one cell type depends on the proportion of another tissue, as would be expected in a paracrine mechanism. The stroma transcript with the highest dependence on tumor percentage was TGF-β2. Another such stroma cell gene for which immunohistochemistry was practical was desmin, which showed altered staining in the tumor-associated stroma. In fact, a large number of typical stroma cell genes displayed dependence on the proportion of tumor, adding evidence to the speculation that tumor- associated stroma differs from non-associated stroma. Tumor-stroma paracrine signaling can be reflected in peritumor halos of altered gene expression that can present a much bigger target for detection than the tumor cells alone.

The methods provided herein provide a straightforward approach using simple and multiple linear regression to identify genes whose expression in tissue is specifically correlated with a specific cell type (e.g., in prostate tissue with either tumor cells, BPH epithelial cells or stromal cells). Context-dependent expression that is not readily attributable to single cell types is also recognized. The investigative approach described here is also applicable to a wide variety of tumor marker discovery investigations in a variety of tissues and organs. The exemplary prostate analysis results presented herein demonstrate the ability to identify a large number of gene candidates as specific products of various cells involved in prostate cancer pathogenesis. A model for cell-specific gene expression is established by both (1) determination of the proportion of each constituent cell type (e.g., epithelium, stroma, tumor, or other discriminating entity) within a given type of tissue or specimen (e.g., prostate, breast, colon, marrow, and the like) and (2) assay of the expression profile (e.g., RNA or protein) of that same tissue or specimen. In some embodiments, cell type specific expression of a gene can be determined by fitting this model to data from a collection of tissue samples.

The methods provided herein can include a step of determining the relative content of each cell type in a heterogeneous sample. Identification of a cell type in a sample can include identifying cell types that are present in a sample in amounts greater than about 1%, 2%, 3%, 4% or 5% or greater than 1%, 2%, 3%, 4% or 5%. Any of a variety of known methods for cell type identification can be used herein.

For example, cell type can be determined by an individual skilled in the ability to identify cell types, such as a pathologist or a histologist. In another example, cell types can be determined by cell sorting and/or flow cytometry methods known in the art.

The methods provided herein can be used to determine that the nucleotide or proteins are differentially expressed in at least one cell type relative to at least one other cell type. Such genes include those that are up-regulated (i.e., expressed at a higher level), as well as those that are down-regulated (i.e., expressed at a lower level). Such genes also include sequences that have been altered (i.e., truncated sequences or sequences with substitutions, deletions or insertions, including point mutations) and show either the same expression profile or an altered profile. In certain embodiments, the genes can be from humans; however, as will be appreciated by those in the art, genes from other organisms can be useful in animal models of disease and drug evaluation; thus, other genes are provided, from vertebrates, including mammals, including rodents (e.g., rats, mice, hamsters, and guinea pigs), primates, and farm animals (e.g., sheep, goats, pigs, cows, and horses). In some cases, prokaryotic genes can be useful. Gene expression in any of a variety of organisms can be determined by methods provided herein or otherwise known in the art. Gene products measured according to the methods provided herein can be nucleic acid molecules, including, but not limited to mRNA or an amplicate or complement thereof, polypeptides, or fragments thereof. Methods and compositions for the detection of nucleic acid molecules and proteins are known in the art. For example, oligonucleotide probes and primers can be used in the detection of nucleic acid molecules, and antibodies can be used in the detection of polypeptides.

In the methods provided herein, one or more gene products can be detected. In some embodiments, two or more gene products are detected. In other embodiments, 3 or more, 4 or more, 5 or more, 7 or more, 10 or more 15 or more, 20 or more 25, or more, 35 or more, 50 or more, 75 or more, or 100 or more gene products can be detected in the methods provided herein.

The expression levels of the marker genes in a sample can be determined by any method or composition known in the art. The expression level can be determined by isolating and determining the level (i.e., amount) of nucleic acid transcribed from each marker gene. Alternatively, or additionally, the level of specific proteins translated from mRNA transcribed from a marker gene can be determined.

Determining the level of expression of specific marker genes can be accomplished by determining the amount of mRNA, or polynucleotides derived therefrom, or protein present in a sample. Any method for determining protein or RNA levels can be used. For example, protein or RNA is isolated from a sample and separated by gel electrophoresis. The separated protein or RNA is then transferred to a solid support, such as a filter. Nucleic acid or protein (e.g., antibody) probes representing one or more markers are then hybridized to the filter by hybridization, and the amount of marker-derived protein or RNA is determined. Such determination can be visual, or machine-aided, for example, by use of a densitometer. Another method of determining protein or RNA levels is by use of a dot-blot or a slot-blot. In this method, protein, RNA, or nucleic acid derived therefrom, from a sample is labeled. The protein, RNA or nucleic acid derived therefrom is then hybridized to a filter containing oligonucleotides or antibodies derived from one or more marker genes, wherein the oligonucleotides or antibodies are placed upon the filter at discrete, easily-identifiable locations. Binding, or lack thereof, of the labeled protein or RNA to the filter is determined visually or by densitometer. Proteins or polynucleotides can be labeled using a radiolabel or a fluorescent (i.e., visible) label.

Methods provided herein can be used to detect mRNA or amplicates thereof, and any fragment thereof. In one example, introns of mRNA or amplicate or fragment thereof can be detected. Processing of mRNA can include splicing, in which introns are removed from the transcript. Detection of introns can be used to detect the presence of the entire mRNA, and also can be used to detect processing of the mRNA, for example, when the intron region alone (e.g., intron not attached to any exons) is detected.

In another embodiment, methods provided herein can be used to detect polypeptides and modifications thereof, where a modification of a polypeptide can be a post-translation modification such as lipidylation, glycosylation, activating proteolysis, and others known in the art, or can include degradational modification such as proteolytic fragments and ubiquitinated polypeptides.

These examples are not intended to be limiting; other methods of determining protein or RNA abundance are known in the art.

Alternatively, proteins can be separated by two-dimensional gel electrophoresis systems. Two-dimensional gel electrophoresis is well-known in the art and can involve isoelectric focusing along a first dimension followed by SDS-PAGE electrophoresis along a second dimension. See, e.g., Hames et al. (1990) Gel Electrophoresis of Proteins: A Practical Approach, IRL Press, New York; Shevchenko et al. (1996) Proc. Natl. Acad. ScL USA 93: 1440-1445; Sagliocco et al. (1996) Yeast 12:1519-1533; and Lander (1996) Science 274:536-539. The resulting electropherograms can be analyzed by numerous techniques, including mass spectrometric techniques, western blotting and immunoblot analysis using polyclonal and monoclonal antibodies. Alternatively, marker-derived protein levels can be determined by constructing an antibody microarray in which binding sites comprise immobilized antibodies, such as monoclonal antibodies, specific to a plurality of protein species encoded by the cell genome. Antibodies can be present for a substantial fraction of the marker-derived proteins of interest. Methods for making monoclonal antibodies are well known (see, e.g., Harlow and Lane (1988) Antibodies: A Laboratory Manual, Cold Spring Harbor, N.Y., which is incorporated in its entirety for all purposes). In one embodiment, monoclonal antibodies are raised against synthetic peptide fragments designed based on genomic sequence of the cell. With such an antibody array, proteins from the cell are contacted to the array, and their binding is assayed with assays known in the art. The expression, and the level of expression, of proteins of diagnostic or prognostic interest can be detected through immunohistochemical staining of tissue slices or sections.

In another embodiment, expression of marker genes in a number of tissue specimens can be characterized using a tissue array (Kononen et al. (1998) Nat. Med. 4:844-847). In a tissue array, multiple tissue samples are assessed on the same microarray. The arrays allow in situ detection of RNA and protein levels; consecutive sections allow the analysis of multiple samples simultaneously.

In some embodiments, polynucleotide microarrays are used to measure expression so that the expression status of each of the markers above is assessed simultaneously. In one embodiment, the microarrays provided herein are oligonucleotide or cDNA arrays comprising probes hybridizable to the genes corresponding to the marker genes described herein. A microarray as provided herein can comprise probes hybridizable to the genes corresponding to markers able to distinguish cells, identify phenotypes, identify a disease or disorder, or provide a prognosis of a disease or disorder (e.g., a classifier as described herein). For example, provided herein are polynucleotide arrays comprising probes to a subset or subsets of at least 2, 5, 10, 15, 20, 30, 40, 50, 75, 100, or more than 100 genetic markers, up to the full set of markers present in a classifier as described in the Examples below. Also provided herein are probes to markers with a modified t statistic greater than or equal to 2.5, 3, 3.5, 4, 4.5 or 5. Also provided herein are probes to markers with a modified t statistic less than or equal to -2.5, -3, -3.5, -4, -4.5 or -5. In specific embodiments, the invention provides combinations such as arrays in which the markers described herein comprise at least 50%, 60%, 70%, 80%, 85%, 90%, 95% or 98% of the probes on the combination or array.

General methods pertaining to the construction of microarrays comprising the marker sets and/or subsets above are known in the art as described herein.

Microarrays can be prepared by selecting probes that comprise a polypeptide or polynucleotide sequence, and then immobilizing such probes to a solid support or surface. For example, the probes can comprise DNA sequences, RNA sequences, or antibodies. The probes can also comprise amino acid, DNA and/or RNA analogues, or combinations thereof. The probes can be prepared by any method known in the art.

The probe or probes used in the methods of the invention can be immobilized to a solid support which can be either porous or non-porous. For example, the probes of the can be attached to a nitrocellulose or nylon membrane or filter. Alternatively, the solid support or surface can be a glass or plastic surface. In another embodiment, hybridization levels are measured to microarrays of probes consisting of a solid phase on the surface of which are immobilized a population of probes. The solid phase can be a nonporous or, optionally, a porous material such as a gel. In another embodiment, the microarrays are addressable arrays, such as positionally addressable arrays. More specifically, each probe of the array can be located at a known, predetermined position on the solid support such that the identity (i.e., the sequence) of each probe can be determined from its position in the array (i.e., on the support or surface).

A skilled artisan will appreciate that positive control probes, e.g., probes known to be complementary and hybridizable to sequences in target polynucleotide molecules, and negative control probes, e.g., probes known to not be complementary and hybridizable to sequences in target polynucleotide molecules, can be included on the array. In one embodiment, positive controls can be synthesized along the perimeter of the array. In another embodiment, positive controls can be synthesized in diagonal stripes across the array. Other variations are known in the art. Probes can be immobilized on the to solid surface by any of a variety of methods known in the art.

In certain embodiments, this model can be further extended to include sample characteristics, such as cell or organism phenotypes, allowing cell type specific expression to be linked to observable indicia such as clinical indicators and prognosis (e.g., clinical disease progression, response to therapy, and the like). In one embodiment, a model for prostate tissue is provided, resulting in identification of cell-type-specific markers of cancer, epithelial hypertrophy, and disease progression. In another embodiment, a method for studying differential gene expression between subjects with cancers that relapse and those with cancers that do not relapse, is disclosed. Also provided is the framework for studying mixed cell type samples and more flexible models allowing for cross-talk among genes in a sample. Also provided are extensions to defining differences in expression between samples with different characteristics, such as samples from subjects who subsequently relapse versus those who do not.

Statistical Treatment The methods provided herein include determining the regression relationship between relative cell content and measured expression levels. For example, the regression relationship can be determined by determining the regression of measured expression levels on cell proportions. Statistical methods for determining regression relationships between variables are known in the art. Such general statistical methods can be used in accordance with the teachings provided herein regarding regression of measured expression levels on cell proportions.

The methods provided herein also include calculating the level of analytes in each cell type based on the regression relationship between relative cell content and expression levels. The regression relationship can be determined according to methods provided herein, and, based on the regression relationship, the level of a particular analyte can be calculated for a particular cell type. The methods provided herein can permit the calculation of any of a variety of analyte for particular cell types. For example, the methods provided herein can permit calculation of a single analyte for a single cell type, or can permit calculation of a plurality of analytes for a single cell type, or can permit calculation of a single analyte for a plurality of cell types, or can permit calculation of a plurality of analytes for a plurality of cell types. Thus, the number of analytes whose level can be calculated for a particular cell type can range from a single analyte to the total number of analytes measured (e.g., the total number of analytes measured using a microarray). In another embodiment, the total number of cell types for which analyte levels can be calculated can range from a single cell type, to all cell types present in a sample at sufficient levels. The levels of analyte for a particular cell type can be used to estimate expression levels of the corresponding gene, as provided elsewhere herein.

The methods provided herein also can include identifying genes differentially expressed in a first cell type relative to a second cell type. Expression levels of one or more genes in a particular cell type can be compared to one or more additional cell types.

Differences in expression levels can be represented in any of a variety of manners known in the art, including mathematical or statistical representations, as provided herein. For example, differences in expression level can be represented as a modified t statistic, as described elsewhere herein.

The methods provided herein also can serve as the basis for methods of indicating the presence of a particular cell type in a subject. The methods provided herein can be used for identifying the expression levels in particular cell types. Using any of a variety of classifier methods known in the art, such as a naϊve B ayes classifier, gene expression levels in cells of a sample from a subject can be compared to reference expression levels to determine the presence of absence, and, optionally, the relative amount, of a particular cell type in the sample. For example, the markers provided herein as associated with prostate tumor, stroma or BPH can be selected in a prostate tumor classifier in accordance with the modified t statistic associated with each marker provided in the Tables herein. Methods for using a modified t statistic in classifier methods are provided herein and also are known in the art. In another embodiment, the methods provided herein can be used in phenotype-indicating methods such as diagnostic or prognostic methods, in which the gene expression levels in a sample from a subject can be compared to references indicative of one or more particular phenotypes.

For purposes of exemplification, and not for purposes of limitation, an exemplary method of determining gene expression levels in one or more cell types in a heterogeneous cell sample is provided as follows. Suppose that there are four cell types: BPH, Tumor, Stroma, J] ₁ I Stroma, ( VsUc At ! and Cystic

Atrophy. Supposing that each cell type has a (possibly) different distribution for y, the expression level for a gene j, denoted by:

and that sample k has proportions

A, lU'H ' ^' k I\ΛΛ^I - ^A . ⁰ U - WX^ ^Av vt i< .V i ^ ij .'i -, * of each cell type is studied. The distribution of the expression level for gene j is then if the expression levels are additive in the cell proportions as they would be if each cell's expression level depends only on the type of cell (and not, say, on what other types of cells can be present in the sample). In a later section this formulation is extended to cases in which the expression of a given cell type depends on what other types of cells are present. The average expression level in a sample is then the weighted average of the expectations with weights corresponding to the cell proportions:

or i where

This is the known form for a multiple linear regression equation (without specifying an intercept), and when multiple samples are available one can estimate the β _y -. Once these estimates are in hand, estimates for the differences in gene expression of two cell types are of the form:

and standard methods for testing linear hypotheses about the coefficients β _y - can be applied to test whether the average expression levels of cell types Z ₁ and z ₂ are different. The term 'expression levels' as used in this exemplification of the method is used in a generic sense: 'expression levels' could be readings of mRNA levels, cRNA levels, protein levels, fluorescent intensity from a feature on an array, the logarithm of that reading, some highly post-processed reading, and the like. Thus, differences in the coefficients can correspond to differences, log ratios, or some other functions of the underlying transcript abundance. For computational convenience, one may in certain embodiments use Z = XT and γ =

T ^" β setting up T so that one column of T has all zeroes but for a one in position Z ₁ and a minus one in position z ₂ such as / 1 1 i 0 \

I 1 i 1 Ci

I 1 i\ I ⁾ I

\ ! C) 0 0 /

The columns of Z that result are the unit vector (all ones), X _k,BPH + X _{k, Tumor?} % _k,BPH — X _k , _T u _m o _r , and % _k ,st _r o _m a- With this setup, twice the coefficient of % _k , _BPH " X _k , _T u _m o _r estimates the average difference in expression level of a tumor cell versus a BPH cell. With this parametrization, standard software can be used to provide an estimate and a tesmodified t statistic for the average difference of tumor and BPH cells. Further, this can simplify the specification of restricted models in which two or more of the tissue components have the same average expression level.

The data for a study can contain a large number of samples from a smaller number of different men. It is plausible that the samples from one man may tend to share a common level of expression for a given gene, differences among his cells according to their type notwithstanding. This will tend to lead to positive covariance among the measurements of expression level within men. Ordinary least squares (OLS) estimates are less than fully efficient in such circumstances. One alternative to OLS is to use a weighted least squares approach that treats a collection of samples from a single subject as having a common (non- negative) covariance and identical variances.

The estimating equation for this setup can be solved via iterative methods using software such as the gee library from R (Hiaka and Gentleman (1996) /. Comp. Graph. Stat. 5:299-314). When the estimated covariance is negative - as sometimes happens when there is an extreme outlier in the dataset - it can be fixed at zero. Also the sandwich estimate (Liang and Zeger (1986) Biometrika 73: 13-22) of the covariance structure can be used.

The estimating equation approach will provide a tesmodified t statistic for a single transcript. Assessment of differential expression among a group of 12625 transcripts is handled by permutation methods that honor a suitable null model. That null model is obtained by regressing the expression level on all design terms except for the 'BPH - tumor' term using the exchangeable, non-negative correlation structure just mentioned. For performing permutation tests, the correlation structure in the residuals can be accounted for. Let Ki be the set of n\ indexes of samples for subject 1. First, we find y _jk - yj _k = e _jk , k e K ₁ , as the residuals from that fitted null model for subject 1. The inverse square root of the correlation matrix of these residuals is used to transform them, i.e., g _j = φ ^~1/2 e _j. , where φ is the (block diagonal) correlation matrix obtained by substituting the estimate of r from gee as the off-diagonal elements of blocks corresponding to measurements for each subject and β _j. and g _j. are the vector of residuals and transformed residuals for all subjects for gene j. Asymptotically, the e _jk have means and covariances equal to zero. Random permutations of these, g _j. , i = 1,..., M, are obtained and used to form pseudo-observations:

This permutation scheme preserves the null model and enforces its correlation structure asymptotically.

In certain embodiments, the contribution of each type of cell does not depend on what other cell types are present in the sample. However, there can be instances in which contribution of each type of cell does depend on other cell types present in the sample. It may happen that putatively 'normal' cells exhibit genomic features that influence both their expression profiles and their potential to become malignant. Such cells would exhibit the same expression pattern when located in normal tissue, but are more likely to be found in samples that also have tumor cells in them. Another possible effect is that signals generated by tumor cells trigger expression changes in nearby cells that would not be seen if those same cells were located in wholly normal tissue. In either case, the contribution of a cell may be more or less than in another tissue environment leading to a setup in which the contributions of individual cell types to the overall profile depend on the proportions of all types present, viz.

gj (μ\ Xk) ^ y [ ^■ Xkifij i vl Xk)

as do the expected proportions

E ₃ (y[X _A: ) ^ y ^* x _ki Ef (y\ X _k )

or ( Xk ) Uk The methods used herein above can still be applied in the context provided some calculable form is given for Q ₁₁ (X _k ). One choice is given by

where Φ _j is a 4 x m matrix of unknown coefficients and R(X _k ) is a column vector of m elements. This reduces to the case in which each cell's expression level depends only on the type of cell when Φ _j is 4 x 1 matrix and R(Xu) is just ' 1 ' .

Consider the case:

(and recall that ∑ _} X _{k }} = 1 •) Here the subscript for Tumor has been abbreviated T etc., for brevity. This setup provides that BPH (B), tumor, and cystic atrophy (C) cells have expression profiles that do not depend on the other cell types in the sample. However, the expression levels of stromal cells (S) depend on the proportion of tumor cells as reflected by the coefficient δ _j . Notice that is linear in Xi _B , X _k τ, X _k s, X _k c, and Xi _s Xι _T with the unknown coefficients being

multipliers of those terms. So, the unknowns in this case are linear functions of the gene expression levels and can be determined using standard linear models as was done earlier. The only change here is the addition of the product of X _k s and X _{k τ} . Such a product, when significant, is termed an "interaction" and refers to the product archiving a significance level owing to a correlation of Xi _s with Xi _τ . Thus, it is possible to accommodate variations in gene expression that occur when the level of a transcript in one cell type is influenced by the amount of another cell type in the sample. In one aspect, a setup involving a dependency of tumor on the amount of stroma

oΛ i--T , i.'r . ^l '1 t T -f '-V . re... ^■ > ^■■

the expression for XkΦ _j R(Xk) is precisely as it was just above.

Accordingly, one can screen for dependencies by including as regressors products of the proportions of cell types. In certain embodiments, it may not be possible to detect interactions if two different cell types experience equal and opposite changes — one type expressing more with increases in the other and the other expressing less with increases in the first. In one embodiment, dependence of gene expression refers to the dependence of gene expression in one cell type on the level of gene expression in another cell type. In another embodiment, dependence of gene expression refers to the dependence of gene expression in one cell type on the amount of another cell type.

The contribution of each type of cell can depend on what other cell types are present in the sample, but also can depend on other characteristics of the sample, such as clinical characteristics of the subject who contributed it. For example, clinical characteristics such as disease symptoms, disease prognosis such as relapse and/or aggressiveness of disease, likelihood of success in treating a disease, likelihood of survival, condition in which a particular treatment regimen is likely to be more effective than another treatment regimen, can be correlated with cell expression. For example, cell type specific gene expression can differ between a subject with a cancer that does not relapse after treatment and a subject with a cancer that does relapse after treatment. In this case, the contribution of a cell type may be more or less than in another subject leading to an instance in which the contributions of individual cell types to the overall profile depend on the characteristics of the subject or sample. Here, the model used earlier is extended to allow for dependence on a vector of sample specific covariates, Zi. as do the expected proportions:

or

The methods used herein above can still be applied in this context provided some reasonable form is given for β \ _j (X _k ,Z _k ). One useful choice is given by:

Where Φ _j is a 4 x m matrix of unknown coefficients and R(Zu) is a column vector of m elements.

Consider how this would be used to study differences in gene expression among subjects who relapse and those who do not. In this case, Z _k is an indicator variable taking the value zero for samples of subjects who do not relapse and one for those who do. Then

and Φ _j is a four by two matrix of coefficients:

Notice that this leads to

The v coefficients give the average expression of the different cell types in subjects who do not relapse, while the δ coefficients give the difference between the average expression of the different cell types in subjects who do relapse and those who do not. Thus, a non-zero value of 5τ would indicate that in tumor cells, the average expression level differs for subjects who relapse and those who do not. The above equation is linear in its coefficients, so standard statistical methods can be applied to estimation and inference on the coefficients. Extensions that allow β to depend on both cell proportions and on sample covariates can be determined according to the teachings provided herein or other methods known in the art.

Nucleic Acids Provided herein are tables and exhibits listing probe sets and genes associated with the probe set, including, for some tables, GENBANK accession number, and/or locus ID. The tables may include modified t statistics for an Affymetrix microarrays, including associated t statistics for BPH, tumor, stroma and cystic atrophy, for example. Probe IDs for the microarray that map to Probe IDs for a different microarray, and the mapping itself, also may be provided, where the mapping can represent Probe IDs of microarrays that can hybridize to the same gene. By virtue of such mapping, Probe IDs can be associated with nucleotide sequences. Tables also may list the top genes identified as up- and down- regulated in prostate tumor cells of relapse patients, calculated by linear regression including all samples with prostate cancer. Genes that have greater than, for example, a 1.5 fold ratio of predicted expression between relapse and non-relapse tissue can be identified, as can an absolute difference in expression that exceeds the expression level reported for most genes queried by the array.

The tables provided herein also may list the top genes identified as up- and down- regulated in tumors and/or prostate stroma of relapse patients, calculated by linear regression including all samples with prostate cancer. Exemplary genes whose expression can be examined in methods for identifying or characterizing a sample may be provided, as well as Probe IDs that can be used for such gene expression identification.

Splice variants of genes also may be useful for determining diagnosis and prognosis of prostate cancer. As will be understood in the art, multiple splicing combinations are provided for some genes. Reference herein to one or more genes (including reference to products of genes) also contemplates reference to spliced gene sequences. Similarly, reference herein to one or more protein gene products also contemplates proteins translated from splice variants. Exemplary, non-limiting examples of genes whose products can be detected in the methods provided herein include IGF-I, microsimino protein, and MTA-I. In one embodiment detection of the expression of one or more of these genes can be performed in combination with detection of expression of one or more additional genes as listed in the tables herein. Uses of probes and detection of genes identified in the tables may be described and exemplified herein. It is contemplated herein that uses and methods similar to those exemplified can be applied to the probe and gene nucleotide sequences in accordance with the teachings provided herein.

The isolated nucleic acids can contain least 10 nucleotides, 25 nucleotides, 50 nucleotides, 100 nucleotides, 150 nucleotides, or 200 nucleotides or more, contiguous nucleotides of a gene listed herein. In another embodiment, the nucleic acids are smaller than 35, 200 or 500 nucleotides in length.

Also provided are fragments of the above nucleic acids that can be used as probes or primers and that contain at least about 10 nucleotides, at least about 14 nucleotides, at least about 16 nucleotides, or at least about 30 nucleotides. The length of the probe or primer is a function of the size of the genome probed; the larger the genome, the longer the probe or primer required for specific hybridization to a single site. Those of skill in the art can select appropriately sized probes and primers. Probes and primers as described can be single- stranded. Double stranded probes and primers also can be used, if they are denatured when used. Probes and primers derived from the nucleic acid molecules are provided. Such probes and primers contain at least 8, 14, 16, 30, 100 or more contiguous nucleotides. The probes and primers are optionally labeled with a detectable label, such as a radiolabel or a fluorescent tag, or can be mass differentiated for detection by mass spectrometry or other means. Also provided is an isolated nucleic acid molecule that includes the sequence of molecules that is complementary to a nucleotide. Double-stranded RNA (dsRNA), such as RNAi is also provided.

Plasmids and vectors containing the nucleic acid molecules are also provided. Cells containing the vectors, including cells that express the encoded proteins are provided. The cell can be a bacterial cell, a yeast cell, a fungal cell, a plant cell, an insect cell or an animal cell. For recombinant expression of one or more genes, the nucleic acid containing all or a portion of the nucleotide sequence encoding the genes can be inserted into an appropriate expression vector, i.e., a vector that contains the elements for the transcription and translation of the inserted protein coding sequence. Transcriptional and translational signals also can be supplied by the native promoter for the genes, and/or their flanking regions. Also provided are vectors that contain nucleic acid encoding a gene listed herein.

Cells containing the vectors are also provided. The cells include eukaryotic and prokaryotic cells, and the vectors are any suitable for use therein.

Prokaryotic and eukaryotic cells containing the vectors are provided. Such cells include bacterial cells, yeast cells, fungal cells, plant cells, insect cells and animal cells. The cells can be used to produce an oligonucleotide or polypeptide gene products by (a) growing the above-described cells under conditions whereby the encoded gene is expressed by the cell, and then (b) recovering the expressed compound.

A variety of host-vector systems can be used to express the protein coding sequence. These include, but are not limited to, mammalian cell systems infected with virus (e.g., vaccinia virus and adenovirus); insect cell systems infected with virus (e.g., baculovirus); microorganisms such as yeast containing yeast vectors; or bacteria transformed with bacteriophage, DNA, plasmid DNA, or cosmid DNA. The expression elements of vectors vary in their strengths and specificities. Depending on the host-vector system used, any one of a number of suitable transcription and translation elements can be used.

Any methods known to those of skill in the art for the insertion of nucleic acid fragments into a vector can be used to construct expression vectors containing a chimeric gene containing appropriate transcriptional/translational control signals and protein coding sequences. These methods can include in vitro recombinant DNA and synthetic techniques and in vivo recombinants (genetic recombination). Expression of nucleic acid sequences encoding polypeptide can be regulated by a second nucleic acid sequence so that the genes or fragments thereof are expressed in a host transformed with the recombinant DNA molecule(s). For example, expression of the proteins can be controlled by any promoter/enhancer known in the art.

Proteins Protein products of the genes listed herein, derivatives, and analogs can be produced by various methods known in the art. For example, once a recombinant cell expressing such a polypeptide, or a domain, fragment or derivative thereof, is identified, the individual gene product can be isolated and analyzed. This is achieved by assays based on the physical and/or functional properties of the protein, including, but not limited to, radioactive labeling of the product followed by analysis by gel electrophoresis, immunoassay, cross-linking to marker- labeled product, and assays of protein activity or antibody binding.

Polypeptides can be isolated and purified by standard methods known in the art (either from natural sources or recombinant host cells expressing the complexes or proteins), including but not restricted to column chromatography (e.g., ion exchange, affinity, gel exclusion, reversed-phase high pressure and fast protein liquid), differential centrifugation, differential solubility, or by any other standard technique used for the purification of proteins. Functional properties can be evaluated using any suitable assay known in the art.

Manipulations of polypeptide sequences can be made at the protein level. Also contemplated herein are polypeptide proteins, domains thereof, derivatives or analogs or fragments thereof, which are differentially modified during or after translation, e.g., by glycosylation, acetylation, phosphorylation, amidation, derivatization by known protecting/blocking groups, proteolytic cleavage, linkage to an antibody molecule or other cellular ligand. Any of numerous chemical modifications can be carried out by known techniques, including but not limited to specific chemical cleavage by cyanogen bromide, trypsin, chymotrypsin, papain, V8 protease, NaBH4, acetylation, formulation, oxidation, 5 reduction, metabolic synthesis in the presence of tunicamycin and other such agents.

In addition, domains, analogs and derivatives of a polypeptide provided herein can be chemically synthesized. For example, a peptide corresponding to a portion of a polypeptide provided herein, which includes the desired domain or which mediates the desired activity in vitro can be synthesized by use of a peptide synthesizer. Furthermore, if desired, nonclassical o amino acids or chemical amino acid analogs can be introduced as a substitution or addition into the polypeptide sequence. Non-classical amino acids include but are not limited to the D-isomers of the common amino acids, a-amino isobutyric acid, 4-aminobutyric acid, Abu, 2-aminobutyric acid, .epsilon.-Abu, e-Ahx, 6-amino hexanoic acid, Aib, 2-amino isobutyric acid, 3 -amino propionoic acid, ornithine, norleucine, norvaline, hydroxyproline, sarcosine,5 citrulline, cysteic acid, t-butylglycine, t-butylalanine, phenylglycine, cyclohexylalanine,

.beta.-alanine, fluoro-amino acids, designer amino acids such as .beta.-methyl amino acids, Ca-methyl amino acids, Na-methyl amino acids, and amino acid analogs in general. Furthermore, the amino acid can be D (dextrorotary) or L (levorotary). 0 Screening Methods

Oligonucleotide or polypeptide gene products can be used in a variety of methods to identify compounds that modulate the activity thereof. Nucleotide sequences and genes can be identified in different cell types and in the same cell type in which subject have different phenotypes. Methods are provided herein for screening compounds can include contacting5 cells with a compound and measuring gene expression levels, wherein a change in expression levels relative to a reference identifies the compound as a compound that modulates a gene expression.

Also provided herein are methods for identification and isolation of agents, such as compounds that bind to products of the genes listed herein. The assays are designed to0 identify agents that bind to the RNA or polypeptide gene product. The identified compounds are candidates or leads for identification of compounds for treatments of tumors and other disorders and diseases.

A variety of methods can be used, as known in the art. These methods can be performed in solution or in solid phase reactions. Methods for identifying an agent, such as a compound, that specifically binds to an oligonucleotide or polypeptide encoded by a gene as listed herein also are provided. The method can be practiced by (a) contacting the gene product with one or a plurality of test agents under conditions conducive to binding between the gene product and an agent; and (b) identifying one or more agents within the one or plurality that specifically binds to the gene product. Compounds or agents to be identified can originate from biological samples or from libraries, including, but are not limited to, combinatorial libraries. Exemplary libraries can be fusion-protein-displayed peptide libraries in which random peptides or proteins are presented on the surface of phage particles or proteins expressed from plasmids; support- bound synthetic chemical libraries in which individual compounds or mixtures of compounds are presented on insoluble matrices, such as resin beads, or other libraries known in the art.

Modulators of the Activity of Gene products

Provided herein are compounds that modulate the activity of a gene product. These compounds can act by directly interacting with the polypeptide or by altering transcription or translation thereof. Such molecules include, but are not limited to, antibodies that specifically bind the polypeptide, antisense nucleic acids or double-stranded RNA (dsRNA) such as RNAi, that alter expression of the polypeptide, antibodies, peptide mimetics and other such compounds.

Antibodies are provided, including polyclonal and monoclonal antibodies that specifically bind to a polypeptide gene product provided herein. An antibody can be a monoclonal antibody, and the antibody can specifically bind to the polypeptide. The polypeptide and domains, fragments, homologs and derivatives thereof can be used as immunogens to generate antibodies that specifically bind such immunogens. Such antibodies include but are not limited to polyclonal, monoclonal, chimeric, single chain, Fab fragments, and an Fab expression library. In a specific embodiment, antibodies to human polypeptides are produced. Methods for monoclonal and polyclonal antibody production are known in the art. Antibody fragments that specifically bind to the polyeptide or epitopes thereof can be generated by techniques known in the art. For example, such fragments include but are not limited to: the F(ab')2 fragment, which can be produced by pepsin digestion of the antibody molecule; the Fab' fragments that can be generated by reducing the disulfide bridges of the F(ab')2 fragment, the Fab fragments that can be generated by treating the antibody molecular with papain and a reducing agent, and Fv fragments.

Peptide analogs are commonly used in the pharmaceutical industry as non-peptide drugs with properties analogous to those of the template peptide. These types of non-peptide compounds are termed peptide mime tics or pep tidomime tics (Luthman et al., A Textbook of Drug Design and Development, 14:386-406, 2nd Ed., Harwood Academic Publishers (1996); Joachim Grante (1994) Angew. Chem. Int. Ed. Engl., 33:1699-1720; Fauchere (1986) /. Adv. Drug Res., 15:29; Veber and Freidinger (1985) TINS, p. 392; and Evans et al. (1987) /. Med. Chem. 30:1229). Peptide mimetics that are structurally similar to therapeutically useful peptides can be used to produce an equivalent or enhanced therapeutic or prophylactic effect. Preparation of peptidomimetics and structures thereof are known to those of skill in this art.

Prognosis and Diagnosis

Polypeptide products of the coding sequences (e.g., genes) listed herein can be detected in diagnostic methods, such as diagnosis of tumors and other diseases or disorders. Such methods can be used to detect, prognose, diagnose, or monitor various conditions, diseases, and disorders. Exemplary compounds that can be used in such detection methods include polypeptides such as antibodies or fragments thereof that specifically bind to the polypeptides listed herein, and oligonucleotides such as DNA probes or primers that specifically bind oligonucleotides such as RNA encoded by the nucleic acids provided herein.

A set of one or more, or two or more compounds for detection of markers containing a particular nucleotide sequence, complements thereof, fragments thereof, or polypeptides encoded thereby, can be selected for any of a variety of assay methods provided herein. For example, one or more, or two or more such compounds can be selected as diagnostic or prognostic indicators. Methods for selecting such compounds and using such compounds in assay methods such as diagnostic and prognostic indicator applications are known in the art. For example, the Tables provided herein list a modified t statistic associated with each marker, where the modified t statistic indicate the ability of the associated marker to indicate (by presence or absence of the marker, according to the modified t statistic) the presence or absence of a particular cell type in a prostate sample. In another embodiment, marker selection can be performed by considering both modified t statistics and expected intensity of the signal for a particular marker. For example, markers can be selected that have a strong signal in a cell type whose presence or absence is to be determined, and also have a sufficiently large modified t statistic for gene expression in that cell type. Also, markers can be selected that have little or no signal in a cell type whose presence or absence is to be determined, and also have a sufficiently large negative modified t statistic for gene expression in that cell type.

Exemplary assays include immunoassays such as competitive and non-competitive assay systems using techniques such as western blots, radioimmunoassays, ELISA (enzyme linked immunosorbent assay), sandwich immunoassays, immunoprecipitation assays, precipitin reactions, gel diffusion precipitin reactions, immunodiffusion assays, agglutination assays, complement-fixation assays, immunoradiometric assays, fluorescent immunoassays and protein A immunoassays. Other exemplary assays include hybridization assays which can be carried out by a method by contacting a sample containing nucleic acid with a nucleic acid probe, under conditions such that specific hybridization can occur, and detecting or measuring any resulting hybridization.

Kits for diagnostic use are also provided, that contain in one or more containers an anti-polypeptide antibody, and, optionally, a labeled binding partner to the antibody. A kit is also provided that includes in one or more containers a nucleic acid probe capable of hybridizing to the gene-encoding nucleic acid. In a specific embodiment, a kit can include in one or more containers a pair of primers (e.g., each in the size range of 6-30 nucleotides) that are capable of priming amplification. A kit can optionally further include in a container a predetermined amount of a purified control polypeptide or nucleic acid.

The kits can contain packaging material that is one or more physical structures used to house the contents of the kit, such as invention nucleic acid probes or primers, and the like. The packaging material is constructed by well known methods, and can provide a sterile, contaminant-free environment. The packaging material has a label which indicates that the compounds can be used for detecting a particular oligonucleotide or polypeptide. The packaging materials employed herein in relation to diagnostic systems are those customarily utilized in nucleic acid or protein-based diagnostic systems. A package is to a solid matrix or material such as glass, plastic, paper, foil, and the like, capable of holding within fixed limits an isolated nucleic acid, oligonucleotide, or primer of the present invention. Thus, for example, a package can be a glass vial used to contain milligram quantities of a contemplated nucleic acid, oligonucleotide or primer, or it can be a microtiter plate well to which microgram quantities of a contemplated nucleic acid probe have been operatively affixed. The kits also can include instructions for use, which can include a tangible expression describing the reagent concentration or at least one assay method parameter, such as the relative amounts of reagent and sample to be admixed, maintenance time periods for reagent/sample admixtures, temperature, buffer conditions, and the like.

Pharmaceutical Compositions and Modes of Administration Pharmaceutical compositions containing the identified compounds that modulate expression of a gene or bind to a gene product are provided herein. Also provided are combinations of such a compound and another treatment or compound for treatment of a disease or disorder, such as a chemotherapeutic compound.

Expression modulator or binding compound and other compounds can be packaged as separate compositions for administration together or sequentially or intermittently.

Alternatively, they can be provided as a single composition for administration or as two compositions for administration as a single composition. The combinations can be packaged as kits.

Compounds and compositions provided herein can be formulated as pharmaceutical compositions, for example, for single dosage administration. The concentrations of the compounds in the formulations are effective for delivery of an amount, upon administration, that is effective for the intended treatment. In certain embodiments, the compositions are formulated for single dosage administration. To formulate a composition, the weight fraction of a compound or mixture thereof is dissolved, suspended, dispersed or otherwise mixed in a selected vehicle at an effective concentration such that the treated condition is relieved or ameliorated. Pharmaceutical carriers or vehicles suitable for administration of the compounds provided herein include any such carriers known to those skilled in the art to be suitable for the particular mode of administration.

In addition, the compounds can be formulated as the sole pharmaceutically active ingredient in the composition or can be combined with other active ingredients. The active compound is included in the pharmaceutically acceptable carrier in an amount sufficient to exert a therapeutically useful effect in the absence of undesirable side effects on the subject treated. The therapeutically effective concentration can be determined empirically by testing the compounds in known in vitro and in vivo systems. The concentration of active compound in the drug composition depends on absorption, inactivation and excretion rates of the active compound, the physicochemical characteristics of the compound, the dosage schedule, and amount administered as well as other factors known to those of skill in the art. Pharmaceutically acceptable derivatives include acids, salts, esters, hydrates, solvates and prodrug forms. The derivative can be selected such that its pharmacokinetic properties are superior to the corresponding neutral compound. Compounds are included in an amount effective for ameliorating or treating the disorder for which treatment is contemplated.

Formulations suitable for a variety of administrations such as perenteral, intramuscular, subcutaneous, alimentary, transdermal, inhaling and other known methods of administration, are known in the art. The pharmaceutical compositions can also be administered by controlled release means and/or delivery devices as known in the art. Kits containing the compositions and/or the combinations with instructions for administration thereof are provided. The kit can further include a needle or syringe, which can be packaged in sterile form, for injecting the complex, and/or a packaged alcohol pad. Instructions are optionally included for administration of the active agent by a clinician or by the patient. The compounds can be packaged as articles of manufacture containing packaging material, a compound or suitable derivative thereof provided herein, which is effective for treatment of a diseases or disorders contemplated herein, within the packaging material, and a label that indicates that the compound or a suitable derivative thereof is for treating the diseases or disorders contemplated herein. The label can optionally include the disorders for which the therapy is warranted.

Methods of Treatment The compounds provided herein can be used for treating or preventing diseases or disorders in an animal, such as a mammal, including a human. In one embodiment, the method includes administering to a mammal an effective amount of a compound that modulates the expression of a particular gene (e.g., a gene listed herein) or a compound that binds to a product of a gene , whereby the disease or disorder is treated or prevented. Exemplary inhibitors provided herein are those identified by the screening assays. In addition, antibodies and antisense nucleic acids or double-stranded RNA (dsRNA), such as RNAi, are contemplated.

In a specific embodiment, as described hereinabove, gene expression can be inhibited by antisense nucleic acids. The therapeutic or prophylactic use of nucleic acids of at least six nucleotides, up to about 150 nucleotides, that are antisense to a gene or cDNA is provided. The antisense molecule can be complementary to all or a portion of the gene. For example, the oligonucleotide is at least 10 nucleotides, at least 15 nucleotides, at least 100 nucleotides, or at least 125 nucleotides. The oligonucleotides can be DNA or RNA or chimeric mixtures or derivatives or modified versions thereof, single-stranded or double-stranded. The oligonucleotide can be modified at the base moiety, sugar moiety, or phosphate backbone. The oligonucleotide can include other appending groups such as peptides, or agents facilitating transport across the cell membrane, hybridization-triggered cleavage agents or intercalating agents. RNA interference (RNAi) (see, e.g., Chuang et al. (2000) Proc. Natl. Acad. Sci.

U.S.A. 97:4985) can be employed to inhibit the expression of a nucleic acid. Interfering RNA (RNAi) fragments, such as double-stranded (ds) RNAi, can be used to generate loss-of-gene function. Methods relating to the use of RNAi to silence genes in organisms including, mammals, C. elegans, Drosophila and plants, and humans are known. Double-stranded RNA (dsRNA)-expressing constructs are introduced into a host, such as an animal or plant using, a replicable vector that remains episomal or integrates into the genome. By selecting appropriate sequences, expression of dsRNA can interfere with accumulation of endogenous mRNA. RNAi also can be used to inhibit expression in vitro. Regions include at least about 21 (or 21) nucleotides that are selective (i.e., unique) for the selected gene are used to prepare the RNAi. Smaller fragments of about 21 nucleotides can be transformed directly (i.e., in vitro or in vivo) into cells; larger RNAi dsRNA molecules can be introduced using vectors that encode them. dsRNA molecules are at least about 21 bp long or longer, such as 50, 100, 150, 200 and longer. Methods, reagents and protocols for introducing nucleic acid molecules in to cells in vitro and in vivo are known to those of skill in the art.

In an exemplary embodiment, nucleic acids that include a sequence of nucleotides encoding a polypeptide of a gene as listed herein can be administered to promote polypeptide function, by way of gene therapy. Gene therapy refers to therapy performed by administration of a nucleic acid to a subject. In this embodiment, the nucleic acid produces its encoded protein that mediates a therapeutic effect by promoting polypeptide function. Any of the methods for gene therapy available in the art can be used (see, Goldspiel et al., Clinical Pharmacy 12:488-505 (1993); Wu and Wu, Biotherapy 3:87-95 (1991); Tolstoshev, An. Rev. Pharmacol. Toxicol. 32:573-596 (1993); Mulligan, Science 260:926-932 (1993); and Morgan and Anderson, An. Rev. Biochem. 62:191-217 (1993); TIBTECH 11 (5):155-215 (1993).

In some embodiments, vaccines based on the genes and polypeptides provided herein can be developed. For example genes can be administered as DNA vaccines, either single genes or combinations of genes. Naked DNA vaccines are generally known in the art. Methods for the use of genes as DNA vaccines are well known to one of ordinary skill in the art, and include placing a gene or portion of a gene under the control of a promoter for expression in a patient with cancer. The gene used for DNA vaccines can encode full-length proteins, but can encode portions of the proteins including peptides derived from the protein. For example, a patient can be immunized with a DNA vaccine comprising a plurality of nucleotide sequences derived from a particular gene. In another embodiment, it is possible to immunize a patient with a plurality of genes or portions thereof. Without being bound by theory, expression of the polypeptide encoded by the DNA vaccine, cytotoxic T-cells, helper T-cells and antibodies are induced that recognize and destroy or eliminate cells expressing the proteins provided herein.

DNA vaccines can include a gene encoding an adjuvant molecule with the DNA vaccine. Such adjuvant molecules include cytokines that increase the immunogenic response to the polypeptide encoded by the DNA vaccine. Additional or alternative adjuvants are known to those of ordinary skill in the art and find use in the invention. Animal Models and Transgenics

Also provided herein, the nucleotide the genes, nucleotide molecules and polypeptides disclosed herein find use in generating animal models of cancers, such as lymphomas and carcinomas. As is appreciated by one of ordinary skill in the art, when one of the genes provided herein is repressed or diminished, gene therapy technology wherein antisense RNA directed to the gene will also diminish or repress expression of the gene. An animal generated as such serves as an animal model that finds use in screening bioactive drug candidates. In another embodiment, gene knockout technology, for example as a result of homologous recombination with an appropriate gene targeting vector, will result in the absence of the protein. When desired, tissue-specific expression or knockout of the protein can be accomplished using known methods.

It is also possible that a protein is overexpressed in cancer. As such, transgenic animals can be generated that overexpress the protein. Depending on the desired expression level, promoters of various strengths can be employed to express the transgene. Also, the number of copies of the integrated transgene can be determined and compared for a determination of the expression level of the transgene. Animals generated by such methods find use as animal models and are additionally useful in screening for bioactive molecules to treat cancer.

Computer Programs and Methods

The various techniques, methods, and aspects of the methods provided herein can be implemented in part or in whole using computer-based systems and methods. In another embodiment, computer-based systems and methods can be used to augment or enhance the functionality described above, increase the speed at which the functions can be performed, and provide additional features and aspects as a part of or in addition to those of the invention described elsewhere in this document. Various computer-based systems, methods and implementations in accordance with the above-described technology are presented below. A processor-based system can include a main memory, such as random access memory (RAM), and can also include a secondary memory. The secondary memory can include, for example, a hard disk drive and/or a removable storage drive, representing a floppy disk drive, a magnetic tape drive, or an optical disk drive. The removable storage drive reads from and/or writes to a removable storage medium. Removable storage medium refers to a floppy disk, magnetic tape, optical disk, and the like, which is read by and written to by a removable storage drive. As will be appreciated, the removable storage medium can comprise computer software and/or data.

In alternative embodiments, the secondary memory may include other similar means for allowing computer programs or other instructions to be loaded into a computer system. Such means can include, for example, a removable storage unit and an interface. Examples of such can include a program cartridge and cartridge interface (such as the found in video game devices), a movable memory chip (such as an EPROM or PROM) and associated socket, and other removable storage units and interfaces, which allow software and data to be transferred from the removable storage unit to the computer system.

The computer system can also include a communications interface. Communications interfaces allow software and data to be transferred between computer system and external devices. Examples of communications interfaces can include a modem, a network interface (such as, for example, an Ethernet card), a communications port, a PCMCIA slot and card, and the like. Software and data transferred via a communications interface are in the form of signals, which can be electronic, electromagnetic, optical or other signals capable of being received by a communications interface. These signals are provided to communications interface via a channel capable of carrying signals and can be implemented using a wireless medium, wire or cable, fiber optics or other communications medium. Some examples of a channel can include a phone line, a cellular phone link, an RF link, a network interface, and other communications channels. In this document, the terms computer program medium and computer usable medium are used to refer generally to media such as a removable storage device, a disk capable of installation in a disk drive, and signals on a channel. These computer program products are means for providing software or program instructions to a computer system.

Computer programs (also called computer control logic) are stored in main memory and/or secondary memory. Computer programs can also be received via a communications interface. Such computer programs, when executed, permit the computer system to perform the features of the invention as discussed herein. In particular, the computer programs, when executed, permit the processor to perform the features of the invention. Accordingly, such computer programs represent controllers of the computer system.

In an embodiment where the elements are implemented using software, the software may be stored in, or transmitted via, a computer program product and loaded into a computer system using a removable storage drive, hard drive or communications interface. The control logic (software), when executed by the processor, causes the processor to perform the functions of the invention as described herein.

In another embodiment, the elements are implemented in hardware using, for example, hardware components such as PALs, application specific integrated circuits

(ASICs) or other hardware components. Implementation of a hardware state machine so as to perform the functions described herein will be apparent to person skilled in the relevant art(s). In yet another embodiment, elements are implanted using a combination of both hardware and software. In another embodiment, the computer-based methods can be accessed or implemented over the World Wide Web by providing access via a Web Page to the methods of the invention. Accordingly, the Web Page is identified by a Universal Resource Locator (URL). The URL denotes both the server machine and the particular file or page on that machine. In this embodiment, it is envisioned that a consumer or client computer system interacts with a browser to select a particular URL, which in turn causes the browser to send a request for that URL or page to the server identified in the URL. The server can respond to the request by retrieving the requested page and transmitting the data for that page back to the requesting client computer system (the client/server interaction can be performed in accordance with the hypertext transport protocol (HTTP)). The selected page is then displayed to the user on the client's display screen. The client may then cause the server containing a computer program of the invention to launch an application to, for example, perform an analysis according to the methods provided herein.

Prostate-Associated Genes Provided herein are probe and gene sequences that can be indicative of the presence and/or absence of prostate cancer in a subject. Also provided herein are probe and gene sequences that can be indicative of presence and/or absence of benign prostatic hyperplasia (BPH) in a subject. Also provided herein are probe and gene sequences that can be indicative of a prognosis of prostate cancer, where such a prognosis can include likely relapse of prostate cancer, likely aggressiveness of prostate cancer, likely indolence of prostate cancer, likelihood of survival of the subject, likelihood of success in treating prostate cancer, condition in which a particular treatment regimen is likely to be more effective than another treatment regimen, and combinations thereof. In one embodiment, the probe and gene sequences can be indicative of the likely aggressiveness or indolence of prostate cancer. As provided in the methods and Tables herein, probes have been identified that hybridize to one or more nucleic acids of a prostate sample at different levels according to the presence or absence of prostate tumor, BPH and stroma in the sample. The probes provided herein are listed in conjunction with modified t statistics that represent the ability of that particular probe to indicate the presence or absence of a particular cell type in a prostate sample. Use of modified t statistics for such a determination is described elsewhere herein, and general use of modified t statistics is known in the art. Accordingly, provided herein are nucleotide sequences of probes that can be indicative of the presence or absence of prostate tumor and/or BPH cells, and also can be indicative of the likelihood of prostate tumor relapse in a subject.

Also provided in the methods and Tables herein are nucleotide and predicted amino acid sequences of genes and gene products associated with the probes provided herein.

Accordingly, as provided herein, detection of gene products (e.g., mRNA or protein) or other indicators of gene expression, can be indicative of the presence or absence of prostate tumor and/or BPH cells, and also can be indicative of the likelihood of prostate tumor relapse in a subject. As with the probe sequences, the nucleotide and amino acid sequences of these gene products are listed in conjunction with modified t statistics that represent the ability of that particular gene product or indicator thereof to indicate the presence or absence of a particular cell type in a prostate sample.

Methods for determining the presence of prostate tumor and/or BPH cells, the likelihood of prostate tumor relapse in a subject, the likelihood of survival of prostate cancer, the aggressiveness of prostate tumor, the indolence of prostate tumor, survival, and other prognoses of prostate tumor, can be performed in accordance with the teachings and examples provided herein. Also provided herein, a set of probes or gene products can be selected according to their modified t statistic for use in combination (e.g., for use in a microarray) in methods of determining the presence of prostate tumor and/or BPH cells, and/or the likelihood of prostate tumor relapse in a subject. Also provided herein, the gene products identified as present at increased levels in prostate cancer or in subjects with likely relapse of cancer, can serve as targets for therapeutic compounds and methods. For example an antibody or siRNA targeted to a gene product present at increased levels in prostate cancer can be administered to a subject to decrease the levels of that gene product and to thereby decrease the malignancy of tumor cells, the aggressiveness of a tumor, indolence of a tumor, survival, or the likelihood of tumor relapse. Methods for providing molecules such as antibodies or siRNA to a subject to decrease the level of gene product in a subject are provided herein or are otherwise known in the art.

In some embodiments, gene products identified as present at decreased levels in prostate cancer or in subjects with likely relapse of cancer, can serve as subjects for therapeutic compounds and methods. For example a nucleic acid molecule, such as a gene expression vector encoding a particular gene, can be administered to a individual with decreased levels of the particular gene product to increase the levels of that gene product and to thereby decrease the malignancy of tumor cells, the aggressiveness of a tumor, indolence of a tumor, likelihood of survival, or the likelihood of tumor relapse. Methods for providing gene expression vectors to a subject to increase the level of gene product in a subject are provided herein or are otherwise known in the art.

As used herein, the term "prostate cancer signature" refers to genes that exhibit altered expression (e.g., increased or decreased expression) with prostate cancer as compared to control levels of expression (e.g., in normal prostate tissue). Genes included in a prostate cancer signature can include any of those listed in the tables presented herein (e.g., Tables 3 and 4). For example, one or more (e.g., two, three, four, five, six, seven, eight nine, ten, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, or more) of the genes listed in Table 3 can be are present in a prostate tissue sample (e.g., a prostate tissue sample containing normal stroma, prostate cancer cells, or both) at a level greater than or less than the level observed in normal, non-cancerous prostate tissue. In some cases, a prostate cancer signature can be a gene expression profile in which at least 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 percent of the genes listed in a table herein (e.g., Table 3 or Table 4) are expressed at a level greater than or less than their corresponding control levels in non-cancerous tissue.

As used herein, the terms "prostate cell-type predictor" genes and "prostate tissue predictor" genes refer to genes that can, based on their expression levels, serve as indicators as to whether a particular sample of prostate tissue contains particular cell types (e.g., prostate cancer cells, normal stromal cells, epithelial cells of benign prostate hyperplasia, or epithelial cells of dilated cystic glands). Such genes also can indicate the relative amounts of such cell types within the prostate tissue sample. In some embodiments, this document features methods for identifying a subject as having or not having prostate cancer, comprising: (a) providing a prostate tissue sample from the subject; (b) measuring the level of expression for prostate cancer signature genes in the sample; (c) comparing the measured expression levels to reference expression levels for the prostate cancer signature genes; and (d) if the measured expression levels are significantly greater or less than the reference expression levels, identifying the subject as having prostate cancer, and if the measured expression levels are not significantly greater or less than the reference expression levels, identifying the subject as not having prostate cancer. The prostate tissue sample may not include tumor cells, or the prostate tissue sample may include tumor cells and stromal cells. The prostate cancer signature genes can be selected from the genes listed in the Tables herein (e.g., in Table 3 or Table 4). The method can include determining whether measured expression levels for ten or more prostate cancer signature genes are significantly greater or less than reference expression levels for the ten or more prostate cancer signature genes, and classifying the subject as having prostate cancer that is likely to relapse if the measured expression levels are significantly greater or less than the reference expression levels, or classifying the subject as having prostate cancer not likely to relapse if the measured expression levels are not significantly greater or less than the reference expression levels. The ten or more prostate cancer signature genes can be selected from the genes listed in Table 3 or Table 4 herein, for example. The method can include determining whether measured expression levels for twenty or more prostate cancer signature genes are significantly greater or less than reference expression levels for the twenty or more prostate cancer signature genes, and classifying the subject as having prostate cancer that is likely to relapse if the measured expression levels are significantly greater or less than the reference expression levels, or classifying the subject as having prostate cancer not likely to relapse if the measured expression levels are not significantly greater or less than the reference expression levels. The twenty or more prostate cancer signature genes can be selected from the genes listed in Table 3 or Table 4 herein, for example.

This document also features methods for determining the prognosis of a subject diagnosed as having prostate cancer, comprising: (a) providing a prostate tissue sample from the subject; (b) measuring the level of expression for prostate cancer signature genes in the sample; (c) comparing the measured expression levels to reference expression levels for the prostate cancer signature genes; and (d) if the measured expression levels are not significantly greater or less than the reference expression levels, identifying the subject as having a relatively better prognosis than if the measured expression levels are significantly greater or less than the reference expression levels, or if the measured expression levels are significantly greater or less than the reference expression levels, identifying the subject as having a relatively worse prognosis than if the measured expression levels are not significantly greater or less than the reference expression levels. The prostate tissue sample may not include tumor cells, or the prostate tissue sample may include tumor cells and stromal cells. The prostate cancer signature genes can be selected from the genes listed in the Tables herein (e.g., Table 8A or 8B). In addition, this document provides methods for identifying a subject as having or not having prostate cancer, comprising: (a) providing a prostate tissue sample from the subject, wherein the sample comprises prostate stromal cells; (b) measuring expression levels for one or more genes in the stromal cells, wherein the one or more genes are prostate cancer signature genes; (c) comparing the measured expression levels to reference expression levels for the one or more genes, wherein the reference expression levels are determined in stromal cells from non-cancerous prostate tissue; and (d) if the measured expression levels are significantly greater or less than the reference expression levels, identifying the subject as having prostate cancer, and if the measured expression levels are not significantly greater or less than the reference expression levels, identifying the subject as not having prostate cancer. The prostate tissue sample may not include tumor cells, or the prostate tissue sample may include tumor cells and stromal cells. The prostate cancer signature genes can be selected from the genes listed in Table 3 or Table 4 herein, for example.

This document also provides methods for determining a prognosis for a subject diagnosed as having prostate cancer, comprising: (a) providing a prostate tissue sample from the subject, wherein the sample comprises prostate stromal cells; (b) measuring expression levels for one or more genes in the stromal cells, wherein the one or more genes are prostate cancer signature genes; (c) comparing the measured expression levels to reference expression levels for the one or more genes, wherein the reference expression levels are determined in stromal cells from non-cancerous prostate tissue; and (d) if the measured expression levels are not significantly greater or less than the reference expression levels, identifying the subject as having a relatively better prognosis than if the measured expression levels are significantly greater or less than the reference expression levels, or if the measured expression levels are significantly greater or less than the reference expression levels, identifying the subject as having a relatively worse prognosis than if the measured expression levels are not significantly greater or less than the reference expression levels. The prostate tissue sample may not include tumor cells, or the prostate tissue sample may include tumor cells and stromal cells. The prostate cancer signature genes can be selected from the genes listed in the tables herein (e.g., Table 3 or Table 4).

Further, this document features a method for identifying a subject as having or not having prostate cancer, comprising: (a) providing a prostate tissue sample from the subject; (b) measuring expression levels for one or more prostate cell-type predictor genes in the sample; (c) determining the percentages of tissue types in the sample based on the measured expression levels; (d) measuring expression levels for one more prostate cancer signature genes in the sample; (e) determining a classifier based on the percentages of tissue types and the measured expression levels; and (f) if the classifier falls into a predetermined range of prostate cancer classifiers, identifying the subject as having prostate cancer, or if the classifier does not fall into the predetermined range, identifying the subject as not having prostate cancer. Steps (b) and (d) can be carried out simultaneously.

This document also features a method for determining a prognosis for a subject diagnosed with and treated for prostate cancer, comprising: (a) providing a prostate tissue sample from the subject; (b) measuring expression levels for one or more prostate tissue predictor genes in the sample; (c) determining the percentages of tissue types in the sample based on the measured expression levels; (d) measuring expression levels for one more prostate cancer signature genes in the sample; (e) determining a classifier based on the percentages of tissue types and the measured expression levels; and (f) if the classifier falls into a predetermined range of prostate cancer relapse classifiers, identifying the subject as being likely to relapse, or if the classifier does not fall into the predetermined range, identifying the subject as not being likely to relapse. Steps (b) and (d) are carried out simultaneously.

In some embodiments, methods as described herein can be used for identifying the proportion of two or more tissue types in a tissue sample. Such methods can include, for example: (a) using a set of other samples of known tissue proportions from a similar anatomical location as the tissue sample in an animal or plant, wherein at least two of the other samples do not contain the same relative content of each of the two or more cell types; (b) measuring overall levels of one or more gene expression or protein analytes in each of the other samples; (c) determining the regression relationship between the relative proportion of each tissue type and the measured overall levels of each gene expression or protein analyte in the other samples; (d) selecting one or more analytes that correlate with tissue proportions in the other samples; (e) measuring overall levels of one or more of the analytes in step (d) in the tissue sample; (f) matching the level of each analyte in the tissue sample with the level of the analyte in step (d) to determine the predicted proportion of each tissue type in the tissue sample; and (g) selecting among predicted tissue proportions for the tissue sample obtained in step (f) using either the median or average proportions of all the estimates. The tissue sample can contain cancer cells (e.g., prostate cancer cells).

Methods described herein can be used for comparing the levels of two or more analytes predicted by one or more methods to be associated with a change in a biological phenomenon in two sets of data each containing more than one measured sample. Such methods can comprise: (a) selecting only analytes that are assayed in both sets of data; (b) ranking the analytes in each set of data using a comparative method such as the highest probability or lowest false discovery rate associated with the change in the biological phenomenon; (c) comparing a set of analytes in each ranked list in step (b) with each other, selecting those that occur in both lists, and determining the number of analytes that occur in both lists and show a change in level associated with the biological phenomenon that is in the same direction; and (d) calculating a concordance score based on the probability that the number of comparisons would show the observed number of change in the same direction, at random. In step (a), the length of each list can be varied to determine the maximum concordance score for the two ranked lists.

The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.

EXAMPLES Example 1 - Diagnosis of Prostate Cancer without Tumor Cells Using Differentially

Expressed Genes in Stroma Adjacent to Tumors

Over one million prostate biopsies are performed in the U.S. every year. Pathology examination is not definitive in a significant percentage of cases, however, due to the presence of equivocal structures or continuing clinical suspicion. To investigate gene expression changes in the tumor microenvironment vs. normal stroma, gene expression profiles from 15 volunteer biopsy specimens were compared to profiles from 13 specimens containing largely tumor-adjacent stroma. As described below, more than a thousand significant expression changes were identified and filtered to eliminate possible age-related genes, as well as genes that also are expressed at detectable levels in tumor cells. A stroma- specific classifier was constructed based on the 114 remaining unique candidate genes (131 Affymetrix probe sets). The classifier was tested on 380 independent cases, including 255 tumor-bearing cases and 125 non-tumor cases (normal biopsies, normal autopsies, remote stroma as well as pure tumor adjacent stroma). The classifier predicted the tumor status of patients with an average accuracy of 97.4% (sensitivity = 98.0% and specificity = 89.7%), whereas a randomly generated and trained classifier had no diagnostic value. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for categorizing stroma as "presence of tumor" and "non-presence of tumor."

Prostate Cancer Patients Samples and Expression Analysis: Datasets 1 and 2 (Table 1) were obtained using post-prostatectomy frozen tissue samples. All tissues, except where noted, were collected at surgery and escorted to pathology for expedited review, dissection, and snap freezing in liquid nitrogen. RNA for expression analysis was prepared directly from frozen tissue following dissection of OCT (optimum cutting temperature compound) blocks with the aid of a cryostat. For expression analysis, 50 micrograms (10 micrograms for biopsy tissue) of total RNA samples were processed for hybridization to Affymetrix GeneChips. Dataset 1 consists of 109 post-prostatectomy frozen tissue samples from 87 patients.

Twenty-two cases were analyzed twice using one sample from a tumor-enriched specimen and one sample from a non-tumor specimen (more than 1.5 cm away from the tumor), usually the contralateral lobe. In addition, Dataset 1 contains 27 prostate biopsy specimens obtained as fresh snap frozen biopsy cores from 18 normal participants in a clinical trial to evaluate the role of Difluoromethylornithine (DFMO) to decrease the prostate size of normal men (Simoneau et al. (2008) Cancer Epidemiol. Biomarkers Prev. 17:292-299). Finally, Dataset 1 contains 13 cases of normal prostates obtained from the rapid autopsy program of the Sun Health Research Institute, from subjects with an average age of 82 years.

Dataset 2 contains 136 samples from 82 patients, where 54 cases were analyzed as pairs of tumor-enriched samples and, for most cases, non-tumor tissue obtained from the same OCT block as tumor-adjacent tissue. This series includes specimens for which expression coefficients were validated (Stuart et al. (2004) Proc. Natl. Acad. ScL U.S.A. 101:615-620).

Expression analysis for Datasets 1 and 2 was carried out using Affymetrix U133Plus2 and U133A GeneChips, respectively; the expression data are publicly available at GEO database on the World Wide Web at ncbi.nlm.nih.gov/geo, with accession numbers GSE17951 (Dataset 1) and GSE8218 (Dataset 2). For both datasets, cell type distributions for the four principal cell types (tumor epithelial cells, stroma cells, epithelial cells of BPH, and epithelial cells of dilated cystic glands) were determined from frozen sections prepared immediately before and after the sections pooled for RNA preparation by three (Dataset 1 ) or four (Dataset 2) pathologists whose estimates were averaged as described (Stuart et al., supra). The distributions of tumor percentage for Dataset 1 and 2 are shown in Figures IB and 1C.

Dataset 3 consists of a published series (Stephenson et al. (2005) Cancer 104:290- 298) of 79 cases for which expression data were measured with Affymetrix U133A chips. The cell composition was not documented at the time of data collection. Cell composition was estimated using multigene signatures that are invariant with tumor surgical pathology parameters of Gleason and stage by the CellPred program (World Wide Web at webarraydb.org), which confirmed that all 79 samples included tumor cells, with tumor content ranging from 24% to 87% (Figure ID). Dataset 4 includes 57 samples from 44 patients, including 13 tumor-adjacent stroma samples and 44 tumor-bearing samples. Gene expression in these 57 samples was measured with Affymetrix U133A GeneChips. Tumor percentage (ranging from 0% to 80%, Figure IE) was approximated using the CellPred program.

Dataset 5 consists of 4 pooled normal stromal samples and 12 tumor samples gleaned by Laser Capture Micro dissection (LCM) using frozen tissue samples. Each pooled normal stroma sample was pooled from two LCM captured stroma samples from specimens from which no tumor was recovered in the surgical samples available for the research protocol described herein, whereas tumor samples were LCM-captured prostate cancer cells. Gene expression in these 16 samples (using 10 micrograms of total RNA) was measured using Affymetrix U133Plus2 chips.

Compared to U133A (with ~ 22,000 probe sets) used for Datasets 2, 3 and 4, the U133Plus2 platform used for Datasets 1 and 5 had about 30,000 more probe sets. To attain an analysis across multiple datasets, only the probes common to these two platforms were used, i.e., only about 22,000 common probe sets in each Dataset were considered. First, Dataset 1 was quantile-normalized using function 'normalizeQuantiles()' of LIMMA routine (Dalgaard (2002) Statistics and Computing: Introductory Statistics with R, p. 260, Springer- Verlag Inc., New York. Datasets 2-5 were then quantile-normalized by referencing normalized Dataset 1 with a modified function 'REFnormalizeQuantiles(),' which is available from ZJ.

Table 1. Datasets used in the study

Data Platform Subj. Array Array: Ref.

Num. Num. Tumor/Nontumor/Normal

P=87 109 69/40/0

1 U133Plus2 B=18 27 0/0/27 GSE17951

Training A=I 3 13 0/0/13 + Test

2 U133A P=82 136 65/71/0 GSE08218 Test

3 U133A P=79 79 79/0/0 Stephenson et al., supra Test

4 Test U133A P=44 57 44/13/0 http://www.ebi.ac.uk/microarray- as/ae/browse.html?keywords=E-

TABM-26

Test U133P2 L=20 16 12/0/4 GSE17951

¹ P, B, A, and L represent patient, normal biopsy, normal rapid autopsy, and LCM, respectively. Datasets 1 and 2 were collected from five participating institutions in San Diego County, CA. Demographic, Pathology, and clinical values are individually recorded (Shadow charts) and maintained in the UCI SPECS consortium database including tracking sheets of elapsed times following surgery during sample handling.

Statistical tools implemented in R. : The Linear Models for Microarray Data (LIMMA package from Bioconductor, on the World Wide Web at bioconductor.org) was used to detect differentially expressed genes. Prediction Analysis of Microarray (PAM, implemented by the PAMR package from Bioconductor) was used to develop an expression-based classifier from training set and then applied to the test sets without any change (Guo et al. (2007) Biostatistics 8:86-100). Fisher's Exact Test was used to demonstrate the efficiency of the classifier when it was tested on remote stroma versus tumor adjacent stroma. Fisher's test was used instead of chi-square because chi-square test is not suitable when the expected values in any of the cells of the table are below 10. All statistical analysis was done using R language (World Wide Web at r-project.org).

Multiple Linear Regression Model: A multiple linear regression (MLR) model was used to describe the observed Affymetrix intensity of a gene as the summation of the contributions from different types of cells given the pathological cell constitution data: where g is the expression value for a gene, p is the percentage data determined by the pathologists, and βs are the expression coefficients associated with different cell types. In model (1), C is the number of tissue types under consideration. In the present case, three major tissue types were included, i.e., tumor, stroma, and BPH. β _} is the estimate of the relative expression level in cell type j (i.e., the expression coefficient) compared to the overall mean expression level βo. The regression model was applied to the patient cases in Dataset 1 to obtain the model parameters (/Ts) and their corresponding p-values, which were used to aid subsequent gene screening. The application to prostate cancer expression data and validation by immunohistochemistry and by correlation of derived β _} values with LCM- derived samples assayed by qPCR has been described (Stuart et al., supra).

Identification of stroma-derived genes and development of the diagnostic classifier: It was hypothesized that stroma within and directly adj acent to prostate cancer epithelial cell formations of infiltrating tumors exhibit significant RNA expression changes compared to normal prostate stroma. To obtain an initial comparison of tumor-adjacent stroma to normal stroma, normal fresh frozen biopsy tissue was used as a source of normal stroma. Out of 27 normal biopsy samples, 15 were selected from 15 different participants. The remaining 12 biopsy samples were reserved for testing. Gene expression microarray data were obtained and compared to 13 tumor-bearing patient cases from Dataset 1 selected to tumor (T) greater 5 than 0% but less than 10% tumor cell content (the average stroma content is -80%). These criteria ensured that the majority of stroma tissues included were close to tumor, while T < 10% ensures that the impact from tumor cells was minimal since the aim was to capture altered expression signals from stroma cells rather than from tumor cells.

As the number of biopsies available was limited, a permutation strategy was adopted o to maximize their use. First 13 of the 15 normal biopsy samples were selected and their gene expression was compared to the 13 tumor-adjacent stroma samples using the moderated t-test implemented in the LIMMA package of R (Dalgaard, supra). This comparison yielded 3888 expression changes between these two groups with ap value < 0.05.

A substantial difference in age existed between the normal stroma group (average age5 = 51.9 years) and the tumor-adjacent stroma group (average age = 60.6 years). The overall gene expression of the 13 normal stroma samples used for training was compared to that of 13 normal prostate specimens obtained from the rapid autopsy program (see above), with an average age of 82 years. The comparison revealed 8898 significant expression changes (p < 0.05), of which 2210 also were detected in the comparison of normal stroma samples0 between tumor-adjacent stroma (Figure 2A). To eliminate potential impact from aging related genes, only 3888 - 2210 = 1678 genes were used for further inquiry.

A potential issue related to using patient cases with 10% > T > 0% was that the detected expression changes may have included expression changes specific to tumor cells or epithelium cells rather than only to stroma cells. To reduce the possibility that epithelial-cell5 derived expression changes dominated, a secondary gene screening via MLR analysis was used. MLR was used to determine cell-specific gene expression based on "knowledge" of the percent cell composition of the samples of Dataset 1 as determined by a panel of four pathologists (Stuart et al., supra; the distribution is shown in Figure IB for 109 samples from 87 patients of Dataset 1). Thus, the expression data of 109 patient samples was fit with an0 MLR model by which the comparative signal from individual cell types (i.e., expression coefficients, /Ts) and corresponding p-values were calculated as described by Stuart et al. (supra). Model diagnostics showed that the fitted model for significant genes (with any significant /Ts) accounted for > 70% of the total variation (or the variation of e in Equation 1 was < 30% of the total variation), indicating a plausible modeling scheme. Cell-type specific expression coefficients were then used to identify genes that are largely expressed in stroma by eliminating genes expressed in epithelial cells at greater than 10% of the expression in stroma cells, i.e., β _τ < — β _s . Thus from the 1678 genes of the initial analysis, 160 candidate

probe sets with three criteria were selected: (1) β _s > 0, (2) β _s > 10x βτβ _s > 10x /J ₂ . , and (3) p

(β _s ) < 0.1. When the values of the β _s 's were compared to the βf s, it became apparent that the expression levels of these 160 probe sets in stroma cells were substantially higher than in tumor cells (Figure 2B). Moreover, the average β _s of these 160 probe sets was 0.011 , which was more than two-fold increased compared to the average of any β _s > 0. Thus, the 160 selected probe sets were among the highest expressed stroma genes observed.

The second step for the permutation analysis was then carried out. The above procedure was repeated using a different selections of 13 biopsy samples of the 15 until all 105 possible combinations of 13 normal biopsy samples drawn from 15 ( C ₁ " = 105 , where

C™ is the number of combinations of m elements chosen from a total of n elements) was complete. A total of 339 probe sets (Table 3) were generated by the 105 -fold gene selection procedure with a frequency of selection as summarized in Figure IA. Permutation increased the basis set by 339/160, or a 2-fold amplification. Probe sets with at least 50 occurrences (about 50%) of the 105-fold permutation were selected for classifier construction. Prediction Analysis for Microarrays (PAM; Tibshirani et al. (2002) Proc. Natl. Acad. ScL U.S.A. 99:6567-6572) was used to build a diagnostic classifier. The training set (Table 2, line 1) included all 15 normal biopsies and the 13 tumor-adjacent stroma samples that were used for the derivation of significant differences. Of the 146 PAM-input probe sets, 131 were retained following the 10-fold cross validation procedure of PAM, leading to a prediction accuracy of 96.4%. The separation of normal and tumor-adjacent stroma cases of the training set by the Classifier is illustrated into two distinct populations is shown in Figure 2C. The complete list of 146 probe-sets, including 131 probe-sets selected by PAM, is given in Table 4. Many of these genes are known by their function and expression in mesenchymal derivatives such as muscle, nerve, and connective tissue.

Table 2. Operating characteristics (OC) for training analysis and tests.

Accuracy Sensitivity

Dataset Case Num. Specificity %

% %

1 Training set 1 28 (15 + 13) 96.4 92.3 100

Test set

Tumor

2 Tumor-bearing 1 55 (68 - 13) 96.4 96.4 NA

3 Tumor-bearing 2 65 100 100 NA

4 Tumor-bearing 3 79 100 100 NA

5 Tumor-bearing 4 44 100 100 NA

Normals

6 Biopsies (1) 1 7 100 NA 100

7 Biopsies (2) 1 5 60 NA 60

8 Rapid autopsies 1 13 92.3 NA 92.3

Manual Microdissected I

LCM

9 Tumor-adjacent Stroma 2 71 97.1 97.1 NA

10 Tumor-adjacent Stroma 4 13 100 100 NA

11 Tumor-adjacent Stroma 1 12 75 75 NA

12 Tumor-bearing LCM 5 12 100 100 NA

13 Normal Stroma LCM 5 4 100 NA 100

Testing with independent datasets: The 131-element classifier was then tested on numerous prostate samples not used for training, including 55 tumor-bearing cases from Dataset 1 and 65 tumor-bearing cases from Dataset 2. Also included were two additional datasets of 79 tumor-bearing cases (Dataset 3) and 44 tumor-bearing cases (Dataset 4), where both the samples and expression analyses were from separate institutes (Table 1). These four test sets were composed entirely of tumor bearing samples (Table 2, lines 2 to 5). In all four tests, almost all samples (n = 243) were recognized as "tumor" with high average accuracy -99%. Figure IB gives the distribution of tumor percentages for the 109 patient cases of Dataset 1. Two misclassified test samples occurred at T = 20% and 25% (marked with "*" in Figure IB) and therefore are not restricted to the presence of high tumor content. The classification method utilizing PAM did not involve any "knowledge" of cell type content and therefore is successful on samples with a broad range of tumor epithelial cells, including samples with just a low percentage of epithelial cells. Such samples consist of over 90% stroma cells. For the test cases of Dataset 2, tumor cell composition ranges from 2% to 80% (Figure 1C). For Datasets 3 and 4, the tumor epithelium component was not assessed but was estimated using the CellPred program. This yielded estimates of 24% to over 80% stroma cell content for Dataset 3, and as little as 0% to over 80% stroma cell content for Dataset 4 (Figures ID and IE). These observations suggested that the classifier is accurate in the classification of independent tumor-bearing samples as "presence of tumor" and does not depend upon "recognition" of gene expression if the tumor epithelial component.

The classifier also was tested using specimens composed mainly of normal prostate stroma and epithelium. First, the classifier was tested on the 12 remaining biopsies from the DMFO study which were separated into two groups. Group 1 (Table 2, line 6) included second biopsies of the same participants whose first biopsy samples were included in the training set, and therefore are not completely independent cases. Group 2 (Table 2, line 7) included the five biopsy samples of cases not used for training. These samples were devoid of tumor but contained normal epithelial components, typically ranging from -35% to -45%. Microarray data were obtained for these 12 cases and used for testing. The biopsy samples in group 1 were accurately (100%) identified as non-tumor. For group 2, two out of five biopsy samples were categorized as "presence of tumor." When the histories for these cases were consulted, however, it was found that both had consistently exhibited elevated PSA levels of 6.1, 9.6, and 8 ng/ml (normal values < 3 ng/ml), respectively, although no tumor was observed in either of two sets of sextant biopsies obtained from these cases. All other donors of normal biopsies exhibited normal PSA values. The classifier was then tested on 13 specimens obtained by rapid autopsy of individuals dying of unrelated causes (Table 2, line 5 8). Twelve out of these 13 cases (i.e., 92.3%), were classified as nontumor. Histological examination of all embedded tissue of the two "misclassified" cases revealed multiple foci of small "latent" tumors. The 25 samples which were drawn from normal tissues were correctly classified as having no tumor present, or were classified in accordance with abnormal features that were subsequently uncovered. These results provide further support for the o ability of the classifier to discriminate between normal and abnormal prostate tissues in the absence of histologically recognizable tumor cells in the samples studied.

Validation by manual microdissection and LCM of tumor-adjacent and remote stroma: Based on the strong performance with mixed tissue test samples, experiments were 5 conducted to validate the classifier by developing histologically confirmed pure tumor- adjacent stroma samples. Tumor-bearing tissue mounted in OCT blocks in a cryostat were examined by frozen section to visualize the location of the tumor. The OCT-embedded block was etched with a single straight cut with a scalpel to divide the embedded tissue into a tumor zone and tumor-adjacent stroma. Subsequent cryosections were separated into two0 halves and used for H and E staining to confirm their composition. For sections of tumor- adjacent stroma with a large area (i.e., ~ 10 mm ), multiple frozen sections were pooled and used for RNA preparation and microarray hybridization. A final frozen section was stained and examined to confirm that it was free of tumor cells. For smaller areas of the tumor- adjacent zone, the adjacent tissue was removed as a piece, remounted in reverse orientation5 and a final frozen section was made to confirm that the piece was free of tumor cells. This tissue was then used for RNA preparation and expression analysis.

Seventy-one tumor-adjacent stroma samples were obtained from the samples of Dataset 2, 13 from the samples of Dataset 4, and 12 from the samples of Dataset 1, using the manual microdissection method. These tumor-adjacent stroma samples were then used for 0 expression analysis. The expression values for the 131 classifier probe sets were tested using the PAM procedure. Accuracies of 97.1%, 100%, and 75% were observed for the classification as "presence of tumor" (Table 2, lines 9-11). These results indicate an overall accuracy of 94.7% for the 96 independent samples.

Finally, examined laser capture microdissected samples were prepared from the samples of Dataset 5. Twelve tumor cell samples were prepared as 100% prostate cancer 5 cells, while four pooled stroma control samples were prepared from cases where no tumor had been recovered in the surgical samples available for the research protocol. These samples were categorized by the classifier as 100% "presence of tumor" and 100% "no presence of tumor," respectively.

Since several cases (especially from Dataset 1) appeared "misclassified," it was of o interest to know how far from a known tumor site the expression changes characteristic of tumor stroma may extend. There was insufficient tissue for a systematic analysis of samples at various known distances, but 28 cases from Dataset 1 were available that were greater than 1.5 cm from the tumor sites of the same gland and generally were from the contralateral lobe of the donor gland. Array data was collected from all pieces and categorized by the 5 classifier. Only ten of the 28 samples (35.7%) were categorized as tumor-associated stroma. This distribution of classifications was compared to the distribution for the original 12 tumor- adjacent stroma samples manually prepared from samples of Dataset 1 (Table 2, line 11) using the Fisher Exact Test. The distribution for the 28 "remote" samples was significantly different than the category distribution for the 12 authentic tumor-adjacent stroma samples of0 the same cases as judged by a Fischer Exact test, p = 0.038. This result strongly suggests that the expression changes of tumor-adjacent stroma are not inevitable in stroma taken from arbitrary sites of the same tumor-bearing glands, and likely reflect that proximity to tumor affects the expression changes of the genes of the classifier developed here. 5 Comparison with random-gene classifiers: To further validate the 131-element diagnostic classifier, 100 randomized experiments were carried out. In each experiment, 1,700 probe sets were randomly selected from the 12,901 probe set basis, which was obtained by subtracting 9376 aging related probe sets from the entire 22277 probe sets, where 9376 aging related expression changes were defined exactly as before. Finally, the0 sampled probe sets were screened with the same MLR criteria used for development of the 131-element classifier, i.e., (1) β _s > 0, (2) β _s > lOxβr, and (3) p (β _s < 0.1). In each random experiment, the genes that survived the MLR filter were used to develop a classifier with PAM exactly as for the 131-probe set classifier. PAM selected an average of 6.2 probe sets (« 131), and the average performance of these random-gene classifiers based on the tests of other datasets are summarized in Table 5. These random-gene classifiers failed to detect the presence of tumor in most of the test sets. The random classifier was particularly poor, however, in defining a normal distribution for Dataset 1, leading an 8.7% (Table 5, line 2) sensitivity suggesting a bias toward "no presence of tumor." This correlated with the second lack of normal distribution due to a similar bias toward "no presence of tumor," but this time affecting the normal tissues and thereby giving rise to the appearance of accuracy with an average of 82.3% (Table 5, average lines 6-9 and 13). In general, however, the random model tended to be a normal distribution with poor accuracies in the range of 12.9% to 19.2%, indicating that the results obtained with the developed 131-probe set classifier cannot be attributed to chance.

Table 3. Basis set of genes, derived as described herein.

Probe Set ID Gene Title Gene logFC t P Adj. B

Symbol P

200067_x_at sorting nexin 3 SNX3 -0.13 -1.85 0.07 0.34 -4.82

200685_at splicing factor, SFRSU -0.16 -2.19 0.04 0.24 -4.20 arginine/serine-rich 11

200788_s_at phosphoprotein enriched in PEA15 -0.22 -2.34 0.03 0.20 -3.91 astrocytes 15

201022_s_at destrin (actin depolymerizing DSTN -0.14 -2.07 0.05 0.27 -4.43 factor)

201312_s_at SH3 domain binding glutamic SH3BGR -0.19 -1.84 0.08 0.34 -4.82 acid-rich protein like L

201313_at enolase 2 (gamma, neuronal) ENO2 -0.36 -2.15 0.04 0.25 -4.29

201344_at ubiquitin-conjugating enzyme UBE2D2 -0.38 -2.96 0.01 0.09 -2.59

E2D 2 (UBC4/5 homolog, yeast)

201380_at cartilage associated protein CRTAP -0.22 -2.00 0.05 0.29 -4.56

201389_at integrin, alpha 5 (fibronectin ITGA5 -0.50 -2.46 0.02 0.17 -3.67 receptor, alpha polypeptide)

201430_s_at dihydropyrimidinase-like 3 DPYSL3 -0.35 -1.85 0.08 0.34 -4.82

201431 s at dihydropyrimidinase-like 3 DPYSL3 -0.40 -2.78 0.01 0.12 -3.00

201540_at four and a half LIM domains 1 FHLl -0.23 -1.94 0.06 0.31 -4.66

201560_at chloride intracellular channel 4 CLIC4 -0.15 -1.73 0.09 0.37 -5.01

201566_x_at inhibitor of DNA binding 2, ID2 0.40 2.73 0.01 0.13 -3.11 dominant negative helix -loop- helix protein

201655_s_at heparan sulfate proteoglycan 2 HSPG2 -0.18 -1.19 0.25 0.57 -5.75 201667_at gap junction protein, alpha 1 , GJAl -0.17 -1.75 0.09 0.36 -4.97

43kDa

20184 l_s_at heat shock 27kDa protein 1 HSPBl -0.44 -3.97 0.00 0.02 -0.12 201843_s_at EGF-containing fibulin-like EFEMPl -0.32 -2.21 0.04 0.23 -4.17 extracellular matrix protein 1 201980_s_at Ras suppressor protein 1 RSUl -0.17 -1.79 0.08 0.35 -4.91 201981_at pregnancy-associated plasma PAPPA -0.24 -1.51 0.14 0.45 -5.34 protein A, pappalysin 1 202073_at optineurin OPTN -0.29 -1.93 0.06 0.31 -4.68 202192_s_at growth arrest-specific 7 GAS7 -0.43 -1.96 0.06 0.30 -4.62 202196_s_at dickkopf homolog 3 (Xenopus DKK3 -0.15 -1.29 0.21 0.53 -5.63 laevis)

202202_s_at laminin, alpha 4 LAMA4 -0.35 -1.83 0.08 0.34 -4.85 202362_at RAPlA, member of RAS RAPlA -0.32 -1.94 0.06 0.31 -4.65 oncogene family

202422_s_at acyl-CoA synthetase long- ACSL4 -0.16 -1.08 0.29 0.62 -5.87 chain family member 4 202432_at protein phosphatase 3 PPP3CB -0.17 -1.81 0.08 0.35 -4.89

(formerly 2B), catalytic subunit, beta isoform 202440_s_at suppression of tumorigenicity ST5 -0.17 -1.26 0.22 0.54 -5.66

5 202522_at phosphatidylinositol transfer PITPNB -0.16 -2.85 0.01 0.11 -2.85 protein, beta

202565_s_at supervillin SVIL -0.36 -2.45 0.02 0.18 -3.69 202588_at adenylate kinase 1 AKl -0.18 -1.96 0.06 0.30 -4.63 202613_at CTP synthase CTPS -0.21 -1.71 0.10 0.38 -5.03 202620_s_at procollagen-lysine, 2- PLOD2 -0.13 -1.34 0.19 0.51 -5.57 oxoglutarate 5-dioxygenase 2 202685_s_at AXL receptor tyrosine kinase AXL -0.30 -1.79 0.08 0.35 -4.92 202796_at synaptopodin SYNPO -0.22 -1.29 0.21 0.53 -5.63 202806_at drebrin 1 DBNl -0.43 -4.08 0.00 0.02 0.17 20293 l_x_at bridging integrator 1 BINl -0.27 -2.39 0.02 0.19 -3.82 20315 l_at microtubule-associated protein MAPlA -0.69 -4.02 0.00 0.02 0.03

IA 203178_at glycine amidinotransferase (L- GATM -0.24 -1.39 0.18 0.49 -5.51 arginineiglycine amidinotransferase) 203299_s_at adaptor-related protein AP1S2 -0.41 -2.77 0.01 0.12 -3.01 complex 1, sigma 2 subunit 203389_at kinesin family member 3C KIF3C -0.26 -2.39 0.02 0.19 -3.82 203436_at ribonuclease P/MRP 3OkDa RPP30 -0.14 -1.61 0.12 0.41 -5.19 subunit

203438_at stanniocalcin 2 STC2 -0.37 -1.80 0.08 0.35 -4.90 203456_at PRAl domain family, member PRAF2 -0.28 -2.07 0.05 0.27 -4.44

2 203501_at plasma glutamate PGCP -0.30 -2.27 0.03 0.22 -4.05 carboxypeptidase 203597_s_at WW domain binding protein 4 WBP4 -0.34 -3.56 0.00 0.04 -1.17

(formin binding protein 21) 203705_s_at frizzled homolog 7 FZD7 0.25 1.46 0.15 0.47 -5.41 (Drosophila)

203729_at epithelial membrane protein 3 EMP3 -0.31 -1.45 0.16 0.47 -5.43 203766_s_at leiomodin 1 (smooth muscle) LMODl -0.36 -2.04 0.05 0.28 -4.49 203939_at 5 '-nucleotidase, ecto (CD73) NT5E -0.49 -3.80 0.00 0.03 -0.54 204030_s_at schwannomin interacting SCHIPl -0.32 -1.91 0.07 0.32 -4.71 protein 1 204036_at lysophosphatidic acid receptor LPARl -0.31 -1.85 0.07 0.33 -4.81

1 204058_at malic enzyme 1, NADP(+)- MEl -0.34 -2.21 0.03 0.23 -4.17 dependent, cytosolic 204059_s_at malic enzyme 1, NADP(+)- MEl -0.35 -1.96 0.06 0.30 -4.63 dependent, cytosolic 204115_at guanine nucleotide binding GNGU -0.22 -1.34 0.19 0.51 -5.57 protein (G protein), gamma 11 204134_at phosphodiesterase 2A, cGMP- PDE2A -0.16 -1.41 0.17 0.49 -5.48 stimulated 204159_at cyclin-dependent kinase CDKN2C -0.46 -3.42 0.00 0.05 -1.49 inhibitor 2C (pi 8, inhibits

CDK4) 204302_s_at KIAA0427 KIAA042 -0.10 -1.10 0.28 0.61 -5.85

I

204303_s_at KIAA0427 KIAA042 -0.35 -2.17 0.04 0.24 -4.25

I

204304_s_at prominin 1 PROMl 0.59 1.26 0.22 0.55 -5.67

204365_s_at receptor accessory protein 1 REEPl -0.29 -2.18 0.04 0.24 -4.23

204396_s_at G protein-coupled receptor GRK5 -0.46 -2.09 0.05 0.27 -4.40 kinase 5 204410_at eukaryo tic translation EIFlAY -0.21 -1.56 0.13 0.43 -5.27 initiation factor IA, Y-linked 204517_at peptidylprolyl isomerase C PPIC -0.17 -1.98 0.06 0.30 -4.60

(cyclophilin C)

204557_s_at DAZ interacting protein 1 DZIPl -0.21 -1.57 0.13 0.43 -5.25 204570_at cytochrome c oxidase subunit C0X7A1 -0.37 -1.56 0.13 0.43 -5.27

Vila polypeptide 1 (muscle) 204584_at Ll cell adhesion molecule LlCAM -1.20 -3.10 0.00 0.08 -2.26 204627_s_at integrin, beta 3 (platelet ITGB3 -0.82 -3.51 0.00 0.04 -1.28 glycoprotein Ilia, antigen

CD61) 204628_s_at integrin, beta 3 (platelet ITGB3 -0.31 -2.42 0.02 0.18 -3.75 glycoprotein Ilia, antigen

CD61)

204639_at adenosine deaminase ADA -0.38 -1 .27 0 .21 0.54 -5 .66 204736_s_at chondroitin sulfate CSPG4 -0.55 -3 .29 0 .00 0.06 -1 .81 proteoglycan 4 204777_s_at mal, T-cell differentiation MAL -0.99 -3.32 0.00 0.06 -1.74 protein

204939_s_at phospholamban PLN -0.45 -2.53 0.02 0.16 -3.53 204940_at phospholamban PLN -0.49 -2.45 0.02 0.18 -3.70 204963_at sarcospan (Kras oncogene- SSPN -0.26 -1.97 0.06 0.30 -4.61 associated gene) 205076_s_at myotubularin related protein MTMRU -0.57 -2.92 0.01 0.10 -2.69 11

20511 l_s_at phospholipase C, epsilon 1 PLCEl -0.35 -1.53 0.14 0.44 -5.30 205132_at actin, alpha, cardiac muscle 1 ACTCl -0.99 -3.28 0.00 0.06 -1.83 20523 l_s_at epilepsy, progressive EPM2A -0.42 -2.97 0.01 0.09 -2.56 myoclonus type 2A, Lafora disease (laforin) 205257_s_at amphiphysin AMPH -0.22 -1.75 0.09 0.37 -4.98 205265_s_at SPEG complex locus SPEG -0.31 -1.68 0.10 0.39 -5.09 205303_at potassium inwardly-rectifying KCNJ8 -0.42 -2.88 0.01 0.10 -2.77 channel, subfamily J, member

8 205304_s_at potassium inwardly-rectifying KCNJ8 -0.24 -1.83 0.08 0.34 -4.84 channel, subfamily J, member

8 205325_at phytanoyl-CoA 2-hydroxylase PHYHIP -0.42 -1.49 0.15 0.46 -5.37 interacting protein 205368_at family with sequence FAM 13 IB -0.27 -2.31 0.03 0.21 -3.98 similarity 131, member B 205384_at FXYD domain containing ion FXYDl -0.52 -1.81 0.08 0.34 -4.87 transport regulator 1

(phospholemman)

205398_s_at SMAD family member 3 SMAD3 -0.22 -1.52 0.14 0.45 -5.33 205433_at butyrylcholinesterase BCHE -0.93 -2.52 0.02 0.16 -3.55 205475_at scrapie responsive protein 1 SCRGl -0.45 -1.87 0.07 0.33 -4.78 205478_at protein phosphatase 1, PPPlRlA -0.36 -1.58 0.12 0.43 -5.24 regulatory (inhibitor) subunit

IA 205554_s_at deoxyribonuclease I-like 3 DNASEl 0.35 1.57 0.13 0.43 -5.25 L3

205561_at potassium channel KCTD 17 -0.32 -2.77 0.01 0.12 -3.02 tetramerisation domain containing 17 20561 l_at tumor necrosis factor (ligand) TNFSF12 -0.29 -2.18 0.04 0.24 -4.22 superfamily, member 12 205618_at proline rich GIa (G- PRRGl -0.16 -1.26 0.22 0.54 -5.66 carboxyglutamic acid) 1 205632_s_at phosphatidylinositol-4- PIP5K1B -0.43 -1.96 0.06 0.30 -4.63 phosphate 5-kinase, type I, beta 205674_x_at FXYD domain containing ion FXYD2 -0.14 -1.10 0.28 0.61 -5.85 transport regulator 2 205792_at WNTl inducible signaling WISP2 -0.66 -1.89 0.07 0.32 -4.74 pathway protein 2

205954_at retinoid X receptor, gamma RXRG -0.53 -3.47 0.00 0.04 -1.38 205973_at fasciculation and elongation FEZl -0.35 -2.38 0.02 0.19 -3.83 protein zeta 1 (zygin I) 206024_at 4-hydroxyphenylpyruvate HPD -0.57 -2.79 0.01 0.12 -2.98 dioxygenase

206132_at mutated in colorectal cancers MCC 0.48 2. 01 0 .05 0.29 -4. 53 20620 l_s_at mesenchyme homeobox 2 ME0X2 -0.53 -1 .65 0 .11 0.40 -5. 13 206283_s_at T-cell acute lymphocytic TALI -0.26 -1 .93 0 .06 0.31 -4. 68 leukemia 1

206289_at homeobox A4 HOXA4 -0.29 -2.36 0.03 0.20 -3.88

206306_at ryanodine receptor 3 RYR3 -0.46 -1.85 0.07 0.33 -4.81

20633 l_at calcitonin receptor-like CALCRL -0.27 -1.80 0.08 0.35 -4.90

206382_s_at brain-derived neurotrophic BDNF -0.62 -2.89 0.01 0.10 -2.74 factor

206423_at angiopoietin-like 7 ANGPTL -0.47 -1.94 0.06 0.31 -4.66

I

206425_s_at transient receptor potential TRPC3 -0.57 -3.31 0.00 0.06 -1.77 cation channel, subfamily C, member 3

206510_at SIX homeobox 2 SIX2 -0.60 -1.61 0.12 0.42 -5.19

206525_at gamma-aminobutyric acid GABRRl 0.15 1.07 0.29 0.62 -5.88

(GABA) receptor, rho 1

206560_s_at melanoma inhibitory activity MIA -0.19 -1.72 0.10 0.38 -5.03

206580_s_at EGF-containing fibulin-like EFEMP2 -0.21 -1.29 0.21 0.53 -5.63 extracellular matrix protein 2

206874_s_at ___ ___ -0.44 -4.27 0.00 0.01 0.66

206898_at cadherin 19, type 2 CDH 19 -0.48 -2.00 0.05 0.29 -4.56

20707 l_s_at aconitase 1 , soluble ACOl -0.27 -2.90 0.01 0.10 -2.72

207303_at phosphodiesterase 1C, PDElC -0.24 -1.74 0.09 0.37 -5.00 calmodulin-dependent 7OkDa

207332_s_at transferrin receptor (p90, TFRC 0.18 1.32 0.20 0.52 -5.59

CD71)

207437_at neuro-oncological ventral NOVAl -0.43 -1.58 0.13 0.43 -5.24 antigen 1

207554_x_at thromboxane A2 receptor TBXA2R -0.44 -2.86 0.01 0.11 -2.82

207834_at fibulin 1 FBLNl -0.35 -1.98 0.06 0.30 -4.59

207876_s_at filamin C, gamma (actin FLNC -0.45 -2.98 0.01 0.09 -2.55 binding protein 280)

208131_s_at prostaglandin 12 (prostacyclin) PTGIS -0.28 -2.02 0.05 0.28 -4.51 synthase

208760_at Ubiquitin-conjugating enzyme UBE2I -0.24 -1.84 0.08 0.34 -4.83

E2I (UBC9 homolog, yeast)

208789_at polymerase I and transcript PTRF -0.42 -2.27 0.03 0.22 -4.06 release factor

208792_s_at clusterin CLU -0.15 -1.03 0.31 0.64 -5.92

208869_s_at GABA(A) receptor-associated GABARA -0.19 -2.73 0.01 0.13 -3.11 protein like 1 PLl

209015_s_at DnaJ (Hsp40) homolog, DNAJB6 -0.29 -2.61 0.01 0.15 -3.36 subfamily B, member 6

209086_x_at melanoma cell adhesion MCAM -0.61 -4.06 0.00 0.02 0.12 molecule

209087_x_at melanoma cell adhesion MCAM -0.40 -2.32 0.03 0.21 -3.96 molecule

209167_at glycoprotein M6B GPM6B -0.22 -2.14 0.04 0.25 -4.30

209168_at glycoprotein M6B GPM6B -0.18 -1.59 0.12 0.42 -5.22

209169_at glycoprotein M6B GPM6B -0.34 -3.16 0.00 0.07 -2.13

209170_s_at glycoprotein M6B GPM6B -0.23 -1.61 0.12 0.41 -5.19

209191 at tubulin, beta 6 TUBB6 -0.51 -2.92 0.01 0.10 -2.67

209242 at paternally expressed 3 PEG3 -0.25 -1.64 0.11 0.41 -5.15 209263_x_at tetraspanin 4 TSPAN4 -0.17 -1.42 0.17 0.48 -5.46

209288_s_at CDC42 effector protein (Rho CDC42EP -0.21 -1.86 0.07 0.33 -4.79

GTPase binding) 3 i 209293_x_at inhibitor of DNA binding 4, ID4 0.18 1.60 0.12 0.42 -5.21 dominant negative helix -loop- helix protein 209298_s_at intersectin 1 (SH3 domain ITSNl -0.21 -1.66 0.11 0.40 -5.12 protein) 209356_x_at EGF-containing fibulin-like EFEMP2 -0.23 -1.49 0.15 0.46 -5.36 extracellular matrix protein 2 209362_at mediator complex subunit 21 MED21 -0.26 -2.58 0.02 0.15 -3.43 209454_s_at TEA domain family member 3 TEAD3 -0.23 -1.71 0.10 0.38 -5.04 209488_s_at RNA binding protein with RBPMS -0.33 -1.83 0.08 0.34 -4.84 multiple splicing 209524_at hepatoma-derived growth HDGFRP -0.14 -2.18 0.04 0.24 -4.22 factor, related protein 3 J 209543_s_at CD34 molecule CD34 -0.15 -1.58 0.12 0.42 -5.23 209612_s_at alcohol dehydrogenase IB ADHlB -0.41 -1.20 0.24 0.57 -5.74

(class I), beta polypeptide 209613_s_at alcohol dehydrogenase IB ADHlB -0.63 -1.96 0.06 0.30 -4.63

(class I), beta polypeptide 209614_at alcohol dehydrogenase IB ADHlB -0.24 -1.89 0.07 0.32 -4.75

(class I), beta polypeptide 20965 l_at transforming growth factor TGFBlIl -0.42 -2.62 0.01 0.14 -3.35 beta 1 induced transcript 1 209685_s_at protein kinase C, beta 1 PRKCBl -0.26 -1.29 0.21 0.53 -5.63 209686_at S 100 calcium binding protein SlOOB -0.94 -3.82 0.00 0.03 -0.50

B 209758_s_at microfibrillar associated MFAP5 -1.48 -7.89 0.00 0.00 10.08 protein 5 209764_at mannosyl (beta-1,4-)- MGAT3 -0.17 -1.65 0.11 0.40 -5.14 glycoprotein beta-l,4-N- acetylglucosaminyltransferase 209765_at ADAM metallopeptidase ADAM 19 -0.36 -1.78 0.09 0.36 -4.93 domain 19 (meltrin beta) 209843_s_at SRY (sex determining region SOXlO -0.61 -5.58 0.00 0.00 4.16

Y)-box 10

209859_at tripartite motif -containing 9 TRIM9 -0.19 -1.09 0.28 0.61 -5.85 209915_s_at neurexin 1 NRXNl -0.80 -4.05 0.00 0.02 0.08 20998 l_at cold shock domain containing CSDC2 -0.56 -2.43 0.02 0.18 -3.73

C2, RNA binding 210198_s_at proteolipid protein 1 PLPl -1.18 -4.91 0.00 0.00 2.36

(Pelizaeus -Merzbacher disease, spastic paraplegia 2, uncomplicated)

210201_x_at bridging integrator 1 BINl -0.29 -2.54 0.02 0.16 -3.52 210270_at regulator of G-protein RGS6 -0.17 -1.55 0.13 0.43 -5.28 signaling 6 210277_at adaptor-related protein AP4S1 -0.22 -1.34 0.19 0.51 -5.57 complex 4, sigma 1 subunit 210280_at myelin protein zero (Charcot- MPZ -1.20 -5.02 0.00 0.00 2.64 Mane-Tooth neuropathy IB)

210319_x_at msh homeobox 2 MSX2 0.45 2.31 0.03 0.21 -3.98

210432_s_at sodium channel, voltage-gated, SCN3A -0.46 -1.94 0.06 0.31 -4.66 type III, alpha subunit

210632_s_at sarcoglycan, alpha (5OkDa SGCA -0.58 -2.55 0.02 0.16 -3.49 dystrophin-associated glycoprotein)

210736_x_at dystrobrevin, alpha DTNA -0.22 -1.59 0.12 0.42 -5.23

210814_at transient receptor potential TRPC3 -0.75 -3.30 0.00 0.06 -1.80 cation channel, subfamily C, member 3

210852_s_at aminoadipate-semialdehyde AASS 0.24 2.06 0.05 0.27 -4.46 synthase

210869_s_at melanoma cell adhesion MCAM -0.71 -3.93 0.00 0.02 -0.21 molecule

210872_x_at growth arrest-specific 7 GAS7 -0.17 -1.32 0.20 0.52 -5.59

210941_at protocadherin 7 PCDH7 0.31 2.05 0.05 0.28 -4.46

211006_s_at potassium voltage-gated KCNBl -0.31 -1.89 0.07 0.32 -4.75 channel, Shab-related subfamily, member 1

211275_s_at glycogenin 1 GYGl -0.20 -1.66 0.11 0.40 -5.12

211276_at transcription elongation factor TCEAL2 -0.52 -2.89 0.01 0.10 -2.75

A (SΙI)-like 2

211340_s_at melanoma cell adhesion MCAM -0.46 -3.05 0.00 0.08 -2.38 molecule

211347_at CDC 14 cell division cycle 14 CDC 14B -0.21 -2.21 0.03 0.23 -4.16 homolog B (S. cerevisiae)

211348_s_at CDC 14 cell division cycle 14 CDC 14B -0.17 -1.72 0.10 0.38 -5.02 homolog B (S. cerevisiae)

211491_at adrenergic, alpha-lA-, ADRAlA -0.28 -1.80 0.08 0.35 -4.90 receptor

211562_s_at leiomodin 1 (smooth muscle) LMODl -0.39 -1.67 0.11 0.39 -5.10

211564_s_at PDZ and LIM domain 4 PDLIM4 -0.16 -1.05 0.30 0.63 -5.90

211673_s_at molybdenum cofactor MOCSl -0.19 -1.23 0.23 0.55 -5.70 synthesis 1

211677_x_at cell adhesion molecule 3 CADM3 -0.21 -2.08 0.05 0.27 -4.41

211717_at ankyrin repeat domain 40 ANKRD4 -0.28 -2.76 0.01 0.12 -3.03

U

211954_s_at importin 5 IPO5 -0.15 -2.05 0.05 0.28 -4.46

211964_at collagen, type IV, alpha 2 COL4A2 -0.39 -2.27 0.03 0.22 -4.06

212086_x_at lamin A/C LMNA 0.25 1.74 0.09 0.37 -5.00

212097_at caveolin 1 , caveolae protein, CAVl -0.38 -4.57 0.00 0.01 1.46

22kDa

212119_at ras homolog gene family, RHOQ -0.18 -2.08 0.05 0.27 -4.42 member Q

212120_at ras homolog gene family, RHOQ -0.31 -2.60 0.01 0.15 -3.39 member Q

212274 at lipin 1 LPINl -0.48 -3.92 0.00 0.02 -0.25

212358_at CAP-GLY domain containing CLIP3 -0.47 -2.34 0.03 0.20 -3.92 linker protein 3

212385_at transcription factor 4 TCF4 0.30 2.07 0.05 0.27 -4.43 212457_at transcription factor binding to TFE3 -0.25 -2.38 0.02 0.19 -3.84

IGHM enhancer 3 212509_s_at matrix-remodelling associated MXRA7 -0.27 -2.66 0.01 0.14 -3.26

7 212526_at spastic paraplegia 20 (Troyer SPG20 -0.17 -1.91 0.07 0.32 -4.71 syndrome)

212565_at serine/threonine kinase 38 like STK38L -0.58 -3.83 0.00 0.03 -0.47 212589_at related RAS viral (r-ras) RRAS2 -0.29 -2.84 0.01 0.11 -2.86 oncogene homolog 2 212610_at protein tyrosine phosphatase, PTPNU -0.23 -2.24 0.03 0.22 -4.12 non-receptor type 11 (Noonan syndrome 1) 212647_at related RAS viral (r-ras) RRAS -0.39 -1.71 0.10 0.38 -5.05 oncogene homolog 212707_s_at RAS p21 protein activator 4 /// FLJ21767 -0.20 -1.40 0.17 0.49 -5.49 hypothetical protein FLJ21767 ///

/// similar to HSPC047 protein LOClOOl

/// similar to RAS p21 protein 32214 /// activator 4 LOClOOl

33005 /// RASA4 212747_at ankyrin repeat and sterile ANKSlA -0.17 -1.41 0.17 0.49 -5.48 alpha motif domain containing

IA 212764_at zinc finger E-box binding ZEBl -0.24 -1.79 0.08 0.35 -4.92 homeobox 1 212793_at dishevelled associated DAAM2 -0.56 -3.95 0.00 0.02 -0.17 activator of morphogenesis 2 212848_s_at chromosome 9 open reading C9orf3 -0.27 -2.22 0.03 0.23 -4.16 frame 3 212886_at coiled-coil domain containing CCDC69 -0.59 -3.96 0.00 0.02 -0.13

69 212887_at Sec23 homolog A (S. SEC23A -0.20 -1.86 0.07 0.33 -4.79 cerevisiae)

212992_at AHNAK nucleoprotem 2 AHNAK2 -0.60 -2.71 0.01 0.13 -3.14

213010_at protein kinase C, delta binding PRKCDB -0.47 -1.99 0.06 0.29 -4.57 protein P

213107_at TRAF2 and NCK interacting TNIK 0.40 2.03 0.05 0.28 -4.49 kinase

213181_s_at molybdenum cofactor MOCSl -0.21 -1.57 0.13 0.43 -5.25 synthesis 1

213203_at small nuclear RNA activating SNAPC5 -0.15 -1.56 0.13 0.43 -5.27 complex, polypeptide 5,

19kDa

21323 l_at dystrophia myotonica, WD DMWD -0.30 -2.40 0.02 0.19 -3.79 repeat containing

213274_s_at cathepsin B CCTTSSBB --00..3300 -1.53 0.14 0.44 -5.32

213428 s at collagen, type VI, alpha 1 CCOOLL66AA11 --00..2211 -1.37 0.18 0.50 -5.52

213480_at vesicle-associated membrane VVAAMMPP44 --00..2244 -2.61 0.01 0.15 -3.36 protein 4

213545_x_at sorting nexin 3 SSNNXX33 --00..1111 -1.41 0.17 0.49 -5.48 213547_at cullin-associated and CAND2 -0.31 -2.41 0.02 0.18 -3.77 neddylation-dissociated 2

(putative)

213630_at NAC alpha domain containing NACAD -0.18 -1.42 0.16 0.48 -5.46 213675_at CDNA FLJ25106 fis, clone -0.44 -3.25 0.00 0.06 -1.92

CBRO 1467 213764_s_at microfibrillar associated MFAP5 -1.73 -7.18 0.00 0.00 8.33 protein 5 213765_at microfibrillar associated MFAP5 -1.36 -6.40 0.00 0.00 6.31 protein 5

213808_at Clone 23688 mRNA sequence -0.43 -2.16 0.04 0.25 -4.26 213847_at peripherin PRPH -0.93 -4.12 0.00 0.02 0.27 213924_at Metallophosphoesterase 1 MPPEl -0.26 -1.72 0.10 0.38 -5.02 214023_x_at tubulin, beta 2B TUBB2B -0.75 -4.21 0.00 0.01 0.51 214027_x_at desmin /// family with DES /// -0.42 -1.97 0.06 0.30 -4.61 sequence similarity 48, FAM48A member A 214039_s_at lysosomal associated protein LAPTM4 -0.17 -1.20 0.24 0.57 -5.73 transmembrane 4 beta B 214078_at Primary neuroblastoma cDNA, -0.35 -1.44 0.16 0.47 -5.43 clone:Nbla04246, full insert sequence 214121_x_at PDZ and LIM domain 7 PDLIM7 -0.32 -1.68 0.10 0.39 -5.08

(enigma) 214122_at PDZ and LIM domain 7 PDLIM7 -0.30 -2.74 0.01 0.13 -3.09

(enigma)

214159_at Phospholipase C, epsilon 1 PLCEl -0.27 -1.79 0.08 0.35 -4.91 214174_s_at PDZ and LIM domain 4 PDLIM4 -0.23 -1.43 0.16 0.48 -5.45 214175_x_at PDZ and LIM domain 4 PDLIM4 -0.27 -1.54 0.14 0.44 -5.30 214212_x_at f ermitin family homolog 2 FERMT2 -0.42 -3.00 0.01 0.09 -2.50

(Drosophila) 214247_s_at dickkopf homolog 3 (Xenopus DKK3 -0.17 -1.51 0.14 0.45 -5.34 laevis) 214297_at chondroitin sulfate CSPG4 -0.45 -1.78 0.09 0.36 -4.94 proteoglycan 4 214306_at optic atrophy 1 (autosomal OPAl -0.27 -2.67 0.01 0.14 -3.23 dominant) 214368_at RAS guanyl releasing protein RASGRP -0.23 -2.08 0.05 0.27 -4.40

2 (calcium and DAG- 2 regulated)

214434_at heat shock 7OkDa protein 12A HSPA 12A -0.57 -3.40 0.00 0.05 -1.54 214439_x_at bridging integrator 1 BINl -0.29 -2.56 0.02 0.16 -3.47 214449_s_at ras homolog gene family, RHOQ -0.18 -1.81 0.08 0.34 -4.88 member Q 214600_at TEA domain family member 1 TEADl -0.28 -1.61 0.12 0.42 -5.19

(SV40 transcriptional enhancer factor)

214606_at tetraspanin 2 TSPAN2 -0.54 -4.01 0 .00 0.02 -0.02 214643_x_at bridging integrator 1 BINl -0.23 -2.16 0 .04 0.25 -4.27 214696_at chromosome 17 open reading C17orf91 0.50 1.92 0 .07 0.31 -4.70 frame 91 214767_s_at heat shock protein, alpha- HSPB 6 -0.88 -4.27 0.00 0.01 0.66 crystallin-related, B 6

214954_at sushi domain containing 5 SUSD5 -0.98 -3.42 0.00 0.05 -1.51 214987_at CDNA clone — -0.29 -1.94 0.06 0.31 -4.66

IMAGE:4801326 215000_s_at fasciculation and elongation FEZ2 -0.14 -1.99 0.06 0.29 -4.57 protein zeta 2 (zygin II) 215104_at nuclear receptor interacting NRIP2 -0.94 -4.62 0.00 0.01 1.59 protein 2 215306_at MRNA; cDNA -0.48 -2.66 0.01 0.14 -3.26

DKFZp586N2020 (from clone

DKFZp586N2020) 215534_at MRNA; cDN A — -0.46 -2.46 0.02 0.17 -3.68

DKFZp586C1923 (from clone

DKFZp586C1923) 216096_s_at neurexin 1 NRXNl -0.37 -1.68 0.10 0.39 -5.08 216500_at HL 14 gene encoding beta- — -0.29 -2.31 0.03 0.21 -3.98 galactoside-binding lectin, 3' end, clone 2 216894_x_at cyclin-dependent kinase CDKNlC -0.27 -2.45 0.02 0.18 -3.69 inhibitor 1C (p57, Kip2) 217066_s_at dystrophia myotonica-protein DMPK -0.29 -2.11 0.04 0.26 -4.37 kinase 217589_at RAB40A, member RAS RAB40A 0.37 1.49 0.15 0.46 -5.36 oncogene family 217764_s_at RAB31, member RAS RAB 31 -0.21 -1.38 0.18 0.50 -5.51 oncogene family

217820_s_at enabled homolog (Drosophila) EENNAAHH -0.19 -2.12 0.04 0.26 -4.33 217880_at cell division cycle 27 homolog CCDDCC2277 -0.16 -1.54 0.13 0.44 -5.30

(S. cerevisiae) 218087_s_at sorbin and SH3 domain SORBSl -0.18 -2.00 0.05 0.29 -4.56 containing 1 218094_s_at dysbindin (dystrobrevin DDBBNNDDDD22 --00..4411 -3.66 0.00 0.03 -0.90 binding protein 1) domain /// SYSl- containing 2 /// SYSl- DBNDD2

DBNDD2 218183_at chromosome 16 open reading CC1166oorrff55 --00..1166 -1.63 0.11 0.41 -5.16 frame 5 218204_s_at FYVE and coiled-coil domain FFYYCCOOll --00..1166 -1.57 0.13 0.43 -5.25 containing 1 218208_at PQ loop repeat containing 1 /// LOClOOl -0.23 -1.79 0.08 0.35 -4.91 hypothetical protein 31178 ///

LOC100131178 PQLCl

218266_s_at frequenin homolog FREQ -0.46 -2.32 0.03 0.21 -3.95

(Drosophila) 218345_at transmembrane protein 176A TMEM 17 -0.27 -1.05 0.30 0.63 -5.90

6A

218435_at DnaJ (Hsp40) homolog, DNAJC 15 -0.49 -2.55 0.02 0.16 -3.48 subfamily C, member 15 218545_at coiled-coil domain containing CCDC91 -0.31 -2.97 0.01 0.09 -2.57

91 218597_s_at CDGSH iron sulfur domain 1 CISDl -0.18 -2.24 0.03 0.22 -4.12

218648_at CREB regulated transcription CRTC3 -0.33 -3.39 0.00 0.05 -1.58 coactivator 3

21865 l_s_at La ribonucleoprotein domain LARP6 -0.34 -4.00 0.00 0.02 -0.03 family, member 6

218660_at dysferlin, limb girdle muscular DYSF -0.55 -3.49 0.00 0.04 -1.33 dystrophy 2B (autosomal recessive)

218668_s_at RAP2C, member of RAS RAP2C -0.22 -1.51 0.14 0.45 -5.34 oncogene family

218683_at polypyrimidine tract binding PTBP2 -0.18 -1.63 0.11 0.41 -5.17 protein 2

218691_s_at PDZ and LIM domain 4 PDLIM4 -0.42 -2.50 0.02 0.16 -3.58

218711_s_at serum deprivation response SDPR 0.41 2.63 0.01 0.14 -3.32

(phosphatidylserine binding protein)

218818_at four and a half LIM domains 3 FHL3 -0.36 -2.29 0.03 0.21 -4.02

218864_at tensin 1 TNSl -0.30 -1.72 0.10 0.38 -5.03

218877_s_at tRNA methyltransferase 11 TRMTU 0.44 2.93 0.01 0.10 -2.66 homolog (S. cerevisiae)

218975_at collagen, type V, alpha 3 COL5A3 -0.32 -1.79 0.08 0.35 -4.91

219058_x_at tubulointerstitial nephritis TINAGLl -0.14 -1.50 0.14 0.45 -5.35 antigen-like 1

219073_s_at oxysterol binding protein-like OSBPLlO -0.37 -2.24 0.03 0.22 -4.11

10

219091_s_at multimerin 2 MMRN2 -0.44 -3.79 0.00 0.03 -0.57

219102_at reticulocalbin 3, EF-hand RCN3 -0.14 -1.57 0.13 0.43 -5.25 calcium binding domain

219314_s_at zinc finger protein 219 ZNF219 -0.51 -4.66 0.00 0.01 1.70

219336_s_at activating signal cointegrator 1 ASCCl -0.16 -1.59 0.12 0.42 -5.23 complex subunit 1

219416_at scavenger receptor class A, SCARA3 -0.57 -2.45 0.02 0.18 -3.71 member 3

21945 l_at methionine sulfoxide reductase MSRB2 -0.42 -2.07 0.05 0.27 -4.43

B2

219488_at alpha 1,4-galactosyl transferase A4GALT -0.14 -1.56 0.13 0.43 -5.26

(globotriaosylcer amide synthase)

219534_x_at cyclin-dependent kinase CDKNlC -0.23 -1.86 0.07 0.33 -4.80 inhibitor 1C (p57, Kip2)

219563_at chromosome 14 open reading C14orfl39 -0.38 -2.33 0.03 0.20 -3.95 frame 139

219656_at protocadherin 12 PCDH12 -0.26 -1.82 0.08 0.34 -4.86

219689_at sema domain, immunoglobulin SEMA3G -0.22 -1.23 0.23 0.56 -5.71 domain (Ig), short basic domain, secreted,

(semaphorin) 3G

219746_at D4, zinc and double PHD DPF3 -0.18 -1.66 0.11 0.40 -5.12 fingers, family 3

219902_at betaine-homocysteine BHMT2 -0.33 -2.26 0.03 0.22 -4.07 methyltransferase 2 219909 at matrix metallopeptidase 28 MMP28 -0.54 -3.44 0.00 0.05 -1.45

220050_at chromosome 9 open reading C9orf9 -0.32 -2.10 0.04 0.26 -4.37 frame 9

22009 l_at solute carrier family 2 SLC2A6 -0.18 -1.37 0.18 0.50 -5.53

(facilitated glucose transporter), member 6

220103_s_at mitochondrial ribosomal MRPS 18C 0.21 1.82 0.08 0.34 -4.87 protein S 18C

220148_at aldehyde dehydrogenase 8 ALDH8A -0.45 -1.58 0.12 0.43 -5.23 family, member Al 1

220244_at loss of heterozygosity, 3, LOH3CR 0.47 1.93 0.06 0.31 -4.67 chromosomal region 2, gene A 2A

220276_at RERG/RAS-like RERGL -0.54 -1.75 0.09 0.37 -4.98

220722_s_at solute carrier family 5 (choline SLC5A7 -0.41 -2.27 0.03 0.22 -4.05 transporter), member 7

220765_s_at LIM and senescent cell LIMS2 -0.41 -2.81 0.01 0.11 -2.93 antigen-like domains 2

220879_at ___ ___ 0.20 2.17 0.04 0.24 -4.25

220975_s_at CIq and tumor necrosis factor ClQTNFl -0.25 -1.89 0.07 0.32 -4.75 related protein 1

221014_s_at RAB33B, member RAS RAB33B -0.38 -2.47 0.02 0.17 -3.66 oncogene family

221030_s_at Rho GTPase activating protein ARHGAP -0.27 -1.66 0.11 0.40 -5.11

24 24

221127_s_at regulated in glioma RIG -0.19 -1.74 0.09 0.37 -4.99

221193_s_at zinc finger, CCHC domain ZCCHClO -0.20 -1.43 0.16 0.48 -5.45 containing 10

221204_s_at cartilage acidic protein 1 CRTACl -0.56 -4.18 0.00 0.01 0.44

221246_x_at tensin 1 TNSl -0.27 -3.41 0.00 0.05 -1.53

221276_s_at syncoilin, intermediate SYNCl -0.29 -1.63 0.11 0.41 -5.17 filament 1

221447_s_at glycosyltransferase 8 domain GLT8D2 0.57 2.29 0.03 0.21 -4.02 containing 2

221480_at heterogeneous nuclear HNRNPD -0.36 -2.27 0.03 0.22 -4.06 ribonucleoprotein D (AU-rich element RNA binding protein l, 37kDa)

221502_at karyopherin alpha 3 (importin KPNA3 -0.20 -2.16 0.04 0.24 -4.26 alpha 4)

221527_s_at par-3 partitioning defective 3 PARD3 -0.16 -1.59 0.12 0.42 -5.23 homolog (C. elegans)

221634_at ribosomal protein L23a RPL23AP -0.21 -2.04 0.05 0.28 -4.48 pseudogene 7 7

221667_s_at heat shock 22kDa protein 8 HSPB 8 -0.40 -2.29 0.03 0.21 -4.02

221748_s_at tensin 1 TNSl -0.14 -1.62 0.12 0.41 -5.18

221886_at DENN/MADD domain DENND2 -0.33 -1.83 0.08 0.34 -4.84 containing 2A A

222066_at Erythrocyte membrane protein EPB41L1 -0.20 -1.76 0.09 0.36 -4.97 band 4.1-like l

222101 s at dachsous 1 (Drosophila) DCHSl -0.26 -1.56 0.13 0.43 -5.27

22222 l_x_at EH-domain containing 1 EHDl -0.20 -2.43 0.02 0.18 -3.74 222257 _s_at angiotensin I converting ACE2 -0.38 -1.96 0.06 0.30 -4.62 enzyme (peptidyl-dipeptidase

A) 2

32094_ at carbohydrate (chondroitin 6) CHST3 -0.19 -1.09 0.29 0.62 -5.86 sulfotransferase 3

32625_ at natriuretic peptide receptor NPRl -0.22 -2.46 0.02 0.17 -3.68

A/guanylate cyclase A

(atrionatriuretic peptide receptor A)

336 at thromboxane A2 receptor TBXA2R -0.65 -3.37 0.00 0.05 -1.62

33760_ at peroxisomal biogenesis factor PEX14 -0.24 -1.74 0.09 0.37 -5.00

14

35776_ at intersectin 1 (SH3 domain ITSNl -0.20 -1.62 0.12 0.41 -5.18 protein)

35846_ at thyroid hormone receptor, THRA -0.46 -3.87 0.00 0.02 -0.38 alpha (erythroblastic leukemia viral (v-erb-a) oncogene homolog, avian)

37996_ s_at dystrophia myotonica-protein DMPK -0.39 -1.83 0.08 0.34 -4.84 kinase

38290_ at regulator of G-protein RGS 14 -0.17 -1.18 0.25 0.57 -5.76 signaling 14

44702_ at synapse defective 1, Rho SYDEl -0.38 -2.45 0.02 0.18 -3.69

GTPase, homolog 1 (C. elegans)

45714_ at host cell factor Cl regulator 1 HCFClRl -0.24 -1.29 0.21 0.53 -5.63

(XPOl dependent)

52255 s at collagen, type V, alpha 3 COL5A3 -0.42 -2.05 0.05 0.28 -4.47

Table 4. 146 diagnostic probe sets with incidence number greater than 50 for 105- fold gene selection procedure. The 15 shaded probe sets at the bottom are deselected by PAM when the 146 probe sets were used as input for training.

Probe set Gene symbol Gene title LogFC ¹

213764_s_at MFAP5 microfibrillar associated protein 5 -1.73 209758_s_at MFAP5 microfibrillar associated protein 5 -1.48 213765 at MFAP5 microfibrillar associated protein 5 -1.36 myelin protein zero (Charcot-Marie-Tooth

210280 at MPZ neuropathy IB) -1.20 proteolipid protein 1 (Pelizaeus-Merzbacher

210198_s_at PLPl disease, spastic paraplegia 2, uncomplicated) -1.18 215104_at NRIP2 nuclear receptor interacting protein 2 -0.94 213847 at PRPH peripherin -0.93 heat shock protein, alpha-crystallin-related,

214767_s_at HSPB6 B6 -0.88 209843 s at SOXlO SRY (sex determining region Y)-box 10 -0.61 209686 _at SlOOB SlOO calcium binding protein B -0.94

209915 _s_at NRXNl neurexin 1 -0.80

214023 _x_at TUBB2B tubulin, beta 2B -0.75

214954 _at SUSD5 sushi domain containing 5 -0.98

204584 _at LlCAM Ll cell adhesion molecule -1.20

204777 _s_at MAL mal, T-cell differentiation protein -0.99

205132 _at ACTCl actin, alpha, cardiac muscle 1 -0.99

203151 _at MAPlA microtubule-associated protein IA -0.69

210869 _s_at MCAM melanoma cell adhesion molecule -0.71 integrin, beta 3 (platelet glycoprotein Ilia,

204627 _s_at ITGB3 antigen CD61) -0.82

209086 _x_at MCAM melanoma cell adhesion molecule -0.61

219314 _s_at ZNF219 zinc finger protein 219 -0.51

221204 _s_at CRTACl cartilage acidic protein 1 -0.56

212886 _at CCDC69 coiled-coil domain containing 69 -0.59 transient receptor potential cation channel,

210814 _at TRPC3 subfamily C, member 3 -0.75 dishevelled associated activator of

212793 _at DAAM2 morphogenesis 2 -0.56

212565 _at STK38L serine/threonine kinase 38 like -0.58

214606 _at TSPAN2 tetraspanin 2 -0.54

336_at TBXA2R thromboxane A2 receptor -0.65 dysferlin, limb girdle muscular dystrophy 2B

218660 _at DYSF (autosomal recessive) -0.55

214434 _at HSPA12A heat shock 7OkDa protein 12A -0.57

212274 _at LPINl lipin 1 -0.48

206874 _s_at — -0.44

203939 _at NT5E 5'-nucleotidase, ecto (CD73) -0.49

205954 _at RXRG retinoid X receptor, gamma -0.53

219909 _at MMP28 matrix metallopeptidase 28 -0.54 transient receptor potential cation channel,

206425 _s_at TRPC3 subfamily C, member 3 -0.57

205433 _at BCHE butyrylcholinesterase -0.93 thyroid hormone receptor, alpha (erythroblastic leukemia viral (v-erb-a)

35846_ at THRA oncogene homolog, avian) -0.46

204736 _s_at CSPG4 chondroitin sulfate proteoglycan 4 -0.55

202806 _at DBNl drebrin 1 -0.43

212097 _at CAVl caveolin 1, caveolae protein, 22kDa -0.38

201841 _s_at HSPBl heat shock 27kDa protein 1 -0.44

206382 _s_at BDNF brain-derived neurotrophic factor -0.62

219091 _s_at MMRN2 multimerin 2 -0.44

205076 _s_at MTMRl 1 myotubularin related protein 11 -0.57 cyclin-dependent kinase inhibitor 2C (pi 8,

204159 _at CDKN2C inhibits CDK4) -0.46

212992 at AHNAK2 AHNAK nucleoprotein 2 -0.60 206024_at HPD 4-hydroxyphenylpyruvate dioxygenase -0.57

DBNDD2 /// SYSl- dysbindin (dystrobrevin binding protein 1)

218094_s_at DBNDD2 domain containing 2 /// SYS 1 -DBNDD2 -0.41

211276_at TCEAL2 transcription elongation factor A (SΙI)-like 2 -0.52

209191_at TUBB6 tubulin, beta 6 -0.51 213675_at CDNA FLJ25106 fis, clone CBR01467 -0.44

211340_s_at MCAM melanoma cell adhesion molecule -0.46 sarcoglycan, alpha (5OkDa dystrophin-

210632_s_at SGCA associated glycoprotein) -0.58

La ribonucleoprotein domain family, member

218651_s_at LARP6 6 -0.34

207876_s_at FLNC filamin C, gamma (actin binding protein 280) -0.45 tRNA methyl transferase 11 homolog (S.

218877_s_at TRMTIl cerevisiae) 0.44

219416_at SCARA3 scavenger receptor class A, member 3 -0.57 cold shock domain containing C2, RNA

20998 l_at CSDC2 binding -0.56

214212_x_at FERMT2 fermitin family homolog 2 (Drosophila) -0.42

207554_x_at TBXA2R thromboxane A2 receptor -0.44 epilepsy, progressive myoclonus type 2A,

20523 l_s_at EPM2A Lafora disease (laforin) -0.42

MRNA; cDNA DKFZp586N2020 (from 215306_at clone DKFZp586N2020) -0.48

DnaJ (Hsp40) homolog, subfamily C,

218435_at DNAJC15 member 15 -0.49

WW domain binding protein 4 (formin

203597_s_at WBP4 binding protein 21) -0.34 potassium inwardly-rectifying channel,

205303_at KCNJ8 subfamily J, member 8 -0.42 integrin, alpha 5 (fibronectin receptor, alpha

201389_at ITGA5 polypeptide) -0.50

204940_at PLN phospholamban -0.49

LIM and senescent cell antigen-like domains

220765_s_at LIMS2 2 -0.41 adaptor-related protein complex 1 , sigma 2

203299_s_at AP1S2 subunit -0.41 ubiquitin-conjugating enzyme E2D 2

201344_at UBE2D2 (UBC4/5 homolog, yeast) -0.38

218648_at CRTC3 CREB regulated transcription coactivator 3 -0.33

204939_s_at PLN phospholamban -0.45

201431_s_at DPYSL3 dihydropyrimidinase-like 3 -0.40

MRNA; cDNA DKFZp586C1923 (from

215534_at clone DKFZp586C1923) -0.46

209169_at GPM6B glycoprotein M6B -0.34

209651 at TGFBlIl transforming growth factor beta 1 induced -0.42 transcript 1 serum deprivation response

218711 s at SDPR (phosphatidylserine binding protein) 0.41

CAP-GLY domain containing linker protein

212358 _at CLIP3 3 -0.47

218691 _s_at PDLIM4 PDZ and LIM domain 4 -0.42

218266 _s_at FREQ frequenin homolog (Drosophila) -0.46

210319 _x_at MSX2 msh homeobox 2 0.45

218545 _at CCDC91 coiled-coil domain containing 91 -0.31 synapse defective 1 , Rho GTPase, homolog 1

44702_ at SYDEl (C. elegans) -0.38

221014 _s_at RAB33B RAB33B, member RAS oncogene family -0.38

221246 _x_at TNSl tensin 1 -0.27

208789 _at PTRF polymerase I and transcript release factor -0.42 solute carrier family 5 (choline transporter),

220722 _s_at SLC5A7 member 7 -0.41

209087 _x_at MCAM melanoma cell adhesion molecule -0.40

221667 _s_at HSPB 8 heat shock 22kDa protein 8 -0.40 potassium channel tetramerisation domain

205561 _at KCTD 17 containing 17 -0.32

213808 _at — Clone 23688 mRNA sequence -0.43

202565 _s_at SVIL supervillin -0.36

211964 _at COL4A2 collagen, type IV, alpha 2 -0.39

219563 _at C14orfl39 chromosome 14 open reading frame 139 -0.38

214122 at PDLIM7 PDZ and LIM domain 7 (enigma) -0.30 related RAS viral (r-ras) oncogene homolog

212589_at RRAS2 2 -0.29 fasciculation and elongation protein zeta 1

205973_at FEZl (zygin I) -0.35

218818_at FHL3 four and a half LIM domains 3 -0.36

212120_at RHOQ ras homolog gene family, member Q -0.31

219073 s at OSBPLlO oxysterol binding protein-like 10 -0.37 heterogeneous nuclear ribonucleoprotein D

(AU-rich element RNA binding protein 1 ,

221480_at HNRNPD 37kDa) -0.36

20707 l_s_at ACOl aconitase 1, soluble -0.27

211717_at ANKRD40 ankyrin repeat domain 40 -0.28

201313_at ENO2 enolase 2 (gamma, neuronal) -0.36 integrin, beta 3 (platelet glycoprotein Ilia,

204628_s_at ITGB3 antigen CD61) -0.31

204303_s_at KIAA0427 KIAA0427 -0.35

214439_x_at BINl bridging integrator 1 -0.29

DnaJ (Hsp40) homolog, subfamily B,

209015_s_at DNAJB6 member 6 -0.29 cullin-associated and neddylation-dissociated

213547 at CAND2 2 (putative) -0.31 malic enzyme 1 , NADP(+)-dependent,

204058. _at MEl cytosolic -0.34

219902_ _at BHMT2 betaine-homocysteine methyltransferase 2 -0.33

214306. _at OPAl optic atrophy 1 (autosomal dominant) -0.27

210201. _x_ _at BINl bridging integrator 1 -0.29

212509. _s_ at MXRA7 matrix-remodelling associated 7 -0.27

213231. _at DMWD dystrophia myotonica, WD repeat containing -0.30

EGF-containing fibulin-like extracellular

201843 _s_ at EFEMPl matrix protein 1 -0.32

206289 _at HOXA4 homeobox A4 -0.29

203501 at PGCP plasma glutamate carboxypeptidase -0.30 cyclin-dependent kinase inhibitor 1C (p57,

216894 x at CDKNlC Kip2) -0.27

HLl 4 gene encoding beta-galactoside-

216500. _at ... binding lectin, 3' end, clone 2 -0.29

220050 _at C9orf9 chromosome 9 open reading frame 9 -0.32

209362 _. _at MED21 mediator complex subunit 21 -0.26

202931. _x_at BINl bridging integrator 1 -0.27

213480. _at VAMP4 vesicle-associated membrane protein 4 -0.24 tumor necrosis factor (ligand) superfamily,

205611. _at TNFSF 12 member 12 -0.29

204365. _s_at REEPl receptor accessory protein 1 -0.29

203389. _at KIF3C kinesin family member 3C -0.26 family with sequence similarity 131, member

205368 _at FAM131B B -0.27

217066. _s_at DMPK dystrophia myotonica-protein kinase -0.29 transcription factor binding to IGHM

212457 at TFE3 enhancer 3 -0.25

2QQ685j-t SMlSIl spicing factor, arginine/serine-rieh 11 -0 ₊ 16

200788 _m s _m at PEA15 phosphoprotem enriched in astrocytes 15 -0.22

2O2522 _m at PlTPNB phosphatidylinositol transfer protein, beta ~0J 6

2O8869 _m s _m at GABARAPLl GABA(A) receptor-associated protein like 1 -0J9 hepatøma-derived growth factor, related

205524 at HDGEEP3 protein s -OJ 4 CDC 14 cell division cycle i4homolog B (S.

211347 _al CDC14B cerevisiae) -0.21

211677 _χ_ .at CADM3 cell adhesion molecule 3 -0.21 protein tyrosine phosphatase, non-receptor

212610 _at PTPNU type il (Noouan syndrome 1} -0.23

212848 _s_ at C9orf3 chromosome 9 open reading frame 3 -0.27

214643 _χ_ .at BINl bridging integrator 1 -0.23

217820 at ENAH enabled bomolog (Drosophila) -OJ 9

218597 at CISDl CDGSH iron sulfur domain 1 -0.18

221502 _w .at KPNA3 karyopherin alpha 3 (importin alpha 4) -0.20

222221 _W .X _* .at EHDl EH-domain containing 1 -0.20

32625... ^' at NPRl natriuretic peptide receptor A/guanylate -0.22 cyclase A (atrkaiatriuretic peptide receptor A) logFC is the logarithm Fold Change as tumorous stroma being compared to normal stroma.

+/- represents up-/down- regulated expression level in tumorous stroma.

Table 5. Comparison of 131-element classifier to classifiers generated from 'random' genes, 'i' and 'ii' denote the 131-probeset classifier and random-gene classifiers, respectively.

Dataset Case Num. Accuracy % Sensitivity % Specificity

% i ii i ii i ii

1 Training set 1 26 96.4 67.1 92.3 32.5 100 97.1 (13+13)

Test set

Tumor

2 Tumor-bearing 1 55 (68-13) 96.4 8.7 96.4 8.7 NA NA

3 Tumor-bearing 2 65 100 12.9 100 12.9 NA NA

4 Tumor-bearing 3 79 100 13.4 100 13.4 NA NA

5 Tumor-bearing 4 44 100 15.9 100 15.9 NA NA

Normal

6 Biopsies (1) 1 7 100 98.8 NA NA 100 98.8

7 Biopsies (2) 1 5 60.0 100 NA NA 60.0 100

8 Rapid autopsies 1 13 92.3 67.5 NA NA 92.3 67.5

Manuel

Midrodissected/LCM

9 Tumor-adjacent

2 71 97.1 13.6 97.1 13.6 NA NA Stroma

10 Tumor-adj acent

4 13 100 15.9 100 15.9 NA NA Stroma

11 Tumor-adj acent

1 12 75.0 5.8 75.0 5.8 NA NA Stroma

12 Tumor-bearing 5 12 100 19.2 100 19.2 NA NA

13 Pooled normal

5 4 100 79.4 NA NA 100 79.4 stroma

Example 2 - Development of Predictive Biomarkers of Prostate Cancer Three methods utilized in the development of predictive gene signature of prostate cancer are described in this example. First, an analytical method based on a linear combination model for the determination of the percent cell composition of the tumor epithelial cells and the stoma cells from array data of mixed cell type prostate tissue is described. The method utilizes fixed expression coefficients of a small (<100) genes that with expression characteristics that are distinct for tumor epithelial and stroma cells.

Second, a new method for the determination of tumor cell specific biomarkers for the prediction of relapse of prostate cancer using an extended linear combination model is described and validated. A gene profile based on the expression of RNA of prostate cancer epithelial cells that predicts the differential gene expression of relapse (aggressive) vs. non relapse (indolent) prostate cancer is derived. These genes are validated by their identification in independent sets of prostate cancer patients (technical retrospective validation) is described. This method may be used to identify aggressive prostate cancer from data obtained at the time of diagnosis. The method and profiles are novel.

Third, an analogous new method for the determination of stroma cell specific biomarkers for the prediction of relapse of prostate cancer is described. Thus the predictions are based on non tumor cell types. A gene profile based on the expression of RNA of stroma cells of tumor-bearing prostate tissue that predicts the differential gene expression of relapse (aggressive) vs. non relapse (indolent) prostate cancer that is validated by prediction of differences of an independent set of prostate cancer patients (technical retrospective validation) is described. These methods and profiles may be used to identify aggressive prostate cancer from data obtained at the time of diagnosis. The results further indicate that the microenvironment of tumor foci of prostate cancer exhibit altered gene expression at the time of diagnosis which is distinct in non relapse and relapsed prostate cancer.

Datasets: The goals of this study were to continue development of predicative biomarkers of prostate cancer. In particular the goal of this study is to use independent datasets to validate genes deduced as predictive based on studies of dataset 1 {infra vide). Here "dataset" refers to the array-based RNA expression data of all cases of a given set together with the clinical data defining whether a given case relapsed (recurred cancer) or remained disease free, a censored quantity. Only the categorical value, relapsed or non relapsed, is used in the analyses described here.

The three datasets used for this study included 1) 148 Affymetrix U133A array data acquired from 91 patients (publicly available in the GEO database as accession no. GSE8218) which is the principal dataset utilized in previous studies; 2) Illumina (of Illumina Inc., San Diego) beads arrays data from 103 patients as analyzed on 115 arrays, a published dataset (Bibilova et al. (2007) Genomics 89:666-672); and 3) Affymetrix U133A array data from 79 patients, also a published dataset (Stephenson et al., supra). These are referred to in this example as datasets 1 , 2, and 3 respectively. For the purposes herein, relapsed prostate cancer is taken as a surrogate of aggressive disease, while non-relapse is taken as indolent disease with a variable degree of indolence that is directly proportional to the disease-free survival time. Dataset 1 contains 40 non- relapse patients and 47 relapse patients; dataset 2 contains 75 non -relapse patients and 22 relapse patients, and dataset 3 contains 42 non-relapse patients and 37 relapse patients. The first two datasets samples have various amount of different tissue and cell types, including tumor cells, stroma cells (a collective term for fibroblasts, myofibroblasts, smooth muscle, and small amounts of nerve and vascular elements), BPH (epithelial cells of benign prostate hypertrophy) and dilated cystic glands (AKA "atrophic" cystic glands), as estimated by four pathologists (Stuart et al., supra) for dataset 1 and one pathologist for dataset 2. Dataset 3 samples were tumor-enriched samples. In this study, published datasets 2 and 3 were used for the purpose of validation only. A major goal of this study was to use "external" published datasets to validate the properties deduced for genes based on analysis of the dataset 1.

Determination of Cell Specific Gene Expression in Prostate Cancer. Using linear models applied to microarray data from prostate tissues with various amounts of different cell types as estimated by a team of four pathologists, identified genes were identified as being specifically expressed in different cell types (tumor, stroma, BPH and dilated cystic glands) of prostate tissue following published methods (Stuart et al., supra). Thus, the following linear models were applied for generating tissue specific genes.

Model 1 - For any gene i, the hybridization intensity, G ₁ , from an Affymetrix GeneChip is due to the sum of the cell contributions to the total mRNA: ^' tumor P stroma ^' stroma P BPH ^' BPH P dilated cystic gland ^' dilated cystic gland ) ,

Where a "cell contribution" is the amount of the cellular component, P _ceIltype , multiplied times the characteristic expression level of gene i by that cell type, β . Only the β values are unknown and are determined by simple or multiple linear regressions. Note that in general a minimum of four estimates of G ₁ (i.e. four cases) are required to estimate four unknown β whereas in practice many dozens of cases are available so that the unknown coefficients are "over determined".

Model 2 - Since the epithelia of dilated cystic glands were not a major component of prostate tissue, it may be removed from the linear model to simplify the model. ^' "tumor + P stroma ^' "stroma + P BPH ^' "BPH >ι

Models 3-6 - To further simplify the model, cell composition also can be considered as two different cell types, usually one specific cell type and all the other cell types were grouped together.

C i = ( \H R tumor - P tumor + H Rnon— tumor ' P non-tumor \ Λ i \M stroma stroma i non— stroma non— stroma 'ι

^ ⁷ I ^~ \PBPH ^' "BPH ^" " ^" Pnon-BPH ^' non-BPH )ι i \H dilated cystic gland dilated cystic gland P non— dilated cystic gland non— dilated cystic gland J ₁

The gene lists (with/?<0.001) developed from models 3 and 4 using dataset 1 are listed in Table 6.

A New Method for Determination of Cell Type Composition Prediction Using Gene Expression Profiles: Using linear models based on a small list of cell specific genes, i.e., genes from Table 6, the approximate percentage of cell types in samples hybridized to the array may be estimated using only the microarray data utilizing model 3. Potentially all of the genes in Table 6 can be used for cell percent composition prediction. For each individual gene, a new sample's gene expression value from microarray data can be fitted to models 3-6, for a prediction of corresponding cell type percentage. Each gene employed in model 3 provides an estimate of percent tumor cell composition. The median of the predictions based on multiple genes was used to generate a more reliable result estimate of tumor cell content. These prediction genes can be selected/ranked by either their correlation coefficient (for correlation between gene expression level and cell type percentage) or by combination of genes with the best prediction power. In the present case, only a very limited number of genes (8-52 genes) were used for such a prediction. Even fewer genes might be sufficient. To validate the method of tumor or stroma percent composition determination, the known percent composition figures of dataset 1 were used to predict the tumor cell and stroma cell compositions for dataset 2 with known cell composition. For example, the number of genes used for cell type (tumor epithelial cells or stroma cells) prediction between dataset 1 and dataset 2 ranges from 8 to 52 genes, which are listed in Table 7A. The Pearson correlation coefficient between predicted cell type percentage (tumor epithelial cells or stroma cells) and pathologist estimated percentage ranged from 0.7 to 0.87. Tissue (tumor or stroma) specific genes identified from dataset 2 and used for prediction are listed in Table 7B.

Since dataset 1 and dataset 2 data were based on different array platforms, the cross- platform normalization were applied using median rank scores (MRS) method (Warnat et al. (2005) BMC Bioinformatics 6:265). Figures 3A and 3B illustrate the use of the parameters of dataset 1 to predict the cell composition of dataset 2. The Pearson correlation coefficients for the correlation of the observed and calculated cell type compositions is 0.74 and 0.70 respectively. The converse calculations of utilizing the parameters of dataset 2 to calculate the tumor and stroma cell percent compositions of dataset 1 are shown in Figures 3C and 3D, respectively. The Pearson correlation coefficients were 0.87 and 0.78 respectively. The range of Pearson coefficients among four pathologists determined independently for composition estimates of the same samples in dataset 1 is 0.85 - 0.95 (Stuart et al., supra). Thus, the in silico estimates have a correlation that is almost completely subsumed in variation among pathologists, indicating that the in silico estimates are at least similar in performance to a pathologist and leaving open the possibility that the in silico estimates are more accurate than the pathologists.

A New Method for Determination of Cell Specific Relapse Related Genes of Prostate Cancer. Using dataset 1, the genes correlating with patient relapse status were estimated using the following linear models. Model 7

Q -β< p , β< p , β< p , β< p , i r^ tumor _a l tumor r^ stroma, i stroma r^ BPH ,ι BPH r^ dilated cystic gland ,ι dilated cystic gland rs( Vv tumor, i P tumor 4- Y t stroma,ι P stroma 4- Y t BPH ,ι P BPH 4- Y t dilated cystic gland ,ι P dilated cystic gland ^ '

For any gene i , G ₁ (the array reported gene intensity) = the sum of 4 cell type contributions for non relapsed cases (β _celltype , x Percent _celltype ) + Sum of 4 cell type 5 contributions for relapsed cases ( γ _celltype , x P ^ercent _ce u _tyPe ) ^{+ error} term. RS may be either 0 or 1 where 0 is utilized for all non relapse cases and RS = 0 is utilized for relapse cases. Thus when RS=O the expression coefficients β' for non relapse cases are determined while when RS = 1 the coefficients (β'+ γ) are determined. Coefficients are numerically determined by multiple linear regression using least squares determination of best fit coefficients ± error. 1 o The differences in expression between non relapse (β') and relapse (β'+ γ) is just γ and the significance γ may be estimated by T-test and other standard statistical methods.

Model 8-11 - The following models also were implemented to simplify the models,: i H tumor , i tumor H relapse status ,ι H int eraction^ tumor '

C i = Iβ' stroma ,ι P stroma + i β' relapse status ,ι RS + i β' int eraction ,ι P stroma ' RS C ,ι tumor ' ^ i H dilated cystic gland ,ι tumor H relapse status ,ι H int eraction ,ι dilated cystic gland '

Only the samples with >0% tumor epithelial cells were used for the above analysis to remove those far-stroma samples (i.e., non-tumor cell bearing samples). This exclusion of "far-stroma" accommodates the possibility that stroma may contain expression changes

20 characteristic of prostates with cancer, but that these changes might be confined to stroma regions near tumor cells. Because multiple samples are used from some subjects, the estimating equations approach implemented in the "gee" library for R (i.e., the open source R bioinformatics analysis package) was used (Zeger and Liang (1986) Biometrics 42: 121-130). Cell type (tumor epithelial cells or stroma cells) specific genes showed significant (p <

25 0.005) expression level changes between relapse and non-relapse samples using model 8-9, are listed in Tables 8 A and 8B.

The gene list was then validated using independent dataset 3 to test whether any of the same genes were independently identified. Since dataset 3 has unknown tumor/stroma content, the method was first used for predicting tumor/stroma percentage (Figures 4A-4C) before testing the prediction potential of the genes of Tables 8 A and 8B. Cell type (tumor epithelial cells or stroma cells) specific relapse related genes were generated using/? < 0.01 as a cut-off. There were 15 genes that were significantly associated with relapse in tumor 5 cells in both datasets. Twelve genes agreed in identity and sign (direction in relapse). The null hypothesis that 12 genes agreeing and identity and sign was not different from random was tested, yielding ap < 0.007. Thus these genes appear validated by the criterion of coincidence. The process is summarized in Table 9. These significant genes presented in both dataset 1 and 3 together with three additional genes that did not agree in sign between the o two datasets are plotted in Figure 5A which compares the expression coefficients for these genes in both datasets. Almost all of these genes showed consistency between two datasets, with a Pearson Correlation Coefficient of 0.83. Thus the coincident genes also agree in amplitude. These genes are listed in Table 10.

An analogous analysis was carried for the determination of stroma cell specific genes5 (Figure 5B, Table 9). Sixteen genes exhibited correlation with relapse in both datasets, and all of these genes had the same direction in both datasets (p < 0.001). The 16 genes exhibit a Pearson Correlation Coefficient of 0.93. This result indicates that a stroma cell based classifier may have predictive information about relapse. These genes determined from the analysis of datasets 1 and 3 are listed in Table 11. 0 An analogous analysis was carried out using datasets 1 and 2 with a significance cut off of 0.2 for dataset 2 (Table 9). Thirteen coincident genes were identified at this threshold even though the array of dataset three is relatively small (-500 genes). Ten of these 13 genes had the same direction in relapse in both datasets (p < 0.011), as shown in Figure 5C. Thus, these 10 genes are validated in an independent dataset by the criterion of coincidence in5 independent datasets. The common 10 genes which had the same direction are listed in Table 12. One gene, PPAP2B (Affymetrix ID: 212230_at) is down-regulated in relapse cases and is in common with those of datasets 1 and 2.

A similar analysis for stroma-specifically expressed genes revealed BTG2 as a stroma specific relapse gene (Affymetrix ID: 201235_s_at) as a common gene in dataset 1 and 2 that0 exhibited up-regulation in both datasets. These results indicate that three sets of validated genes with significant differential expression may be extracted once tumor percentage is taken into account, which may be useful in the prediction of relapse by analysis of expression data obtained at the time of diagnosis.

Table 6. Tissue Specific Genes detected using dataset 1 (p < 0.005). Regular font: up- regulated genes; Italics: down-regulated genes.

Tumor Specific Genes Stroma Specific Genes 36830_at 202555_s_at

209424_s_at 201496 x at 203954 x_at 212730 at

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214598 at 207977_s_at 214455_at 209616_s_at

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214266_s_at 200852_x_at 212647_at 212652_s_at

221127 _s_at 200947_s_at 202719_s_at 218434_s_at

209016_s_at 209665_at 211985 _s_at 211715 _s_at

201841_s_at 202941_at 212423_at 203115_at

208949_s_at 209605_at 209436_at 201647_s_at

201369_s_at 211733_x_at 204268_at 202718_at

209655_s_at 212347 x at 208690 s at 212204 at 203603_s_at 213244_at 217763 _s_at 211417 _x_at

205803_s_at 221428_s_at 204971_at 217168_s_at

206433_s_at 209108 _at 219410_at 212989_at

212914_at 201825_s_at 212993_at 209228_x_at

203748_x_at 203545_at 206580_s_at 221245 _s_at

218824_at 203616_at 204472_at 203124_s_at

205608_s_at 201116_s_at 201430_s_at 210996_s_at

201313_at 220226_at 211562_s_at 201760 _s_at

202075_s_at 200654_at 204163_at 209919_x_at

204396_s_at 205925_s_at 202133_at 213812_s_at

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214104_at 202178_at 210096_at 221923 _s_at

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211813_x_at 221754_s_at 211964 _at 201704 _at

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211340_s_at 201660_at 208789_at 202929_s_at

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209191 _at 218531_at 207957_s_at 213148_at

209129 _at 200681_at 200930_s_at 202503_s_at

204964_s_at 205566_at 204041_at 209625_at

217767 _at 203164_at 221935 _s_at 210108_at

213564_x_at 202023_at 202994_s_at 209504_s_at

221872_at 207275 _s_at 209488_s_at 222315_at

203562_at 201130_s_at 218224_at 218979_at

209685_s_at 217823 _s_at 204731_at 201577 _at

219250_s_at 221781_s_at 203498_at 215407_s_at

204036_at 37117 _at 203881_s_at 205133_s_at

211126_s_at 205942_s_at 201147 _s_at 209367_at

201438_at 215380_s_at 213994_s_at 200970_s_at

214212_x_at 219518_s_at 206938_at 202605_at

213568_at 200971_s_at 205609_at 63825_at

201631_s_at 221874_at 201645 _at 205505_at

202440_s_at 212978_at 209496_at 218025_s_at

212977_at 210720_s_at 212067_s_at 206110_at

221541_at 218188_s_at 204364_s_at 204942_s_at

200923_at 201724 _s_at 212236_x_at 217111_at

220595_at 208737_at 212813_at 203219_s_at

204284_at 218909_at 218380_at 204019_s_at

208747_s_at 209531_at 212230_at 212295_s_at

203131 at 201417 at 218418 s at 209855_s_at 201242_s_at 202893_at 205132_at 221024 _s_at

204463_s_at 218086_at 200931_s_at 221865 _at

204464_s_at 51158_at 209427 _at 203386_at

201843_s_at 219411_at 204288_s_at 210719_s_at

202748_at 218258_at 218730_s_at 221880 _s_at

202018_s_at 201583_s_at 218980_at 220432_s_at

208966_x_at 209825_s_at 213371_at 202546_at

209209_s_at 222121_at 203706 _s_at 211423_s_at

200897_s_at 204388_s_at 205856_at 217736_s_at

209487_at 219850_s_at 221748 _s_at 207098_s_at

210869_s_at 204389_at 200907_s_at 200606_at

211896_s_at 215108_x_at 222162_s_at 219388_at

219295_s_at 201196 _s_at 209286_at 213085_s_at

209335_at 209478 _at 204955_at 200078_s_at

211663_x_at 214733_s_at 212843_at 206860_s_at

202566_s_at 205769_at 205157_s_at 202668_at

204570_at 209030_s_at 204069_at 218248_at

209074_s_at 201014_s_at 200953_s_at 219584_at

201348_at 202005_at 203851_at 211559 _s_at

201957_at 206068_s_at 205725_at 206303_s_at

202202_s_at 203029_s_at 212226_s_at 205248_at

213428_s_at 203430_at 208131_s_at 217776 _at

201497_x_at 219015_s_at 200621_at 201963 _at

213992_at 200700_s_at 211748_x_at 202769 _at

21861 l_at 212181_s_at 207977_s_at 213325_at

212254_s_at 205102_at 207876_s_at 209585_s_at

209948_at 204319_s_at 206116_s_at 208580_x_at

217757_at 200670_at 204273 _at 202790 _at

204457_s_at 266_s_at 201787 _at 204141_at

221505_at 210787_s_at 209651_at 218696_at

201540_at 206770_s_at 204931_at 209514_s_at

200986_at 214106_s_at 202283_at 210480_s_at

200906_s_at 203042_at 209687_at 212744 _at

203729_at 210715_s_at 201842 _s_at 209934_s_at

218718_at 212448_at 201431_s_at 215432_at

214091_s_at 212115 _at 209156_s_at 202428_x_at

202196 _s_at 87100_at 202269_x_at 217014 _s_at

204400_at 200656_s_at 202007_at 209693_at

201105 _at 213892_s_at 219167_at 211596_s_at

209288_s_at 208658_at 201150 _s_at 222258_s_at

214505_s_at 203030_s_at 202565_s_at 204394_at

200762_at 220014_at 209616_s_at 208788_at

212136_at 217912 _at 214247_s_at 213288_at

203423_at 210293_s_at 209283_at 209031_at

201641_at 211724_x_at 212187_x_at 221589 _s_at

213093_at 202148_s_at 217728 _at 213712_at

202995_s_at 221019_s_at 201539 _s_at 201951_at

204939_s_at 212183_at 210298_x_at 203180_at

204894_s_at 201193 _at 205547_s_at 208190_s_at

215016_x_at 201582_at 207030_s_at 203642_s_at

210139 s at 208527 x at 209167_at 218211 s at 219685_at 202770 _s_at 209291_at 202826_at

201495_x_at 210951_x_at 213068_at 208180_s_at

203065_s_at 212745_s_at 209351_at 219017 _at

205549_at 207843_x_at 209170 _s_at 219405_at

203324_s_at 217775 _s_at 202222_s_at 205645_at

219478_at 40093_at 202992_at 203717_at

209210_s_at 212252_at 213746_s_at 201079 _at

203323_at 204776_at 208791_at 209389_x_at

212768_s_at 210738_s_at 208792_s_at 210041_s_at

204135 _at 222067_x_at 205564_at 202688_at

213071_at 201848_s_at 204734_at 210652_s_at

202274 _at 205221_at 201058_s_at 203946_s_at

209540_at 209366_x_at 205382_s_at 202088_at

209355_s_at 219266_at 205242_at 202457_s_at

33767_at 210337_s_at 201496 x at 200832_s_at

201615_x_at 201131 _s_at 202722 _s_at

209541_at 202786_at 209706_at

212724_at 208546_x_at 204583_x_at

213139_at 202740_at 220933_s_at

212233_at 220926_s_at 214404_x_at

203903_s_at 211070_x_at 213246_at

207480_s_at 213920_at 222209_s_at

208790_s_at 209094_at 200969_at

210299_s_at 220380_at 213285_at

221747 _at 215779_s_at 202429_s_at

205935_at 202708_s_at 210387_at

201820_at 213106_at 203911_at

209292_at 200790_at 217875_s_at

212992_at 209911 _x_at 221802 _s_at

202409_at 208490_x_at 201128_s_at

203766_s_at 204751_x_at 219118_at

203186_s_at 212310_at 219667_s_at

212730_at 203041_s_at 210130_s_at

212097_at 216623_x_at 203739_at

217897 _at 214329_x_at 204231_s_at

203951_at 212281_s_at 215726_s_at

200859_x_at 210317 _s_at 205052_at

222043_at 217850 _at 214765 _s_at

221667_s_at 218922_s_at 201849 _at

211276_at 213555_at 209460_at

201667_at 201413_at 222277 _at

214752_x_at 217752_s_at 213587_s_at

212865_s_at 210222_s_at 210377_at

218087_s_at 204582_s_at 213622_at

203296_s_at 221561_at 222075_s_at

208937_s_at 202286_s_at 202525_at

214027_x_at 74694_s_at 204485_s_at

202555_s_at 209806_at 212543_at

207390_s_at 209163 _at 220116_at

209763_at 212255_s_at 214774 _x_at

204083 s at 205924_at 203304 at 208650_s_at 218035_s_at

203644_s_at 201596 _x_at

217901 _at 205597_at

214463_x_at 209844_at

219127_at 217973 _at

201562_s_at 209459_s_at

219117 _s_at 202427 _s_at

218254_s_at 214290_s_at

221582_at 214469_at

209696_at 219312_s_at

216905_s_at 209623_at

200935_at 219736_at

203485_at 211137_s_at

202687_s_at 46323_at

212640_at 219856_at

202089_s_at 218186_at

218189_s_at 206302_s_at

214651_s_at 212686_at

201952_at 203007_x_at

215017 _s_at 202454_s_at

208837_at 206558_at

203857_s_at 202043_s_at

212812_at 214087_s_at

209935_at 205830_at

201662_s_at 209173_at

204973_at 205780_at

200644_at 218280_x_at

204305_at 204875_s_at

220161 _s_at 209369_at

201923_at 202890_at

221732_at 205776_at

208579_x_at 212789 _at

219806_s_at 221669 _s_at

202489_s_at 218638_s_at

201563_at 217979 _at

217080 _s_at 36830_at

214455_at 218835_at

210328_at 203954_x_at

211478_s_at 210339_s_at

209340_at 203397_s_at

210788_s_at 220192_x_at

203716_s_at 209114_at

206214_at 209398_at

219476_at 212449_s_at

204667_at 211689 _s_at

215071_s_at 203216_s_at

209854_s_at 206858_s_at

203917 _at 212445_s_at

205862_at 201690 _s_at

200862_at 212412_at

203474 at 203243 s at 209624_s_at 211303 _x_at

212218_s_at 204623_at

201688_s_at 215363_x_at

205542_at 205347_s_at

201839_s_at 219360_s_at

202345_s_at 203196_at

213506_at 203953_s_at

218313_s_at 205860_x_at

214598_at 216920_s_at

221424_s_at 215806_x_at

217487 _x_at 221577 _x_at

216804_s_at 211144_x_at

201689_s_at 209813_x_at

204934_s_at 209425_at

21777 l_at 209426_s_at

203908_at 209424 s at

203242 s at

Table 7A. Tissue (tumor or stroma) specific genes used for prediction. Regular font: up-regulated genes. Italics: down-regulated genes. Tumor Specific Gene List 1 - genes used for tumor percentage prediction based on models developed by dataset 1. Tumor Specific Gene List 2 - genes used for tumor percentage prediction based on models developed by dataset 2. Stroma Specific Gene List 1 - genes used for stroma percentage prediction based on models developed by dataset 1. Stroma Specific Gene List 2 - genes used for stroma percentage prediction based on models developed by dataset 2.

Tumor Specific Tumor Specific Stroma Specific Stroma Specific Gene List 1 Gene List 2 Gene List 1 Gene List 2

211194_s_at 201739_at 214460_at 202088_at 209854_s_at

202310_s_at 209854_s_at 201394_s_at 20093 l_s_at 200795_at

216062_at 33322_i_at 202525_at 209854_s_at 207169_x_at

211872_s_at 209706_at 201577_at 205780_at 212647_at

215240_at 205780_at 205645_at 217487_x_at 201131_s_at

204748_at 205780_at 203425_s_at 221788_at 214800_x_at

204742_s_at 201577_at 202404_s_at 202089_s_at 202404_s_at

204926_at 209706_at 200795_at 211194 s at 219960_s_at

205042_at 200931_s_at 214800_x_at 201615_x_at

222043_at 202088_at 207169_x_at 205541_s_at

212984_at 202436_s_at 209854_s_at 203084_at

215775_at 209283_at 207956_x_at

204742_s_at 202088_at 201995 _at

203698_s_at 202088_at 205645_at

209771_x_at 215350 at 201577 _at

202089_s_at 201394 _s_at

209771_x_at 202525_at

201839_s_at 214460 at

205834_s_at

209935_at

211834_s_at

221788 _at

210930_s_at

212230_at

202089_s_at

201409_s_at

201555 _at

33322_i_at

217487 _x_at

201744 _s_at

201215_at

211748_x_at

221788 _at

215564_at

201555 _at

33322_i_at

211964 at Table 7B. Tissue (tumor or stroma) specific genes identified from dataset 2 used for prediction. Table 8A. Tissue (tumor or stroma) specific relapse related genes.

Table 8B. Tissue (tumor or stroma) specific relapse related genes. Normal font: up- regulated genes. Italics: down-regulated genes.

Table 10. Tumor specific relapse related genes, identified by both dataset 1 and dataset 3 using linear model.

Table 11. Stroma specific relapse related genes, identified by both dataset 1 and dataset 3 using linear model. Table 12. Tumor specific relapse related genes, identified by both dataset 1 and dataset 2 using linear model.

Example 3 - In silico estimates of tissue components in cancer tissue based on expression profiling data

This example relates to the use of linear models to predict the tissue component of prostate samples based on microarray data. This strategy can be used to estimate the proportion of tissue components in each case and thereby reduce the impact of tissue proportions as a major source of variability among samples. The prediction model was tested by 10-fold cross validation within each data set, and also by mutual prediction across independent data sets.

Prostate cancer microarray data sets: Four publicly available prostate cancer data sets (datasets 1 through 4) with pathologist-estimated tissue component information were included in this study (Table 13). For all data sets, four major tissue components (tumor cells, stroma cells, epithelial cells of BPH, and epithelial cells of dilated cystic glands) were determined from sections prepared immediately before and after the sections pooled for RNA preparation by pathologists. The tissue component distributions for the four data sets are shown in Table 13. Four publicly available microarray data sets (datasets 5 through 8) also were collected.

These included a total of 238 arrays that were generated from 219 tumor enriched and 19 non-tumor parts of prostate tissue, as shown in Table 14. Dataset 5 consists of two groups (37 recurrence and 42 non-recurrence) for a total of 79 cases. The samples used in these four datasets do not have associated details of tissue component information.

Selection of Genes for Model-Training: Subsets of genes were selected to train the prediction model using two strategies. In the first strategy, each gene was ranked by the correlation coefficient between its intensity values and the percentage of a given tissue component across all samples. In the second strategy, the genes were ranked by their F- statistic, a measure of their fit in the multiple linear regression model as described below. The two strategies produced very similar results.

Multiple Linear Regression Model: A multi-variate linear regression model was used for prediction of tissue components. This is based on the assumption that the observed gene expression intensity of a gene is the summation of the contributions from different types of cells: g = β _o + ∑β _]P] + e, (1)

where g is the expression value for a gene, p _} is the percentage of a given tissue component determined by the pathologists, and/? is the expression coefficient associated with a given cell type. In this model, C is the number of tissue types under consideration. In the current study, only β' s of two major tissue types, tumor and stroma, were estimated to minimize the noise caused by other minority cell types. The contribution of other cell types to the total intensity g is subsumed into β ₀ and e. Note that β _} is suggestive of the relative expression level in cell type j compared to the overall mean expression level β ₀ . The regression model was used to predict the percentage of tissue components after the parameters were determined on a training data set.

Cross-validation within data sets: Ten-fold cross-validation was used to estimate the prediction error rates for each data set. Briefly, one tenth of the samples were randomly selected as the test set using a boot strapping strategy and the remaining nine tenths of the samples were used as training set. Prediction models are constructed using the training sets with a pre-defined number of genes selected with the strategy mentioned above. The prediction is then tested on the test set. The sample selection and prediction step are repeated 10 times using different test samples each time until all the samples are used as test samples only once. This whole procedure is repeated five times using different sets of 10% of the data in each iteration to generate reliable results.

Validation between data sets: Mutual predictions were performed among datasets 1, 2, 3 and 4 to assess the applicability of prediction models across different data sets. Because the microarray platforms differ among the four data sets, quantile normalization are applied to preprocess the microarray data (Bolstad et al. (2003) Bioinformatics 19:185-193) with one modification. Quantile normalization method was applied on the test data set with the entire training set as the reference. This change means that the training set that is used to build prediction models will not be re-calculated and the prediction models will likely stay the same.

The mapping of probe sets from different Affymetrix platforms is based on the array comparison files downloaded from the Affymetrix website (World Wide Web at affymetrix.com). Probe sets of Probes in Affymetrix U133A array are a sublist of those in Affymetrix U133Plus2.0 array, and the DNA sequences of the common probes of two platforms are identical, suggesting these two platforms are very similar. The Illumina DASL platform used in data set 4 only provided gene symbols as the probe annotation, which was used to map to Affymetrix platforms. The numbers of genes mapped among different platforms are shown in Table 15.

Prediction on data sets that do not have pathologist' s estimates of tissue proportions: Datasets 5, 6, 7, and 8 do not have previous estimates of tissue composition (Table 14). Datasets 1, 5, and 6 were generated from Affymetrix U133A arrays. Thus, the prediction models constructed with data set 1 were used to predict tissue components of samples used in datasets 5 and 6. Likewise, datasets 2, 7, and 8 were generated with Affymetrix U133Plus2.0 arrays, so prediction models constructed with dataset 2 were used to predict tissue components of samples used in datasets 7 and 8. The modified quantile normalization method described above was used for preprocessing the test data sets.

Comparison of in silico predictions and pathologist' s estimates within the same data set: Four sets of microarray expression data for which tissue percentages had been determined by pathologists (Table 13), were used to develop in silico models that could predict tissue percentages in other samples that had array data but did not have pathologist data on tissue percentages. The discrepancies between in silico predictions and pathologist's estimates were measured by the mean absolute difference between values predicted in silico and the observation values estimated by pathologists. Ten-fold cross-validation was used to estimate the prediction discrepancies for datasets 1, 2, 3 and 4. To determine the best number of genes for constructing prediction model, the most significant 5, 10, 20, 50, 100 or 250 genes were compared. The prediction results are shown in Figures 6A and 6B, and Tables 16 and 17.

Among the four datasets, dataset 1 has the most similar in silico prediction to the pathologist's estimation, with 8% average discrepancy rate for tumor and 16% average discrepancy rate for stroma using the 250-gene model. This may because: 1) this dataset has four pathologists' estimation of tissue components, which will certainly be more accurate than that by one pathologist; 2) fresh frozen tissues were used which generate intact RNA for profiling; and/or 3) relatively larger sample size. Dataset 4 has the least accurate prediction, which may be because: 1) the dataset was generated from degraded total RNA samples from the FFPE blocks; and/or 2) the total number of genes on the Illumina DASL array platform are much less than that of other array platforms (511 probes versus 12626 or more probe sets for the other data sets).

The predictions of tumor components are slightly better than that of stroma, which may be explained in part by the fact that prostate stroma is a mixture of fibroblast cells, smooth muscle cells, blood vessels et al.

As shown in Figure 6, the prediction model does not require many genes. The prediction model can reliable predict tumor components with as few as 10 genes, and predict stroma components with 50 genes.

Dataset 2 contains twelve laser capture micro-dissected tumor samples, the average in silico predicted tumor components for these samples are 91 % in average. Assuming these samples really are all nearly pure tumor then the error rate is 9% or less for these samples, which is close to the average error rates of all samples in dataset 2.

The possibility of predicting of two other prostate cell types - the epithelial cells of BPH and dilated cystic glands by extending the current multi-variate model - also were explored. It was found that in silico prediction on these two tissue components are much less accurate than tumor and stroma component, largely because their percentage values are usually small and the pathologists differed in their estimates of these tissues. The extended prediction model including these tissues also slightly lowers the prediction accuracy of tumor and stroma components.

In the original study for dataset 3, agreement analysis on the tissue components that were estimated by four pathologists were assessed as inter-observer Pearson correlation coefficients. The average coefficients for tumor and stroma were 0.92 and 0.77. This is better than the correlation coefficients between in silico prediction and pathologist's estimation for the same dataset, which is 0.72 for the tumor component and 0.57 for stroma component. However, pathologists reviewed the same sections and the tissue components of the adjacent but non-identical samples processed for array assay may differ.

One indication that the prediction model may be optimized to the limits of the data available is the fact that the discrepancy between in silico predicted tissue components and pathologist's estimate for the predictions made on the test sets is often barely 1% different from that of the predictions made on the training set. See the example of 250-gene model as below. Data on other models were very similar.

Data set 1 (training/test): tumor 7.6%/8.1%; stroma 11.7%/12.8%.

Data set 2 (training/test): tumor 8.4%/9.5%; stroma 11.5%/12.5%.

Data set 3 (training/test): tumor 10.3%/11.4%; stroma 15.2%/17.3%.

Data set 4 (training/test): tumor 11.9%/12.5%; stroma 14.7%/15.4%. To construct the best prediction models from each data set, a 10-fold permutation strategy was adopted to select the most suitable genes to be used in the final prediction model. To construct a n (i.e., 5, 10, 20, 50, 100, 250) gene model for each data set, only nine tenths of randomly chosen samples were used in the multi-variate linear regression analysis for selecting the n most significant genes. This step was repeated nine more times until all the samples were used nine times, which also means that all samples were skipped once. All selected genes (n x 10) were pooled and ranked by their incidence. The n genes with the most hits, which are listed in Table 18, were used to construct prediction models that are integrated into CellPred program, as described below.

Comparison between in silico predictions across data sets and pathologist's estimates:

Discrepancies for predictions made across different data sets are shown in Table 19. The 250-gene model is used for the mutual prediction. The prediction models constructed on fewer genes also were performed, and the prediction was less accurate than the 250-gene model. In general, the in silico predictions across different datasets are less similar to the pathologist's estimates than the in silico prediction made within the same dataset. However, 5 the discrepancy in predictions across datasets is similar to the discrepancy within datasets when the array platforms are very similar (Affymetrix U133A and U133Plus2.0) and sample types are the same (i.e., fresh frozen sample). For the example of datasets 1 and 2, the prediction discrepancy is 11.0% for tumor and 16.7% for stroma when data set 1 was used as a training set, whereas vice versa, the numbers are 11.6% for tumor and 11.8% for stroma. In o the case that microarray platforms and sample types vary (between fresh frozen and FFPE, for example), the cross data set prediction error rates increase and vary largely from 12.1% ~ 28.6% for tumor and 14.7% to 38.2% for stroma depending on the comparison. The mutual prediction results strongly suggest that the feasibility of tissue components prediction across data sets when array platform and sample type are the same. For other cases, prediction of 5 tissue percentages is also possible, but has a large error.

In silico prediction of tissue components of samples in publicly available prostate data sets: The in silico predicted tumor and stroma components of 238 samples used in datasets 5, 6, 7, and 8 are documented in Table 17. When 219 of 238 samples were prepared as tumor-0 enriched prostate tissue, the in silico predicted tumor proportions for these 219 samples showed a wide range from 0 to 87% tumor cells. There are 44 (20.1%) samples predicted with less than 30% tumor cells, as shown in Figure 7A. These 44 samples with low amounts of predicted tumor appeared in dataset 5 (5 out of 79 tumor samples, 6.3%), dataset 6 (7 out of 44 tumor samples, 15.9%), dataset 7 (2 out of 13 tumor samples, 15.4%), and dataset 8 (305 out of 83 tumor samples, 36.1%), suggesting a large variation of tumor enrichment occurred in all the different data sets.

Dataset 5 includes information regarding recurrence of cancer after prostatectomy for patients, which was used to divide the samples into two groups for comparison (Stephenson, supra). The average tumor tissue component predicted for the recurrence group (58.5%) was0 noted to be about 10% higher than that of non-recurrence group (48.0%), as shown in Figure 7B. Unless recognized and taken into account, this skew has the potential to provide false data regarding recurrence. Thus, tumor-specific genes are enriched in univariate analysis of the recurrent cases simply because such genes are naturally enriched in samples with more tumor cells.

To further illustrate this effect, the percentage of tumor predicted on dataset 5 using the dataset 1 in silico model was plotted as the x axis in a heat map with the non-recurrence and recurrence groups plotted separately. The Y axis consists of the expression levels in data set 5 of the top 100 (50 up- and 50 down-regulated) significant differential expressed genes between tumor and normal tissue identified in dataset 6. The gradient effects from left to right on two groups (non-recurrence and recurrence group) of samples from dataset 5 shows that expression levels of tissue specific genes selected from dataset 6 greatly correlate with the in silico predicted tumor contents with the prediction models developed from dataset 1. Moreover, samples in the recurrence group show slightly higher expression levels in up- regulated genes and lower expression level in down-regulated genes (also shown in Figure 7B), indicating that the tumor components vary among two groups that may cause bias if two groups were compared directly without corrections.

Software for prostate cancer tissue prediction: CellPred, a web service freely available on the World Wide Web at webarraydb.org, was designed for prediction of the tissue components of prostate samples used in high-throughput expression studies, such as microarrays. CellPred was developed on a LAMP system (a GNU Linux server with Apache, MySQL and Python). The modules were written in python (World Wide Web at python.org) while analysis functions were written in R language (World Wide Web at r-project.org). The R script for modeling / training / prediction is downloadable from the World Wide Web at webarraydb.org/softwares/CellPred/. Users have the option to choose the number of genes for constructing the model. Genes used for generating the model are provided as an output file. Other details about the program can be found in the online help document.

Users can upload their own data sets for construction of prediction models. However, as an example, data has already been uploaded to allow prediction models constructed on datasets 1, 2 and 3 to be used for making predictions for a user-supplied data set. The user needs to upload the Affymetrix CeI file or any other type of microarray intensity file processed appropriately to make it compatible for making predictions. The most accurate prediction is made for Affymetrix U133A, U133Plus2.0 and U95Av2 array data using the prediction models developed on dataset 1, 2, or 3 respectively. For all other types of microarray platforms, prediction is likely quite noisy. In such cases, probes/probe sets on the platform of the test sets will be mapped to the probes on the training set of choice based on the gene symbols, gene IDs (i.e. GenBank IDs, refSeq IDs) or a mapping file (Xia et al. (2009) Bioinformatics 25:2425-2429). Modified quantile normalization is integrated for preprocessing the intensity values of the test arrays. Then the prediction is made on the test sets using the prediction models constructed with the training set. High-throughput expression sequence tags are accepted by the program if the data are condensed into a file equivalent to an intensity file, along with gene names or IDs that can be mapped to the training data sets.

Table 13. Prostate cancer microarray data sets with known tissue component information.

Data Set 1 Data Set 2 Data Set 3 Data Set 4

U133A U133Plus2 U95Av2 Illumina DASL

Microarray Platform arrays

Fresh Fresh Fresh Frozen FFPE

Sample Type Frozen Frozen n. of Arrays 136 149 88 114

Sample Source Prostatectomy 132 110 88 114

Autopsy * 4 13

LCM ** 16 ^Λ

Prostate 10

Biopsy

Data Source GSE8218 GSE17951 GSE1431*** n. of Probes or Probe

Sets 22283 54675 12626 511 n. of Pathologists 4 1 4 1

Tumor ( ⁰ Ic) Maximum 80 100 80 90

Mean 20 26 17 24

Minimum 0 0 0 0

Stroma (%) Maximum 100 100 100 100

Mean 61 63 59 54

Minimum 4 0 4 0

Epithelium from BPH

(%) Maximum 50 53 55 60

Mean 11 6 12 14

Minimum 0 0 0 0

Atrophic Gland (%) Maximum 20 49 32 50

Mean 6 4 7 7

Minimum 0 0 0 0

* Autopsy prostate samples from normal subjects.

** Laser capture micro-dissected samples; ^Λ 12 tumor samples and 4 stroma samples.

*** Stuart et al., supra

**** Bibikova et al. (2007) Genomics 89:666-672

Table 14. Prostate cancer microarray data sets without known tissue component information.

Data Set 5 Data Set 6 Data Set 7 Data Set 8

Array Platform U133A U133A U133Plus2 U133Plus2 n. of Arrays 79 57 19 83

Fresh Fresh Frozen Fresh Fresh

Sample Type Frozen Frozen Frozen

Tumor-enriched 13

Samples 79 44 83

Stroma Samples 0 13 6 O

* http://www.ebi.ac.uk/microarray- as/ae/brυwse.html?keywords=π-

Data Source TΛBM-26 GSE3225 GSE2109

Table 15. In silico tissue components (tumor/stroma) prediction discrepancies (%) and correlation coefficients compared to pathologist's estimates using 10-fold cross validation.

Data Set Data Set Data Set Data Set 1 2 3 4

Tumor

5-gene model Cells 10.1/0.78 22.9/0.41 16.5/0.48 16.1/0.64 Stroma 20.8/0.51 28.4/0.38 31.9/0.16 21.5/0.5

Tumor

10-gene model Cells 8.5/0.83 12.6/0.84 11.6/0.7 13.7/0.71 Stroma 18/0.57 19.6/0.61 21.7/0.52 17.8/0.62

Tumor

20-gene model Cells 8.2/0.85 11.8/0.86 10.5/0.74 14.7/0.63 Stroma 15.9/0.64 16.6/0.72 18.6/0.5 18.6/0.6

Tumor

50-gene model Cells 8.4/0.86 11.7/0.85 10.9/0.72 13.9/0.69 Stroma 13.3/0.72 14.3/0.78 18.3/0.55 16.9/0.66

100-gene Tumor model Cells 8/0.87 10.6/0.87 10.6/0.75 12.7/0.7 Stroma 12.9/0.74 13.5/0.79 17.1/0.56 15.6/0.7

250-gene Tumor model Cells 8.1/0.87 9.5/0.9 11.4/0.72 12.5/0.73 Stroma 12.8/0.73 12.5/0.82 17.3/0.57 15.4/0.72

Table 16. Number of probes/probe sets mapped across different microarray platforms.

U133A U133Plus2.0 U95Av2 Illumina DASL array

U133A - - - -

U133Plus2.0 22277 - - -

U95Av2 12310 12323 - -

Illumina DASL array 359 359 330 -

Table 17. In silico predicted tissue components for datasets 5, 6, 7 and 8 (%).

Data Sets sample name sample type Platform Tumor Stroma

Data Set 5 SL_U133A_PG_12 tumor-enriched samples U133A 75 25

Data Set 5 SL_U133A_PG_42 tumor-enriched samples U133A 42 48

Data Set 5 SL_U133A_PG_45 tumor-enriched samples U133A 42 58

Data Set 5 SL_U133A_PG_50 tumor-enriched samples U133A 70 30

Data Set 5 SL_U133A_PG_53 tumor-enriched samples U133A 69

Data Set 5 SL_U133A_PG_8 tumor-enriched samples U133A 38 60

Data Set 5 SL_U133A_PR22.T tumor-enriched samples U133A 61 29

Data Set 5 SL_U133A_PR24.T tumor-enriched samples U133A 63 34

Data Set 5 SL_U133A_PR25.T tumor-enriched samples U133A 61 31

Data Set 5 SL_U133A_PR28.T tumor-enriched samples U133A 35 65

Data Set 5 SL_U133A_PR31.T tumor-enriched samples U133A 52 47

Data Set 5 SL_U133A_PR32.T tumor-enriched samples U133A 60 33

Data Set 5 SL_U133A_PR33.T tumor-enriched samples U133A 39 46

Data Set 5 SL_U133A_PR35.T tumor-enriched samples U133A 62 37

Data Set 5 SL_U133A_PR37.T tumor-enriched samples U133A 77 23

Data Set 5 SL_U133A_PR39.T tumor-enriched samples U133A 31 69

Data Set 5 SL_U133A_PR40.T tumor-enriched samples U133A 47 52

Data Set 5 SL_U133A_PR41.T tumor-enriched samples U133A 25 75

Data Set 5 SL_U133A_PR42.T tumor-enriched samples U133A 61 32

Data Set 5 SL_U133A_PR43.T tumor-enriched samples U133A 66 34

Data Set 5 SL_U133A_PR44.T tumor-enriched samples U133A 35 53

Data Set 5 SL_U133A_PR45.T tumor-enriched samples U133A 37 31

Data Set 5 SL_U133A_PR47.T tumor-enriched samples U133A 66 34

Data Set 5 SL_U133A_PR50.T tumor-enriched samples U133A C ^l 48 45

Data Set 5 SL_U133A_PR52.T tumor-enriched samples U133A 69 30

Data Set 5 SL_U133A_PR53.T tumor-enriched samples U133A 56 42

Data Set 5 SL_U133A_PR54.T tumor-enriched samples U133A 65 35

Data Set 5 SL_U133A_PR55.T tumor-enriched samples U133A 25 47

Data Set 5 SL_U133A_PR56.T tumor-enriched samples U133A 51 31

Data Set 5 SL_U133A_PR57.T tumor-enriched samples U133A 27 57

Data Set 5 SL_U133A_PR58.T tumor-enriched samples U133A 33 42

Data Set 5 SL_U133A_PR59.T.REP tumor-enriched samples U133A 32 68

Data Set 5 SL_U133A_PR60.T tumor-enriched samples U133A 55 45

Data Set 5 SL_U133A_PR61.T tumor-enriched samples U133A 60 35

Data Set 5 SL_U133A_PR62.T tumor-enriched samples U133A 24 50

Data Set 5 SL_U133A_PR64.T tumor-enriched samples U133A 45 55

Data Set 5 SL_U133A_PR65.T tumor-enriched samples U133A 57 43

Data Set 5 SL_U133A_PR66.T tumor-enriched samples U133A 53 47

Data Set 5 SL_U133A_PR68.T tumor-enriched samples U133A 45 42

Data Set 5 SL_U133A_PR69.T tumor-enriched samples U133A 33 56

Data Set 5 SL_U133A_PR70.T tumor-enriched samples U133A 29 71

Data Set 5 SL_U133A_PR71.T tumor-enriched samples U133A 35 48

Data Set 5 SL_U133A_PG_13 tumor-enriched samples U133A 67 33

Data Set 5 SL_U133A_PG_15 tumor-enriched samples U133A 33 64

Data Set 5 SL_U133A_PG_37 tumor-enriched samples U133A 72 28

Data Set 5 SL_U133A_PG_41 tumor-enriched samples U133A 59 35 Data Set 5 SL_U133A_PG_46 tumor-enriched samples U133A 49 51

Data Set 5 SL_U133A_PG_52 tumor-enriched samples U133A 64 36

Data Set 5 SL_U133A_PR10.T tumor-enriched samples U133A 60 40

Data Set 5 SL_U133A_PR11.T tumor-enriched samples U133A 35 61

Data Set 5 SL_U133A_PR12.Trpt tumor-enriched samples U133A 46 54

Data Set 5 SL_U133A_PR13.T tumor-enriched samples U133A 60 31

Data Set 5 SL_U133A_PR14.T tumor-enriched samples U133A 41 46

Data Set 5 SL_U133A_PR15.T tumor-enriched samples U133A 52 39

Data Set 5 SL_U133A_PR16.T tumor-enriched samples U133A 87 13

Data Set 5 SL_U133A_PR17.T tumor-enriched samples U133A 61 31

Data Set 5 SL_U133A_PR18.T tumor-enriched samples U133A 73 27

Data Set 5 SL_U133A_PR19.T tumor-enriched samples U133A 68 32

Data Set 5 SL_U133A_PRl.Tredo tumor-enriched samples U133A 39 45

Data Set 5 SL_U133A_PR20.T tumor-enriched samples U133A 57 43

Data Set 5 SL_U133A_PR21.Trep tumor-enriched samples U133A 62 38

Data Set 5 SL_U133A_PR26.T tumor-enriched samples U133A 34 66

Data Set 5 SL_U133A_PR27.T tumor-enriched samples U133A 42 51

Data Set 5 SL_U133A_PR29.T tumor-enriched samples U133A 82 18

Data Set 5 SL_U133A_PR2.Tredo tumor-enriched samples U133A 50 50

Data Set 5 SLJJ 133 A_PR3.TREDO tumor-enriched samples U133A 59 41

Data Set 5 SL_U133A_PR48.T tumor-enriched samples U133A 74 26

Data Set 5 SL_U133A_PR49.T tumor-enriched samples U133A 53 38

Data Set 5 SLJJ 133 A J ^> R4.TREDO tumor-enriched samples U133A 30 60

Data Set 5 SLJJISSAJ^RSI.T tumor-enriched samples U133A 58 30

Data Set 5 SLJJ 133 A _PR5.TREDO tumor-enriched samples U133A 82 18

Data Set 5 SLJJ133A_PR63.T tumor-enriched samples U133A 48 51

Data Set 5 SLJJ 133 A J^Rό.TREDO tumor-enriched samples U133A 61 39

Data Set 5 SLJJ133AJ ^> R72.T tumor-enriched samples U133A 72 28

Data Set 5 SLJJBSAJ ³ RVS-T tumor-enriched samples U133A 68 21

Data Set 5 SLJJ133AJ ^> R74.B tumor-enriched samples U133A 84 16

Data Set 5 SLJJ 133 A _PR7.TRED02 tumor-enriched samples U133A 49 32

Data Set 5 SLJJ 133 A _PR8.TREDO tumor-enriched samples U133A 76 24

Data Set 5 SLJJ 133 A _PR9.TREDO tumor-enriched samples U133A 56 44

Data Set 6 A- 1940339465. CEL tumor-enriched samples U133A 37 33

Data Set 6 A-2393346053.CEL tumor-enriched samples U133A 62 30

Data Set 6 A-3010184133.CEL tumor-enriched samples U133A 67 28

Data Set 6 A-3435720971.CEL tumor-enriched samples U133A 59 35

Data Set 6 A-4418592762.CEL tumor-enriched samples U133A 62 30

Data Set 6 A-4464625690.CEL tumor-enriched samples U133A 12 34

Data Set 6 A-4472570235.CEL tumor-enriched samples U133A 61 36

Data Set 6 A-4917290232.CEL tumor-enriched samples U133A 74 19

Data Set 6 A-4963842013.CEL tumor-enriched samples U133A 18 63

Data Set 6 A-5173529673.CEL tumor-enriched samples U133A 62 38

Data Set 6 A-5292628126.CEL tumor-enriched samples U133A 37 39

Data Set 6 A-5642567629.CEL tumor-enriched samples U133A 80 18

Data Set 6 A-7270793196.CEL tumor-enriched samples U133A 0 84

Data Set 6 A-7350218006.CEL tumor-enriched samples U133A 20 53

Data Set 6 A-8500920543.CEL tumor-enriched samples U133A 44 45

Data Set 6 A-9763059872.CEL tumor-enriched samples U133A 43 36

Data Set 6 111T-A.CEL tumor-enriched samples U133A 44 43 Data Set 6 A-135T.CEL tumor-enriched samples U133A 38 39

Data Set 6 A-169T.CEL tumor-enriched samples U133A 45 49

Data Set 6 A-171T.CEL tumor-enriched samples U133A 62 38

Data Set 6 A-185N.CEL stroma samples U133A 0 69

Data Set 6 185T-A.CEL tumor-enriched samples U133A 49 31

Data Set 6 195T-A.CEL tumor-enriched samples U133A 46 42

Data Set 6 A-226T.CEL tumor-enriched samples U133A 43 46

Data Set 6 A-237T.CEL tumor-enriched samples U133A 37 57

Data Set 6 A-23N.CEL stroma samples U133A 19 78

Data Set 6 A-23T.CEL tumor-enriched samples U133A 48 52

Data Set 6 243T-A.CEL tumor-enriched samples U133A 53 38

Data Set 6 246T-A.CEL tumor-enriched samples U133A 45 55

Data Set 6 A-257T.CEL tumor-enriched samples U133A 58 39

Data Set 6 A-340N.CEL stroma samples U133A 25 52

Data Set 6 340T.CEL tumor-enriched samples U133A 32 68

Data Set 6 357T.CEL tumor-enriched samples U133A 51 49

Data Set 6 362T.CEL tumor-enriched samples U133A 46 54

Data Set 6 370T.CEL tumor-enriched samples U133A 36 50

Data Set 6 A-399N.CEL stroma samples U133A 0 63

Data Set 6 399T.CEL tumor-enriched samples U133A 15 85

Data Set 6 405T.CEL tumor-enriched samples U133A 38 39

Data Set 6 A-EPO 1N.CEL stroma samples U133A 0 77

Data Set 6 A-EP01T.CEL tumor-enriched samples U133A 24 73

Data Set 6 A-EP02N.CEL stroma samples U133A 5 71

Data Set 6 A-EP02T.CEL tumor-enriched samples U133A 38 62

Data Set 6 A-EP03N.CEL stroma samples U133A 8 56

Data Set 6 A-EP03T.CEL tumor-enriched samples U133A 41 53

Data Set 6 A-EP04N.CEL stroma samples U133A 0 65

Data Set 6 A-EP04T.CEL tumor-enriched samples U133A 30 53

Data Set 6 A-EP06N.CEL stroma samples U133A 0 76

Data Set 6 A-EP06T.CEL tumor-enriched samples U133A 38 61

Data Set 6 A-V16N.CEL stroma samples U133A 7 69

Data Set 6 A-V16T2.CEL tumor-enriched samples U133A 13 73

Data Set 6 A-V19N.CEL stroma samples U133A 0 67

Data Set 6 A-V19T.CEL tumor-enriched samples U133A 32 56

Data Set 6 A-V21N.CEL stroma samples U133A 10 82

Data Set 6 A-V21T.CEL tumor-enriched samples U133A 58 42

Data Set 6 A-V29N.CEL stroma samples U133A 0 82

Data Set 6 A-V29T.CEL tumor-enriched samples U133A 42 38

Data Set 6 A-V30T.CEL tumor-enriched samples U133A 41 30

Data Set 7 GSM74875.CEL stroma samples U133P2 9 91

Data Set 7 GSM74876.CEL stroma samples U133P2 21 68

Data Set 7 GSM74877.CEL stroma samples U133P2 2 98

Data Set 7 GSM74878.CEL stroma samples U133P2 19 76

Data Set 7 GSM74879.CEL stroma samples U133P2 10 90

Data Set 7 GSM74880.CEL stroma samples U133P2 9 91

Data Set 7 GSM74881.CEL tumor-enriched samples U133P2 33 67

Data Set 7 GSM74882.CEL tumor-enriched samples U133P2 26 74

Data Set 7 GSM74883.CEL tumor-enriched samples U133P2 37 63

Data Set 7 GSM74884.CEL tumor-enriched samples U133P2 41 59 Data Set 7 GSM74885.CEL tumor-enriched samples U133P2 32 68

Data Set 7 GSM74886.CEL tumor-enriched samples U133P2 34 66

Data Set 7 GSM74887.CEL tumor-enriched samples U133P2 34 66

Data Set 7 GSM74888.CEL tumor-enriched samples U133P2 82 18

Data Set 7 GSM74889.CEL tumor-enriched samples U133P2 76 24

Data Set 7 GSM74890.CEL tumor-enriched samples U133P2 61 39

Data Set 7 GSM74891.CEL tumor-enriched samples U133P2 59 41

Data Set 7 GSM74892.CEL tumor-enriched samples U133P2 75 25

Data Set 7 GSM74893.CEL tumor-enriched samples U133P2 72 28

Data Set 8 GSM38079. CEL tumor-enriched samples U133P2 29 71

Data Set 8 GSM46837.CEL tumor-enriched samples U133P2 58 42

Data Set 8 GSM46866.CEL tumor-enriched samples U133P2 40 60

Data Set 8 GSM 137971. CEL tumor-enriched samples U133P2 54 46

Data Set 8 GSM138038.CEL tumor-enriched samples U133P2 48 36

Data Set 8 GSM 152575. CEL tumor-enriched samples U133P2 51 49

Data Set 8 GSM 152611. CEL tumor-enriched samples U133P2 64 32

Data Set 8 GSM152617.CEL tumor-enriched samples U133P2 23 73

Data Set 8 GSM152622.CEL tumor-enriched samples U133P2 19 76

Data Set 8 GSM 152631. CEL tumor-enriched samples U133P2 20 80

Data Set 8 GSM152772.CEL tumor-enriched samples U133P2 38 62

Data Set 8 GSM152778.CEL tumor-enriched samples U133P2 59 41

Data Set 8 GSM152783.CEL tumor-enriched samples U133P2 36 64

Data Set 8 GSM179790.CEL tumor-enriched samples U133P2 27 73

Data Set 8 GSM179792.CEL tumor-enriched samples U133P2 31 69

Data Set 8 GSM 179843. CEL tumor-enriched samples U133P2 28 72

Data Set 8 GSM179849.CEL tumor-enriched samples U133P2 15 85

Data Set 8 GSM102498.CEL tumor-enriched samples U133P2 46 54

Data Set 8 GSMl 02510.CEL tumor-enriched samples U133P2 35 65

Data Set 8 GSM117726.CEL tumor-enriched samples U133P2 57 43

Data Set 8 GSM117727.CEL tumor-enriched samples U133P2 36 64

Data Set 8 GSMl 17741. CEL tumor-enriched samples U133P2 29 69

Data Set 8 GSM76640.CEL tumor-enriched samples U133P2 28 49

Data Set 8 GSM76648.CEL tumor-enriched samples U133P2 45 55

Data Set 8 GSM88977.CEL tumor-enriched samples U133P2 57 43

Data Set 8 GSM89017.CEL tumor-enriched samples U133P2 59 41

Data Set 8 GSM102435.CEL tumor-enriched samples U133P2 22 78

Data Set 8 GSM53061. CEL tumor-enriched samples U133P2 32 68

Data Set 8 GSM53114.CEL tumor-enriched samples U133P2 30 60

Data Set 8 GSM53152.CEL tumor-enriched samples U133P2 62 38

Data Set 8 GSM53162.CEL tumor-enriched samples U133P2 67 33

Data Set 8 GSM76516.CEL tumor-enriched samples U133P2 44 56

Data Set 8 GSM76544.CEL tumor-enriched samples U133P2 17 83

Data Set 8 GSM76553.CEL tumor-enriched samples U133P2 55 45

Data Set 8 GSM325799.CEL tumor-enriched samples U133P2 45 55

Data Set 8 GSM325802.CEL tumor-enriched samples U133P2 11 89

Data Set 8 GSM325804.CEL tumor-enriched samples U133P2 33 67

Data Set 8 GSM325810.CEL tumor-enriched samples U133P2 23 77

Data Set 8 GSM353882.CEL tumor-enriched samples U133P2 49 51

Data Set 8 GSM353884.CEL tumor-enriched samples U133P2 19 81

Data Set 8 GSM353891.CEL tumor-enriched samples U133P2 52 48 Data Set 8 GSM353892.CEL tumor-enriched samples U133P2 56 44

Data Set 8 GSM353893.CEL tumor-enriched samples U133P2 29 65

Data Set 8 GSM353894.CEL tumor-enriched samples U133P2 23 61

Data Set 8 GSM353899.CEL tumor-enriched samples U133P2 33 67

Data Set 8 GSM353910.CEL tumor-enriched samples U133P2 44 56

Data Set 8 GSM353917.CEL tumor-enriched samples U133P2 41 59

Data Set 8 GSM353940.CEL tumor-enriched samples U133P2 29 71

Data Set 8 GSM 179901. CEL tumor-enriched samples U133P2 56 44

Data Set 8 GSM 179903. CEL tumor-enriched samples U133P2 27 73

Data Set 8 GSM179954.CEL tumor-enriched samples U133P2 58 42

Data Set 8 GSM203677.CEL tumor-enriched samples U133P2 17 83

Data Set 8 GSM203707.CEL tumor-enriched samples U133P2 24 76

Data Set 8 GSM203711.CEL tumor-enriched samples U133P2 30 70

Data Set 8 GSM203715.CEL tumor-enriched samples U133P2 37 63

Data Set 8 GSM203722.CEL tumor-enriched samples U133P2 25 75

Data Set 8 GSM203740.CEL tumor-enriched samples U133P2 45 55

Data Set 8 GSM203764.CEL tumor-enriched samples U133P2 47 53

Data Set 8 GSM203778.CEL tumor-enriched samples U133P2 59 39

Data Set 8 GSM203786.CEL tumor-enriched samples U133P2 52 48

Data Set 8 GSM231872.CEL tumor-enriched samples U133P2 57 43

Data Set 8 GSM231876.CEL tumor-enriched samples U133P2 10 90

Data Set 8 GSM231881. CEL tumor-enriched samples U133P2 24 76

Data Set 8 GSM231888.CEL tumor-enriched samples U133P2 28 72

Data Set 8 GSM231894.CEL tumor-enriched samples U133P2 30 70

Data Set 8 GSM231944.CEL tumor-enriched samples U133P2 37 63

Data Set 8 GSM231951. CEL tumor-enriched samples U133P2 23 57

Data Set 8 GSM231957.CEL tumor-enriched samples U133P2 57 43

Data Set 8 GSM231978.CEL tumor-enriched samples U133P2 41 59

Data Set 8 GSM231979.CEL tumor-enriched samples U133P2 36 57

Data Set 8 GSM231990.CEL tumor-enriched samples U133P2 29 71

Data Set 8 GSM277677.CEL tumor-enriched samples U133P2 12 82

Data Set 8 GSM277683.CEL tumor-enriched samples U133P2 55 45

Data Set 8 GSM277694.CEL tumor-enriched samples U133P2 40 60

Data Set 8 GSM301659.CEL tumor-enriched samples U133P2 15 85

Data Set 8 GSM301665.CEL tumor-enriched samples U133P2 3 78

Data Set 8 GSM301666.CEL tumor-enriched samples U133P2 14 66

Data Set 8 GSM301670.CEL tumor-enriched samples U133P2 30 70

Data Set 8 GSM301674.CEL tumor-enriched samples U133P2 16 84

Data Set 8 GSM301679.CEL tumor-enriched samples U133P2 42 58

Data Set 8 GSM301701.CEL tumor-enriched samples U133P2 34 66

Data Set 8 GSM301709.CEL tumor-enriched samples U133P2 46 54

Data Set 8 GSM38053.CEL tumor-enriched samples U133P2 39 61 Table 18. Genes identified by permutation strategy to select the most suitable genes for the final prediction model

DataSet geneModel uniquelD Gene Symbol Gene Description

Data Set 1 5 gene model 202555_s_at MYLK myosin, light polypeptide kinase /// myosin, light polypeptide kinase Data Set 1 5 gene model 219360_s_at TRPM4 transient receptor potential cation channel, subfamily M, member 4 Data Set 1 5 gene model 209825_s_at UCK2 uridine-cytidine kinase 2 gap junction protein, beta 1, 32kDa (connexin 32, Charcot-Marie-Tooth

Data Set 1 5 gene model 204973_at GJBl neuropathy, X-linked) Data Set 1 5 gene model 214027_x_at DES /// FAM48A desmin /// family with sequence similarity 48, member A Data Set 1 10 gene model 202222_s_at DES desmin Data Set 1 10 gene model 205547_s_at TAGLN transgelin Data Set 1 10 gene model 203766_s_at LMODl leiomodin 1 (smooth muscle) Data Set 1 10 gene model 217728_at S100A6 SlOO calcium binding protein A6 (calcyclin) Data Set 1 10 gene model 209825_s_at UCK2 uridine-cytidine kinase 2 clusterin (complement lysis inhibitor, SP-40,40, sulfated glycoprotein 2,

Data Set 1 10 gene model 208792_s_at CLU testosterone-repressed prostate message 2, apolipoprotein J) Data Set 1 10 gene model 212412_at PDLIM5 PDZ and LIM domain 5 Data Set 1 10 gene model 219360_s_at TRPM4 transient receptor potential cation channel, subfamily M, member 4 Data Set 1 10 gene model 201061_s_at STOM stomatin Data Set 1 10 gene model 209283_at CRYAB crystallin, alpha B Data Set 1 20 gene model 200982_s_at ANXA6 annexin A6 Data Set 1 20 gene model 218094_s_at C20orf35 chromosome 20 open reading frame 35 Data Set 1 20 gene model 20395 l_at CNNl calponin 1, basic, smooth muscle Data Set 1 20 gene model 209356_x_at EFEMP2 EGF-containing fibulin-like extracellular matrix protein 2 Data Set 1 20 gene model 206580_s_at EFEMP2 EGF-containing fibulin-like extracellular matrix protein 2 Data Set 1 20 gene model 201590_x_at ANX A2 annexin A2 Data Set 1 20 gene model 219167_at RASL12 RAS-like, family 12 Data Set 1 20 gene model 201105_at LGALSl lectin, galactoside-binding, soluble, 1 (galectin 1) Data Set 1 20 gene model 206558_at SIM2 single-minded homolog 2 (Drosophila) Data Set 1 20 gene model 217728_at S100A6 SlOO calcium binding protein A6 (calcyclin) Data Set 1 20 gene model 202148_s_at PYCRl pyrroline-5-carboxylate reductase 1 Data Set 1 20 gene model 205547_s_at TAGLN transgelin Data Set 1 20 gene model 209825_s_at UCK2 uridine-cytidine kinase 2 Data Set 1 20 gene model 212412_at PDLIM5 PDZ and LIM domain 5 Data Set 1 20 gene model 209283 at CRYAB crystallin, alpha B

Data Set 1 20 gene model 205645 _at REPS2 RALBPl associated Eps domain containing 2

Data Set 1 20 gene model 203766 _s_at LMODl leiomodin 1 (smooth muscle) clusterin (complement lysis inhibitor, SP-40,40, sulfated glycoprotein 2

Data Set 1 20 gene model 208792 _s_at CLU testosterone-repressed prostate message 2, apolipoprotein J)

Data Set 1 20 gene model 201061 _s_at STOM stomatin keratin 5 (epidermolysis bullosa simplex, Dowling-Meara/Kobner/Weber-

Data Set 1 20 gene model 201820 _at KRT5 Cockayne types)

Data Set 1 50 gene model 200621 _at CSRPl cysteine and glycine-rich protein 1

Data Set 1 50 gene model 212236 _x_at KRT 17 keratin 17

Data Set 1 50 gene model 205856 _at SLC 14Al solute carrier family 14 (urea transporter), member 1 (Kidd blood group)

Data Set 1 50 gene model 207949 _s_at ICAl islet cell autoantigen 1, 69kDa glucosaminyl (N-acetyl) transferase 1, core 2 (beta-l,6-N-acetylglucosa-

Data Set 1 50 gene model 205505 _at GCNTl minyltransferase)

Data Set 1 50 gene model 205935 _at FOXFl forkhead box Fl

Data Set 1 50 gene model 213503 _x_at ANX A2 annexin A2

Data Set 1 50 gene model 210427 _x_at ANX A2 annexin A2

Data Set 1 50 gene model 208816 _x_at ANXA2P2 annexin A2 pseudogene 2 fibroblast growth factor receptor 2 (bacteria-expressed kinase, keratinocyte growth factor receptor, craniofacial dysostosis 1, Crouzon

Data Set 1 50 gene model 203638 _s_at FGFR2 syndrome, Pfeiffer syndrome, Jackson-Weiss syndrome)

Data Set 1 50 gene model 203892 _at WFDC2 WAP four-disulfide core domain 2

Data Set 1 50 gene model 210986 _s_at TPMl tropomyosin 1 (alpha)

Data Set 1 50 gene model 202565 _s_at SVIL supervillin

Data Set 1 50 gene model 203228 _at PAFAH1B3 platelet-activating factor acetylhydrolase, isoform Ib, gamma subunit 29kDa

Data Set 1 50 gene model 213288 _at OACT2 O-acyltransferase (membrane bound) domain containing 2

Data Set 1 50 gene model 204394 _at SLC43A1 solute carrier family 43, member 1

Data Set 1 50 gene model 203243 _s_at PDLIM5 PDZ and LIM domain 5

Data Set 1 50 gene model 201431 _s_at DPYSL3 dihydropyrimidinase-like 3

Data Set 1 50 gene model 219736 _at TRIM36 tripartite motif -containing 36

Data Set 1 50 gene model 201058 _s_at MYL9 myosin, light polypeptide 9, regulatory

Data Set 1 50 gene model 212509 _s_at MXRA7 matrix-remodelling associated 7

Data Set 1 50 gene model 46323 at CANTl calcium activated nucleotidase 1

Data Set 1 50 gene model 205309 _at SMPDL3B sphingomyelin phosphodiesterase, acid-like 3B

Data Set 1 50 gene model 209545 _s_at RIPK2 receptor-interacting serine-threonine kinase 2

Data Set 1 50 gene model 209763 at CHRDLl chordin-like 1

Data Set 1 50 gene model 205687_at UBPH ubiquitin-binding protein homolog serpin peptidase inhibitor, clade F (alpha-2 antiplasmin, pigment

Data Set 1 50 gene model 202283_at SERPINFl epithelium derived factor), member 1

Data Set 1 50 gene model 203323_at CAV2 caveolin 2

Data Set 1 50 gene model 210869_s_at MCAM melanoma cell adhesion molecule

Data Set 1 50 gene model 212116_at RFP ret finger protein

Data Set 1 50 gene model 221732_at CANTl calcium activated nucleotidase 1

Data Set 1 50 gene model 219478_at WFDCl WAP four-disulfide core domain 1

Data Set 1 50 gene model 218865_at MOSCl MOCO sulphurase C-terminal domain containing 1

Data Set 1 50 gene model 200897_s_at KIAA0992 palladin

Data Set 1 50 gene model 203632_s_at GPRC5B G protein-coupled receptor, family C, group 5, member B

Data Set 1 50 gene model 211576_s_at SLC 19Al solute carrier family 19 (folate transporter), member 1

Data Set 1 50 gene model 212886_at DKFZP434C171 DKFZP434C171 protein

Data Set 1 50 gene model 202949_s_at FHL2 four and a half LIM domains 2

Data Set 1 50 gene model 208690_s_at PDLIMl PDZ and LIM domain 1 (elfin)

Data Set 1 50 gene model 217912_at DUSlL dihydrouridine synthase 1-like (S. cerevisiae)

Data Set 1 50 gene model 206580_s_at EFEMP2 EGF-containing fibulin-like extracellular matrix protein 2

Data Set 1 50 gene model 212097_at CAVl caveolin 1 , caveolae protein, 22kDa

Data Set 1 50 gene model 202274_at ACTG2 actin, gamma 2, smooth muscle, enteric

Data Set 1 50 gene model 212813_at JAM3 junctional adhesion molecule 3

Data Set 1 50 gene model 201105_at LGALSl lectin, galactoside-binding, soluble, 1 (galectin 1) phosphoribosylaminoimidazole carboxylase, phosphoribosyl-

Data Set 1 50 gene model 201014_s_at PAICS aminoimidazole succinocarboxamide synthetase

Data Set 1 50 gene model 206558_at SIM2 single-minded homolog 2 (Drosophila)

Data Set 1 50 gene model 202440_s_at ST5 suppression of tumorigenicity 5

Data Set 1 50 gene model 200795_at SPARCLl SPARC-like 1 (mast9, hevin)

Data Set 1 50 gene model 212724_at RND3 Rho family GTPase 3

Data Set 1 100 gene model 202740_at ACYl aminoacylase 1

Data Set 1 100 gene model 204400_at EFS embryonal Fyn-associated substrate

Data Set 1 100 gene model 204570_at COX7A1 cytochrome c oxidase subunit Vila polypeptide 1 (muscle)

Data Set 1 100 gene model 201272_at AKRlBl aldo-keto reductase family 1 , member B 1 (aldose reductase)

Data Set 1 100 gene model 201284_s_at APEH N-acylaminoacyl-peptide hydrolase

Data Set 1 100 gene model 214156_at MYRIP myosin VIIA and Rab interacting protein

Data Set 1 100 gene model 203562_at FEZl fasciculation and elongation protein zeta 1 (zygin I)

Data Set 1 100 gene model 209170_s_at GPM6B glycoprotein M6B

protein phosphatase 3 (formerly 2B), catalytic subunit, alpha

Data Set 1 100 gene model 202429_s_at PPP3CA isoform (calcineurin A alpha)

Data Set 1 100 gene model 212680_x_at PPP1R14B protein phosphatase 1, regulatory (inhibitor) subunit 14B

Data Set 1 100 gene model 213996_at YPELl yippee-like 1 (Drosophila)

KDEL (Lys-Asp-Glu-Leu) endoplasmic reticulum protein

Data Set 1 100 gene model 200700_s_at KDELR2 retention receptor 2 similar to Interferon-induced transmembrane protein 3 (Interferon-

Data Set 1 100 gene model 216565_x_at LOC391020 inducible protein 1-8U)

Data Set 1 100 gene model 213001_at ANGPTL2 angiopoietin-like 2

Data Set 1 100 gene model 221586_s_at E2F5 E2F transcription factor 5, pl30-binding

Data Set 1 100 gene model 20097 l_s_at SERPl stress-associated endoplasmic reticulum protein 1

Data Set 1 100 gene model 200923_at LGALS3BP lectin, galactoside-binding, soluble, 3 binding protein

Data Set 1 100 gene model 202073 at OPTN optineurin

Data Set 1 100 gene model 203498_at DSCRlLl Down syndrome critical region gene 1-like 1

Data Set 1 100 gene model 206860_s_at FLJ20323 hypothetical protein FLJ20323

Data Set 1 100 gene model 217973_at DCXR dicarbonyl/L-xylulose reductase

Data Set 1 100 gene model 209616_s_at CESl carboxylesterase 1 (monocyte/macrophage serine esterase 1)

Data Set 1 100 gene model 204754_at HLF Hepatic leukemia factor

Data Set 1 100 gene model 209550_at NDN necdin homolog (mouse) prostaglandin 12 (prostacyclin) synthase /// prostaglandin 12

Data Set 1 100 gene model 20813 l_s_at PTGIS (prostacyclin) synthase

Data Set 1 100 gene model 203729 at EMP3 epithelial membrane protein 3

Data Set 1 100 gene model 203892_at WFDC2 WAP four-disulfide core domain 2

Data Set 1 100 gene model 202794_at INPPl inositol polyphosphate- 1 -phosphatase pleckstrin homology domain containing, family C (with FERM

Data Set 1 100 gene model 209210_s_at PLEKHCl domain) member 1

Data Set 1 100 gene model 209191_at TUBB 6 tubulin, beta 6

Data Set 1 100 gene model 217897_at FXYD6 FXYD domain containing ion transport regulator 6

Data Set 1 100 gene model 209434_s_at PPAT phosphoribosyl pyrophosphate amidotransferase

Data Set 1 100 gene model 202427_s_at BRP44 brain protein 44

Data Set 1 100 gene model 20404 l_at MAOB monoamine oxidase B

Data Set 1 100 gene model 202177_at GAS6 growth arrest-specific 6

Data Set 1 100 gene model 212067_s_at ClR complement component 1 , r subcomponent

Data Set 1 100 gene model 214247_s_at DKK3 dickkopf homolog 3 (Xenopus laevis)

Data Set 1 100 gene model 205780 at BIK BCL2 -interacting killer (apoptosis-inducing)

Data Set 1 100 gene model 205776_at FMO5 flavin containing monooxygenase 5

Data Set 1 100 gene model 220192_x_at SPDEF SAM pointed domain containing ets transcription factor

Data Set 1 100 gene model 218922_s_at LASS4 LAGl longevity assurance homolog 4 (S. cerevisiae)

Data Set 1 100 gene model 200907_s_at KIAA0992 palladin

Data Set 1 100 gene model 207836_s_at RBPMS RNA binding protein with multiple splicing fibroblast growth factor receptor 2 (bacteria-expressed kinase, keratinocyte growth factor receptor, craniofacial dysostosis 1,

Data Set 1 100 gene model 203638 s at FGFR2 Crouzon syndrome, Pfeiffer syndrome, Jackson-Weiss syndrome)

Data Set 1 100 gene model 203242_s_at PDLIM5 PDZ and LIM domain 5

Data Set 1 100 gene model 209624_s_at MCCC2 methylcrotonoyl-Coenzyme A carboxylase 2 (beta)

Data Set 1 100 gene model 212736_at C16orf45 chromosome 16 open reading frame 45

Data Set 1 100 gene model 206116 s at TPMl tropomyosin 1 (alpha)

Data Set 1 100 gene model 212843_at NCAMl neural cell adhesion molecule 1

Data Set 1 100 gene model 202947_s_at GYPC glycophorin C (Gerbich blood group)

Data Set 1 100 gene model 207876_s_at FLNC filamin C, gamma (actin binding protein 280)

Data Set 1 100 gene model 204069_at MEISl Meisl, myeloid ecotropic viral integration site 1 homolog (mouse)

Data Set 1 100 gene model 209087_x_at MCAM melanoma cell adhesion molecule

Data Set 1 100 gene model 212236_x_at KRT 17 keratin 17

Data Set 1 100 gene model 204394_at SLC43A1 solute carrier family 43, member 1

Data Set 1 100 gene model 212115_at C16orf34 chromosome 16 open reading frame 34

Data Set 1 100 gene model 202074_s_at OPTN optineurin clusterin (complement lysis inhibitor, SP-40,40, sulfated glycoprotein

Data Set 1 100 gene model 222043_at CLU 2, testosterone-repressed prostate message 2, apolipoprotein J)

Data Set 1 100 gene model 206858_s_at HOXC6 homeo box C6

Data Set 1 100 gene model 218418_s_at ANKRD25 ankyrin repeat domain 25

Data Set 1 100 gene model 213924_at MPPEl Metallophosphoesterase 1

Data Set 1 100 gene model 202504_at TRIM29 tripartite motif -containing 29

Data Set 1 100 gene model 205937 at CGREFl cell growth regulator with EF-hand domain 1

Data Set 1 100 gene model 208837_at TMED3 transmembrane emp24 protein transport domain containing 3

Data Set 1 100 gene model 216804_s_at PDLIM5 PDZ and LIM domain 5

Data Set 1 100 gene model 20391 l_at RAPlGAl RAPl, GTPase activating protein 1

Data Set 1 100 gene model 210299_s_at FHLl four and a half LIM domains 1

Data Set 1 100 gene model 210427_x_at ANX A2 annexin A2

Data Set 1 100 gene model 210987_x_at TPMl tropomyosin 1 (alpha)

Data Set 1 100 gene model 210243 s at B4GALT3 UDP-Gal:betaGlcNAc beta 1,4- galactosyltransferase, polypeptide 3

Data Set 1 100 gene model 209665_at CYB561D2 cytochrome b-561 domain containing 2

Data Set 1 100 gene model 210986_s_at TPMl tropomyosin 1 (alpha)

Data Set 1 100 gene model 203243_s_at PDLIM5 PDZ and LIM domain 5

Data Set 1 100 gene model 205856_at SLC 14Al solute carrier family 14 (urea transporter), member 1 (Kidd blood group)

Data Set 1 100 gene model 200974_at ACTA2 actin, alpha 2, smooth muscle, aorta serpin peptidase inhibitor, clade F (alpha-2 antiplasmin, pigment epithelium

Data Set 1 100 gene model 202283_at SERPINFl derived factor), member 1

Data Set 1 100 gene model 209545 s at RIPK2 receptor-interacting serine-threonine kinase 2

Data Set 1 100 gene model 203228_at PAFAH1B3 platelet-activating factor acetylhydrolase, isoform Ib, gamma subunit 29kDa

Data Set 1 100 gene model 201058 s at MYL9 myosin, light polypeptide 9, regulatory

Data Set 1 100 gene model 205309_at SMPDL3B sphingomyelin phosphodiesterase, acid-like 3B

Data Set 1 100 gene model 212116_at RFP ret finger protein

Data Set 1 100 gene model 212509_s_at MXRA7 matrix-remodelling associated 7

Data Set 1 100 gene model 209118_s_at TUB A3 tubulin, alpha 3

Data Set 1 100 gene model 202565_s_at SVIL supervillin

Data Set 1 100 gene model 218865_at MOSCl MOCO sulphurase C-terminal domain containing 1

Data Set 1 100 gene model 203632_s_at GPRC5B G protein-coupled receptor, family C, group 5, member B

Data Set 1 100 gene model 20143 l_s_at DPYSL3 dihydropyrimidinase-like 3

Data Set 1 100 gene model 207949_s_at ICAl islet cell autoantigen 1, 69kDa potassium large conductance calcium-activated channel, subfamily M,

Data Set 1 100 gene model 209948_at KCNMBl beta member 1

Data Set 1 100 gene model 209426_s_at AMACR alpha-methylacyl-CoA racemase

Data Set 1 100 gene model 209424 s at AMACR alpha-methylacyl-CoA racemase

Data Set 1 100 gene model 209425_at AMACR alpha-methylacyl-CoA racemase

Data Set 1 100 gene model 204083_s_at TPM2 tropomyosin 2 (beta)

Data Set 1 100 gene model 204934_s_at HPN hepsin (transmembrane protease, serine 1)

Data Set 1 100 gene model 211276_at TCEAL2 transcription elongation factor A (SΙI)-like 2

Data Set 1 100 gene model 201061_s_at STOM stomatin gap junction protein, beta 1, 32kDa (connexin 32, Charcot-Marie-Tooth

Data Set 1 100 gene model 204973 at GJBl neuropathy, X-linked)

Data Set 1 100 gene model 200824_at GSTPl glutathione S -transferase pi

Data Set 1 100 gene model 202555_s_at MYLK myosin, light polypeptide kinase /// myosin, light polypeptide kinase

Data Set 1 100 gene model 214027_x_at DES /// FAM48A desmin /// family with sequence similarity 48, member A

Data Set 1 250 gene model 222199_s_at BIN3 bridging integrator 3

Data Set 1 250 gene model 209623_at MCCC2 methylcrotonoyl-Coenzyme A carboxylase 2 (beta)

Data Set 1 250 gene model 202889_x_at MAP7 microtubule-associated protein 7 Data Set 1 250 gene model 200862_at DHCR24 24-dehydrocholesterol reductase Data Set 1 250 gene model 217736 s at EIF2AK1 eukaryotic translation initiation factor 2-alpha kinase 1 T cell receptor gamma constant 2 /// T cell receptor gamma constant 2 /// T cell receptor gamma variable 9 /// T cell receptor gamma variable 9 /// similar to T-cell receptor gamma chain C region PT-gamma-1/2 /// similar

TRGC2 /// TRGV9 to T-cell receptor gamma chain C region PT-gamma-1/2 /// similar to T-cell

/// LOC442532 /// receptor gamma chain V region PT -gamma- 1/2 precursor /// similar to T-cell

LOC442670 /// receptor gamma chain V region PT -gamma- 1/2 precursor /// TCR gamma

Data Set 1 250 gene model 209813_x_at TARP alternate reading frame protein /// TCR gamma alternate reading frame protein

TRGC2 /// TRGV9 T cell receptor gamma constant 2 /// T cell receptor gamma variable 9 ///

/// LOC442532 /// similar to T-cell receptor gamma chain C region PT-gamma-1/2 /// similar to

LOC442670 /// T-cell receptor gamma chain V region PT-gamma-1/2 precursor /// TCR

Data Set 1 250 gene model 215806_x_at TARP gamma alternate reading frame protein Data Set 1 250 gene model 222121 at SGEF Src homology 3 domain-containing guanine nucleotide exchange factor

TRGC2 /// TRGV9 T cell receptor gamma constant 2 /// T cell receptor gamma variable 9

/// LOC442532 /// /// similar to T-cell receptor gamma chain C region PT-gamma-1/2 ///

LOC442670 /// similar to T-cell receptor gamma chain V region PT-gamma-1/2 precursor

Data Set 1 250 gene model 216920_s_at TARP /// TCR gamma alternate reading frame protein Data Set 1 250 gene model 202729_s_at LTBPl latent transforming growth factor beta binding protein 1 Data Set 1 250 gene model 204667_at FOXAl forkhead box Al Data Set 1 250 gene model 209584_x_at APOBEC3C apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3C Data Set 1 250 gene model 203662_s_at TMODl tropomodulin 1 Data Set 1 250 gene model 203629_s_at COG5 component of oligomeric golgi complex 5 Data Set 1 250 gene model 201839_s_at TACSTDl tumor-associated calcium signal transducer 1 Data Set 1 250 gene model 201128_s_at ACLY ATP citrate lyase Data Set 1 250 gene model 214106_s_at GMDS GDP-mannose 4,6-dehydratase Data Set 1 250 gene model 210224_at MRl major histocompatibility complex, class I-related Data Set 1 250 gene model 20207 l_at SDC4 syndecan 4 (amphiglycan, ryudocan) Data Set 1 250 gene model 214733_s_at YIPFl Yipl domain family, member 1 Data Set 1 250 gene model 219806_s_at FN5 FN5 protein Data Set 1 250 gene model 213506_at F2RL1 coagulation factor II (thrombin) receptor-like 1 Data Set 1 250 gene model 221565_s_at FAM26B family with sequence similarity 26, member B Data Set 1 250 gene model 219920_s_at GMPPB GDP-mannose pyrophosphorylase B Data Set 1 250 gene model 221027 s at PLA2G12A phospholipase A2, group XIIA /// phospholipase A2, group XIIA

Data Set 1 250 gene model 209086_x_at MCAM melanoma cell adhesion molecule Data Set 1 250 gene model 207957_s_at PRKCB 1 Protein kinase C, beta 1 Data Set 1 250 gene model 221880_s_at LOC400451 hypothetical gene supported by AK075564; BC060873 Data Set 1 250 gene model 221669_s_at ACAD8 acyl-Coenzyme A dehydrogenase family, member 8 Data Set 1 250 gene model 205248_at C21orf5 chromosome 21 open reading frame 5 Data Set 1 250 gene model 206656_s_at C20orf3 chromosome 20 open reading frame 3 Data Set 1 250 gene model 202566_s_at SVIL supervillin Data Set 1 250 gene model 214765_s_at ASAHL N-acylsphingosine amidohydrolase (acid ceramidase)-like Data Set 1 250 gene model 210652_s_at Clorf34 chromosome 1 open reading frame 34 Data Set 1 250 gene model 202202_s_at LAMA4 laminin, alpha 4 Data Set 1 250 gene model 201605_x_at CNN2 calponin 2 Data Set 1 250 gene model 21255 l_at CAP2 CAP, adenylate cyclase-associated protein, 2 (yeast) Data Set 1 250 gene model 201136_at PLP2 proteolipid protein 2 (colonic epithelium-enriched) Data Set 1 250 gene model 218328_at COQ4 coenzyme Q4 homolog (yeast) Data Set 1 250 gene model 219786_at MTL5 metallothionein-like 5, testis-specific (tesmin) Data Set 1 250 gene model 206375_s_at HSPB3 heat shock 27kDa protein 3 Data Set 1 250 gene model 212563_at BOPl block of proliferation 1 Data Set 1 250 gene model 218792_s_at BSPRY B -box and SPRY domain containing Data Set 1 250 gene model 209270_at LAMB 3 laminin, beta 3 Data Set 1 250 gene model 221898_at PDPN podoplanin Data Set 1 250 gene model 206110_at HIST1H3H histone 1, H3h Data Set 1 250 gene model 213547_at CAND2 cullin-associated and neddylation-dissociated 2 (putative) Data Set 1 250 gene model 204345_at COL16A1 collagen, type XVI, alpha 1 Data Set 1 250 gene model 208579_x_at H2BFS H2B histone family, member S Data Set 1 250 gene model 205850_s_at GAB RB 3 gamma-aminobutyric acid (GABA) A receptor, beta 3 Data Set 1 250 gene model 205304_s_at KCNJ8 potassium inwardly-rectifying channel, subfamily J, member < Data Set 1 250 gene model 201284_s_at APEH N-acylaminoacyl-peptide hydrolase Data Set 1 250 gene model 208490_x_at HIST1H2BF histone 1, H2bf Data Set 1 250 gene model 218944_at PYCRL pyrroline-5-carboxylate reductase-like Data Set 1 250 gene model 209154_at TAX1BP3 Taxi (human T-cell leukemia virus type I) binding protein 3 Data Set 1 250 gene model 215380_s_at C7orf24 chromosome 7 open reading frame 24 Data Set 1 250 gene model 219517_at ELL3 elongation factor RNA polymerase II-like 3 Data Set 1 250 gene model 213275_x_at CTSB cathepsin B prion protein (p27-30) (Creutzfeld-Jakob disease, Gerstmann-

Data Set 1 250 gene model 201300_s_at PRNP Strausler-Scheinker syndrome, fatal familial insomnia)

Data Set 1 250 gene model 204294_at AMT aminomethyltransferase (glycine cleavage system protein T) ADAM metallopeptidase with thrombospondin type 1 motif, 5

Data Set 1 250 gene model 219935_at ADAMTS5 (aggrecanase-2)

Data Set 1 250 gene model 201030_x_at LDHB lactate dehydrogenase B

Data Set 1 250 gene model 217890_s_at PARVA parvin, alpha

Data Set 1 250 gene model 213148_at LOC257407 hypothetical protein LOC257407

Data Set 1 250 gene model 20393 l_s_at MRPL12 mitochondrial ribosomal protein Ll 2

Data Set 1 250 gene model 214077_x_at MEIS4 Meisl, myeloid ecotropic viral integration site 1 homolog 4 (mouse)

Data Set 1 250 gene model 221505_at ANP32E acidic (leucine-rich) nuclear phosphoprotein 32 family, member E

Data Set 1 250 gene model 218087_s_at SORBSl sorbin and SH3 domain containing 1

Data Set 1 250 gene model 217764_s_at RAB31 RAB31, member RAS oncogene family

Data Set 1 250 gene model 20501 l_at LOH11CR2A loss of heterozygosity, 11, chromosomal region 2, gene A

Data Set 1 250 gene model 213293 s at TRIM22 tripartite motif -containing 22

Data Set 1 250 gene model 20423 l_s_at FAAH fatty acid amide hydrolase

Data Set 1 250 gene model 200878_at EPASl endothelial PAS domain protein 1

Data Set 1 250 gene model 203296_s_at ATP1A2 ATPase, Na+/K+ transporting, alpha 2 (+) polypeptide

Data Set 1 250 gene model 202724_s_at FOXOlA forkhead box O1A (rhabdomyosarcoma)

Data Set 1 250 gene model 201952_at ALCAM activated leukocyte cell adhesion molecule

Data Set 1 250 gene model 208658_at PDIA4 protein disulfide isomerase family A, member 4

Data Set 1 250 gene model 203857_s_at PDIA5 protein disulfide isomerase family A, member 5

Data Set 1 250 gene model 219395_at RBM35B RNA binding motif protein 35B

Data Set 1 250 gene model 209776_s_at SLC 19Al solute carrier family 19 (folate transporter), member 1

Data Set 1 250 gene model 209806_at HIST1H2BK histone 1, H2bk

Data Set 1 250 gene model 211144_x_at TRGC2 T cell receptor gamma constant 2

Data Set 1 250 gene model 216905_s_at ST14 suppression of tumorigenicity 14 (colon carcinoma, matriptase, epithin) solute carrier family 25 (mitochondrial carrier; dicarboxylate

Data Set 1 250 gene model 218275 at SLC25A10 transporter), member 10

Data Set 1 250 gene model 20392 l_at CHST2 carbohydrate (N-acetylglucosamine-6-O) sulfotransferase 2 protein phosphatase 3 (formerly 2B), catalytic subunit, alpha isoform

Data Set 1 250 gene model 202429_s_at PPP3CA (calcineurin A alpha)

Data Set 1 250 gene model 201185_at HTRAl HtrA serine peptidase 1

Data Set 1 250 gene model 204141_at TUBB2 tubulin, beta 2

Data Set 1 250 gene model 219561_at COPZ2 coatomer protein complex, subunit zeta 2

Data Set 1 250 gene model 204123_at LIG3 ligase III, DNA, ATP-dependent

Data Set 1 250 gene model 204777_s_at MAL mal, T-cell differentiation protein

Data Set 1 250 gene model 205157_s_at KRT 17 keratin 17 Data Set 1 250 gene model 212347_x_at MXD4 MAX dimerization protein 4 Data Set 1 250 gene model 213143 at LOC257407 hypothetical protein LOC257407 Data Set 1 250 gene model 202920_at ANK2 ankyrin 2, neuronal Data Set 1 250 gene model 21755 l_at LOC441453 similar to olfactory receptor, family 7, subfamily A, member 17 Microtubule-associated protein IB /// Homo sapiens, clone IMAGE:

Data Set 1 250 gene model 212233_at MAPlB 5535936, mRNA Data Set 1 250 gene model 205429_s_at MPP6 membrane protein, palmitoylated 6 (MAGUK p55 subfamily member 6) Data Set 1 250 gene model 202180_s_at MVP major vault protein Data Set 1 250 gene model 213982_s_at RABGAPlL RAB GTPase activating protein 1-like Data Set 1 250 gene model 211126_s_at CSRP2 cysteine and glycine-rich protein 2 Data Set 1 250 gene model 205132_at ACTC actin, alpha, cardiac muscle Data Set 1 250 gene model 213071 at DPT dermatopontin Data Set 1 250 gene model 208430_s_at DTNA dystrobrevin, alpha Data Set 1 250 gene model 206453_s_at NDRG2 NDRG family member 2 Data Set 1 250 gene model 218979_at C9orf76 chromosome 9 open reading frame 76 Data Set 1 250 gene model 22075 l_s_at C5orf4 chromosome 5 open reading frame 4 Data Set 1 250 gene model 213564_x_at LDHB lactate dehydrogenase B Data Set 1 250 gene model 20965 l_at TGFBlIl transforming growth factor beta 1 induced transcript 1 Data Set 1 250 gene model 218224_at PNMAl paraneoplastic antigen MAl Data Set 1 250 gene model 203219_s_at APRT adenine phosphoribosyltransferase Data Set 1 250 gene model 201798_s_at FER1L3 fer- 1-like 3, myoferlin (C. elegans) Data Set 1 250 gene model 201462_at SCRNl secernin 1 Data Set 1 250 gene model 212254_s_at DST dystonin Data Set 1 250 gene model 204352_at TRAF5 TNF receptor-associated factor 5 Data Set 1 250 gene model 201583_s_at SEC23B Sec23 homolog B (S. cerevisiae) Data Set 1 250 gene model 218073_s_at TMEM48 transmembrane protein 48 Data Set 1 250 gene model 209934 s at ATP2C1 ATPase, Ca++ transporting, type 2C, member 1 SWI/SNF related, matrix associated, actin dependent regulator of

Data Set 1 250 gene model 204099_at SMARCD3 chromatin, subfamily d, member 3 prostaglandin-endoperoxide synthase 1 (prostaglandin G/H synthase

Data Set 1 250 gene model 205128 _x_ _at PTGSl and cyclooxygenase) Data Set 1 250 gene model 219127 _at MGCl 1242 hypothetical protein MGCl 1242 Data Set 1 250 gene model 203281 _s_ at UBElL ubiquitin-activating enzyme El-like Data Set 1 250 gene model 203705 S at FZD7 frizzled homolog 7 (Drosophila)

Data Set 1 250 gene model 217979_at TM4SF13 Tetraspanin 13 Data Set 1 250 gene model 823_at CX3CL1 chemokine (C-X3-C motif) ligand 1 Data Set 1 250 gene model 210298 x at FHLl four and a half LIM domains 1 Data Set 1 250 gene model 208789_at PTRF polymerase I and transcript release factor transcription factor 7-like 1 (T-cell specific, HMG-box) ///

Data Set 1 250 gene model 221016_s_at TCF7L1 transcription factor 7-like 1 (T-cell specific, HMG-box) Data Set 1 250 gene model 200807_s_at HSPDl heat shock 6OkDa protein 1 (chaperonin) Data Set 1 250 gene model 201900_s_at AKRlAl aldo-keto reductase family 1, member Al (aldehyde reductase) guanylate binding protein 1, interferon-inducible, 67kDa ///

Data Set 1 250 gene model 202269_x_at GBPl guanylate binding protein 1, interferon-inducible, 67kDa Data Set 1 250 gene model 204793_at GPRASPl G protein-coupled receptor associated sorting protein 1 Data Set 1 250 gene model 212187_x_at PTGDS prostaglandin D2 synthase 2IkDa (brain) Data Set 1 250 gene model 201923_at PRDX4 peroxiredoxin 4 Data Set 1 250 gene model 21075 l_s_at RGN regucalcin (senescence marker protein-30) Data Set 1 250 gene model 209288_s_at CDC42EP3 CDC42 effector protein (Rho GTPase binding) 3 Data Set 1 250 gene model 207414_s_at PCSK6 proprotein convertase subtilisin/kexin type 6 Data Set 1 250 gene model 204875_s_at GMDS GDP-mannose 4,6-dehydratase Data Set 1 250 gene model 219405_at TRIM68 tripartite motif -containing 68 Data Set 1 250 gene model 205364_at ACOX2 acyl-Coenzyme A oxidase 2, branched chain Data Set 1 250 gene model 214404_x_at SPDEF SAM pointed domain containing ets transcription factor Data Set 1 250 gene model 202732_at PKIG protein kinase (cAMP-dependent, catalytic) inhibitor gamma CD59 antigen pi 8-20 (antigen identified by monoclonal antibodies

Data Set 1 250 gene model 212463_at CD59 16.3A5, EJ16, EJ30, EL32 and G344) Data Set 1 250 gene model 217762_s_at RAB31 RAB31, member RAS oncogene family Data Set 1 250 gene model 201850_at CAPG capping protein (actin filament), gelsolin-like Data Set 1 250 gene model 217763_s_at RAB31 RAB31, member RAS oncogene family Data Set 1 250 gene model 213010_at PRKCDBP protein kinase C, delta binding protein Data Set 1 250 gene model 219518_s_at ELL3 elongation factor RNA polymerase II-like 3 Data Set 1 250 gene model 201689_s_at TPD52 tumor protein D52 Data Set 1 250 gene model 214505_s_at FHLl four and a half LIM domains 1 Data Set 1 250 gene model 201601_x_at IFITMl interferon induced transmembrane protein 1 (9-27) Data Set 1 250 gene model 209074_s_at TU3A TU3A protein Data Set 1 250 gene model 218427_at SDCCAG3 serologically defined colon cancer antigen 3 Data Set 1 250 gene model 204753_s_at HLF hepatic leukemia factor Data Set 1 250 gene model 214598 at CLDN8 claudin 8

Data Set 1 250 gene model 20163 l_s_at IER3 immediate early response 3 Data Set 1 250 gene model 204400_at EFS embryonal Fyn-associated substrate Data Set 1 250 gene model 21777 l_at GOLPH2 golgi phosphoprotein 2 Data Set 1 250 gene model 219152_at PODXL2 podocalyxin-like 2 Data Set 1 250 gene model 202454_s_at ERBB 3 v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian) Data Set 1 250 gene model 214039_s_at LAPTM4B lysosomal associated protein transmembrane 4 beta Data Set 1 250 gene model 205303_at KCNJ8 potassium inwardly-rectifying channel, subfamily J, member 8 Data Set 1 250 gene model 209583_s_at CD200 CD200 antigen Data Set 1 250 gene model 205743_at STAC SH3 and cysteine rich domain Data Set 1 250 gene model 204284_at PPP1R3C protein phosphatase 1, regulatory (inhibitor) subunit 3C Data Set 1 250 gene model 21861 l_at IER5 immediate early response 5 Data Set 1 250 gene model 207030_s_at CSRP2 cysteine and glycine-rich protein 2 Data Set 1 250 gene model 201690_s_at TPD52 tumor protein D52 Data Set 1 250 gene model 214091_s_at GPX3 glutathione peroxidase 3 (plasma) Data Set 1 250 gene model 211724_x_at FLJ20323 hypothetical protein FLJ20323 /// hypothetical protein FLJ20323 Data Set 1 250 gene model 201539_s_at FHLl four and a half LIM domains 1 Data Set 1 250 gene model 201060_x_at STOM stomatin protein phosphatase IA (formerly 2C), magnesium-dependent, alpha isoform /// protein phosphatase IA (formerly 2C), magnesium-dependent,

Data Set 1 250 gene model 203966_s_at PPMlA alpha isoform Data Set 1 250 gene model 203851 at IGFBP6 insulin-like growth factor binding protein 6 Data Set 1 250 gene model 200903_s_at AHCY S-adenosylhomocysteine hydrolase Data Set 1 250 gene model 215016_x_at DST dystonin Data Set 1 250 gene model 20929 l_at ID4 inhibitor of DNA binding 4, dominant negative helix -loop-helix protein Data Set 1 250 gene model 207480_s_at MEIS2 Meisl, myeloid ecotropic viral integration site 1 homolog 2 (mouse) Data Set 1 250 gene model 219856_at Clorfl lό chromosome 1 open reading frame 116 Data Set 1 250 gene model 201272_at AKRlBl aldo-keto reductase family 1 , member B 1 (aldose reductase) Data Set 1 250 gene model 216251_s_at KIAA0153 KIAAO 153 protein Data Set 1 250 gene model 213085_s_at KIBRA KIBRA protein Data Set 1 250 gene model 205769_at SLC27A2 solute carrier family 27 (fatty acid transporter), member 2 Data Set 1 250 gene model 203423_at RBPl retinol binding protein 1, cellular

SlOO calcium binding protein A4 (calcium protein, calvasculin,

Data Set 1 250 gene model 203186_s_at S100A4 metastasin, murine placental homolog) Data Set 1 250 gene model 212445_s_at NEDD4L neural precursor cell expressed, developmentally down-regulated 4-like Data Set 1 250 gene model 220933 s at ZCCHC6 zinc finger, CCHC domain containing 6

Data Set 1 250 gene model 218186_at RAB25 RAB 25, member RAS oncogene family protein tyrosine phosphatase-like (proline instead of catalytic arginine),

Data Set 1 250 gene model 212640_at PTPLB member b

Data Set 1 250 gene model 209550 at NDN necdin homolog (mouse)

Data Set 1 250 gene model 201348_at GPX3 glutathione peroxidase 3 (plasma)

Data Set 1 250 gene model 207266_x_at RBMSl RNA binding motif, single stranded interacting protein 1 UDP-N-acetyl-alpha-D-galactosamineipolypeptide N-acetylgalactosaminyl

Data Set 1 250 gene model 203397_s_at GALNT3 transferase 3 (GalNAc-T3)

Data Set 1 250 gene model 218198_at DHX32 DEAH (Asp-Glu-Ala-His) box polypeptide 32 serpin peptidase inhibitor, clade G (Cl inhibitor), member 1

Data Set 1 250 gene model 200986_at SERPINGl (angioedema, hereditary)

Data Set 1 250 gene model 221582_at HIST3H2A histone 3, H2a

Data Set 1 250 gene model 204570_at COX7A1 cytochrome c oxidase subunit Vila polypeptide 1 (muscle)

Data Set 1 250 gene model 200644_at MARCKSLl MARCKS-like 1

Data Set 1 250 gene model 201667_at GJAl gap junction protein, alpha 1 , 43kDa (connexin 43) 3-hydroxybutyrate dehydrogenase (heart, mitochondrial) ///

Data Set 1 250 gene model 211715_s_at BDH 3-hydroxybutyrate dehydrogenase (heart, mitochondrial)

Data Set 1 250 gene model 217080_s_at HOMER2 homer homolog 2 (Drosophila)

Data Set 1 250 gene model 219121_s_at RBM35A RNA binding motif protein 35 A

Data Set 1 250 gene model 218223 s at CKIP-I CK2 interacting protein 1; HQ0024c protein

Data Set 1 250 gene model 213288_at OACT2 O-acyltransferase (membrane bound) domain containing 2

Data Set 1 250 gene model 209863_s_at TP73L tumor protein p73-like

Data Set 1 250 gene model 202005_at ST14 suppression of tumorigenicity 14 (colon carcinoma, matriptase, epithin)

Data Set 1 250 gene model 203324_s_at CAV2 caveolin 2

Data Set 1 250 gene model 205265_s_at APEGl aortic preferentially expressed gene 1

Data Set 1 250 gene model 208747_s_at CIS complement component 1 , s subcomponent

Data Set 1 250 gene model 212647_at RRAS related RAS viral (r-ras) oncogene homolog

Data Set 1 250 gene model 214156_at MYRIP myosin VIIA and Rab interacting protein

Data Set 1 250 gene model 203065 s at CAVl caveolin 1 , caveolae protein, 22kDa

Data Set 1 250 gene model 200923_at LGALS3BP lectin, galactoside-binding, soluble, 3 binding protein

Data Set 1 250 gene model 203748_x_at RBMSl RNA binding motif, single stranded interacting protein 1

Data Set 1 250 gene model 205578_at ROR2 receptor tyrosine kinase-like orphan receptor 2 RNA-binding region (RNPl, RRM) containing 1 /// RNA-binding

Data Set 1 250 gene model 212430_at RNPCl region (RNPl, RRM) containing 1

Data Set 1 250 gene model 218980_at FHOD3 formin homology 2 domain containing 3

Data Set 1 250 gene model 200895_s_at FKBP4 FK506 binding protein 4, 59kDa

Data Set 1 250 gene model 219829_at ITGB 1BP2 integrin beta 1 binding protein (melusin) 2

Data Set 1 250 gene model 201482_at QSCN6 quiescin Q6 asparagine-linked glycosylation 8 homolog (yeast, alpha- 1,3 -glucosyl-

Data Set 1 250 gene model 203545_at ALG8 transferase)

Data Set 1 250 gene model 217973_at DCXR dicarbonyl/L-xylulose reductase

Data Set 1 250 gene model 201315_x_at IFITM2 interferon induced transmembrane protein 2 (1-8D)

Data Set 1 250 gene model 203706_s_at FZD7 frizzled homolog 7 (Drosophila)

Data Set 1 250 gene model 221462_x_at KLK15 kallikrein 15

Data Set 1 250 gene model 209170 s at GPM6B glycoprotein M6B

Data Set 1 250 gene model 204993_at GNAZ guanine nucleotide binding protein (G protein), alpha z polypeptide

Data Set 1 250 gene model 209114_at TSPANl tetraspanin 1

Data Set 1 250 gene model 219685_at TMEM35 transmembrane protein 35

Data Set 1 250 gene model 20969 l_s_at DOK4 docking protein 4

Data Set 1 250 gene model 212203_x_at IFITM3 interferon induced transmembrane protein 3 (1-8U)

Data Set 1 250 gene model 205542_at STEAPl six transmembrane epithelial antigen of the prostate 1

Data Set 1 250 gene model 212680_x_at PPP1R14B protein phosphatase 1, regulatory (inhibitor) subunit 14B

Data Set 1 250 gene model 1598_g_at GAS6 growth arrest-specific 6

Data Set 1 250 gene model 209340_at UAPl UDP-N-acteylglucosamine pyrophosphorylase 1 prostaglandin 12 (prostacyclin) synthase /// prostaglandin 12 (prostacyclin)

Data Set 1 250 gene model 20813 l_s_at PTGIS synthase

Data Set 1 250 gene model 213004_at ANGPTL2 angiopoietin-like 2

Data Set 1 250 gene model 203892 at WFDC2 WAP four-disulfide core domain 2

Data Set 1 250 gene model 20391 l_at RAPlGAl RAPl, GTPase activating protein 1

Data Set 1 250 gene model 206860_s_at FLJ20323 hypothetical protein FLJ20323

Data Set 1 250 gene model 209696_at FBPl fructose-l,6-bisphosphatase 1

Data Set 1 250 gene model 210547 x at ICAl islet cell autoantigen 1, 69kDa

Data Set 1 250 gene model 204734_at KRT 15 keratin 15 fibroblast growth factor receptor 2 (bacteria-expressed kinase, keratinocyte growth factor receptor, craniofacial dysostosis 1, Crouzon syndrome,

Data Set 1 250 gene model 203638_s_at FGFR2 Pfeiffer syndrome, Jackson- Weiss syndrome)

Data Set 1 250 gene model 20097 l_s_at SERPl stress-associated endoplasmic reticulum protein 1 similar to Interferon-induced transmembrane protein 3 (Interferon-inducible

Data Set 1 250 gene model 216565_x_at LOC391020 protein 1-8U)

Data Set 1 250 gene model 209434_s_at PPAT phosphoribosyl pyrophosphate amidotransferase

Data Set 1 250 gene model 209804_at DCLRElA DNA cross-link repair IA (PS02 homolog, S. cerevisiae) Data Set 1 250 gene model 202893_at UNC 13B unc-13 homolog B (C. elegans)

UDP-N-acetyl-alpha-D-galactosamineipolypeptide N-acetyl-

Data Set 1 250 gene model 218313_s_at GALNT7 galactosaminyltransferase 7 (GaINAc -T7) Data Set 2 5 gene model 200982_s_at ANXA6 annexin A6 Data Set 2 5 gene model 205304_s_at KCNJ8 potassium inwardly-rectifying channel, subfamily J, member 8 Data Set 2 5 gene model 227554_at LOC402560 Hypothetical LOC402560 Data Set 2 5 gene model 235867_at GSTM3 glutathione S-transferase M3 (brain) Data Set 2 5 gene model 213556_at LOC390940 similar to R28379_l Data Set 2 10 gene model 213924_at MPPEl Metallophosphoesterase 1 Data Set 2 10 gene model 205303_at KCNJ8 potassium inwardly-rectifying channel, subfamily J, member 8 Data Set 2 10 gene model 208792_s_at CLU clusterin Data Set 2 10 gene model 230087_at PRIMAl proline rich membrane anchor 1 Data Set 2 10 gene model 218094_s_at DBNDD2 dysbindin (dystrobrevin binding protein 1) domain containing 2 Data Set 2 10 gene model 205304_s_at KCNJ8 potassium inwardly-rectifying channel, subfamily J, member 8 Data Set 2 10 gene model 1553102_a_at CCDC69 coiled-coil domain containing 69 Data Set 2 10 gene model 227554_at LOC402560 Hypothetical LOC402560 Data Set 2 10 gene model 209434_s_at PPAT phosphoribosyl pyrophosphate amidotransferase Data Set 2 10 gene model 231118_at ANKRD35 ankyrin repeat domain 35 Data Set 2 20 gene model 201798_s_at FER1L3 fer-1-like 3, myoferlin (C. elegans) Data Set 2 20 gene model 222043_at CLU clusterin Data Set 2 20 gene model 219670_at Clorfl65 chromosome 1 open reading frame 165 Data Set 2 20 gene model 223843_at SCARA3 scavenger receptor class A, member 3 Data Set 2 20 gene model 203323_at CAV2 caveolin 2 Data Set 2 20 gene model 230067_at FLJ30707 Hypothetical protein FLJ30707 Data Set 2 20 gene model 212736_at C16orf45 chromosome 16 open reading frame 45 Data Set 2 20 gene model 221898_at PDPN podoplanin phosphorylase, glycogen; muscle (McArdle syndrome, glycogen

Data Set 2 20 gene model 205577_at PYGM storage disease type V)

SWI/SNF related, matrix associated, actin dependent regulator of

Data Set 2 20 gene model 204099_at SMARCD3 chromatin, subfamily d, member 3 Data Set 2 20 gene model 224710_at RAB34 RAB34, member RAS oncogene family Data Set 2 20 gene model 203151_at MAPlA microtubule-associated protein IA Data Set 2 20 gene model 201590_x_at ANXA2 annexin A2 Data Set 2 20 gene model 210427 x at ANXA2 annexin A2

Data Set 2 20 gene model 218421_at CERK ceramide kinase Data Set 2 20 gene model 209356_x_at EFEMP2 EGF-containing fibulin-like extracellular matrix protein 2 Data Set 2 20 gene model 208792_s_at CLU clusterin Data Set 2 20 gene model 219525_at FLJ10847 hypothetical protein FLJ10847 Data Set 2 20 gene model 204777_s_at MAL mal, T-cell differentiation protein Data Set 2 20 gene model 213503_x_at ANX A2 annexin A2 Data Set 2 50 gene model 1552701_a_at COPl caspase-1 dominant-negative inhibitor pseudo-ICE Data Set 2 50 gene model 204115_at GNGU guanine nucleotide binding protein (G protein), gamma 11 Data Set 2 50 gene model 244111_at KA21 truncated type I keratin KA21 Data Set 2 50 gene model 22075 l_s_at C5orf4 chromosome 5 open reading frame 4 Data Set 2 50 gene model 244050_at PTPLAD2 protein tyrosine phosphatase-like A domain containing 2 Data Set 2 50 gene model 214027_x_at DES /// FAM48A desmin /// family with sequence similarity 48, member A Data Set 2 50 gene model 222744_s_at TMLHE trimethyllysine hydroxylase, epsilon Data Set 2 50 gene model 1553995_a_at NT5E 5'-nucleotidase, ecto (CD73) Data Set 2 50 gene model 20879 l_at CLU clusterin Data Set 2 50 gene model 201136_at PLP2 proteolipid protein 2 (colonic epithelium-enriched) Data Set 2 50 gene model 226047_at MRVIl Murine retrovirus integration site 1 homolog Data Set 2 50 gene model 236383_at Transcribed locus Data Set 2 50 gene model 211562_s_at LMODl leiomodin 1 (smooth muscle) Data Set 2 50 gene model 222669_s_at SBDS Shwachman-Bodian-Diamond syndrome Data Set 2 50 gene model 207030_s_at CSRP2 cysteine and glycine-rich protein 2 phosphodiesterase 4A, cAMP-specific (phosphodiesterase E2

Data Set 2 50 gene model 204735_at PDE4A dunce homolog, Drosophila) Data Set 2 50 gene model 218864_at TNSl tensin 1 Data Set 2 50 gene model 214369_s_at RASGRP2 RAS guanyl releasing protein 2 (calcium and DAG-regulated) Data Set 2 50 gene model 205578_at ROR2 receptor tyrosine kinase-like orphan receptor 2 SWI/SNF related, matrix associated, actin dependent regulator of

Data Set 2 50 gene model 204099_at SMARCD3 chromatin, subfamily d, member 3 Data Set 2 50 gene model 213309_at PLCL2 phospholipase C-like 2 Data Set 2 50 gene model 207836_s_at RBPMS RNA binding protein with multiple splicing Data Set 2 50 gene model 20392 l_at CHST2 carbohydrate (N-acetylglucosamine-6-O) sulfotransferase 2 Data Set 2 50 gene model 20395 l_at CNNl calponin 1, basic, smooth muscle Data Set 2 50 gene model 217111_at AMACR alpha-methylacyl-CoA racemase Data Set 2 50 gene model 210869_s_at MCAM melanoma cell adhesion molecule Data Set 2 50 gene model 226926_at ZD52F10 dermokine

Data Set 2 50 gene model 220034_at IRAK3 interleukin-1 receptor-associated kinase 3 Data Set 2 50 gene model 238151_at TUBB6 Tubulin, beta 6 Data Set 2 50 gene model 201842_s_at EFEMPl EGF-containing fibulin-like extracellular matrix protein 1 Data Set 2 50 gene model 20965 l_at TGFBlIl transforming growth factor beta 1 induced transcript 1 Data Set 2 50 gene model 203632_s_at GPRC5B G protein-coupled receptor, family C, group 5, member B Data Set 2 50 gene model 49452_at ACACB acetyl-Coenzyme A carboxylase beta Data Set 2 50 gene model 203766_s_at LMODl leiomodin 1 (smooth muscle) Data Set 2 50 gene model 22538 l_at LOC399959 hypothetical gene supported by BX647608 potassium large conductance calcium-activated channel, subfamily

Data Set 2 50 gene model 209948_at KCNMBl M, beta member 1 Data Set 2 50 gene model 235657_at — Transcribed locus Data Set 2 50 gene model 213426_s_at CAV2 caveolin 2 Data Set 2 50 gene model 205088_at CXorfό chromosome X open reading frame 6 Data Set 2 50 gene model 227006_at PPP1R14A protein phosphatase 1, regulatory (inhibitor) subunit 14A Data Set 2 50 gene model 211276_at TCEAL2 transcription elongation factor A (SΙI)-like 2 transcription factor 7-like 1 (T-cell specific, HMG-box) /// transcription

Data Set 2 50 gene model 221016_s_at TCF7L1 factor 7-like 1 (T-cell specific, HMG-box) Data Set 2 50 gene model 207390_s_at SMTN smoothelin Data Set 2 50 gene model 211340_s_at MCAM melanoma cell adhesion molecule Data Set 2 50 gene model 228080_at LAYN layilin Data Set 2 50 gene model 214767_s_at HSPB6 heat shock protein, alpha-cry stallin-related, B 6 Data Set 2 50 gene model 242170_at ZNF154 Zinc finger protein 154 (pHZ-92) phosphorylase, glycogen; muscle (McArdle syndrome, glycogen

Data Set 2 50 gene model 205577_at PYGM storage disease type V) Data Set 2 50 gene model 230519_at FLJ30707 hypothetical protein FLJ30707 Data Set 2 50 gene model 222043_at CLU clusterin Data Set 2 100 gene model 203892_at WFDC2 WAP four-disulfide core domain 2

Full-length cDNA clone CS0DJ013YP06 of T cells (Jurkat cell line)

Data Set 2 100 gene model 23991 l_at — Cot 10-normalized of Homo sapiens (human) Data Set 2 100 gene model 216548_x_at HMG4L high-mobility group (nonhistone chromosomal) protein 4-like Data Set 2 100 gene model 207016_s_at ALDH 1A2 aldehyde dehydrogenase 1 family, member A2 Data Set 2 100 gene model 210224_at MRl major histocompatibility complex, class I-related Data Set 2 100 gene model 226638_at ARHGAP23 Rho GTPase activating protein 23 Data Set 2 100 gene model 214369_s_at RASGRP2 RAS guanyl releasing protein 2 (calcium and DAG-regulated) Data Set 2 100 gene model 227188_at C21orf63 chromosome 21 open reading frame 63

Data Set 2 100 gene model 205478_at PPPlRlA protein phosphatase 1, regulatory (inhibitor) subunit IA Data Set 2 100 gene model 202949_s_at FHL2 four and a half LIM domains 2 Data Set 2 100 gene model 235593_at ZFHXlB zinc finger homeobox Ib Data Set 2 100 gene model 228202_at PLN Phospholamban Data Set 2 100 gene model 204940_at PLN phospholamban Data Set 2 100 gene model 206030_at ASPA aspartoacylase (Canavan disease) Data Set 2 100 gene model 212358_at CLIPR-59 CLIP-170-related protein Data Set 2 100 gene model 227862_at LOC388610 hypothetical LOC388610 Data Set 2 100 gene model 227236_at TSPAN2 tetraspanin 2

Full-length cDNA clone CSODIOOl YP15 of Placenta Cot 25-normalized

Data Set 2 100 gene model 225288_at of Homo sapiens (human) Data Set 2 100 gene model 218691 s at PDLIM4 PDZ and LIM domain 4 caspase 1, apoptosis-related cysteine peptidase (interleukin 1, beta,

Data Set 2 100 gene model 1552703_s_ at CASPl /// COPl convertase) /// caspase- 1 dominant-negative inhibitor pseudo-ICE Data Set 2 100 gene model 231292_at EID3 El A-like inhibitor of differentiation 3 Data Set 2 100 gene model 210102_at LOH11CR2A loss of heterozygosity, 11, chromosomal region 2, gene A guanine nucleotide binding protein (G protein), alpha activating

Data Set 2 100 gene model 206355_at GNAL activity polypeptide, olfactory type Data Set 2 100 gene model 227742_at CLIC6 chloride intracellular channel 6 Data Set 2 100 gene model 231202_at ALDH 1L2 aldehyde dehydrogenase 1 family, member L2 Data Set 2 100 gene model 205132_at ACTC actin, alpha, cardiac muscle Data Set 2 100 gene model 209087_x_at MCAM melanoma cell adhesion molecule Data Set 2 100 gene model 236936_at Data Set 2 100 gene model 211126_s_at CSRP2 cysteine and glycine-rich protein 2 Data Set 2 100 gene model 202794_at INPPl inositol polyphosphate- 1 -phosphatase Data Set 2 100 gene model 241803_s_at

EDG2 /// endothelial differentiation, lysophosphatidic acid G-protein-coupled

Data Set 2 100 gene model 204037_at LOC644923 receptor, 2 /// hypothetical protein LOC644923 Data Set 2 100 gene model 204993_at GNAZ guanine nucleotide binding protein (G protein), alpha z polypeptide Data Set 2 100 gene model 1555630_a_at RAB34 RAB 34, member RAS oncogene family Data Set 2 100 gene model 209789_at CORO2B coronin, actin binding protein, 2B Data Set 2 100 gene model 244167_at SERGEF Secretion regulating guanine nucleotide exchange factor Data Set 2 100 gene model 20385 l_at IGFBP6 insulin-like growth factor binding protein 6 Data Set 2 100 gene model 229648_at Transcribed locus Data Set 2 100 gene model 202196_s_at DKK3 dickkopf homolog 3 (Xenopus laevis)

Data Set 2 100 gene model 226303_at PGM5 phosphoglucomutase 5

Data Set 2 100 gene model 20143 l_s_at DPYSL3 dihydropyrimidinase-like 3

Data Set 2 100 gene model 213746_s_at FLNA filamin A, alpha (actin binding protein 280)

Data Set 2 100 gene model 212091_s_at COL6A1 collagen, type VI, alpha 1

Data Set 2 100 gene model 1569956_at — Homo sapiens, clone IMAGE:4413783, mRNA

Data Set 2 100 gene model 203650_at PROCR protein C receptor, endothelial (EPCR) natriuretic peptide receptor B/guanylate cyclase B (atrionatriuretic

Data Set 2 100 gene model 204310_s_at NPR2 peptide receptor B)

Data Set 2 100 gene model 222669_s_at SBDS Shwachman-Bodian-Diamond syndrome

Data Set 2 100 gene model 205578_at ROR2 receptor tyrosine kinase-like orphan receptor 2

Data Set 2 100 gene model 212813_at JAM3 junctional adhesion molecule 3

Data Set 2 100 gene model 23027 l_at — Homo sapiens, clone IMAGE:4512785, mRNA

Data Set 2 100 gene model 236383_at — Transcribed locus

Data Set 2 100 gene model 210880_s_at EFS embryonal Fyn-associated substrate

Data Set 2 100 gene model 206813_at CTFl cardiotrophin 1

Data Set 2 100 gene model 45297_at EHD2 EH-domain containing 2

Data Set 2 100 gene model 20062 l_at CSRPl cysteine and glycine-rich protein 1

Data Set 2 100 gene model 226280_at — CDNA FLJ43545 fis, clone PROST2011631

Data Set 2 100 gene model 213170_at GPX7 glutathione peroxidase 7

Data Set 2 100 gene model 1552785_at FLJ37549 hypothetical protein FLJ37549

Data Set 2 100 gene model 203370_s_at PDLIM7 PDZ and LIM domain 7 (enigma)

Data Set 2 100 gene model 223842_s_at SCARA3 scavenger receptor class A, member 3

Data Set 2 100 gene model 206465_at ACSBGl acyl-CoA synthetase bubblegum family member 1

Data Set 2 100 gene model 201136_at PLP2 proteolipid protein 2 (colonic epithelium-enriched)

Data Set 2 100 gene model 43427_at ACACB acetyl-Coenzyme A carboxylase beta phosphodiesterase 4A, cAMP-specific (phosphodiesterase E2

Data Set 2 100 gene model 204735_at PDE4A dunce homolog, Drosophila)

Data Set 2 100 gene model 213010_at PRKCDBP protein kinase C, delta binding protein

Data Set 2 100 gene model 223095_at MARVELDl MARVEL domain containing 1

Data Set 2 100 gene model 226304_at HSPB6 heat shock protein, alpha-crystallin-related, B6

Data Set 2 100 gene model 243209_at KCNQ4 potassium voltage-gated channel, KQT-like subfamily, member 4

Data Set 2 100 gene model 244111_at KA21 truncated type I keratin KA21

Data Set 2 100 gene model 1552701_a_at COPl caspase-1 dominant-negative inhibitor pseudo-ICE

Data Set 2 100 gene model 207836_s_at RBPMS RNA binding protein with multiple splicing

Data Set 2 100 gene model 211564_s_at PDLIM4 PDZ and LIM domain 4

Data Set 2 100 gene model 208690_s_at PDLIMl PDZ and LIM domain 1 (elfin) Data Set 2 100 gene model 207030_s_at CSRP2 cysteine and glycine-rich protein 2 Data Set 2 100 gene model 217111_at AMACR alpha-methylacyl-CoA racemase Data Set 2 100 gene model 214027_x_at DES /// FAM48A desmin /// family with sequence similarity 48, member A Data Set 2 100 gene model 211562_s_at LMODl leiomodin 1 (smooth muscle) Data Set 2 100 gene model 244050_at PTPLAD2 protein tyrosine phosphatase-like A domain containing 2 Data Set 2 100 gene model 1553995_a_at NT5E 5'-nucleotidase, ecto (CD73) Data Set 2 100 gene model 204069_at MEISl Meisl, myeloid ecotropic viral integration site 1 homolog (mouse) Data Set 2 100 gene model 206122_at SOX15 SRY (sex determining region Y)-box 15 Data Set 2 100 gene model 210869_s_at MCAM melanoma cell adhesion molecule Data Set 2 100 gene model 204115_at GNGU guanine nucleotide binding protein (G protein), gamma 11 Data Set 2 100 gene model 22538 l_at LOC399959 hypothetical gene supported by BX647608 Data Set 2 100 gene model 226926_at ZD52F10 dermokine

SWI/SNF related, matrix associated, actin dependent regulator of

Data Set 2 100 gene model 204099_at SMARCD3 chromatin, subfamily d, member 3 Data Set 2 100 gene model 205088_at CXorfό chromosome X open reading frame 6 Data Set 2 100 gene model 203632_s_at GPRC5B G protein-coupled receptor, family C, group 5, member B Data Set 2 100 gene model 20392 l_at CHST2 carbohydrate (N-acetylglucosamine-6-O) sulfotransferase 2 Data Set 2 100 gene model 228080_at LAYN layilin Data Set 2 100 gene model 218864_at TNSl tensin 1 Data Set 2 100 gene model 20395 l_at CNNl calponin 1, basic, smooth muscle Data Set 2 100 gene model 22075 l_s_at C5orf4 chromosome 5 open reading frame 4 Data Set 2 100 gene model 20879 l_at CLU clusterin Data Set 2 100 gene model 212886_at CCDC69 coiled-coil domain containing 69 Data Set 2 100 gene model 229480_at LOC402560 hypothetical LOC402560 Data Set 2 100 gene model 209434_s_at PPAT phosphoribosyl pyrophosphate amidotransferase Data Set 2 100 gene model 213556_at LOC390940 similar to R28379_l Data Set 2 100 gene model 231118_at ANKRD35 ankyrin repeat domain 35 Data Set 2 100 gene model 205083_at AOXl aldehyde oxidase 1 Data Set 2 250 gene model 202274_at ACTG2 actin, gamma 2, smooth muscle, enteric Data Set 2 250 gene model 213290_at COL6A2 collagen, type VI, alpha 2 Data Set 2 250 gene model 210139_s_at PMP22 peripheral myelin protein 22 Data Set 2 250 gene model 229127_at ATP5J ATP synthase, H+ transporting, mitochondrial FO complex, subunit F6 Data Set 2 250 gene model 209427_at SMTN smoothelin Data Set 2 250 gene model 223786 at CHST6 carbohydrate (N-acetylglucosamine 6-0) sulfotransferase 6

Data Set 2 250 gene model 206600_s_at SLC16A5 solute carrier family 16 (monocarboxylic acid transporters), member 5 Data Set 2 250 gene model 219213_at JAM2 junctional adhesion molecule 2 Data Set 2 250 gene model 206580_s_at EFEMP2 EGF-containing fibulin-like extracellular matrix protein 2 Data Set 2 250 gene model 228141_at LOC493869 Similar to RIKEN cDNA 2310016C16 Data Set 2 250 gene model 227862_at LOC388610 hypothetical LOC388610 Data Set 2 250 gene model 204570_at COX7A1 cytochrome c oxidase subunit Vila polypeptide 1 (muscle) Data Set 2 250 gene model 227998_at S100A16 SlOO calcium binding protein A16 Data Set 2 250 gene model 228726_at Data Set 2 250 gene model 213106_at Data Set 2 250 gene model 205392_s_at CCL14 /// CCL15 chemokine (C-C motif) ligand 14 /// chemokine (C-C motif) ligand 15 Data Set 2 250 gene model 238657_at UBXD3 UBX domain containing 3 aldo-keto reductase family 1, member Cl (dihydrodiol dehydrogenase 1;

Data Set 2 250 gene model 216594_x_at AKRlCl 20-alpha (3-alpha)-hydroxysteroid dehydrogenase) Data Set 2 250 gene model 212647_at RRAS related RAS viral (r-ras) oncogene homolog Data Set 2 250 gene model 230264_s_at AP1S2 adaptor-related protein complex 1 , sigma 2 subunit Data Set 2 250 gene model 210619_s_at HYALl hyaluronoglucosaminidase 1 Data Set 2 250 gene model 224724_at SULF2 sulfatase 2 Data Set 2 250 gene model 225242_s_at CCDC80 coiled-coil domain containing 80 Data Set 2 250 gene model 218454_at FLJ22662 hypothetical protein FLJ22662 Data Set 2 250 gene model 220933_s_at ZCCHC6 zinc finger, CCHC domain containing 6 Data Set 2 250 gene model 230933_at Transcribed locus Data Set 2 250 gene model 218423_x_at VPS54 vacuolar protein sorting 54 (S. cerevisiae) Data Set 2 250 gene model 218660_at DYSF dysferlin, limb girdle muscular dystrophy 2B (autosomal recessive) Data Set 2 250 gene model 213139_at SNAI2 snail homolog 2 (Drosophila) Data Set 2 250 gene model 228494_at PPP1R9A protein phosphatase 1, regulatory (inhibitor) subunit 9A prion protein (p27-30) (Creutzfeldt-Jakob disease, Gerstmann-Strausler-

Data Set 2 250 gene model 201300_s_at PRNP Scheinker syndrome, fatal familial insomnia) Data Set 2 250 gene model 214212_x_at PLEKHCl pleckstrin homology domain containing, family C (with FERM domain) member 1 Data Set 2 250 gene model 200795_at SPARCLl SPARC-like 1 (mast9, hevin) Data Set 2 250 gene model 1556696_s_at FLJ42709 Hypothetical gene supported by AK124699 Data Set 2 250 gene model 200859_x_at FLNA filamin A, alpha (actin binding protein 280) Data Set 2 250 gene model 207480_s_at MEIS2 Meisl, myeloid ecotropic viral integration site 1 homolog 2 (mouse) Data Set 2 250 gene model 202222_s_at DES desmin Data Set 2 250 gene model 201060_x_at STOM stomatin Data Set 2 250 gene model 220795 s at KIAA1446 likely ortholog of rat brain-enriched guanylate kinase-associated protein

Data Set 2 250 gene model 212097_at CAVl caveolin 1 , caveolae protein, 22kDa

Data Set 2 250 gene model 227826_s_at SORBS2 Sorbin and SH3 domain containing 2

Data Set 2 250 gene model 1555127_at MOCSl molybdenum cofactor synthesis 1

Data Set 2 250 gene model 212793_at DAAM2 dishevelled associated activator of morphogenesis 2

Data Set 2 250 gene model 213001_at ANGPTL2 angiopoietin-like 2

Data Set 2 250 gene model 205560_at PCSK5 proprotein convertase subtilisin/kexin type 5

Data Set 2 250 gene model 201234_at ILK integrin-linked kinase

Data Set 2 250 gene model 227899_at VIT vitrin

Data Set 2 250 gene model 234015_at NAALADL2 N-acetylated alpha-linked acidic dipeptidase-like 2

Data Set 2 250 gene model 227066_at MOBKL2C MOBl, Mps One Binder kinase activator-like 2C (yeast)

Data Set 2 250 gene model 209118_s_at TUB A3 tubulin, alpha 3

Data Set 2 250 gene model 202422_s_at ACSL4 acyl-CoA synthetase long-chain family member 4

Data Set 2 250 gene model 242874_at C14orfl61 Chromosome 14 open reading frame 161

Data Set 2 250 gene model 236270_at NFATC4 nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 4

Data Set 2 250 gene model 221748_s_at TNSl tensin 1 /// tensin 1

Data Set 2 250 gene model 204793_at GPRASPl G protein-coupled receptor associated sorting protein 1

Data Set 2 250 gene model 238115_at DNAJC18 DnaJ (Hsp40) homolog, subfamily C, member 18

Data Set 2 250 gene model 22091 l_s_at KIAA1305 KIAA1305

Data Set 2 250 gene model 227233_at TSPAN2 tetraspanin 2

Data Set 2 250 gene model 227565_at — Transcribed locus

Data Set 2 250 gene model 229014_at FLJ42709 hypothetical gene supported by AK124699

Data Set 2 250 gene model 201425_at ALDH2 aldehyde dehydrogenase 2 family (mitochondrial)

Data Set 2 250 gene model 226225_at MCC mutated in colorectal cancers

Data Set 2 250 gene model 242086_at SPATA6 Spermatogenesis associated 6

Data Set 2 250 gene model 239183_at ANGPTLl angiopoietin-like 1

Data Set 2 250 gene model 1568868_at FLJ16008 FLJ 16008 protein

Data Set 2 250 gene model 202148_s_at PYCRl pyrroline-5-carboxylate reductase 1

Data Set 2 250 gene model 204030_s_at SCHIPl schwannomin interacting protein 1 natriuretic peptide receptor B/guanylate cyclase B (atrionatriuretic

Data Set 2 250 gene model 214066_x_at NPR2 peptide receptor B)

Data Set 2 250 gene model 221436_s_at CDCA3 cell division cycle associated 3 /// cell division cycle associated 3

Data Set 2 250 gene model 209685_s_at PRKCBl protein kinase C, beta 1

Data Set 2 250 gene model 227486_at NT5E 5'-nucleotidase, ecto (CD73)

Data Set 2 250 gene model 1559477_s_at MEISl Meisl, myeloid ecotropic viral integration site 1 homolog (mouse)

Data Set 2 250 gene model 217220_at

Data Set 2 250 gene model 232276_at HS6ST3 heparan sulfate 6-0-sulfotransferase 3

Data Set 2 250 gene model 58916_at KCTD14 potassium channel tetramerisation domain containing 14

Data Set 2 250 gene model 238463_at — Homo sapiens, clone IMAGE:5309572, mRNA

Data Set 2 250 gene model 220974_x_at SFXN3 sideroflexin 3 /// sideroflexin 3

Data Set 2 250 gene model 209735_at ABCG2 ATP-binding cassette, sub-family G (WHITE), member 2

Data Set 2 250 gene model 228113_at RAB37 RAB37, member RAS oncogene family

Data Set 2 250 gene model 223395_at ABI3BP ABI gene family, member 3 (NESH) binding protein

Data Set 2 250 gene model 235897_at COPZ2 coatomer protein complex, subunit zeta 2

Data Set 2 250 gene model 241310_at — Transcribed locus

Data Set 2 250 gene model 202409_at Cllorf43 chromosome 11 open reading frame 43

Data Set 2 250 gene model 210632_s_at SGCA sarcoglycan, alpha (5OkDa dystrophin-associated glycoprotein)

Data Set 2 250 gene model 204879_at PDPN podoplanin

Data Set 2 250 gene model 213068_at DPT dermatopontin

UDP glucuronosyltransferase 2 family, polypeptide B28 /// UDP

Data Set 2 250 gene model 211682_x_at UGT2B28 glucuronosyltransferase 2 family, polypeptide B28

Data Set 2 250 gene model 205547_s_at TAGLN transgelin

Data Set 2 250 gene model 220113_x_at POLRlB polymerase (RNA) I polypeptide B, 128kDa

Data Set 2 250 gene model 57588_at SLC24A3 solute carrier family 24 (sodium/potassium/calcium exchanger), member 3

Data Set 2 250 gene model 1554206_at TMLHE trimethyllysine hydroxylase, epsilon

Data Set 2 250 gene model 204688_at SGCE sarcoglycan, epsilon

Data Set 2 250 gene model 228584_at SGCB sarcoglycan, beta (43kDa dystrophin-associated glycoprotein)

Data Set 2 250 gene model 203510_at MET met proto-oncogene (hepatocyte growth factor receptor)

Data Set 2 250 gene model 226955_at FLJ36748 hypothetical protein FLJ36748

Data Set 2 250 gene model 208335_s_at DARC Duffy blood group, chemokine receptor

Data Set 2 250 gene model 204418_x_at GSTM2 glutathione S -transferase M2 (muscle)

Data Set 2 250 gene model 22054 l_at MMP26 matrix metallopeptidase 26

Data Set 2 250 gene model 204955_at SRPX sushi-repeat-containing protein, X-linked

Data Set 2 250 gene model 207397_s_at HOXD13 homeobox D13

Data Set 2 250 gene model 22572 l_at SYNPO2 synaptopodin 2

Data Set 2 250 gene model 225782_at MSRB3 methionine sulfoxide reductase B 3

Data Set 2 250 gene model 227827_at SORBS2 Sorbin and SH3 domain containing 2

Data Set 2 250 gene model 221870_at EHD2 EH-domain containing 2

Data Set 2 250 gene model 223623_at ECRG4 esophageal cancer related gene 4 protein

Data Set 2 250 gene model 225020_at DAB2IP DAB2 interacting protein

Data Set 2 250 gene model 20813 l_s_at PTGIS prostaglandin 12 (prostacyclin) synthase /// prostaglandin 12 (prostacyclin)

synthase

Data Set 2 250 gene model 238526_at RAB3IP RAB 3 A interacting protein (rabin3) Data Set 2 250 gene model 204750_s_at DSC2 desmocollin 2 Data Set 2 250 gene model 212276_at LPINl lipin 1 Data Set 2 250 gene model 229839_at SCARA5 Scavenger receptor class A, member 5 (putative) Data Set 2 250 gene model 230986_at KLF8 Kruppel-like factor 8 Data Set 2 250 gene model 238877 at ___ Data Set 2 250 gene model 204422_s_at FGF2 fibroblast growth factor 2 (basic) Data Set 2 250 gene model 228554_at ___ MRNA; cDNA DKFZp586G0321 (from clone DKFZp586G0321) Data Set 2 250 gene model 204430_s_at SLC2A5 solute carrier family 2 (facilitated glucose/fructose transporter), member 5 Data Set 2 250 gene model 217728_at S100A6 SlOO calcium binding protein A6 (calcyclin) Data Set 2 250 gene model 204149_s_at GSTM4 glutathione S -transferase M4

GABPA /// GA binding protein transcription factor, alpha subunit 6OkDa /// GA

Data Set 2 250 gene model 210188_at GABPAP binding protein transcription factor, alpha subunit pseudogene Data Set 2 250 gene model 231137_at ACSBGl Acyl-CoA synthetase bubblegum family member 1 Data Set 2 250 gene model 226627_at 8-Sep septin 8 Data Set 2 250 gene model 201841 s at HSPBl heat shock 27kDa protein 1 Data Set 2 250 gene model 227249_at NDEl NudE nuclear distribution gene E homolog 1 (A. nidulans) Data Set 2 250 gene model 209583_s_at CD200 CD200 molecule Data Set 2 250 gene model 201348_at GPX3 glutathione peroxidase 3 (plasma) Data Set 2 250 gene model 219761_at CLEClA C-type lectin domain family 1, member A Data Set 2 250 gene model 214247_s_at DKK3 dickkopf homolog 3 (Xenopus laevis) Data Set 2 250 gene model 224964_s_at GNG2 guanine nucleotide binding protein (G protein), gamma 2 Data Set 2 250 gene model 229313_at ___ Data Set 2 250 gene model 209763_at CHRDLl chordin-like 1 Data Set 2 250 gene model 221781 s at DNAJClO DnaJ (Hsp40) homolog, subfamily C, member 10 Data Set 2 250 gene model 218980_at FHOD3 formin homology 2 domain containing 3 Data Set 2 250 gene model 214121_x_at PDLIM7 PDZ and LIM domain 7 (enigma)

Transcribed locus, strongly similar to NP_079045.1 adipocyte-specific

Data Set 2 250 gene model 226834_at adhesion molecule; CAR-like membrane protein [Homo sapiens] Data Set 2 250 gene model 1559266_s_at FLJ45187 hypothetical protein LOC387640 Data Set 2 250 gene model 244710_at FLJ32786 hypothetical protein FLJ32786 Data Set 2 250 gene model 225912_at TP53INP1 tumor protein p53 inducible nuclear protein 1 Data Set 2 250 gene model 225464_at FRMD6 FERM domain containing 6 Data Set 2 250 gene model 210096 at CYP4B1 cytochrome P450, family 4, subfamily B, polypeptide 1

Data Set 2 250 gene model 213386 _at RNF20 Ring finger protein 20

Data Set 2 250 gene model 204058 _at MEl Malic enzyme 1 , NADP(+)-dependent, cytosolic

Full-length cDNA clone CSODIOOl YP15 of Placenta Cot 25-normalized

Data Set 2 250 gene model 225288 _at of Homo sapiens (human)

Data Set 2 250 gene model 239503 _at ___ CDNA clone IMAGE:5301910

Data Set 2 250 gene model 241198 _s_at Cllorf70 chromosome 11 open reading frame 70

Data Set 2 250 gene model 228195 _at MGC 13057 Hypothetical protein MGC 13057

Data Set 2 250 gene model 210105 _s_at FYN FYN oncogene related to SRC, FGR, YES

Data Set 2 250 gene model 205384 _at FXYDl FXYD domain containing ion transport regulator 1 (phospholemman)

Data Set 2 250 gene model 225968 _at PRICKLE2 prickle-like 2 (Drosophila)

Data Set 2 250 gene model 220532 _s_at LR8 LR8 protein

Data Set 2 250 gene model 207957 _s_at PRKCBl Protein kinase C, beta 1

Data Set 2 250 gene model 206816 _s_at SPAG8 sperm associated antigen 8

Data Set 2 250 gene model 200911 _s_at TACCl transforming, acidic coiled-coil containing protein 1

Data Set 2 250 gene model 226436 _at RASSF4 Ras association (RaIGDS/ AF-6) domain family 4

Data Set 2 250 gene model 204400 _at EFS embryonal Fyn-associated substrate

Data Set 2 250 gene model 244289 _at LOC134466 hypothetical protein LOC 134466

Data Set 2 250 gene model 238484 _s_at ___ MRNA; clone CD 43T7

Data Set 2 250 gene model 32094_ at CHST3 carbohydrate (chondroitin 6) sulfotransferase 3

Data Set 2 250 gene model 228260 _at ELAVL2 ELAV (embryonic lethal, abnormal vision, Drosophila)-like 2 (Hu antigen B)

Data Set 2 250 gene model 204205 _at APOBEC3G apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3G

Data Set 2 250 gene model 212914 _at CBX7 chromobox homolog 7

Data Set 2 250 gene model 206625 _at RDS retinal degeneration, slow

Data Set 2 250 gene model 222666 _s_at RCLl RNA terminal phosphate cyclase-like 1

Data Set 2 250 gene model 222744 _s_at TMLHE trimethyllysine hydroxylase, epsilon

Data Set 2 250 gene model 219478 _at WFDCl WAP four-disulfide core domain 1 fibroblast growth factor receptor 1 (fms-related tyrosine kinase 2,

Data Set 2 250 gene model 211535 _s_at FGFRl Pfeiffer syndrome)

Data Set 2 250 gene model 209191 _at TUBB 6 tubulin, beta 6

Data Set 2 250 gene model 225790 _at MSRB 3 methionine sulfoxide reductase B 3

Data Set 2 250 gene model 238613 _at ZAK sterile alpha motif and leucine zipper containing kinase AZK

Data Set 2 250 gene model 241386 _at ___ Transcribed locus

Data Set 2 250 gene model 203939 _at NT5E 5'-nucleotidase, ecto (CD73) serpin peptidase inhibitor, clade G (Cl inhibitor), member 1, (angioedema,

Data Set 2 250 gene model 200986 _at SERPINGl hereditary)

Data Set 2 250 gene model 204940_at PLN phospholamban Data Set 2 250 gene model 225798_at tcag7.981 juxtaposed with another zinc finger gene 1 Data Set 2 250 gene model 222722_at OGN osteoglycin (osteoinductive factor, mimecan) Data Set 2 250 gene model 203619_s_at FAIM2 Fas apoptotic inhibitory molecule 2 Data Set 2 250 gene model 220233_at FBXO17 F-box protein 17

Transcribed locus, strongly similar to NP_057364.1 carboxylesterase 4-like;

Data Set 2 250 gene model 231672_at carboxylesterase-related protein [Homo sapiens] Data Set 2 250 gene model 204894_s_at AOC3 amine oxidase, copper containing 3 (vascular adhesion protein 1) Data Set 2 250 gene model 202794_at INPPl inositol polyphosphate- 1 -phosphatase Data Set 2 250 gene model 221935_s_at C3orf64 chromosome 3 open reading frame 64 Data Set 2 250 gene model 20796 l_x_at MYHl 1 myosin, heavy polypeptide 11, smooth muscle Data Set 2 250 gene model 205973_at FEZl fasciculation and elongation protein zeta 1 (zygin I) Data Set 2 250 gene model 223734_at OSAP ovary-specific acidic protein Data Set 2 250 gene model 228802_at RBPMS2 RNA binding protein with multiple splicing 2 Data Set 2 250 gene model 204939_s_at PLN phospholamban Data Set 2 250 gene model 227188_at C21orf63 chromosome 21 open reading frame 63 Data Set 2 250 gene model 202242_at TSPAN7 tetraspanin 7 Data Set 2 250 gene model 227915_at ASB2 ankyrin repeat and SOCS box-containing 2 Data Set 2 250 gene model 201185_at HTRAl HtrA serine peptidase 1 Data Set 2 250 gene model 205475_at SCRGl scrapie responsive protein 1 Data Set 2 250 gene model 203892_at WFDC2 WAP four-disulfide core domain 2 Data Set 2 250 gene model 210102_at LOHl 1CR2A loss of heterozygosity, 11, chromosomal region 2, gene A Data Set 2 250 gene model 228585_at ENTPDl Ectonucleoside triphosphate diphosphohydrolase 1 Data Set 2 250 gene model 209686_at SlOOB SlOO calcium binding protein, beta (neural) Data Set 2 250 gene model 232298_at LOC401093 hypothetical LOC401093 Data Set 2 250 gene model 212509_s_at MXRA7 matrix-remodelling associated 7 Data Set 2 250 gene model 203068_at KLHL21 kelch-like 21 (Drosophila) Data Set 2 250 gene model 65718_at GPR 124 G protein-coupled receptor 124 Data Set 2 250 gene model 203729_at EMP3 epithelial membrane protein 3 Data Set 2 250 gene model 212274_at LPINl lipin 1 Data Set 2 250 gene model 214606_at TSPAN2 tetraspanin 2 Data Set 2 250 gene model 202796_at SYNPO synaptopodin Data Set 2 250 gene model 209343 at EFHDl EF-hand domain family, member Dl

Full-length cDNA clone CS0DF020YJ04 of Fetal brain of Homo sapiens

Data Set 2 250 gene model 227115_at (human)

Data Set 2 250 gene model 205573_s_at SNX7 sorting nexin 7

Data Set 2 250 gene model 208789_at PTRF polymerase I and transcript release factor

Data Set 2 250 gene model 219167_at RASL12 RAS-like, family 12

Data Set 2 250 gene model 213415_at CLIC2 chloride intracellular channel 2

Data Set 2 250 gene model 205132_at ACTC actin, alpha, cardiac muscle

Data Set 2 250 gene model 228807_at

Data Set 2 250 gene model 202949_s_at FHL2 four and a half LIM domains 2

Data Set 2 250 gene model 218691_s_at PDLIM4 PDZ and LIM domain 4

Data Set 2 250 gene model 224929_at LOC340061 hypothetical protein LOC340061

Data Set 2 250 gene model 231798_at NOG Noggin

Data Set 2 250 gene model 231292_at EID3 El A-like inhibitor of differentiation 3

Data Set 2 250 gene model 227742_at CLIC6 chloride intracellular channel 6

Data Set 2 250 gene model 24348 l_at RHOJ ras homolog gene family, member J

Data Set 2 250 gene model 236936_at

Data Set 2 250 gene model 206194_at HOXC4 homeobox C4

Data Set 2 250 gene model 221747_at TNSl Tensin 1 /// Tensin 1

Data Set 2 250 gene model 235737_at TSLP thymic stromal lymphopoietin

Data Set 2 250 gene model 223506_at ZC3H8 zinc finger CCCH-type containing 8

Data Set 2 250 gene model 211864_s_at FER1L3 fer-1-like 3, myoferlin (C. elegans)

Data Set 2 250 gene model 228202_at PLN Phospholamban

Data Set 2 250 gene model 235898_at — Transcribed locus

Data Set 2 250 gene model 238584_at IQCA IQ motif containing with AAA domain

Data Set 2 250 gene model 207547_s_at FAM107A family with sequence similarity 107, member A

Data Set 2 250 gene model 229480_at LOC402560 hypothetical LOC402560

Data Set 2 250 gene model 212886_at CCDC69 coiled-coil domain containing 69

Data Set 2 250 gene model 227976_at LOC644538 hypothetical protein LOC644538

Data Set 2 250 gene model 209434_s_at PPAT phosphoribosyl pyrophosphate amido transferase

Data Set 2 250 gene model 205083_at AOXl aldehyde oxidase 1

Data Set 2 250 gene model 213556_at LOC390940 similar to R28379_l

Data Set 2 250 gene model 205304_s_at KCNJ8 potassium inwardly-rectifying channel, subfamily J, member !

Data Set 2 250 gene model 227554_at LOC402560 Hypothetical LOC402560

Data Set 2 250 gene model 231118_at ANKRD35 ankyrin repeat domain 35

Data Set 2 250 gene model 230087_at PRIMAl proline rich membrane anchor 1

Data Set 2 250 gene model 200982_s_at ANXA6 annexin A6

Data Set 2 250 gene model 1553102_a_at CCDC69 coiled-coil domain containing 69

Data Set 2 250 gene model 203324_s_at CAV2 caveolin 2 Data Set 2 250 gene model 221898_at PDPN podoplanin Data Set 2 250 gene model 235867_at GSTM3 glutathione S-transferase M3 (brain) Data Set 2 250 gene model 205303_at KCNJ8 potassium inwardly-rectifying channel, subfamily J, member 8 Data Set 2 250 gene model 209356_x_at EFEMP2 EGF-containing fibulin-like extracellular matrix protein 2 Data Set 2 250 gene model 218094_s_at DBNDD2 dysbindin (dystrobrevin binding protein 1) domain containing 2 Data Set 2 250 gene model 204777_s_at MAL mal, T-cell differentiation protein Data Set 2 250 gene model 208792_s_at CLU clusterin Data Set 2 250 gene model 242170_at ZNF154 Zinc finger protein 154 (pHZ-92) Data Set 2 250 gene model 213924_at MPPEl Metallophosphoesterase 1 Data Set 2 250 gene model 209488_s_at RBPMS RNA binding protein with multiple splicing Data Set 3 5 gene model 1251_g_at RAPlGAP RAPl GTPase activating protein

SWI/SNF related, matrix associated, actin dependent regulator of

Data Set 3 5 gene model 32565_at SMARCD3 chromatin, subfamily d, member 3 Data Set 3 5 gene model 36495 at FBPl fructose-l,6-bisphosphatase 1 ANXA2 /// ANXA2P1 ///

Data Set 3 5 gene model 31444_s_at ANXA2P3 annexin A2 /// annexin A2 pseudogene 1 /// annexin A2 pseudogene 3

Data Set 3 5 gene model 575_s_at TACSTDl tumor-associated calcium signal transducer 1

Data Set 3 10 gene model 36495_at FBPl fructose-l,6-bisphosphatase 1

Data Set 3 10 gene model 33121_g_at RGSlO regulator of G-protein signalling 10 gap junction protein, beta 1, 32kDa (connexin 32, Charcot-Marie-Tooth

Data Set 3 10 gene model 39598_at GJB 1 neuropathy, X-linked) procollagen-proline, 2-oxoglutarate 4-dioxygenase (proline 4-hydroxylase),

Data Set 3 10 gene model 36666_at P4HB beta polypeptide Data Set 3 10 gene model 40060_r_at PDLIM5 PDZ and LIM domain 5 Data Set 3 10 gene model 3693 l_at TAGLN transgelin Data Set 3 10 gene model 34203_at CNNl calponin 1, basic, smooth muscle Data Set 3 10 gene model 32444_at ATP6V0E2L ATPase, H+ transporting VO subunit E2-like (rat) Data Set 3 10 gene model 3253 l_at GJAl gap junction protein, alpha 1 , 43kDa (connexin 43) Data Set 3 10 gene model 34800_at LRIGl leucine-rich repeats and immunoglobulin-like domains 1 Data Set 3 20 gene model 38098_at LPINl lipin 1 procollagen-proline, 2-oxoglutarate 4-dioxygenase (proline 4-hydroxylase),

Data Set 3 20 gene model 691_g_at P4HB beta polypeptide

Data Set 3 20 gene model 36785_at HSPBl heat shock 27kDa protein 1

Data Set 3 20 gene model 38716_at CAMKK2 calcium/calmodulin-dependent protein kinase kinase 2, beta

Data Set 3 20 gene model 35071_s_at GMDS GDP-mannose 4,6-dehydratase

Data Set 3 20 gene model 36495_at FBPl fructose-l,6-bisphosphatase 1

Data Set 3 20 gene model 35823_at PPIB peptidylprolyl isomerase B (cyclophilin B)

Data Set 3 20 gene model 32135_at SREBFl sterol regulatory element binding transcription factor 1

Data Set 3 20 gene model 38435_at PRDX4 peroxiredoxin 4

Data Set 3 20 gene model 37000_at BRP44 brain protein 44

Data Set 3 20 gene model 34885_at SYNGR2 synaptogyrin 2

Data Set 3 20 gene model 41163_at TMED3 transmembrane emp24 protein transport domain containing 3 ras-related C3 botulinum toxin substrate 3 (rho family, small GTP binding

Data Set 3 20 gene model 39965_at RAC3 protein Rac3)

Data Set 3 20 gene model 37648_at TTLL12 tubulin tyrosine ligase-like family, member 12

Data Set 3 20 gene model 33121_g_at RGSlO regulator of G-protein signalling 10

Data Set 3 20 gene model 33396_at GSTPl glutathione S -transferase pi

Data Set 3 20 gene model 41839_at GASl growth arrest-specific 1

Data Set 3 20 gene model 34678_at FER1L3 fer-1-like 3, myoferlin (C. elegans)

Data Set 3 20 gene model 40776_at DES desmin amyloid beta (A4) precursor protein-binding, family A, member 2 binding

Data Set 3 20 gene model 41306_at APBA2BP protein

Data Set 3 50 gene model 37730_at SNDl staphylococcal nuclease domain containing 1

Data Set 3 50 gene model 37809_at HOX A9 homeobox A9

Data Set 3 50 gene model 36624_at IMPDH2 IMP (inosine monophosphate) dehydrogenase 2

Data Set 3 50 gene model 38044_at FAM107A family with sequence similarity 107, member A

Data Set 3 50 gene model 35071_s_at GMDS GDP-mannose 4,6-dehydratase

Data Set 3 50 gene model 39315_at ANGPTl angiopoietin 1

Data Set 3 50 gene model 36791_g_at TPMl tropomyosin 1 (alpha)

Data Set 3 50 gene model 37958_at TMEM47 transmembrane protein 47

Data Set 3 50 gene model 36073_at NDN necdin homolog (mouse)

Data Set 3 50 gene model 32971_at C9orf61 chromosome 9 open reading frame 61

Data Set 3 50 gene model 32542_at FHLl four and a half LIM domains 1

Data Set 3 50 gene model 41163_at TMED3 transmembrane emp24 protein transport domain containing 3

Data Set 3 50 gene model 38719_at NSF N-ethylmaleimide-sensitive factor

Data Set 3 50 gene model 41696_at C7orf24 chromosome 7 open reading frame 24

Data Set 3 50 gene model 33308_at GUSB glucuronidase, beta

Data Set 3 50 gene model 41812_s_at NUP210 nucleoporin 21OkDa

Data Set 3 50 gene model 41742_s_at OPTN optineurin

Data Set 3 50 gene model 37917 at FLJ20323 hypothetical protein FLJ20323

Data Set 3 50 gene model 40437_at TMEM87A transmembrane protein 87A tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation

Data Set 3 50 gene model 1424_s_at YWHAH protein, eta polypeptide Data Set 3 50 gene model 34739_at FNBPlL formin binding protein 1-like Data Set 3 50 gene model 37000_at BRP44 brain protein 44 Data Set 3 50 gene model 37599_at AOXl aldehyde oxidase 1 Data Set 3 50 gene model 829_s_at GSTPl glutathione S -transferase pi Data Set 3 50 gene model 38262_at ___ Clone 23620 mRNA sequence Data Set 3 50 gene model 33371_s_at RAB31 RAB31, member RAS oncogene family Data Set 3 50 gene model 33611_g_at CLDN8 claudin 8 Data Set 3 50 gene model 36617_at IDl inhibitor of DNA binding 1, dominant negative helix -loop-helix protein Data Set 3 50 gene model 40674_s_at HOXC6 homeobox C6 Data Set 3 50 gene model 661_at GASl growth arrest-specific 1 Data Set 3 50 gene model 38435 at PRDX4 peroxiredoxin 4 Data Set 3 50 gene model 39031 at COX7A1 cytochrome c oxidase subunit Vila polypeptide 1 (muscle) Data Set 3 50 gene model 39099_at SEC23A Sec23 homolog A (S. cerevisiae) Data Set 3 50 gene model 32787_at ERBB 3 v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian) Data Set 3 50 gene model 3693 l_at TAGLN transgelin Data Set 3 50 gene model 36432_at MCCC2 methylcrotonoyl-Coenzyme A carboxylase 2 (beta) Data Set 3 50 gene model 41745_at IFITM3 interferon induced transmembrane protein 3 (1-8U) Data Set 3 50 gene model 32314_g_at TPM2 tropomyosin 2 (beta) Data Set 3 50 gene model 36673_at MPI mannose phosphate isomerase

SWI/SNF related, matrix associated, actin dependent regulator of

Data Set 3 50 gene model 456_at SMARCD3 chromatin, subfamily d, member 3 Data Set 3 50 gene model 34775 at TSPANl tetraspanin 1 Data Set 3 50 gene model 38098_at LPINl lipin 1 Data Set 3 50 gene model 38716_at CAMKK2 calcium/calmodulin-dependent protein kinase kinase 2, beta Data Set 3 50 gene model 1237_at IER3 immediate early response 3 Data Set 3 50 gene model 33891_at CLIC4 chloride intracellular channel 4 ras-related C3 botulinum toxin substrate 3 (rho family, small GTP

Data Set 3 50 gene model 39965_at RAC3 binding protein Rac3) Data Set 3 50 gene model 41306 at APBA2BP amyloid beta (A4) precursor protein-binding, family A, member 2

binding protein

Data Set 3 50 gene model 1257_ s_at QSCN6 quiescin Q6

Data Set 3 50 gene model 41273 _at MXRA7 matrix-remodelling associated 7 potassium large conductance calcium-activated channel, subfamily M,

Data Set 3 50 gene model 38298 _at KCNMBl beta member 1

Data Set 3 100 gene model 37043 _at ID3 inhibitor of DNA binding 3, dominant negative helix -loop-helix protein

Data Set 3 100 gene model 37539 _at RGLl ral guanine nucleotide dissociation stimulator-like 1

Data Set 3 100 gene model 39351 _at CD59 CD59 molecule, complement regulatory protein

Data Set 3 100 gene model 38422 _s_at FHL2 four and a half LIM domains 2

Data Set 3 100 gene model 31684 _at ANXA2P1 annexin A2 pseudogene 1

Data Set 3 100 gene model 38739 _at ETS2 v-ets erythroblastosis virus E26 oncogene homolog 2 (avian)

Data Set 3 100 gene model 36591 _at TUBAl tubulin, alpha 1 (testis specific)

Data Set 3 100 gene model 36614 _at HSPA5 heat shock 7OkDa protein 5 (glucose-regulated protein, 78kDa)

Data Set 3 100 gene model 32109 _at FXYDl FXYD domain containing ion transport regulator 1 (phospholemman)

Data Set 3 100 gene model 38634 _at RBPl retinol binding protein 1 , cellular

Data Set 3 100 gene model 37326 _at PLP2 proteolipid protein 2 (colonic epithelium-enriched)

Data Set 3 100 gene model 35771 _at DEAFl deformed epidermal autoregulatory factor 1 (Drosophila) fibroblast growth factor receptor 2 (bacteria-expressed kinase, keratinocyte growth factor receptor, craniofacial dysostosis 1, Crouzon syndrome,

Data Set 3 100 gene model 1363_ at FGFR2 Pfeiffer syndrome, Jackson- Weiss syndrome)

Data Set 3 100 gene model 40674 _s_at HOXC6 homeobox C6

Data Set 3 100 gene model 36617 _at IDl inhibitor of DNA binding 1, dominant negative helix -loop-helix protein

Data Set 3 100 gene model 38802 _at PGRMCl progesterone receptor membrane component 1

Data Set 3 100 gene model 34793 _s_at PLS3 plastin 3 (T isoform) cyclin-dependent kinase 7 (MO15 homolog, Xenopus laevis, cdk-activating

Data Set 3 100 gene model 33317 _at CDK7 kinase)

Data Set 3 100 gene model 34310 _at APRT adenine phosphoribosyltransferase

Data Set 3 100 gene model 38328 _at SLC25A13 solute carrier family 25, member 13 (citrin)

Data Set 3 100 gene model 35631 _at POLR2H polymerase (RNA) II (DNA directed) polypeptide H

Data Set 3 100 gene model 36650 _at CCND2 cyclin D2

Data Set 3 100 gene model 1814_ at TGFBR2 transforming growth factor, beta receptor II (70/8OkDa)

Data Set 3 100 gene model 34320 _at PTRF polymerase I and transcript release factor

Data Set 3 100 gene model 33610 _at CLDN8 claudin 8

Data Set 3 100 gene model 38326 _at G0S2 G0/G1 switch 2

Data Set 3 100 gene model 212 at ROR2 receptor tyrosine kinase-like orphan receptor 2

HIST1H2AD ///

Data Set 3 100 gene model 31693_f_at HIST1H3D histone 1, H2ad /// histone 1, H3d Data Set 3 100 gene model 37599_at AOXl aldehyde oxidase 1 Data Set 3 100 gene model 38921_at PDElB phosphodiesterase IB, calmodulin-dependent Data Set 3 100 gene model 41720_r_at FADSl fatty acid desaturase 1 Data Set 3 100 gene model 33102_at ADD3 adducin 3 (gamma) Data Set 3 100 gene model 35071_s_at GMDS GDP-mannose 4,6-dehydratase

HIST2H2AA ///

LOC653610 /// histone 2, H2aa /// similar to Histone H2A.O (H2A/o) (H2A.2) (H2a-615)

Data Set 3 100 gene model 286_at H2A/R /// histone H2A/r

HIST2H2AA ///

LOC653610 /// histone 2, H2aa /// similar to Histone H2A.O (H2A/o) (H2A.2) (H2a-615)

Data Set 3 100 gene model 32609_at H2A/R /// histone H2A/r Data Set 3 100 gene model 153_f_at HIST1H2BJ histone 1, H2bj Data Set 3 100 gene model 31524_f_at HIST1H2BI histone 1, H2bi Data Set 3 100 gene model 3297 l_at C9orf61 chromosome 9 open reading frame 61 Data Set 3 100 gene model 32819_at HIST1H2BK histone 1, H2bk Data Set 3 100 gene model 1662_r_at Data Set 3 100 gene model 35127_at HIST1H2AE histone 1, H2ae Data Set 3 100 gene model 36347_f_at HIST1H2BN histone 1, H2bn Data Set 3 100 gene model 37485_at SLC27A2 solute carrier family 27 (fatty acid transporter), member 2 Data Set 3 100 gene model 37761_at BAIAP2 BAIl-associated protein 2 Data Set 3 100 gene model 31528_f_at HIST1H2BM histone 1, H2bm Data Set 3 100 gene model 1929_at ANGPTl angiopoietin 1 Data Set 3 100 gene model 37917_at FLJ20323 hypothetical protein FLJ20323 Data Set 3 100 gene model 35576_f_at HIST1H2BL histone 1, H2bl Data Set 3 100 gene model 33308_at GUSB glucuronidase, beta Data Set 3 100 gene model 33766_at VIPRl vasoactive intestinal peptide receptor 1 Data Set 3 100 gene model 34769_at FAAH fatty acid amide hydrolase Data Set 3 100 gene model 35628_at TM7SF2 transmembrane 7 superfamily member 2 Data Set 3 100 gene model 38719_at NSF N-ethylmaleimide-sensitive factor Data Set 3 100 gene model 35770_at ATP6AP1 ATPase, H+ transporting, lysosomal accessory protein 1 Data Set 3 100 gene model 41812_s_at NUP210 nucleoporin 21OkDa Data Set 3 100 gene model 38279_at GNAZ guanine nucleotide binding protein (G protein), alpha z polypeptide Data Set 3 100 gene model 31816_at GAA glucosidase, alpha; acid (Pompe disease, glycogen storage disease type II)

Data Set 3 100 gene model 32700 _at GBP2 guanylate binding protein 2, interferon-inducible

Data Set 3 100 gene model 32151 _at RANGAPl Ran GTPase activating protein 1

Data Set 3 100 gene model 32526 _at JAM3 junctional adhesion molecule 3

Data Set 3 100 gene model 41139 _at MAGEDl melanoma antigen family D, 1 solute carrier family 25 (mitochondrial carrier; adenine nucleotide

Data Set 3 100 gene model 40436 g at SLC25A6 translocator), member 6

Data Set 3 100 gene model 1980_ s_at NME2 non-metastatic cells 2, protein (NM23B) expressed in

Data Set 3 100 gene model 770 a t GPX3 glutathione peroxidase 3 (plasma)

Data Set 3 100 gene model 40069 _at SVIL supervillin

Data Set 3 100 gene model 37713 _at ACYl aminoacylase 1

Data Set 3 100 gene model 36073 _at NDN necdin homolog (mouse)

Data Set 3 100 gene model 1519_ at ETS2 v-ets erythroblastosis virus E26 oncogene homolog 2 (avian)

Data Set 3 100 gene model 33708 _at SLC43A1 solute carrier family 43, member 1 glucosaminyl (N-acetyl) transferase 1, core 2 (beta-l,6-N-acetyl-

Data Set 3 100 gene model 38218 _at GCNTl glucosaminyltransferase)

Data Set 3 100 gene model 39852 _at SPG20 spastic paraplegia 20, spartin (Troyer syndrome)

Data Set 3 100 gene model 40521 _at RGL2 ral guanine nucleotide dissociation stimulator-like 2

Data Set 3 100 gene model 34050 _at ACSMl acyl-CoA synthetase medium-chain family member 1 solute carrier family 25 (mitochondrial carrier; adenine nucleotide

Data Set 3 100 gene model 40435 _at SLC25A6 translocator), member 6

Data Set 3 100 gene model 37630 _at CHRDLl chordin-like 1

Data Set 3 100 gene model 2011 s_at BIK BCL2 -interacting killer (apoptosis-inducing)

Data Set 3 100 gene model 38146 _at ST18 suppression of tumorigenicity 18 (breast carcinoma) (zinc finger protein)

Data Set 3 100 gene model 39082 _at ANXA6 annexin A6

Data Set 3 100 gene model 39243 _s_at PSIPl PC4 and SFRS 1 interacting protein 1

Data Set 3 100 gene model 41814 _at FUCAl fucosidase, alpha-L- 1, tissue

Data Set 3 100 gene model 38044 _at FAM107A family with sequence similarity 107, member A

Data Set 3 100 gene model 36432 _at MCCC2 methylcrotonoyl-Coenzyme A carboxylase 2 (beta)

Data Set 3 100 gene model 36160 _s_at PTPRN2 protein tyrosine phosphatase, receptor type, N polypeptide 2

Data Set 3 100 gene model 34739 _at FNBPlL formin binding protein 1-like

Data Set 3 100 gene model 36596 _r_at GATM glycine amidinotransferase (L-arginine:glycine amidinotransferase)

Data Set 3 100 gene model 31685 _at FEV FEV (ETS oncogene family)

Data Set 3 100 gene model 1911_ s_at GADD45A growth arrest and DNA-damage-inducible, alpha tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation

Data Set 3 100 gene model 1424_ s_at YWHAH protein, eta polypeptide

Data Set 3 100 gene model 40301_at GPR161 G protein-coupled receptor 161

Data Set 3 100 gene model 39315_at ANGPTl angiopoietin 1

Data Set 3 100 gene model 34213_at WWCl WW, C2 and coiled-coil domain containing 1

Data Set 3 100 gene model 38435_at PRDX4 peroxiredoxin 4

Data Set 3 100 gene model 33900_at FSTL3 follistatin-like 3 (secreted glycoprotein)

Data Set 3 100 gene model 38791_at DDOST dolichyl-diphosphooligosaccharide-protein glycosyltransferase

Data Set 3 100 gene model 1597_at GAS6 growth arrest-specific 6

Data Set 3 100 gene model 41207_at C9orf3 chromosome 9 open reading frame 3

Data Set 3 100 gene model 38262_at — Clone 23620 mRNA sequence

Data Set 3 100 gene model 33611_g_at CLDN8 claudin 8

Data Set 3 100 gene model 37000_at BRP44 brain protein 44

Data Set 3 100 gene model 634_at PRSS8 protease, serine, 8 (prostasin)

Data Set 3 250 gene model 1248_at POLR2H polymerase (RNA) II (DNA directed) polypeptide H

Data Set 3 250 gene model 36955_at LMAN2 lectin, mannose-binding 2

Data Set 3 250 gene model 33135_at SLC19A1 solute carrier family 19 (folate transporter), member 1

Data Set 3 250 gene model 41804_at FLJ22531 hypothetical protein FU22531

Data Set 3 250 gene model 33924_at RAB6IP1 RAB6 interacting protein 1

Data Set 3 250 gene model 40663_at REPS2 RALBPl associated Eps domain containing 2

Data Set 3 250 gene model 4077 l_at MSN moesin

Data Set 3 250 gene model 37939_at APOBEC3C apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like 3C

Data Set 3 250 gene model 36452_at SYNPO synaptopodin

Data Set 3 250 gene model 37407_s_at MYHIl myosin, heavy polypeptide 11, smooth muscle

Data Set 3 250 gene model 33824_at KRT8 keratin 8

Data Set 3 250 gene model 773_at MYHIl myosin, heavy polypeptide 11, smooth muscle

Data Set 3 250 gene model 41137_at PPP1R12B protein phosphatase 1, regulatory (inhibitor) subunit 12B

Data Set 3 250 gene model 41281_s_at PEXlO peroxisome biogenesis factor 10

Data Set 3 250 gene model 330_s_at

Data Set 3 250 gene model 39714_at SH3BGRL SH3 domain binding glutamic acid-rich protein like

Data Set 3 250 gene model 41788_i_at TSC22D2 TSC22 domain family, member 2

Data Set 3 250 gene model 36761_at OVOL2 ovo-like 2 (Drosophila)

Data Set 3 250 gene model 39100_at SPOCKl sparc/osteonectin, cwcv and kazal-like domains proteoglycan (testican) 1

Data Set 3 250 gene model 33466_at LOC90355 hypothetical gene supported by AF038182; BC009203

Data Set 3 250 gene model 35630_at LLGL2 lethal giant larvae homolog 2 (Drosophila)

Data Set 3 250 gene model 37929_at IGSF4 immunoglobulin superfamily, member 4

Data Set 3 250 gene model 39356_at NEDD4L neural precursor cell expressed, developmentally down-regulated 4-like

Data Set 3 250 gene model 297_g_at —

Data Set 3 250 gene model 1270_at RAPlGAP RAPl GTPase activating protein

Data Set 3 250 gene model 32435_at RPL19 ribosomal protein L19

Data Set 3 250 gene model 35147_at MCF2L MCF.2 cell line derived transforming sequence-like

Data Set 3 250 gene model 3933 l_at TUBB2A tubulin, beta 2A

Data Set 3 250 gene model 1225_g_at PCTKl PCTAIRE protein kinase 1

Data Set 3 250 gene model 33448_at SPINTl serine peptidase inhibitor, Kunitz type 1

TRGC2 /// TRGV2

/// TRGV9 /// T cell receptor gamma constant 2 /// T cell receptor gamma variable 2 ///

TARP /// T cell receptor gamma variable 9 /// TCR gamma alternate reading frame

Data Set 3 250 gene model 41468 at LOC642083 protein /// hypothetical protein LOC642083

Data Set 3 250 gene model 38410_at CETN2 centrin, EF-hand protein, 2

Data Set 3 250 gene model 1693_s_at TIMPl TIMP metallopeptidase inhibitor 1

Data Set 3 250 gene model 33876_at WWTRl WW domain containing transcription regulator 1 serpin peptidase inhibitor, clade F (alpha-2 antiplasmin, pigment

Data Set 3 250 gene model 40856_at SERPINFl epithelium derived factor), member 1 fibroblast growth factor receptor 1 (fms-related tyrosine kinase 2,

Data Set 3 250 gene model 2057_g_at FGFRl Pfeiffer syndrome)

Data Set 3 250 gene model 37247_at TCF21 transcription factor 21

Data Set 3 250 gene model 39170_at CD59 CD59 molecule, complement regulatory protein

Data Set 3 250 gene model 37576_at PCP4 Purkinje cell protein 4

Data Set 3 250 gene model 35871_s_at SLC4A4 solute carrier family 4, sodium bicarbonate cotransporter, member 4

Data Set 3 250 gene model 34955_at ABCC4 ATP-binding cassette, sub-family C (CFTR/MRP), member 4

Data Set 3 250 gene model 31528_f_at HIST1H2BM histone 1, H2bm

Data Set 3 250 gene model 36790_at TPMl tropomyosin 1 (alpha)

Data Set 3 250 gene model 36533_at PTGIS prostaglandin 12 (prostacyclin) synthase

Data Set 3 250 gene model 40127_at SFXN3 sideroflexin 3

Data Set 3 250 gene model 41504 s at MAF v-maf musculoaponeurotic fibrosarcoma oncogene homolog (avian)

Data Set 3 250 gene model 39544_at DMN desmuslin

Data Set 3 250 gene model 501_g_at CYP2J2 cytochrome P450, family 2, subfamily J, polypeptide 2

Data Set 3 250 gene model 34684_at RECQL RecQ protein-like (DNA helicase Ql -like)

Data Set 3 250 gene model 718_at HTRAl HtrA serine peptidase 1

Data Set 3 250 gene model 35285_at SLC4A4 solute carrier family 4, sodium bicarbonate cotransporter, member 4

ClR /// complement component 1 , r subcomponent /// similar to Complement

Data Set 3 250 gene model 39409_at LOC643676 CIr subcomponent precursor (Complement component 1, r subcomponent)

Data Set 3 250 gene model 34091_s_at VIM vimentin Data Set 3 250 gene model 32535_at FBNl fibrillin 1 Data Set 3 250 gene model 36757_at HIST1H3H histone 1, H3h Data Set 3 250 gene model 39165_at NIFUN NifU-like N-terminal domain containing Data Set 3 250 gene model 35365_at ILK integrin-linked kinase Data Set 3 250 gene model 32553_at MAZ MYC-associated zinc finger protein (purine-binding transcription factor) Data Set 3 250 gene model 32543_at CALR calreticulin Data Set 3 250 gene model 36589_at AKRlBl aldo-keto reductase family 1 , member B 1 (aldose reductase) Data Set 3 250 gene model 39697_at HSDl 1B2 hydroxysteroid (11-beta) dehydrogenase 2 Data Set 3 250 gene model 33710_at OACT5 O-acyltransferase (membrane bound) domain containing 5 Data Set 3 250 gene model 32566_at CHPF chondroitin polymerizing factor Data Set 3 250 gene model 38831_f_at GNB2 guanine nucleotide binding protein (G protein), beta polypeptide 2 steroid-5-alpha-reductase, alpha polypeptide 2 (3-oxo-5 alpha-steroid

Data Set 3 250 gene model 565_at SRD5A2 delta 4-dehydrogenase alpha 2) Data Set 3 250 gene model 36204_at PTPRF protein tyrosine phosphatase, receptor type, F Data Set 3 250 gene model 38324_at LSR lipolysis stimulated lipoprotein receptor Data Set 3 250 gene model 40422_at IGFBP2 insulin-like growth factor binding protein 2, 36kDa sphingomyelin phosphodiesterase 1 , acid lysosomal (acid

Data Set 3 250 gene model 32574_at SMPDl sphingomyelinase) Data Set 3 250 gene model 41368_at SLC13A3 solute carrier family 13 (sodium-dependent dicarboxylate transporter), member 3

TAFlO RNA polymerase II, TATA box binding protein (

Data Set 3 250 gene model 868_at TAFlO TBP)-associated factor, 3OkDa Data Set 3 250 gene model 34843_at ZNF516 zinc finger protein 516 Data Set 3 250 gene model 35749_at TADA3L transcriptional adaptor 3 (NGGl homolog, yeast)-like Data Set 3 250 gene model 1243_at DDB2 damage-specific DNA binding protein 2, 48kDa Data Set 3 250 gene model 38292_at HOMER2 homer homolog 2 (Drosophila)

3-hydroxymethyl-3-methylglutaryl-Coenzyme A lyase

Data Set 3 250 gene model 38425_at HMGCL (hydroxymethylglutaricaciduria) Data Set 3 250 gene model 39752_at CYB561D2 cytochrome b-561 domain containing 2 Data Set 3 250 gene model 37016_at ECHSl enoyl Coenzyme A hydratase, short chain, 1, mitochondrial Data Set 3 250 gene model 40570_at FOXOlA forkhead box O1A (rhabdomyosarcoma) Data Set 3 250 gene model 1135_at GRK5 G protein-coupled receptor kinase 5 Data Set 3 250 gene model 33862_at PPAP2B phosphatidic acid phosphatase type 2B Data Set 3 250 gene model 37704_at BCKDHA branched chain keto acid dehydrogenase El, alpha polypeptide Data Set 3 250 gene model 1985_s_at NMEl non-metastatic cells 1, protein (NM23 A) expressed in

Data Set 3 250 gene model 32747_at ALDH2 aldehyde dehydrogenase 2 family (mitochondrial) Data Set 3 250 gene model 38408_at TSPAN7 tetraspanin 7 Data Set 3 250 gene model 36232_at FGF13 fibroblast growth factor 13 Data Set 3 250 gene model 40548_at BICDl bicaudal D homolog 1 (Drosophila) Data Set 3 250 gene model 40775_at ITM2A integral membrane protein 2A Data Set 3 250 gene model 36690_at NR3C1 nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor) Data Set 3 250 gene model 37225_at ANKRD15 ankyrin repeat domain 15 Data Set 3 250 gene model 39366_at PPP1R3C protein phosphatase 1, regulatory (inhibitor) subunit 3C Data Set 3 250 gene model 37343_at ITPR3 inositol 1 ,4,5-triphosphate receptor, type 3

HNRPAl /// heterogeneous nuclear ribonucleoprotein Al /// hypothetical protein

Data Set 3 250 gene model 34987_s_at LOC644245 LOC644245 Data Set 3 250 gene model 36676_at RPN2 ribophorin II Data Set 3 250 gene model 33253_at TRIM14 tripartite motif-containing 14 Data Set 3 250 gene model 40300_g_at GPR161 G protein-coupled receptor 161

SWI/SNF related, matrix associated, actin dependent regulator of chromatin,

Data Set 3 250 gene model 34695_at SMARCD2 subfamily d, member 2 Data Set 3 250 gene model 36965_at ANK3 ankyrin 3, node of Ranvier (ankyrin G) Data Set 3 250 gene model 36950_at TMED9 transmembrane emp24 protein transport domain containing 9 Data Set 3 250 gene model 33404_at CAP2 CAP, adenylate cyclase-associated protein, 2 (yeast) asparagine-linked glycosylation 3 homolog (S. cerevisiae, alpha-1,3-'

Data Set 3 250 gene model 38161_at ALG3 mannosyltransf erase) Data Set 3 250 gene model 37930_at ATP7B ATPase, Cu++ transporting, beta polypeptide Data Set 3 250 gene model 37022_at PRELP proline/arginine-rich end leucine-rich repeat protein

SWI/SNF related, matrix associated, actin dependent regulator of

Data Set 3 250 gene model 32579_at SMARCA4 chromatin, subfamily a, member 4 Data Set 3 250 gene model 32246_g_at METTL3 methyltransferase like 3 Data Set 3 250 gene model 39657_at KRT4 keratin 4 Data Set 3 250 gene model 39925_at COL9A2 collagen, type IX, alpha 2 Data Set 3 250 gene model 914_g_at ERG v-ets erythroblastosis virus E26 oncogene like (avian) Data Set 3 250 gene model 1120_at GSTM3 glutathione S -transferase M3 (brain) Data Set 3 250 gene model 36147_at SSR2 signal sequence receptor, beta (translocon-associated protein beta) Data Set 3 250 gene model 36515_at GNE glucosamine (UDP-N-acetyl)-2-epimerase/N-acetylmannosamine kinase Data Set 3 250 gene model 31575_f_at Data Set 3 250 gene model 34699_at CD2AP CD2-associated protein Data Set 3 250 gene model 32573_at SFRS9 splicing factor, arginine/serine-rich 9

Data Set 3 250 gene model 36660_at RABUA RABIlA, member RAS oncogene family tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation

Data Set 3 250 gene model 409_at YWHAQ protein, theta polypeptide

Data Set 3 250 gene model 1798_at SLC39A6 solute carrier family 39 (zinc transporter), member 6

Data Set 3 250 gene model 41750_at PDIA6 protein disulfide isomerase family A, member 6

Data Set 3 250 gene model 38684_at ATP2C1 ATPase, Ca++ transporting, type 2C, member 1

Data Set 3 250 gene model 4088 l_at ACLY ATP citrate lyase

UDP-N-acetyl-alpha-D-galactosamineipolypeptide N-acetylgalactosaminyl-

Data Set 3 250 gene model 38041_at GALNTl transferase 1 (GaINAc-Tl)

Data Set 3 250 gene model 34823_at DPP4 dipeptidyl-peptidase 4 (CD26, adenosine deaminase complexing protein 2)

Data Set 3 250 gene model 254 at H3F3A H3 histone, family 3A

Data Set 3 250 gene model 32203_at C20orfl8 chromosome 20 open reading frame 18

Data Set 3 250 gene model 32506_at TBClDl TBCl (tre-2/USP6, BUB2, cdclό) domain family, member 1

Data Set 3 250 gene model 39023_at IDHl isocitrate dehydrogenase 1 (NADP+), soluble

Data Set 3 250 gene model 36252_at CTFl cardiotrophin 1

Data Set 3 250 gene model 36572_r_at ARL6IP ADP-ribosylation factor-like 6 interacting protein

Data Set 3 250 gene model 38010_at BNIP3 BCL2/adenovirus ElB 19kDa interacting protein 3

Data Set 3 250 gene model 153_f_at HIST1H2BJ histone 1, H2bj

Data Set 3 250 gene model 38666_at PSCDl pleckstrin homology, Sec7 and coiled-coil domains l(cytohesin 1) phosphoribosylaminoimidazole carboxylase, phosphoribosylaminoimidazole

Data Set 3 250 gene model 39056_at PAICS succinocarboxamide synthetase

Data Set 3 250 gene model 31532 at MDSl myelodysplasia syndrome 1

Data Set 3 250 gene model 32245_at METTL3 methyltransferase like 3

HIST2H2AA ///

LOC653610 /// histone 2, H2aa /// similar to Histone H2A.O (H2A/o) (H2A.2) (H2a-615)

Data Set 3 250 gene model 32609_at H2A/R /// histone H2A/r

HIST2H2AA ///

LOC653610 /// histone 2, H2aa /// similar to Histone H2A.O (H2A/o) (H2A.2) (H2a-615)

Data Set 3 250 gene model 286_at H2A/R /// histone H2A/r

Data Set 3 250 gene model 40607_at DPYSL2 dihydropyrimidinase-like 2

ARHGAP8 ///

Data Set 3 250 gene model 37117_at LOC553158 Rho GTPase activating protein 8 /// PRR5-ARHGAP8 fusion

Data Set 3 250 gene model 39236_s_at FAAH fatty acid amide hydrolase

Data Set 3 250 gene model 31662_at VPS45A vacuolar protein sorting 45A (yeast)

Data Set 3 250 gene model 36894_at CBX7 chromobox homolog 7

Data Set 3 250 gene model 40786_at PPP2R5C protein phosphatase 2, regulatory subunit B (B56), gamma isoform Data Set 3 250 gene model 38354_at CEBPB CCAAT/enhancer binding protein (C/EBP), beta Data Set 3 250 gene model 36591_at TUBAl tubulin, alpha 1 (testis specific) Data Set 3 250 gene model 1739_at FOLHl folate hydrolase (prostate-specific membrane antigen) 1 Data Set 3 250 gene model 33358_at PPMlH protein phosphatase IH (PP2C domain containing) Data Set 3 250 gene model 36963_at PGD phosphogluconate dehydrogenase Data Set 3 250 gene model 1513_at Data Set 3 250 gene model 1336_s_at PRKCB 1 protein kinase C, beta 1 Data Set 3 250 gene model 34835_at NCSTN nicastrin Data Set 3 250 gene model 41585_at KIAA0746 KIAA0746 protein Data Set 3 250 gene model 1514_g_at

BOPl ///

Data Set 3 250 gene model 35615_at LOC653119 block of proliferation 1 /// similar to block of proliferation 1

O-linked N-acetylglucosamine (GIcNAc) transferase (UDP-N-acetyl-

Data Set 3 250 gene model 38614_s_at OGT glucosamineipolypeptide-N-acetylglucosaminyl transferase) Data Set 3 250 gene model 41098_at DAAM2 dishevelled associated activator of morphogenesis 2 Data Set 3 250 gene model 34840_at SERINC5 Serine incorporator 5 Data Set 3 250 gene model 36986_at LYPLA2 lysophospholipase II Data Set 3 250 gene model 32224_at FCHSD2 FCH and double SH3 domains 2 Data Set 3 250 gene model 38527_at NONO non-POU domain containing, octamer-binding Data Set 3 250 gene model 41720_r_at FADS 1 fatty acid desaturase 1 Data Set 3 250 gene model 41526_at HMG20B high-mobility group 2OB Data Set 3 250 gene model 38986_at PDIA3 protein disulfide isomerase family A, member 3 Data Set 3 250 gene model 35146_at TGFBlIl transforming growth factor beta 1 induced transcript 1 Data Set 3 250 gene model 39063_at ACTC actin, alpha, cardiac muscle Data Set 3 250 gene model 40841_at TACCl transforming, acidic coiled-coil containing protein 1 Data Set 3 250 gene model 3681 l_at LOXLl lysyl oxidase-like 1 Data Set 3 250 gene model 40994_at GRK5 G protein-coupled receptor kinase 5 Data Set 3 250 gene model 37573_at ANGPTL2 angiopoietin-like 2 Data Set 3 250 gene model 36937_s_at PDLIMl PDZ and LIM domain 1 (elfin) Data Set 3 250 gene model 3721 l_at BDHl 3-hydroxybutyrate dehydrogenase, type 1 Data Set 3 250 gene model 31816_at GAA glucosidase, alpha; acid (Pompe disease, glycogen storage disease type II) Data Set 3 250 gene model 36126_at COASY Coenzyme A synthase Data Set 3 250 gene model 32798_at GSTM3 glutathione S-transferase M3 (brain) Data Set 3 250 gene model 33863 at HYOUl hypoxia up-regulated 1

Data Set 3 250 gene model 37956_at ALDH3B2 aldehyde dehydrogenase 3 family, member B2 Data Set 3 250 gene model 39521_at SLC12A4 solute carrier family 12 (potassium/chloride transporters), member 4 Data Set 3 250 gene model 1020_s_at CIB 1 calcium and integrin binding 1 (calmyrin) Data Set 3 250 gene model 34291_at FARSLA phenylalanine-tRNA synthetase-like, alpha subunit Data Set 3 250 gene model 38151_at LOHl 1CR2A loss of heterozygosity, 11, chromosomal region 2, gene A Data Set 3 250 gene model 40666_at ENTPD5 ectonucleoside triphosphate diphosphohydrolase 5 Data Set 3 250 gene model 1121_g_at GSTM3 glutathione S-transferase M3 (brain) Data Set 3 250 gene model 518_at NR1H2 nuclear receptor subfamily 1, group H, member 2 Data Set 3 250 gene model 3563 l_at POLR2H polymerase (RNA) II (DNA directed) polypeptide H Data Set 3 250 gene model 212_at ROR2 receptor tyrosine kinase-like orphan receptor 2 Data Set 3 250 gene model 37761_at BAIAP2 BAIl-associated protein 2 Data Set 3 250 gene model 37582_at KRT 15 keratin 15 Data Set 3 250 gene model 32108_at SPR sepiapterin reductase (7,8-dihydrobiopterin:NADP+ oxidoreductase) Data Set 3 250 gene model 35127_at HIST1H2AE histone 1, H2ae Data Set 3 250 gene model 33362_at CDC42EP3 CDC42 effector protein (Rho GTPase binding) 3 Data Set 3 250 gene model 32544_s _^ at RSUl Ras suppressor protein 1 Data Set 3 250 gene model 3978 l_at IGFB P4 insulin-like growth factor binding protein 4 Data Set 3 250 gene model 41870_at PDPN podoplanin Data Set 3 250 gene model 31791_at TP73L tumor protein p73-like Data Set 3 250 gene model 39753_at ITGA5 integrin, alpha 5 (fibronectin receptor, alpha polypeptide) Data Set 3 250 gene model 39123_s_at TRPCl transient receptor potential cation channel, subfamily C, member 1

FOLHl /// folate hydrolase (prostate-specific membrane antigen) 1 /// growth-

Data Set 3 250 gene model 1740_g_at PSMAL inhibiting protein 26 Data Set 3 250 gene model 31527_at RPS2 ribosomal protein S2 Data Set 3 250 gene model 3571 l_at GLS2 glutaminase 2 (liver, mitochondrial) Data Set 3 250 gene model 1931_at ABCC4 ATP-binding cassette, sub-family C (CFTR/MRP), member 4 Data Set 3 250 gene model 41139_at MAGEDl melanoma antigen family D, 1 Data Set 3 250 gene model 32260_at PEA 15 phosphoprotein enriched in astrocytes 15 Data Set 3 250 gene model 36093_at FLJ30092 AF-I specific protein phosphatase

SlOO calcium binding protein A4 (calcium protein, calvasculin, metastasin,

Data Set 3 250 gene model 38087_s_at S 100 A4 murine placental homolog) Data Set 3 250 gene model 37743_at FEZl fasciculation and elongation protein zeta 1 (zygin I) Data Set 3 250 gene model 296_at Data Set 3 250 gene model 35783_at VAMP3 vesicle-associated membrane protein 3 (cellubrevin) Data Set 3 250 gene model 38653 at PMP22 peripheral myelin protein 22

Data Set 3 250 gene model 37827_r_at DOPEY2 dopey family member 2 Data Set 3 250 gene model 37043_at ID3 inhibitor of DNA binding 3, dominant negative helix -loop-helix protein Data Set 3 250 gene model 39124_r_at TRPCl transient receptor potential cation channel, subfamily C, member 1 Data Set 3 250 gene model 40414_at VARS valyl-tRNA synthetase Data Set 3 250 gene model 32533_s_at VAMP5 vesicle-associated membrane protein 5 (myobrevin) Data Set 3 250 gene model 33883_at EFS embryonal Fyn-associated substrate Data Set 3 250 gene model 1815_g_at TGFBR2 transforming growth factor, beta receptor II (70/8OkDa) Data Set 3 250 gene model 1585_at ERBB 3 v-erb-b2 erythroblastic leukemia viral oncogene homolog 3 (avian) Data Set 3 250 gene model 1470_at POLD2 polymerase (DNA directed), delta 2, regulatory subunit 5OkDa Data Set 3 250 gene model 41223_at COX5A cytochrome c oxidase subunit Va Data Set 3 250 gene model 39396_at LYPLAl lysophospholipase I Data Set 3 250 gene model 37680_at AKAP12 A kinase (PRKA) anchor protein (gravin) 12 Data Set 3 250 gene model 36677_at COPB2 coatomer protein complex, subunit beta 2 (beta prime)

HIST1H2AD ///

Data Set 3 250 gene model 31693_f_at HIST1H3D histone 1, H2ad /// histone 1, H3d Data Set 3 250 gene model 36618_g_at IDl inhibitor of DNA binding 1, dominant negative helix -loop-helix protein Data Set 3 250 gene model 34162_at RBPMS RNA binding protein with multiple splicing Data Set 3 250 gene model 924_s_at PPP2CB protein phosphatase 2 (formerly 2A), catalytic subunit, beta isoform Data Set 3 250 gene model 38780_at AKRlAl aldo-keto reductase family 1, member Al (aldehyde reductase) Data Set 3 250 gene model 38635_at SSR4 signal sequence receptor, delta (translocon-associated protein delta) Data Set 3 250 gene model 31524_f_at HIST1H2BI histone 1, H2bi Data Set 3 250 gene model 31684_at ANXA2P1 annexin A2 pseudogene 1 Data Set 3 250 gene model 1452_at LMO4 LIM domain only 4 Data Set 3 250 gene model 41225_at DUSP3 dual specificity phosphatase 3 (vaccinia virus phosphatase VHl -related) Data Set 3 250 gene model 40327_at HOXB 13 homeobox B 13 Data Set 3 250 gene model 37599_at AOXl aldehyde oxidase 1 Data Set 3 250 gene model 33610_at CLDN8 claudin 8 Data Set 3 250 gene model 41289_at NCAMl neural cell adhesion molecule 1 Data Set 3 250 gene model 33709_at PDE9A phosphodiesterase 9A Data Set 3 250 gene model 38396_at 3 ¹ UTR of hypothetical protein (ORFl) Data Set 3 250 gene model 36521_at DZIPl DAZ interacting protein 1 Data Set 3 250 gene model 38429_at FASN fatty acid synthase Data Set 3 250 gene model 33630 s at SPTBN2 spectrin, beta, non-erythrocytic 2 Data Set 3 250 gene model 40093_at BCAM basal cell adhesion molecule (Lutheran blood group) Data Set 3 250 gene model 844_at PPPlRlA protein phosphatase 1, regulatory (inhibitor) subunit IA

Data Set 3 250 gene model 38183_at FOXFl forkhead box Fl Data Set 3 250 gene model 34264_at RUSCl RUN and SH3 domain containing 1 Data Set 3 250 gene model 38326_at G0S2 G0/G1 switch 2 Data Set 3 250 gene model 39351 at CD59 CD59 molecule, complement regulatory protein Data Set 3 250 gene model 38921_at PDElB phosphodiesterase IB, calmodulin-dependent Data Set 3 250 gene model 33932_at GSPTl Gl to S phase transition 1 Data Set 3 250 gene model 38642_at ALCAM activated leukocyte cell adhesion molecule Data Set 3 250 gene model 35742_at C16orf45 chromosome 16 open reading frame 45 Data Set 3 250 gene model 39169_at SEC61G Sec61 gamma subunit Data Set 4 5 gene model AKAP2 Data Set 4 5 gene model CAVl Data Set 4 5 gene model TACSTDl Data Set 4 5 gene model HPN_varl Data Set 4 5 gene model CAMKK2 Data Set 4 10 gene model rap IGAP Data Set 4 10 gene model RAB3B Data Set 4 10 gene model TACSTDl Data Set 4 10 gene model EXTl Data Set 4 10 gene model TGFB 3 Data Set 4 10 gene model LOC 129642 Data Set 4 10 gene model SYNEl Data Set 4 10 gene model GI_10437016 Data Set 4 10 gene model AKAP2 Data Set 4 10 gene model ITGB3 Data Set 4 20 gene model MLCK Data Set 4 20 gene model IFI27 Data Set 4 20 gene model MLP Data Set 4 20 gene model GNAZ Data Set 4 20 gene model STOM Data Set 4 20 gene model TACSTDl Data Set 4 20 gene model KIP2 Data Set 4 20 gene model RRAS Data Set 4 20 gene model TIMP2 Data Set 4 20 gene model ILK

Data Set 4 20 gene model XLKDl

Data Set 4 20 gene model EXTl

Data Set 4 20 gene model STEAP

Data Set 4 20 gene model PYCRl

Data Set 4 20 gene model GSTPl

Data Set 4 20 gene model MEIS2

Data Set 4 20 gene model CDHl

Data Set 4 20 gene model RAB3B

Data Set 4 20 gene model SYNEl

Data Set 4 20 gene model GI_10437016

Data Set 4 50 gene model SIATl

Data Set 4 50 gene model GI_4884218

Data Set 4 50 gene model LIM

Data Set 4 50 gene model CCK

Data Set 4 50 gene model NBLl

Data Set 4 50 gene model PAICS

Data Set 4 50 gene model NKX3-1

Data Set 4 50 gene model BMPRlB

Data Set 4 50 gene model REPS2

Data Set 4 50 gene model IFI27

Data Set 4 50 gene model ARFIP2

Data Set 4 50 gene model D-PCa-2_mRNA

Data Set 4 50 gene model ATP2C1

Data Set 4 50 gene model EDNRB

Data Set 4 50 gene model BCL2_beta

Data Set 4 50 gene model GI_3360414

Data Set 4 50 gene model Pl

Data Set 4 50 gene model MKI67

Data Set 4 50 gene model CLU

Data Set 4 50 gene model MMP2

Data Set 4 50 gene model PLS3

Data Set 4 50 gene model GALNT3

Data Set 4 50 gene model LSAMP

Data Set 4 50 gene model ERBB 3

Data Set 4 50 gene model LTBP4

Data Set 4 50 gene model SPARCLl Data Set 4 50 gene model TGFB2_cds Data Set 4 50 gene model HPN_var2 Data Set 4 50 gene model KIAK0002 Data Set 4 50 gene model TNFSFlO Data Set 4 50 gene model KIAA0172 Data Set 4 50 gene model memD Data Set 4 50 gene model DNAH5 Data Set 4 50 gene model PDLIM7 Data Set 4 50 gene model SIM2 Data Set 4 50 gene model KIP2 Data Set 4 50 gene model STRA13 Data Set 4 50 gene model TGFBR3 Data Set 4 50 gene model HNF-3 -alpha Data Set 4 50 gene model GNAZ Data Set 4 50 gene model EXTl Data Set 4 50 gene model STAC Data Set 4 50 gene model MEIS2 Data Set 4 50 gene model MLP Data Set 4 50 gene model MLCK Data Set 4 50 gene model TACSTDl Data Set 4 50 gene model XLKDl Data Set 4 50 gene model PYCRl Data Set 4 50 gene model STEAP Data Set 4 50 gene model CDHl Data Set 4 100 gene model TRAF5 Data Set 4 100 gene model LIPH Data Set 4 100 gene model TP73 Data Set 4 100 gene model CALMl Data Set 4 100 gene model TSPAN-I Data Set 4 100 gene model SEC14L2 Data Set 4 100 gene model CD38 Data Set 4 100 gene model ROBOl Data Set 4 100 gene model GSTM3 Data Set 4 100 gene model SLC39A6

Data Set 4 100 gene model ALDH 1A2 Data Set 4 100 gene model TU3A Data Set 4 100 gene model RGSlO Data Set 4 100 gene model UBl Data Set 4 100 gene model TRIM29 Data Set 4 100 gene model KAIl Data Set 4 100 gene model DCC Data Set 4 100 gene model ECT2 Data Set 4 100 gene model NKX3-1 Data Set 4 100 gene model NTNl Data Set 4 100 gene model GSTM5 Data Set 4 100 gene model IFI27 Data Set 4 100 gene model EZH2 Data Set 4 100 gene model PROKl Data Set 4 100 gene model TRPM8 Data Set 4 100 gene model CLULl Data Set 4 100 gene model ZABCl Data Set 4 100 gene model MOAT-B Data Set 4 100 gene model LIM Data Set 4 100 gene model MET Data Set 4 100 gene model NY-REN-41 Data Set 4 100 gene model KIAA0389 Data Set 4 100 gene model RPL13A Data Set 4 100 gene model PCGEMl Data Set 4 100 gene model MAL Data Set 4 100 gene model ITPRl Data Set 4 100 gene model GASl Data Set 4 100 gene model DHCR24 Data Set 4 100 gene model SPDEF Data Set 4 100 gene model SIATl Data Set 4 100 gene model PTTGl Data Set 4 100 gene model MYBL2 Data Set 4 100 gene model PPP1R12A Data Set 4 100 gene model ANGPTL2 Data Set 4 100 gene model PRSS8

Data Set 4 100 gene model TGFB2

Data Set 4 100 gene model CCK

Data Set 4 100 gene model HNMP-I

Data Set 4 100 gene model XBPl

Data Set 4 100 gene model SRD5A2

Data Set 4 100 gene model ANX A2

Data Set 4 100 gene model D-PCa-2_mRNA

Data Set 4 100 gene model KIAA0003

Data Set 4 100 gene model SLC14A1

Data Set 4 100 gene model GDF15

Data Set 4 100 gene model HSD17B4

Data Set 4 100 gene model PAICS

Data Set 4 100 gene model COL5A2

Data Set 4 100 gene model REPS2

Data Set 4 100 gene model NBLl

Data Set 4 100 gene model ARFIP2

Data Set 4 100 gene model BMPRlB

Data Set 4 100 gene model D-PCa-2_varl

Data Set 4 100 gene model GJAl

Data Set 4 100 gene model DF

Data Set 4 100 gene model GALNT3

Data Set 4 100 gene model PLS3

Data Set 4 100 gene model Pl

Data Set 4 100 gene model HOXC6

Data Set 4 100 gene model EDNRB

Data Set 4 100 gene model ZAKI-4

Data Set 4 100 gene model SYT7

Data Set 4 100 gene model TBX A2R

Data Set 4 100 gene model MMP2

Data Set 4 100 gene model FBPl

Data Set 4 100 gene model AMACR

Data Set 4 100 gene model SLIT3

Data Set 4 100 gene model BC008967

Data Set 4 100 gene model CNNl

Data Set 4 100 gene model KIAA0869

Data Set 4 100 gene model BIK Data Set 4 100 gene model XLKDl Data Set 4 100 gene model CRYAB Data Set 4 100 gene model AKAP2 Data Set 4 100 gene model TMSNB Data Set 4 100 gene model HPN_varl Data Set 4 100 gene model CAVl Data Set 4 100 gene model ILK Data Set 4 100 gene model ITGB3 Data Set 4 100 gene model TGFB 3 Data Set 4 100 gene model CAMKK2 Data Set 4 100 gene model LOC 129642 Data Set 4 100 gene model PYCRl Data Set 4 100 gene model rap IGAP Data Set 4 100 gene model ITGA5 Data Set 4 100 gene model STOM Data Set 4 100 gene model CDHl Data Set 4 100 gene model TACSTDl Data Set 4 100 gene model GSTPl Data Set 4 100 gene model DNAH5 Data Set 4 250 gene model ESMl Data Set 4 250 gene model MT3 Data Set 4 250 gene model RIG Data Set 4 250 gene model PEX5 Data Set 4 250 gene model SERPINB5 Data Set 4 250 gene model KLK2 Data Set 4 250 gene model KLK3 Data Set 4 250 gene model RET_var2 Data Set 4 250 gene model RBPl Data Set 4 250 gene model CKTSFlBl Data Set 4 250 gene model ODCl Data Set 4 250 gene model BMP5 Data Set 4 250 gene model PPFIA3 Data Set 4 250 gene model HSA250839 Data Set 4 250 gene model ERBB2

Data Set 4 250 gene model SLC2A3 Data Set 4 250 gene model TRAPl Data Set 4 250 gene model HUEL Data Set 4 250 gene model OXCT Data Set 4 250 gene model OSBPL8 Data Set 4 250 gene model PMIl Data Set 4 250 gene model CDC42BPA Data Set 4 250 gene model BC-2 Data Set 4 250 gene model PTGDR Data Set 4 250 gene model THBSl Data Set 4 250 gene model MMP7 Data Set 4 250 gene model CPXM Data Set 4 250 gene model NDUF A2 Data Set 4 250 gene model ITGAl Data Set 4 250 gene model NGFB Data Set 4 250 gene model DDRl Data Set 4 250 gene model PTOVl Data Set 4 250 gene model LOC283431 Data Set 4 250 gene model ADAMTSl Data Set 4 250 gene model GI_2094528 Data Set 4 250 gene model GUCY1A3 Data Set 4 250 gene model KIAA1946 Data Set 4 250 gene model HGF Data Set 4 250 gene model SPARC Data Set 4 250 gene model AKR1C3 Data Set 4 250 gene model HLTF Data Set 4 250 gene model TROAP Data Set 4 250 gene model TNFRSF6 Data Set 4 250 gene model LOX Data Set 4 250 gene model ITGBl Data Set 4 250 gene model MAP2K1IP1 Data Set 4 250 gene model GALNTl Data Set 4 250 gene model SNDl Data Set 4 250 gene model HNRPAB Data Set 4 250 gene model GI 1178507

Data Set 4 250 gene model D-PCa-2_var2 Data Set 4 250 gene model MMP9 Data Set 4 250 gene model PTEN Data Set 4 250 gene model MCM2 Data Set 4 250 gene model BTG2 Data Set 4 250 gene model CD44 Data Set 4 250 gene model CST3 Data Set 4 250 gene model COLlAl Data Set 4 250 gene model PRCl Data Set 4 250 gene model ALG-2 Data Set 4 250 gene model PGM3 Data Set 4 250 gene model C7 Data Set 4 250 gene model JUNB Data Set 4 250 gene model NIPA2 Data Set 4 250 gene model SULFl Data Set 4 250 gene model COBLLl Data Set 4 250 gene model PIMl Data Set 4 250 gene model BCL2_alpha Data Set 4 250 gene model ERG_varl Data Set 4 250 gene model CCNE2 Data Set 4 250 gene model RGSU Data Set 4 250 gene model SFN Data Set 4 250 gene model CDHU Data Set 4 250 gene model MME Data Set 4 250 gene model RGS5 Data Set 4 250 gene model G6PD Data Set 4 250 gene model ITSN Data Set 4 250 gene model LUM Data Set 4 250 gene model NRIPl Data Set 4 250 gene model GI_839562 Data Set 4 250 gene model ID2 Data Set 4 250 gene model FGF 18 Data Set 4 250 gene model ALDH4A1 Data Set 4 250 gene model LIPH Data Set 4 250 gene model NSP

Data Set 4 250 gene model CALDl

Data Set 4 250 gene model IMPDH2

Data Set 4 250 gene model KIP

Data Set 4 250 gene model DKFZp434C0931

Data Set 4 250 gene model CTHRCl

Data Set 4 250 gene model CRISP3

Data Set 4 250 gene model UCHL5

Data Set 4 250 gene model FBPl

Data Set 4 250 gene model BC008967

Data Set 4 250 gene model CRYAB

Data Set 4 250 gene model AMACR

Data Set 4 250 gene model KIAA0869

Data Set 4 250 gene model CNNl

Data Set 4 250 gene model AKAP2

Data Set 4 250 gene model BIK

Data Set 4 250 gene model CAVl

Data Set 4 250 gene model SLIT3

Data Set 4 250 gene model TMSNB

Data Set 4 250 gene model ITGB3

Data Set 4 250 gene model MEIS2

Data Set 4 250 gene model HPN_varl

Data Set 4 250 gene model XLKDl

Data Set 4 250 gene model rap IGAP

Data Set 4 250 gene model MLP

Data Set 4 250 gene model CAMKK2

Data Set 4 250 gene model CAV2

Data Set 4 250 gene model TGFB 3

Data Set 4 250 gene model CDHl

Data Set 4 250 gene model TACSTDl

Data Set 4 250 gene model RAB3B

Data Set 4 250 gene model NTRK3

Data Set 4 250 gene model KIP2

Data Set 4 250 gene model RRAS

Data Set 4 250 gene model ITGA5

Data Set 4 250 gene model STEAP

Data Set 4 250 gene model ILK Data Set 4 250 gene model KIAA0172 Data Set 4 250 gene model SYNEl Data Set 4 250 gene model GNAZ Data Set 4 250 gene model PYCRl Data Set 4 250 gene model LOC 129642 Data Set 4 250 gene model MMP2 Data Set 4 250 gene model EXTl Data Set 4 250 gene model GSTPl Data Set 4 250 gene model ERBB 3 Data Set 4 250 gene model GI_10437016 Data Set 4 250 gene model STOM Data Set 4 250 gene model STAC Data Set 4 250 gene model FOLHl Data Set 4 250 gene model DNAH5 Data Set 4 250 gene model TIMP2 Data Set 4 250 gene model PDLIM7 Data Set 4 250 gene model TGFBR3 Data Set 4 250 gene model HNF-3 -alpha Data Set 4 250 gene model SIM2 Data Set 4 250 gene model MLCK Data Set 4 250 gene model memD Data Set 4 250 gene model TNFSFlO Data Set 4 250 gene model KIAK0002 Data Set 4 250 gene model MAL Data Set 4 250 gene model STRA13 Data Set 4 250 gene model ARFIP2 Data Set 4 250 gene model MKI67 Data Set 4 250 gene model TBXA2R Data Set 4 250 gene model ZAKI-4 Data Set 4 250 gene model BCL2_beta Data Set 4 250 gene model CLU Data Set 4 250 gene model Pl Data Set 4 250 gene model GALNT3 Data Set 4 250 gene model GASl

Data Set 4 250 gene model COL5A2

Data Set 4 250 gene model LTBP4

Data Set 4 250 gene model PLS3

Data Set 4 250 gene model GI_4884218

Data Set 4 250 gene model SYT7

Data Set 4 250 gene model HPN_var2

Data Set 4 250 gene model TGFB2_cds

Data Set 4 250 gene model HOXC6

Data Set 4 250 gene model PAICS

Data Set 4 250 gene model LSAMP

Data Set 4 250 gene model NBLl

Data Set 4 250 gene model GDF15

Data Set 4 250 gene model ITPRl

Data Set 4 250 gene model REPS2

Data Set 4 250 gene model ANGPTL2

Data Set 4 250 gene model BMPRlB

Data Set 4 250 gene model GI_3360414

Data Set 4 250 gene model ATP2C1

Data Set 4 250 gene model RPL13A

Data Set 4 250 gene model SPARCLl

Data Set 4 250 gene model PRSS8

Data Set 4 250 gene model SLC14A1

Data Set 4 250 gene model DF

Data Set 4 250 gene model D-PCa-2_mRNA

Data Set 4 250 gene model EDNRB

Data Set 4 250 gene model SIATl

Data Set 4 250 gene model D-PCa-2_varl

Data Set 4 250 gene model XBPl

Data Set 4 250 gene model KIAA0003

Data Set 4 250 gene model VCL

Data Set 4 250 gene model KIAA0389

Data Set 4 250 gene model HNMP-I

Data Set 4 250 gene model MOAT-B

Data Set 4 250 gene model SRD5A2

Data Set 4 250 gene model PPP1R12A

Data Set 4 250 gene model IFI27 Data Set 4 250 gene model PCGEMl Data Set 4 250 gene model ZABCl Data Set 4 250 gene model HSD17B4 Data Set 4 250 gene model PPAP2B Data Set 4 250 gene model SPDEF Data Set 4 250 gene model TP73 Data Set 4 250 gene model RGSlO Data Set 4 250 gene model ANX A2 Data Set 4 250 gene model DHCR24 Data Set 4 250 gene model CCK Data Set 4 250 gene model NY-REN-41 Data Set 4 250 gene model MYBL2 Data Set 4 250 gene model NTNl Data Set 4 250 gene model NKX3-1 Data Set 4 250 gene model TGFB2 Data Set 4 250 gene model GJAl Data Set 4 250 gene model MET Data Set 4 250 gene model EZH2 Data Set 4 250 gene model PTTGl Data Set 4 250 gene model FZD7 Data Set 4 250 gene model TRPM8 Data Set 4 250 gene model DCC Data Set 4 250 gene model UBl Data Set 4 250 gene model CLULl Data Set 4 250 gene model LIM Data Set 4 250 gene model SCUBE2 Data Set 4 250 gene model toml-like Data Set 4 250 gene model TSPAN-I Data Set 4 250 gene model SEC14L2 Data Set 4 250 gene model SERPINFl Data Set 4 250 gene model GSTM5 Data Set 4 250 gene model CALMl Data Set 4 250 gene model DATl Data Set 4 250 gene model MCCC2

Data Set 4 250 gene model BNIP3

Data Set 4 250 gene model TFAP2C

Data Set 4 250 gene model KAIl

Data Set 4 250 gene model TGFB 1

Data Set 4 250 gene model NEFH

Data Set 4 250 gene model ALDH 1A2

Data Set 4 250 gene model ECT2

Data Set 4 250 gene model COL4A2

Data Set 4 250 gene model TU3A

Data Set 4 250 gene model CHAFlA

Data Set 4 250 gene model CD38

Data Set 4 250 gene model CES 1

Data Set 4 250 gene model DKFZP564B 167

Data Set 4 250 gene model STEAP2

Data Set 4 250 gene model COL4A1

Data Set 4 250 gene model SLC39A6

Data Set 4 250 gene model UNC5C

Data Set 4 250 gene model TMEPAI

Data Set 4 250 gene model GI_2056367

Data Set 4 250 gene model Prostein

Data Set 4 250 gene model GPR43

Data Set 4 250 gene model GI_22761402

Data Set 4 250 gene model PROKl

Data Set 4 250 gene model TRIM29

Data Set 4 250 gene model ANTXRl

Table 19. In silico tissue components (tumor/stroma) prediction discrepancies (%) and correlation coefficients compared to pathologist's estimates across data sets.

Test

SetYTraining

Set Data Set 1 Data Set 2 Data Set 3 Data Set 4

Data Set 1 NA 11.6/11.8(0.82/0.73) 23.7/27(0.86/0 .74) 13.3/18.8(0.82/0 .75)

Data Set 2 11/16.7(0.89/0.76) NA 22.1/38.2(0.84/0 .63) 28.6/25.8(0.79/0 .72)

Data Set 3 14.5/15.1(0.76/0.64 13.7/22.3(0.75/0.59) NA 17.4/14.7(0.71/0 .59)

Data Set 4 12.1/24.5(0.76/0.62) 12.7/23.7(0.73/0.62) 12.8/19.9(0.72/0 .61) NA

Example 4 - Identification of Tissue Specific Genes in Prostate Cancer

Genes specifically expressed in different cell types (tumor, stroma, BPH and atrophic gland) of prostate tissue were identified.

Tissue Content Prediction Using Gene Expression Profile

Using linear models based on a small list of tissue specific genes, the tissue components of samples hybridized to the array is predictable. These genes are listed in Table 20.

Tissue Specific Relapse Related Genes

Some tissue specific genes showed significant expression level changes between relapse and non-relapse samples. The gene list is shown in Table 8 above.

Table 20. Tissue specific genes for tissue prediction.

Example 5 - Development of Predictive Biomarkers of Prostate Cancer

Cancer gene expression profiling studies often measure bulk tumor samples that contain a wide range of mixtures of multiple cell types. The differences in tissue components add noise to any measurement of expression in tumor cells. Such noise would be reduced by taking tissue percentages into account. However, such information does not exist for most available datasets.

Linear models for predicting tissue components (tumor, stroma, and benign prostatic hyperplasia) using two large public prostate cancer expression microarray datasets whose tissue components were estimated by pathologists (datasets 1 and 2) were developed. Mutual in silico predictions of tissue percentages between datasets 1 and 2 correlated with pathologists' estimates for tumor, stroma and BPH (pairwise comparisons for each tissue p < 0.0001). The model from dataset 2 was used to predict tissue percentages of a third large public dataset, for which tissue percentages were unknown. Then datasets 1 and 3 were used to identify candidate recurrence- related genes. The number of concordant recurrence-related markers significantly increased when the predicted tissue components were used. The most significant candidates are listed herein. This is the first known endeavor that finds genes predicative of outcome in two or more independent prostate cancer datasets. Given that tumors are highly heterogeneous and include many irrelevant changes, some markers in adjacent stroma or epithelial tissues could be reliable alternative sensors for recurrent versus non-recurrent cancers. The candidate biomarkers associated with recurrence after prostatectomy are included here.

Previously, a modification of the linear combination model of Stuart et al. 2004 was demonstrated and validated. This method is then employed to correct the independent data to that expected based on cell composition. The corrected data is used to validate genes discovered by analysis of the data to exhibit significant differential expression between non-recurrent and recurrent (aggressive) prostate cancer. The biomarkers of this and previous approaches are compared.

Herein, the result of further manipulation of the data is presented in Table form. A list of genes is provided that cross validate across the U01/SPECS dataset (dataset 1, which has tissue percentage estimated) and the dataset of Stephenson et al. (supra), dataset 3 where tissue percentages are estimated by applying a model based on tissue percentages in Bibilova et al. (supra).

Previous reports summarized efforts toward the development of enhanced methods and specification of genes for the prediction of the outcome of prostate cancer. The current report summarizes continued development of predictive biomarkers of Prostate Cancer.

The goals of this study are to continue development of predicative biomarkers of prostate cancer. In particular the goal of the work summarized here is to use independent datasets to validate genes deduced as predictive based on studies of dataset 1 (infra vide). Here "dataset" refers to the array-based RNA expression data of all cases of a given set together with the clinical data defining whether a given case recurred or remained disease free, a censored quantity. Only the categorical value, recurrent or non recurrent, is used in the analyses described here. For the purposes of the present work, recurrent prostate cancer is taken as a surrogate of aggressive disease while a non-recurrent patient is taken as indolent disease with a variable degree of indolence that is directly proportional to the disease-free survival time. The dataset 1 contains 26 non-recurrent patients, 29 recurrent patients, the dataset 2 contains 63 non-recurrent patients, 18 recurrent patients, and the dataset 3 contains 29 non-recurrent patients and 42 recurrent patients. The data used for this analysis are subsets of previous datasets. Only samples containing more than 0% tumor and follow-up times longer than 2 years for non-recurrent and 4 years for recurrent cases were included for this particular analysis. The first two datasets' samples have various amount of different tissue and cell types, including tumor cells, stroma cells (a collective term for fibroblasts, myofibroblasts, smooth muscle, and small amounts of nerve and vascular elements), BPH (epithelial cells of benign prostate hypertrophy) and dilated cystic glands (AKA "atrophic" cystic glands), as estimated by four pathologists (Stuart et al., supra) for dataset 1 and one pathologist for dataset 2. Dataset 3 samples were tumor-enriched samples, as claimed by the authors (a coauthor of that study, Steven Goodison, is also a coauthor of Stuart et al. PNAS 2004). In this study, published datasets 2 and 3 were used for the purpose of validation only. A major goal of this study is to use "external" published datasets to validate the properties deduced for genes based on analysis of the dataset 1.

Linear regression analysis was performed on the SPECS (dataset 1 ) and Goodison (dataset 3) arrays, separately. Estimates of significance of association with recurrence were determined as described in previous updates. The accompanying table filters this data as follows. First, genes associated with recurrence with/; < 0.1 in any tissue in either dataset were retained. Those genes that showed expression changes that were concordant between datasets were retained. However, the confidence in tissue assignment is not great because stroma and tumor tissue percentages are naturally anti-correlated. Thus, the data was also filtered for genes with/; < 0.1 which appeared to move in opposite directions in these two tissues across datasets as these are about as likely to be real changes and concordant changes in one tissue across datasets. In addition, genes that had ap < 0.01 in one tissue in one dataset were also retained even if the other dataset did not show a significant change, if the fold change in either stroma or tumor was consistent across datasets and there was at least a two-fold change in both datasets. Following these procedures and criteria we observed the results listed in Table 21. This is the first known endeavor that finds genes predicative of outcome in two or more independent prostate cancer datasets. In addition, some of the identified prognosticators are likely to occur in stroma or in BPH rather than in tumor. Such markers in stroma or BPH may be more easily observed as these tissues are more prevalent and more genetically homogeneous than tumor cells.

Table 21: Prognosticators for prostate cancer recurrence after prostatectomy.

(A) Genes predicted to be down regulated in prostate tumor cells or up regulated in prostate stroma cells in patients in which prostate cancer will recur after prostatectomy.

(Al) Genes predicted to have expression changes greater than 2-fold in the current datasets.

201042 _at 203932 _at 211573 _x_at

201169_s_at 203973 _s_at 211635 ^' _x_at

201170_s_at 204070_at 211637 ' _x_at

201288_at 204135_at 211644_x_at

201465 _s_at 204670_x_at 211650 _x_at

201531_at 206332_s_at 211798_x_at

201566_x_at 206360_s_at 213541_s_at

201720 _s_at 206392 _s_at 214669 _x_at

201721 _s_at 208966 _x_at 214768 _x_at

202269_x_at 209138_x_at 214777 _at

202531_at 209457_at 214836_x_at

202627_s_at 209823_x_at 214916_x_at

202628_s_at 210915_x_at 215121_x_at

202643_s_at 211003_x_at 215193_x_at

203290_at 211430_s_at

(A2) Genes predicted to have expression changes less than 2-fold in the current datasets.

179 _at 203028 _s_at 204438 _at

200748_s_at 203052_at 204446_s_at

200795_at 203269_at 204561_x_at

201367_s_at 203416_at 204789_at

201496_x_at 203591_s_at 204790_at

201539_s_at 203640_at 204820_s_at

201540_at 203748_x_at 204890_s_at

201645_at 203758_at 204940_at

201650_at 203760_s_at 205375_at

202205_at 203851_at 205459_s_at

202283_at 203923_s_at 205476_at

202574_s_at 204116_at 205508_at

202637_s_at 204192_at 205582_s_at

202748_at 204265 _s_at 206366 _x_at 207201 jsjxt 211633_x_at 216984_x_at

207334_sjjt 211639_x_at 217227 _x jjt

207629_sjJt 211649_x_at 217236 _x _at

208110 _x jit 211835_at 217239 _x jjt

208146 js_at 212016_s_at 217326 _x jit

208278 _s _at 212230jjt 217360 _x jit

208461_at 212613_at 217384 _x jit

208734_x_at 212860jjt 217478 _s jit

208889_s_at 212938_at 217691 _x jit

209182_s_at 213095_x_at 217883 _at

209320_at 213176 _s jit 218047_at

209346_s_at 213193_x_at 218087 ^' _s _at

209402 _s_at 213293_s_at 218232_at

209447_at 213422_s_at 218301_at

209685 _s_at 213497_at 218368_s_at

209873_s_at 213556_at 218718_at

209880_s_at 213958_at 218965_s_at

210051_at 214040_s_at 219202_at

210166_at 214219_x_at 219256_s_at

210190_at 214252_s_at 219541_at

210225 _x jjt 214326_x_at 219677_at

210298_x_at 214450jjt 221237_s_at

210299_s_at 214551js_at 221293_s_at

210785 _s_at 214567_s_at 221667 jijjt

210845 _s jjt 215116_s_at 221882_s_at

210933_s_at 215388_s_at 222079_at

211230_s_at 216224_s_at 22210OjJt

211628 x at 216248 s at 222210 at

(B) Genes predicted to be up regulated in prostate tumor cells or down regulated in prostate stroma cells inpatients in which prostate cancer will recur after prostatectomy. (Bl) Genes predicted to have expression changes greater than 2 -fold in the current datasets. 201660jjt 213510_x_at 218518_at

201661jsjΛt 214109_at 218519_at

201824_at 215363_x_at 218930_s_at

203791 _at 217483 _at 219368 _at

205311 _at 217487 _x_at 219685 _at

205489 _at 217566 _s _at 220724 _at

205860 _x jit 217894 jjt 221802 _s_at

213331jsjJt 218224_at

(B2) Genes predicted to have expression changes less than 2-fold in the current datasets. 201782 js jjt 202322 ji jit 202592 _at

202053_sjιt 202337 jit 202596_at

202056_at 202352_s_at 202892_at

202070 s at 202538_s_at 202903 jit 202919_at 207769_s_at 218260_at

202959_at 208281_x_at 218291_at

203207_s_at 208839_s_at 218296_x_at

203359_s_at 208873_s_at 218333_at

203503_s_at 208942_s_at 218344_sjjt

203531_at 20911 l_at 218373 jxt

203538_at 209162_s_at 218403 jxt

203667_at 209274_s_at 218499 jxt

203814_s_at 209585_s_at 218510jcjit

203869_at 209662_at 21852IjSjJLt

204045_at 209817 _at 218532 js jxt

204159_at 210988_s_at 218583 jsjxt

204173 _at 212208_at 218633 _xjjA

204496_at 212530_at 218896 js jxt

204554_at 212652_s_at 218962 js jxt

205005_s_at 213026_at 219007 jit

205055_at 213031_s_at 219038 jxt

205107_s_at 21321 ^' / 'jxt 219174 jit

205160_at 213555_at 219206 ji jit

205161_s_at 213701_at 219451 jxt

205303_at 213794_s_at 219467 jit

205371_s_at 213893_x_at 219833 _s jxt

205565_s_at 214455_at 219997 _s jxt

205609_at 214527 _s_at 220094 _s jxt

205830_at 214811 _at 220606 _s jxt

205953_at 215412_x_at 221265 jsjit

205955_at 216105_x_at 221559_sjJt

206571_s_at 216308_x_at 221826jιt

206587_at 217645 _at 222011 jijxt

206920_s_at 217775_s_at 222081 jxt

206973_at 218009_s_at 47530 at

207071_s_at 218085_at

207628_s_at 218197_s_at

207747 s at 218230 at

(C) Genes predicted to be down regulated in benign prostatic hyperplasia in patients in which prostate cancer will recur after prostatectomy.

(Cl) Genes predicted to have expression changes greater than 2 -fold in the current datasets.

204282_s_at 207769 ja jxt

200924_s_at 204775_at 208141 jsjjt

201418 js jxt 206328 jit 210128js_at

202415 _s jxt 206866 jit 210678 _sjxt

203421 jxt 206894 jit 211512 _sjxt

203577 jit 206964_at 212389jxt

203590_at 207631_at 214311 jxt 214316_x_at 218372_at 220562_at

214819_at 218778_x_at 221141_x_at

216397_s_at 218965_s_at 222080_s_at

217264 _s_at 219082 _at

217660_at 220388_at

(C2) Genes predicted to have expression changes less than 2-fold in the current datasets.

200051_at 208906_at 218144_s_at

201640_x_at 209202_s_at 218744_s_at

202159_at 209927_s_at 219111 _s_at

203128_at 212127_at 219379_x_at

203162_s_at 212292_at 219986 _s_at

203321_s_at 212456_at 221418_s_at

206109_at 212931_at 221525 _at

207484_s_at 213057_at 221800_s_at

207896_s_at 214778_at 34260_at

208110_x_at 216199_s_at

208278_s_at 217468_at

(D) Genes predicted to be up regulated in benign prostatic hyperplasia in patients in which prostate cancer will recur after prostatectomy.

(Dl) Genes predicted to have expression changes greater than 2 -fold in the current datasets.

200795_at 209274_s_at

201304_at 209362_at

201435_s_at 209406_at

201554_x_at 210299_s_at

201617 _x_at 210986 _s_at

201745 _at 210987 _x_at

202118_s_at 211562_s_at

202437 _s_at 211749 _s_at

202538_s_at 212698_s_at

203065_s_at 213325_at

203224_at 214455_at

203640_at 216304_x_at

204045_at 218718_at

204438_at 218730_s_at

204725_s_at 218962_s_at

204940 _at 219410 _at

205105_at 219685_at

205549_at 219902_at

205609_at 222150_s_at

206434_at 222209_s_at

208800_at

208839_s_at

208884_s_at

208924_at (D2) Genes predicted to have expression changes less than 2-fold in the current datasets.

201133_s_at

201447_at

201448_at

201865_x_at

202056_at

202265_at

202442_at

202666_s_at

202918_s_at

202919_at

203225_s_at

203544_s_at

203562_at

204496_at

205140_at

205659_at

207483_s_at

208290_s_at

208767_s_at

208925_at

209821_at

209882_at

210371_s_at

211727 _s_at

211760_s_at

212112_s_at

212397_at

212408_at

212530_at

212607_at

212652_s_at

213102_at

213168_at

213374_x_at

213988_s_at

214686_at

215171_s_at

216115_at

217900 _at

218209_s_at

218583_s_at

218729_at

218989_x_at

219230 at 219292_at 221553 at

Example 6 - Development of Predictive Biomarkers of Prostate Cancer

Datasets used in this Study

The two datasets used for this study include 1) 148 Affymetrix Ul 33 A arrays from 91 patients we acquired (publicly available in the GEO database as accession no. GSE8218, not otherwise published, also referred to as "our data") which is the principal data set utilized in previous studies; 2) Illumina (of Illumina Inc., San Diego) beads arrays data from 103 patients as analyzed on 115 arrays, a published data set (Bibikova et al., supra);

The two datasets samples have various amount of different tissue and cell types, including tumor cells, stroma cells (a collective term for fibroblasts, myofibroblasts, smooth muscle, and small amounts of nerve and vascular elements), BPH (epithelial cells of benign prostate hypertrophy) and dilated cystic glands (AKA "atrophic" cystic glands), as estimated by four pathologists (Stuart et al., supra) for dataset 1 and one pathologist for dataset 2.

Determination of cell specific gene expression in prostate cancer

Linear models (Model 1-3, below) were applied to microarray data from prostate tissues with various amounts of different cell types as estimated by a team of four pathologists. We identified genes specifically expressed in different cell types (tumor, stroma, BPH and dilated cystic glands) of prostate tissue following our published methods (Stuart et al. 2003). Model 1-3:

Cell composition can also be considered as two different cell types; one specific cell type versus all the other cell types, grouped together. i tumor H non-tumor non-tumor Λ i \H stroma stroma H non— stroma non— stroma Λ ^i — VP BPH ^' * BPH ~"~ Pnon-BPH ^' * non-BPH Ii

The correlation (between probe hybridization intensity and tissue percentages) parameters, such as intercept, slope, probability, standard error, was developed for all the genes on the array from model 1 , 2 and 3 using dataset 1 and dataset 2. A new method for the determination of cell type composition prediction using gene expression profiles

Using linear models 1-3, the approximate percents of cell types in samples hybridized to the array may be estimated using only the microarray data based on a sub-list of genes on the array. For example, each gene employed in Model 1 provides an estimate of percent tumor cell composition. We used the median of the predictions based on multiple genes for each tissue type. In our case, only a very limited number of the best tissue-specific genes (5-41 genes) were used for the prediction. Even fewer genes might be sufficient.

In order to validate the method of tumor or stroma percent composition determination, we utilized the known percent composition figures of data set 1 to predict the tumor cell and stroma cell compositions for data set 2 with known cell composition. For example, the number of genes used for cell type (tumor epithelial cells, stroma cells or BPH epithelial cells) prediction between dataset 1 and dataset 2 ranges from 5 to 41 non-redundant genes, which are listed in Table 20 herein. The Pearson correlation coefficient between predicted cell type percentage (tumor epithelial cells, stroma cells or BPH epithelial cells) and pathologist estimated percentage ranges from 0.45-0.87.

Since dataset 1 and dataset 2 data were based on different array platforms, the cross- platform normalization were applied using median rank scores (MRS) method (Warnat et al., supra).

The method of deducing cell type percentage from array data of whole prostate tissue as illustrated here is claimed as novel. Figures 8A, 4B and 4C illustrate the use of the parameters of data set 1 to predict the cell composition of data set 2. The Pearson correlation coefficients for the correlation of the observed and calculated cell type compositions is 0.74, 0.70 and 0.45 respectively. The converse calculations of utilizing the parameters of data set 2 to calculate the tumor and stroma cell percent compositions of data set 1 are shown in Figure 8D, 4E and 4F respectively, The Pearson Correlation Coefficients are 0.87, 0.78 and 0.57 respectively. The range of Pearson coefficients among four pathologist for composition estimates of the same samples in dataset 1 are 0.92, 0.77 and 0.73 for tumor, stroma and BPH cells respectively (Stuart et al. supra). Thus, the in silico estimates have a correlation that is almost completely subsumed in variation among pathologist, indicating that the in silico estimates are at least similar in performance to a pathologist and leaving open the possibility that the in silico estimates are more accurate than the pathologists.

Example 7 - Evaluation of Predictive Signatures of Prostate Cancer Dietary factors have long been considered major factors influencing the development and progression of prostate cancer and Dr. Gordon Saxe of UCSD has published small scale clinical trials showing that diet and life style alterations have a significant impact on the progression of relapsed prostate cancer (Nguyen, Major et al. 2006); (Saxe, Major et al. 2006)). The UCI SPECS study has accepted a "piggy back" project funded by a subcontract from UCSD (G. Saxe, PI) for carrying out a computerized survey of dietary habits of all patients recruited into the SPECS trial at UCI and UCSD. The questionnaire is self administered by providing a laptop computer to postoperative patients and is directly transmitted to Viocare (world wide web at viocare.com), the developers for the questionnaire, where the results are evaluated and provided with comparative statistics for study use. Blood samples are obtained and assessed for carotenoid carotenoids, vitamin D, and other dietary markers (as a validation of reported habits), as well as sex steroid hormones, IG-I, IGFBP-3, and cytokines. Body mass and BMI is measured by standard anthropometry and dexascanning will be introduced shortly to enable more precise evaluation of body composition. The information will be used to independently model diet/nutrition - disease outcome associations and also correlated with our gene expression results to examine diet-gene interactions.

Bioinformatics Identification and Technical Validation of expression biomarkers using Independent test sets of prostate cancer cases. This is focused on the technical and experimental validation of candidate genes that have been identified as differentially expressed in relapsed (aggressive) and non-relapsed (indolent, good prognosis) prostate cancer. Efforts utilized standard approaches such as recursive partitioning (Koziol 2008)PAM, and VSM to identify potential biomarkers. These efforts showed that genes could be defined that preferentially identified cases that relapse early, within two years of prostatectomy, but were not general. This may be due to the heterogeneity of expression in prostate cancer and the need to identify different signatures for different subclasses of prostate cancer, i.e. the development of a true classifier drawn from the appropriate signatures. Efforts have led to significant progress toward this goal. Two factors are particularly significant. First we have made extensive use of multiple linear regression (MLR) analysis first developed by us for analysis of expression of prostate cancer during the predecessor "Director's Challenge" project (Stuart 2004). Second, we have utilized our data set of 147 Ul 33 arrays together with five additional independent data sets of expression data (Table 22). The data sets of Table 22 are a unique resource for validation. The extended MLR approach provides for determining cell-type specific gene expression for four cell types in non-relapsed prostate cancer cases and for the determination of significant changes in expression for the four cell types for relapsed cases, i.e. significantly differentially expressed genes by cell-type in high risk cases. This model is summarized in equation 1:

Q - β< p , β< p , β< p , β< p , wi r tumor, i tumor r stroma, i stroma r BPH ,ι BPH r alleys gland ,ι dilcys gland

(eqn. 1) ri( ^v / tumor, i P tumor 4- V / stroma, i P stroma 4- V t BPH ,ι P BPH 4- V / alleys gland ,ι P odilcys gland ^ ' where G ₁ is the observed Affymetrix total Gene expression, the β are the cell-type specific expression coefficients, the P's are the percent of each cell type of the samples applied to the arrays, and the γ's are the differentially expressed component of gene expression for the relapsed cases. When rs=0, no relapse cases are included and the equation is that for gene expression by nonrelapse cases only. The percentages, P, may be determined by examination of H and E slides of the tissue used for RNA preparation by a team of four experienced pathologists. Only two of the six data sets (our cases and those of the Illumina data set, Table 22) have had P's determined by pathologists. Therefore it was first necessary to estimate the percent cell type distribution in all cases of the other four data sets. This was done by using profiles of 40-80 genes for each cell type identified as described (Stuart 2004) that do not vary whether a case is relapse or nonrelapse and are independent of Gleason etc. This method was validated by predicting the percent tumor and stroma cell content of the cases of the Illumina data set which confirmed that the method was accurate (Wang 2007; Wang 2008).

We then applied equation one to our data to identify genes with significant (p < 0.01) differential expression in relapsed cases. To validate these genes the process was repeated with each of the five data sets. For each data set we considered a gene as validated if (1) the γ again exhibited/? < 0.01, (2) were represented by identical Affymetrix probe sets or mapped probe set, and (3) exhibited the same direction change in differential expression. For the tumor cells and stroma cell probe sets, the magnitude of differential expression (the γ) of the two data sets are highly correlated (r _pea rson > 0.7). Approximately 1000 probe sets were identified that were validated in our data set and one other data set. The number of genes validated in this way is highly significantly greater than the number that may be expected to meet the validation criteria for two data sets by chance. These probe sets represent approximately 693 unique genes owing to a number of genes that were validated in two or more pairs of data sets. Numerous genes correspond to those previously reported by others as related to outcome in prostate cancer and these and many others are functionally related to processes thought important in the progression of prostate cancer. For example several members of the Wnt signal transduction pathway are apparent and are being examined using the TMA.

Discussion. The statistical and biochemical properties of many of these genes support the conclusion that an important signature of outcome for prostate cancer has been obtained. We believe that this is the first use of multiple independent data sets for the validation of signatures of outcome for prostate cancer. Not all validated genes exhibit significant differential expression on all data sets. This provides a picture of the diversity of expression of genes as they appear in independent data sets. Thus, it is possible to construct a true classifier that represents the diversity of all six data sets and this effort is underway. The recognition of diversity among published data sets by a consistent set of criteria provides an explanation for the difficulty of finding a signature based on analyses of one or two data sets.

Experimental validation. As originally proposed, archived prostate cancer cases of the predecessor "Director's Challenge" program that have not been examined by expression analysis are being measured using the Ul 33 plus 2 platform. These cases were recruited in the period 2000 - 2004. Approximately 25% of these cases have exhibited evidence of relapse. Thus, these cases provide additional valuable material for validating the predictive properties of the recently developed classifiers. The candidate biomarker genes and their ability to function in classifiers identified above will be tested by comparison of the categorization of these new cases with observed survival results. Approximately 300 fresh frozen prostate cancer cases with clinical follow-up have been characterized with respect to tumor content and approximately 80 have sufficient tumor content for analysis. The percent cell-type distribution has been determined by one pathologist and will be refined by use of the four pathologist analysis. Nearly all cases analyzed have yielded excellent RNA and to date 63 cases have been applied to U133 plus 2 arrays and 27 of these cases also have been applied to EXON arrays. Purified RNA and DNA have been banked from all of these cases and may be used, for example, for PCR validation. The analyzed cases were chosen to (2) maximize tumor content and (2) to be approximately equally divided among relapse and nonrelapse cases in order to maximize statistical power for the testing of differential expression. Owing to these criteria, only 15-20 additional cases from the set of 300 will be useful.

The goal of this set of studies is to identify SNP variations and to determine whether particular SNPs correlate with gene expression changes. The potential significance of this study is that SNP sequence maybe determined for any patient from somatic cells such a blood cells or buccal smears. Thus SNP changes that are found to correlate with predictive expression changes may provide to a much more versatile predictive assay. Moreover this information may provide an understanding of the basis of the of the differential expression changes in terms of the properties of location of the correlated SNP.

The platform that is being utilized by D. Duggan is the Illumina one million SNP array and technology. This is the largest coverage array available and provides for sampling of >1 million SNP sequences. The arrays focus on SNP sites near known genes. Over half of all sampled SNPs are within 10 Kb of a gene.

Twenty one nontumor samples from tumor-bearing prostates have been provided and have now been examined on the Illumina platform. These samples are taken from the same 300- case validation set being analyzed by U133 plus 2 and Exon arrays. Approximately equal numbers of know relapse and nonrelapse cases have been provided. All cases have been used to prepare both RNA and DNA. The RNA is archived while the DNA has been applied to the Illumina platform. All cases analyzed have yielded over 90% present calls indicating excellent DNA qc. The data from these first 42 samples will be used for an interim analysis. Owing to the open ended nature of correlating all differentially expressed genes with multiple SNPs, power of the analysis increases with sample numbers and the current plan is to utilize all samples provided to U133 plus 2 arrays to the SNP analysis included relapse and nonrelapse cases.

Tissue microarray development. The goal is to fabricate prostate cancer TMAs to (1) validate newly identified biomarkers, (2) to validate cell-type specific express on the protein level, and (3) to identify antibody reagents for prognostic assay development. To date 494 prostate cancer cases have been provided and 254 have been used for TMA fabrication (Table 23). The major criterion for the selection of cases is that >5 years of survival data be available (except for normal prostate controls) and most of the cases from UCI and LBVA (Long Beach Veterans Administration Medical Center, an associated hospital of the UCI SOM) have 10-19 years of survival data. The original clinical slides of all cases are examined by two pathologists (P. Carpenter and J. Wang-Rodriquez) who regrade Gleason scores and color-encircle zones for core punching. Cores are taken to represent tumor, BPH, tumor-adjacent stroma, far stroma, dilated cystic glands and, where applicable, PIN. TMA fabrication is carried out at the Burnham Institute for Medical Research (S. Krajewski and J. Reed), All chosen fields are represented by two cores. Thus typically each case is represented by 5 x 2 = 10 cores. To date 254 cases array contains -1000 cores. The four cell types are placed on separate slide arrays so that specialized studies of one cell type do not needlessly consume material. The 494 cases that have been collected for the TMA are entirely independent of all other cases of this study. For approximately two dozen "Director's Challenge" cases that have been used for U133 plus 2 expression analysis there is FFPE tissue which will be applied to the TMA as a means of directly comparing RNA expression and IHC results.

In addition to multiple cell types, several unique features are being developed. Normal prostate control tissue is being incorporated to represent the same cell types as for the cancer cases. These are provided by Sun Health Research Institute (T. Beach and J. Rodgers) based on their rapid autopsy program. These cases are carefully vetted by two pathologists (P. Carpenter and J. Wang-Rodriquez). In addition the time from death to freezing for all cases is recorded and averages 4.25 h for all 65 cases acquired so far but 3.9 h for the cases of the last year. As a further assessment of quality, RNA has been assessed using the Agilent Bioanalyzer for 38 cases (Y. Wang and H. Yao) which indicates intact RNA in 80% of cases and degraded RNA in 10% of cases. Thus, these normal prostates promise to provide an extensive and approximately age- appropriate control panel. A small number of cases contain prostate cancer and may provide an opportunity to determine protein expression differences between clinical and occult disease.

Another unique feature of the TMAs is the collaborative development of quantization being carried out between the BIMR and Aperio Biotechnologies of San Marcos, CA. This system provides very high resolution line scanning which is stored on a devoted server at BIMR. Specialized software allows retrieval of high power images of any field for remote viewing by participating pathologists via a secure web-based portal (Scancope). Thus finished TMAs are being examined by two pathologists to determine that selected cores indeed represent the Gleason pattern and cell type intended. Moreover, the software provides a database for the survival data associated with each case. Algorithms have been developed by Allen Olson and colleagues of Aperio for the separation of two colors of TMAs labeled with two antibodies developed with different chromagens. In this method a standard antibody that identifies tumor such a AMACR is used for IHC in parallel with a test antibody (second color). Only pixels of the test antibody labeling that colocalizes with AMACR are then selected for correlation with survival data. An example of two color separation using our TMA was published recently (Krajewska, Olson et al. 2007). Quantification is in advanced stages of development.

Numerous antibodies have been screened for use on FFPE sections and 36 have been optimized, applied to one or more of the TMA slides, and digitized as summarized in Table 24. Several antibodies with known behavior in prostate cancer (anti-PSMA, AMACR, E-Cadherin, beta-Catenin, etc.) have been chosen to characterize the arrays while others (anti-Frzd7. SFRPl, PAP, ANX2, etc.) correspond to predicative biomarkers of this study. A number of apoptosis related biomarkers have be identified and the use of BCL-B as a biomarker in prostate and other epithelial tumors has been published recently (Krajewska 2008; Krajewska 2008b).

It is planned to (1) emphasize visual and electronic scoring of the IHC-labeled TMA, (2) validate electronic scoring and (3) evaluate the relationship of antibody labeling and outcome parameters using the Cox-proportional hazard analysis of Kaplan-Meier plots. A second priority will be to continue to expand the TMA to the full 594 case array.

Prognostic test of predicative gene profiles. The goal is to recruit new prostate cancer cases and utilize fresh surgical specimens and biopsies to assess outcome using the current predictive gene profile and to prospectively compare the predicted outcome to observed outcome during year five and as a follow-on long term project. Cases for this study are being recruited in four centers: NWU, UCI, UCSD (SDVA and Thornton Hospitals), and SKCC (Kaiser Permanent Hospital, San Diego). In addition, plans are underway to add the UCI-associated hospital in Long Beach, LBVA. The total number of cases recruited over the past year and from the inception of the study is summarized in Table 25 and associated Demographic, Grading, and Staging data is summarized in Tables 26 and 27. Nearly 1500 cases have been recruited by informed consent to date, over 1300 frozen tissues obtained of which approximately 520 contain tumor. The original goal is to validate selected biomarkers by PCR. Should array costs continue to decrease it may be possible to carryout complete pangenomic expression analysis. By present RNA requirements, conservatively 260 samples would support this effort. Many of these cases have provided blood and post-DRE urine specimens (Table 25) as a further basis for the determination of biomarker expression in more accessible fluids. Shadow charts with baseline data and follow-up data are being developed for all cases.

Diet SPECS study. Patients being recruited for the prostate cancer prospective are being consented to participate in the "piggy back" SPECS diet survey study. To date 27 cases have been consented of which 21 have had blood drawn and provided to the NIH-sponsored General Clinical Research Centers of USCD and UCI (Table 28). In addition 8 patients have completed the computerized questionnaire (Table 28). It is the planned to extend the UCI study to include a second clinic of Dr. D. Ornstein at UCI in addition to the present clinic of A. Ahlering and to continue to enroll all future patients that will be recruited for the prospective study at UCI and UCSD over the coming year. A longer range goal of this study is to utilize the present observational study as a proof of principle that sample acquisition and data base resources are available for the development of a potential phase II trial in which relapsed patients may be offered participation in a randomized intervention trial to test the efficacy of diet and life style change to modify the subsequent course of disease. This initiative will require the development of a new proposal for follow-on funding to the SPECS study.

References

Bibikova, M., E. Chudin, et al. (2007). "Expression signatures that correlated with Gleason score and relapse in prostate cancer." Genomics 89(6): 666-72.

Koziol, J., Jia, Zhenyu, and Mercola, Dan (2008). "The Wisdom of the Commons: Ensemble Tree Classifiers for Prostate Cancer Prognosis." B iof informatics (in revision).

Krajewska, M., Jane N. Winter, Daina Variakojis, Alan Lichtenstein, Dayong Zhai, Michael

Cuddy, Xianshu Huang, Frederic Luciano, Cheryl H. Baker, Hoguen Kim, Eunah Shin, Susan Kennedy, Allen H. Olson, Andrzej Badzio, Jacek Jassem, Ivo Meinhold-Heerlein, Michael J. Duffy, Aaron D. Schimmer, Ming Tsao, Ewan Brown, Dan Mercola, Stan Krajewski, John C. Reed. (2008). " BcI-B expression in human epithelial and non- epithelial malignancies." Proceedings of the 99th Annual Meeting of the American

Association for Cancer Research; 2008 Apr 12-16; San Diego, CA. (abstract no. 2180. ). Krajewska, M., A. H. Olson, et al. (2007). "Claudin-1 immunohistochemistry for distinguishing malignant from benign epithelial lesions of prostate." Prostate 67(9): 907-10. Krajewska, M., Shinichi Kitada, Jane N. Winter, Daina Variakojis, Alan Lichtenstein, Dayong

Zhai, Michael Cuddy, Xianshu Huang, Frederic Luciano, Cheryl H. Baker, Hoguen

Kim6, Eunah Shin, Susan Kennedy, Allen H. Olson, Andrzej Badzio, Jacek Jassem, Ivo

Meinhold-Heerlein, Michael J. Duffy, Aaron D. Schimmer, Ming Tsao3, Ewan Brown,

Anne Sawyers, Michael Andreeff, Dan Mercola, Stan Krajewski and John C. (2008b).

Reed. BcI-B Expression in Human Epithelial and Nonepithelial Malignancies Clinical

Cancer Research 14, 14: 3011-3021. LaTulippe, E., J. Satagopan, et al. (2002). "Comprehensive gene expression analysis of prostate cancer reveals distinct transcriptional programs associated with metastatic disease. "

Cancer Res 62(15): 4499-506. Nguyen, J. Y., J. M. Major, et al. (2006). "Adoption of a plant-based diet by patients with recurrent prostate cancer." Integr Cancer Ther 5(3): 214-23. Saxe, G. A., J. M. Major, et al. (2006). "Potential attenuation of disease progression in recurrent prostate cancer with plant-based diet and stress reduction." Integr Cancer Ther 5(3): 206-

13. Singh, D., P. G. Febbo, et al. (2002). "Gene expression correlates of clinical prostate cancer behavior." Cancer Cell 1(2): 203-9. Stephenson, A. J., A. Smith, et al. (2005). "Integration of gene expression profiling and clinical variables to predict prostate carcinoma recurrence after radical prostatectomy." Cancer

104(2): 290-8. Stuart, R. O., W. Wachsman, et al. (2004). "In silico dissection of cell-type-associated patterns of gene expression in prostate cancer." Proc Natl Acad Sci U S A 101(2): 615-20. Wang, Y., Zhenyu Jia, Michael McClelland, and Dan Mercola. (2008). "In silico estimates of tissue percentage improve cross-validation of potential relapse biomarkers in prostate cancer and adjacent stroma. ." Proceedings of the 99th Annual Meeting of the American

Association for Cancer Research; 2008 Apr 12-16; San Diego, CA. (abstract no. 999.). Wang, Y. K., James; Goodison, Steve; JainJua, Yu, Mercola, Dan, McClelland, Michael.

(2007). "Toward the development of a predicative signature of prostate cancer."

Proceedings of the American Association of Cancer Research, Annual Meeting 2007. Yu, Y. P., D. Landsittel, et al. (2004). "Gene expression alterations in prostate cancer predicting tumor aggression and preceding development of malignancy." J Clin Oncol 22(14): 2790-

9.

The goal of these studies remains the development of a multigene profile that identifies at the time of diagnosis, prostate cancer patients with poor prognosis and good prognosis. Biomarkers have been identified that are validated in at least one independent data set of six data sets available. Moreover the biomarkers represent the diversity of expression among independent data sets. Thus, a true classifier may be formed for the prognosis of prostate cancer.

Current biomarker information is be utilized to develop a test based on the use of FFPE patient tissue, a widely available resource, that may provide improved guidance for prostate cancer patients.

A 254-case TMA is being used to validate selected biomarkers at the protein expression level. The TMA is composed of cases that are independent of the cases utilized to define the biomarkers. Antibodies that perform well may be useful reagents for the development of an IHC- based assay for determining outcome using FFPE prostatectomy tissue or using preoperative biopsy tissue.

Pangenomic expression data has been collected on 60 cases archived from the "Director's Challenge" program and 25 of these cases have also been profiled on the Illumina million SNP chip. This analysis will continue and when suitable numbers are available, SNP alterations that correlate with expression changes will be determined in order that blood cells may provide a means to determine susceptibility to expression of genes associated with behavior to define SNPs with predictive properties. SNPs can be assessed from any tissue, buccal smears or prostate cancer. Patients that are reliably recognized as belonging to either of these groups will be provided with increased knowledge of the likely outcome of their disease and, therefore, may opt for a wider and more appropriate spectrum of treatment. Patients are being recruited for prospective testing. In addition, certain dietary features are being determined by questionnaire and blood analysis. Patient of this cohort that relapse but do not seek immediate hormonal or radiation therapy may be offered a diet-life style intervention trial. In particular, the over use of radical prostatectomy may be reduced at considerably decreased morbidity, anguish, and expense.

A variety of efforts have been initiated to translate the results into practical tests. High throughput gene expression analysis will allow us to use all 1000 probe sets that we have determined have predictive value to assess risk and compare the assessment to the clinical indicators of risk such as preop PSA, Gleason, and stage and well as outcome over the next few years. Strong indications of predictive value will indicate that biopsy samples should routinely be made available in the fresh state for RNA analysis and provide preoperative information about patients at high risk of disease that may not be cured by surgery and may provide guidance of who would profit from adjuvant therapy. Finally, patients that relapse following surgery commonly have slowly rising PSA values (low PSA doubling time) and many specialists do not immediately recommend hormone or radiation treatment. Such cases may be offered a diet regimen. Our current "piggy back" observational diet study may set the frame work for evaluating the role of diet. In addition the gene signature of such patients will be known and correlations may be carried out to assess whether there is a signature predictive of response. Similarly, by correlating the response to treatment with the known gene expression results, other signatures predictive of response-to-therapy may be determined. These possibilities require that our prospective cohort be examined by expression analysis which requires a large number of arrays not provided for in the original proposal. Thus, work with the prospective cohort will require additional funding for continuation of the translation of the SPECS studies and planning needs to focus on this issue.

Table 22. Data Sets Utilized for Identification and Validation of Biomarkers of Relapse of Prostate Cancer Following Prostatectomy

Time to

Dat Relaps Non- Relapse preO a Array Targets e Relapse data P- Gleaso TNM

Sets platform ^d (total) (total) available? PSA n stage Ref. i a,b U133A2 22,283 85 57 yes yes yes yes yes 1 partial

(only for relapse

T Illumina 511 25 84 samples) no yes yes no 2

3 ^C U133A 22,283 37 42 no yes yes yes no 3

4 U95Av2 12,626 8 13 no no no no no 4

U95Av2,B

5 ,C 37,891 23 25 yes yes yes yes no 5

6 U95Av2 12,626 9 14 no yes yes yes no 6 a Contains data on tissue percentages.

These data sets contain information on follow-up time. Relapse i was defined as PSA reaches detectable level after prostatectomy within the first four years . All non-relapse cases were cases followed-up over two years and showed no sign of relapse. c These data sets contain information on follow-up time. Relapse > vas defined as three consecutive PSA increases >0.1ng/ml within the first four years. All non-relapse cases were cases followed-up over two years and showed no sign of relapse.

Number of target transcripts represented on the array.

Ref. 1, (Stuart, Wachsman et al. 2004) Ref. 2, (Bibikova, Chudin et al. 2007) Ref. 3, (Stephenson, Smith et al. 2005) Ref. 4, (Singh, Febbo et al. 2002) Ref. 5, (Yu, Landsittel et al. 2004) Ref. 6, (LaTulippe, Satagopan et al. 2002)

Table 23: UCI SPECS Tissue Microarray (TMA) Development Status

Table 24. Antibodies applied to the SPECS TMA

Table 25. Summary of samples collected for prospective study during the current funding period and since the inception of the study.

Interval Summary of Consented SPECS Patients since 7-1-07 Characteristic SKC NWU UCSD/VAMC- UCI

C SD

(KPH

Consented Cases 45 335 295 85

BPH 9 47

Prostate Cancer 339 100

Tissues Obtained (frozen) 40 267 147

Samples with Tumor 45% 34(13%) 53 (62%)

Samples without Tumor 55% unknown 32 (48%)

Sample Review Pending 238 0

Mean Sample Tumor % 16%

Banked Plasma 40 78 215 55

Banked Urine 40 78 238 (94 postDRE) 39

Consented SPECS Patients since inception of the study (9/30/05)

SKC NWU ¹ UCSD/VAMC-SD UCI

(KPH

;

Consented (TOTAL 1489) 59 711 404 304

Mean Age 60.5 62.4 64(41-85) 62

BPH 0 10 81

Mean PSA (ng/ml) unknown 2.8(<0.15-30.8) 6.66 overall av

Prostate Cancer 59 274 175 213

Mean PSA (ng/ml) 5.6±3.6 7.53(0.22-77.8) 6.66 overall av

Tissues Obtained (frozen) 59 572 210 420

Samples with Tumor 127 30% 213(51%)

Samples without Tumor Unknown 30% 145 (49%)

Sample Review Pending 466 40% 0

Mean Sample Tumor % 12.2% 53%

Banked Plasma 59 176 317 209

Banked Urine 59 174 339(94postDRE) 174 (postDRE)

Number/percent NED since surg 75%

Number/percent chemical 3% 0 relapse (PSA > 0.2 ng/ml)

Number/percent neg postop 74% 150

PSA

Number/percent pos postop PSA 8% 3

Number pending PSA 18% Table 26. Ethnicity of Consented Cases for Prospective Analysis

Table 27. Gleason Score Distribution and Stage Distribution for Consented Cases for Prospective Analysis Table 28. Summary of cases consented for the observational diet SPECS study

The challenge of developing predictive signatures for the outcome of newly diagnosed prostate cancer based on expression analysis and genetic changes of tumor and non- tumor cells

Linear regression analysis was used to determine the average gene expression profile of four cell types, including tumor and stroma cells, in a set of 88 prostatectomy samples (1). By combining these cases with 55 additional cases with Affymetrix U133A gene expression data, we were able to select 63 cases in which disease relapsed over a period of three or more years following prostatectomy. Linear regression analysis of the non-relapse and relapse sets revealed changes in hundreds of gene expression values, including genes primarily expressed in stroma cells that were associated with the relapse status. These genes were used to generate classifiers using two other independent Affymetrix expression datasets generated from enriched prostate tumors. One dataset of 79 samples (37 relapse, Affymetrix Ul 33 A array; training-set) was used as the training set (2), and one dataset of 48 samples (23 relapse, Affymetrix U95Av2/U95B/U95C array was used as the test-set (3). Probe sets across platforms were mapped using the Affymetrix array comparison spreadsheet and normalized using quantile discretization (4). Classifier genes were determined by use of recursive partitioning (RP) in which a handful of genes are used sequentially for classification (5), as well as Prediction Analysis of Microarrays (PAM)(6), in which case outcomes were predicted via a nearest shrunken centroid method from gene expression data (1) . RP classification trees using up to five genes, and sometimes including preoperative PSA, routinely classified each independent dataset into three survival groups, non- relapse, early relapse, and late relapse with/? < 0.005 . Classifiers generated by PAM using tumor specific genes predicted by linear regression as input was as good (accuracy, sensitivity, specificity) as the best classifiers using all of the expression data, indicating an enrichment for relevant genes by the linear regression method (SVM was dropped from here since it did not perform better than PAM). However classifier performance decreased with increased disease- free survival of the cases. A 59-gene classifier determined by PAM using all cases of the training set with times-to-relapse of < 2 years yielded a specificity of 75.9% and a sensitivity of 88.0% with an overall accuracy of 73.4% when tested with the second independent data set for cases of the same time period. All three performance values decreased continuously upon inclusion of longer time periods to < 4 y. No reliable PAM classifiers could be generated for late relapse cases. RP consistently yielded a major group of nonrelapse cases and two classes of relapse cases, one of which consists of very early relapse cases with disease-free survival of < 2 years. The distinction of late relapse cases from nonrelapse cases using PAM remains a challenge and may reflect the similarity of gene expression profiles of nonrelapse cases from those destined to relapse relatively late after diagnosis. Prediction of early relapse at the time of diagnosis may be a realistic 6 go"- al.

1. Stuart, R., et al. PNAS 2004;201:615-20; 2. Stephenson et al. Cancer. 2005;104:290-8. 3. Yu Y., et al. J. Clin. Oncol. 2004;22:1790. 4. Warnat, P., et al. BMC Bioinformatics. 2005;6:265. 5. Koziol, J., et al. Cancer Res. 2003;9:5120-6. 6. Tibshirani, R. et al. PNAS 2002;99:6567-72.

A New Bi-Model Approach for the Development of a Classifier for Predicting Outcomes of Prostate Cancer Patients

Prostate cancer is the most common malignancy of males. However, the majority of cases are "indolent" and may not threaten lives. In order to improve disease management, reliable molecular indicators are needed to distinguish the indolent cancer from the cancer that will progress. Statistical methods, such as hierarchical clustering, PAM and SVM, have been widely used for classifier development for various cancers. However, those methods can not be immediately applied to prostate cancer research because the tissue samples collected from patients are very heterogeneous in cell composition. The observed expression level of any gene for a given sample is not solely for tumor cells; rather, it is the sum of contributions from all types of cells within that sample. In current study, we propose a novel method where the expression level of any gene is illustrated with a linear model considering the contributions from different types of cells and their interactions with aggression phases (relapse or non-relapse). ANOVA is used to identify cell specific relapse associated genes that possess discriminative power. The expression patterns of those selected genes may be described using two Gaussian models on the basis of disease phases; thus they can be used for predicting outcomes of newly diagnosed. The new method is compared to other conventional methods based on simulated data. A predictive classifier is created by training a real dataset generated for prostate cancer research. The performance of the new classifier is compared to the nomogram and other clinical parameters with predictive value.

In silico estimates of tissue percentage improve cross-validation of potential relapse biomarkers in prostate cancer and adjacent stroma.

Differences in RNA levels that correlated with relapse versus non-relapse were calculated for two public expression microarray data sets using two models. One model did not take into account tumor and stroma tissue percentages in each sample, and the other used these percentages in a linear model. The latter model led to a highly significant increase in the number of candidate relapse-associated biomarkers cross-validated between both data sets. Many of these relapse-associated changes in transcript levels occurred in adjacent stroma. Estimates of tissue percentages based on expression data applied between data sets correlated almost as well as multiple pathologists correlated with each other within a data set. This in silico model to predict tissue percentage was applied to a third public data set, for which no tissue percentages exist. Cross-validation of relapse-associated genes between data sets was again highly significantly improved using the linear model, and included changes in stroma. The third data set was heavily skewed towards a previously unrecognized higher tumor percentage in relapse versus non- relapse cases, a bias that is taken into account by the linear model. In summary, the use of tissue percentages determined by a pathologist or inferred from in silico data increased the power to detect concordant changes associated with a clinical parameter in separate data sets, and assigned these changes to different tissue compartments. The strategy should be applicable for biomarkers other than RNA and for samples from any type of disease that contains measurable mixed tissues.

Improved identification of RNA prognostic biomarkers for prostate cancer using in silico tissue percentage estimates

Although many studies of detecting RNA-based prognosticators for prostate cancer have been performed, they have limited agreement with each other. One contributing factor may be the variations in the proportion of tissue components in prostate tissue samples, which leads to considerable noise and even misleading results in mining microarrays data.

We assembled six microarray data sets for RNA expression in prostate cancer samples with associated relapse information, including two large data sets of our own. Our two datasets, and one other, included estimates of tissue percentages made by pathologists. These data sets were used to identify genes that were then used to build a simple linear model for tissue percentage prediction. Estimates of tissue percentages based on expression data applied between data sets correlated almost as well as multiple pathologists correlated with each other within a data set.

Using a multiple linear regression (MLR) model which integrates tissue component percentages, we identified a list of tumor- and reactive stroma-associated prognostic RNA biomarkers in all six data sets. The level of each RNA is expressed as a linear model of contributions from the different cell types and their interactions with relapse status g = b _o + + RS x T^ γ p + e, where g is expression intensity, C is the number of cell

types, RS is relapse status indicator, e is random error, and I? 'sand ^'s are regression coefficients. ANOVA is used to identify cell specific genes that are differentially expressed between relapsed and non-relapsed cases, i.e., the genes with significant γ's . Markers were then cross-validated between the six different microarray data sets. There were 185 genes that occurred in more than one data set, and 152 of 185 (82.2%) showed the same direction of change in differential expression between relapse and non-relapse patient samples (p<10 ^~ ). Most of these prognostic markers were not previously identified by other studies and some were potentially differentially expressed in stroma. In summary, the use of tissue percentages determined by a pathologist or inferred from in silico data increased the power to detect differential expressed genes associated with a clinical parameter and assigned these changes to different tissue compartments. The strategy should be applicable for biomarkers other than RNA and for samples from any type of disease that contains measurable mixed tissues.

A Bi-Model Classifier that Allows RNA Expression in Mixed Tissues to Be Used in Prostate Cancer Prognosis

Introduction: Reliable molecular indicators are needed to distinguish indolent prostate cancer from cancer that will progress. Statistical methods, such as hierarchical clustering, PAM and SVM, have been widely used to develop classifiers of prognostic molecular markers that estimate risk. However, one barrier to the efficient use of classifiers in prostate cancer is the variable mixture of different cell types in most clinical samples. The observed level of any marker for a given sample is due to the sum of contributions from all types of cells within the tumor. Elsewhere [1], we propose a novel classification method in which the expression level of any gene is expressed as a linear model of contributions from the different cell types and their interactions with relapse status. While this method provides biomarkers with greater confidence by deconvoluting the effect of tissue percentages in each sample, the problem of how to construct a classifier for mixed populations remains.

Methods: We propose that the expression patterns of prognostic RNAs may be described using either of two Gaussian models, one for relapsed cases and the other one for non-relapsed cases, both of which include calculation with cell constitute information. A likelihood-ratio statistic ( LR ) can be developed by contrasting the probability of being risk free to the probability of undergoing relapse based on fitting expression values of selected biomarkers and the cell composition data of each sample to these two differential models. A patient is diagnosed as having high risk of relapse if LR ≥ Jc ₁ , or is diagnosed as being of low risk if LR ≤ Jc ₂ , where

Jc ₁ and Jc ₂ are pre-selected cutoffs with Jc ₁ > 1 > Jc ₂ .

Results: In a simulation study, the new method outperformed the conventional classification methods PAM and SVM. A prognostic classifier was then created by training an expression dataset generated from Affymetrix U133P2 arrays from prostatectomies with known tissue compostion, which yielded a 50 gene classifier with an accuracy of 94% following cross validation. When the predictive classifier was applied to an independent "test" data set based on

35 Affymetrix U133A arrays, an accuracy of 80% was achieved

Conclusion: This novel classifier may be useful for assessing risk of relapse at the time of diagnosis in clinical samples with variable amounts of cancer tissue.

Reference: [1] Wang, Y., et al., Proc. 100 ^th Annual meeting of the AACR. [abstract].

The prostate tumor microenvironment exhibits numerous differentially expressed genes useful for diagnosis

Introduction: There are over one million prostate biopsies performed in the U.S. annually. Pathology examination misses the tumor entirely in a few percent of cases. In an additional 10- 20% of cases the biopsies are not definitive due to atypical foci, PIN, or other caveats, often leading to a "repeat biopsy" in 6-12 months. We observed that the microenvironment of prostate tumor cells exhibits numerous differential gene expression changes compared to remote stroma tissue of the same cases. Such changes could be useful to form a classifier for the diagnosis of prostate cancer when tumor is present in very low amounts or is barely missed by a biopsy. Methods: A training set of 105 prostate cancer cases was created with known cell type composition for the three major cell types of tumor tissue (tumor epithelial cells, epithelial cells of BPH and stroma cells) as assessed by four pathologists. RNA expression was measured on U133plus2 GeneChips. A linear model defined the total signal as the sum of expression values of the three cell types each weighted by its percent composition figure for a given case: Gi = βtumor Ptumor +βstroma Pstroma ÷βBPHPBPH where Gi is the fluorescence intensity for a gene of a case, Pi are the percents of the indicated cell type and βi are cell-specific expression coefficients (signal/percent cell type). The model was applied separately to tumor-bearing tissues and tumor-free remote stroma tissues. Differential gene expression was derived by subtraction of the values for the two series. Results: The -200 most significant differences were used as input to PAM. Tenfold cross- validation dichotomized the training set into tumor-bearing and remote stroma tissues, yielding a classifier of 36 genes that had a 94% accuracy. This classifier was then tested using an independent set of 82 cases, as well as 13 control normal prostate stroma tissues. The classifier had an accuracy of 83% on the test set. Correct classification was also achieved for five of six biopsies from normal males and all seven cases from the rapid autopsy. Several genes such as myosin VI, collagen IX, and destrin, known to be highly expressed in mesenchymal derivatives, are preferentially expressed in tumor-adjacent stroma.

Conclusions: The differential gene expression changes observed here most likely represent differences in expression between tumor-adjacent stroma and remote stroma. These differences may be due to paracrine or "field effect" mechanisms involving interaction with the tumor adjacent to the affected stroma. The reaction of stroma to nearby prostate cancer is well-known but, as observed here, involves many more gene changes than previously recognized. These changes can be exploited to develop a classifier that accurately categorizes tumor-bearing tissues, remote tissues of the same cases and normal tissues. Such a classifier could enhance diagnosis from false negative and equivocal biopsy results.

Table 29. 125 Genes generated by one of the two methods for identifying reactive stroma genes

201021 _s_ at destrin (actin depolymerizing factor) DSTN

91826_ at EPS8-like 1 EPS8L1

216338 _s_ at Yipl domain family, member 3 YIPF3

201189 _s_ at inositol 1,4,5-triphosphate receptor, type 3 ITPR3

219259 _at sema domain, immunoglobulin domain (Ig), SEMA4A transmembrane domain (TM) and short cytoplasmic domain, (semaphorin) 4 A

Table 30. 36 Genes generated by one of the two methods for identifying reactive stroma genes

Example 8 - Quantitative Tissue Imaging For Clinical Diagnosis and Prognosis of Prostate Cancer

SPECIFIC AIMS

Projects that use antibodies for clinical diagnosis or prognosis must take into account the huge biological differences that occur between patients and between clinical samples. One way to minimize the clinical variation is to use a panel of diagnostic or prognostic antibodies, each of which are known to capture relevant information in a subset of patients or a subset of clinical samples. However, there are also technical challenges that cause difference in staining within and between samples. One way to minimize the impact of technical variation would be to multiplex diagnostic and prognostic markers together with "reference" antibodies that that identify within tissues particular cell type rather than outcomes. These reference antibodies, under the same technical influences and in the same tissue section, can then be used to identify the signals observed for the diagnostic and prognostic antibodies of the relevant cell types which can then be quantified far more accurately than would be possible using separate hybridizations. In the case of prostate cancer, where diagnostic and prognostic antibodies are likely to be relevant in a highly variable and often rare fraction of the cancer cells or adjacent stroma cells in a patient or clinical sample, and where changes from normal tissue may often be subtle rather than "all-or-nothing", it is likely that only the inclusion of reference antibodies in the same visualization will make it possible to identify the distinct clinically relevant regions with any confidence.

Fortunately, the technology that would be able to perform multiplex antibody staining of individual samples exists with the use of fluorescent dyes. The overall goal over this two phase project is to develop an automated quantitative image-based assay of the expression level of a panel of 5-10 diagnostic and 5-10 prognostic antibody biomarkers in Prostate cancer. Quantification of each antibody biomarker will be carried for specific cell types by utilizing co- localization of each test antibody biomarker of the panel with a reference antibody that is known to specifically identify total epithelium or tumor epithelial cells or tumor-adjacent stroma cells.

In Phase 1 of this project we will focus on the identification and characterization of the reference antibodies that reliably identify total epithelium or tumor epithelium or tumor adjacent stroma in both formalin-fixed and paraffin-embedded (FFPE) and frozen tissue sections. It is likely that a set of reference markers that distinguish different types of epithelial/tumor and fibroblast/smooth muscle stroma, could be useful for automated screening of samples for diagnosis. Phase II will then build on this reference set with additional markers of diagnostic and prognostic use.

In phase I, whole frozen and FFPE sections as well as prostate cancer tissue microarrays (TMAs) will be used to survey candidate reference antibodies and the reproducibility, variability, and accuracy of labeling will be determined for all cases of the TMA as well as by comparison to standard cell lines and normal prostate tissue specimens. This aim is non-trivial as antibodies can have optima for immunohistochemistry that differ markedly from each other. Optimizing a multiplex application may require examining may different types of antibody for each marker as well as a variety of conditions in order to uncover a standard conditions and a standard set of antibodies. Reproducibility, variability, and accuracy of the intensity data will be carefully assessed using positive and negative controls, TMA statistics, and repeated hybridizations on different days for adjacent slices of tissue, including the TMAs. Data storage consistent with the DICOM standard will take place by porting our data to a freeware database and visualization system (ConQuest) . The quantitative properties of the multiplex antibody system will be generated automatically using the proprietary scanning microcytometer developed by VaIa Sciences Inc. using multiple fluorphores and validated by comparison to direct visual assessment of the binding location and intensity of representative candidate antibody biomarkers. Each section used for quantitative immunofluorescence (IF) will then be used to prepare DAB (bisdiazobenzidene) chromagen labeled version with hematoxyl counter stain and provided to a panel of four pathologists for estimation of labeling intensity and percent positively labeled epithelial cells or tumor epithelial cells or tumor-adjacent stroma cells. Visual scores for DAB and for fluorescence labeled sections will by quantitative compared to the automated output of the VaIa system, using a linear model of the relationship between automated intensity and visual intensity. There is no strict necessity for an antibody to map exactly to a tissue type as assessed by a pathologist, but the scorings should be consistently different for any particular sample, in order to be confident that the antibody is measuring something slightly different, consistently. Zones of authentic tumor and stroma will be defined and the coincidence with colocalized pixels or cells will be quantitatively evaluated.

Workflow will be streamlined and then an SOP created to allow automatic image analysis to be completed with 4-5 days.

B. Background and Significance

Overview

Despite advances in our understanding of cancer and the development of new therapeutics, cancer remains the number two killer in the US with mortality rates of many cancers remaining relatively unchanged for decades. Prostate cancer is the most common cancer and second leading cause of cancer-related death among males of Western countries [1-3]. While PSA screening has been a valuable marker increasing early detection of prostate cancer, PSA testing currently suffers from several limitations including lack of specificity and inability to accurately predict disease progression [1, 2, 4-8]. There is a critical unmet need to identify reliable novel biomarkers to assist in early detection of prostate cancer, and, most critically, to determine risk of prostate cancer rercurrence following initial therapy such as prostatectomy. Currently the major treatment modality for newly diagnosed prostate cancer remains radical prostatectomy. Radical prostatectomy provides an excellent outcome for organ-confined disease. However, 15%-20% or more of all surgical patients ultimately experience rercurrence indicating the presence of residual disease, local invasion and/or metastatic deposits at the time of surgery [7- H]. Traditional clinical parameters including tumor staging, Gleason score, and PSA levels, stage or their combinations based on preoperative values have not adequately predicted the patient risk of rercurrence [11, 12]. It is now recognized that prostate cancer exhibits hundreds of altered gene expression changes many of which may represent genes that directly influence outcome [13-19]. However a recent consensus statement by a panel of prostate SPORE leaders (the Inter-SPORE Prostate Biomarkers Study and NBN Pilot group) has tersely summarized that few or none have proven reliable enough to advance to clinical use (http://prostatenbnpilot.nci.nih.gov/abou tpilot_ipbs.asp).

We are developing a new test using novel methods that identify cell-specific biomarkers that can be applied at the time of diagnosis to determine whether the tumor has the potential to recur after surgery. The development of a clinical test capable of distinguishing indolent and aggressive forms of the disease at the time of diagnosis will provide crucial guidance. First, this information will provide guidance as to who needs treatment thereby providing the option of avoiding surgery and the associated morbidity for those patients with a high risk of recurrence. Second, this information will also provide guidance as to who may profit from postsurgery or immediate adjuvant therapy thereby utilizing a period of many months or years during which recurrence otherwise could develop unopposed. Moreover, integration of gene expression signatures with clinical data has recently been shown to improve the accuracy of predicting progression, and metastasis [13, 14, 20]. One purpose of this proposal is the translation of a prostate cancer gene expression classifier into an antibody panel capable of rapid and reliable prediction of disease recurrence using (a) generally available clinical material such as biopsy specimens or, (b) as a guide to adjuvant therapy and patient counseling using post prostatectomy surgical pathology blocks. A crucial advantage of protein markers over RNA markers is that the protein markers provide spatial resolution of cell types and can detect cell-type-localized co- expression of markers, information that is lost in bulk RNA samples.

Moreover there remain critical challenges to diagnosis by biopsy. Over one million prostate biopsies are carried out per year in the U.S. . Most are negative. Approximately 20% of these negative biopsies are judged insufficient for a definitive diagnosis owing to small foci or read as "atypical glands" only seen or other ambiguities, i.e. -100,000 such cases per year. The microenvironment of these sites contains potential information for diagnosis. We have observed that the tumor adjacent stroma of prostate cancer exhibits hundreds of altered mRNA expression changes and have derived a gene list that accurately identifies tumor adjacent stroma tissue. Thus, antibodies of selected gene products may be potentially useful to assist in diagnosis of traditionally nondiagnositic biopsies.

Importance of identifying diagnostic and prognostic prostate biomarkers.

To date, only a limited number of diagnostic biomarkers that are differentially regulated in prostate carcinoma have been identified such as prostate-specific antigen [2, 5, 6, 23-25], prostate specific membrane antigen [26, 27], and human glandular kallikrein 2 [10, 28-32], and PC A3. While these antigens have been useful in the development of early diagnostics and for the directed delivery of therapeutics to prostate cancer in preclinical models [33, 34] these markers do not address the need to identify biomarkers that characterize early or advanced stages of prostate carcinogenesis and metastasis. Recent studies have identified circulating urokinase-like plasminogen activator receptor forms that may be used alone or in combination with other prostate cancer biomarkers (hK2,PSA) to predict the presence of prostate cancer [35]. Other potential prognostic markers include early prostate cancer antigen (EPCA), AMACR, human kallikrein 11, macrophage inhibitory cytokine 1 (MIC-I), PCA3, and prostate cancer specific autoantibodies [5, 36-42].

The search for novel prostate cancer biomarkers has turned to the use of global genomic and proteomic profiling to facilitate the discovery of multiple markers with both diagnostic and prognostic significance [5, 18, 36-42]. Gene-expression profiling comparing gene expression from normal prostate tissue, BPH tissue, and prostate cancer tissue has identified many potential genes that are differentially regulated in prostate cancer [14, 15]. These include hepsin, a serine protease, alpha-methylacyl-CoA racemase (AMACR), macrophage inhibitory cytokine (MIC-I), and insulin-like growth factor binding protein 3 (IGFBP3) [40], TGFβl, IL-6, and many others. Validation of these markers at the protein level from patient tissue or serum samples and clinical validation of these markers as true diagnostic and prognostic tools are necessary. While some of these candidates have appeared in meta analyses (e.g., Rhodes, 2002), as noted, the recent consensus statement of the InterSPORE study has noted that none have proven sufficiently reliable for clinical use and none have been used to form a panel that predicts outcome of multiple independent case sets.

Current clinical parameters including Gleason score, PSA, and tumor staging have been inadequate in predicting patient outcome . Combinations of clinical criteria have been assembled into predictive nomograms in attempts to improve diagnosis of indolent vs. advanced disease [11, 12]. While these studies suggest improved diagnostic and prognostic capabilities, those based solely on preoperative clinical values perform less well and they await widespread clinical validation. One major challenge has been that the majority of prostate cancers share similar histological features (Gleason score) or clinical markers (PSA) but exhibit widely different clinical outcomes . Recently multigene profiles of biomarkers that are predictive of the outcome of prostate cancer at the time of diagnosis have been developed [14, 20, 44-46]. Singh identified a 5 -gene classifier capable of predicting prostate cancer recurrence better than clinical parameters of preop PSA or tumor stage [46]. Stephenson identified a set of 10 genes highly correlative with prostate cancer recurrence. An analysis combining clinical variables with the 10- gene classifier greatly improved prediction of clinical outcome [20] . Henshall identified >200 genes that correlate with prostate cancer recurrence better than preoperative PSA [14]. From these studies it is clear that molecular correlates have the potential to provide a considerable increase in information related to outcome than current clinical parameters. In addition to prediction of outcome, it is likely that several of these unique biomarkers are functional and therefore provide intervention opportunities. The proper identification of the molecular determinants predictive of prostate cancer rercurrence, their validation at the protein level, and the translation of the data into a robust clinical test is the challenge addressed in our current proposal. We have developed improvements in both the identification and validation of candidate genes that will enable a rapid and robust transition to a clinical test.

Improved gene lists

We have developed new methods that have helped in the development of gene signatures for the diagnosis and for prognosis based on expression values of tissue obtained at about the time of the original diagnosis. First, as described herein, we have used a linear combination model together with knowledge of cell composition as determined by a panel of four pathologist to determine gene expression by cell type [18]. These studies revealed cohorts of genes that are differentially expressed by tumor epithelium compared to epithelium of PBH or dilated cystic glands or stroma [18]. This observation has important practical considerations. While most global genome studies have looked at differences between normal and cancerous prostate epithelial cells, considering the contribution of stromal cells as "contamination", we have found that stroma exhibit dozens of significantly differential gene expression changes between tumor-adjacent stroma and stroma remote from tumor sites [18] and dozens of differential expression changes between tumor- adjacent stroma of recurrent PCa cases compared to nonrecurrent cases [43]; [44]. We have identified two separate subsets of genes. The first consists of tumor epithelium specific and stroma cells specific genes that are differentially expressed between recurrent PCa ("aggressive" cancer, relapsed PCa) and nonrecurrent PCa ("indolent" cancer, nonrelapsed PCa). Since nearly all PCa tissue specimens contain stroma or reactive stroma in the immediate microenvironment of tumor, the proper inclusion of antibodies sensitive to stromal change provides an important ingredient of a "classifier" for prognostic use. These expression changes may be used to predict outcome ([43] [44]).

Second, we have identified a separate subset of tumor-adjacent stroma specific genes. These genes are differentially expressed between tumor- adjacent stroma and remote stroma. These expression changes may be used to detect tumor- adjacent stroma at foci of "nondiagnostic" or "atypical" tumor in biopsies of equivocal cases thereby potentially converting "nondiagnostic" cases to a definitive determination. We propose to use these gene lists as the starting point for the development of panels of 5-10 antibodies for application to biopsy or postoperative FFPE tissue specimens that are routinely available for all patients with a confirmed or suspected diagnosis of prostate cancer. While RNA may be retrieved from these samples , the preservation of a particular set of transcripts with the crucial information in all cases and in proportion to the amounts in fresh tissue is problematic. In contrast, antibody based diagnosis from FFPE is well established . In Phase II we plan to utilize a high throughput scanning microscope to identify the best antibodies for inclusion in the panels. TMAs consisting of 254 prostate cancer cases, normal prostate tissue and defined cell lines will be used for the survey. The TMAs to be used here have been constructed to contain cores especially rich in tumor-adjacent stroma and remote stroma. These cores will allow us to evaluate whether the differential expression observed between relapsed and nonrelpased cases may be observed in adjacent nontumor tissue or even in remote nontumor tissue and to confirm that diagnosis based on tumor-adjacent stroma is reliable. Additional potential applications include the detection of tumor-adjacent stroma in "negative" biopsies that may have narrowly "missed" frank tumor. This possibility is of considerable significance given that most of the million biopsies performed each year are "negative".

Biomarker validation using tissue microarrays (TMAs).

The heterogeneous nature of DNA changes in prostate cancer makes it unlikely that a single biomarker will be adequate for proper determination of prostate cancer severity and risk of rercurrence. What is needed is the identification of a panel of biomarkers that can be shown to correlate with different aspects of disease progression and risk of rercurrence in the population of cancer patients. The screening of tissue by use of microarrays (TMAs) is ideal for identification of markers that statistically correlate with disease progression and outcome [45-48]. Screening of TMAs is a powerful tool for validation of the microarray results, for extension of the RNA expression results to protein expression and for the identification of antibodies of biomarkers that are widely expressed and readily available from samples routinely taken at time of diagnosis. TMAs are constructed using hundreds of different patient samples that span the entire range of clinical pathology and outcome. Furthermore, it requires only small amounts of tissue that can be collected at the time of diagnosis such as biopsy samples and is amendable to high throughput analysis using multiple antibody probes. TMAs may be made from selected archived cases with clinical annotation spanning many years detailing survival and other parameters, such as treatment history.

Numerous studies have used TMAs to identify or validate prostate cancer biomarkers associated with disease progression, response to therapy, rercurrence, and metastasis [45-48, 49, 50]. TMA analysis was used to validate a seven antibody panel derived from a 48 gene expression signature enabling more accurate classification between Gleason grade 3 and 4 tumors [47]. Multiple TMA studies have identified several markers indicative of prostate cancer progression including Amacr (alpha-methyl acyl racemase) AMACR, AR, Bcl-2, CDlO, ECAD, Ki67, and p53 [45]. TMA analysis has identified 13 genes associated with prostate cancer rercurrence. These include AKT, D-catenin, NFKB, Stat-3, hMSH2, Hepsin, PIMl, syndecan-1, Bcl-2, Ki 67, and ECAD [45]. Few have been formed into a coherent predictive panel and evaluated as a panel. Therefore, the performance of a panel compared to individual antibodies and the potential of combinations to overcome the diversity of prostate cancer is unknown. Nearly all studies ignore the stroma although smooth muscle alpha actin has been examined by Rowley and coworkers [51]. Others suffer the caveats noted by interSPORE group. Several, such as AMACR are utilized as an aid to diagnosis in surgical pathology but are not used routinely in risk assessment. We propose the systematic evaluation of over 50 predicted prognostic biomarkers (Phase I and Phase II) taken from a predictive panel of known performance at the RNA level.

High throughput analysis and quantification.

The current study will address several obstacles that have precluded the development of a rapid and reliable biomarker panel ready for clinical testing. While TMAs contain a wealth of potential data, the ability to properly identify and quantify the cell-specific staining patterns of antibodies currently relies on manual identification or pattern recognition programs that are both time consuming and subject to bias and error. Therefore we will utilize an automated digitizing scanning system developed by VaIa Sciences Inc. This system can rapidly record histological sections labeled with up to 10 distinct fluorophores with pixel level subcellular resolution including for TMAs and display each color separately. The system has been acquired by Beckman Coulter Instruments Inc. (Fullerton, CA)

(http ://w ww .beckm artcoulter.com/hr/pressroom/oc pressR eleases detai 1.asp? Key=4764&Date 1 =12/11/2003) and developed as the Beckman-Coulter IC 100 system. Our application requires only two colors. The reference antibody will be applied to locate all epithelial cells or the subset of epithelial tumor cells or stroma cells and a test antibody will be applied in with a second fluorophore and the pixels of colocalization of test antibody with bonafide epithelia or tumor or stroma will be determined as well as the pixels of not colocalized with target cells. The intensity of antibody labeling at target sites will then be integrated, normalized and compared to nonlocalized binding or to the known clinical outcome. Thus specificity, sensitivity, and accuracy may be determined by existing technology and software . As a gold standard, Phase I will establish the utility of the reference antibodies in comparison to the visual results of a panel of pathologists. Phase II Studies

• Development of clinical studies. Phase II will involve forming and validating the multiplex application of antibodies as prognositic panel and as a diagnositic panel in clinical trials. The diagnoistic and clinicaol performance of candidate antibodies will be determined. Teo pandel will be formed composed of antibodies with (1) maximum performance by the criteria of intensity, specificity, and sensitivity and (2) superior accuracy with subsets of cases not equally achieved by other antibodies.

• Acquisition and tests of monoclonal versions of panel members. All polyconal antibodies will be converted to monoclonal counterparts by commercial license from existin vendors or commission using sources that can provide GMP product. GMP manufacture of the predictive antibody will be initiated and a clinical protocol developed for recruitment and testing on prostate cancer patients in a CLIA setting.

• Expansion of biomarker discovery/validation platform; In Phase II we will continue to validate novel prostate cancer gene classifiers on an expanding set of TMAs. We will also examine whether circulating protein biomarkers have predictive value.

C. Preliminary data

Cl. Derivation of diagnositic and predictive genes signatures.

While the importance of the tumor microenvironment on tumor progression and metastasis has been well documented [19, 40, 49, 51-54], very few studies such as Tuxhorn et al. (2002) [51] and [55] have identified genetic markers of reactive stroma. We have utilized linear regression to define expression profiles of the four major cell types contained within prostate tissue samples including tumor cells, stromal cells, and two additional normal epithelial components [18]. In the linear model, the observed expression of any gene (the expression array result for that gene) in a complex piece of dissected prostate tissue used for RNA preparation and Affymetrix analysis is considered to be due to the sum of contributions from the principal cell types in the sample. Each contribution is in turn due to the proportion or percent of each cell type in the sample and the characteristic expression coefficient for the particular gene in a particular cell type: (egn. 1) G ^ ⁷ I = H β' tumor, i ^L P tumor + ^ Fβ' stroma, i ^L P stroma + ^T H β' BPH ,ι ^L P BPH + ^T H β' alleys gland ,ι ^L P alleys gland

where G ₁ is the observed Affymetrix total Gene expression, β' are the cell-type specific expression coefficients, and the P's are the percent of each cell type of the sample used for the array. The percentages, P, may be determined by examination of H and E slides of the tissue used for RNA preparation by a team of four experienced pathologists. The expression coefficients are determined by multiple linear regression (MLR) analysis. For grossly microdissected tissue enriched in tumor, there are four major cell types as expressed in eqn. 1. We showed that there is very high and statistically significant agreement both between and amongst the four pathologists for the determination of cell-type percentages [18]. In this initial study we sought to determine genes that were consistently expressed predominately by one cell type or another without regard to outcome, i.e. genes that were characteristic of cell type in prostate cancer specimens. We observed 3384 genes were statistically significantly expressed predominately by one cell type. For example, 1096 were consistently expressed by tumor epithelial cells while 496 genes were significantly associated with BPH epithelial cells. Cell type specific expression has been validated by comparison to the literature, by quantitative PCR of LCM samples, and by immunohistochemistry [18].

C.I.A. Diagnostic multigene signature. These initial studies indicate that numerous, perhaps hundreds, of genes may be differentially expressed in the microenviroment of tumor cells which may be useful in diagnosis in supplement to or even in the absence of data from the tumor cell component [18]. Three methods have employed to identify such genes. We adopted the model that it is mainly tumor- adjacent stroma that exhibits the most and largest differential expression changes between the microenviroment around tumor cells and normal or remote stroma. We also assumed that stroma remote from tumor sites of PCa-bearing prostate glands could be used to approximate the expression of normal stroma. We utilized publicly available expression data from 91 cases applied to 148 U133A Affymetrix GeneChips (GEO accession number GSE8218). These cases were the same as those previously studied on the U95av platform [18] plus additional cases. The percent cell composition determined exactly as described [18]. The goal is to find the genes that have altered expression levels between normal stroma cells and the stroma cells close to the tumor cells. We divided U133A samples into two subgroups: 91 tumor-bearing cases and 57 non-tumor-bearing portions of tissue from the same cases. These portions are largely remote stroma. We then applied eqn. 1 to each set thereby determining two β values for stroma: tumor-adjacent stroma and tumor-remote stroma. Note that neither recurrence status or any other clinical parameter such as the Gleason score indicating differences among the tumor bearing portions was considered. Thus only β characteristic of stroma were determined together with a least-squares estimate of error for each β value. Note also that β which are large relative to error must be uniformly or characteristic of tumor-adjacent stroma or remote stroma, i.e. independent of clinical values such as Gleason scores that might indicate differences in aggressiveness. Such β favor high T values in significance tests. The significant differences between the β values for tumor-adjacent stroma and remote stroma were determined. This method produced 208 genes. These significant genes are candidate genes as specifically differentially expressed in the tumor-adjacent microenvironment. In a second method eqn 1 was extended to include a cross-product:

G = β ' p + β < p + β < p + β < p

+ ^β (

Eqn 2

The cross-product term is used for modeling the interaction between tumor and stroma cells. The significant interaction can be treated as the altered expression trait of stroma caused by the adjacent tumor cells. Egn 2 was applied to the Ul 33 A plus data set thereby 1820 significant cross-product terms (-8% of the probe sets). Finally a third gene list was determined by application of Egn. 2 to and independent set of 91 cases measured on the pangenomic Affymetrix U133A plus2 GeneChips (unpublished data, D. Mercola). This third data set could be used as a test set for the genes determined using the Ul 33 A arrays however the differences in platform means that testing can not be applied without cross platform normalization, a process that introduces additional error. Therefore we applied eqn. 2 to the third data set ab initio and sought genes that met the same significance criterion yielding 4533 significant cross-product terms (also ~ 8% of probe sets). Finally we asked which of these genes were common with to all three determinations (the maximum intersect is 208 genes). This three-way intersect yielded 90 genes, i.e. 90 genes which appeared on all three calculations using the two different case sets. These genes may be used to diagnosis the presence of tumor-adjacent gene changes entirely from stroma tissue in the absence of tumor cells.

To test the consistency of these genes PAM (Prediction Analysis for Microarrays) was employed using all 90 genes as a classifier to distinguish tumor and nontumor tissues of the Ul 33 A and the U133 plus2 data sets. This method does not utilize information of percent cell type composition.

First, we extracted relevant expression values for these 90 genes from U133plus2 data as a training set. Then we used PAM to analyze these extracted expression data, with tumor/non- tumor as relevant classification variable. Via cross validation, PAM identified 21 genes out of 90 as the best predictor for classification variable. The classifier was tested on the Ul 33 A data which yielded a specificity of 100% and a sensitivity of 94.4% (accuracy > 94.4%).

Conclusions. The observations indicate that it is possible to diagnosis the presence of prostate cancer in a large proportion of cases solely from an analysis of the expression of tumor-adjacent tissue, i.e. in the absence of tumor cells. This has a very important potential application to the understanding of patient biopsy material. Moreover, by repeating the above analysis by applying egns. 1 and 2 only to U133A, (two list input in forming the intersect) the final analysis would be free of any input from the test set and stringently objective. We plan to the 21 gene set in this way and to use the resulting list as the starting point for the identification of antibodies suitable for formation of a diagnosis panel for Phase II.

C.l.B. Prognostic multigene signature. MLR may be extended to identify genes differentially expressed by a given cell type between indolent and aggressive tumor cases where "aggression" is defined by chemical recurrence. In the simplest application of this method, eqn. 1 is applied separately to each class of cases - indolent or aggressive cases - and significant differences in β for these two classes of cases for each cell type are determined. Using these methods for a series of 91 patients examined on 131 U133A GeneChips, we observed 1212 genes were significantly and differentially expressed by tumor cells (p < 0.05).

In order to validate these differential expression changes, the process was then repeated using the independent 86 cases assessed on the U133A plus2 platform. Again, no cross platform normalization is required. 1373 significantly differentially expressed (p < 0.05) genes were identified. "Validated" genes were then defined by four criteria: (i) two or more probe sets of each platform mapped to the same gene; (ii) where multiple probe sets for the same gene were present, all probe sets for the same gene met criteria (iii) and (iv); (iii) differential expression changes for each case set were significant with/? < 0.05, (iv) the differential expression of identified genes are in the same direction for each case set. We observed that 18 tumor cell specific genes and 19 stroma cell specific gene met these criteria. The chances that that 37 genes could appear to meet the significance criteria for both case sets and be of the same sign by chance is a vanishingly small/? < zx indicating supporting that the validated gene list is specific. Moreover, the magnitude of differential express of these genes for the two cases sets is positively and significantly correlated (Figure 9) further demonstrating the relatedness of the validated genes. None of the genes are the same as those determined for the diagnostic multigene signature.

Conclusions. These preliminary calculations indicate that it is readily possible to identify multigene signatures that exhibit reproducible differential expression changes that discriminate indolent for aggressive disease. These calculations account for the cell type heterogeneity that is an essential part of the structure of prostate cancer and leads to the heterogeneity of sample collections assessed by others. Therefore our approach may overcome a major problem plaguing the development of a reliable prognostic classifier. In addition we employed two independent data sets. As a result of accounting for percent cell type composition, we have observed separate gene signatures for tumor epithelial cells and for tumor-adjacent stroma cells. Thus, it may be possible to utilize tissue with sparse tumor content to enhance the prognostic value of the specimens. We plan to use the 38 identified genes as a starting point for the identification and screen of antibodies for our antibody panel in Phase II. This study with TMAs will further validate the prognostic properties of our signature. Numerous additional studies are in progress. We need to test our classifier on published independent data sets by calculation of operating characteristics. We plan to use PAM to further refine our gene list and assess the accuracy by as for the diagnostic profile. These and other refinements are in progress.

C.2. Fully Automated Fluorescence and Absorption Microscopy Analyses. The scanning microscopy and separate image representation from multiple color labeled slides to be used here has been developed by VaIa Sciences Inc. of San Diego by J. Price, President and CEO, and co- workers and has been utilized for a variety of publications (61 -84). This system, known as the Q3DM Eidaq™ 100 robotic microscopy instrument runs on the Beckman Coulter's CytoShop™ version 2.0. This instrument includes a Nikon (Melville, NY) Eclipse microscope with an automated stage interfaced to a fluorescence light source and filter wheel of up to 10 narrow band base optical filters in the range 413 nm - 663 nm. Numerous supporting software packages has been developed. The system is supported by a variety of antibody-based kits prepared by VaIa. Each product contains staining reagents that are targeted towards particular proteins of interest along with a software program (Thora™) that can be used on virtually any computer system. The original instrumentation was developed by a predecessor company, Q3DM Inc. by J. Price focused on the development of high throughput microscopy instrumentation oriented primarily toward automated fluorescence image cytometry (61-84). This instrumentation was designed with accurate image segmentation (81. 83, 84), fluorescent excitation arc lamp stabilization (68, 82), and autofocus for producing fluorescence imaging (69). This system was sold to Beckman Coulter and developed as the Beckman-Coulter IC 100. The current instrumentation is a further generation scanning microcytomer and includes a slide holder hotel for automated scanning of 100 prepared slides.

Two modes, immunofluorescence (IF) with fluorophore-labeled antibodies and mmunohistochemistry using absorption chromophores will be employed in the present study. For both methods spectral separation of multiple labeled sections is achieved by capturing multiple images using multiple fixed band pass filters. Up to ten fixed band pass filters are automatically rotated into the optical path of the light either in front of the light source or in front of the camera. Therefore up to 10 images per section are recorded on a monochrome CCD camera creating a "spectral stack". Spectral unmixing from the data of the spectral stack is sensitive to errors in registration of images of the spectral stack to chromatic aberration. Multiple precautions have been included in the software correct for effects .

For IF the narrow emission of fluorophores of different colors are resolved directly by the appropriate filter of the spectral stack and the corresponding image may be used for pixel-level analysis (for examples see Progozhina et al 2007).

For IHC the broad absorption bands of typical chromophores such as DAB (bisdiazobenzidene), hematoxyln, and others require analysis of multiple images of the spectral stack as previously developed (3). Briefly, spectral unmixing of the observed intensity is based on a model expressed in matrix notation as a linear combination of chormophores where each chromophore contribution is the product of amount of binding and fluorescence intensity or absorption in a given wavelength range. Emission and absorption spectra for all chromaphores to be used here are known and the desired unknown are relative amounts of each chromaphore contributing to a given pixel intensity. These are determined by the method of Non-negative Matrix Factorization (NMF) ϊUb C! -ύ. unpublished !. Effective multicolor separation of tissue images usually requires knowledge of the individual chromaphores interacting with the tissue. Based on NMF, the VaIa system is the first system capable of performing this color decomposition in a fully automated manner without reference to individual chromaphore-tissue absorption or fluorescence spectra. Instrumentation and software implementing these methods have been developed, characterized and validated on TMAs using objective standards and expert visual scoring and the results are described in reference (Kaiήnoyhoh oi al. impish bailed, Rabinovich et al. 2006).

Supportive additional features of imaging technology and software include: (i) the ability to regroup broken core images which are common in TMA fabrication. None of the currently available software other than that of VaIa has addressed this to our knowledge. This problem solved this problem by using the K-means clustering algorithm (53, 54), which provides an automatic method for grouping objects (e.g., pixels) based on distance. Details can be found in the VaIa TMA software "framework" article (Rabinovich et al. 2006). (ii) Online viewing, computerized entry of TMA Scoring and Storage is implemented. The tissue microarray core images are organized by software for viewing, interactive entry of expression scores and storing of the data in an organized format. The user can click on any of these thumbnails to view an enlarged image of the entire core and/or a full magnification subfield of the image of the core. Data can then be entered by selecting the data entry pop-up window. The storage format for the images is standard TIF or BMP. Further details can be found in reference (Rabinovich et al. 2006). (iii) Fully Automated Densitometry IF- or IHC-labeled TMAs using Unsupervised Multispectral Unmixing has been developed and implemented (Rabinovich et al. 2006). Figure 11 summarizes major steps in data acquisition and analysis.

We propose to utilize reference antibodies in one color to identify particular cell types and double label the same section in a second color to localize a candidate or test antibody binding. The amount of test antibody binding to target cells such as tumor cells will be determined by colocalization: determination of the pixels of test antibody binding at the site (pixels) of reference antibody labeling. The integrated pixel values of non-colocalized test antibody also will be determined as a measure of lack of specificity.

Two separate uses of colocalization are planned. For routine high throughput screening of candidate antibodies (Phase II), IF will be used as IF has is more sensitive, enjoys greater dynamic range and more amenable to the application of multiple proven antibodies to patient material. For characterization of reference antibodies (Phase I) by comparison to the gold standard of visual score by an expert panel of pathologist, IHC will used in order to provide slides that can be directly assessed by pathologists and compared to the results of colocalization by spectral deconvolution.

C.3. Accuracy of Spectral Unmixing of IHC labeled TMAs: comparison to single labeling and to visual scoring. Cell type specific labeling of candidate biomarkers in an automated fashion proposed here relies on colocalization of candidate antibodies with the cell of interest as identified by a reference antibody using a second color. The resolution of separate fluorphore labeling patterns from multiple labeled tissue section may be obtained directly from images of multiple narrow band base filters. However absorption/transmission based images of IHC are more challenging and require spectral separation using nonmatrix factorization (NMF). We have evaluated this approach by using double labeled TMAs by the following procedure. Using a set of 97 cores, we first applied the DAB stain and captured 437 multispectral image stacks <9), an average of 4.5 fields of view per core. We then added the hematoxylin stain and acquired a second image stack. The second stack served as the input to our algorithm and the resulting decomposition, which estimated the DAB staining, was compared with the first stack, which serves as the ground truth. We then experimentally evaluated the use of NMF for the color decomposition problem. While reconstruction error represents a quantitative measure, it does not provide a standard for judging how accurately the estimated components represent the dye concentrations. We quantified the performance by comparing the ground truth single-stained image to the corresponding automatically extracted component of the doubly-stained tissue sample as proposed by Rabinovich et al. (Rubsno^ Uc. h α ah unpublished).

Using this procedure the average decomposition error over all samples was 6.73% with standard deviation of 1.81%. This therefore provides one objective assessment of the accuracy of spectral devolution in comparison to the single chromophore labeled section.

With the accuracy of densitometry via multispectral unmixing established, we asked how this quantitative measurement compares with the subjective scoring of a human expert. A panel of four trained pathologists (M. Krajewska, S. Krajewski, D. Mercola, A. Shabaik) evaluated the 97 tissue biopsies for the expression of antibody protein (DAB). The scoring was performed according to pathology conventions and each tissue section was graded on a scale from 0.0 to 3.0 in increments of 0.5. For correlation of the visual and analytical results, we analyzed the performance of a linear model y=mx +c, where x is the score reported by NMF decomposition, y is the pathologist's score, m is the slope and c is the _y-intercept. Linear regression was used to fit the model. The fitting error for regression may be an indication of the prediction error of the model. However, depending on the complexity of the model and the amount of data available, the regression error can be significantly different from the true prediction error of the model. Thus, an effort was made to estimate the prediction error and report it instead of the fitting error. The simplest and most widely used method for reporting prediction error when the data is scarce is cross-validation (86). Ten-fold cross validation resulted in a mean squared error of 0.02 with a standard deviation of 0.01. This is equivalent to a root mean squared (RMS) error of 0.163, which also translates to an average of 5.4% error on the pathologist's scale. A major result of the validation study is that the 5.4% error is considerably larger than the corresponding signal: noise ratio of the camera detector. Thus the validation makes available a greatly increased dynamic range of electronic signal detection of the camera-based microscope over the visual system with a "noise" value of ~ 3 x 5.4% = 16.2% vs. < 1% for the camera. The increased dynamic range for quantified antibody binding overcome a major limitation of antibody labeling using visual or IHC methods and greatly increases the ability to identify antibodies that correlate with survival data and other important clinical co variants. This advantage is extended many times for fluorescence -based antibody labeling.

Another decomposition of the form A = BC that is widely used is Independent Component Analysis (ICA) (Uyvarinen, J., Karhurten, and F. Oj a. Independent Component Analysis, John Wiley & Sons, 2001). ICA is based on the assumption that the matrix A is the result of the superposition of a number of stochastically independent processes. This is a more reasonable description of the staining process where each stain can be assumed to be independent of the other stain. Classically, however, ICA algorithms do not enforce non-negativity and that makes them unsuited for stain recovery as well. We experimentally evaluated the use of NMF and ICA for the color decomposition problem. While reconstruction error represents a simple quantitative measure, it does not provide a standard for judging how accurately the estimated components represent the dye concentrations. We quantify the performance by comparing the ground truth single-stained DAB image to the corresponding automatically extracted component of the doubly-stained DAB/hematoxyln tissue sample. Quantitatively, the overall for four images sets was 50% larger for ICA compared to NMF (the images are available at hppt://vision.ucsd.edu/). Both NMF and ICA provide good results however there is an observable increase in fidelity to ground truth for the NMF analysis. We propose to utilize NMF for the studies proposed here.

Conclusions. 1. These studies provide support for the ability to successfully decompose multicolor labeled TMAs to component images. The application proposed here is simpler as separate 2D images are unnecessary. We plan to extract a subset of pixel intensities, those of chromaphore A that are co-localized with the pixels of chromaphore B where chromaphore A predominately binds to cells of interest such as tumor or epithelial cells or stroma cells. We have not completed this task however only minor modifications to existing software, pixel integration, is required and is proposed as a milestone of Phase I. The data of co-localized chromaphore B, the test chromaphore, would then be analyzed by Cox-regression and ANOVA analysis with covariates of disease progression currently available for the cases of the PCa TMA. 2, The automated ability to scan TMAs and extract quantified data will greatly facilitate antibody screening.

C. 4. Multicolor IF separation at the subcellualar level. The design goal of the VaIa scanning robotic microscope is subcellular segmentation using pixel level resolution. It is important to note, therefore, that this capability exceeds the needs of cellular resolution required here which is well within current level of the instrumentation development. This was insured by the successful development of an automated membrane algorithm of the Thora package (Prigozhina 2007). For example mouse skin tumors were labeled with three fluorophores, two to identify proteins of interest, the membrane binding E-cadherin and the epithelial localizing antibody anti-K-14, and a cell localizing label for nuclei, DAPI. In this context, K14 is a putative marker for tumorigenic epidermal cells that invade the deeper skin layers. Cells exhibiting K14 signal (high red channel fluorescence) were clustered within the tumor loci. Areas of the section that stained brightly for K14 stained relatively dimly for cadherins, whereas surrounding tissue stained poorly for K14 and brightly for cadherins. To quantify K14 and cadherins, Thora separated the three primary cellular compartments (membrane, nucleus, and cytosol) from the dualcolor image of pan- cadherin and nuclear fluorescence. Thora estimated the cell boundaries in both the normal cells bordering the tumor where the cadherin signal was strong and in the tumor where it was relatively weak. To measure cadherin reduction in K14-positive cells, TMIs (total membrane intensity by pixel integration by boundary recognition) in the cadherin channel were collated for K14 cells with ACI (average cytoplasmic intensity) of 30 (the ACI range was 0 ACI 255 for the 8-bit images). By visual inspection and comparison of the intensity measurements of different cellular regions, ACI values below 30 arose from background staining that was not cell-specific. The mean pan-cadherin TMI for K14-positive cells was just 34% of that for K14- negative cells, and this difference was highly significant (P < 0.01). Thus, the K14-positive cells representing invading tumor exhibited quantifiably reduced cadherin expression relative to the surrounding cells. Other examples and details of the development have been described in detail (Prigo/ina 2007).

For the applications proposed in this SBIR project membrane boundary recognition is less crucial as it is only necessary to identify zones of tumor epithelial cells and zones of nonepithelial stroma and those subareas of test antibody labeling that colocalize with either tumor or, for nonspecific labeling nontumor labeling. It is of course important to recognize that colocalized tumor labeling may only be increased on average compared to non tumor labeling and, like cadherin, this may be readily quantified.

C. 5. TMA construction.

The Prostate cancer TMAs to be used here have been fabricated as part of the NIH-supported UCI SPECS (Strategic Partners for the Evaluation of Cancer Signatures) consortium at the Burnham Institute of Medical Research, a consortium member of the UCI SPECS program and are available here as an NIH resource of NIH-sponsored projects. The TMAs have been specifically fabricated to validate the cell-specificity of candidate biomarkers of prostate cancer. 272 cases with known clinical outcome have been included to date. FFPE blocks and clinical follow-up were retrieved from two participating institutes of the SPECS consortium according to an IRB-approved and HIPPA-compliant protocol and consist of cases provided by SKCC (60 cancer cases, 12 normal cases) with the rest of the cases drawn from UCI that have 10-19 years of clinical follow-up with clinical characteristics as previously described in T. Ahlering and co workers [75]. All cases have been re-examined by two clinical pathologists who confirmed the Gleason score and defined areas of tumor, BPH, stroma adjacent to tumor, stroma away from tumor, and epithelium of dilated cystic glands and PIN cores. In order to validate cell-specific binding properties of candidate biomarker antibodies, each case on the TMAs is represented by 4-5 cores from 4-5 zones of pure cell types as defined by two pathologists. Duplicate cores from the chosen zones were used for array fabrication so that all zones are represented in duplicate. Thus these TMAs are unusual in that they have 4-5 x 2 cores per case on the array. The TMAs are under continuous construction with the next phase to include 100 additional UCI cases so that the arrays available for the proposed study will exceed the present 272 case set. The prototype array at the 66 case stage have been utilized for the evaluation of several potential antibody by markers including Claudin I and BcI-B (Krajewska et al. 2007; Krajewska el al.

2008).

C. 6. Colocalization. The studies of Krajewska et al. (Krajewska 2007;Krajewska 2008) utilized double antibody labeling of the same TMA section using anti-Claudin I and anti-cytokeratin in the double chromagen mode. For colocalization the two color were separated using a segmentation program developed by Aperio Technologies and represented individually and provide clear indication of the epithelial binding pattern of anti-Claudin-I. Pixel count and quantification of colorcalization as well as nonlocalized binding is readily possible although non specific binding for anti-Claudin-I is neglible in this example. The method is less easily generalized to three or more colors or to IF as yet and therefore is less versatile than the Thora system of VaIa preferred for this application however it provides further illustration of our early experience in the methods proposed here.

Conclusions. Candidate gene expression levels for diagnosis and prognosis have been derived. Methods for the high throughput and quantitative assessment of labeling by corresponding antibodies are available. The wedding of this methods promises to provide the means of developing reference and assessment antibodies for new ICON-compliant clinical assays which sovle significant unmet needs.

Phase I. Here we focus on attaining milestones that support the goal of demonstrating that reference antibodies and methods are available for the reliable and quantitative identification of cells of interest for use in Phase II, the systematic assessment of candidate biomarker antibodies for the development of panels for the multiplex determination of diagnosis and prognosis

Milestone 1. Develop an automated optimized imaging assay and SOP for prostate stroma and epithelial/tumor cells using three or more antibodies for immunohistochemistry and immunofluorescence.

Unstained sections of formalin-fixed paraffin-embedded prostate tumors, unstained sections of our prostate cancer TMAs and frozen sections of frozen prostate carcinoma-bearing tissues will be utilized. FFPE blocks will be taken from the extensive collection used for construction of the TMAs. Frozen tissues are available from the UCI SPECS program. Antibodies for the labeling of all epithelial structures, just tumor epithelium, and the fibroblast/myofibroblasts component of stroma will be optimized separately for all three tissue preparations. Screening studies will be carried out using chromagen labeling by indirect IHC using DAB for ease of visual monitoring and optimization will be extended to indirect IF.

Panepithelial labeling. Panepithelial labeling will be used as a reference to define candidate antibody biomarker labeling that colocalizes with bonafide epithelium in prostate cancer sections and therefore to derive a ratio of epitheliahnonepithelial labeling as a measure of specificity. Panepithelial labeling will be optimized for two antibodies and the best one of these used for all subsequent studies. Anti-high molecular cytokeratin (anti-HMW keratin; Dako clone 34βE12 mouse monoclonal anticytokeratin) will be used at the starting conditions that we have previously employed for the prostate cancer TMAs (Krajewski 2007). The antibody labels squamous, ductal and complex epithelia containing cytokeratins 1, 5, 10, and 14 (68, 58, 56.5' and 50 kDa proteins).

A second anti-panepithelial antibody is AE3/AE4 (Dako AE3/AE4 MNFl 16 mouse monoclonal antihuman) which is in standard clinical use in the Pathology Department at UCI for the identification of epithelial components especially in the investigation of metastatic spread of carcinomas in distant tissues. The antibody labels multiple cytokeratins (65-67, 64, 59, 58, 56.5, 56, 54, 52, 50, 48 and 40 kDa cytokeratins) in either FFPE or frozen tissue.

Tumor epithelial cell labeling. Tumor epithelial cell labeling will be used as a reference to define the colocalization of labeling by candidate antibody biomarkers with bonafide tumor cells and therefore to derive the ratio tumor cell labling:non tumor cell labeling as a measure of specificity. Prostate cancer tumor epithelial cell labeling provides a more specific reference site for co-localization studies to be carried out in Phase II but is a challenging reference target owing to the limited number of antigens accepted as expressed in prostate cancer epithelial cells independent of the degree of differentiation or other histological properties such as Gleason score. We previously examined the expression pattern at the RNA level for a series of 55 tumors where expression could be resolved to the principal cells types (tumor epithelial cells, BPH epithelial cells, dilated cystic gland lining epithelium and stroma) which revealed that several classically expressed antigens such as PSMA (prostate specific membrane antigen), PAP (prostate acid phosphatase), and AMACR (α-methyl acyl CoA racemase) where significantly expressed at the RNA in nearly all tumor cells independent of grade and stage (Stuart ct al. 2004). In this study we validated the protein expression was specific in seven representative cases (Stuart et al. 2004) using IHC.

Anti-AMACR is now in widespread clinical use for the identification of metastatic prostate cancer and has been reviewed extensively (e.g. Rubin 2004). In an analysis of anti- AMACR labeling of a prostate cancer TMA of 70 cases including "foamy" cell carcinoma with low expression of AMACR , labeling was detected in 91% percent of cases (Rubin 2004). Specificity and sensitivity were examined by quantitative receiver operator characteristic which yields an AUC was 0.9 (p < 0.00001). These values are highly encouraging for the approach proposed here. It is not necessary to identify all prostate cancer cells but rather label a statistically valid sampling in order to assess, on this sample, the colocalization properties of candidate antibody biomarkers. Thus, a 91% labeling efficiency is very acceptable. We will employ the same commercial antibody and procedures as for Rubin et al. (Rubin 2004): mouse monoclonal anti-AMACR p504s (Zeta Corp., Sierra Madre, CA) at a starting dilution for optimization (see below) of 1:25. The optimization protocol to be used here encompasses the conditions of Rubin et al. (Rubin 2004). A major potential advantage of anti-AMACR is that the weak or absent labeling of normal epithelial components will facilitate quantification of nonspecific labeling ("noncolocalized labeling") by candidate biomarker antibodies to be developed in Phase II.

Other potential tumor epithelial cell antibodies include anti-PSMA, anti-PSA, and anti-PAP. Antibodies to these products react with epithelium of normal and malignant cells. Anti- PSMA is extensively studied, is FDA approved (clone 7El 1) for radiological detection of PCa metastases, labels nearly 100% of tumors in histological sections, and consistently label tumors at greater intensity that benign prostate epithelium (Chang 2004). We will optimize the labeling of FFPE, TMAs, and frozen sections test with our quantitative IF methods can exploit this property to distinguish tumor from benign labeling in comparison to anti- AMACR and visual scoring. We will utilize a mouse monoclonal anti-human PSMA (Dako clone 3E6).

Stroma cell labeling. "Stroma" as used here is a collective term consistent largely of fibroblasts, myofibroblasts and less proportion of vascular, neural, and other elements.

Fibroblast and myofibroblasts labeling will be used as a reference to identify colocalization of stroma-binding candidate biomarker antibodies and to derive the ration of stroma :nonstroma labeling by the candidate antibodies. Widely accepted markers that may make suitable reference antibodies consist of anti-desmin, anti-vimentin, and smooth type α- actin and others (Castellucci 1996; Tuxhorn 2002; Λyala 2003; Tomas 2004: Ao 2006; Jiang 2007). We have previously utilized anti-desmin for the IHC analysis of prostate cancer (Stuart 2004). Considerable literature has accumulated indicating that Vimentin and smooth muscle type α-alpha vary in expression in PCa depending on the extent of epithelial- mesenchymal transformation and reactive stroma formation, two processes that correlate with aggression (Tuxhora 2002; Ayala 2003;lIyanagisawa 2007; Yang 2008)). These phenomena appear to be proximal to the site of PCa. These markers therefore have the potential to delimit the "field" effects that are associated with differential gene expression of tumor-adjacent stroma. These observation correlate well with our observations that tumor- adjacent stroma contain numerous differentially expressed genes useful for diagnosis and for prognosis. Indeed, as noted, the mRNA levels of desmin and vimentin are significantly increased in stroma of our PCa samples compared to the epithelial components (Stuart ct al. 2004). We plane, therefore, to optimize all three antibodies and determine their suitability as reference antibodies for stroma in general and tumor-adjacent stroma in particular. Previously characterized stroma reference antibodies include: anti-desmin mouse monoclonal antibody Dako clone D33 (Stuart 2004); anti-vimentin goat polyclonal sera cat. No. AB1620 from Chemicon (Temecula, CA) (Tuxhora 2002); and anti-smooth muscle α-actin Dako clone IA4 (Tuxhora 2002). For the development of stable renewable reagent sources it is highly desirable to work with monoclonal antibodies where source licensing can be organized. Therefore for anti-vimentin we will also examin mouse monoclona antibody from Dako, clone V9.

Optimization and SOP development. The primary antibodies will be applied using an automated immunostainer (DAKO Universal Staining System) and employing the Envision- Plus-horseradish peroxidase system (DakoCytomation, Inc.) secondary labeling system for DAB. FFPE sections will be deparaffinized by xylene overnight followed by microwave treatment and 0.4 power for 30 min. in a 6.0- pH citrate buffer. No enzymes or other

"antigen retrieval" processes will be applied here or any of the labeling conditions considered here in order to minimize the variables required in developing panels of multiple antibodies with compatible protocols (Phase II). Sections will be pre-treated with normal mouse serum for 40 min. and washed in PBS with automated stirring three times. For optimization, primary antibodies will be applied at room temperature for 40 min in two-fold serial dilution from 1:30 through 1:960 or higher dilutions if practical. The optimal titre (as well as the preceding and following titre value) as judged by visual appearance (D. Mercola, F.C.A.P.) of specific labeling intensity to background labeling intensity will be re-tested on sections with increased deparaffinization steps (see IF procedure) including an over night baking step and reduced as well as extended microwaving to check for an improvement in signal to background labeling intensity. Finally, the time and temperature of application of the primary antibody will be optimized by comparing exposure to primary antibodies for 2 h and 24 h at room temperature and 24 at 4 deg. C.

These steps will be applied to both FFPE and frozen sections of fresh tissue. In the case of fresh tissue, we will utilize samples that have been cryopreserved in liquid nitrogen from the time of initial freezing. All samples for the UCI SPECS project are obtained directly from the O.R. and processed by an expedited surgical pathology grossing procedure. Sample for research are taken from tissue adjacent to the grossly identified tumor site or, for "remote" tissue control samples, taken from the contralateral prostate. Tracking sheets are maintained on all samples giving the elapsed time from the O.R. to freezing. Representative samples are used for RNA q.c. as an indication of preservation by analysis of total RNA using an Agilent Bioanalyzer which indicates high levels of preservation in over 95% of samples. Frozen sections will be prepared from these tissues directly from the frozen state without thawing. The sections will be fixed for 60 sec. in 95% methanol or 100% acetone or 70% EtOH all at - 22 deg. C, air-dried, and used directly for antibody optimization.

TMA confirmation. Optimized labeling protocols developed on FFPE sections will be tested by application to our TMA with 272 cases including cores of tumor- adjacent and remote stroma. Labeling of the TMAs will provide information of the generality of labeling across cases and the reproducibility of specific labeling for tumor and stroma. To insure that optimization has been achieved for the TMAs, the last steps of the optimization procedure will be repeated using the TMA sections, i.e. the application of primary antibody using the three best titre values and the following steps. Progress will be monitored by visual inspection of the DAB labeled slides (D. Mercola, F.C.A.P). Optimal conditions will be judged by the most cases of the TMA that reflect the desired criteria of the greatest differential expression between target cell type with "background" intensity. All informative slides will be stored in a temperature controlled laboratory for scanning and quantitative assessment of variability, accuracy, and reproducibility assessment of Milestones 3 and 4.

Immunofluorescence. Immunofluoresce is the intended method of choice owing to the much higher dynamic range and sensitivity of antigen detection. Indeed, we anticipate that primary antibodies can be extended to high titres by factors of 10x or more. The major challenge is selection of conditions that minimize "background" or "autofluorescence". Background fluorescence can be minimize by using fluorophores with long wavelength emission (>500 nm), use of sections with rigorous deparaffinization procedures (i.e. the overnight deparaffinzation xylene treatment and used of prolong baking of unstained FFPE sections, above), use of pretested acid washed slides and coverslipping reagents, and use of a configuration of the robotic microscope with optical filter wheel located before the monochrome CCD camera. These methods have been optimized previously (Rabinovich 2006). The characterized flurophore-conjugated secondary antibodies to be used previously that will be applied here are: Texas Red-labeled goat anti-mouse (catalog number 115-075- 146, Jackson Laboratories, Bar Harbor, ME) and Alexa Fluor 488-labeled goat anti-mouse (catalog number A21121, Molecular Probes, Eugene, OR). These reagents can be used at dilutions in the range 1:1,000 to 1:10,000. The optimum concentration will be determined for sections of our TMAs.

Visual assessment of optimum conditions require counter staining. Sections will be stained with DAPI (Molecular Probes, Eugene, OR) at 75 ng/ml (in 10 mM TRIS, 10 mM EDTA, 100 mM NaCl) for 45 min prior to sealing with coverslips. Visual assessment will be carried out by J. Price and D. Mercola.

Milestone 2. Storage and visualization will utilize exiting technology of the VaIa Sciences Inc. system. All data will also be placed in a free database that is DICOM compliant.

In this project the bulk of data collection, storage, and analysis will be by the VaIa Science robotic scanning microscope and associated software and storage capacity. As reviewed here (Preliminary Studies), Throra and associated software for data acquisition, analysis and storage are advanced. These are most completely described in the specialty publications of Rabinovich et al. (Rabinovich 2006) and Prignoshima et al. (Prigoshina 2007). Moreover Proveri Inc. and VaIa Sciences Inc. are committed to the development of completely DICOM complaint storage and data sharing (http://www.sph. sc.edu/cornd/rorden/dicora.htral). The primary data of the assay proposed here, a multiplexed antibody assay utilizing indirect IF, will consist of a spectral stack of multiple color images of histological section of biopsies or postprostatectomy tissue sections together with standard hematoxylin and eosin stained sections of the same section used for IF labeling. Such images represent a novel data set for diagnosis and prognosis without direct precedent in the DICOM standard. Since Phase II is focused on product development for diagnosis and prognosis in the CLIA reference lab setting, VaIa Science Inc. is very interested in developing a DICOM-compatible format for the storage and transmission of primary tissue images. It is planned to develop a demonstration format using DICOM heading and other features in analogy of other imaging systems.

Milestone 3. SOPs will be developed for specimen collection, processing, and stability of the cell types in the imaging assay.

SOPs for the acquisition of tissues and blocks have been developed by the UCI SPECS program and are maintained as date pdf files and in an SOP workbook. These SOPs describe procedure for informed-consent based patient recruitment at all participating sides and methods of tissue collection at O.R rooms, expedited processing and storage together with diagrammatic illustrations of dissection procedures and additional tracking forms for each specimen. All procedures are UCI IRB-approved and HIPPA-compliant. In addition the UCI SPECS program maintains "shadow charts" for all recruited patients including the signed witness informed consent, tracking sheets, and CRFs of baseline clinical data together with source documentation of all values recorded in the SPECS data base. The data base is maintained on a devoted server hosted by a participating institute, the Sidney Kimmel Cancer Center of San Diego, in a locked server room under the control of the SKCC IT department. The server is accessed remotely via a password protected web-based portal by approved clinical coordinators and the data base manager. All personnel are UCI employees. The SOPs will be incorporated into the SOPs generated for phase I of this project.

SOPs describing the optimized procedures and reagents of Milestone 1 will be developed as final condions are determined. The methods for the fabrication of the TMAs will be included. These will include methods for periodic testing to insure stability of the labeling results. The current TMAs contain cores of fixed cultured prostate cells including standard tumor cells (LnCAP, PC3, DU145, M12) and normal immortalized cells (RWPEl, p69) will will be used to record quantified labeling intensity. Upon the completion of Milestone 1 , multiple section of the TMA block containing cell cores will be prepared as a master lot for periodic qc and for standardizing new lots of renewable reagents. These procedures will be included in the SOPs.

It is a major goal of phase II to initiate a prospective validation program using newly recruited clinical patients and UCI and applying the multiplex panel to research biopsies and post surgery tissue specimens in the CLIA lab of the molecular pathology core of the UCI Department of Pathology and Laboratory Medicine. In anticipation of this study, All SOPs, master lot preparations, and DICOM-capatible image storage will be coordinated with CLIA requirements of this laboratory.

Specific Aim 1: Generation and initial characterization of predictive antibodies.

1. Acquisition of 25 candidate antibodies against antigens identified as predictive of prostate cancer progression or recurrence based upon the preliminary studies (Section C). 5 2. Western analysis and IHC analysis of 25 candidate antibodies in order to confirm cell-specific expression and specificity.

3. Prioritize antibodies for testing on TMAs (Aim 2) based upon the intensity of cell- specific tissue labeling, the specificity as judged by the observation of predominate binding to a protein of the predicted molecular weight in Western analysis, and o sensitivity as judged by percent of cells of the expected type in IHC labeled tissue sections.

Specific Aim 2: Validation of prostate cancer predictive antibodies on tissue microarrays (TMAs).

1. IHC analysis of 6-10 prioritized candidate antibodies on TMAs constructed from 254 5 annotated clinical prostate cancer cases. Analysis will consist of the determination of manual "immunoscores" by three pathologists.

2. Kaplan-Meier analysis comparison of immunoscores with clinical outcomes for 5-8 candidate antibodies.

3. Prioritize antibodies for clinical development based upon sensitivity, specificity, and0 accuracy as determined from the Kaplan-Meier analysis of Aim 2-2 and the magnitude of the differential expression between non-recurrent and recurrent cases. Antibodies also will be prioritized by their ability to contribute to a classifier" panel of antibodies, i.e. the minimum number of antibodies that encompass the "diversity" of the 254 cases. The measure of "encompassing diversity" will be the number of5 cases whose survival category is uniquely recognized by that antibody. These criteria insure the development of the smallest antibody panel necessary. Since the TMAs are fabricated from cases entirely independent of those used for MLR, confirmation of differential express here extends the generality of the biomarker antibodies and, ipso facto, extends the biomarkers to the protein level. The panel of antibodies successful0 at this level will represent both significant changes in tumor cell expression between recurrent and nonrecurrent cases and will include tumor microenvironment changes in between recurrent and nonrecurrent cases, a key ingredient in building a robust classifier.

Specific Aim 3: Automated and improved quantification of TMA readout.

1. Quantify and validate the two-color separation method by (i) quantification of pixel 5 intensity of test antibodies only at the locus pixels of specific cell types such as all epithelium or all prostate cancer as defined by cell-specific markers such as anti- cytokeratin or anti-Amacr (Aim 2-1) and (ii) validate the quantification approach by correlation with visual immunoscores. Pearson and Spearman correlation coefficients will be determined, together with probabilities of the correlation coefficients as well o as the degree of relatedness (slope) of visual and quantified scores.

D. Methods

Specific Aim 1: Generation and initial characterization of predictive antibodies to epithelial and stroma tumor antigens. Antibodies against known prostate cancer antigens and against putative prostate cancer biomarkers identified by gene expression analysis will be 5 obtained from commercial sources and characterized using Western blotting and immunohistochemistry. Candidate antibodies that demonstrate the ability to detect discrete proteins on Western Blots prepared from fresh prostate tissue samples (stroma or tumor) and the ability to differentially label cell types in paraffin-embedded prostate cancer tissue sections will identified. Their ability to predict clinical outcome will be tested in specific aim0 2.

D.l.a. Description of Antibodies

Commercial antibodies will be purchased, if available. Other antibodies will be generated (Lampire Biologicals, San Diego, CA). Numerous antibodies used in our separate projects5 have been developed in cooperation with Lampire Biologicals [50, 68-74].

Three classes of antibodies will be tested:

1. Antibodies that label prostate tumor cells, normal epithelium, or stromal cells to be used as internal standards will be used to identify specific cell-types within prostate tissue samples. Those on hand of particular importance for the identification of epithelial components include anti-high molecular weight cytokeratin (HMW cytokeratin), anti-PSA, anti-PAP, anti-PSMA, and anti-Amacr. Those intended for the identification of stroma include anti- Desmin and anti-smooth muscle alpha actin (Anti-ACTA). We have optimized all of these 5 for use with FFPE tissue sections and described results in previous studies [18, 67].

2. Antibodies against potential prognostic markers identified by gene expression analysis. Twelve commercially available antibodies against predicted antigens have been obtained and screened using standard sections of FFPE prostate cancer tissue blocks. Five of these antibodies are very promising for detailed characterization as proposed here. Antibodies that o are not available or exhibit poor labeling or background properties in screening will be commissioned de novo as described below.

3. The selection and screening of additional antibodies will be prioritized by starting with antibodies to gene products that exhibit the largest differential labeling (largest difference in immunoscore or normalized pixel intensity) between nonrecurrent and recurrent prostate5 cancer cases. As noted above, approximately half of the antibodies screened so far do exhibit excellent signal to background properties on test sections of FFPE prostate cancer.

D.l.b. Criteria for inclusion of antibodies for TMA analysis will include: path to monoclonal antibody production. 0 1. Antibodies are suggested by the results of MLR (Preliminary Data, Section Cl).

Candidate antibodies first will be vetted by Western analysis to test for the detection of antigen of correct molecular weight in prostate tumor tissue extracts or alternative molecular weights previously reported as prostate cancer-variants. Previous experience [18] has revealed that an important factor in meeting these criteria is knowledge of the origin of the5 antigen. The linear regression results identify probe sets of Affymetrix GeneChips which correspond to precise genes and introns of genes. Commercial antibodies against recombinant proteins or large fragments of proteins likely correspond to the identified gene product and so are useful for testing whether genes of probe sets are expressed at the protein level. Similarly, commercial antibodies against highly pure native proteins of a carefully0 characterized molecular weight that agrees with that expected value on the basis of the Affymetrix -predicted gene product also may be expected to be confirmed by Western analysis. However, antibodies produced against proteins purified from natural sources may contain alternative spliced products and/or other gene family member proteins as well as closely related proteins or fragments that are difficult to separate during purification may lead to antibodies reactive to a range of molecular weights with an unclear relationship to the 5 gene product corresponding to the Affymetrix probe set. Monoclonal antibodies against recombinant or synthetic peptides more often meet the need for single gene product specificity and will be preferred. In addition monoclonal (mouse, rat) define a potentially renewable resource that may be contracted as a stable supplier of test kit reagents. Therefore, all polyclonal antibodies characterized here for inclusion on the final antibody classifier will o replicated by the commissioned preparation of the corresponding monoclonal antibody as part of phase II.

2. Consistent and robust IHC signal of antigens from formalin-fixed and paraffin-embedded (FFPE) tissue. TMAs provide a major advantage in that the fraction of cases exhibiting increased or decreased IHC signal may be quantified readily. In order to develop an assay5 with maximum reproducibility, methods that minimize reliance on "antigen retrieval" strategies will be adopted. This will select for robust antibodies capable of recognizing antigens on archived samples.

3. Consistent and robust IHC signal of antigens from archived (>10 years) FFPE tissue. IHC labeling intensity for each antibody will be correlated with the age of the sample on the0 TMA. An advantage of our TMAs is the presence of cases from 2 to 19 years old.

4. Cell-specific labeling. Cell identity (normal epithelium, stroma, BPH) will be determined by manual inspection or staining with cell-specific antibodies. IHC intensity for each antibody will be immunoscored for staining intensity and cell specificity as described below (Sections D.2.c. or D.3.b.) 5

D.l.b. Tissue source for Western blotting. Tissues will be obtained from the UCI SPECS prostate project tissue bank. This is a resource of the NIH-supported UCI SPECS prostate project. Prostate samples were obtained from patients (UCI) that were preoperatively staged as having organ-confined prostate cancer. Institutional Review Board-approved informed0 consent for participation in this project was obtained from all patients. Tissue samples were collected in the operating room, and specimens were immediately transported to institutional pathologists who provided fresh portions of grossly identifiable or suspected tumor tissue and separate portions of uninvolved tissues that were excess to patient care needs (surgical pathology staging and confirmatory diagnosis). All excess tissue was snap frozen upon receipt and maintained in liquid nitrogen until used for frozen section preparation at -22°C. Fifty five percent of all cases collected in this series contained histologically confirmed tumor tissue. Portions of frozen samples enriched for tumor, stroma, BPH, and dilated cystic glands are identified by examination of frozen sections. When suitable tissues are identified, thick frozen sections of 20 microns are collected in separate Eppendorf tubes for lysis and Western analysis.

Additionally, the ability of antibodies to visualize antigens of correct MW on Western blots from tissue extracts established from a panel of human prostate cell lines will be determined. This panel will include androgen resistant prostate cancer cells (PC3, DU145), androgen sensitive prostate cancer cells (LnCAP), primary immortalized RWPE-I epithelial cells. Cancer cells of alternative derivation (lung, breast, colon), and several normal cell lines

(fibroblasts, myoblasts) (ATCC) (these cells have also been applied to the TMAs as sections of formalin-fixed cell pellets).

D.l.c. Western blotting Tissues or cultured cells will be lysed in either Ix Laemmli solution lacking bromophenol blue or in RIPA buffer (0.15 mM NaCl/0.05 mM Tris HCl, pH 7.2/1% Triton X-100/1% sodium deoxycholate/0.1% sodium dodecyl sulfate) containing protease inhibitors including the caspase inhibitors 100 μM Z-Asp-2.6-dichlorobenzoyloxymethyl-ketone (Bachem) and Z-Val-Ala-Asp-fmk (Calbiochem). Total protein content will be quantified by either the Bradford or bicinchoninic acid methods (Pierce). SDS/PAGE and immunoblotting with enhanced chemiluminescence -based detection (Amersham Pharmacia) will be performed [50, 69-71]. .

Antibody reactivity will be semiquantified by comparison of reaction intensity of tissue and cellular extracts with extracts of prostate cancer cells (PC3, LNCaP) and negative control cells (bacterial cultures and female normal breast epithelial cells, MCFlOA) of known total protein mass.

D.l.d Immunohistochemistry. Our methods for optimization and detection of antibody labeling have been described extensively [50, 68-74]. Briefly, the cell specificity of the identified antibody for normal and malignant prostate tissue will be tested by comparing the binding patterns on a series of normal and malignant prostate tissue specimens. FFPE tissue sections (5 μm) will be deparaffinized, microwave-heated, and immunolabeled by indirect staining using either a conjugated secondary antibody for avidin-biotin complex formation with horseradish peroxidase (HRP) using the Vecta labeling reagents (Vector Laboratories) followed by addition of diaminobenzidine (DAB) for colorimetric detection or the Envision-Plus-HRP system (Dako) with a Dako Universal Staining System. A range of antibody concentrations will be tested to optimize signal detection and specificity. For all tissues examined, the immunostaining procedure will be performed in parallel by using either preimmune serum (polyclonals) to verify specificity, or the antiserum reabsorbed with 5-10 μg/ml of synthetic peptide or recombinant protein immunogen where available. Positive controls for cell-type specificity will be determined by staining sections with a "cocktail" of antibodies directed against pan-cytokeratin (Sigma) to identify epithelial cells and antibodies against Desmin, alpha-smooth muscle actin, or prolyl-4-hydroxylase to identify stromal cells

Specific Aim 2: Validation of prostate cancer predictive antibodies on tissue microarrays (TMAs). Our TMAs have been constructed from archived prostate tissue samples with known clinical outcomes from SKCC and UCI. IHC staining will be performed using antibodies developed in Specific Aim 1. IHC staining levels will be immunoscored (below) and compared to clinical outcomes by Kaplan-Meier analysis. Significance of discrimination of survival groups will be determined by the Cox Proportional Hazards model.

Visual determination is carried out by three pathologists (SK, MK, and DAM) and averaged. Candidate antibodies demonstrating the greatest sensitivity, specificity, and accuracy for the prediction of clinical outcome by the Kaplan-Meier criterion will be selected for the antibody panel for prognostic validation of clinical samples in Phase II.

D2.b. Immunohistochemistry on TMAs. Immunohistochemistry on TMAs will be performed as described previously [50, 69-71] and above (Section D.l.d.)

D.2.C. Immunoscoring of TMA readouts

Immunoscores are determined visually and are formed as a product of the percent of a given cell type that is positive 1-100 percent) times the intensity on a three point scale yielding a range of values from 1-300 [68-70, 72, 73]. For the three -point scale intensity is j judged as 0, negative; 1+, weak; 2+, moderate; and 3+, strong [70]. Samples will be additionally scored for percentage of immunopositive malignant cells, estimating the percentage in increments of 10% (0%, 10%, 20%, 30%, and so on) from a minimum of five representative medium- power fields. The scoring will then be based on the percentage of immunopositive cells (0 to 100) multiplied by staining intensity score (0/1/2/3), yielding scores of 0 to 300. Scoring is conducted in a joint session of the three pathologists utilizing the original glass slides and a multihead microscrope in order to insure identical viewing times and field exposures. The reproducibility and agreement among pathologists following this format has been assessed [18] and immunoscoring using the above scales has been used in several studies [50, 69-71].

D.l.d. Statistical Analysis

Data will be analyzed using the JMP Statistics software package (SAS Institute, Cary, NC), and STATISTICA Software (StatSoft, Tulsa, OK). Comparisons of antibody immunostaining data with patient survival will be made using the Cox proportional hazards model and the comparison of Kaplan-Meier survival curves. An unpaired t test method was used for correlation of immunoscores with the available patient data. All statistical methods will be supervised by our biostatistician, Zhenyu Jia, consultant for Phases I and II of this project (see Biosketch, Z. Jia and letter). Antibody performance will be judged by conventional operating characteristics (accuracy, sensitivity, and specificity) but also by criteria that produce the smallest panels that maximizes the percent of cases of the TMA accurately discriminated as aggressive or nonagressive by survival and other criteria. This is an important consideration, as a true classifier panel should contain biomarkers effective with cases that other biomarkers may be insensitive to, i.e. cover the diversity of prostate cancer. Thus, individual antibodies will be scored by the number of cases unique classified with very large or very small odds ratios that other antibodies fail to distinguish (i.e. the number of unique cases accurately classified). These criteria further insure that the minimum number of antibodies to discriminate all amendable cases of the TMA will be formed.

Specific Aim 3: Automation and improved quantification of TMA readout. The discriminatory power and the rate of characterization of the prognostic antibodies identified in Specific Aim 2 may be improved using image analysis that provides for quantitative determination of antibody labeling intensity. Rapid scanning, digitization, and the use of a newly developed algorithm for two-color separation are established at the BIMR largely as the developmental work of one of the applicants (SK). Digitized IHC labeled prostate TMA are maintain on a server located at the BIMR and accessible by all participants via a secure portal (https://scanscope.burnham.org/Login.php). This greatly facilitates the monitoring of IHC results and planning of next steps and immunoscoring sessions. UCI SPECS pathologists utilize high resolution line scanned H and E and IHC images of this site for immunoscoring of other projects and confirmed the histological features of the TMAs such as Gleason scores, presence of PIN, etc. This technology allows for automated quantification of cell-specific antibody staining of TMA samples without reliance on "shape recognition" or manual inspection to determine cell-type. This technology will be tested using the panel of prognostic antibodies developed in the first two specific aims. Specific Aim 3: Automation and improved quantification of TMA readout. D.3.a. Double labeling. Double labeling places constraints on the combination of standard (anti-PSMA, anti-

AMACR, and anti-cytokeratin) and candidate antibody combinations owing to the need to use secondary antibodies for the development of two different chromagens. The methods that we have previously used for double labeling (Krajewski 2007; Krajewska 2008) will be followed closely. In general candidate antibodies will be derived from rabbit sera. Indirect IHC using biotin labeled an ti -rabbit IgG will be applied for development of DAB (3,31- diaminobenzidine chromagen, DAKOCytomation; brown). Mouse monoclonal antibodies to AMACR, PSMA, or cytokeratin will be identified by addition of biotin-labeled anti-mouse for development of the black SG precipitate (Serotec; SG chromagen, Vector Lab., Inc.; black). No or very light counter staining with Nuclear Red (DAKOCytomation) will be applied

D.3.b. Validation of prostate cancer predictive antibodies on tissue microarrays

(TMAs). Color unmixing has been validated for sections labeled with hematoxyln and DAB (Preliminary Data). As noted, actual isolation of subsets of pixels that co-localize with epithelial or tumor cells is a milestone of Phase I. Validation will be extended to DAB and SG double labeled sections and to colocalized integrated and normalize pixel values. For this purpose it is important to note that visual scores are traditional obtained as the product of the intensity of labeling (on a 0 to 3+ scale) times the percent of tumor or epithelial cells that exhibit positive labeling. Here both factors will be used to validate co-localization. A test system utilizing a polyclonal anti-AMACR (DAB) and monoclonal anti-cytokeratin (SG) alone and in combination will be applied to both the tumor TMA and to the BPH TMA.

First, analogous to the hematoxyln-DAB system, deconvolution results (reconstructed DAB image and reconstructed SG image) for the combination labeling will be compared to individual labeling (ground truth). These tests will define the accuracy as percent error +/- standard deviation for each chromagen. Second, colocalized pixel sums for AMACR labeling as a "standard" for binding to a high percentage of tumor cells will be determined. This is the sum of pixel intensity for DAB at pixels positive for SG. The pixel sum for DAB will be normalized to SG for all cases to correct for the variable amount of total epithelium on each core. The normalized sums are expected to be maximal for tumor sections where AMACR expression is commonly positive in most cells of most tumors but to exhibit minimum overlap in cases of BPH. Indeed simple thresholding may succeed defining a single value that best separates average tumor from average BPH. This may be expected since AMACR labeling will be applied based on optimization of tumor sections. Third, visual score by two pathologists (S. Krajewski and D. Mercola) will be acquired for all the single- antibody (DAB or SG) labeled TMAs. The results of spectral unmixing for DAB and SG will be compared to visual scoring for these chromagens as for the previous studies. Finally, the normalized DAB pixel sum is expected accurately correlate with the percent tumor cell component determined by the pathology and especially to correlate with the ration of percent DAB positive tumor cells over percent positive SG cytokeratin cells Thus, globally we predict:

Case average co-localization pixel sum for AMACR (DAB) ~ Case average vis. % positive

AMACR

Case average pixel sum for Cytokeratin (SG) Case average vis. % positive

Cytokeratin

On a case by case basis plots of normalize DAB/SG vs. percent DAB positive/percent SG are predicted to have a high Pearson correlation with a slope ~ 1 and error similar to the preliminary Results of <10%. Validation of spectral unmizing for this chromaphore system will provide a major milestone of Phase I and means of automated antibody biomarker screening of Phase II.

Candidate stroma biomarker antibodies will be treated in a converse fashion. Mutually exclusive pixel sums (all pixels other than cytokeratin-positive pixels) will be integrated. This guarantees that epithelial components. These values will be normalized to the nonepithelial pixel sum intensity for a trichrome stain of the TMA using a second spectral unmixing calculation to identify connective tissue component (blue).

Antibodies

We are aware that the quantification method being developed here has numerous additional standardization issues. It is entirely dependent on the properties of reference antibodies to define "cell-type". Antiamacr is in wide clinical use for the identification of prostate tumor cells in non prostate tissue in the presence of other components including glands. Nevertheless it is not unchallenged and "negative" results have been noted to occur for up to 30% of prostate cancer cells [76-81]. Thus pixels identified by these criteria may only "sample" a large proportion of tumor cells. This may be acceptable unless particular classes of tumor cells such as those expressing genes correlating with, say, rercurrence, are preferentially negative. It will be important to utilize other criteria such as visual inspection by trained pathologist and the use of other faithful tumor cell markers reveal significant bias. We have identified a large panel of genes that are preferentially expressed by prostate tumor cells [18]. In addition, standard alternatives such as antiPSA and antiPSMA may be compared to determine labeling deficiency by antiAmacr.

We have chosen to concentrate on the use of monoclonal antibodies for these studies as they generally display higher specificity and consistency compared to polyclonals and are therefore better adapted to commercialization into clinical development. Polyclonal antibodies are commercially available and might prove to be more sensitive in FFPE tissues, and therefore may be explored. Commissioned monoclonal antibodies are amenable to clear definition of ownership and path to market.

Many antibodies against prostate cancer tissues are commercially available. However, antibodies against important biomarkers that are not currently commercially available or that fail to meet quality control specified in specific aim 1 will be made using peptide antigens (Lampire Biologicals, San Diego, CA) as for previous studies [50, 68-74].

Finally an important challenge in Phase II will be the combining of multiple antibodies with possible individual optimization protocols to a single tissue section. If this can not be achieved conveniently, i.e. without serial application, the panel will be applied on multiple slides using 2-3 different antibodies of the panel per slide. Although less convenient, the use of two or possible three serial sections of patient biopsy tissue does materially effect the ability to derive prognosis from our predictive antibody panel.

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Example 9 - Conversion of a novel RNA-based prognostic test for prostate cancer into a clinical assay

A. SPECIFIC AIMS. Nomograms are sets of clinical parameters that are used to estimate the risk of prostate cancer recurrence [1, 2]. We propose to improve on the current nomograms by including predictions based on gene expression.

We have used a novel strategy to identify and validate genes whose expression correlates with prostate cancer progression in either tumor tissue or in stroma near to tumor, across multiple independent microarray datasets. We will convert this set of expression differences into a clinical assay. Our proposed strategy involves monitoring a panel of RNAs, including some RNAs that predict the risk of disease recurrence, some RNAs for housekeeping genes (internal controls), and some RNAs that are used to determine the tissue composition of a prostate sample (tumor, stroma, BPH). The inclusion of RNAs to monitor tissue percentage allows only suitable prognostic markers to be monitored in each sample; those prognostic markers that are directed towards the primary tissue in that particular sample.

We will use an RNA detection strategy (QuantiGene Plex 2.0) that works on both fresh frozen and FFPE samples, and that can accurately monitor up to 36 different RNAs, simultaneously. The assay runs on the FDA-approved Luminex platform, already used in clinical labs. We will first screen our candidate RNAs for those that perform well on this platform using RNA from fresh frozen samples with known microarray expression patterns. Panels will then be applied to 150 tumor-enriched FFPE samples and 150 stroma-enriched (near to tumor), from prostate cancer patients, with up to two decades of clinical history. The best performing subset of genes will be assembled into two panels for clinical use, one for use in stroma-enriched samples, and the other to be used in tumor-enriched samples.

The long-term goal is to validate the classifiers in a prospective study on newly recruited prostatectomy samples.

B. BACKGROUND AND SIGNIFICANCE.

Cancer and the Need for Prognostic Markers. Prostate cancer is the most common malignancy of males in the United States [3]. Patients newly diagnosed with advanced prostate cancer that do not yet have evidence of metastases are generally advised to submit to invasive therapies such as radical prostatectomy or radiation treatment. However, the majority of prostate cancers are a slow growing indolent form with a low risk of mortality. Patients with early stage disease and extremely favorable nomogram scores, suggesting indolence of the cancer, can instead opt for intensive vigilance. We propose the development of a gene-expression-based clinical test that makes a differential prognostic prediction between indolent and aggressive forms of prostate cancer. This test would provide an additional key aid to prostate cancer patients, and doctors, in making their treatment decisions, and will be particularly useful for those patients that are not at the extremes of the current nomogram scoring systems [1, 2],

While other studies to detect RNA-based prognosticators for prostate cancer have been performed, they have limited agreement with each other, and very limited overlap with prognosticators found by other methods [4-7]. We have developed a different method that identifies prognostic markers and we have cross-validated them across different data sets (detailed below). We now propose to convert a panel of these prognosticators into a useful clinical assay. We will use the QuantiGene Plex 2.0 Assay (Panomics, Inc., Fremont, CA), which is as sensitive as real time PCR but can be much more extensively multiplexed [8, 9]. The assay can detect up to 36 targets per well. The assay is based on the branched DNA (bDNA) technology, which amplifies signal directly from captured target RNA without purification or reverse transcription. RNA quantitation is performed directly from fresh frozen tissue or from formalin-fixed, paraffin-embedded (FFPE) tissue homogenates, and is relatively insensitive to RNA degradation and to chemical modifications introduced by formalin-fixation [10, H]. The method is already in the FDA-approved clinical diagnostic VERSANT 3.0 assays for HIV, HCV and HBV viral load [12] and has been used in biomarker discovery, secondary screening, microarray validation, quantification of RNAi knockdowns and predictive toxicology [11, 13-15].

C. PRELIMINARY STUDIES. The key to this project is the set of genes that we will put into the prognostic assay. We describe how we obtained these genes in some detail here.

We previously developed methods to determine the genes preferentially expressed by the three major cell types of tumor-bearing prostate tissue: tumor epithelial cells, benign epithelial cells (BPH) and stromal cells [16]. We have now extended this method so that we can now identify transcription changes that correlate with early cancer recurrence in one or more of these three cell types. In addition to transcription changes in tumor cells that correlate with recurrence, we find that prognostic changes also occur in stroma near to tumor but not in BPH. We have validated a subset of these new recurrence-related genes using independent publicly available microarray data sets. Table 31 summarizes the data sets we have analyzed from various sources, including our own prostatectomy samples. Table 31: Prostate cancer expression microarray data sets

Data Array Non-

Sets platform Targets Recurrent Recurrent Reference

Our unpublished

1 U133Plus2 54,675 27 38 data

Our unpublished

2 U133A 22,283 30 26 data

3 Illumina 511 18 63 [4]

4 U133A 22,283 29 42 [7]

5 U95Av2 12,626 8 13 [6]

6 U95Av2 12,626 9 14 [5]

Identification of cell-specific genes. Most previous experiments to determine expression profiles of solid tumors using microarrays involved "enriched" tumor fractions. There are three limitations of this strategy. First, samples vary in purity, introducing an error due to various amounts of accompanying tissue types. Second, the change in gene expression of other cell types is subsumed in a single number, obscuring the unique profiles of these accompanying cell types. Third, substantial amounts of stroma are intrinsic to the structure of nearly all prostate tumors. We devised a method for the decon volution of average cell- specific gene expression from a set of samples containing different mixtures of cell types [16]. Estimates of the amount of three major cell types were made: tumor epithelial cells (tumor, T), epithelium of benign prostatic hyperplasia (BPH, B), and stromal cells (S, including pooled smooth muscle, connective tissue, infiltrating immune cells, and vascular elements). The amount of mRNA (Affymetrix signal intensity, G _1} ) from a given gene is the sum of the amount of each cell type multiplied by the intrinsic expression, β _ιp of that gene by the given cell type:

^G ,j = β BPH, _J * BPH^ ⁺ βτ, _] ^X _T , _ι ⁺ β _S , _] ^X _S , _ι + £ _υ (1) where X ₁ is the proportion of each cell type and ε is the error. The model identified hundreds of genes significantly more expressed in only one tissue and examples were validated by laser capture micro-dissection and immunohistochemistry [16].

In silico estimates of tissue percentage?,. Estimates of tissue percentages made by pathologists for all the samples in data set 1 , 2 and 3 allowed identification of individual transcript levels that correlated best with tissue percentage. The expression levels of each of these overlapping genes were fitted to a simple linear model for each tissue type and were ranked by their correlation coefficient. A subset of the top genes from one data set was subsequently used to predict tissue percentage in the other data set. The Pearson correlation coefficients between predicted cell type percentage (tumor, stroma and BPH cells) and pathologist's estimates for all pairwise predictions of the three data sets range from 0.45-0.87 (p<0.001 in all comparisons).

Estimation of cell type percentage proved to be highly relevant. In data set 4, recurrent cases had a systematically higher percentage of tumor tissue than non-recurrent cases. Unless recognized and taken into account, this skew would generate false expression-derived estimates regarding recurrence. Identification of cell-specific biomarkers of aggressive prostate cancer. We have now extended equation 1 to identify genes specific to cell-type and aggression, for cases with known follow-up history. To obtain cell-specific gene expression for both recurrent and nonrecurrent cases, the summation of equation 1 is simply segregated to reserve terms with β _] coefficients for non-recurrent cases and denoting recurrent cases (rs) at the end with a separate coefficient, γ

Multiple linear regression (MLR) analysis was carried out leading to the calculation of all β _] , all Y _j , and their associated t-statistic values. Thus, estimates of the intrinsic expression of three cell types (T, S and BPH) for non-recurrent and recurrent prostate cancer were derived.

In data set 1 (U133Plus2.0 array), for example, 928 differentially regulated genes were identified in early recurrent cancer types at an adjusted p value of less than 0.05, including 405 tumor- and 561 stroma-related prognostic genes. In both data sets 1 and 2, the most significant changes were observed in the stromal tissue portion of specimens that were from near tumor (reactive stroma). The ability to look for changes in expression in stroma during recurrence is one of the major advantages of our approach.

Confirmation of Prognostic Genes using Independent Data Sets (Cross-Validation). The six available expression microarray data sets with information on prostate cancer recurrence (Table 31) allowed identification of that subset of candidate prognosticators that could be validated. We filtered all sets for γ with/? < 0.05; then mapped identical Affymetrix probes (data set 1, 2, 4, 5 and 6) or gene symbol (data set 2). Finally, we identified genes that occurred in both compared data sets, and showed the same direction of change in differential expression between recurrent and non-recurring samples. Overall, 152 of 185 (82.2%) genes were concordant across pairs of data sets (p<10 ^~ ). About one third of the 152 concordant genes correspond to those previously reported by others as related to outcome in prostate cancer. About a quarter may be in error (false discovery rate given that 31 of 185 were not concordant). Some sets of genes are functionally related to biological processes considered important in the progression of prostate cancer, exemplified by several members of the Wnt signal transduction pathway.

The enormous tissue percentage diversity among published data sets (all "tumor enriched" 5 sets had some samples with less than 30% tumor, according to our in silico analysis) and a frequent bias in tumor percentages between recurrent and non-recurrent cases (leading to any tumor-specific gene being erroneously associated with recurrence) provides two explanations for the previous struggle of the community to find a valid recurrence-specific signature in any one data set. o Gene Expression Quantification Using the QuantiGene Plex 2.0 assay. We have tested the sensitivity and the technical and biological accuracy of the assay using a panel of genes in a 10-Plex. The ten-gene panel included two housekeeping genes and eight genes with cell type percentage predictive power for prostate tumor, stroma, and BPH. The assay was performed on 12 fresh frozen prostate cancer samples and 9 FPEE samples with various amounts of 5 tumor, stroma, and BPH.

A standard curve for the housekeeping gene ribosomal protein S20 proved that the Plex 2.0 assay is highly reproducible and sensitive with a wide dynamic range (not shown).

Transcripts for all ten genes were accurately measured over a wide dynamic range when0 the template amount was over 33ng. The gene expression levels for all eight tissue-specific genes detected by either the Plex 2.0 assay, or the Affymetrix U133P2 array using the same RNA samples, had correlation coefficients ranging from 0.64 to 0.89. Moreover, all eight tissue-enriched genes showed good correlations with their respective cell type percentages in FFPE samples.These preliminary experiments demonstrate that the Plex 2.0 assay is a very5 sensitive and reproducible method, consistent with microarray data.

D. RESEARCH DESIGN AND METHODS. The thousands of tissue specific genes and over 150 candidate prognostic genes that we have identified will vary in their practical usefulness. F thermore, not all of these genes will translate to a particular assay platform,0 due to circumstances such as splicing variants that may not behave identically. This project will find a subset of high performance genes for our chosen assay strategy, gleaned from among the many high-confidence candidate genes we have identified.

We will convert the gene markers into an assay that can be easily adapted in a clinical lab, using the Plex 2.0 assay on FFPE samples (no RNA extraction or reverse transcription required). For probe validation, assays will be performed on 24 total RNA samples which already have previously reported microarray data. Probes that correlate best with the microarray data will be used to analyze 150 FFPE samples with annotated recurrence status (over a decade of post-surgery follow-up in most cases). A classifier that can distinguish indolent and/or aggressive cases will be developed and outcome prediction accuracy will be estimated by cross-validation.

Step 1. Select Candidate Genes for Further Validation. We have selected a list of gene biomarkers for further analysis, including 75 prognostic marker genes from our studies and 25 that are found in at least one of our datasets and in the literature, 30 tissue component prediction genes, and 4 houslφeping genes which represent relatively low, medium and high expression levels.

Step 2. QuantiGene Plex Assay Probe Design and Validation. Frozen Tissue Samples. 24 total RNA samples that already have Affymetrix gene expression data will be used in the Plex 2.0 assay. The RNA samples will be selected to encompass a wide range of tissue percentages and equal numbers of non-recurrent and recurrent cases. Probes of the Plex 2.0 assay will be designed by Panomics. Each panel of the Plex 2.0 assay will contain up to 36 genes. We will test four panels, totaling 130 or more candidate genes. The assay will be performed using our Bio-Plex system which relies on FACS sorting of fluorescently encoded beads.

Selection of Genes for Future Use. Genes that show significant correlation between the Plex assay and Affymetrix assay will be kept for further analysis. Genes with very low signal or low variance in these assays will be eliminated from further analysis. We will combine the top performing genes into three panels (36 genes per panel) for further study. If necessary, more potentially useful prognostic or tissue-enriched transcripts will be screened.

Step 3. Develop Classifiers for Recurrence Prediction. FFPE Samples. We will acquire a set of 150 archived prostate cancer samples from the SPECS study for validation. Two samples will be selected from each block. One will be tumor-enriched (>70% tumor cells) and the other stroma-enriched (>70% stroma cells near to tumor: "Reactive stroma") as estimated by pathologists. These blocks have 8-20 years of associated clinical data and represent a range of overall survival and time to recurrence. Gleason scores range from 5-8. Samples will be coded for blind analysis. Plex 2.0 Assays will be performed on the three panels of above selected genes.

Outcome Prediction. We will first use a subset of the samples with the pathologists' estimates of cell type percentages to develop linear models of cell type component prediction. Cell type percentages of the remaining samples will be estimated using these linear models and the most predictive markers will be identified to be retained in the ultimate clinical assay.

Samples will be divided into tumor-enriched samples, stroma-enriched samples. Those samples that prove not to be suitably enriched will be set aside. We will use the appropriate tissue-enriched samples to develop classifiers that distinguish aggressive and indolent cancers using Prediction Analysis for Microarrays (PAM) [17] and Support Vector Machine (SVM) [18, 19] approaches. Misclassification error will be estimated by the 10-fold cross- validation or the leave one out strategy. These tools will be implemented in R (3ltij)i//wwvv.r- ]>rojectΛ>rg/). Two classifiers will be developed, one for tumor-enriched samples and one for stroma-enriched samples.

We will also attempt in silico correction of transcript levels based on the tissue percentage markers present in each multiplex. We will attempt to adjust signals to reflect the tissue percentages by simple linear regression and determine if this variable improves disease outcome prediction. Pre- and post operation PSA, pathology T stage, and Gleason scores are available for all cases. Thus, using these parameters plus our RNA-based classifier, the nomogram-predicted disease free survival can be calculated.

Final predictive set. The initial four panels of up to 36 genes, each, will be reduced to three panels after initial screening. Then these three panels used in the FFPE study will be further condensed into just two panels that contain only useful genes for tissue percentage estimation and for prognosis: one panel for stroma-enriched samples and one for tumor- enriched samples. Both panels will measure up to 10 RNAs for estimating tissue percentage, 25 RNAs for prognosis, and 3 or more housekeeping controls.

Further studies.

Application to Biopsies. We have found biopsies to be an excellent source of RNA. If any stroma biomarkers are associated with recurrence, we will test the Plex 2.0 assay on 10 of our hundreds of snap frozen biopsy samples to determine technical feasibility. It is possible that biopsies that are negative for cancer may still have regions that are close enough to the missed tumor that they show "reactive" gene changes. This would revolutionize the assessment of patients that are negative for cancer upon biopsy.

More Sophisticated Class Prediction Algorithms. In this project, we propose to use in silico cell type composition prediction to estimate tumor percentages only for sample quality control. However, knowledge of tissue composition opens up opportunities for many intellectual advances in data analysis. We are developing a new classification method which takes advantage of cell composition information without rejecting any high quality data, and results in better performance than PAM and S VM -based predictions [20] .

Signaling Pathway Analysis for Understanding Prostate Cancer Progression. Our preliminary study on pathway analysis shows that our newly identified predictive markers for recurrence are significantly enriched for elements involved in cancer related pathways, exemplified by the Wnt signaling pathway. One of our long term goals is to explore the mechanisms of cancer-related pathways that are cross-validated in multiple data sets using tools such as DAVID (The Database for Annotation, Visualization and Integrated Discovery) [21, 22]. These pathways are potential targets for novel therapeutic treatment.

5 1. Unique in silico tissue composition prediction strategy based on gene expression profiling. Large variations in the proportion of tissue components in prostate cancer tissue samples lead to considerable noise and even misleading results in mining microarrays data for prognosticators. We have generated and validated linear models for tissue component estimations based on gene expression levels. Lists of 10-20 genes that define tumor, stroma o and BPH tissue, allow the proportion of each of these tissues to be determined from gene expression profiles, alone. This novel approach of in silico tissue component prediction will be used for quality control by determining the major cell components in each clinical RNA sample.

2. Unique prognostic gene biomarkers. Using a multiple linear regression model 5 which integrates tissue component percentages, we have identified a list of tumor- and reactive stroma-associated prognostic biomarkers, which can distinguish indolent and aggressive prostate cancer. Markers were then cross-validated between different microarray data sets produced by different research groups. Most of these prognostic markers were not previously identified by other studies. This is a simple and yet novel approach to find better,0 more precise, prognosticators for disease progression.

3. Accurate and sensitive multiple gene expression quantitation. A single prostate cancer prognostic marker is unlikely to be able to classify patients. Instead, a group of markers will be needed to account for the genetic variability of patients and the variability in cancer progression. The QuantiGene Plex 2.0 assay (Panomics, Inc) allows simultaneous5 quantification of multiple RNA targets directly from tissue homogenates. The assay does not require RNA purification, reverse transcription, or target amplification, because it combines branched DNA (bDNA) signal amplification technology and xMAP® (multi-analyte profiling) beads. The assay uses the FDA approved Luminex system already found in clinical labs. Our data prove the accuracy and sensitivity of the assay, and the ability to predict tissue proportions in FFPE samples. We will convert a large number of previously identified and successfully cross-validated prognostic genes into the QuantiGene assay system that can then be easily adopted by clinical labs. The QuantiGene assay gene panel will be tested on our 5 large collection of FFPE samples that have up to decades of patient data after surgery.

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5 Example 10 - Increasing Sample Size Does Not Boost Power If Confounding Factors Are

Not Controlled — A Study of Prostate Cancer with Microarray Analysis of prostate cancer data

We recently published a dataset for prostate cancer study (publicly available at GEO database with access number GSE8218) [3]. This dataset consists of 136 samples from 82 o patients who went through prostatectomy. Of these 82 patients, 45 underwent disease relapse, 33 did not and the remaining 4 were unknown. Here we used the 130 samples with definitive relapse status for this study. In some cases, more than one sample was collected from different regions of prostate of the same patient, for example, from tumor-enriched microdissected tissue and from nontumor tissue from ≥1.5 cm from tumor (usually the5 contralateral lobe). For each sample which was used for microarray assay, four pathologists independently reviewed the hematoxylin and eosin (H&E) stained sections and estimated the percentages of three major cell components, i.e., tumor, stroma and BPH. The goal of this study is to identify genes that are associated with disease progression in tumor cells or maybe in other types of cells which indicate gene expression changes in the tumor micro-0 environment [16].

At first, we did differential analysis on all the 130 samples using the LIMMA package (http://www.bioconductor.org) in R [5]. We identified 602 altered genes between relapse and non-relapse groups by the criterion of B > 0, where B represents log-likelihood- ratio of being differentially expressed versus being equivalently expressed. Thus, B > 05 indicates that the gene under consideration has altered expression between relapse and non- relapse groups. The same criterion applied to the gene selection in the subsequent analyses. We then randomly selected a subset of 40, 45, ..., 120, 125 samples from the data and carried out differential expression analysis respectively. If increase of sample size boosts power, we expect to see that more genes are detected when sample size becomes larger and the overlap0 of the signatures detected at different sample sizes is large, i.e., the circles and squares in Figure 12 are supposed to stay close to each other and go upward steadily. Nevertheless, as shown in Figure 12, the number of detected genes fluctuated as sample size increased with maximum detection (666 genes) when 120 randomly selected samples were used (circles). We compared different gene lists identified to the longest gene list of 666 genes in Figure 12 (squares) which showed only moderate overlap.

5 Next, we selected samples by stepwise enriching the tumor or stroma components which are two major types of cells in prostate tissue. Specifically, we used T ₅ k% (k = 0, 5, ..., 70, 75) as cutoff for sample selection, where T stands for the percentage for tumor component. The number of genes identified in each case were summarized in Figure 13 A. The maximum detection (602 genes) occurred when all 130 samples were included in the o analysis. However, the overlap between these 602 genes with the gene lists detected at other points were very low (the squares were very much separated from the circles). In particular, the overlap between these 602 genes with the gene lists detected for tumor enriched samples in the right half of the plot was very low, indicating that many of the

602 genes were false discoveries due to the diversity in terms of cell composition of5 samples. This suggested that employing all the 130 samples available is not the optimal strategy. However, there was another peak for the curve indicated by the circles when 40 samples (with tumor component greater than 35%) were used. The overlap between the detected genes at this point (as new reference gene list) with other gene lists near this point (sample size 22 to 49) was plotted in Figure 13B. The overlaps were high (≥80%, curves0 indicated by circles and squares stuck together within this region), suggesting consistent discoveries among these assays (Figure 13B). We observed that at the right end of the plot the number of detected genes rises at sample size = 17 and less but the overlap with the list of 247 genes (identified at sample size = 40; Table 33) kept dropping. This odd behavior was ascribed to the tiny sample size, for example, only 4 to 17 samples were included, which5 diminished power but enlarged chance of incurring false positives.

A similar phenomenon was observed when we investigate relapse-associated stromal genes. There were two peaks for the genes predicted to associated with recurrence (circles) at sample size 70 and 92 in the right half of the plot (stroma enriched samples). The overlap between the genes identified at these two points and gene lists around these two points (24 to0 106) were fairly high (>76%, see Figures 13C and 13D). In the left half of the plot, the detection rates were also high when most samples were included (sample size = 128 in Figure 13E; sample size = 130 in Figure 13F). However, the overlap between the detected genes at those points and gene lists identified at right end of the plot is very low, indicating that many detected genes were false positives if most samples were included. Note that the sample size at the right end of these plots is still reasonably large (34 to 60) compared to that of plots for genes putatively from tumor; therefore, we did not see the bending up of the curve indicated by the circles that occurs in Figures 13A-13B which indicated increased false positives. However, owing to the reduced power caused by fewer samples, many interested genes were missed (low detection rate at the right end of the plots compared to the detection rates when sample size = 70 to 92). The original paper dealt with the heterogeneous samples via using a multiple-linear- regression (MLR) model by which the observed Affymetrix gene expression values are described as linear combination of the contribution from different types of cells [3] [17]. Specifically, the following model was applied to the expression data for each gene, g = b _o + ∑b _]P] + l {RS = l)x∑ γ _]P] +ε, (1)

where g is the observed expression for a gene, b ₀ is the grand mean, C = 3 indicating 3 types of cell component, p ₃ is the percentage of cell type j, b _} represent the expression of this gene in cell type j when the case is non-relapse, γ _} is the extra expression (either up- or down-regulated) in cell type j when the case relapses, and finally 1(RS = 1) is an indicator variable with / = 1 if the case relapses (denoted by RS = 1) and / = 0 if the case does not recur (denoted by RS = 0). We reanalyzed the data with exactly the same method and detected 119 relapse-associated genes in tumor and 247 relapse-associated gene in stroma. These two gene lists have 36 and 169 genes in common respectively with the 247 genes identified for tumor (sample size = 40 in Figure 13B) and 666 genes identified in stroma (sample size = 70 in Figure 13C) by t-test. We considered that the MLR analysis was more desirable than t-test (e.g., LIMMA) because (1) using the percentage data as covariates for regression analysis is more accurate than selecting samples based on the percentage cutoff, and (2) all samples are effectively used for calculation leading to increased power. However, precise percentage estimation data are not commonly available for many studies; in most cases, samples were only roughly classified into either tumor-enriched or stroma-enriched categories. Therefore, t-test still applies prevalently. To compare the results from these two analyses (t-test based on enriched samples and MLR), we added green/gold curve to each plot of Figure 12 and Figure 13 denoting the overlap between each identified gene lists by t-test and tumor/stroma genes identified with MLR. Here we assume that cell-type specific genes identified with

MLR are more reliable based on above reasoning; thus, we try to validate results of t- test by MLR results. For random experiment (Figure 12), the overlaps were limited and did not demonstrate any visible pattern as sample size increased. However, for stepwise enrichment experiment (Figure 13), the overlaps were much improved and showed bell- shaped pattern as expected (with maximum at peaks of blue curves Figure 13B- 13D). We presume that these 247 tumor genes and 666 stroma genes identified by t-test were most close to reality because the optimal subset of samples were used by balancing sample size and homogeneity between samples. We also calculated the empirical /rvalues for the overlap between tumor/stroma gene lists identified with these two approaches as follows.

Suppose we calculate significance level for overlap of two tumor gene lists, i.e., 119 genes by MLR and 247 genes by t-test. Let count = 0. From -22,000 genes, we randomly selected two gene lists of length 119 and 247, respectively. Not that 119 and 247 are the lengths of genes identified separately by t-test and MLR. If the overlap of the two randomly selected gene lists is equal or greater than 36 (observed overlap between these two tumor gene lists), we let count increase by 1. We repeated this process 10,000 times and the/?-value of the observed overlap of tumor genes is calculated as p = count /10000.

By the same means, we calculated the significance level for overlap of two stroma gene lists as well. Both /rvalues for tumor overlapping genes and stroma overlapping genes were < 0.0001. This again verified the discoveries by t-test with stepwise enriched samples. Simulated study In this section, we generated a dataset consisting of 200 samples each of which is composed of three types of cells. This is to mimic the situation we are facing for prostate cancer study. We randomly assigned the 200 samples into either case group (denoted by 1) or control group (denoted by 0). Here case means aggressive prostate cancers which will progress even after surgical removal prostate gland; while control denotes indolent prostate cancer which will not recur after prostatectomy. For each sample, the percentages of three cell types were simulated as follows. We let cell type 3 (BPH) be the minority cell which takes up tolO% volume in tissues; thus, we first generated the percentage of cell type 3 (x3) from uniform distribution U(O, 0.1). We then generated the percentage of cell type 1 (xl for tumor) from U(O, 1 - x3), and the percentage of cell type 2 (x2 for stroma) is therefore 1 - xl - x3. For each sample, we simulated expression data for 1000 gene as follows. We let gene 1 5 to 60 have altered expression in cell type 1 between case and control. The differences in terms of expression for gene 1 to 20, gene 21 to 40 and gene 41 to 60 are set to 0.5, 1.0 and 2.0, respectively. The same setting was used for generating differentially expressed genes for cell type 2 (gene 61 to 120). Due to the small load for cell type 3, we assume that the difference in cell type 3 between case and control is undetectable, so we did not simulate o differentially expressed genes for cell type 3.

First, we randomly selected a subset of 40, 50, ..., 190, 200 samples from the data and carried out differential expression analysis using LIMMA. The sensitivity, specificity and false discovery rate had been logged in each situation. Such analysis was repeated 100 times and the average operating characteristic is summarized in Figure 14. The sensitivity or power5 went up as sample size increased, however, the detection rate was limited (maximum 46.7%). Note that the specificity and false discovery rate were steadily satisfactory (very close to 0).

Considering the heterogeneity in cell composition, we then selected samples by stepwise enriching one type of cell. Specifically, we included samples with xl ₅ k% (k = 0, 5, ..., 85, 90) in expression comparison procedure, and then identified genes that are 0 differentially expressed in cell type 1 between case and control. With varying cutoff, the number of samples included in analysis and the sensitivity or power achieved by these samples are summarized in Table 32. Obviously, the maximum sensitivity or power is 73.3% which is much higher than any figures attained by randomly selected sample in Figure 14. In addition, the maximum sensitivity or power achieved when xl ₅ 65%, neither too small nor5 too large in terms of the content of cell type 1 (or the number of samples included in the calculation). If the selected cutoff is too small, most samples will be included. This is like what we observed in previous assay when sample size is close to upper limit (see Figure 14). In this case, the variation caused by mixed tissue is likely to impair detection power. However, if the selected cutoff is too large, too few samples will be included in the analysis,0 leading to a reduced power. For example, if we use xl ₅ 90% for sample selection, only 9 samples (5 controls and 4 cases) were selected. The sensitivity or power in this situation is only 43%. This is very similar to the observation in prostate cancer data analysis which showed a bending-down detection curve when sample size is near 0 (Figure 13A- 13B). There is a trade off between size and level of homogeneity of samples. Both factors positively contribute to power but never benefit from each other as if type I and type II errors in statistical hypothesis test. This lesson tells us that carefully selecting samples from resource is superior to utilizing all available samples indiscriminately.

Finally, we applied MLR to the simulated data and the results were much improved compared to the regular t-test with enriched samples (Table 32). This is what we expected and attested plausibility of validating results of t-test by using results of MLR analysis.

Table 32. Operating characteristics for MLR analysis.

Sensitivity Specificity

Tumor genes 91.7% 96.0% Stroma genes 96.7% 96.0%

References

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3. Stuart, R.O., Wachsman, W., Berry, CC, Wang-Rodriguez, J., Wasserman, L., Klacansky, L, Masys, D., Arden, K., Goodison, S., McClelland, M., Wang, Y.P., Sawyers, A., Kalcheva, L, Tarin, D., Mercola, D.: In silico dissection of cell-type-associated patterns of gene expression in prostate cancer. Proceedings of the National Academy of Sciences of the United States of America 101(2) (2004) 615-620

4. Koziol, J.A., Feng, A.C, Jia, Z. Y., Wang, Y.P., Goodison, S., McClelland, M., Mercola, D.: The wisdom of the commons: ensemble tree classifiers for prostate cancer prognosis. Bioinformatics 25(1) (2009) 54-60 5. Smyth, G. K.: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3 (2004) Article 3

6. Tusher, V.G., Tibshirani, R., Chu, G.: Significance analysis of microarrays applied to the ionizing radiation response. Proceedings of the National Academy of Sciences of the United

States of America 98 (2001) 5116-5121

7. Jia, Z., Xu, S.: Bayesian mixture model analysis for detecting differentially expressed genes. International Journal of Plant Genomics 2008 (2008) Article ID 892927, 12 pages

8. Fan, C, Oh, D.S., Wessels, L., Weigelt, B., Nuyten, D.S.A., Nobel, A.B., van't Veer, LJ., Perou, CM.: Concordance among gene-expression-based predictors for breast cancer. New England Journal of Medicine 355(6) (2006) 560-569

9. Chang, H. Y., Sneddon, J.B., Alizadeh, A.A., Sood, R., West, R.B., Montgomery, K., Chi, J.T., van de Rijn, M., Botstein, D., Brown, P.O.: Gene expression signature of fibroblast serum response predicts human cancer progression: Similarities between tumors and wounds. Plos Biology 2(2) (2004) 206-214

10. Paik, S., Shak, S., Tang, G., Kim, C, Baker, J., Cronin, M., Baehner, F.L., Walker, M.G., Watson, D., Park, T., Hiller, W., Fisher, E.R., Wickerham, D.L., Bryant,

J., Wolmark, N.: A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. New England Journal of Medicine 351(27) (2004) 2817-2826 11. Sorlie, T., Perou, CM., Tibshirani, R., Aas, T., Geisler, S., Johnsen, H., Hastie, T., Eisen, M.B., van de Rijn, M., Jeffrey, S.S., Thorsen, T., Quist, H., Matese, J.C., Brown, P.O., Botstein, D., Lonning, P.E., Borresen-Dale, A.L.: Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proceedings of the National Academy of Sciences of the United States of America 98(19) (2001) 10869-10874 12. Sorlie, T., Tibshirani, R., Parker, J., Hastie, T., Marron, J.S., Nobel, A., Deng, S., Johnsen, H., Pesich, R., Geisler, S., Demeter, J., Perou, CM., Lonning, P.E., Brown, P.O., Borresen-Dale, A.L., Botstein, D.: Repeated observation of breast tumor subtypes in independent gene expression data sets. Proceedings of the National Academy of Sciences of the United States of America 100(14) (2003) 8418-8423 13. Sotiriou, C, Neo, S.Y., McShane, L.M., Korn, E.L., Long, P.M., Jazaeri, A., Martiat, P., Fox, S.B., Harris, A.L., Liu, E.T.: Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proceedings of the National Academy of Sciences of the United States of America 100(18) (2003) 10393-10398

14. van de Vijver, MJ., He, Y.D., van 't Veer, LJ., Dai, H., Hart, A.A.M., Voskuil, D.W., Schreiber, GJ., Peterse, J.L., Roberts, C, Marton, MJ., Parrish, M., Atsma, D., Witteveen, A., Glas, A., Delahaye, L., van der Velde, T., Bartelink, H., Rodenhuis, S., Rutgers, E.T., Friend, S.H., Bernards, R.: A gene-expression signature as a predictor of survival in breast cancer. New England Journal of Medicine 347(25) (2002) 1999-2009

15. van't Veer, LJ., Dai, H. Y., van de Vijver, MJ., He, Y.D.D., Hart, A.A.M., Mao, M., Peterse, H.L., van der Kooy, K., Marton, MJ., Witteveen, A.T., Schreiber, GJ., Kerkhoven, R.M., Roberts, C, Linsley, P. S., Bernards, R., Friend, S. H.: Gene expression profiling predicts clinical outcome of breast cancer. Nature 415(6871) (2002) 530-536

16. Cunha, G.R., Hayward, S.W., Wang, Y.Z., Ricke, W.A.: Role of the stromal microenvironment in carcinogenesis of the prostate. International Journal of Cancer 107(1) (2003) 1-10 17. Jia, Z., Wang, Y., Koziol, J., McClelland, M., Mercola, D.: A new bi-model classifier for predicting outcomes of prostate cancer patients, in JSM Proceedings, Biometrics Section. Denver, CO: American Statistical Association. (2008)

Table 33. Prognostic prostate cancer genes (biomarkers) in stroma cells identified by t-test following triage of training cases based on calculated low tumor cell percentage

Probe.Set.ID Gene.Title

9212 209724 _s_at zinc finger protein 161 homolog (mouse)

8569 209075 _s_at iron-sulfur cluster scaffold homolog (E. coli)

5558 206031 _s_at ubiquitin specific peptidase 5 (isopeptidase T)

2137 202609 _at epidermal growth factor receptor pathway substrate 8

17587 218222 _x_at aryl hydrocarbon receptor nuclear translocator

20870 221507 _at transportin 2 (importin 3, karyopherin beta 2b)

3319 203792 _x_at polycomb group ring finger 2

254 200726 _at protein phosphatase 1 , catalytic subunit, gamma isoform

687 201159 _s_at N-myristoyltransferase 1

18431 219067 _s_at non-SMC element 4 homolog A (S. cerevisiae)

9148 209659 _s_at cell division cycle 16 homolog (S. cerevisiae)

10469 211023 _at pyruvate dehydrogenase (lipoamide) beta

21176 221816 _s_at PHD finger protein 11

3636 204109 _s_at nuclear transcription factor Y, alpha

11450 212064 _x_at MYC-associated zinc finger protein (purine-binding transcription factor)

4295 204768 _s_at flap structure-specific endonuclease 1

12711 213330 _s_at stress-induced-phosphoprotein 1 (Hsp70/Hsp90-organizing protein)

18080 218716 _x_at mitochondrial translation optimization 1 homolog (S. cerevisiae)

728 201200 _at cellular repressor of E1 A-stimulated genes 1

1825 202297 _s_at RER1 retention in endoplasmic reticulum 1 homolog (S. cerevisiae)

18419 219055 _at S1 RNA binding domain 1

3811 204284 _at protein phosphatase 1 , regulatory (inhibitor) subunit 3C

8782 209288 _s_at CDC42 effector protein (Rho GTPase binding) 3

12103 212718 _at poly(A) polymerase alpha

3791 204264 _at carnitine palmitoyltransferase Il

17188 217823 _s_at ubiquitin-conjugating enzyme E2, J1 (UBC6 homolog, yeast)

21817 34868_at Smg-5 homolog, nonsense mediated mRNA decay factor (C. elegans)

12250 212865 _s_at collagen, type XIV, alpha 1

11396 212009 s at stress-induced-phosphoprotein 1 (Hsp70/Hsp90-organizing protein)

11407 212021 _s_at antigen identified by monoclonal antibody Ki-67

21773 32541_at protein phosphatase 3 (formerly 2B), catalytic subunit, gamma isoform

15404 216032 _s_at ERGIC and golgi 3

2460 202931 _x_at bridging integrator 1

17360 217995 _at sulfide quinone reductase-like (yeast)

8725 209231 _s_at dynactin 5 (p25)

21295 221935 _s_at chromosome 3 open reading frame 64

22178 65517_at adaptor-related protein complex 1 , mu 2 subunit

20785 221422 _s_at chromosome 9 open reading frame 45

17290 217925 _s_at chromosome 6 open reading frame 106

2905 203378 _at PCF1 1 , cleavage and polyadenylation factor subunit, homolog (S. cerevisiae)

14114 214738 _s_at NIMA (never in mitosis gene a)- related kinase 9

2706 203178 _at glycine amidinotransferase (L-arginine:glycine amidinotransferase)

19211 219847 _at histone deacetylase 1 1

17855 218490 _s_at zinc finger protein 302

10113 210648 _x_at sorting nexin 3

20886 221523 _s_at Ras-related GTP binding D

11565 212179 _at splicing factor, arginine/serine-rich 18

19134 219770 _at glycosyltransferase-like domain containing 1

5199 205672 _at xeroderma pigmentosum, complementation group A

3167 203640 _at muscleblind-like 2 (Drosophila)

10433 210986 _s_at tropomyosin 1 (alpha)

88 200067 _x_at sorting nexin 3

13818 214439 _x_at bridging integrator 1

2399 202871 _at TNF receptor-associated factor 4

11570 212184 _s_at mitogen-activated protein kinase kinase kinase 7 interacting protein 2

9418 209932 _s_at deoxyuridine triphosphatase

21148 221788 _at CDNA FLJ11614 fis, clone HEMBA1004015

12476 213093 _at protein kinase C, alpha

13966 214588 _s_at Microfibri liar-associated protein 3

2851 203324 _s_at caveolin 2

21207 221847 _at hypothetical protein LOC100129361

18159 218795 _at acid phosphatase 6, lysophosphatidic

11533 212147 _at Smg-5 homolog, nonsense mediated mRNA decay factor (C. elegans)

873 201345 s at ubiquitin-conjugating enzyme E2D 2 (UBC4/5 homolog, yeast)

14634 215260_s_at transcription factor 3 (E2A immunoglobulin enhancer binding factors E12/E47)

16339 216969 _s_at kinesin family member 22

12895 213514 _s_at diaphanous homolog 1 (Drosophila)

1911 202383 _at jumonji, AT rich interactive domain 1 C

11497 212111 _at syntaxin 12

4074 204547 _at RAB40B, member RAS oncogene family

19713 220349 _s_at endo-beta-N-acetylglucosaminidase

6528 207002 _s_at pleiomorphic adenoma gene-like 1

17271 217906 _at kelch domain containing 2

7906 208405 _s_at CD164 molecule, sialomucin

9685 210201 _x_at bridging integrator 1

12557 213175 _s_at small nuclear ribonucleoprotein polypeptides B and B1

5636 206110 _at histone cluster 1 , H3h

3411 203884 _s_at RAB11 family interacting protein 2 (class I)

795 201267 _s_at proteasome (prosome, macropain) 26S subunit, ATPase, 3

4490 204963 _at sarcospan (Kras oncogene-associated gene)

14375 215000 _s_at fasciculation and elongation protein zeta 2 (zygin II)

21934 39549_at neuronal PAS domain protein 2

9513 210028 _s_at origin recognition complex, subunit 3-like (yeast)

14256 214881 _s_at upstream binding transcription factor, RNA polymerase I

9676 210192 _at ATPase, aminophospholipid transporter (APLT), class I, type 8A, member 1

17714 218349 _s_at Zwilch, kinetochore associated, homolog (Drosophila)

758 201230 _s_at ariadne homolog 2 (Drosophila)

6748 207223 _s_at ROD1 regulator of differentiation 1 (S. pombe)

11624 212238 _at additional sex combs like 1 (Drosophila)

9009 209516 _at SMYD family member 5

9763 210283 _x_at poly(A) binding protein interacting protein 1 /// hypothetical LOC645139 /// similar to poly(A) binding protein interacting protein 1 isof

2347 202819 _s_at transcription elongation factor B (SIII), polypeptide 3 (1 1 OkDa, elongin A)

3641 204114 _at nidogen 2 (osteonidogen)

17544 218179 _s_at chromosome 4 open reading frame 41

2420 202892 _at cell division cycle 23 homolog (S. cerevisiae)

17880 218515 _at chromosome 21 open reading frame 66

12084 212699 _at secretory carrier membrane protein 5

18062 218698 _at APAF1 interacting protein

5138 205611 at tumor necrosis factor (ligand) superfamily, member 12

8201 208706_s_at eukaryotic translation initiation factor 5

13554 214175 _x_at PDZ and LIM domain 4

4466 204939 _s_at phospholamban

8451 208956 _x_at deoxyuridine triphosphatase

10085 210620 _s_at general transcription factor NIC, polypeptide 2, beta 1 1 OkDa

17458 218093 _s_at ankyrin repeat domain 10

19049 219685 _at transmembrane protein 35

20799 221436 _s_at cell division cycle associated 3

17196 217831 _s_at NSFL1 (p97) cofactor (p47)

8707 209213 _at carbonyl reductase 1

11700 212315 _s_at nucleoporin 21 OkDa

12779 213398 _s_at chromosome 14 open reading frame 124

17874 218509 _at lipid phosphate phosphatase-related protein type 2

12018 212633 _at KIAA0776

11483 212097 _at caveolin 1 , caveolae protein, 22kDa

11077 211675 _s_at MyoD family inhibitor domain containing

13258 213878 _at Pyridine nucleotide-disulphide oxidoreductase domain 1

3045 203518 _at lysosomal trafficking regulator

13715 214336 _s_at coatomer protein complex, subunit alpha

6056 206530 _at RAB30, member RAS oncogene family

21792 33760_at peroxisomal biogenesis factor 14

12821 213440 _at RAB1 A, member RAS oncogene family

11882 212497 _at mitogen-activated protein kinase 1 interacting protein 1 -like

2181 202653 _s_at membrane-associated ring finger (C3HC4) 7

1361 201833 _at histone deacetylase 2

5330 205803 _s_at transient receptor potential cation channel, subfamily C, member 1

2493 202964 _s_at regulatory factor X, 5 (influences HLA class Il expression)

18531 219167 _at RAS-like, family 12

14074 214698 _at ROD1 regulator of differentiation 1 (S. pombe)

7438 207922 _s_at macrophage erythroblast attacher

17412 218047 _at oxysterol binding protein-like 9

2057 202529 _at phosphoribosyl pyrophosphate synthetase-associated protein 1

2857 203330 _s_at syntaxin 5

462 200934 _at DEK oncogene (DNA binding)

11200 211804 s at cyclin-dependent kinase 2

535 201007 _at hydroxyacyl-Coenzyme A dehydrogenase/3-ketoacyl-Coenzyme A thiolase/enoyl-Coenzyme A hydratase (trifunctional protein), beta

3466 203939_ .at 5'-nucleotidase, ecto (CD73)

12354 212971_ .at cysteinyl-tRNA synthetase

1302 201774_ _s_at non-SMC condensin I complex, subunit D2

3552 204025_ _s_at programmed cell death 2

13816 214437_ _s_at serine hydroxymethyltransferase 2 (mitochondrial)

3313 203786_ _s_at tumor protein D52-like 1

550 201022_ _s_at destrin (actin depolymerizing factor)

11942 212557_ _at zinc finger protein 451

450 200922_ .at KDEL (Lys-Asp-Glu-Leu) endoplasmic reticulum protein retention receptor 1

20636 221273_ _s_at ring finger protein 208 /// similar to ring finger protein 208

2546 203017_ _s_at synovial sarcoma, X breakpoint 2 interacting protein

10425 210978_ _s_at transgelin 2

20106 220742_ _s_at N-glycanase 1

6380 206854_ _s_at mitogen-activated protein kinase kinase kinase 7

12864 213483_ .at peptidylprolyl isomerase domain and WD repeat containing 1

19458 220094 _s_at coiled-coil domain containing 9OA

4482 204955, .at sushi-repeat-containing protein, X-linked

3927 204400_ .at embryonal Fyn-associated substrate

20553 221190_ _s_at chromosome 18 open reading frame 8

14854 215481_ _s_at peroxisomal biogenesis factor 5

9947 210470_ _x_at non-POU domain containing, octamer-binding

7458 207943_ _x_at pleiomorphic adenoma gene-like 1

18479 219115_ _s_at interleukin 20 receptor, alpha

1794 202266_ _at TRAF and TNF receptor associated protein

18133 218769_ _s_at ankyrin repeat, family A (RFXANK-like), 2

7033 207511_ _s_at chromosome 2 open reading frame 24

11562 212176_ .at splicing factor, arginine/serine-rich 18

4578 205051_ _s_at v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog

1960 202432 _at protein phosphatase 3 (formerly 2B), catalytic subunit, beta isoform

7579 208070, _s_at REV3-like, catalytic subunit of DNA polymerase zeta (yeast)

1655 202127_ .at PRP4 pre-mRNA processing factor 4 homolog B (yeast)

14198 214823_ _at zinc finger protein 204 (pseudogene)

4467 204940 _at phospholamban

19299 219935 at ADAM metallopeptidase with thrombospondin type 1 motif, 5 (aggrecanase-2)

12388 213005 _s_at KN motif and ankyrin repeat domains 1

3233 203706_ _s_at frizzled homolog 7 (Drosophila)

16813 217448_ _s_at TOX high mobility group box family member 4 /// similar to KIAA0737 protein

20865 221502_ _at karyopherin alpha 3 (importin alpha 4)

11630 212244 _at glutamate receptor, ionotropic, N-methyl D-aspartate-like 1A /// GRINL1 A combined protein

1593 202065_ _s_at protein tyrosine phosphatase, receptor type, f polypeptide (PTPRF), interacting protein (liprin), alpha 1

8726 209232_ _s_at dynactin 5 (p25)

17131 217766_ _s_at transmembrane protein 5OA

3776 204249_ _s_at LIM domain only 2 (rhombotin-like 1 ) deleted in azoospermia 1 /// deleted in azoospermia 3 /// deleted in azoospermia 2 /// deleted in azoospermia 4 /// similar to deleted i

7785 208281_ _x_at like

17228 217863_ _at protein inhibitor of activated STAT, 1

14501 215127_ _s_at RNA binding motif, single stranded interacting protein 1

13906 214527_ _s_at polyglutamine binding protein 1

12674 213293_ _s_at tripartite motif-containing 22

6464 206938_ _at steroid-5-alpha-reductase, alpha polypeptide 2 (3-oxo-5 alpha-steroid delta 4-dehydrogenase alpha 2)

2711 203183_ _s_at SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily d, member 1

12083 212698_ _s_at septin 10

9042 209550, .at necdin homolog (mouse)

11083 211681_ _s_at PDZ and LIM domain 5

20841 221478_ _at BCL2/adenovirus E1 B 19kDa interacting protein 3-like

18981 219617_ .at chromosome 2 open reading frame 34

13702 214323_ _s_at UPF3 regulator of nonsense transcripts homolog A (yeast)

8662 209168_ .at glycoprotein M6B

13151 213771_ _at interferon regulatory factor 2 binding protein 1

20946 221584_ _s_at potassium large conductance calcium-activated channel, subfamily M, alpha member 1

1131 201603, .at protein phosphatase 1 , regulatory (inhibitor) subunit 12A

20510 221147_ _x_at WW domain containing oxidoreductase

14312 214937_ _x_at pericentriolar material 1

19162 219798_ _s_at methylphosphate capping enzyme

20996 221634_ .at ribosomal protein L23a pseudogene 7

17452 218087_ _s_at sorbin and SH3 domain containing 1

975 201447_ _at TIA1 cytotoxic granule-associated RNA binding protein

3991 204464 _s_at endothelin receptor type A

4563 205036 at LSM6 homolog, U6 small nuclear RNA associated (S. cerevisiae)

19141 219777__at GTPase, IMAP family member 6

11488 212102_ _s_at karyopherin alpha 6 (importin alpha 7)

1730 202202_ _s_at laminin, alpha 4

6437 206911_ _at tripartite motif-containing 25

15666 216294_ _s_at KIAA1 109

2220 202692_ _s_at upstream binding transcription factor, RNA polymerase I

8786 209292_ .at Inhibitor of DNA binding 4, dominant negative helix-loop-helix protein

1846 202318_ _s_at SUMO1/sentrin specific peptidase 6

12643 213262_ _at spastic ataxia of Charlevoix-Saguenay (sacsin)

12288 212904_ .at leucine rich repeat containing 47

5630 206104_ .at ISL LIM homeobox 1

15760 216389_ _s_at WD repeat domain 23

3217 203690_ _at tubulin, gamma complex associated protein 3

1721 202193_ .at LIM domain kinase 2

12866 213485 _, _s_at ATP-binding cassette, sub-family C (CFTR/MRP), member 10

18742 219378_ .at NMDA receptor regulated 1 -like

15919 216549_ _s_at TBC1 domain family, member 22B

3932 204405_ _x_at DIM1 dimethyladenosine transferase 1 -like (S. cerevisiae)

12080 212695_ .at cryptochrome 2 (photolyase-like)

12365 212982_ .at zinc finger, DHHC-type containing 17

14210 214835_ _s_at succinate-CoA ligase, GDP-forming, beta subunit

8870 209377 _s_at high mobility group nucleosomal binding domain 3

4427 204900 _, _x_at Sin3A-associated protein, 3OkDa

2850 203323 _, .at caveolin 2

3965 204438_ _at mannose receptor, C type 1 /// mannose receptor, C type 1 -like 1

17047 217682_ _at CDNA FLJ37032 fis, clone BRACE201 1265

1661 202133_ .at WW domain containing transcription regulator 1

17157 217792_ .at sorting nexin 5

18811 219447_ _s_at solute carrier family 35, member C2 /// hypothetical protein LOC100128167

1890 202362_ _at RAP1 A, member of RAS oncogene family

10969 211564_ _s_at PDZ and LIM domain 4

11680 212294_ .at guanine nucleotide binding protein (G protein), gamma 12

1095 201567_ _s_at golgi autoantigen, golgin subfamily a, 4

8812 209318 _x_at pleiomorphic adenoma gene-like 1

2833 203306 s at solute carrier family 35 (CMP-sialic acid transporter), member A1

4220 204693 _at CDC42 effector protein (Rho GTPase binding) 1

5568 206042 _x_ at small nuclear ribonucleoprotein polypeptide N /// SNRPN upstream reading frame

20179 220815 _at catenin (cadherin-associated protein), alpha 3

279 200751 _s_ at heterogeneous nuclear ribonucleoprotein C (C1/C2)

12687 213306 _at multiple PDZ domain protein

9307 209821 _at interleukin 33

18058 218694 _at armadillo repeat containing, X-linked 1

1678 202150 _s_ at neural precursor cell expressed, developmentally down-regulated 9

11506 212120 at ras homolog gene family, member Q

Table 34. Prognostic prostate cancer genes (biomarkers) in stroma cells identified by t-test following triage of training cases based on calculated low stroma cell percentage

The multiple linear regression method was extended to divide tumor cases into those with good outcome (never relapsed following surgery, i.e. appear to be cured) from bad outcome, i.e. in several months or years following surgery their tumor reappeared. The genes that are specifically differentially expressed in the bad outcome cases were identified (the list). These genes or a subset of them may be measure in a new patient to determine whether he matches a good or bad outcome profile. In summary, differences in RNA levels that correlated with relapse versus non-relapse were calculated for four expression microarray data sets (data set 1, 2, 3 and 4) using multiple linear regression models which used these percentages in a linear model. Many of these relapse-associated changes in transcript levels occurred in adjacent stroma. Data set 3 does not have pathologist's estimation of tissue percentage and in silico tissue prediction model was used to predict tissue percentages. The identified genes are listed in Tables 35-42.

Table 35 - dataset 1 (stroma) log base 2 uniquelD GeneSymb GeneDesc fold change probS

203381_ _s_at APOE apolipoprotein E 2.912890091 4.87E-05

201465_ _s_at JUN v-jun sarcoma virus 17 oncogene homolog (avian) 2.770294931 0.005679 likely ortholog of mouse limb-bud and heart gene /// likely ortholog of

221011 _s_at LBH mouse limb-bud and heart gene 2.758949055 0.008186

203382_ _s_at APOE apolipoprotein E 2.683386924 2.64E-06

201428_ .at CLDN4 claudin 4 2.681309084 4.34E-05

203953 _s_at CLDN3 claudin 3 2.547551209 0.009406

203400_ _s_at TF transferrin 2.544986614 0.000238

200923_ .at LGALS3BP lectin, galactoside-binding, soluble, 3 binding protein 2.278659056 3.32E-05

212016_ _s_at PTBP1 polypyrimidine tract binding protein 1 2.119201234 0.004097

213986 _s_at C19orf6 chromosome 19 open reading frame 6 2.088646742 0.005784

203954_ _x_at CLDN3 claudin 3 2.084660768 0.001916

219127_ .at MGC1 1242 hypothetical protein MGC1 1242 2.072553416 0.002204

201670_ _s_at MARCKS myristoylated alanine-rich protein kinase C substrate 2.071304593 0.003602

200796_ _s_at MCL1 myeloid cell leukemia sequence 1 (BCL2-related) 2.065871088 0.000335

21 1919 s at CXCR4 chemokine (C-X-C motif) receptor 4 /// chemokine (C-X-C motif) receptor 2.007551333 0.005134

208960_ _s_at KLF6 Kruppel-like factor 6 1 .98241705 0.001315

203878 _. _s_at MMP1 1 matrix metallopeptidase 11 (stromelysin 3) 1.946981553 0.000644

208961_ _s_at KLF6 Kruppel-like factor 6 1.928324021 0.001944

201418_ _s_at SOX4 SRY (sex determining region Y)-box 4 1.886396014 0.00012

209201_ _x_at CXC R4 chemokine (C-X-C motif) receptor 4 1.875714407 0.004034

218010_ _x_at C20orf149 chromosome 20 open reading frame 149 1.875322213 0.001173

201169_ _s_at BHLHB2 basic helix-loop-helix domain containing, class B, 2 1.860022182 0.001173

TRBV21 -1 ///

TRBV19 ///

TRBV5-4 /// T cell receptor beta variable 21 -1 /// T cell receptor beta variable 19 /// T

TRBV3-1 /// cell receptor beta variable 5-4 /// T cell receptor beta variable 3-1 /// T

21 1796_ _s_at TRBC1 cell receptor beta constant 1 1.852987215 0.006799

210827_ _s_at ELF3 E74-like factor 3 (ets domain transcription factor, epithelial-specific ) 1.852964164 0.00592

201235_ _s_at BTG2 BTG family, member 2 1.834631974 0.005075

204103 at CCL4 chemokine (C-C motif) ligand 4 1.830777096 0.002071 major histocompatibility complex, class II, DQ beta 1 /// major

21 1656_x_at HLA-DQB1 histocompatibility complex, class II, DQ beta 1 1.826633903 0.000461

217478_s_at HLA-DMA major histocompatibility complex, class II, DM alpha 1.804916186 0.002235

21 1991_s_at HLA-DPA1 major histocompatibility complex, class II, DP alpha 1 1.778213183 0.009104

201467_s_at NQO1 NAD(P)H dehydrogenase, quinone 1 1.724578298 0.008336

214063 s at TF transferrin 1.719846565 5.39E-06 aldo-keto reductase family 1 , member C1 (dihydrodiol dehydrogenase 1 ;

204151_ _x_at AKR1 C1 20-alpha (3-alpha)-hydroxysteroid dehydrogenase) 1.703218877 0.005958

213002_ .at MARCKS Myristoylated alanine-rich protein kinase C substrate 1.695018082 0.003443

212884_ _x_at APOE Apolipoprotein E 1.690228647 3.37E-05

210130 _s_at TM7SF2 transmembrane 7 superfamily member 2 1.636804642 0.0033

203876_ _s_at MMP1 1 matrix metallopeptidase 11 (stromelysin 3) 1.633132547 0.004348 chromosome 19 open reading frame 27 /// chromosome 19 open

221267_ _s_at C19orf27 reading frame 27 1.591104024 0.000266

203206_ _at FAM53B family with sequence similarity 53, member B 1.546295766 0.009117 integrin, beta 2 (antigen CD18 (p95), lymphocyte function-associated

202803_ _s_at ITGB2 antigen 1 ; macrophage antigen 1 (mac-1 ) beta subunit) 1.525053858 0.001955

204805_ _s_at H1 FX H1 histone family, member X 1 .51 177251 0.000833

203085 s at TGFB1 transforming growth factor, beta 1 (Camurati-Engelmann disease) 1.496141641 0.006601

201 102 _s_at PFKL phosphofructokinase, liver 1.494139392 0.003596

222037 _at MCM4 MCM4 minichromosome maintenance deficient 4 (S. cerevisiae) 1.492902786 0.008226 ras-related C3 botulinum toxin substrate 2 (rho family, small GTP

213603 _s_at RAC2 binding protein Rac2) 1.421863261 0.001516

211065 _x_at PFKL phosphofructokinase, liver /// phosphofructokinase, liver 1.419074476 0.003626

209872 _s_at PKP3 plakophilin 3 1 .41708755 0.003264

218500 _at C8orf55 chromosome 8 open reading frame 55 1 .40325073 0.000829

222001 _x_at ... LOC440669 1.381519268 0.001264

201721 _s_at LAPTM5 lysosomal associated multispanning membrane protein 5 1.364245523 0.004506

204480 _s_at C9orf16 chromosome 9 open reading frame 16 1.3621 19551 0.002395

202424 _at MAP2K2 mitogen-activated protein kinase kinase 2 1.348325659 0.00821 1

218400 _at OAS3 2'-5'-oligoadenylate synthetase 3, 10OkDa 1.347681406 0.006973

218388 _at PGLS 6-phosphogluconolactonase 1.331769877 0.003923

1405_i_ .at CCL5 chemokine (C-C motif) ligand 5 1.330972453 0.006929

209083 _at CORO1 A coronin, actin binding protein, 1 A 1.330478184 0.00188

217580 _x_at ARL6IP2 ADP-ribosylation factor-like 6 interacting protein 2 1.32680891 1 0.000724

212575 _at C19orf6 chromosome 19 open reading frame 6 1.322896709 0.008636

201137 _s_at HLA-DPB1 major histocompatibility complex, class II, DP beta 1 1.317915963 0.000445

222329 _x_at ANKRD17 Ankyrin repeat domain 17 1.305908564 0.003892

213756 _s_at HSF1 heat shock transcription factor 1 1 .28322941 0.001029

21 1528 _x_at HLA-G HLA-G histocompatibility antigen, class I, G 1.254908775 0.007445

208306 _x_at HLA-DRB5 Major histocompatibility complex, class II, DR beta 3 1.252929469 0.009424

203669 _s_at DGAT1 diacylglycerol O-acyltransferase homolog 1 (mouse) 1.251384492 0.002648

209034 _at PNRC1 proline-rich nuclear receptor coactivator 1 1 .22881177 0.00706

218387 _s_at PGLS 6-phosphogluconolactonase 1.218121181 0.000973

216641 _s_at LAD1 ladinin 1 1.216435002 0.001758

41047_ at C9orf16 chromosome 9 open reading frame 16 1.216180961 0.00049

215256 _x_at SNX26 sorting nexin 26 1.214981235 0.007326

203655 _at XRCC1 X-ray repair complementing defective repair in Chinese hamster cells 1 1.208264842 5.44E-06

216905 _s_at ST14 suppression of tumorigenicity 14 (colon carcinoma, matriptase, epithin) 1.204813841 5.15E-05

219709 _x_at C16orf24 chromosome 16 open reading frame 24 1.201397295 0.002638 MCM5 minichromosome maintenance deficient 5, cell division cycle 46

216237 _s_at MCM5 (S. cerevisiae) 1.196420347 0.00013

214630 at CYP1 1 B2 cytochrome P450, family 1 1 , subfamily B, polypeptide 2 1.192597779 0.00666

216180 _s_at SYNJ2 synaptojanin 2 1.181448547 0.003155

219986 _, _s_at ACAD10 acyl-Coenzyme A dehydrogenase family, member 10 1.170864828 0.008871 catenin (cadherin-associated protein), delta 2 (neural plakophilin-related

209617_ _s_at CTNND2 arm-repeat protein) 1.151479966 0.008055

205462_ _s_at HPCAL1 hippocalcin-like 1 1.147720101 0.004591

221410_ _x_at PCDHB3 protocadherin beta 3 1.130409165 0.007588

216526_ _x_at HLA-C major histocompatibility complex, class I, C 1.129954807 0.002574

202574_ _s_at CSNK1 G2 casein kinase 1 , gamma 2 1.127593225 0.003117

210974_ _s_at AP3D1 adaptor-related protein complex 3, delta 1 subunit 1.121470759 0.007399 Solute carrier family 7 (cationic amino acid transporter, y+ system),

216603_ .at SLC7A8 member 8 1.115393112 0.002945

218414_ _s_at NDE1 nudE nuclear distribution gene E homolog 1 (A. nidulans) 1.109296453 0.005488

201750_ _s_at ECE1 endothelin converting enzyme 1 1.105981324 0.001328

213072_ .at CYHR1 cysteine/histidine-rich 1 1.098248049 0.001836

202005 _, .at ST14 suppression of tumorigenicity 14 (colon carcinoma, matriptase, epithin) 1.075171037 4.02E-05

217456_ _x_at HLA-E major histocompatibility complex, class I, E 1.074487488 0.009378

219189_ .at FBXL6 F-box and leucine-rich repeat protein 6 1.069653172 0.002043

209969 _s_at STAT1 signal transducer and activator of transcription 1 , 91 kDa 1.061838965 0.006707

205050 _, _s_at MAPK8IP2 mitogen-activated protein kinase 8 interacting protein 2 1.052857019 0.00051 1

217834_ _s_at SYNCRIP synaptotagmin binding, cytoplasmic RNA interacting protein 1.038522393 0.005806

204192_ .at CD37 CD37 antigen 1.029167905 0.00175

203421 .at TP53I11 tumor protein p53 inducible protein 1 1 1.024354916 0.00015

205131 _, _x_at CLEC11 A C-type lectin domain family 11 , member A 1.023564752 0.006935

65133_i_ _at ZNHIT4 zinc finger, HIT type 4 1 .01215222 0.005505

205312_ .at SPM spleen focus forming virus (SFFV) proviral integration oncogene spi1 1.008672462 0.000102

209266_ _s_at SLC39A8 solute carrier family 39 (zinc transporter), member 8 1.007342042 0.008898

206782_ _s_at DNAJC4 DnaJ (Hsp40) homolog, subfamily C, member 4 1 .00438868 0.005432 transcription factor 3 (E2A immunoglobulin enhancer binding factors

210776_ _x_at TCF3 E12/E47) 0.984301782 0.002414

205641 _s_at TRADD TNFRSFIA-associated via death domain 0.981998647 0.00963

215616 _, _s_at JMJD2B jumonji domain containing 2B 0.974423755 0.005036

218524_ .at E4F1 E4F transcription factor 1 0.972090779 0.00304

209166_ _s_at MAN2B1 mannosidase, alpha, class 2B, member 1 0.96538801 1 0.00909

210783 x at CLEC11 A C-type lectin domain family 11 , member A 0.964623024 0.00825

212312 _at BCL2L1 BCL2-like 1 /// BCL2-like 1 0.956568938 0.003998

201309_ _x_at C5orf13 chromosome 5 open reading frame 13 0.943074822 0.009672

21 1622_ _s_at ARF3 ADP-ribosylation factor 3 /// ADP-ribosylation factor 3 0.940173907 0.002443

221525_ _at DKFZp76112123 hypothetical protein DKFZp761 12123 0.931597762 3.08E-06

219040_ _at CORO7 coronin 7 0.931315331 0.000566

210862_ _s_at SARDH sarcosine dehydrogenase 0.925137006 0.000327

218900_ .at CNNM4 cyclin M4 0.916639249 0.001065

218161_ _s_at CLN6 ceroid-lipofuscinosis, neuronal 6, late infantile, variant 0.902839478 0.008702

1729_at TRADD TNFRSF1 A-associated via death domain 0.901121935 0.007888

204876_ .at ZNF646 zinc finger protein 646 0.8981329 0.0083 cadherin, EGF LAG seven-pass G-type receptor 3 (flamingo homolog,

205165_ .at CELSR3 Drosophila) 0.896931251 0.003155

201806 _s_at ATXN2L ataxin 2-like 0.882606769 0.002438

209906_ .at C3AR1 complement component 3a receptor 1 0.880463808 0.003461

218151_ _x_at GPR172A G protein-coupled receptor 172A 0.876852613 0.002933 nuclear factor of kappa light polypeptide gene enhancer in B-cells

209973_ _at NFKBIL1 inhibitor-like 1 0.869189181 0.003246

MCM5 minichromosome maintenance deficient 5, cell division cycle 46

201755_ .at MCM5 (S. cerevisiae) 0.86573888 0.005865

218060_ _s_at FLJ 13154 hypothetical protein FLJ13154 0.861692665 0.008244

34260_at KIAA0683 KIAA0683 gene product 0.861468194 0.002057

219227_ .at FLJ 14166 hypothetical protein FLJ 14166 0.844958683 0.00054

206179_ _s_at TPPP brain-specific protein p25 alpha 0.840038822 0.006931

220408_ _x_at FAM48A family with sequence similarity 48, member A 0.834810375 0.003026

205459_ _s_at NPAS2 neuronal PAS domain protein 2 0.834483527 0.00215

202828 _s_at MMP14 matrix metallopeptidase 14 (membrane-inserted) 0.832258469 0.008966

215660_ _s_at MAST2 microtubule associated serine/threonine kinase 2 0.825371167 0.006286

217399_ _s_at FOXO3A forkhead box O3A 0.818163753 0.006906

203713_ _s_at LLGL2 lethal giant larvae homolog 2 (Drosophila) 0.8061 16539 0.00397

218778 _x_at EPS8L1 EPS8-like 1 0.804607035 0.0041 15

217635_ _s_at POLG polymerase (DNA directed), gamma 0.79874289 0.008862

207484_ _s_at EHMT2 euchromatic histone-lysine N-methyltransferase 2 0.794528405 0.001093

KIR3DL2 /// killer cell immunoglobulin-like receptor, three domains, long cytoplasmic

217318 x at KIR2DL5A /// tail, 2 /// killer cell immunoglobulin-like receptor, two domains, long 0.785885277 0.007021

KIR3DL3 /// cytoplasmic tail, 5A /// killer cell immunoglobulin-like receptor, three

KIR3DP1 /// domains, long cytoplasmic tail, 3 /// killer cell immunoglobulin-like

KIR2DL5B receptor, three domains, pseudogene 1 /// killer cell immunoglobulin-like receptor, two domains, long cytoplasmic tail, 5B

216269_ _s_at ELN elastin (supravalvular aortic stenosis, Williams-Beuren syndrome) 0.782926943 0.004981

2031 10_ .at PTK2B PTK2B protein tyrosine kinase 2 beta 0.782294713 0.00925

221956_ .at LRCH4 leucine-rich repeats and calponin homology (CH) domain containing 4 0.776309624 0.000405

209346_ _s_at PI4KII phosphatidylinositol 4-kinase type Il 0.776079 0.00567

213772 _s_at GGA2 golgi associated, gamma adaptin ear containing, ARF binding protein 2 0.775961201 0.000385

205056_ _s_at GPR162 G protein-coupled receptor 162 0.772934808 0.004891

221827_ .at C20orf18 chromosome 20 open reading frame 18 0.770810095 0.008335

UDP-Gal:betaGlcNAc beta 1 ,4- galactosyltransferase, polypeptide 1 ///

21 1631 _x_at B4GALT1 UDP-Gal:betaGlcNAc beta 1 ,4- galactosyltransferase, polypeptide 1 0.769453748 0.006482

204561_ _x_at APOC2 apolipoprotein C-Il 0.768790428 0.006691 pleckstrin homology, Sec7 and coiled-coil domains, binding protein ///

209606 at PSCDBP pleckstrin homology, Sec7 and coiled-coil domains, binding protein 0.767819454 0.000536 transcription factor AP-2 alpha (activating enhancer binding protein 2

204654_s_at TFAP2A alpha) 0.765548068 0.005104 215501_s_at DUSP10 dual specificity phosphatase 10 0.764372765 0.001137 201638 s at CPSF1 cleavage and polyadenylation specific factor 1 , 16OkDa 0.760097664 0.0073 mediator of RNA polymerase Il transcription, subunit 25 homolog (yeast)

/// mediator of RNA polymerase Il transcription, subunit 25 homolog

2081 10 _x_at MED25 (yeast) 0.754612516 0.004973

203904_ _x_at CD82 CD82 antigen 0.75448255 0.002761

218033_ _s_at SNN stannin 0.75046509 0.003139

221819_ _at RAB35 RAB35, member RAS oncogene family 0.750420795 0.002767

204740_ _at CNKSR1 connector enhancer of kinase suppressor of Ras 1 0.733951109 0.00534

207684_ _at TBX6 T-box 6 0.733730783 0.001236

Similar to immunoglobulin M chain /// (fetal) Ig rearranged H-chain VDJ- region mRNA, clone 60P2 /// Immunoglobulin heavy chain variable

217239_ _x_at ... region (VH III family) from IgM rheumatoid factor 0.729465376 0.004266

222206_ _s_at NCLN nicalin homolog (zebrafish) 0.726650572 0.003808

221769_ _at SPSB3 splA/ryanodine receptor domain and SOCS box containing 3 0.714802911 0.008419

203665_ _at HMOX1 heme oxygenase (decycling) 1 0.713086293 0.003608

216397 s at BOP1 block of proliferation 1 0.710439053 0.00661

216486_ _x_at ZNF79 zinc finger protein 79 (pT7) 0.70278943 0.00744

210618_ .at RAP1 GA1 RAP1 , GTPase activating protein 1 0.701787237 0.008246

220279_ .at TRIM17 tripartite motif-containing 17 0.701696588 0.005732

212860_ _at ZDHHC18 zinc finger, DHHC-type containing 18 0.692179196 0.001172 growth differentiation factor 1 /// LAG1 longevity assurance homolog 1

206397_ _x_at GDF1 /// LASS1 (S. cerevisiae) 0.681040048 0.00893

216253_ _s_at PARVB parvin, beta 0.68008837 0.004465

203388_ _at ARRB2 arrestin, beta 2 0.676275378 0.008795

212909 _at LYPD1 LY6/PLAUR domain containing 1 0.66440939 0.007554

220565_ .at CCR10 chemokine (C-C motif) receptor 10 0.654341383 0.00317

202835_ .at TXN L4A thioredoxin-like 4A 0.639106937 0.008589

209664_ _x_at N FATC 1 nuclear factor of activated T-cells, cytoplasmic, calcineurin-dependent 1 0.634834568 0.005169

217348_ _x_at ARHGEF15 Rho guanine nucleotide exchange factor (GEF) 15 0.629597609 0.009076

221964_ .at TULP3 tubby like protein 3 0.586438249 0.005707

212541_ .at FLAD1 Fad1 , flavin adenine dinucleotide synthetase, homolog (yeast) 0.582246433 0.008041

216928_ _at TAL1 T-cell acute lymphocytic leukemia 1 0.552267983 0.009048

207129 _, _at CA5B carbonic anhydrase VB, mitochondrial 0.548399587 0.005982

222226 _, .at SAA3P serum amyloid A3 pseudogene 0.540537928 0.004708

212210 _, .at DKFZP586J0619 DKFZP586J0619 protein 0.531909885 0.007105

221302 _, .at KLF15 Kruppel-like factor 15 0.529771199 0.006305

214154 _, _s_at PKP2 plakophilin 2 0.515217352 0.009465

219794 _, .at FLJ 10979 hypothetical protein FLJ 10979 0.497258115 0.001629

221605 _, _s_at PIPOX pipecolic acid oxidase -0.50431013 0.008656

209640 _, .at PML promyelocytic leukemia -0.55944007 0.004261

210197 _, .at ITPK1 inositol 1 ,3,4-triphosphate 5/6 kinase -0.57382402 0.002728

204627 _, _s_at ITGB3 integrin, beta 3 (platelet glycoprotein IMa, antigen CD61 ) -0.63106292 0.004923

207776 _, _s_at CACN B2 calcium channel, voltage-dependent, beta 2 subunit -0.65065517 0.00724

205743 _, .at STAC SH3 and cysteine rich domain -0.65441404 0.005835

218515 .at C21orf66 Chromosome 21 open reading frame 66 -0.65673727 0.004143

213556 _, .at LOC390940 similar to R28379_1 -0.67610942 0.00278

204101 _, .at MTM1 myotubularin 1 -0.68206735 0.008859

202783 _, .at NNT nicotinamide nucleotide transhydrogenase -0.68667172 0.006744

206648 _, .at ZNF571 zinc finger protein 571 -0.7028254 0.005997

203339 at SLC25A12 solute carrier family 25 (mitochondrial carrier, Aralar), member 12 -0.7041933 0.009364

205610_at MYOM1 myomesin 1 (skelemin) 185kDa -0.71121509 0.006047 221347_at CHRM5 cholinergic receptor, muscarinic 5 -0.71869285 0.002223 221883_at PKNOX1 PBX/knotted 1 homeobox 1 -0.72499071 0.008725 210751_s_at RGN regucalcin (senescence marker protein-30) -0.73027121 0.00376

NY-REN-7 ///

214945_at LOC389347 NY-REN-7 antigen /// similar to KIAA0752 protein -0.730904 0.005715 203661 _s_at TMOD1 tropomodulin 1 -0.74604145 0.003351

TRIM34 /// tripartite motif-containing 34 /// tripartite motif-containing 6 and tripartite

221044_s_at TRIM6-TRIM34 motif-containing 34 -0.74753844 0.007554 203067_at PDHX pyruvate dehydrogenase complex, component X -0.76522922 0.007264 dihydrolipoamide S-acetyltransferase (E2 component of pyruvate

213149_at DLAT dehydrogenase complex) -0.77065763 0.008295

219217_at FLJ23441 hypothetical protein FLJ23441 -0.82350455 0.004043

222184_at Clone 25015 mRNA sequence -0.82451004 0.005988

219464_at CA14 carbonic anhydrase XIV -0.85151131 0.001788

203678_at KIAA1018 KIAA1018 protein -0.85238538 0.008184

203493_s_at PIG8 trans lokin -0.86026192 0.009846

218284_at SMAD3 SMAD, mothers against DPP homolog 3 (Drosophila) -0.86334284 0.007209

203340_s_at SLC25A12 solute carrier family 25 (mitochondrial carrier, Aralar), member 12 -0.86434437 0.003291

212549_at STAT5B signal transducer and activator of transcription 5B -0.89053653 0.000704

202520_s_at MLH1 mutL homolog 1 , colon cancer, nonpolyposis type 2 (E. coli) -0.89205598 0.002419

219292_at THAP1 THAP domain containing, apoptosis associated protein 1 -0.9081982 0.003527

203295_s_at ATP1A2 ATPase, Na+/K+ transporting, alpha 2 (+) polypeptide -0.91450659 0.005197

205088_at CXorf6 chromosome X open reading frame 6 -0.93252411 0.006048

213455_at LOC283677 hypothetical LOC283677 -0.93533955 0.004083

204273_at EDNRB endothelin receptor type B -0.95041426 0.001573

213661_at DKFZP586H2123 regeneration associated muscle protease -0.97931329 0.000103

214660_at PELO Pelota homolog (Drosophila) -0.99965707 0.001013

209314_s_at HBS1 L HBS1 -like (S. cerevisiae) -1.01951087 0.003451

218043_s_at AZI2 5-azacytidine induced 2 -1.02481557 0.009637

217721_at 7-Sep Septin 7 -1.02559647 1.04E-05

222150_s_at LOC54103 hypothetical protein LOC54103 -1.03784254 0.000709

212297_at ATP13A3 ATPase type 13A3 -1.04040016 0.003333

205348 s at DNCM dynein, cytoplasmic, intermediate polypeptide 1 -1.04536482 0.004605

216944_ _s_at ITPR1 inositol 1 ,4,5-triphosphate receptor, type 1 -1 .05338499 0.002504

212757_ _s_at CAMK2G calcium/calmodulin-dependent protein kinase (CaM kinase) Il gamma -1 .05360528 0.003609

219764_ .at FZD10 frizzled homolog 10 (Drosophila) -1 .06313139 0.008364

205355_ _at ACADSB acyl-Coenzyme A dehydrogenase, short/branched chain -1 .07225397 0.00024

21 1423_ _s_at SC5DL sterol-C5-desaturase (ERG3 delta-5-desaturase homolog, fungal)-like -1 .09101617 0.00355

218842_ .at FLJ21908 hypothetical protein FLJ21908 -1 .09131478 0.009608

201517_ .at NCBP2 nuclear cap binding protein subunit 2, 2OkDa -1 .09569572 0.003334

202629_ _at APPBP2 amyloid beta precursor protein (cytoplasmic tail) binding protein 2 -1 .09597872 0.006989

203368_ _at CRELD1 cysteine-rich with EGF-like domains 1 -1 .10296371 0.006024

218926_ .at MYNN myoneurin -1 .10682045 0.005249 mannosyl (alpha-1 ,6-)-glycoprotein beta-1 ,2-N-

203102_ _s_at MGAT2 acetylglucosaminyltransferase -1 .12479778 0.009853

204640 _s_at SPOP speckle-type POZ protein -1 .14057396 0.008894

209085_ _x_at RFC1 replication factor C (activator 1 ) 1 , 145kDa -1 .14678312 0.009435

216887_ _s_at LDB3 LIM domain binding 3 -1 .15951792 6.69E-05

218446_ _s_at FAM18B family with sequence similarity 18, member B -1 .19486247 0.008233

203607 _at INPP5F inositol polyphosphate-5-phosphatase F -1 .21721914 0.00778

208962_ _s_at FADS1 fatty acid desaturase 1 -1 .23095952 0.009261

203301_ _s_at DMTF1 cyclin D binding myb-like transcription factor 1 -1 .23757435 0.002026

209616_ _s_at CES1 carboxylesterase 1 (monocyte/macrophage serine esterase 1 ) -1 .23772255 0.006259

205091_ _x_at RECQL RecQ protein-like (DNA helicase Q1 -like) -1 .26382967 0.002155

212408_ .at TOR1AIP1 torsin A interacting protein 1 -1 .26884999 0.000794

2091 15 _. .at UBE1 C ubiquitin-activating enzyme E1 C (UBA3 homolog, yeast) -1 .28406789 0.006194

205609_ .at ANGPT1 angiopoietin 1 -1 .29995843 0.000936

221556_ .at CDC14B CDC14 cell division cycle 14 homolog B (S. cerevisiae) -1 .33129421 0.000541

212470_ .at SPAG9 sperm associated antigen 9 -1 .33265021 0.0011 11

218356_ .at FTSJ2 FtsJ homolog 2 (E. coli) -1 .33926239 0.009562

203359_ _s_at MYCBP c-myc binding protein -1 .35166348 0.001626

217976_ _s_at DNCLI1 dynein, cytoplasmic, light intermediate polypeptide 1 -1 .35231538 0.006603

202364_ .at MXM MAX interactor 1 /// MAX interactor 1 -1 .37647331 0.007104

218285_ _s_at DHRS6 dehydrogenase/reductase (SDR family) member 6 -1 .39658249 0.009223

203066 at GALNAC4S-6ST B cell RAG associated protein -1 .39669919 0.002459 fibroblast growth factor receptor 2 (bacteria-expressed kinase,

203638 s at FGFR2 keratinocyte growth factor receptor, craniofacial dysostosis 1 , Crouzon -1 .39817144 0.003227

syndrome, Pfeiffer syndrome, Jackson-Weiss syndrome)

202566_ _s_at SVIL supervillin -1 .41043106 0.003067

202666_ _s_at ACTL6A actin-like 6A -1 .41181602 0.00432

217645_ _at C14orf1 12 Chromosome 14 open reading frame 112 -1 .42306469 0.001156

214464_ _at CDC42BPA CDC42 binding protein kinase alpha (DMPK-like) -1 .42358933 0.007193

DCN1 , defective in cullin neddylation 1 , domain containing 1 (S.

218583_ _s_at DCUN1 D1 cerevisiae) -1 .42862207 8.49E-05

218696_ _at EIF2AK3 eukaryotic translation initiation factor 2-alpha kinase 3 -1 .43324674 0.002507

218458_ _at GCL germ cell-less homolog 1 (Drosophila) -1 .43562848 0.000817

204847_ .at ZBTB11 zinc finger and BTB domain containing 11 -1 .45652565 0.003812

212508_ .at MOAP1 modulator of apoptosis 1 -1 .46228963 0.009805

209512_ _at HSDL2 hydroxysteroid dehydrogenase like 2 -1 .46513595 0.005694

221559 _s_at MIS12 MIS12 homolog (yeast) -1 .48224324 0.005882

208857_ _s_at PCMT1 protein-L-isoaspartate (D-aspartate) O-methyltransferase -1 .51253251 0.00136

204496_ .at STRN3 striatin, calmodulin binding protein 3 -1 .51677917 6.66E-07

209248_ _at GHITM growth hormone inducible transmembrane protein -1 .53299358 0.004751

205501_ _at ... CDNA FLJ25677 fis, clone TST04054 -1 .54125794 0.004143

217894_ .at KCTD3 potassium channel tetramerisation domain containing 3 -1 .54669027 0.00242

218432_ .at FBXO3 F-box protein 3 -1 .55082805 0.006021

212476_ .at CENTB2 centaurin, beta 2 -1 .57248456 0.000834

2131 11 .at PIP5K3 phosphatidylinositol-3-phosphate/phosphatidylinositol 5-kinase, type -1 .59084559 0.00612

220595_ .at PDZRN4 PDZ domain containing RING finger 4 -1 .59949653 0.002427

205202_ .at PCMT1 protein-L-isoaspartate (D-aspartate) O-methyltransferase -1 .601 14799 0.00871

213510_ _x_at LOC220594 TL132 protein -1 .60917878 0.004543

2141 10 _s_at ... Similar to lymphocyte-specific protein 1 -1 .61029591 0.005937

208990_ _s_at HNRPH3 heterogeneous nuclear ribonucleoprotein H3 (2H9) -1 .616026 0.002598

221543_ _s_at SPFH2 SPFH domain family, member 2 -1 .6331 1048 0.008106

205407_ .at RECK reversion-inducing-cysteine-rich protein with kazal motifs -1 .63514289 0.009982

213331_ _s_at NEK1 NIMA (never in mitosis gene a)-related kinase 1 -1 .642141 14 0.004306

212806_ .at KIAA0367 KIAA0367 -1 .67732615 0.008455

208927_ .at SPOP speckle-type POZ protein -1 .69297515 0.000958

204793_ .at GPRASP1 G protein-coupled receptor associated sorting protein 1 -1 .72462939 0.007174

209005_ .at FBXL5 F-box and leucine-rich repeat protein 5 -1.7915898 0.005041

218499 at MASK Mst3 and SOK1 -related kinase -1 .81965417 0.00809

213714 _at CACN B2 calcium channel, voltage-dependent, beta 2 subunit -1 .86889018 0.001614

218683 _at PTB P2 polypyrimidine tract binding protein 2 -1 .88451684 0.002306

217776 _at RDH11 retinol dehydrogenase 11 (all-trans and 9-cis) -1 .88682983 0.005793

213371 _at LDB3 LIM domain binding 3 -1 .89439777 0.000105

219685 _at TMEM35 transmembrane protein 35 -2.01663255 0.004303

212397 _at RDX radixin -2.01924579 0.006075

205083 _at AOX 1 aldehyde oxidase 1 -2.1175354 0.001062

221553 _at DKFZp564K142 implantation-associated protein -2.16829747 0.000416

204412 _s_ at NEFH neurofilament, heavy polypeptide 20OkDa -2.18137802 0.006076

201617 _x_ at CALD1 caldesmon 1 -2.40787957 0.00202

209763 _at CHRDL1 chordin-like 1 -2.61605999 0.001164

217775 _s_ at RDH11 retinol dehydrogenase 11 (all-trans and 9-cis) -2.71917266 0.002765

212530 _at NEK7 NIMA (never in mitosis gene a)-related kinase 7 -2.86398048 3.07E-05

33767_ at NEFH neurofilament, heavy polypeptide 20OkDa -3.4120129 0.005992

203946 S at ARG2 arginase, type Il -3.7410222 0.008657

Table 36 - dataset 1 (tumor) log base

2 fold uniquelD Gene Symbol Gene Title change p value

214455_ .at HIST1 H2BC histone 1 , H2bc 4.358572 0.000901

214469_ .at HIST1 H2AE histone 1 , H2ae 4.062953 0.00297

205347_ _s_at TMSL8 thymosin-like 8 3.770383 0.001191 trefoil factor 1 (breast cancer, estrogen-inducible

205009_ .at TFF1 sequence expressed in) 3.595782 0.004612

215779_ _s_at HIST1 H2BG histone 1 , H2bg 3.42953 4.59E-05

218280 x at HIST2H2AA histone 2, H2aa 2.907689 8.37E-05

AFFX-r2-Ec- bioB-M_ at bioB biotin synthesis, sulfur insertion? 2.742559 0.002921

214472_ .at HIST1 H3D histone 1 , H3d 2.69731 0.009665

205501_ .at — CDNA FLJ25677 fis, clone TST04054 2.609067 0.001198 solute carrier family 33 (acetyl-CoA transporter), member

203164 at SLC33A1 1 2.565196 0.001372

208180 _. _s_at HIST1 H4H histone 1 , H4h 2.558291 0.000912

207076 _s_at ASS argininosuccinate synthetase 2.34241 1 0.000472

AFFX-r2-Ec- bioB-3 at bioB Biotin synthase /// biotin synthesis, sulfur insertion? 2.167319 0.006457

AFFX-BioB-

M_at bioB Biotin synthase /// biotin synthesis, sulfur insertion? 2.150147 0.004866

201962 s at RNF41 ring finger protein 41 2.095234 0.001988

AFFX-r2-Ec- bioC-5_ at bioC biotin biosynthesis; reaction prior to pimeloyl CoA 2.030088 0.004128

206110 _. _at HIST1 H3H histone 1 , H3h 1.973389 0.001869

214683 _. _s_at CLK1 CDC-like kinase 1 1.967806 0.002233

222067 _x_at HIST1 H2BD histone 1 , H2bd 1.967367 4.18E-05

208546 _. _x_at HIST1 H2BH histone 1 , H2bh 1.956574 9.08E-06

214290 _. _s_at HIST2H2AA histone 2, H2aa 1.926708 0.000733

200888 _s_at RPL23 ribosomal protein L23 1.901666 0.002246

AFFX-BioC-

3 at bioC biotin biosynthesis; reaction prior to pimeloyl CoA 1.854454 0.00595

201636 _. _at FXR1 fragile X mental retardation, autosomal homolog 1 1.850738 1 .92E-05

201031 s at HNRPH1 heterogeneous nuclear ribonucleoprotein H1 (H) 1.844054 0.001705

AFFX-r2-Ec- bioC-3 at bioC biotin biosynthesis; reaction prior to pimeloyl CoA 1.804444 0.004125

218085 _. _at CHMP5 chromatin modifying protein 5 1.775694 0.002059

202666 _. _s_at ACTL6A actin-like 6A 1.772138 0.001759

213704 _. _at RABGGTB Rab geranylgeranyltransferase, beta subunit 1.746765 0.006789

LSM5 homolog, U6 small nuclear RNA associated (S.

202903 _. _at LSM5 cerevisiae) 1.691493 0.00515 procollagen-proline, 2-oxoglutarate 4-dioxygenase

207543 s at P4HA1 (proline 4-hydroxylase), alpha polypeptide I 1.659193 0.008062 protein phosphatase 3 (formerly 2B), catalytic subunit,

202425_x_at PPP3CA alpha isoform (calcineurin A alpha) 1.647563 0.008555 208490 x at HIST1 H2BF histone 1 , H2bf 1.637573 0.000595 similar to 60S ribosomal protein L29 (Cell surface heparin

LOC283412 /// binding protein HIP) /// similar to 60S ribosomal protein

216177 at LOC284064 L29 (Cell surface heparin binding protein HIP) 1.635539 0.001375

215088 s at SDHC succinate dehydrogenase complex, subunit C, integral 1.624131 0.00957

membrane protein, 15kDa

218268_ .at TBC1 D15 TBC1 domain family, member 15 1.597015 0.008018

209248_ .at GHITM growth hormone inducible transmembrane protein 1.572931 0.003096

219375_ _at CEPT1 choline/ethanolamine phosphotransferase 1 1.551122 0.001931

217850_ _at GN L3 guanine nucleotide binding protein-like 3 (nucleolar) 1.533389 0.00342

217775_ _s_at RDH1 1 retinol dehydrogenase 11 (all-trans and 9-cis) 1.531528 0.00246

208523_ _x_at HIST1 H2BI histone 1 , H2bi 1.499348 1 .43E-05

201407_ _s_at PPP1 CB protein phosphatase 1 , catalytic subunit, beta isoform 1.488251 0.004231

HIST1 H2AD ///

214522_ _x_at HIST1 H3D histone 1 , H2ad /// histone 1 , H3d 1.485638 0.003751

218643_ _s_at CRIPT postsynaptic protein CRIPT 1.485566 0.006706

218605_ _at TFB2M transcription factor B2, mitochondrial 1.482999 0.002284

206100_ _at CPM carboxypeptidase M 1.480325 0.008133

215071_ _s_at HIST1 H2AC histone 1 , H2ac 1.461777 0.000361

208527_ _x_at HIST1 H2BE histone 1 , H2be 1.449344 0.003147

208828_ _at POLE3 polymerase (DNA directed), epsilon 3 (p17 subunit) 1.408762 0.005599

209797_ _at TMEM4 transmembrane protein 4 1.399449 0.005354

219048_ .at PIGN phosphatidylinositol glycan, class N 1.303567 0.005323

20991 1_ _x_at HIST1 H2BD histone 1 , H2bd 1.281097 0.000582

219889_ .at FRAT1 frequently rearranged in advanced T-cell lymphomas 1.228008 0.005585

217368_ _at ... 1.204159 0.005441

214542_ _x_at HIST1 H2AI histone 1 , H2ai 1.201637 0.001861 DCN1 , defective in cullin neddylation 1 , domain

218583_ _s_at DCUN1 D1 containing 1 (S. cerevisiae) 1.195575 0.003238

208646_ _at RPS14 ribosomal protein S14 1.161299 0.003706

205355_ _at ACADSB acyl-Coenzyme A dehydrogenase, short/branched chain 1.155481 0.004407

201542_ .at SAR 1 A SAR1 gene homolog A (S. cerevisiae) 1.152993 0.008667

222279 at FLJ35429 hypothetical protein FLJ35429 1.146027 0.001236

AFFX-r2-P1 - cre-3_at ... 1.14421 0.009331

214481_ .at HIST1 H2AM Histone 1 , H2am 1.124449 0.000726

54632_at THADA thyroid adenoma associated 1.1 1474 0.006338

202244_ .at PSMB4 proteasome (prosome, macropain) subunit, beta type, 4 1.109895 0.004437

209755 at NMNAT2 nicotinamide nucleotide adenylyltransferase 2 1.090024 0.009247

BUB3 budding uninhibited by benzimidazoles 3 homolog

201456_ _s_at BUB3 (yeast) 1.077827 0.008934

218926_ .at MYNN myoneurin 1.074881 0.009357

202361_ _at SEC24C SEC24 related gene family, member C (S. cerevisiae) 1.00409 0.009599

218882_ _s_at WDR3 WD repeat domain 3 0.998624 0.004399 sterol-C5-desaturase (ERG3 delta-5-desaturase homolog,

211423_ _s_at SC5DL fungal)-like 0.997797 0.007958

204496_ _at STRN3 striatin, calmodulin binding protein 3 0.989084 0.003394

220445_ _s_at CSAG2 CSAG family, member 2 0.956467 0.003469

202029_ _x_at RPL38 ribosomal protein L38 0.932832 0.003047

218712_ .at C1orf109 chromosome 1 open reading frame 109 0.924297 0.002317

219940_ _s_at FLJ1 1305 hypothetical protein FLJ 11305 0.906085 0.003188

218615_ _s_at TMEM39A transmembrane protein 39A 0.905335 0.008103

202231_ .at hfl-B5 dendritic cell protein 0.895731 0.007247

SMC2 structural maintenance of chromosomes 2-like 1

213253 .at SMC2L1 (yeast) 0.881586 0.007795

217347_ .at ... 0.879581 0.003568

217598_ .at CINP Cyclin-dependent kinase 2-interacting protein 0.860204 0.00932 enhancer of rudimentary homolog (Drosophila) ///

200043_ _at ERH enhancer of rudimentary homolog (Drosophila) 0.858756 0.002082

216382 _s_at TNRC21 trinucleotide repeat containing 21 0.847784 0.007086

200025_ _s_at RPL27 ribosomal protein L27 /// ribosomal protein L27 0.817494 0.008265

204120_ _s_at ADK adenosine kinase 0.786247 0.002474

218598_ _at RINT-1 Rad50-interacting protein 1 0.77458 0.008568

201377_ _at UBAP2L ubiquitin associated protein 2-like 0.76542 0.004964

217019_ .at RPS4X ribosomal protein S4, X-linked 0.746504 0.00445

214583_ .at RSC1 A1 regulatory solute carrier protein, family 1 , member 1 0.731596 0.00277

220167_ _s_at TP53TG3 TP53TG3 protein 0.723961 0.006065

216317 x at RHCE Rhesus blood group, CcEe antigens 0.72354 0.005877

Rhesus blood group, CcEe antigens /// Rhesus blood

215819_s_at RHCE /// RHD group, D antigen 0.704674 0.003491

221465_at OR6A2 olfactory receptor, family 6, subfamily A, member 2 0.698869 0.005448

218104_at TEX10 testis expressed sequence 10 0.697213 0.007688

219464 at CA14 carbonic anhydrase XIV 0.675065 0.003769

204191 _at IFNAR1 interferon (alpha, beta and omega) receptor 1 0.646818 0.008478

RCD1 required for cell differentiationi homolog (S.

213903 _s_at RQCD1 pombe) 0.634636 0.007719

208241 _at NRG1 neuregulin 1 0.612895 0.009468

21971 1 _at ZNF586 zinc finger protein 586 0.588872 0.003791

210315 _at SYN2 synapsin Il 0.567586 0.008766

207418 _s_at DDO D-aspartate oxidase 0.546003 0.009539

210197 _at ITPK1 inositol 1 ,3,4-triphosphate 5/6 kinase 0.522487 0.007189

216643 _at MGC29898 Hypothetical protein MGC29898 0.488018 0.006114

214599 _at IVL involucrin 0.479332 0.006939

213888 _s_at TRAF3IP3 TRAF3 interacting protein 3 -0.51908 0.002587 potassium voltage-gated channel, KQT-like subfamily,

211217 _s_at KCNQ1 member 1 -0.54572 0.002446

207238 _s_at PTPRC protein tyrosine phosphatase, receptor type, C -0.5739 0.009871 splA/ryanodine receptor domain and SOCS box

219677 _at SPSB1 containing 1 -0.61119 0.009945

TAF6-like RNA polymerase II, p300/CBP-associated

21321 1 _s_at TAF6L factor (PCAF)-associated factor, 65kDa -0.71299 0.008806

21541 1 _s_at TRAF3IP2 TRAF3 interacting protein 2 -0.72938 0.003376

207334 _s_at TGFBR2 transforming growth factor, beta receptor Il (70/8OkDa) -0.74428 0.005555

PLSCR3 /// phospholipid scramblase 3 /// hypothetical protein

56197_ at MGC40107 MGC40107 -0.77096 0.000375

221638 _s_at STX16 syntaxin 16 -0.77643 0.008744

MAP/microtubule affinity-regulating kinase 2 ///

211082 _x_at MARK2 MAP/microtubule affinity-regulating kinase 2 -0.77848 0.000404

217798 _at CNOT2 CCR4-NOT transcription complex, subunit 2 -0.78477 0.009738

213815 _x_at C19orf29 chromosome 19 open reading frame 29 -0.80837 0.003387

TAF6-like RNA polymerase II, p300/CBP-associated

213209 _at TAF6L factor (PCAF)-associated factor, 65kDa -0.80961 0.003306

MCM5 minicnromosome maintenance deficient 5, cell

216237 _s_at MCM5 division cycle 46 (S. cerevisiae) -0.8119 0.002492

215994 _x_at KIAA0676 KIAA0676 protein -0.82092 0.009066

21921 1 _at USP18 ubiquitin specific peptidase 18 -0.82139 0.002558

202180 _s_at MVP major vault protein -0.8218 0.003661

202594 at LEPROTL1 leptin receptor overlapping transcript-like 1 -0.82262 0.005424

220319 _s_at MYLIP myosin regulatory light chain interacting protein -0.82297 0.006616

203167 _at TIMP2 TIMP metallopeptidase inhibitor 2 -0.8333 0.00033

212054 _x_at KIAA0676 KIAA0676 protein -0.83381 0.003755

201638 _s_at CPSF1 cleavage and polyadenylation specific factor 1 , 16OkDa -0.83768 0.001261

217635 _s_at POLG polymerase (DNA directed), gamma -0.84235 0.00803

218062 _x_at CDC42EP4 CDC42 effector protein (Rho GTPase binding) 4 -0.84668 0.009831 single immunoglobulin and toll-interleukin 1 receptor (TIR)

52940_ at SIGIRR domain -0.84946 0.00038

206431 _x_at KIAA0676 KIAA0676 protein -0.8733 0.004129

216017 _s_at NAB2 NGFI-A binding protein 2 (EGR1 binding protein 2) -0.90116 0.002577

212155 _at RNF187 ring finger protein 187 -0.90251 0.004585

203733 _at DEXI dexamethasone-induced transcript -0.90678 0.00441

214564 _s_at PCDHGC3 protocadherin gamma subfamily C, 3 -0.9124 0.00484

213932 _x_at HLA-A Major histocompatibility complex, class I, A -0.91489 0.004634 transcription factor 3 (E2A immunoglobulin enhancer

209151 _x_at TCF3 binding factors E12/E47) -0.9237 0.006165

219777 _at GIMAP6 GTPase, IMAP family member 6 -0.92938 0.004519 low density lipoprotein-related protein 1 (alpha-2-

200784 _s_at LRP1 macroglobulin receptor) -0.93157 0.002028 single immunoglobulin and toll-interleukin 1 receptor (TIR)

218921 _at SIGIRR domain -0.93915 0.000191

218337 _at RAM 6 retinoic acid induced 16 -0.94326 0.00886

218454 _at FLJ22662 hypothetical protein FLJ22662 -0.9525 0.001467

210449 _x_at MAPK14 mitogen-activated protein kinase 14 -0.96009 0.003314

TRA@ /// TRDV2 T cell receptor alpha locus /// T cell receptor delta variable

/// TRAV20 /// 2 /// T cell receptor alpha variable 20 /// T cell receptor

210972 _x_at TRAJ17 /// TRAC alpha joining 17 /// T cell receptor alpha constant -0.96124 0.009068

212613 _at BTN3A2 butyrophilin, subfamily 3, member A2 -0.966 0.003251

210594 _x_at MPZL1 myelin protein zero-like 1 -0.9726 0.001493 major histocompatibility complex, class II, DQ beta 1 ///

211656 _x_at HLA-DQB1 major histocompatibility complex, class II, DQ beta 1 -0.97359 0.008209

212969 _x_at EML3 echinoderm microtubule associated protein like 3 -0.97783 0.002431

217399 _s_at FOXO3A forkhead box O3A -0.98874 0.000866

38241 _ at BTN3A3 butyrophilin, subfamily 3, member A3 -0.98912 0.005224

211779 x at AP2A2 adaptor-related protein complex 2, alpha 2 subunit /// -0.98928 0.002939

adaptor-related protein complex 2, alpha 2 subunit

204821_ .at BTN3A3 butyrophilin, subfamily 3, member A3 -0.99177 0.00376

202108_ .at PEPD peptidase D -0.9956 0.001981

205641_ _s_at TRADD TNFRSF1 A-associated via death domain -1.00386 0.005591

208452 _x_at MYO9B myosin IXB -1.00471 0.008675

1729_at TRADD TNFRSF1 A-associated via death domain -1.00623 0.003376

205582_ _s_at GGTLA1 gamma-glutamyltransferase-like activity 1 -1.00714 0.005459

203417_ .at MFAP2 microfibrillar-associated protein 2 -1.01063 0.004099

203567_ _s_at TRIM38 tripartite motif-containing 38 -1.01518 0.00722

221807_ _s_at PP2447 hypothetical protein PP2447 -1.01859 0.002653

212294_ .at GNG12 guanine nucleotide binding protein (G protein), gamma 12 -1.02169 0.007683

213537_ .at HLA-DPA1 major histocompatibility complex, class II, DP alpha 1 -1.02887 0.007775

205057_ _s_at IDUA iduronidase, alpha-L- -1.02952 0.008204

202253_ _s_at DNM2 dynamin 2 -1.0356 0.003634

204989 _, _s_at ITG B4 integrin, beta 4 -1.03812 0.005783

216587_ _s_at FZD8 frizzled homolog 8 (Drosophila) -1.04165 0.000195

209038 _s_at EHD1 EH-domain containing 1 -1.07689 0.000435

200879 _, _s_at EPAS1 endothelial PAS domain protein 1 -1.07827 0.005357 eukaryotic translation initiation factor 3, subunit 4 delta,

208887_ .at EIF3S4 44kDa -1.07907 0.005199

200754 _x_at SFRS2 splicing factor, arginine/serine-rich 2 -1.08153 0.001966

209166_ _s_at MAN2B1 mannosidase, alpha, class 2B, member 1 -1.08936 0.009711

204715_ .at PANX1 pannexin 1 -1.09622 0.002005 RNA binding protein, autoantigenic (hnRNP-associated

201271_ _s_at RALY with lethal yellow homolog (mouse)) -1.10474 0.008964

212201 .at KIAA0692 KIAA0692 protein -1.12509 0.008471

203300 _, _x_at AP1 S2 adaptor-related protein complex 1 , sigma 2 subunit -1.13007 0.000427

200753_ _x_at SFRS2 splicing factor, arginine/serine-rich 2 -1.13784 0.001244

215489_ _x_at HOMER3 homer homolog 3 (Drosophila) -1.15018 0.001966

211065_ _x_at PFKL phosphofructokinase, liver /// phosphofructokinase, liver -1.16035 0.003765

200704_ .at LITAF lipopolysaccharide-induced TNF factor -1.16415 0.006365

210778_ _s_at MXD4 MAX dimerization protein 4 -1.16878 0.001405

203749_ _s_at RARA retinoic acid receptor, alpha -1.17184 0.002959

203669 s at DGAT1 diacylglycerol O-acyltransferase homolog 1 (mouse) -1.17387 0.00441

213072_at CYHR1 cysteine/histidine-πch 1 -1.18141 0.00089

214459_x_at HLA-C major histocompatibility complex, class I, C -1.19253 0.008783

1405_i_at CCL5 chemokine (C-C motif) ligand 5 -1.19347 0.002527

213716_s_at SECTM1 secreted and transmembrane 1 -1.19552 0.000349

201309 x at C5orf13 chromosome 5 open reading frame 13 -1.19692 0.002882 v-erb-b2 erythroblastic leukemia viral oncogene homolog

210930_ _s_at ERBB2 2, neuro/glioblastoma derived oncogene homolog (avian) -1.20882 0.004858

215116_ _s_at DNM1 dynamin 1 -1.211 15 0.000173

DNA segment on chromosome X and Y (unique) 155

210269_ _s_at DXYS155E expressed sequence --11..2211664455 00..0000778888 peptide deformylase-like protein /// component of

219575 _s_at PDF /// COG8 oligomeric golgi complex 8 -1.22854 0.002583

200706_ _s_at LITAF lipopolysaccharide-induced TNF factor -1.23478 0.008528

221785_ .at WIZ widely-interspaced zinc finger motifs -1.23555 0.003656

201137_ _s_at HLA-DPB1 major histocompatibility complex, class II, DP beta 1 -1.26192 0.005489

213901_ _x_at RBM9 RNA binding motif protein 9 -1.26856 0.004455 fibroblast growth factor receptor 1 (fms-related tyrosine

210973_ _s_at FGFR1 kinase 2, Pfeiffer syndrome) --11..2277119922 00..000000662277 mannosyl (alpha-1 ,3-)-glycoprotein beta-1 ,4-N-

220189_ _s_at MGAT4B acetylglucosaminyltransferase, isoenzyme B -1.27367 0.007236

209083_ _at CORO1A coronin, actin binding protein, 1A -1.27403 0.00115

203274_ .at F8A1 coagulation factor Vlll-associated (intronic transcript) 1 -1.28233 0.00521 disabled homolog 2, mitogen-responsive phosphoprotein

210757_ _x_at DAB2 (Drosophila) -1.28618 0.002714

203005_ _at LTBR lymphotoxin beta receptor (TNFR superfamily, member 3) -1.29124 0.006501

203771_ _s_at BLVRA biliverdin reductase A -1.29211 0.001352

CDNA FLJ13267 fis, clone OVARC1000964 /// CDNA

212829_ _at ... FLJ13267 fis, clone OVARC1000964 -1.32372 0.002998

212764_ .at ... ... -1.32423 0.006375

208306_ _x_at HLA-DRB5 Major histocompatibility complex, class II, DR beta 3 -1.32972 0.004616

201609_ _x_at ICMT isoprenylcysteine carboxyl methyltransferase -1.33083 0.001386

204938_ _s_at PLN phospholamban -1.34398 0.005261

217430_ _x_at COL1A1 collagen, type I, alpha 1 -1.35597 0.006694

210202_ _s_at BIN1 bridging integrator 1 -1.36271 0.009731

217757 at A2M alpha-2-macroglobulin -1.36418 0.006867

209129_ _at TRIP6 thyroid hormone receptor interactor 6 1.36573 0.0087

210974_ _s_at AP3D1 adaptor-related protein complex 3, delta 1 subunit -1.3677 0.000717

209350_ _s_at GPS2 G protein pathway suppressor 2 1.37152 0.001217

217912_ _at DUS1 L dihydrouridine synthase 1 -like (S. cerevisiae) 1.39203 0.00833

BTN3A3 /// butyrophilin, subfamily 3, member A3 /// butyrophilin,

204820_ _s_at BTN3A2 subfamily 3, member A2 1.40039 0.000145

Solute carrier family 7 (cationic amino acid transporter, y+

216603_ .at SLC7A8 system), member 8 1.40095 0.007789

201809 _s_at ENG endoglin (Osler-Rendu-Weber syndrome 1 ) 1.40133 0.004058

209356_ _x_at EFEMP2 EGF-containing fibulin-like extracellular matrix protein 2 1.40444 7.15E-05

213125_ .at OLFML2B olfactomedin-like 2B 1.40665 0.008364 chemokine (C-C motif) ligand 5 /// chemokine (C-C motif)

204655_ _at CCL5 ligand 5 1.41262 0.004745

204941_ _s_at ALDH3B2 aldehyde dehydrogenase 3 family, member B2 1.41969 0.006283 myxovirus (influenza virus) resistance 1 , interferon- inducible protein p78 (mouse) /// myxovirus (influenza virus) resistance 1 , interferon-inducible protein p78

202086_ .at MX1 (mouse) 1.42531 0.004155 disabled homolog 2, mitogen-responsive phosphoprotein

201279_ _s_at DAB2 (Drosophila) 1.42604 0.0003

208270_ _s_at RNPEP arginyl aminopeptidase (aminopeptidase B) -1.4374 0.00073

202424_ .at MAP2K2 mitogen-activated protein kinase kinase 2 1.44086 0.006954

216526_ _x_at HLA-C major histocompatibility complex, class I, C 1.44302 0.00023 inhibitor of DNA binding 3, dominant negative helix-loop-

207826_ _s_at ID3 helix protein -1.44509 0.009601

217762_ _s_at RAB31 RAB31 , member RAS oncogene family -1.44782 0.008251

200752_ _s_at CAPN1 calpain 1 , (mu/l) large subunit -1.46225 0.002717 T cell receptor beta variable 19 /// T cell receptor beta

TRBV19 /// variable 19 /// T cell receptor beta constant 1 /// T cell

213193_ _x_at TRBC1 receptor beta constant 1 -1.46489 0.001138

212025_ _s_at FLII flightless I homolog (Drosophila) -1.46962 0.005018

212646_ .at RAFTLIN raft-linking protein -1.47769 0.004248

208947_ _s_at RENT1 regulator of nonsense transcripts 1 -1.48065 0.001272

210892_ _s_at GTF2I general transcription factor II, i -1.49638 0.005113

202326 at EHMT2 euchromatic histone-lysine N-methyltransferase 2 -1.49951 0.004361

209264_ _s_at TSPAN4 tetraspanin 4 -1.50061 0.003844

201 102_ _s_at PFKL phosphofructokinase, liver -1.501 18 0.000642

212940_ .at COL6A1 collagen, type Vl, alpha 1 -1.53744 0.008081

TRBV21 -1 ///

TRBV19 /// T cell receptor beta variable 21 -1 /// T cell receptor beta

TRBV5-4 /// variable 19 /// T cell receptor beta variable 5-4 /// T cell

TRBV3-1 /// receptor beta variable 3-1 /// T cell receptor beta constant

211796_ _s_at TRBC1 1 -1.54546 0.004773

205748_ _s_at RNF126 ring finger protein 126 -1.54774 0.001837 ras-related C3 botulinum toxin substrate 2 (rho family,

213603_ _s_at RAC2 small GTP binding protein Rac2) -1.57268 2.79E-05 chromosome 19 open reading frame 27 /// chromosome

221267_ _s_at C19orf27 19 open reading frame 27 -1.58204 2.26E-05

212687_ _at LIMS1 LIM and senescent cell antigen-like domains 1 -1.58394 0.001718

205462_ _s_at HPCAL1 hippocalcin-like 1 -1.59498 0.000239

214022_ _s_at IFITM1 interferon induced transmembrane protein 1 (9-27) -1.60767 0.005327

203758_ _at CTSO cathepsin O -1.60908 0.00246

217729_ _s_at AES amino-terminal enhancer of split -1.61466 0.000731

209034 _at PNRC1 proline-rich nuclear receptor coactivator 1 -1.61945 0.001488

205683_ _x_at TPSAB1 tryptase alpha/beta 1 -1.62549 0.005441 v-maf musculoaponeurotic fibrosarcoma oncogene

218559_ _s_at MAFB homolog B (avian) -1.62729 0.000277

217456_ _x_at HLA-E major histocompatibility complex, class I, E -1.63437 0.00014 syndecan 2 (heparan sulfate proteoglycan 1 , cell surface-

212154_ .at SDC2 associated, fibroglycan) -1.64398 0.004244

220266_ _s_at KLF4 Kruppel-like factor 4 (gut) -1.65117 0.009049

210084 x at TPSAB1 tryptase alpha/beta 1 -1.65813 0.008036

Disabled homolog 2, mitogen-responsive phosphoprotein

201278 at DAB2 (Drosophila) -1.6602 7.45E-05 ubiquitin-conjugating enzyme E2, J1 (UBC6 homolog,

217823_s_at UBE2J1 yeast) -1.67896 0.005511 201508 at IGFBP4 insulin-like growth factor binding protein 4 -1.68522 0.002388 integrin, beta 2 (antigen CD18 (p95), lymphocyte function- associated antigen 1 ; macrophage antigen 1 (mac-1 ) beta

202803 s at ITGB2 subunit) -1.69425 0.00015

221972_ _s_at SDF4 stromal cell derived factor 4 1.70034 0.000312

204452_ _s_at FZD 1 frizzled homolog 1 (Drosophila) 1.70124 0.000198

221875_ _x_at HLA-F major histocompatibility complex, class I, F 1.70772 0.004049

201641_ _at BST2 bone marrow stromal cell antigen 2 -1.7259 0.002772 guanylate binding protein 1 , interferon-inducible, 67kDa ///

202269_ _x_at GBP1 guanylate binding protein 1 , interferon-inducible, 67kDa 1.72769 0.003229

213290_ .at COL6A2 collagen, type Vl, alpha 2 1.72876 3.96E-05 aldo-keto reductase family 1 , member C1 (dihydrodiol dehydrogenase 1 ; 20-alpha (3-alpha)-hydroxysteroid

216594 _x_at AKR1 C1 dehydrogenase) 1.73395 0.00763

215313_ _x_at HLA-A major histocompatibility complex, class I, A -1.7586 0.000347

217897_ .at FXYD6 FXYD domain containing ion transport regulator 6 1.75875 0.004604 syntrophin, beta 2 (dystrophin-associated protein A1 ,

205315_ _s_at SNTB2 59kDa, basic component 2) 1.75932 0.00632

209261 _s_at NR2F6 nuclear receptor subfamily 2, group F, member 6 1.76202 5.32E-05

204135_ .at DOC1 downregulated in ovarian cancer 1 1.78565 0.007502

201315_ _x_at IFITM2 interferon induced transmembrane protein 2 (1 -8D) -1.7927 0.006411

215923_ _s_at PSD4 pleckstrin and Sec7 domain containing 4 1.79879 0.007615

208966_ _x_at IFH 6 interferon, gamma-inducible protein 16 1.80351 0.0082

212575_ .at C19orf6 chromosome 19 open reading frame 6 1.81288 5.94E-05

Taxi (human T-cell leukemia virus type I) binding protein

215464_ _s_at TAX1 BP3 3 1.81314 3.61 E-05

TPSAB1 ///

216474_ _x_at TPSB2 tryptase alpha/beta 1 /// tryptase beta 2 1.81811 0.004967

208637_ _x_at ACTN 1 actinin, alpha 1 1.82179 0.005568

201540_ _at FHL1 four and a half LIM domains 1 1.82184 0.008282

ELAV (embryonic lethal, abnormal vision, Drosophila)-like

201727_ _s_at ELAVL1 1 (Hu antigen R) 1.84045 0.002956

213293_ _s_at TRIM22 tripartite motif-containing 22 1.85487 0.000663

HLA-DQA1 /// major histocompatibility complex, class II, DQ alpha 1 ///

212671 _s_at HLA-DQA2 major histocompatibility complex, class II, DQ alpha 2 1.85494 0.005757

200795_ .at SPARCL1 SPARC-like 1 (mast9, hevin) 1.88146 0.001011

201601_ _x_at IFITM1 interferon induced transmembrane protein 1 (9-27) -1.8845 0.002461

204806_ _x_at HLA-F major histocompatibility complex, class I, F -1.8901 0.009947

201149 s at TIMP3 TIMP metallopeptidase inhibitor 3 (Sorsby fundus -1.8904 0.001284

dystrophy, pseudoinflammatory)

210072_ .at CCL19 chemokine (C-C motif) ligand 19 -1.89355 0.004993

208998 _. .at UCP2 uncoupling protein 2 (mitochondrial, proton carrier) -1.9024 0.001612

201401_ _s_at ADRBK1 adrenergic, beta, receptor kinase 1 -1.90273 0.00701

213422_ _s_at MXRA8 matrix-remodelling associated 8 -1.91604 8.71 E-05

TRBV19 /// T cell receptor beta variable 19 /// T cell receptor beta

210915_ _x_at TRBC1 constant 1 -1.92403 0.000631

201666_ _at TIMP1 TIMP metallopeptidase inhibitor 1 -1.92682 0.004328

207788_ _s_at SORBS3 sorbin and SH3 domain containing 3 -1.93591 0.003418

202994_ _s_at FBLN1 fibulin 1 -1.93867 0.007311 aldo-keto reductase family 1 , member C1 (dihydrodiol dehydrogenase 1 ; 20-alpha (3-alpha)-hydroxysteroid

204151_ _x_at AKR1 C1 dehydrogenase) 1.95545 0.000524

203942_ _s_at MARK2 MAP/microtubule affinity-regulating kinase 2 1.96174 7.52E-05

208747 _s_at C1 S complement component 1 , s subcomponent 1.97406 0.000158

207808_ _s_at PROS1 protein S (alpha) 1.97435 0.00286

211991_ _s_at HLA-DPA1 major histocompatibility complex, class II, DP alpha 1 1.99289 0.00046

212067_ _s_at C1 R complement component 1 , r subcomponent 2.00528 0.000954

219607_ _s_at MS4A4A membrane-spanning 4-domains, subfamily A, member 4 2.00653 0.000276

211990_ .at HLA-DPA1 major histocompatibility complex, class II, DP alpha 1 2.00996 0.000895

218162_ .at OLFML3 olfactomedin-like 3 2.01228 0.000241

214247_ _s_at DKK3 dickkopf homolog 3 (Xenopus laevis) 2.01752 0.008837

200796_ _s_at MCL1 myeloid cell leukemia sequence 1 (BCL2-related) 2.02536 0.000545

212423_ _at C10orf56 chromosome 10 open reading frame 56 2.05422 0.008811

218718_ .at PDGFC platelet derived growth factor C 2.05444 0.007694

201670_ _s_at MARCKS myristoylated alanine-rich protein kinase C substrate 2.05799 0.0021 13

217478_ _s_at HLA-DMA major histocompatibility complex, class II, DM alpha 2.07162 0.00266

200696_ _s_at GSN gelsolin (amyloidosis, Finnish type) -2.0889 0.001634

212937_ _s_at COL6A1 collagen, type Vl, alpha 1 2.10637 0.000273

203964_ _at NMI N-myc (and STAT) interactor 2.1 1187 0.007323

210130_ _s_at TM7SF2 transmembrane 7 superfamily member 2 2.1 1234 2.97E-06

203766_ _s_at LMOD1 leiomodin 1 (smooth muscle) -2.1405 0.000436

211813_ _x_at DCN decorin 2.14944 0.000876

207016 s at ALDH 1A2 aldehyde dehydrogenase 1 family, member A2 2.15142 0.000544

203131_ _at PDGFRA platelet-derived growth factor receptor, alpha polypeptide 2.15729 0.000843

219295_ _s_at PCOLCE2 procollagen C-endopeptidase enhancer 2 2.15942 0.001554 serpin peptidase inhibitor, clade F (alpha-2 antiplasmin,

202283_ .at SERPINF1 pigment epithelium derived factor), member 1 2.16096 3.69E-05 pleckstrin homology domain containing, family C (with

209209_ _s_at PLEKHC1 FERM domain) member 1 2.16279 0.008575

201044_ _x_at DUSP1 dual specificity phosphatase 1 2.17605 0.003233

202177_ _at GAS6 growth arrest-specific 6 2.17954 0.001073

TIMP metallopeptidase inhibitor 3 (Sorsby fundus

201 147_ _s_at TIMP3 dystrophy, pseudoinflammatory) 2.191 14 0.001147

217767_ .at C3 complement component 3 2.20807 0.000409

204875 _s_at GMDS GDP-mannose 4,6-dehydratase 2.21051 0.001802

203851_ .at IGFBP6 insulin-like growth factor binding protein 6 2.22037 8.04E-05

209487_ .at RBPMS RNA binding protein with multiple splicing 2.22901 0.006667

200974_ .at ACTA2 actin, alpha 2, smooth muscle, aorta -2.2617 0.00324

208790_ _s_at PTRF polymerase I and transcript release factor 2.26239 0.00114

20131 1_ _s_at SH3BGRL SH3 domain binding glutamic acid-rich protein like 2.26733 0.008612 disabled homolog 2, mitogen-responsive phosphoprotein

201280_ _s_at DAB2 (Drosophila) 2.26916 0.00016

211864_ _s_at FER1 L3 fer-1 -like 3, myoferlin (C. elegans) 2.30992 0.009752

214505_ _s_at FHL1 four and a half LIM domains 1 2.31098 0.002028

2091 13_ _s_at HMG20B high-mobility group 20B 2.32179 0.003738 collagen, type III, alpha 1 (Ehlers-Danlos syndrome type

201852_ _x_at COL3A1 IV, autosomal dominant) 2.32476 0.006918

208944 _at TGFBR2 transforming growth factor, beta receptor Il (70/8OkDa) 2.37037 0.008003

201893_ _x_at DCN decorin 2.37312 0.000542

212091_ _s_at COL6A1 collagen, type Vl, alpha 1 2.39348 9.55E-06

213428_ _s_at COL6A1 collagen, type Vl, alpha 1 2.40702 0.001384

219922 _s_at LTB P3 latent transforming growth factor beta binding protein 3 2.44169 0.00145

209286_ .at CDC42EP3 CDC42 effector protein (Rho GTPase binding) 3 2.44278 0.009432

204442_ _x_at LTB P4 latent transforming growth factor beta binding protein 4 2.44389 0.00061

204942_ _s_at ALDH3B2 aldehyde dehydrogenase 3 family, member B2 2.44641 0.006218

201668 _x_at MARCKS myristoylated alanine-rich protein kinase C substrate 2.45345 0.000485

200907 s at KIAA0992 palladin 2.47332 0.007584

204163_ _at EMILIN1 elastin microfibril interfacer 1 2.48498 0.001723

202409_ .at LOC492304 putative insulin-like growth factor Il associated protein 2.53724 0.002963 transforming growth factor, beta 1 (Camurati-Engelmann

203085_ _s_at TGFB1 disease) 2.54778 3.26E-06

212016_ _s_at PTBP1 polypyrimidine tract binding protein 1 2.55013 0.000697

204931_ .at TCF21 transcription factor 21 2.56319 0.001324

200904_ .at HLA-E major histocompatibility complex, class I, E 2.57234 2.07E-05

207961_ _x_at M YH 11 myosin, heavy polypeptide 1 1 , smooth muscle 2.57354 0.004572

CD74 antigen (invariant polypeptide of major

209619_ .at CD74 histocompatibility complex, class Il antigen-associated) 2.58672 0.008069

209488 _. _s_at RBPMS RNA binding protein with multiple splicing 2.60676 0.0031 18

210299_ _s_at FHL1 four and a half LIM domains 1 2.611 11 0.006014

203890_ _s_at DAPK3 death-associated protein kinase 3 2.62193 0.005771

202222_ _s_at DES desmin -2.6422 0.008325

201787_ .at FBLN1 fibulin 1 2.65593 0.000172

221667_ _s_at HSPB8 heat shock 22kDa protein 8 2.72126 0.004204

201539_ _s_at FHL1 four and a half LIM domains 1 2.77375 0.005409

209651 _at TGFB1 I1 transforming growth factor beta 1 induced transcript 1 2.80218 0.007543

206391_ .at RARRES1 retinoic acid receptor responder (tazarotene induced) 1 -2.8163 0.000645

201551_ _s_at LAM P 1 lysosomal-associated membrane protein 1 2.81852 0.005412

203453_ _at SCNN1 A sodium channel, nonvoltage-gated 1 alpha 2.90666 0.002618

201497_ _x_at M YH 11 myosin, heavy polypeptide 11 , smooth muscle 2.98474 0.008825

221872_ .at RARRES1 retinoic acid receptor responder (tazarotene induced) 1 3.05437 0.001816 serpin peptidase inhibitor, clade G (C1 inhibitor), member

200986_ _at SERPING1 1 , (angioedema, hereditary) -3.0594 2.65E-05

212574 _x_at C19orf6 chromosome 19 open reading frame 6 3.13178 1 .84E-05

200923_ .at LGALS3BP lectin, galactoside-binding, soluble, 3 binding protein 3.18309 5.59E-06

214040_ _s_at GSN gelsolin (amyloidosis, Finnish type) 3.18941 4.71 E-07

213986_ _s_at C19orf6 chromosome 19 open reading frame 6 3.19533 6.08E-06

210298 _x_at FHL1 four and a half LIM domains 1 -3.2298 0.002585

202995_ _s_at FBLN1 fibulin 1 3.23923 7.20E-06

214251_ _s_at NUMA1 nuclear mitotic apparatus protein 1 3.31103 0.000616

214752_ _x_at FLNA filamin A, alpha (actin binding protein 280) 3.33076 0.002287

206392 s at RARRES1 retinoic acid receptor responder (tazarotene induced) 1 3.42531 0.0001

216971_ _s_at PLEC1 plectin 1 , intermediate filament binding protein 50OkDa -3.54374 2.04E-05

211564_ _s_at PDLIM4 PDZ and LIM domain 4 -3.58703 0.000624

200859_ _x_at FLNA filamin A, alpha (actin binding protein 280) -3.61765 0.000526

209156_ _s_at COL6A2 collagen, type Vl, alpha 2 -3.65904 0.002408

213176_ _s_at LTB P4 latent transforming growth factor beta binding protein 4 -3.83594 1 .59E-07

213746 s at FLNA filamin A, alpha (actin binding protein 280) -4.20067 0.001409

Table 37 - dataset 2 (stroma) uniquelD slopeS probS HLA-DPB1 1.098455644 0.008690666 DKFZp761 D221 0.934159835 0.007873718

Gl 22761402 0.815995448 0.005679887

Table 38 - dataset 2 (tumor) uniquelD slopeT probT

GI_2094528 5.453123 7.29E-08

GRP 4.33782 1.81 E-10

CSMD1 3.474385 0.003859

MKI67 2.539772 0.001274

MMP9 2.213091 0.003619

GI_22761402 2.036472 3.80E-10

G l_16550429 1.974206 5.21 E-05

NRG1 1.586801 0.008504

CSPG2 1.546295 0.00394

ATP6V1 E2 1.478015 0.007254

SALL4 1.477018 0.002289

KHDRBS3 1.444605 0.001988

CDKN2A 1.350577 0.009437

WISP1 1.146656 0.000726

KIAA0389 0.91715 0.006525

MNAT1 0.912261 0.007793

ARFIP2 0.876391 0.00014

LAMA4 0.864271 0.003849

BAX 0.841 154 0.007583

AML1 0.810606 0.006099

ITGA5 0.780658 0.009293

CTBP1 0.678406 0.009205

KIAA1109 0.67286 0.007531

MAP2K1 IP1 -0.53796 0.007869

G2AN -0.58591 0.006474

Prostein -0.83684 0.008957

AZG P 1 -1 .16268 0.000864

DKFZp586J0119 -1.1717 0.002842

HLA-DPB1 -1 .39602 0.002492

DLG2 -1 .70437 0.000225

PART1 -2.25762 0.000612

MCCC2 -2.27693 1.48E-05

GDEP -2.88929 0.000495

CCK -3.66888 4.84E-05

GABRG2 -5.02487 0.000587

Table 39 - dataset 3 (stroma) log base

2 fold uniquelD Gene Symbol Gene Title change p value

203413_at NELL2 NEL-like 2 (chicken) /// NEL-like 2 (chicken) 5.489204 1.80E-06

218835_at SFTPA2 surfactant, pulmonary-associated protein A2 4.99448 0.000853

213068_at DPT dermatopontin 4.941844 8.05E-10

213791_at PENK proenkephalin 4.627489 1.54E-22

202409_at LOC492304 putative insulin-like growth factor Il associated protein 4.301564 7.02E-25

212187 x at PTGDS prostaglandin D2 synthase 21 kDa (brain) 4.282127 4.81 E-08 prostaglandin D2 synthase 21 kDa (brain) /// prostaglandin D2

21 1663 x at PTGDS synthase 21 kDa (brain) 4.162189 3.70E-11

206211_at SELE selectin E (endothelial adhesion molecule 1 ) 4.141967 0.000374

219230_at FLJ10970 hypothetical protein FLJ10970 4.136925 2.21 E-06

207977_s_at DPT dermatopontin 4.006443 8.87E-07

205431_s_at BMP5 bone morphogenetic protein 5 3.951413 4.18E-08 prostaglandin D2 synthase 21 kDa (brain) /// prostaglandin D2

21 1748 x at PTGDS synthase 21 kDa (brain) 3.934415 1.50E-07

202746_at ITM2A integral membrane protein 2A 3.796333 5.80E-08 major histocompatibility complex, class II, DR beta 1 /// major

221491 x at HLA-DRB1 histocompatibility complex, class II, DR beta 1 3.790244 1.91 E-08

208727_s_at CDC42 cell division cycle 42 (GTP binding protein, 25kDa) 3.757378 0.001057

209170_s_at GPM6B glycoprotein M6B 3.755294 2.75E-16

Fc fragment of IgE, high affinity I, receptor for; alpha polypeptide /// Fc fragment of IgE, high affinity I, receptor for; alpha

21 1734_s_at FCER1 A polypeptide 3.664566 5.87E-12

203382 s at APOE apolipoprotein E 3.63542 1.20E-06

205200_at CLEC3B C-type lectin domain family 3, member B 3.629254 9.42E-25

205404_at HSD11 B1 hydroxysteroid (1 1 -beta) dehydrogenase 1 3.571465 1.15E-17

209167_at GPM6B glycoprotein M6B 3.553804 4.51 E-10

219759 at LRAP leukocyte-derived arginine aminopeptidase 3.542274 4.1 1 E-05

219266_at ZNF350 zinc finger protein 350 3.31832 0.003402

Major histocompatibility complex, class II, DQ beta 1 /// Major

212999_x_at HLA-DQB1 histocompatibility complex, class II, DQ beta 1 3.309683 2.92E-11 pleiotrophin (heparin binding growth factor 8, neurite growth-

209466_x_at PTN promoting factor 1 ) 3.296314 0.000107

206049_at SELP selectin P (granule membrane protein 14OkDa, antigen CD62) 3.293419 1.72E-24 transient receptor potential cation channel, subfamily M, member

219360_s_at TRPM4 4 3.232353 0.000393

213071 at DPT dermatopontin 3.140798 2.80E-08 pleiotrophin (heparin binding growth factor 8, neurite growth- promoting factor 1 ) /// pleiotrophin (heparin binding growth factor

21 1737 x at PTN 8, neurite growth-promoting factor 1 ) 3.135266 0.001864

Major histocompatibility complex, class II, DQ beta 1 /// Major

209480_at HLA-DQB1 histocompatibility complex, class II, DQ beta 1 3.104849 1.14E-06

203381 _s_at APOE apolipoprotein E 3.103849 0.000211

208335 s at FY Duffy blood group 3.05992 2.03E-06

58916 at KCTD14 potassium channel tetramerisation domain containing 14 3.01771 1 2.41 E-08

209168_at GPM6B glycoprotein M6B 3.017066 1.50E-15

221690_s_at NALP2 NACHT, leucine rich repeat and PYD containing 2 3.008828 0.001826 chemokine (C-C motif) ligand 14 /// chemokine (C-C motif) ligand

205392 s at CCL14 /// CCL15 15 2.90463 7.54E-08

220532_s_at LR8 LR8 protein 2.890372 6.71 E-08

201325_s_at EMP1 epithelial membrane protein 1 2.866127 0.000366

218922_s_at LASS4 LAG1 longevity assurance homolog 4 (S. cerevisiae) 2.847613 4.92E-09

205207 at IL6 interleukin 6 (interferon, beta 2) 2.837542 0.000239

205430_at BMP5 bone morphogenetic protein 5 2.825151 9.03E-05

205242_at CXCL13 chemokine (C-X-C motif) ligand 13 (B-cell chemoattractant) 2.796628 2.07E-10

204602_at DKK1 dickkopf homolog 1 (Xenopus laevis) 2.781827 0.007679 major histocompatibility complex, class II, DQ beta 1 /// major

21 1654_x_at HLA-DQB1 histocompatibility complex, class II, DQ beta 1 2.780187 1.03E-06

207430_s_at MSMB microseminoprotein, beta- 2.759453 0.000438 pleiotrophin (heparin binding growth factor 8, neurite growth-

209465 x at PTN promoting factor 1 ) 2.745541 0.004492

205898_at CX3CR1 chemokine (C-X3-C motif) receptor 1 2.741153 0.000124

203868_s_at VCAM1 vascular cell adhesion molecule 1 2.684526 7.86E-06

212884_x_at APOE Apolipoprotein E 2.662926 6.87E-07

210297 s at MSMB microseminoprotein, beta- 2.655622 9.22E-05

209747_at TGFB3 transforming growth factor, beta 3 2.634028 2.10E-08

206071 _s_at EPHA3 EPH receptor A3 2.620419 3.86E-11

205882_x_at AD D3 adducin 3 (gamma) 2.609889 2.58E-08

202747_s_at ITM2A integral membrane protein 2A 2.604758 1.12E-07 Tissue factor pathway inhibitor (lipoprotein-associated

213258_at TFPI coagulation inhibitor) 2.580382 2.48E-08

213395_at MLC1 megalencephalic leukoencephalopathy with subcortical cysts 1 2.572899 4.39E-10

203052_at C2 complement component 2 2.568309 3.99E-06 major histocompatibility complex, class II, DQ beta 1 /// major

212998_x_at HLA-DQB1 histocompatibility complex, class II, DQ beta 1 2.534972 0.000877

201324_at EMP1 epithelial membrane protein 1 2.530238 0.003386

201842_s_at EFEMP1 EGF-containing fibulin-like extracellular matrix protein 1 2.525356 0.001269

215145 s at CNTNAP2 contactin associated protein-like 2 2.514392 0.003233

204545_at PEX6 peroxisomal biogenesis factor 6 2.49286 9.98E-06

220283_at KIAA1822L KIAA1822-like 2.475417 1.19E-13

206682_at CLEC10A C-type lectin domain family 10, member A 2.443806 7.31 E- 12 guanylate binding protein 1 , interferon-inducible, 67kDa ///

202269 x_at GBP1 guanylate binding protein 1 , interferon-inducible, 67kDa 2.438516 3.33E-05

219867_at CHODL chondrolectin 2.414547 7.74E-08 serpin peptidase inhibitor, clade A (alpha-1 antiproteinase,

209443_at SERPINA5 antitrypsin), member 5 2.410818 3.05E-12

204368_at SLCO2A1 solute carrier organic anion transporter family, member 2A1 2.39768 0.000189

222304_x_at OR7E47P olfactory receptor, family 7, subfamily E, member 47 pseudogene 2.381455 0.000604 major histocompatibility complex, class II, DR alpha /// major

208894_at HLA-DRA histocompatibility complex, class II, DR alpha 2.377035 7.05E-05

C-type lectin domain family 7, member A /// C-type lectin domain

221698_s_at CLEC7A family 7, member A 2.371779 2.31 E-07

209201_x_at CXC R4 chemokine (C-X-C motif) receptor 4 2.369772 0.002292

219140_s_at RBP4 retinol binding protein 4, plasma 2.366078 3.36E-21

201034 at AD D3 adducin 3 (gamma) 2.346457 1.49E-05

205326_at RAMP3 receptor (calcitonin) activity modifying protein 3 2.295126 1.88E-07

214761_at ZNF423 zinc finger protein 423 2.278218 5.59E-12

MMP23B ///

2071 18_s_at MMP23A matrix metallopeptidase 23B /// matrix metallopeptidase 23A 2.266738 4.31 E-15

215193_x_at HLA-DRB1 major histocompatibility complex, class II, DR beta 1 2.246954 7.79E-07

2021 12_at VWF von Willebrand factor 2.23189 3.57E-05

216598_s_at CCL2 chemokine (C-C motif) ligand 2 2.226284 0.002007

204394 at SLC43A1 solute carrier family 43, member 1 2.225854 3.83E-06

205073_at CYP2J2 cytochrome P450, family 2, subfamily J, polypeptide 2 2.2213 0.008273

2151 16_s_at DNM1 dynamin 1 2.219382 3.06E-08

209573_s_at C18orf1 chromosome 18 open reading frame 1 2.216492 6.80E-14

205432_at OVG P 1 oviductal glycoprotein 1 , 12OkDa (mucin 9, oviductin) 2.198928 6.02E-07

S100 calcium binding protein A4 (calcium protein, calvasculin,

203186_s_at S100A4 metastasin, murine placental homolog) 2.185376 0.000729

214598_at CLDN8 claudin 8 2.182942 0.001672

209735 at ABCG2 ATP-binding cassette, sub-family G (WHITE), member 2 2.149332 5.36E-08

206030_at ASPA aspartoacylase (Canavan disease) 2.145077 2.69E-05

220988 S at C1 QTNF3 C1 q and tumor necrosis factor related protein 3 /// C1q and 2.142632 0.000899

tumor necrosis factor related protein 3

208814_at HSPA4 Heat shock 7OkDa protein 4 2.140177 0.000139

219269_at FLJ21616 hypothetical protein FLJ21616 2.137572 0.001848

209398_at HIST1 H1 C histone 1 , H1c 2.129817 0.001397

218345_at HCA112 hepatocellular carcinoma-associated antigen 1 12 2.128669 2.01 E-07

213059_at CREB3L1 cAMP responsive element binding protein 3-like 1 2.113389 7.06E-06

201858_s_at PRG1 proteoglycan 1 , secretory granule 2.11 1824 0.00024

210982_s_at HLA-DRA major histocompatibility complex, class II, DR alpha 2.098275 0.000226

205347 s at TMSL8 thymosin-like 8 2.090751 0.002065

204670_x_at HLA-DRB1 major histocompatibility complex, class II, DR beta 1 2.080329 9.22E-07 chemokine (C-X-C motif) receptor 4 /// chemokine (C-X-C motif)

21 1919_s_at CXC R4 receptor 4 2.078332 0.004165

210889 s at FCGR2B Fc fragment of IgG, low affinity lib, receptor (CD32) 2.069481 5.10E-07

204260_at CHGB chromogranin B (secretogranin 1 ) 2.062107 6.67E-08 colony stimulating factor 1 receptor, formerly McDonough feline sarcoma viral (v-fms) oncogene homolog /// colony stimulating factor 1 receptor, formerly McDonough feline sarcoma viral (v-

203104_at CSF1 R fms) oncogene homolog 2.058921 1.12E-05

201752_s_at AD D3 adducin 3 (gamma) 2.051831 1.04E-05

205316_at SLC15A2 Solute carrier family 15 (H+/peptide transporter), member 2 2.050784 0.006469

201666_at TIMP1 TIMP metallopeptidase inhibitor 1 2.047738 0.007282

202242_at TSPAN7 tetraspanin 7 2.045386 1.02E-06 major histocompatibility complex, class II, DR beta 1 /// major

209312_x_at HLA-DRB1 histocompatibility complex, class II, DR beta 1 2.030949 8.04E-06

LGALS3 /// lectin, galactoside-binding, soluble, 3 (galectin 3) /// galectin-3

208949 s at GALIG internal gene 2.014096 8.43E-05

201843_s_at EFEMP1 EGF-containing fibulin-like extracellular matrix protein 1 2.012838 8.96E-05

204955_at SRPX sushi-repeat-containing protein, X-linked 2.010724 0.001692

202643 s at TNFAIP3 tumor necrosis factor, alpha-induced protein 3 2.010615 0.00946

219405_at TRIM68 tripartite motif-containing 68 2.006525 0.005106 elastin (supravalvular aortic stenosis, Williams-Beuren

212670_at ELN syndrome) 2.005247 0.000189

201108 s at THBS1 thrombospondin 1 2.000481 0.008471

201137_s_at HLA-DPB1 major histocompatibility complex, class II, DP beta 1 1.994721 4.66E-05

204037 at EDG2 endothelial differentiation, lysophosphatidic acid G-protein- 1.987127 3.55E-07

coupled receptor, 2

205648_at WNT2 wingless-type MMTV integration site family member 2 1.982491 1.03E-05

201753_s_at AD D3 adducin 3 (gamma) 1.980488 0.001529

218574_s_at LMCD1 LIM and cysteine-rich domains 1 1.970663 2.97E-06

215059_at ... MRNA; cDNA DKFZp564G1 12 (from clone DKFZp564G112) 1.968959 1.37E-09

213601_at SLIT1 slit homolog 1 (Drosophila) 1.968699 1.30E-05

205620_at F10 coagulation factor X 1.948339 1.70E-09

BTN3A3 /// butyrophilin, subfamily 3, member A3 /// butyrophilin, subfamily 3,

204820_s_at BTN3A2 member A2 1.943927 0.002256

KIAA0268 ///

UNQ6077 /// C219-reactive peptide /// AAAP6077 /// similar to C219-reactive

212310_at LOC440751 peptide 1 .94176 0.000253

213418_at HSPA6 heat shock 7OkDa protein 6 (HSP70B ¹ ) 1.937575 0.000133

203619_s_at FAIM2 Fas apoptotic inhibitory molecule 2 1.928405 8.84E-11

208983_s_at PECAM1 platelet/endothelial cell adhesion molecule (CD31 antigen) 1.917568 0.000819

202992_at C7 complement component 7 1.909132 0.000337

202410_x_at IGF2 insulin-like growth factor 2 (somatomedin A) 1.908886 3.76E-10

219602_s_at FAM38B family with sequence similarity 38, member B 1.894541 2.52E-16

210978_s_at TAGLN2 transgelin 2 1.893889 0.000161

213519_s_at LAMA2 laminin, alpha 2 (merosin, congenital muscular dystrophy) 1 .89175 4.73E-05

213844_at HOXA5 homeo box A5 1.891152 0.001277

210508_s_at KCNQ2 potassium voltage-gated channel, KQT-like subfamily, member 2 1 .88277 5.65E-09

221087_s_at APOL3 apolipoprotein L, 3 1.880867 0.000279

320_at PEX6 peroxisomal biogenesis factor 6 1.878554 9.65E-05

205759_s_at SULT2B1 sulfotransferase family, cytosolic, 2B, member 1 1.875884 0.000175

219436_s_at EMCN endomucin 1.869907 9.20E-08 placental growth factor, vascular endothelial growth factor-

209652_s_at PGF related protein 1.8611 16 4.35E-11

219729_at PRRX2 paired related homeobox 2 1.853068 1.75E-06

205978_at KL klotho 1.846235 0.001963 ets variant gene 4 (E1 A enhancer binding protein, E1 AF) /// ets

21 1603_s_at ETV4 variant gene 4 (E1 A enhancer binding protein, E1 AF) 1.840957 3.24E-09

218084_x_at FXYD5 FXYD domain containing ion transport regulator 5 1.839466 1.14E-07

203886 s at FBLN2 fibulin 2 1.831612 0.000219

219991_at SLC2A9 solute carrier family 2 (facilitated glucose transporter), member 9 1.828705 7.10E-11

221944_at LOC283874 Hypothetical protein LOC283874 1.823408 1.33E-09

204103_at CCL4 chemokine (C-C motif) ligand 4 1.822669 0.000153

221529_s_at PLVAP plasmalemma vesicle associated protein 1.820398 0.00012

219747_at FLJ23191 hypothetical protein FLJ23191 1.818384 0.000831

205608_s_at ANGPT1 angiopoietin 1 1.815849 3.89E-06

210002_at GATA6 GATA binding protein 6 1.81 1896 0.004116

2161 14_at NCKIPSD NCK interacting protein with SH3 domain 1 .79883 1.44E-10

211991_s_at HLA-DPA1 major histocompatibility complex, class II, DP alpha 1 1.798669 0.001184

203979_at CYP27A1 cytochrome P450, family 27, subfamily A, polypeptide 1 1.784908 0.000374

217478_s_at HLA-DMA major histocompatibility complex, class II, DM alpha 1.779529 0.00548

CD74 antigen (invariant polypeptide of major histocompatibility

209619_at CD74 complex, class Il antigen-associated) 1.779286 1.70E-05

204311_at ATP1 B2 ATPase, Na+/K+ transporting, beta 2 polypeptide 1.778801 5.05E-21

209823_x_at HLA-DQB1 major histocompatibility complex, class II, DQ beta 1 1.760789 0.00911 1

205818_at DBC1 deleted in bladder cancer 1 1.759038 0.000258

205779_at RAMP2 receptor (calcitonin) activity modifying protein 2 1.758379 1.58E-07

221593_s_at RPL31 ribosomal protein L31 1.757848 0.007055

218857_s_at ASRGL1 asparaginase like 1 1.744043 0.009616

210881_s_at IGF2 insulin-like growth factor 2 (somatomedin A) 1.742502 5.58E-14

210452_x_at CYP4F2 cytochrome P450, family 4, subfamily F, polypeptide 2 1.741893 1.80E-06

KIAA0268 ///

UNQ6077 /// C219-reactive peptide /// AAAP6077 /// similar to C219-reactive

212305_s_at LOC440751 peptide 1.734093 5.87E-06

219050_s_at ZNHIT2 zinc finger, HIT type 2 1.732035 3.14E-09 granzyme K (granzyme 3; tryptase II) /// granzyme K (granzyme

206666_at GZMK 3; tryptase II) 1.720426 7.1 1 E-06

21 1813_x_at DCN decorin 1.719793 8.78E-05

204919_at PRR4 proline rich 4 (lacrimal) 1.713232 0.000424 guanylate binding protein 1 , interferon-inducible, 67kDa ///

202270_at GBP1 guanylate binding protein 1 , interferon-inducible, 67kDa 1.71 1127 0.006492

207114_at LY6G6C lymphocyte antigen 6 complex, locus G6C 1.703258 3.10E-13

205987_at CD1 C CD1 C antigen, c polypeptide 1.695706 2.06E-05

209325 s at RGS16 regulator of G-protein signalling 16 1.693634 2.42E-07

219725_at TREM2 triggering receptor expressed on myeloid cells 2 1.693001 7.13E-08 cytidine monophosphate-N-acetylneuraminic acid hydroxylase

205518_s_at CMAH (CMP-N-acetylneuraminate monooxygenase) 1.684399 0.002381

204122_at TYROBP TYRO protein tyrosine kinase binding protein 1.677151 0.000344 stimulated by retinoic acid gene 6 homolog (mouse) /// stimulated

221701_s_at STRA6 by retinoic acid gene 6 homolog (mouse) 1.669373 2.01 E-06

TIMP metallopeptidase inhibitor 3 (Sorsby fundus dystrophy,

201149_s_at TIMP3 pseudoinflammatory) 1.657828 0.000357

219666_at MS4A6A membrane-spanning 4-domains, subfamily A, member 6A 1.655295 0.000556

21 1896_s_at DCN decorin 1.652321 0.00251

213524_s_at G0S2 G0/G1 switch 2 1 .64485 0.001741

208306_x_at HLA-DRB5 Major histocompatibility complex, class II, DR beta 3 1 .64335 4.75E-05

218380_at NALP1 NACHT, leucine rich repeat and PYD (pyrin domain) containing 1 1.638032 0.000218

210684_s_at DLG4 discs, large homolog 4 (Drosophila) 1.629744 0.000385

201204_s_at RRBP1 Ribosome binding protein 1 homolog 18OkDa (dog) 1.629572 0.005089 adrenergic, alpha-2A-, receptor /// adrenergic, alpha-2A-,

209869_at ADRA2A receptor 1.624862 0.003948

210063_at SARDH sarcosine dehydrogenase 1.620661 9.60E-05

206932_at CH25H cholesterol 25-hydroxylase 1.614504 0.001957 intercellular adhesion molecule 1 (CD54), human rhinovirus

202637_s_at ICAM1 receptor 1.613227 0.000977 endothelial differentiation, lysophosphatidic acid G-protein-

204036_at EDG2 coupled receptor, 2 1.612794 0.005969

221923_s_at NPM1 nucleophosmin (nucleolar phosphoprotein B23, numatrin) 1.607706 6.82E-05

209083 at CORO1A coronin, actin binding protein, 1 A 1.605729 5.68E-05

64486_at CORO1 B coronin, actin binding protein, 1 B 1.6031 19 4.13E-07 cytochrome b-245, beta polypeptide (chronic granulomatous

203923_s_at CYBB disease) 1.595935 2.48E-07

205619_s_at MEOX1 mesenchyme homeo box 1 1.593023 0.000432

209473_at ENTPD1 ectonucleoside triphosphate diphosphohydrolase 1 1.589396 7.98E-05

207691 _x_at ENTPD1 ectonucleoside triphosphate diphosphohydrolase 1 1.583364 0.002043 ribonuclease, RNase A family, k6 /// ribonuclease, RNase A

213566_at RNASE6 family, k6 1.582904 0.003242 ras-related C3 botulinum toxin substrate 2 (rho family, small GTP

213603 s at RAC2 binding protein Rac2) 1.576754 0.006208

204595 s at STC 1 stanniocalcin 1 1.575287 0.000307

220584_at FLJ22184 hypothetical protein FLJ22184 1.573809 0.000495

217511_at KAZALD 1 Kazal-type serine peptidase inhibitor domain 1 1.570805 8.43E-06

220951 _s_at ACF apobec-1 complementation factor 1.563946 1.14E-09

TIMP metallopeptidase inhibitor 3 (Sorsby fundus dystrophy,

201150_s_at TIMP3 pseudoinflammatory) 1.560587 3.64E-06 transient receptor potential cation channel, subfamily C, member

220818_s_at TRPC4 4 1.555885 3.57E-05

220894 x at PRDM12 PR domain containing 12 1.554104 0.000536

205067_at IL1 B interleukin 1 , beta 1.553715 0.001479

201426_s_at VIM vimentin 1.551604 0.000102

215864_at INPP4B Inositol polyphosphate-4-phosphatase, type II, 105kDa 1.549052 1.19E-07

52255_s_at COL5A3 collagen, type V, alpha 3 1.546181 6.91 E-26

CD48 antigen (B-cell membrane protein) /// CD48 antigen (B-cell

2041 18_at CD48 membrane protein) 1.543715 0.001868 chemokine (C-C motif) ligand 5 /// chemokine (C-C motif) ligand

204655_at CCL5 5 1.538412 0.00225

210051_at RAPGEF3 Rap guanine nucleotide exchange factor (GEF) 3 1.536953 0.002695

208189_s_at MYO7A myosin VIIA 1.531819 8.82E-05

220827_at CACNA1 C Calcium channel, voltage-dependent, L type, alpha 1 C subunit 1.531756 2.71 E-10

219407 s at LAMC3 laminin, gamma 3 1.531 194 3.10E-05

201422_at IFI30 interferon, gamma-inducible protein 30 1.528154 0.003337 serpin peptidase inhibitor, clade F (alpha-2 antiplasmin, pigment

202283_at SERPINF1 epithelium derived factor), member 1 1 .52572 0.007076

221372 s at P2RX2 purinergic receptor P2X, ligand-gated ion channel, 2 1.523015 3.08E-08

205859_at LY86 lymphocyte antigen 86 1.522632 8.40E-08

201487_at CTSC cathepsin C 1.520159 0.00021

207704_s_at GAS7 growth arrest-specific 7 1.515128 3.24E-09

206852_at EPHA7 EPH receptor A7 1 .51479 0.002947

216464_x_at GPR44 G protein-coupled receptor 44 1.514045 2.60E-07

206090_s_at DISC1 disrupted in schizophrenia 1 1.505648 0.00607

CD83 antigen (activated B lymphocytes, immunoglobulin

204440 at CD83 superfamily) 1.500999 3.29E-05

206756_at CHST7 carbohydrate (N-acetylglucosamine 6-0) sulfotransferase 7 1.500986 1.1 1 E-08

205582 s at GGTLA1 gamma-glutamyltransferase-like activity 1 1.493693 0.004216

204852_s_at PTPN7 protein tyrosine phosphatase, non-receptor type 7 1.493524 2.75E-05

214219_x_at MAP4K1 mitogen-activated protein kinase kinase kinase kinase 1 1.493364 1.16E-09

203813_s_at SLIT3 slit homolog 3 (Drosophila) 1.490279 4.88E-06

33197_at MYO7A myosin VIIA 1 .48536 1.67E-05

209824_s_at ARNTL aryl hydrocarbon receptor nuclear translocator-like 1.484842 5.05E-05

210785_s_at C1orf38 chromosome 1 open reading frame 38 1.47861 1 0.000219

214781_at ... 1.478349 5.38E-06

208966_x_at IFH 6 interferon, gamma-inducible protein 16 1.471898 0.004326

202827_s_at MMP14 matrix metallopeptidase 14 (membrane-inserted) 1.468315 0.006623

219993_at SOX17 SRY (sex determining region Y)-box 17 1.468222 9.01 E-05

206883_x_at GP9 glycoprotein IX (platelet) 1.462466 1.19E-12

206498_at OCA2 oculocutaneous albinism Il (pink-eye dilution homolog, mouse) 1.454155 3.50E-06 chemokine (C-C motif) receptor 2 /// chemokine (C-C motif)

206978_at CCR2 receptor 2 1.453387 1.88E-05

210971_s_at ARNTL aryl hydrocarbon receptor nuclear translocator-like 1.452728 1.10E-08

221840_at PTPRE protein tyrosine phosphatase, receptor type, E 1.450219 0.008859

201721 s at LAPTM5 lysosomal associated multispanning membrane protein 5 1.449572 0.002062

221065_s_at CHST8 carbohydrate (N-acetylgalactosamine 4-0) sulfotransf erase 8 1.449315 4.63E-06

219143_s_at RPP25 ribonuclease P 25kDa subunit 1.446895 9.50E-06

RBMY1 A1 /// RNA binding motif protein, Y-linked, family 1 , member A1 /// RNA

RBMY2FP /// binding motif protein, Y-linked, family 2, member F pseudogene

RBMY1 F /// /// RNA binding motif protein, Y-linked, family 1 , member F ///

RBMY1 B /// RNA binding motif protein, Y-linked, family 1 , member B /// RNA

RBMY1 D /// binding motif protein, Y-linked, family 1 , member D /// RNA

RBMY1 E /// binding motif protein, Y-linked, family 1 , member E /// RNA

216842_x_at RBMYU binding motif protein, Y-linked, family 1 , member J 1.446631 2.73E-13

203508_at TNFRSF1 B tumor necrosis factor receptor superfamily, member 1 B 1.443054 2.60E-09

216601_at LOC90586 AOC3 pseudogene 1.438038 1.14E-06 major histocompatibility complex, class II, DQ beta 1 /// major

21 1656_x_at HLA-DQB1 histocompatibility complex, class II, DQ beta 1 1.43731 1 0.004604

210288 at KLRG1 killer cell lectin-like receptor subfamily G, member 1 1.436072 3.71 E-06

203417_at MFAP2 microfibrillar-associated protein 2 1.435479 0.00902

215253_s_at DSCR1 Down syndrome critical region gene 1 1.432121 4.58E-05

203088 at FBLN5 fibulin 5 1.430714 0.001472

215161_at CAMK1 G calcium/calmodulin-dependent protein kinase IG 1.430099 6.76E-07 206105_at AFF2 AF4/FMR2 family, member 2 1.42814 1.18E-05 203761 _at SLA Src-like-adaptor /// Src-like-adaptor 1.42803 0.00108 213176_s_at LTBP4 latent transforming growth factor beta binding protein 4 1.425834 0.001008 218072_at COMMD9 COMM domain containing 9 1.423254 7.50E-07 colony stimulating factor 3 receptor (granulocyte) /// colony

203591 _s_at CSF3R stimulating factor 3 receptor (granulocyte) 1.422208 4.64E-06

21 1888_x_at CASP10 caspase 10, apoptosis-related cysteine peptidase 1.420268 4.45E-08

204393_s_at ACPP acid phosphatase, prostate 1.418571 0.002979

205752_s_at GSTM5 glutathione S-transferase M5 1.417412 0.005095

218744_s_at PACSIN3 protein kinase C and casein kinase substrate in neurons 3 1.413561 3.69E-07

219014_at PLAC8 placenta-specific 8 1.408531 0.004038

221409_at OR2S2 olfactory receptor, family 2, subfamily S, member 2 1.407145 3.32E-06

218047_at OSBPL9 oxysterol binding protein-like 9 1.406243 0.000941

205014_at FGFBP1 fibroblast growth factor binding protein 1 1.405974 0.000149

205915_x_at GRIN1 glutamate receptor, ionotropic, N-methyl D-aspartate 1 1.404451 0.001843

209993_at ABCB1 ATP-binding cassette, sub-family B (MDR/TAP), member 1 1.403813 0.000736

203047_at STK10 serine/threonine kinase 10 1.40223 4.81 E-06

219916_s_at RNF39 ring finger protein 39 1.401976 5.02E-05

210237_at ARTN artemin 1.400828 0.002656

213620_s_at ICAM2 intercellular adhesion molecule 2 1.40024 0.001242

221949_at LOC222070 hypothetical protein LOC222070 1.393796 3.46E-07 granzyme H (cathepsin G-like 2, protein h-CCPX) /// granzyme H

210321_at GZMH (cathepsin G-like 2, protein h-CCPX) 1.391566 5.33E-08

220684_at TBX21 T-box 21 1.389553 9.12E-05

202972_s_at FAM13A1 family with sequence similarity 13, member A1 1.387619 0.001369

204923_at CXorf9 chromosome X open reading frame 9 1.385232 2.03E-06

209474_s_at ENTPD1 ectonucleoside triphosphate diphosphohydrolase 1 1.38397 0.000529

21 1312_s_at WISP1 WNT1 inducible signaling pathway protein 1 1.382509 9.48E-09

213745_at ATRNL1 attractin-like 1 1.382329 9.43E-10

202465_at PCOLCE procollagen C-endopeptidase enhancer 1.374989 0.00685

217442_at IGSF4B Immunoglobulin superfamily, member 4B 1.374732 2.13E-11

209734_at NCKAP1 L NCK-associated protein 1 -like 1.374667 3.85E-06

202012 s at EXT2 exostoses (multiple) 2 1.374388 4.43E-06

213744_at ATRNL1 attractin-like 1 1.373034 1.90E-07

207671 _s_at VM D2 vitelliform macular dystrophy 2 (Best disease, bestrophin) 1.372551 3.05E-11

219550_at ROBO3 roundabout, axon guidance receptor, homolog 3 (Drosophila) 1 .37161 0.004909 colony stimulating factor 2 receptor, beta, low-affinity

(granulocyte-macrophage) /// colony stimulating factor 2

205159_at CSF2RB receptor, beta, low-affinity (granulocyte-macrophage) 1.370481 5.48E-05

215283_at LOC400642 hypothetical gene supported by BC041875; BX648984 1.370296 6.23E-05

206296_x_at MAP4K1 mitogen-activated protein kinase kinase kinase kinase 1 1.367559 1.27E-06 calcium/calmodulin-dependent protein kinase (CaM kinase) Il

21 1483 x at CAMK2B beta 1.365512 4.47E-05

207389_at GP1 BA glycoprotein Ib (platelet), alpha polypeptide 1.358464 9.64E-11

221610_s_at STAP2 signal-transducing adaptor protein-2 1.356235 0.001381

203423_at RBP1 retinol binding protein 1 , cellular 1.353443 1.52E-06

220331_at CYP46A1 cytochrome P450, family 46, subfamily A, polypeptide 1 1.349657 4.10E-05

221356_x_at P2RX2 purinergic receptor P2X, ligand-gated ion channel, 2 1.349092 9.10E-05

207244_x_at CYP2A6 cytochrome P450, family 2, subfamily A, polypeptide 6 1.347315 1.60E-05

215869_at ABCA1 ATP-binding cassette, sub-family A (ABC1 ), member 1 1.345443 1.63E-06

218870_at ARHGAP15 Rho GTPase activating protein 15 1.343762 0.000274

210263 at KCNF1 potassium voltage-gated channel, subfamily F, member 1 1.342529 3.03E-05

203562_at FEZ1 fasciculation and elongation protein zeta 1 (zygin I) 1.341803 2.62E-06

205602_x_at PSG7 pregnancy specific beta-1 -glycoprotein 7 1.338459 0.00018 wingless-type MMTV integration site family, member 4 ///

208606 s at WNT4 wingless-type MMTV integration site family, member 4 1.337541 0.000233

207578_s_at HTR4 5-hydroxytryptamine (serotonin) receptor 4 1.337137 1.09E-05

203391 _at FKBP2 FK506 binding protein 2, 13kDa 1.336908 0.001163

207442_at CSF3 colony stimulating factor 3 (granulocyte) 1.336667 8.72E-06

21 1898_s_at EPHB1 EPH receptor B1 1.335632 5.09E-08

206481 _s_at LDB2 LIM domain binding 2 1.333839 0.003625

213854_at SYNGR1 synaptogyrin 1 1.332841 1.22E-08

220275_at CUZD1 CUB and zona pellucida-like domains 1 1.332314 5.94E-10

209335_at DCN decorin 1.3321 15 0.008296

204677 at CDH5 cadherin 5, type 2, VE-cadherin (vascular epithelium) 1.33191 1 0.003247

220323_at FLJ13265 hypothetical protein FLJ13265 1.328675 4.00E-06

216033 s at FYN FYN oncogene related to SRC, FGR, YES 1.326662 0.001122

217143 s at TRA@ /// TRD@ T cell receptor alpha locus /// T cell receptor delta locus 1.325576 2.34E-07

205575_at C1 QL1 complement component 1 , q subcomponent-like 1 1.320599 1.46E-06

206185_at CRYBB1 crystallin, beta B1 1.316754 4.21 E-06

Fc fragment of IgE, high affinity I, receptor for; gamma

204232_at FCER1 G polypeptide 1.316049 0.006303

206901_at MGC11271 hypothetical protein MGC1 1271 1.314038 6.63E-08

204046_at PLCB2 phospholipase C, beta 2 1.313606 1.11E-10

202921 _s_at ANK2 ankyrin 2, neuronal 1.311235 4.22E-05

204174 at ALOX5AP arachidonate 5-lipoxygenase-activating protein 1.30909 0.002048

218975_at COL5A3 collagen, type V, alpha 3 1.308812 3.78E-06

200862_at DHCR24 24-dehydrocholesterol reductase 1.308416 0.000115

206690_at ACCN1 amiloride-sensitive cation channel 1 , neuronal (degenerin) 1.308361 4.65E-07

219607_s_at MS4A4A membrane-spanning 4-domains, subfamily A, member 4 1.306072 0.00058

218931_at RAB17 RAB17, member RAS oncogene family 1.305796 0.000941

203471 _s_at PLEK pleckstrin 1.303243 2.73E-05

216670_at KLK13 kallikrein 13 1.301028 8.63E-06

2201 14 s at STAB2 stabilin 2 1.300534 1.10E-05

203131_at PDGFRA platelet-derived growth factor receptor, alpha polypeptide 1.300041 0.006105

CD79A antigen (immunoglobulin-associated alpha) /// CD79A

205049_s_at CD79A antigen (immunoglobulin-associated alpha) 1.298628 7.07E-05

Fms-related tyrosine kinase 1 (vascular endothelial growth

222033_s_at FLT1 factor/vascular permeability factor receptor) 1.294185 3.73E-05

33579_i_at GALR3 galanin receptor 3 1.293842 7.63E-05

208658_at PDIA4 protein disulfide isomerase family A, member 4 1.292984 0.001238

21 1177_s_at TXNRD2 thioredoxin reductase 2 1.292756 0.008969

217700 at MGC40499 Hypothetical protein MGC40499 1.289032 0.002271

220934_s_at MGC3196 hypothetical protein MGC3196 1.28763 5.49E-08

219173_at MYO15B myosin XVB pseudogene 1.286818 0.003646

203028_s_at CYBA cytochrome b-245, alpha polypeptide 1.286411 0.001666

212611 at DTX4 deltex 4 homolog (Drosophila) 1.283227 0.001018

204912_at IL10RA interleukin 10 receptor, alpha 1.279827 0.002413

213095_x_at AIF1 allograft inflammatory factor 1 1.279464 2.76E-05

215333_x_at GSTM1 glutathione S-transferase M1 1.279061 0.0003

206130 s at ASG R2 asialoglycoprotein receptor 2 1.278545 8.22E-05

203823_at RGS3 regulator of G-protein signalling 3 1.278094 0.007327 fucosyltransferase 3 (galactoside 3(4)-L-fucosyltransferase,

216010_x_at FUT3 Lewis blood group included) 1.277343 3.58E-09

219594_at NINJ2 ninjurin 2 1.274351 0.003505

2051 16_at LAMA2 laminin, alpha 2 (merosin, congenital muscular dystrophy) 1.272944 4.38E-06

209901_x_at AIF1 allograft inflammatory factor 1 1.271631 6.47E-05

215051_x_at AIF1 allograft inflammatory factor 1 1 .27141 0.001009

204468_s_at TIE1 tyrosine kinase with immunoglobulin-like and EGF-like domains 1 1.268268 0.004301

218961_s_at PNKP polynucleotide kinase 3'-phosphatase 1.266761 0.000323

217001_x_at HLA-DOA major histocompatibility complex, class II, DO alpha 1.265885 2.33E-05

203547_at CD4 CD4 antigen (p55) /// CD4 antigen (p55) 1.264987 1.15E-07

206192_at CDSN corneodesmosin 1.262778 2.20E-06

205098_at CCR1 chemokine (C-C motif) receptor 1 1.262468 2.33E-05

CTAG E4 ///

LOC441296 /// CTAGE family, member 4 /// similar to FLJ00261 protein ///

214355_x_at LOC442780 hypothetical protein LOC442780 1.261934 0.000487

21 1465_x_at FUT6 fucosyltransferase 6 (alpha (1 ,3) fucosyltransferase) 1.261367 0.000632

203388_at ARRB2 arrestin, beta 2 1.260163 3.70E-07

21 1439_at SFRS7 splicing factor, arginine/serine-rich 7, 35kDa 1.256575 8.70E-05

213661_at DKFZP586H2123 regeneration associated muscle protease 1.256497 0.004417

204685_s_at ATP2B2 ATPase, Ca++ transporting, plasma membrane 2 1.256397 0.000148

208018_s_at HCK hemopoietic cell kinase 1.255764 1.28E-05

204882_at ARHGAP25 Rho GTPase activating protein 25 1.254924 0.00231 1

21 1107_s_at AURKC aurora kinase C 1.254824 0.000197 transcription elongation factor B (SIII), polypeptide 2 (18kDa,

213877_x_at TCEB2 elongin B) 1 .25469 7.17E-05

219134 at ELTD1 EGF, latrophilin and seven transmembrane domain containing 1 1.254081 0.002919

222302_at ... 1.252659 4.84E-05

210764_s_at CYR61 cysteine-rich, angiogenic inducer, 61 1.252464 0.004922

210992_x_at FCGR2C Fc fragment of IgG, low affinity Nc, receptor for (CD32) 1.252432 0.000325

219812_at MGC2463 hypothetical protein MGC2463 1 .25233 0.000195

218198 at DHX32 DEAH (Asp-Glu-Ala-His) box polypeptide 32 1.250753 0.001116

220424_at NPHS2 nephrosis 2, idiopathic, steroid-resistant (podocin) 1.248843 0.000644

215261 at BTBD9 BTB (POZ) domain containing 9 1.246809 0.00191 1

204179_at MB myoglobin 1.244098 0.001062

216635_at ... MRNA; cDNA DKFZp566M0524 (from clone DKFZp566M0524) 1 .24263 0.006897

221655_x_at EPS8L1 EPS8-like 1 1.242194 0.001336

BCL2-like 14 (apoptosis facilitator) /// BCL2-like 14 (apoptosis

221241_s_at BCL2L14 facilitator) 1.240116 0.008184

218180_s_at EPS8L2 EPS8-like 2 1.239582 0.000931

205574_x_at BMP1 bone morphogenetic protein 1 1.239223 0.000207

214619_at CRHR1 corticotropin releasing hormone receptor 1 1.235597 3.86E-05

221612_at HT017 HT017 protein 1.235493 2.30E-05

219188_s_at LRP16 LRP16 protein 1.232948 0.000289

209166_s_at MAN2B1 mannosidase, alpha, class 2B, member 1 1.232609 0.006471

204683_at ICAM2 intercellular adhesion molecule 2 1.231781 2.80E-05

220846 s at FLJ31031 Chromosome 1 open reading frame 86 1.231 192 2.59E-06

205508_at SCN1 B sodium channel, voltage-gated, type I, beta 1.230635 5.10E-05

202767_at ACP2 acid phosphatase 2, lysosomal 1.230351 0.000279 caspase 1 , apoptosis-related cysteine peptidase (interleukin 1 ,

21 1368_s_at CASP1 beta, convertase) 1 .22893 9.67E-05

205911 at PTHR1 parathyroid hormone receptor 1 1.227918 0.001313

221306_at GPR27 G protein-coupled receptor 27 1.225707 3.54E-07

2061 18_at STAT4 signal transducer and activator of transcription 4 1.225404 6.36E-05

206868_at STARD8 START domain containing 8 1.225035 0.001739

204265_s_at GPSM3 G-protein signalling modulator 3 (AGS3-like, C. elegans) 1.222665 9.26E-05

201893_x_at DCN decorin 1.219407 0.002361

216431_at ... 1.217426 0.000193

220758_s_at ROBO4 roundabout homolog 4, magic roundabout (Drosophila) 1.215253 2.55E-09

201415 at GSS glutathione synthetase 1.21421 1 0.003929

219740_at FLJ12505 hypothetical protein FLJ12505 1.213626 0.006106

221134_at ANGPT4 angiopoietin 4 1.210957 2.90E-06

TEK tyrosine kinase, endothelial (venous malformations, multiple

206702_at TEK cutaneous and mucosal) 1.208943 0.000266

210013_at HPX hemopexin 1.207515 4.72E-07

218753_at XKR8 X KeII blood group precursor-related family, member 8 1.207513 2.39E-05

212974_at DENND3 DENN/MADD domain containing 3 1.205504 9.79E-07

216981 x at SPN sialophorin (gpL1 15, leukosialin, CD43) 1.198066 0.001143

neutrophil cytosolic factor 2 (65kDa, chronic granulomatous

209949_at NCF2 disease, autosomal 2) 1.196403 5.62E-07

220217_x_at SPANXC SPANX family, member C 1.195915 3.34E-05

200916_at TAGLN2 transgelin 2 1.193798 0.005828

206838 at TBX19 T-box 19 1.193417 2.83E-05

204189_at RARG retinoic acid receptor, gamma 1.190107 4.63E-06

212494_at TENC1 tensin like C1 domain containing phosphatase (tensin 2) 1.189217 0.00742

207484_s_at EHMT2 euchromatic histone-lysine N-methyltransferase 2 1 .18355 3.55E-05

209057 x at CDC5L CDC5 cell division cycle 5-like (S. pombe) 1.182148 0.001456

203474_at IQGAP2 IQ motif containing GTPase activating protein 2 1.181481 0.007674

218436_at SIL1 SIL1 homolog, endoplasmic reticulum chaperone (S. cerevisiae) 1 .18025 0.000107

216145_at DGCR14 DiGeorge syndrome critical region gene 14 1 .17926 0.001273

2211 19 at FLJ20184 hypothetical protein FLJ20184 1.179235 3.03E-05

209723_at SERPINB9 serpin peptidase inhibitor, clade B (ovalbumin), member 9 1.178844 0.009258 leukocyte immunoglobulin-like receptor, subfamily A (with TM

208594_x_at LILRA6 domain), member 6 1.178449 0.000131

204550_x_at GSTM1 glutathione S-transferase M1 1.175901 0.009622 interleukin 2 receptor, gamma (severe combined

2041 16_at IL2RG immunodeficiency) 1.175542 0.00388

204442_x_at LTB P4 latent transforming growth factor beta binding protein 4 1.174073 0.004392

221314_at GDF9 growth differentiation factor 9 1.173884 1.20E-07

214498_at ASIP agouti signaling protein, nonagouti homolog (mouse) 1.172638 0.000285

33307_at CGI-96 CGI-96 protein 1.172066 0.003255

213030_s_at PLXNA2 plexin A2 1.171819 0.000205

215085_x_at DLEC1 deleted in lung and esophageal cancer 1 1.171125 5.23E-07

214560_at FPRL2 formyl peptide receptor-like 2 1.170717 2.43E-06

210753_s_at EPHB1 EPH receptor B1 1.170166 3.28E-06

204445_s_at ALOX5 arachidonate 5-lipoxygenase 1 .16959 6.51 E-06

212443_at NBEAL2 neurobeachin-like 2 1.169153 0.001722

38149 at ARHGAP25 Rho GTPase activating protein 25 1.168602 3.29E-06

206475_x_at CSH1 chorionic somatomammotropin hormone 1 (placental lactogen) 1 .16758 3.38E-06

214955_at TMPRSS6 transmembrane protease, serine 6 1.167176 0.001639

206713_at NTNG1 netrin GI 1.166645 0.00048

213851 at MGC52022 Similar to RIKEN cDNA I 810038N08 gene 1 .16496 8.79E-05

218421 at CERK ceramide kinase 1 .16472 0.000359

216637_at ITSN1 lntersectin 1 (SH3 domain protein) 1.164142 0.000137 leukocyte immunoglobulin-like receptor, subfamily A (with TM

21 1101_x_at LILRA2 domain), member 2 1.163844 5.90E-06

209897 s at SLIT2 slit homolog 2 (Drosophila) 1.163723 0.001552

205147_x_at NCF4 neutrophil cytosolic factor 4, 4OkDa 1 .16372 5.96E-05 killer cell immunoglobulin-like receptor, two domains, long

21 1397_x_at KIR2DL2 cytoplasmic tail, 2 1 .16366 0.000218

207669_at KRTHB3 keratin, hair, basic, 3 1.163186 7.51 E-08

206366_x_at XCL2 chemokine (C motif) ligand 2 1.162929 0.004354

206819_at DKFZP434P211 POM121 -like protein 1.162553 5.94E-05

215042_at BMP6 bone morphogenetic protein 6 1.161377 0.000244

209588_at EPHB2 EPH receptor B2 1.160957 1.33E-05

210629_x_at LST1 leukocyte specific transcript 1 1.158822 7.95E-06 cytidine monophosphate-N-acetylneuraminic acid hydroxylase

210571_s_at CMAH (CMP-N-acetylneuraminate monooxygenase) 1.158728 0.000676

219559_at C20orf59 chromosome 20 open reading frame 59 1.158237 0.000201

217275_at TSSK2 testis-specific serine kinase 2 1 .15798 8.86E-05

21 1581_x_at LST1 leukocyte specific transcript 1 1.157886 1.22E-07

201011_at RPN1 ribophorin l 1.157681 0.001796 cartilage intermediate layer protein, nucleotide

206227_at CILP pyrophosphohydrolase 1.155692 7.85E-05

205186_at DNALI1 dynein, axonemal, light intermediate polypeptide 1 1.154983 0.003285

205984_at CRHBP corticotropin releasing hormone binding protein 1 .15356 0.000122

209961 _s_at HGF hepatocyte growth factor (hepapoietin A; scatter factor) 1.152891 0.000229

213897_s_at MRPL23 mitochondrial ribosomal protein L23 1 .15272 2.04E-06

90265_at CENTA1 centaurin, alpha 1 1 .15202 1.23E-05

214181_x_at LST1 leukocyte specific transcript 1 1.149659 0.001679

218843_at FNDC4 fibronectin type III domain containing 4 1.148459 0.000618

214339_s_at MAP4K1 mitogen-activated protein kinase kinase kinase kinase 1 1.146132 0.000207

21 1873_s_at PCDHGA9 protocadherin gamma subfamily A, 9 1.145308 1.52E-05

219593_at SLC15A3 solute carrier family 15, member 3 1.144956 0.000108

209169_at GPM6B glycoprotein M6B 1.144321 3.01 E-06

204319 s at RGS10 regulator of G-protein signalling 10 1.144314 0.001006

2131 12_s_at SQSTM1 sequestosome 1 1.143514 0.00015

209365_s_at ECM1 extracellular matrix protein 1 1.142208 0.000612

206804_at CD3G CD3G antigen, gamma polypeptide (TiT3 complex) 1.141646 7.59E-08

204800_s_at FLJ13639 hypothetical protein FLJ13639 1 .14114 0.006305

221297_at GPRC5D G protein-coupled receptor, family C, group 5, member D 1.140954 1.86E-05

213832_at ... Clone 24405 mRNA sequence 1.140383 0.006423

2191 13_x_at DHRS10 dehydrogenase/reductase (SDR family) member 10 1.139395 0.001779

213895_at EMP1 epithelial membrane protein 1 1.139386 0.004267

206402_s_at NPFF neuropeptide FF-amide peptide precursor 1 .13774 0.00623

219310_at C20orf39 chromosome 20 open reading frame 39 1.137368 0.00271

215550_at SRGAP3 SLIT-ROBO Rho GTPase activating protein 3 1.136009 1.30E-08 suppression of tumorigenicity 14 (colon carcinoma, matriptase,

202005_at ST14 epithin) 1.1355 0.000832

21 1557_x_at SLCO2B1 solute carrier organic anion transporter family, member 2B1 1 .13542 0.009672

208474_at CLDN6 claudin 6 1.135153 0.000232

202947_s_at GYPC glycophorin C (Gerbich blood group) 1.134178 0.000687

207334 s at TGFBR2 transforming growth factor, beta receptor Il (70/8OkDa) 1.132974 0.00068

221640_s_at LRDD leucine-rich repeats and death domain containing 1.132909 0.000615

214234_s_at CYP3A5 cytochrome P450, family 3, subfamily A, polypeptide 5 1.130604 0.006804

217652_at KIAA0892 KIAA0892 1.129858 3.28E-06

205677_s_at DLEU1 deleted in lymphocytic leukemia, 1 1.129101 9.99E-05 solute carrier family 22 (organic cation transporter), member 18

206097_at SLC22A18AS antisense 1.127572 0.001618

203561 _at FCGR2A Fc fragment of IgG, low affinity Na, receptor (CD32) 1 .12705 0.005208 pleckstrin homology domain containing, family G (with RhoGef

217044 s at PLEKHG3 domain) member 3 1.1264 0.000122

220747_at HSPC072 HSPC072 protein 1.126193 3.21 E-05

221578_at RASSF4 Ras association (RalGDS/AF-6) domain family 4 1.125902 2.37E-06

204574_s_at MMP19 matrix metallopeptidase 19 1.125668 0.009642 collagen, type VII, alpha 1 (epidermolysis bullosa, dystrophic,

217312_s_at COL7A1 dominant and recessive) 1.125297 0.006292

209436_at SPON1 spondin 1 , extracellular matrix protein 1.123601 0.000215

200696_s_at GSN gelsolin (amyloidosis, Finnish type) 1.123572 0.003018

21 1738 x at ELA3A elastase 3A, pancreatic /// elastase 3A, pancreatic 1.123323 0.000461

220805 at HRH2 histamine receptor H2 1.123186 9.48E-07

219103_at DDEFL1 development and differentiation enhancing factor-like 1 1.120327 0.00046

215411_s_at TRAF3IP2 TRAF3 interacting protein 2 1.120294 5.14E-05

TIMP metallopeptidase inhibitor 3 (Sorsby fundus dystrophy,

201148_s_at TIMP3 pseudoinflammatory) 1.120049 0.000928

213695_at PON3 paraoxonase 3 1.119743 5.72E-05

218965_s_at RBM21 RNA binding motif protein 21 1 .11951 0.00334

204674_at LRMP lymphoid-restricted membrane protein 1.118861 0.000553

218245 at LRRC54 leucine rich repeat containing 54 1 .11885 0.000789

207376_at VENTX VENT homeobox homolog (Xenopus laevis) 1.117574 8.52E-06

207960_at ... 1.116504 0.000935

215610_at ... Transcribed locus 1.112932 1.05E-08

204358 s at FLRT2 fibronectin leucine rich transmembrane protein 2 1.112246 0.000143

207784_at ARSD arylsulfatase D 1 .11 146 2.37E-05 damage-specific DNA binding protein 2, 48kDa /// LIM homeobox

203409_at DDB2 /// LHX3 3 1.11139 0.005919

219707_at CPNE7 copine VII 1.110303 0.002104

215906_at ... 1.109794 0.000269

212613_at BTN3A2 butyrophilin, subfamily 3, member A2 1.108914 0.004853

209324_s_at RGS16 regulator of G-protein signalling 16 1.108761 6.77E-05

215971_at ... CDNA FLJ12058 fis, clone HEMBB1002092 1.107792 7.39E-06

205129_at NPM3 nucleophosmin/nucleoplasmin, 3 1.107268 0.000756

213223_at RPL28 ribosomal protein L28 1.107263 0.001166 caspase 1 , apoptosis-related cysteine peptidase (interleukin 1 ,

209970_x_at CASP1 beta, convertase) 1.107025 0.000298

208048 at TAC R 1 tachykinin receptor 1 1.107001 1.38E-08

208468_at SOX21 SRY (sex determining region Y)-box 21 1.106596 2.15E-05

215010_s_at BRSK2 BR serine/threonine kinase 2 1.105591 3.18E-07

217509_x_at GRIK5 glutamate receptor, ionotropic, kainate 5 1.105381 1.79E-05

215701 at ... 1.102759 8.62E-05

204220_at GMFG glia maturation factor, gamma 1.102296 0.001946

ELOVL family member 5, elongation of long chain fatty acids

215082_at ELOVL5 (FEN1/Elo2, SUR4/Elo3-like, yeast) 1.101982 9.24E-05

202818 s at TCEB3 transcription elongation factor B (SIII), polypeptide 3 (110kDa, 1.101175 0.000278

elongin A)

214357_at C1orf105 chromosome 1 open reading frame 105 1.101 112 5.08E-05

217285_at DGCR13 DiGeorge syndrome critical region gene 13 1.100987 0.001335

207767_s_at EGR4 early growth response 4 1.099535 0.00051 1

201206_s_at RRBP1 ribosome binding protein 1 homolog 18OkDa (dog) 1.099139 0.006128

215515_at KIRREL Kin of IRRE like (Drosophila) 1.098938 0.000903

214195_at TPP1 tripeptidyl peptidase I 1.098226 7.27E-07

217391_x_at ... 1.098124 0.000774

210958_s_at MAST4 microtubule associated serine/threonine kinase family member 4 1.097492 0.008785

219821_s_at GFOD1 glucose-fructose oxidoreductase domain containing 1 1.095218 3.14E-07

210038_at PRKCQ protein kinase C, theta 1.094495 0.000198

205617_at PRRG2 proline rich GIa (G-carboxyglutamic acid) 2 1.094276 1.1 1 E-06

210795_s_at MEG3 Maternally expressed 3 1.093571 3.76E-07 protein phosphatase 2 (formerly 2A), catalytic subunit, beta

201374_x_at PPP2CB isoform 1.091421 0.001955

210414_at FLRT1 fibronectin leucine rich transmembrane protein 1 1.090188 0.001513

216919 at TP53I1 1 tumor protein p53 inducible protein 1 1 1.090037 0.000901

221565_s_at FAM26B family with sequence similarity 26, member B 1 .08976 0.000534

216535_at IGSF4B immunoglobulin superfamily, member 4B 1 .08963 0.00034

220447_at HRH3 histamine receptor H3 1.089199 0.000484 amyloid beta (A4) precursor protein (peptidase nexin-ll,

222013 x at APP Alzheimer disease) 1.087231 0.000364

210924_at OLFM1 olfactomedin 1 1 .08674 0.000904

214050_at ... 1 .08424 0.000395

216174_at HCRP1 hepatocellular carcinoma-related HCRP1 1.083556 0.000134

215989 at CBX2 chromobox homolog 2 (Pc class homolog, Drosophila) 1.082325 0.000209

220142_at HAPLN2 hyaluronan and proteoglycan link protein 2 1.082306 0.001665

216318_at ... 1.081527 8.88E-06

ATP synthase, H+ transporting, mitochondrial FO complex,

207552 at ATP5G2 subunit c (subunit 9), isoform 2 1.081272 0.00162

204770_at TAP2 transporter 2, ATP-binding cassette, sub-family B (MDR/TAP) 1.080895 0.000451

221310_at FGF14 fibroblast growth factor 14 1.078813 5.24E-06

216730_at ... CDNA: FLJ20908 fis, clone ADSE00417 1.078621 0.000573

209946 at VEGFC vascular endothelial growth factor C 1.078162 0.003348

219716_at APOL6 apolipoprotein L, 6 1.077216 0.002077

219746_at DPF3 D4, zinc and double PHD fingers, family 3 1.077036 2.25E-05

206586_at CNR2 cannabinoid receptor 2 (macrophage) 1.076953 0.000126

21 1469_s_at CXCR6 chemokine (C-X-C motif) receptor 6 1.07681 3.84E-05 integrin, beta 2 (antigen CD18 (p95), lymphocyte function- associated antigen 1 ; macrophage antigen 1 (mac-1 ) beta

202803_s_at ITG B2 subunit) 1.076421 0.009872

21 1125_x_at GRIN1 glutamate receptor, ionotropic, N-methyl D-aspartate 1 1.074783 3.85E-05 killer cell immunoglobulin-like receptor, two domains, short

208198_x_at KIR2DS1 cytoplasmic tail, 1 1.073912 0.003583

204395_s_at GRK5 G protein-coupled receptor kinase 5 1.073395 0.001142

219569_s_at TMEM22 transmembrane protein 22 1.073144 8.29E-05

221347_at CHRM5 cholinergic receptor, muscarinic 5 1.072709 0.003841

212032_s_at PTOV1 prostate tumor overexpressed gene 1 1.070938 0.003341

215689_s_at SHBG sex hormone-binding globulin 1.070663 3.59E-05

FCG R3 k ill Fc fragment of IgG, low affinity Ilia, receptor (CD16a) /// Fc

204006_s_at FCGR3B fragment of IgG, low affinity IMb, receptor (CD16b) 1.069524 0.006449

220279_at TRIM17 tripartite motif-containing 17 1.068909 0.002536

219152_at PODXL2 podocalyxin-like 2 1.068572 0.002093

216204_at ARVCF Armadillo repeat gene deletes in velocardiofacial syndrome 1.067051 7.40E-05

208551 _at HIST1H4G histone 1 , H4g 1.066869 3.87E-05

216471_x_at SSX2 synovial sarcoma, X breakpoint 2 1.064977 2.02E-07 runt-related transcription factor 1 ; translocated to, 1 (cyclin D-

216832_at RUNX1 T1 related) 1.06483 0.000944

222261 _at KIAA1609 KIAA1609 protein 1.064259 0.000799

213381_at C10orf72 Chromosome 10 open reading frame 72 1.064227 0.004801

21 1582_x_at LST1 leukocyte specific transcript 1 1.063432 0.000141

213676_at ... Similar to expressed sequence AW125688 1.063194 0.000577

209975_at CYP2E1 cytochrome P450, family 2, subfamily E, polypeptide 1 1.05977 0.001648

205468_s_at IRF5 interferon regulatory factor 5 1.059766 2.42E-05

204844_at ENPEP glutamyl aminopeptidase (aminopeptidase A) 1.058753 8.44E-08

21 1121_s_at DOK1 docking protein 1 , 62kDa (downstream of tyrosine kinase 1 ) 1.05813 5.73E-05

217033_x_at NTRK3 neurotrophic tyrosine kinase, receptor, type 3 1.057614 0.00093

209199 s at MEF2C MADS box transcription enhancer factor 2, polypeptide C 1.057181 0.002233

(myocyte enhancer factor 2C)

214543_x_at QKI quaking homolog, KH domain RNA binding (mouse) 1.057106 0.000111

21 1472_at PLXNB2 Plexin B2 1.056568 7.40E-05

21 1254_x_at RHAG Rhesus blood group-associated glycoprotein 1.055625 0.000136

217716_s_at SEC61 A1 Sec61 alpha 1 subunit (S. cerevisiae) 1.055173 0.000467

204880_at MGMT O-6-methylguanine-DNA methyltransferase 1.054187 0.0011

207473_at MLN motilin 1.053301 0.000847

202828_s_at MMP14 matrix metallopeptidase 14 (membrane-inserted) 1.053203 3.43E-06

201358_s_at COPB coatomer protein complex, subunit beta 1.052447 0.001585

214574_x_at LST1 leukocyte specific transcript 1 1.051954 2.67E-07

205627_at CDA cytidine deaminase 1.051772 1.81E-05

219170_at FSD1 fibronectin type III and SPRY domain containing 1 1.05101 4.82E-05

Solute carrier family 4, anion exchanger, member 1 (erythrocyte

205592_at SLC4A1 membrane protein band 3, Diego blood group) 1.050982 0.000456

DNA segment on chromosome X (unique) 9879 expressed

221982_x_at DXS9879E sequence 1.050854 2.03E-05 21 1507_s_at MTMR3 myotubularin related protein 3 1.050169 0.001274 206612_at CACNG1 calcium channel, voltage-dependent, gamma subunit 1 1.049659 9.41 E-06 21 1809_x_at COL13A1 collagen, type XIII, alpha 1 1.046958 0.00606 220748_s_at ZNF580 zinc finger protein 580 1.046107 0.007542 neutrophil cytosolic factor 4, 4OkDa /// neutrophil cytosolic factor

207677_s_at NCF4 4, 4OkDa 1.044207 2.78E-06

202206_at ARL7 ADP-ribosylation factor-like 7 1.044112 0.007809

217018_at 1.04192 0.000153 inhibitor of kappa light polypeptide gene enhancer in B-cells,

214398_s_at IKBKE kinase epsilon 1.038623 2.70E-05 219752_at RASAL1 RAS protein activator like 1 (GAP1 like) 1.03815 0.001556 216840_s_at LAMA2 laminin, alpha 2 (merosin, congenital muscular dystrophy) 1.037806 0.003018 203982 s at ABCD4 ATP-binding cassette, sub-family D (ALD), member 4 1.037405 5.80E-06 sirtuin (silent mating type information regulation 2 homolog) 5 (S. cerevisiae) /// sirtuin (silent mating type information regulation 2

221010_s_at SIRT5 homolog) 5 (S. cerevisiae) 1.035556 2.47E-06 213592_at AGTRL1 angiotensin Il receptor-like 1 1.034585 8.33E-05 219652 s at CXorf36 chromosome X open reading frame 36 1.033314 2.06E-06

212283 at AGRN agrin 1.032403 2.05E-05

216700_at TRIO Triple functional domain (PTPRF interacting) 1.03081 1 0.000696

213706_at GPD1 glycerol-3-phosphate dehydrogenase 1 (soluble) 1.030612 5.58E-07

204834_at FGL2 fibrinogen-like 2 1.030404 0.006944

222210 at Kl AA0195 KIAA0195 gene product 1.029435 2.85E-06

217357_at ... 1.027727 0.000145

219365_s_at CAMKV CaM kinase-like vesicle-associated 1.027098 0.000579 RAP2A, member of RAS oncogene family /// RAP2B, member of

214487_s_at RAP2A /// RAP2B RAS oncogene family 1.026884 0.000181

210880_s_at EFS embryonal Fyn-associated substrate 1.024848 0.007538

221001_at X102 X102 gene /// X102 gene 1.024812 4.99E-07

221237_s_at OSBP2 oxysterol binding protein 2 /// oxysterol binding protein 2 1.024408 0.001013

206213_at WNT10B wingless-type MMTV integration site family, member 10B 1.024375 0.000127

214549_x_at SPRR1A small proline-rich protein 1 A 1.024205 0.002567

221005_s_at PTDSS2 phosphatidylserine synthase 2 /// phosphatidylserine synthase 2 1.023927 0.000239

215292_s_at MKL1 megakaryoblastic leukemia (translocation) 1 1.023718 6.17E-05

207648 at DRP2 dystrophin related protein 2 1.023445 0.000731

214460_at LSAMP limbic system-associated membrane protein 1.021305 0.009662

208320_at CABP1 calcium binding protein 1 (calbrain) 1.020849 0.000154

201107_s_at THBS1 thrombospondin 1 1.020722 0.006437

213309_at PLCL2 phospholipase C-like 2 1.020016 7.49E-07

222029_x_at HKE2 HLA class Il region expressed gene KE2 1.019963 0.008569

205614_x_at MST1 macrophage stimulating 1 (hepatocyte growth factor-like) 1.019198 0.00031 1

207835_at FBLN1 fibulin 1 1.018196 0.00032 mediator of RNA polymerase Il transcription, subunit 25 homolog (yeast) /// mediator of RNA polymerase Il transcription, subunit

2081 10_x_at MED25 25 homolog (yeast) 1.017807 2.44E-06

210399 x at FUT6 fucosyltransferase 6 (alpha (1 ,3) fucosyltransferase) 1.017012 0.000529

221391_at TAS2R14 taste receptor, type 2, member 14 1.016935 1.05E-06

89977_at FLJ20581 hypothetical protein FLJ20581 1 .01608 0.000219

210310_s_at FG F5 fibroblast growth factor 5 1.014682 9.00E-05

216343 at PCDHGA3 protocadherin gamma subfamily A, 3 1 .01452 0.000553

214750_at PLAC4 placenta-specific 4 1.014185 0.000205

213681 at CYHR1 cysteine/histidine-rich 1 1.014052 0.004029

216355_at PCDHB17 protocadherin beta 17 pseudogene 1.013899 1.86E-05 215833_s_at SPPL2B signal peptide peptidase-like 2B 1.013712 0.001556 215461_at ZNRF4 zinc and ring finger 4 1.013446 0.000183 207421 _at CA5A carbonic anhydrase VA, mitochondrial 1.013271 5.34E-05 204153_s_at MFNG manic fringe homolog (Drosophila) 1.013229 4.86E-05 215540_at TRA@ T cell receptor alpha locus 1.013047 3.91 E-06 214227_at GNG7 Guanine nucleotide binding protein (G protein), gamma 7 1.012821 1.33E-07 21 1230_s_at PIK3CD phosphoinositide-3-kinase, catalytic, delta polypeptide 1.012684 0.000832 221300_at C15orf2 chromosome 15 open reading frame 2 1.01 1462 6.27E-05 215633_x_at LST1 leukocyte specific transcript 1 1.01 1338 0.000293 206863_x_at 1.010909 0.000248 solute carrier family 6 (neurotransmitter transporter,

215715_at SLC6A2 noradrenalin), member 2 1.010879 1.45E-05

220061 _at FLJ20581 hypothetical protein FLJ20581 1.010812 9.24E-05

200743_s_at TPP1 tripeptidyl peptidase I 1.010374 0.007319

209977_at PLG plasminogen 1.009697 3.08E-08

206967_at CCNT1 cyclin TI 1.009678 0.002755

336_at TBXA2R thromboxane A2 receptor 1.009425 0.005581

213673_x_at SCIRP10 Neuron derived neurotrophic factor 1.009335 0.000132

214646_at HIST1 H3J Histone 1 , H3j 1.009183 0.001027

208250_s_at DMBT1 deleted in malignant brain tumors 1 1 .00721 0.000584

219815_at GAL3ST4 galactose-3-O-sulfotransferase 4 1.006456 8.73E-05

206811_at ADCY8 adenylate cyclase 8 (brain) 1.006309 7.18E-05

215798_at ALDH1 L1 aldehyde dehydrogenase 1 family, member L1 1.006185 0.004725

208014_x_at AD7C-NTP neuronal thread protein AD7c-NTP 1.005731 0.004158

202665_s_at WASPIP Wiskott-Aldrich syndrome protein interacting protein 1.005529 3.84E-06

220538_at ADM2 adrenomedullin 2 1 .00539 0.000509

205688_at TFAP4 transcription factor AP-4 (activating enhancer binding protein 4) 1.004625 0.000174

209873_s_at PKP3 plakophilin 3 1.003699 0.003234

21 1541_s_at DYRK1A dual-specificity tyrosine-(Y)-phosphorylation regulated kinase 1 A 1.003632 0.001865

220027_s_at RASIP1 Ras interacting protein 1 1.003009 0.005147

204811_s_at CACNA2D2 calcium channel, voltage-dependent, alpha 2/delta subunit 2 1.002381 0.000191

221844_x_at CDNA clone IMAGE:6208446 1.00141 1 0.002373

207933 at ZP2 zona pellucida glycoprotein 2 (sperm receptor) 1 .00048 3.25E-05

bile acid Coenzyme A: amino acid N-acyltransferase (glycine N-

206913_at BAAT choloyltransferase) 0.99993 3.36E-05

208257_x_at PSG 1 pregnancy specific beta-1 -glycoprotein 1 0.999826 0.008624

206560_s_at MIA melanoma inhibitory activity 0.999537 0.005338

217423 at TTLL2 Tubulin tyrosine ligase-like family, member 2 0.999515 0.00178

216143_at CLN6 Ceroid-lipofuscinosis, neuronal 6, late infantile, variant 0.999408 1.13E-05 leukocyte immunoglobulin-like receptor, subfamily A (with TM

21 1100_x_at LILRA2 domain), member 2 0.999034 5.40E-07

205032 at ITGA2 integrin, alpha 2 (CD49B, alpha 2 subunit of VLA-2 receptor) 0.998939 0.002165

217108_at ... 0.998791 0.002145

206327_s_at CDH15 cadherin 15, M-cadherin (myotubule) 0.998169 0.001107

215502_at ... IMAGE cDNA clone 26881 0.997939 0.002203

35150_at CD40 CD40 antigen (TNF receptor superfamily member 5) 0.997766 0.000269

210619_s_at HYAL1 hyaluronoglucosaminidase 1 0.996516 8.49E-06

209320_at ADCY3 adenylate cyclase 3 0.996384 0.000106 protein phosphatase 1 , regulatory (inhibitor) subunit 2

207377_at PPP1 R2P9 pseudogene 9 0.996091 1.12E-06

216375_s_at ETV5 ets variant gene 5 (ets-related molecule) 0.995764 5.57E-05

209906_at C3AR1 complement component 3a receptor 1 0.994516 0.005283

209334_s_at PSMD9 proteasome (prosome, macropain) 26S subunit, non-ATPase, 9 0.993686 0.000779

38671 at PLXND1 plexin D1 0.993379 0.007427

209197_at SYT1 1 synaptotagmin Xl 0.993298 0.000702

217207_s_at BTNL3 butyrophilin-like 3 0.992646 0.003392 cytochrome b-245, beta polypeptide (chronic granulomatous

203922 s at CYBB disease) 0.992087 0.000227

216767_at PARVA Parvin, alpha 0.9917 0.000276

215951_at KIAA1055 KIAA1055 protein 0.991587 6.06E-07

214866_at PLAUR plasminogen activator, urokinase receptor 0.9911 17 0.000182

220151 at FLJ10490 hypothetical protein FLJ10490 0.990848 3.63E-07

220125_at DNAM dynein, axonemal, intermediate polypeptide 1 0.990424 0.000197

221627_at TRIM10 tripartite motif-containing 10 0.989742 0.000151

220798_x_at PRG2 plasticity-related gene 2 0.989689 0.000961

207878 at KRT2B cytokeratin 2 0.989357 0.000104

218644 at PLEK2 pleckstrin 2 0.988125 0.000537

216034 at SUHW1 suppressor of hairy wing homolog 1 (Drosophila) 0.988102 0.000254

221792_at RAB6B RAB6B, member RAS oncogene family 0.987376 0.000358

207610_s_at EM R2 egf-like module containing, mucin-like, hormone receptor-like 2 0.986348 1.60E-06 perforin 1 (pore forming protein) /// perforin 1 (pore forming

214617 at PRF1 protein) 0.986237 0.000203 integrin, alpha M (complement component receptor 3, alpha; also known as CD1 1 b (p170), macrophage antigen alpha polypeptide) /// integrin, alpha M (complement component receptor 3, alpha; also known as CD11 b (p170), macrophage

205786_s_at ITGAM antigen alpha polypeptide) 0.98524 0.000228

21 1203 s at CNTN1 contactin 1 0.985236 0.002938

220006_at FLJ12057 hypothetical protein FLJ12057 0.984517 0.003694

203729_at EM P3 epithelial membrane protein 3 0.983296 0.004107

202273_at PDGFRB platelet-derived growth factor receptor, beta polypeptide 0.981 187 0.004208

204495 s at DKFZP434H132 DKFZP434H132 protein 0.98108 0.002842

214194_at KIAA1008 KIAA1008 0.981061 0.00011 1

216320_x_at MST1 macrophage stimulating 1 (hepatocyte growth factor-like) 0.980436 0.000234

205082_s_at AOX1 aldehyde oxidase 1 0.980009 0.00671 1

206362_x_at MAP3K10 mitogen-activated protein kinase kinase kinase 10 0.979762 6.68E-05

208391 _s_at GLP1 R glucagon-like peptide 1 receptor 0.979549 0.001333

203666_at CXCL12 chemokine (C-X-C motif) ligand 12 (stromal cell-derived factor 1 ) 0.979442 0.002696

219403_s_at HPSE heparanase 0.978728 5.65E-09

21 1806 s at KCNJ15 potassium inward Iy- rectifying channel, subfamily J, member 15 0.977504 0.002924

220737_at RPS6KA6 ribosomal protein S6 kinase, 9OkDa, polypeptide 6 0.976875 6.18E-06

220727_at KCNK10 potassium channel, subfamily K, member 10 0.975527 0.000212

220678_at FLJ20712 hypothetical protein FLJ20712 0.974765 3.79E-06

213323 s at ZC3H7B zinc finger CCCH-type containing 7B 0.97462 2.91 E-05

206673_at GPR putative G protein coupled receptor 0.973485 0.000642

21 1718_at MGC2889 hypothetical protein MGC2889 /// hypothetical protein MGC2889 0.973295 3.41 E-05

MADS box transcription enhancer factor 2, polypeptide C

207968_s_at MEF2C (myocyte enhancer factor 2C) 0.972413 0.000414

Transcription elongation factor B (SIII), polypeptide 3 (1 1 OkDa,

213685_at TCEB3 elongin A) 0.972175 0.005332

206707 x at C6orf32 chromosome 6 open reading frame 32 0.971474 0.000624

207902 at IL5RA interleukin 5 receptor, alpha 0.971 188 3.52E-05

207026_s_at ATP2B3 ATPase, Ca++ transporting, plasma membrane 3 0.969925 2.38E-05

220288_at MYO15A myosin XVA 0.969919 0.000667

202509_s_at TNFAIP2 tumor necrosis factor, alpha-induced protein 2 0.969386 5.70E-05

208053_at GUCY2F guanylate cyclase 2F, retinal 0.969147 3.38E-07

220611_at DAB1 disabled homolog 1 (Drosophila) 0.969066 0.000388

219937_at TRHDE thyrotropin-releasing hormone degrading enzyme 0.968986 9.74E-08

215761_at DMXL2 Dmx-like 2 0.968794 3.38E-06 amyloid beta (A4) precursor protein-binding, family B, member 1

219994 at APBB1 IP interacting protein 0.968502 1.25E-07

206367_at REN renin 0.968477 0.001 19 polyamine modulated factor 1 binding protein 1 /// polyamine

221022_s_at PMFBP1 modulated factor 1 binding protein 1 0.968236 3.56E-05

213994_s_at SPON1 spondin 1 , extracellular matrix protein 0.968006 4.23E-06

214558_at GPR12 G protein-coupled receptor 12 0.964812 0.00161 1

21 1438_at TRHR thyrotropin-releasing hormone receptor 0.963805 7.84E-05

216911_s_at HIC2 hypermethylated in cancer 2 0.963379 8.08E-05

207359_at CAMKK2 calcium/calmodulin-dependent protein kinase kinase 2, beta 0.962789 3.20E-05

NADH dehydrogenase (ubiquinone) Fe-S protein 8, 23kDa

203190_at NDUFS8 (NADH-coenzyme Q reductase) 0.962197 0.004504

220196 at MUC16 mucin 16 0.962065 6.60E-06

207408_at SLC22A14 solute carrier family 22 (organic cation transporter), member 14 0.961864 0.00172

217095_x_at NCR1 natural cytotoxicity triggering receptor 1 0.961826 0.001417

203332_s_at INPP5D inositol polyphosphate-5-phosphatase, 145kDa 0.961279 0.00864

220918_at C21orf96 chromosome 21 open reading frame 96 0.961257 0.000842

221948_s_at KELCHL Kelch-like 22 (Drosophila) 0.960731 0.000541

212822_at HEG1 HEG homolog 1 (zebrafish) 0.960187 3.48E-06

219781_s_at LOC51333 mesenchymal stem cell protein DSC43 0.960093 6.58E-05

212161_at AP2A2 adaptor-related protein complex 2, alpha 2 subunit 0.959755 0.004453

53968 at KIAA1698 KIAA1698 protein 0.958987 0.001682

206630_at TYR tyrosinase (oculocutaneous albinism IA) 0.95882 0.000248

207890_s_at MMP25 matrix metallopeptidase 25 0.958581 0.002151

205033_s_at DEFA1 /// DEFA3 defensin, alpha 1 /// defensin, alpha 3, neutrophil-specific 0.958401 5.03E-07

205504 at BTK Bruton agammaglobulinemia tyrosine kinase 0.95831 1 0.000679

208574 at SOX14 SRY (sex determining region Y)-box 14 0.957942 0.000388

221099_at ... 0.957269 0.001654

21 1307_s_at FCAR Fc fragment of IgA, receptor for 0.956468 6.73E-06

214447_at ETS1 v-ets erythroblastosis virus E26 oncogene homolog 1 (avian) 0.955524 0.000177

220743 at ... 0.955227 0.000285

201063_at RCN1 reticulocalbin 1 , EF-hand calcium binding domain 0.954974 0.008775 solute carrier family 7 (cationic amino acid transporter, y+

204588_s_at SLC7A7 system), member 7 0.954551 0.000692

216507_at FLJ90013 Hypothetical protein FLJ90013 0.954496 8.96E-09

203539_s_at CHN2 chimerin (chimaerin) 2 0.953171 3.98E-05

220309_at TTC22 tetratricopeptide repeat domain 22 0.951776 0.003222

217365_at PRAMEF11 PRAME family member 11 0.951383 0.000119

219720 s at C14orf118 chromosome 14 open reading frame 118 0.951278 0.000245

214506_at ADMR adrenomedullin receptor 0.95117 4.74E-06

213945_s_at ... 0.950091 0.000512

221908_at FLJ 14627 Hypothetical protein FLJ 14627 0.950037 4.77E-06 likely ortholog of rat brain-enriched guanylate kinase-associated

220795 s at KIAA1446 protein 0.949336 0.000245

215126_at ... CDNA FLJ42949 fis, clone BRSTN2006583 0.948775 0.000922

206508_at TNFSF7 tumor necrosis factor (ligand) superfamily, member 7 0.948023 2.94E-06

208300_at PTPRH protein tyrosine phosphatase, receptor type, H 0.947916 3.23E-06

208191 x at PSG4 pregnancy specific beta-1 -glycoprotein 4 0.94781 1 0.001919

219243_at GIMAP4 GTPase, IMAP family member 4 0.947322 0.004675

214344_at LOC92973 hypothetical protein LOC92973 0.94722 0.000315

21 1499_s_at MAPK11 mitogen-activated protein kinase 1 1 0.946144 0.000412

214376_at ... Clone 24626 mRNA sequence 0.94599 0.000136

210223_s_at MR1 major histocompatibility complex, class l-related 0.945776 4.67E-05

UDP-GlcNAc:betaGal beta-1 ,3-N-acetylglucosaminyltransferase

4 /// U DP-GIcN Ac:betaGal beta-1 ,3-N-

221240_s_at B3GNT4 acetylglucosaminyltransferase 4 0.943408 0.001771

213045_at MAST3 microtubule associated serine/threonine kinase 3 0.943103 0.000934 cofactor required for Sp1 transcriptional activation, subunit 2,

217120_s_at CRSP2 15OkDa 0.943059 2.81 E-08

210369 at SWAP70 SWAP-70 protein 0.942556 0.001198

210325_at CD1A CD1 a antigen 0.942355 2.44E-05 dysferlin, limb girdle muscular dystrophy 2B (autosomal

218660_at DYSF recessive) 0.941437 4.46E-05

207919_at ART1 ADP-ribosyltransferase 1 0.941 168 0.003683

210140_at CST7 cystatin F (leukocystatin) 0.940948 0.000868

222187_x_at G3BP Ras-GTPase-activating protein SH3-domain-binding protein 0.940886 0.002833

207537_at PFKFB1 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 1 0.940421 0.000124

204016_at LARS2 leucyl-tRNA synthetase 2, mitochondrial 0.939642 0.002428

205874_at ITPKA inositol 1 ,4,5-trisphosphate 3-kinase A 0.939383 0.0017

210060_at PDE6G phosphodiesterase 6G, cGMP-specific, rod, gamma 0.939259 0.000417

205456_at CD3E CD3E antigen, epsilon polypeptide (TiT3 complex) 0.938427 0.003697

204896_s_at PTG E R4 prostaglandin E receptor 4 (subtype EP4) 0.938419 0.000833

219699_at LG I2 leucine-rich repeat LGI family, member 2 0.936908 0.00089 leukocyte immunoglobulin-like receptor, subfamily A (with TM

21 1102_s_at LILRA2 domain), member 2 0.935461 1.89E-05

206168_at ZC3H7B zinc finger CCCH-type containing 7B 0.935152 0.00403

220524_at EPB41 L4B erythrocyte membrane protein band 4.1 like 4B 0.93495 6.07E-05

216501 at VAC 14 Vac14 homolog (S. cerevisiae) 0.934679 0.000341

214609_at PHOX2A paired-like (aristaless) homeobox 2a 0.934516 0.00064

209827_s_at IL16 interleukin 16 (lymphocyte chemoattractant factor) 0.934203 0.000219

220213_at ZNF218 zinc finger protein 218 0.934187 0.009603

206598_at INS insulin 0.933682 0.000163

218409_s_at DNAJC1 DnaJ (Hsp40) homolog, subfamily C, member 1 0.933182 0.002171

40472_at LOC254531 PLSC domain containing protein 0.932897 0.007037

208315_x_at TRAF3 TNF receptor-associated factor 3 0.932159 9.75E-07

218702 at SARS2 seryl-tRNA synthetase 2 0.9321 1 1 0.001478

213733_at MYO1 F myosin IF 0.931715 0.005239

202759_s_at PALM2-AKAP2 PALM2-AKAP2 protein 0.931709 0.006223

221148_at ... 0.931128 0.001105

205291 at IL2RB interleukin 2 receptor, beta /// interleukin 2 receptor, beta 0.929637 0.003666

220792_at PRDM5 PR domain containing 5 0.929591 7.18E-05

218384_at CARHSP1 calcium regulated heat stable protein 1 , 24kDa 0.929406 9.38E-05

210064_s_at UPK1 B uroplakin 1 B 0.929291 0.001785

214443 at PVR poliovirus receptor 0.929274 3.00E-06

221162_at HHLA1 HERV-H LTR-associating 1 0.929239 0.002818

209913_x_at ... 0.929173 0.007018

207330_at PZP pregnancy-zone protein 0.928844 0.00035

209851 _at KIAA0853 KIAA0853 0.926285 0.00018

21 1262 at PCSK6 proprotein convertase subtilisin/kexin type 6 0.926089 0.001872

207066_at HRC histidine rich calcium binding protein 0.925906 0.000666

21 1380_s_at PRKG1 protein kinase, cGMP-dependent, type I 0.924419 0.000129

221629_x_at C8orf30A chromosome 8 open reading frame 3OA 0.923667 0.006093

1405 i at CCL5 chemokine (C-C motif) ligand 5 0.922542 0.009512

206793_at PNMT phenylethanolamine N-methyltransferase 0.922358 2.42E-05

221828_s_at C9orf28 chromosome 9 open reading frame 28 0.922291 0.001901

214863_at ... Full length insert cDNA clone ZC35F11 0.921942 2.04E-06

215484 at TRIM3 Tripartite motif-containing 3 0.920328 0.005954

216082_at NEU3 Sialidase 3 (membrane sialidase) 0.920173 0.002161

207915_at ... 0.919943 7.38E-05

214925_s_at SPTAN 1 spectrin, alpha, non-erythrocytic 1 (alpha-fodrin) 0.919839 0.000206

208463_at GABRA4 gamma-aminobutyric acid (GABA) A receptor, alpha 4 0.919755 0.000125

208429_x_at HNF4A hepatocyte nuclear factor 4, alpha 0.918856 0.004035

206340_at NR1 H4 nuclear receptor subfamily 1 , group H, member 4 0.918757 0.001697

64942_at GPR153 G protein-coupled receptor 153 0.918574 8.95E-05

206625_at RDS retinal degeneration, slow 0.918354 2.14E-05

210135 s at SH 0X2 short stature homeobox 2 0.917223 3.89E-07

206800_at MTHFR 5,10-methylenetetrahydrofolate reductase (NADPH) 0.917052 0.00011

219838_at TTC23 tetratricopeptide repeat domain 23 0.916964 0.001938

RASA4 ///

212707_s_at FLJ21767 RAS p21 protein activator 4 /// hypothetical protein FLJ21767 0.916446 0.004167 cytochrome P450, family 2, subfamily C, polypeptide 18 ///

208126_s_at CYP2C18 cytochrome P450, family 2, subfamily C, polypeptide 18 0.915609 7.20E-05

210258_at RGS13 regulator of G-protein signalling 13 0.915366 8.42E-08 golgi associated, gamma adaptin ear containing, ARF binding

21 1815_s_at GGA3 protein 3 0.915231 0.007716

206123_at LLGL1 lethal giant larvae homolog 1 (Drosophila) 0.91445 0.001826

206663_at SP4 Sp4 transcription factor 0.914144 9.78E-05

209997 x at PCM1 pericentriolar material 1 0.913982 8.80E-05

205106 at MTCP1 mature T-cell proliferation 1 0.913926 0.000114

205403_at IL1 R2 interleukin 1 receptor, type Il 0.913454 0.008767

216514_at ... 0.912848 0.000234

205182_s_at ZNF324 zinc finger protein 324 0.912844 0.003284

205524 s at HAPLN1 hyaluronan and proteoglycan link protein 1 0.91 1608 0.00132

206191_at ENTPD3 ectonucleoside triphosphate diphosphohydrolase 3 0.91 112 0.007054

210442_at IL1 RL1 interleukin 1 receptor-like 1 0.91 1 106 0.000988

208439_s_at FCN2 ficolin (collagen/fibrinogen domain containing lectin) 2 (hucolin) 0.910393 0.002278

205639 at AOAH acyloxyacyl hydrolase (neutrophil) 0.910339 0.001896

207395_at BTN1A1 butyrophilin, subfamily 1 , member A1 0.909998 0.000847

221820_s_at MYST1 MYST histone acetyltransferase 1 0.909873 9.13E-06

210080_x_at ELA3A elastase 3A, pancreatic 0.909449 0.003787 killer cell immunoglobulin-like receptor, three domains, long

21 1389_x_at KIR3DL1 cytoplasmic tail, 1 0.909362 0.00063

PRP19/PSO4 pre-mRNA processing factor 19 homolog (S.

203103_s_at PRPF19 cerevisiae) 0.909287 0.000708

220826_at C21orf77 chromosome 21 open reading frame 77 0.907489 5.77E-05

222346_at LAMA1 laminin, alpha 1 0.905613 0.005743

210441_at CDC14A CDC14 cell division cycle 14 homolog A (S. cerevisiae) 0.905417 3.47E-06

220815_at CTNNA3 catenin (cadherin-associated protein), alpha 3 0.904306 0.00121

201884_at CEACAM5 carcinoembryonic antigen-related cell adhesion molecule 5 0.903545 0.007521 phosphate regulating endopeptidase homolog, X-linked

210617_at PHEX (hypophosphatemia, vitamin D resistant rickets) 0.9035 0.004225

207167_at IGSF2 immunoglobulin superfamily, member 2 0.902945 1.20E-06

209951 _s_at MAP2K7 mitogen-activated protein kinase kinase 7 0.901329 0.002096

213917 at ... 0.900237 2.62E-06

201265_at ... 0.899375 0.006351

205929_at GPA33 glycoprotein A33 (transmembrane) 0.899265 0.003078

207075_at CIAS1 cold autoinflammatory syndrome 1 0.899193 0.000191

220246_at CAMK1 D calcium/calmodulin-dependent protein kinase ID 0.898767 0.004189

210843_s_at MFAP3L microfibrillar-associated protein 3-like 0.898522 0.000468

206531_at DPF1 D4, zinc and double PHD fingers family 1 0.898467 0.000134

217728_at S100A6 S100 calcium binding protein A6 (calcyclin) 0.898182 0.00582

64440 at IL17RC interleukin 17 receptor C 0.897985 0.000724

220571 _at PRDM11 PR domain containing 11 0.897562 0.001026

207353_s_at HMX1 homeo box (H6 family) 1 0.897475 0.00024 solute carrier family 11 (proton-coupled divalent metal ion

217473_x_at SLC1 1A1 transporters), member 1 0.897153 8.45E-06

206486_at LAG3 lymphocyte-activation gene 3 0.897007 0.000625

207568_at CHRNA6 cholinergic receptor, nicotinic, alpha polypeptide 6 0.896591 7.49E-05

21 1372_s_at IL1 R2 interleukin 1 receptor, type Il 0.896299 4.16E-09

213085_s_at KIBRA KIBRA protein 0.896232 0.003845

216354_at ... 0.896107 0.000145

216849_at FLJ16124 FLJ 16124 protein 0.895984 0.000779

213820_s_at STARD5 START domain containing 5 0.89595 0.000457

212045_at GLG1 golgi apparatus protein 1 0.895905 8.27E-05

221724_s_at CLEC4A C-type lectin domain family 4, member A 0.89571 0.000548

217261_at TTTY2 testis-specific transcript, Y-linked 2 0.895076 3.98E-05 solute carrier family 7 (cationic amino acid transporter, y+

214406_s_at SLC7A4 system), member 4 0.894468 4.20E-05

1431_at CYP2E1 cytochrome P450, family 2, subfamily E, polypeptide 1 0.893819 8.64E-09

207501_s_at FGF12 fibroblast growth factor 12 0.892461 9.33E-06

205233_s_at PAFAH2 platelet-activating factor acetylhydrolase 2, 4OkDa 0.892418 0.003492

208417_at FG F6 fibroblast growth factor 6 0.891957 2.75E-05

218742_at NARFL nuclear prelamin A recognition factor-like 0.891804 0.001888

221042_s_at CLMN calmin (calponin-like, transmembrane) 0.891435 0.00507

216678_at WDR10 WD repeat domain 10 0.890952 0.00061 1

21 1902_x_at TRA@ T cell receptor alpha locus 0.890799 0.000465

219692_at KREMEN2 kringle containing transmembrane protein 2 0.890418 0.000429

21 1395_x_at FCGR2C Fc fragment of IgG, low affinity Nc, receptor for (CD32) 0.89012 0.009727

216376_x_at ... 0.889859 0.00407

207360_s_at NTSR1 neurotensin receptor 1 (high affinity) 0.888996 0.000173

205175_s_at KHK ketohexokinase (fructokinase) 0.88859 0.000288

179_at PMS2L11 postmeiotic segregation increased 2-like 11 0.88773 0.001602

222058_at RNF130 ring finger protein 130 0.887109 0.004514

203331_s_at INPP5D inositol polyphosphate-5-phosphatase, 145kDa 0.886934 0.000161

218584_at FLJ21 127 hypothetical protein FLJ21 127 0.886789 0.000317

21 1189 x at CD84 CD84 antigen (leukocyte antigen) 0.886564 0.00031 1

Platelet-derived growth factor beta polypeptide (simian sarcoma

216055_at PDGFB viral (v-sis) oncogene homolog) 0.88617 0.000195

217158_at LOC401525 similar to tumor suppressor deleted in oral cancer-related 1 0.886142 0.001433

216584_at ... 0.885923 0.001746

210838_s_at ACVRL1 activin A receptor type ll-like 1 0.885889 4.69E-07

210461_s_at ABLIM1 actin binding LIM protein 1 0.885788 0.000383

216909_at KIAA0690 KIAA0690 0.885774 4.81 E-05

221997_s_at MRPL52 Mitochondrial ribosomal protein L52 0.885656 0.000125

209407 s at DEAF1 deformed epidermal autoregulatory factor 1 (Drosophila) 0.885363 0.009904

205790_at SCAP1 src family associated phosphoprotein 1 0.885175 0.000807

203888_at THBD thrombomodulin 0.884706 0.005264

216912_at ARHGEF4 Rho guanine nucleotide exchange factor (GEF) 4 0.884548 6.04E-05

214301 s at DPYSL4 Dihydropyrimidinase-like 4 0.884202 0.004768

208242_at RAX retina and anterior neural fold homeobox 0.884195 0.002392

216356_x_at BAIAP3 BAM -associated protein 3 0.882958 0.007904

207258_at DSCR4 Down syndrome critical region gene 4 0.882771 0.001306

203441 s at CDH2 cadherin 2, type 1 , N-cadherin (neuronal) 0.882416 0.001164

221080_s_at DENND1 C DENN/MADD domain containing 1 C 0.882251 1.72E-06

220476_s_at C1orf183 chromosome 1 open reading frame 183 0.881818 0.00247

214969_at MAP3K9 mitogen-activated protein kinase kinase kinase 9 0.881787 0.000995

65585_at FAM86B1 family with sequence similarity 86, member B1 0.881319 0.005841 TCR V alpha 14.1/J alpha 32/C alpha =melanoma antigen- specific T-cell receptor alpha chain {V-J-C junction, alternatively spliced, clone TIL C10-1} [human, CD8+ tumor-infiltrating

216540_at lymphocytes, mRNA Partial, 108 nt] 0.881124 0.005302

202473 x at HCFC1 host cell factor C1 (VP16-accessory protein) 0.880976 0.000185

221859_at SYT13 synaptotagmin XIII 0.880516 0.004475

21 1441_x_at CYP3A43 cytochrome P450, family 3, subfamily A, polypeptide 43 0.879752 0.000585

208260_at AVPR1 B arginine vasopressin receptor 1 B 0.879447 0.004078 serpin peptidase inhibitor, clade A (alpha-1 antiproteinase,

206325_at SERPINA6 antitrypsin), member 6 0.879177 2.10E-05

214070_s_at ATP10B ATPase, Class V, type 10B 0.878654 0.00107

204138 s at ZNF42 zinc finger protein 42 (myeloid-specific retinoic acid-responsive) 0.878602 0.00105

207701 at C22orf24 chromosome 22 open reading frame 24 0.878251 0.003312

206617_s_at RENBP renin binding protein 0.877962 0.006423

Transcribed locus, weakly similar to XP_498467.1 PREDICTED:

217629_at ... hypothetical protein XP_498467 [Homo sapiens] 0.877538 0.000221

216285_at DGCR14 DiGeorge syndrome critical region gene 14 0.876436 2.1 1 E-05

212873_at HA-1 minor histocompatibility antigen HA-1 0.876282 0.006107

2131 13_s_at SLC43A3 solute carrier family 43, member 3 0.87567 0.000886

216865_at COL14A1 collagen, type XIV, alpha 1 (undulin) 0.875622 6.83E-09

214197_s_at SETDB1 SET domain, bifurcated 1 0.874204 0.002121

220032 at FLJ21986 hypothetical protein FLJ21986 0.874084 0.002551

21 1794_at FYB FYN binding protein (FYB-120/130) 0.873983 1.02E-05

214205_x_at TXNL2 thioredoxin-like 2 0.87394 0.005241 solute carrier family 16 (monocarboxylic acid transporters),

204462 s at SLC16A2 member 2 0.873773 4.73E-05

203920_at NR1 H3 nuclear receptor subfamily 1 , group H, member 3 0.872932 0.001946

220873_at REPS2 RALBP1 associated Eps domain containing 2 0.87271 4.44E-05

214391_x_at PTGER1 prostaglandin E receptor 1 (subtype EP1 ), 42kDa 0.872005 2.05E-05

213484_at ... Clone 23700 mRNA sequence 0.871888 0.0054

205150 s at KIAA0644 KIAA0644 gene product 0.87179 0.00065

221313_at GPR52 G protein-coupled receptor 52 0.87171 1 0.00133

221250_s_at MXD3 MAX dimerization protein 3 /// MAX dimerization protein 3 0.871569 0.001593

216688_at ... MRNA; cDNA DKFZp434C2331 (from clone DKFZp434C2331 ) 0.871559 0.002427

208287_at HCG9 HLA complex group 9 0.871307 3.03E-06

216330_s_at POU6F1 POU domain, class 6, transcription factor 1 0.870981 0.00084

21 1590_x_at TBXA2R thromboxane A2 receptor 0.870258 0.00259

205801_s_at RASG R P3 RAS guanyl releasing protein 3 (calcium and DAG-regulated) 0.869696 6.14E-06

219784_at FBXO31 F-box protein 31 0.869542 4.76E-05 endothelial differentiation, lysophosphatidic acid G-protein-

204038_s_at EDG2 coupled receptor, 2 0.869435 0.001318

39763_at HPX hemopexin 0.869381 0.000679

215697_at KIAA0318 RIM binding protein 2 0.868829 0.004419

210865_at FASLG Fas ligand (TNF superfamily, member 6) 0.868775 0.000269 erythrocyte membrane protein band 4.1 like 5 /// erythrocyte

220977 x at EPB41 L5 membrane protein band 4.1 like 5 0.86835 0.001454

220514 at NAP1 L4 Nucleosome assembly protein 1 -like 4 0.867935 2.69E-05

204002_s_at ICA1 islet cell autoantigen 1 , 69kDa 0.867793 0.006207

208303_s_at CRLF2 cytokine receptor-like factor 2 0.867666 0.000715

202999_s_at LOXL2 lysyl oxidase-like 2 0.867293 0.003275

207955_at CCL27 chemokine (C-C motif) ligand 27 0.86648 0.00029

214926_at SPTAN 1 spectrin, alpha, non-erythrocytic 1 (alpha-fodrin) 0.865929 1.02E-05

206602_s_at HOXD3 homeo box D3 0.865777 0.000871

221187_s_at FLJ22688 hypothetical protein FLJ22688 0.86524 0.007743

221754_s_at CORO1 B coronin, actin binding protein, 1 B 0.86466 0.001857

219579 at RAB3IL1 RAB3A interacting protein (rabin3)-like 1 0.864289 0.003327

207463_x_at PRSS3 protease, serine, 3 (mesotrypsin) 0.863898 2.23E-05 single immunoglobulin and toll-interleukin 1 receptor (TIR)

52940_at SIGIRR domain 0.863651 0.000105

202252 at RAB13 RAB13, member RAS oncogene family 0.863116 0.003683

217032_at FOXD4L1 forkhead box D4-like 1 0.862573 0.003821

207432_at VMD2L1 vitelliform macular dystrophy 2-like 1 0.862103 0.0077

205031_at EFNB3 ephrin-B3 0.861805 0.001803

208807_s_at CHD3 chromodomain helicase DNA binding protein 3 0.861487 0.000109

204893_s_at ZFYVE9 zinc finger, FYVE domain containing 9 0.860821 0.004671

215654_at BCAT2 branched chain aminotransferase 2, mitochondrial 0.860771 0.004391

LOC442334 /// ADP-ribosylation factor 1 pseudogene /// similar to dJ133P16.1

216824_at LOC44271 1 (ADP-ribosylation factor 1 ) 0.860345 0.003856

205797_s_at TCP11 L1 t-complex 11 (mouse) like 1 0.860241 7.53E-06

215003_at DGCR9 DiGeorge syndrome critical region gene 9 0.8601 15 0.001958 single immunoglobulin and toll-interleukin 1 receptor (TIR)

218921_at SIGIRR domain 0.859653 0.009406

207637_at KIAA0789 KIAA0789 gene product 0.858257 0.000183 amyloid beta (A4) precursor protein-binding, family B, member 2

216750_at APBB2 (Fe65-like) 0.858248 7.16E-05

220875_at ... 0.858226 0.000393

204701_s_at STOML1 stomatin (EPB72)-like 1 0.858146 0.001115

206199_at CEACAM7 carcinoembryonic antigen-related cell adhesion molecule 7 0.857617 0.002432

220563_s_at SHANK1 SH3 and multiple ankyrin repeat domains 1 0.856415 3.12E-05

220439_at RIN3 Ras and Rab interactor 3 0.856404 0.004883

206807 s at AD D2 adducin 2 (beta) 0.856229 0.004691

21 11 18_x_at ESR2 estrogen receptor 2 (ER beta) 0.856026 9.23E-05

210798_x_at MASP2 mannan-binding lectin serine peptidase 2 0.855827 0.000537

221354_s_at GPR24 G protein-coupled receptor 24 0.855768 1.51E-05

216371_at ... 0.855255 0.00016

210651 s at EPHB2 EPH receptor B2 0.854712 0.002339

204684_at NPTX1 neuronal pentraxin I 0.854664 0.00138

212524_x_at H2AFX H2A histone family, member X 0.854261 0.000333

205988_at CD84 CD84 antigen (leukocyte antigen) 0.854212 0.006972

213003_s_at ... 0.853995 0.005828

21 1884_s_at CIITA class II, major histocompatibility complex, transactivator 0.853094 8.89E-05

MCM2 minichromosome maintenance deficient 2, mitotin (S.

202107_s_at MCM2 cerevisiae) 0.852926 0.00294

UDP-N-acetyl-alpha-D-galactosaminepolypeptide N-

216757_at GALNT7 acetylgalactosaminyltransferase 7 (GalNAc-T7) 0.852494 0.002395

216346_at SEC14L3 SEC14-like 3 (S. cerevisiae) 0.851003 4.87E-05

215295_at DTNB dystrobrevin, beta 0.850667 6.22E-07

219246_s_at FLJ13491 hypothetical protein FLJ13491 0.850492 0.003471

38710 at OTUB1 OTU domain, ubiquitin aldehyde binding 1 0.85044 0.000192

207477_at ... 0.850423 0.00288

208285_at OR7A5 olfactory receptor, family 7, subfamily A, member 5 0.850122 0.008317

210663_s_at KYNU kynureninase (L-kynurenine hydrolase) 0.8498 5.42E-06

208559_at IPF1 insulin promoter factor 1 , homeodomain transcription factor 0.849776 0.000599

219610_at RGNEF Rho-guanine nucleotide exchange factor 0.849436 0.004942

207864_at SCN7A sodium channel, voltage-gated, type VII, alpha 0.848979 0.000165

221140_s_at GPR132 G protein-coupled receptor 132 0.848849 1.80E-05

219457 s at RIN3 Ras and Rab interactor 3 0.848234 0.000158

204272_at LGALS4 lectin, galactoside-binding, soluble, 4 (galectin 4) 0.848178 0.003667

217150_s_at NF2 neurofibromin 2 (bilateral acoustic neuroma) 0.847791 0.005105

216168_at RHOH Ras homolog gene family, member H 0.847527 0.000364

91920_at BCAN brevican 0.846724 0.001148

201596_x_at KRT18 keratin 18 0.846269 0.001081

206655_s_at GP1 BB glycoprotein Ib (platelet), beta polypeptide 0.845864 0.00953

38691_s_at SFTPC surfactant, pulmonary-associated protein C 0.845532 0.009071

207459 x at GYPB glycophorin B (includes Ss blood group) 0.845525 0.004905

207351 s at SH2D2A SH2 domain protein 2A

206358_at PRM1 protamine 1

207349_s_at UCP3 uncoupling protein 3 (mitochondrial, proton carrier) 0.843948 3.37E-06 sema domain, transmembrane domain (TM), and cytoplasmic

220778 x at SEMA6B domain, (semaphorin) 6B 0.843882 0.000109

216182_at SYNJ2 Synaptojanin 2 0.843175 0.001066

205982_x_at SFTPC surfactant, pulmonary-associated protein C 0.842955 0.009112

T-cell receptor active beta-chain (V10-D-J-C) mRNA, clone

216857 at ... PL3.9 0.842781 0.002451

218454_at FLJ22662 hypothetical protein FLJ22662 0.842399 0.002987

216921_s_at KRTHA5 keratin, hair, acidic, 5 0.842264 5.04E-05 serpin peptidase inhibitor, clade A (alpha-1 antiproteinase,

213874_at SERPINA4 antitrypsin), member 4 0.841801 0.00078

AFFX-M27830_3_at ... 0.8415 0.001008

210677_at SOAT2 sterol O-acyltransferase 2 0.841056 0.000485

220107 s at C14orf140 chromosome 14 open reading frame 140 0.840942 2.07E-05

206524_at T T, brachyury homolog (mouse) 0.840398 0.002165

203317_at PSD4 pleckstrin and Sec7 domain containing 4 0.840178 0.008102

214192_at RABEP1 Rabaptin, RAB GTPase binding effector protein 1 0.840071 0.003721

204848_x_at HBG1 hemoglobin, gamma A /// hemoglobin, gamma A 0.840017 0.002107

216705 s at ADA adenosine deaminase 0.839774 0.000153

205447_s_at MAP3K12 mitogen-activated protein kinase kinase kinase 12 0.839549 0.000975

216662_at MYO7B myosin VIIB 0.839088 0.002118

205203_at PLD1 phospholipase D1 , phophatidylcholine-specific 0.838684 0.00398

205495_s_at GNLY granulysin /// granulysin 0.838495 0.005164

206398_s_at CD19 CD19 antigen 0.838434 3.81 E-05

210506_at FUT7 fucosyltransferase 7 (alpha (1 ,3) fucosyltransferase) 0.838299 0.000825

207206_s_at ALOX12 arachidonate 12-lipoxygenase 0.838067 1.87E-05

205847_at PRSS22 protease, serine, 22 0.837831 0.0017

214490_at ARSF arylsulfatase F 0.836557 0.001553 leukocyte immunoglobulin-like receptor, subfamily B (with TM

207697_x_at LILRB2 and ITIM domains), member 2 0.835008 0.00753

216993_s_at COL11 A2 collagen, type Xl, alpha 2 0.834892 0.003152

219718 at FLJ10986 hypothetical protein FLJ10986 0.834723 0.001015

killer cell immunoglobulin-like receptor, two domains, short

21 1532_x_at KIR2DS2 cytoplasmic tail, 2 0.8347 0.000112

217152_at NCOR1 Nuclear receptor co-repressor 1 0.834399 0.008458

APOA1 /// apolipoprotein A-I /// similar to Apolipoprotein A-I precursor (Apo-

204450 x at LOC440837 Al) 0.834267 0.000601

208346_at PPBPL2 pro-platelet basic protein-like 2 0.834157 2.48E-07

220460_at SLCO1 C1 solute carrier organic anion transporter family, member 1 C1 0.833961 0.000188

207925_at CST5 cystatin D 0.83379 0.001118

222254_at ... 0.8335 0.006338

205888_s_at KIAA0555 Jak and microtubule interacting protein 2 0.83333 5.55E-06

V-set and immunoglobulin domain

217222_at containing 6 Similar to Ig heavy chain V-I region HG3 precursor 0.833258 0.000354

215413_at EXOC7 exocyst complex component 7 0.832972 0.001793

202837_at TRAFD1 TRAF-type zinc finger domain containing 1 0.832726 0.000737 killer cell immunoglobulin-like receptor, two domains, short

208122_x_at KIR2DS3 cytoplasmic tail, 3 0.832409 0.00342

213421_x_at PRSS3 protease, serine, 3 (mesotrypsin) 0.832239 0.001226

220577_at GVIN1 GTPase, very large interferon inducible 1 0.832187 0.001014

217606_at ... MRNA; cDNA DKFZp686P24158 (from clone DKFZp686P24158) 0.83157 0.005491

TAF10 RNA polymerase II, TATA box binding protein (TBP)- associated factor, 3OkDa /// TAF10 RNA polymerase II, TATA

200055_at TAF10 box binding protein (TBP)-associated factor, 3OkDa 0.831507 0.000508

204397_at EML2 echinoderm microtubule associated protein like 2 0.831093 0.002218

221407_at CX36 connexin-36 0.830803 0.002076 signaling threshold regulating transmembrane adaptor 1 ///

205484_at SIT1 signaling threshold regulating transmembrane adaptor 1 0.830778 3.05E-05

T cell receptor V alpha gene segment V-alpha-w24, clone

217397_at ... IGRa02 0.830644 0.000102

204874_x_at BAIAP3 BAM -associated protein 3 0.830529 0.006974

206176_at BMP6 bone morphogenetic protein 6 0.829926 0.008036

218624_s_at MGC2752 hypothetical protein MGC2752 0.829469 0.006444

219745_at C10orf77 chromosome 10 open reading frame 77 0.827648 0.000216

210039 s at PRKCQ protein kinase C, theta 0.827445 0.001937

hemoglobin, gamma A /// hemoglobin, gamma A /// hemoglobin,

204419_x_at HBG1 /// HBG2 gamma G /// hemoglobin, gamma G 0.827344 0.003002

206444_at PDE1 B phosphodiesterase 1 B, calmodulin-dependent 0.827286 0.000697

203597_s_at WBP4 WW domain binding protein 4 (formin binding protein 21 ) 0.827136 0.002215

216363 at ... 0.827097 0.000529

202338_at TK1 thymidine kinase 1 , soluble 0.826562 0.000195 transmembrane protease, serine 5 (spinesin) /// transmembrane

221032_s_at TMPRSS5 protease, serine 5 (spinesin) 0.826283 0.000563

209521 _s_at AMOT angiomotin 0.826042 5.88E-05

215712_s_at IGFALS insulin-like growth factor binding protein, acid labile subunit 0.825709 0.000395

204845_s_at ENPEP glutamyl aminopeptidase (aminopeptidase A) 0.825585 2.23E-06

215036_at IGLC2 Immunoglobulin lambda constant 1 (Meg marker) 0.824869 0.000167

207770 x at CSH2 chorionic somatomammotropin hormone 2 0.824469 0.006528

216577_at ... 0.824371 0.00566

217192_s_at PRDM1 PR domain containing 1 , with ZNF domain 0.824299 0.002806

Leucine-rich repeats and calponin homology (CH) domain

216745 x at LRCH1 containing 1 0.824287 0.001504

207682_s_at KIF25 kinesin family member 25 0.824051 1.74E-05

221603_at PEX16 peroxisomal biogenesis factor 16 0.823451 0.004913

41856_at UNC5B Unc-5 homolog B (C. elegans) 0.823341 0.000741

206703_at CHRNB1 cholinergic receptor, nicotinic, beta polypeptide 1 (muscle) 0.823278 0.006994

214312_at FOXA2 Forkhead box A2 0.823255 0.001685

208221 _s_at SLIT1 slit homolog 1 (Drosophila) 0.823089 0.001039

204890_s_at LCK lymphocyte-specific protein tyrosine kinase 0.823026 0.009338 runt-related transcription factor 1 (acute myeloid leukemia 1 ;

21 1182_x_at RUNX1 aml1 oncogene) 0.822846 0.003972 nuclear factor of kappa light polypeptide gene enhancer in B-

214062_x_at NFKBIB cells inhibitor, beta 0.822183 0.003967

Transcribed locus, weakly similar to NP_055301 .1 neuronal

217537_x_at thread protein AD7c-NTP [Homo sapiens] 0.821786 0.000808

215688_at RASG RF1 Ras protein-specific guanine nucleotide-releasing factor 1 0.821194 6.10E-05

220628_s_at SDK2 sidekick homolog 2 (chicken) 0.82084 0.000193

205101_at CIITA class II, major histocompatibility complex, transactivator 0.820817 0.001039

215996_at ... 0.820763 0.003043

206749 at CD1 B CD1 b antigen /// CD1 b antigen 0.820728 0.000449

200715 x at RPL13A ribosomal protein L13a 0.820563 0.000682

207146_at KRTHA2 keratin, hair, acidic, 2 0.820305 5.97E-05

215811_at SNCA Synuclein, alpha (non A4 component of amyloid precursor) 0.819527 0.004545

214817_at UNC13A unc-13 homolog A (C. elegans) 0.819064 2.41 E-05 transient receptor potential cation channel, subfamily V, member

208267_at TRPV5 5 0.818969 2.12E-05

210822_at LOC283345 RPL13-2 pseudogene 0.818945 0.000532

217453_at — MRNA; cDNA DKFZp434E2028 (from clone DKFZp434E2028) 0.818861 0.001843 intercellular adhesion molecule 1 (CD54), human rhinovirus

215485_s_at ICAM1 receptor 0.818667 0.000583 killer cell immunoglobulin-like receptor, two domains, long

208179_x_at KIR2DL3 cytoplasmic tail, 3 0.81765 1.50E-05

219884_at LHX6 LIM homeobox 6 0.81745 0.003771

220630_s_at CHIA chitinase, acidic 0.817113 0.000965

204329_s_at ZNF202 zinc finger protein 202 0.816722 2.97E-05

207129 at CA5B carbonic anhydrase VB, mitochondrial 0.816256 0.001177

205623_at ALDH3A1 aldehyde dehydrogenase 3 family, memberAI 0.815949 0.000173

210708_x_at CASP10 caspase 10, apoptosis-related cysteine peptidase 0.815312 0.001296

222281 _s_at ... 0.815191 0.000175

204689_at HHEX hematopoietically expressed homeobox 0.814661 0.000764

216409_at ACSL6 Acyl-CoA synthetase long-chain family member 6 0.81463 0.001007 natriuretic peptide receptor B/guanylate cyclase B

214066_x_at NPR2 (atrionatriuretic peptide receptor B) 0.814606 3.98E-05

209933_s_at CD300A CD300A antigen 0.813986 0.001651

216696_s_at PRODH2 proline dehydrogenase (oxidase) 2 0.813827 0.003259

220938_s_at GMEB1 glucocorticoid modulatory element binding protein 1 0.81236 0.003955

216271_x_at SYDE1 synapse defective 1 , Rho GTPase, homolog 1 (C. elegans) 0.812133 0.000592

Fanconi anemia, complementation group A /// Fanconi anemia,

203806 s at FANCA complementation group A 0.81 1827 0.000662

210623_at LOC51035 unknown protein LOC51035 0.81 1715 8.90E-06

208160_at FLJ 10232 hypothetical protein FLJ10232 0.81 1444 0.000632

217646_at SURF1 surfeit 1 0.81 1438 0.001205

222179 at ... 0.810842 0.000746

204484 at PIK3C2B phosphoinositide-3-kinase, class 2, beta polypeptide 0.810695 0.00495

220698_at MGC4294 hypothetical protein MGC4294 0.810408 0.000573

212480_at KIAA0376 KIAA0376 protein 0.810372 0.001581

204509_at CA12 carbonic anhydrase XII 0.81026 0.000575

205013_s_at ADORA2A adenosine A2a receptor 0.809904 2.19E-06

221845_s_at CLPB CIpB caseinolytic peptidase B homolog (E. coli) 0.809604 0.003307

221293_s_at DEF6 differentially expressed in FDCP 6 homolog (mouse) 0.80938 0.009208

203364_s_at KIAA0652 KIAA0652 gene product 0.8092 0.001189

216502_at ISG20L2 interferon stimulated exonuclease gene 20kDa-like 2 0.808479 0.000383

216811_at ... 0.80722 0.002073

38707_r_at E2F4 E2F transcription factor 4, p107/p130-binding 0.807098 0.003481 phosphodiesterase 4C, cAMP-specific (phosphodiesterase E1

206791 _s_at PDE4C dunce homolog, Drosophila) 0.806942 0.001461

217072_at CD300A CD300A antigen 0.8069 0.000193

213717_at LDB3 LIM domain binding 3 0.806833 0.003669

205685_at CD86 CD86 antigen (CD28 antigen ligand 2, B7-2 antigen) 0.806044 0.00043 myosin binding protein C, cardiac /// myosin binding protein C,

208040_s_at MYBPC3 cardiac 0.805482 0.006134

206215_at OPCML opioid binding protein/cell adhesion molecule-like 0.804443 0.004654

200024_at RPS5 ribosomal protein S5 /// ribosomal protein S5 0.804373 2.71 E-08

216434_at FLJ20699 Hypothetical protein FLJ20699 0.803173 0.008806

219887_at FLJ10786 hypothetical protein FLJ10786 0.80284 0.002043

215293_s_at FRAG1 FGF receptor activating protein 1 0.802814 0.003327

211001_at TRIM29 tripartite motif-containing 29 0.801186 0.00352

215592_at ... CDNA FLJ12232 fis, clone MAMMA1001206 0.80007 0.00056

222023_at AKAP13 A kinase (PRKA) anchor protein 13 0.799597 1.76E-06

207601_at SULT1 B1 sulfotransferase family, cytosolic, 1 B, member 1 0.799559 0.003508

205804_s_at TRAF3IP3 TRAF3 interacting protein 3 0.799237 0.009694

205038_at ZNFN1A1 Zinc finger protein, subfamily 1A, 1 (Ikaros) 0.798172 0.002108

1294_at UBE1 L ubiquitin-activating enzyme E1 -like 0.798154 0.007833

209958_s_at PTHB1 parathyroid hormone-responsive B1 0.797717 0.001779

206361 _at GPR44 G protein-coupled receptor 44 0.797095 0.000497

2081 17_s_at LAS1 L LAS1 -like (S. cerevisiae) /// LAS1 -like (S. cerevisiae) 0.796825 0.00129

21 1201_at FSHR follicle stimulating hormone receptor 0.796597 0.000255

222331 at 0.796315 0.000304

210221_at CHRNA3 cholinergic receptor, nicotinic, alpha polypeptide 3 0.796218 0.00672

222319_at NAP1 L4 Nucleosome assembly protein 1 -like 4 0.79601 0.004068 acetyl-Coenzyme A acyltransferase 2 (mitochondrial 3-oxoacyl-

202002_at ACAA2 Coenzyme A thiolase) 0.796005 0.001231

36907 at MVK mevalonate kinase (mevalonic aciduria) 0.795749 3.82E-05

205336_at PVALB parvalbumin 0.795282 0.000948

Cofactor required for Sp1 transcriptional activation, subunit 7,

216423_at CRSP7 7OkDa 0.795102 0.001266

206517 at CDH16 cadherin 16, KSP-cadherin 0.794476 0.000345

219554_at RHCG Rhesus blood group, C glycoprotein 0.794444 5.69E-05

206991 _s_at CCR5 chemokine (C-C motif) receptor 5 0.794388 0.001413

21 1365_s_at PCDHA2 protocadherin alpha 2 0.794364 0.001216

21 1261 at ... 0.793915 0.000379

204558_at RAD54L RAD54-like (S. cerevisiae) 0.793736 0.003131

209864_at FRAT2 frequently rearranged in advanced T-cell lymphomas 2 0.793445 0.007654

206886_x_at GH1 growth hormone 1 0.792857 0.000646 thrombopoietin (myeloproliferative leukemia virus oncogene

21 1831_s_at THPO ligand, megakaryocyte growth and development factor) 0.79236 0.009934

203234_at UPP1 uridine phosphorylase 1 0.792279 0.000147

210644_s_at LAIR1 leukocyte-associated Ig-like receptor 1 0.791897 0.00404

214332 s at TSFM Ts translation elongation factor, mitochondrial 0.791705 2.98E-05

213570_at EIF4E2 eukaryotic translation initiation factor 4E member 2 0.79138 0.00194

217447_at MAG myelin associated glycoprotein 0.79125 0.000143

214330_at ATPAF2 ATP synthase mitochondrial F1 complex assembly factor 2 0.790537 0.0039

203475_at CYP19A1 cytochrome P450, family 19, subfamily A, polypeptide 1 0.79051 0.00017

21 1403_x_at VCX2 variable charge, X-linked 2 0.790168 9.40E-05 thyroid hormone receptor, beta (erythroblastic leukemia viral (v-

207044_at THRB erb-a) oncogene homolog 2, avian) 0.789673 0.008531

204987_at ITIH2 inter-alpha (globulin) inhibitor H2 0.789563 0.001902

39854_r_at PNPLA2 patatin-like phospholipase domain containing 2 0.789506 0.001013 calcium-sensing receptor (hypocalciuric hypercalcemia 1 , severe

210577_at CASR neonatal hyperparathyroidism) 0.789336 0.002927

215473 at ... Hypothetical protein LOC284215 0.789187 0.00011 1

205487 s at VGLL1 vestigial like 1 (Drosophila) 0.78883 0.000461

ABO blood group (transferase A, alpha 1 -3-N- acetylgalactosaminyltransferase; transferase B, alpha 1 -3-

214504_at ABO galactosyltransferase) 0.788684 0.002557

216101_at ... Full length insert cDNA clone YR67C11 0.787912 0.001857

208384_s_at MID2 midline 2 0.787341 0.004796

214713_at YLPM1 YLP motif containing 1 0.787238 0.003838

205024 s at RAD51 RAD51 homolog (RecA homolog, E. coli) (S. cerevisiae) 0.786701 1.72E-05

206725_x_at BMP1 bone morphogenetic protein 1 0.786448 0.002992

205666_at FMO1 flavin containing monooxygenase 1 0.78642 0.003307 sema domain, immunoglobulin domain (Ig), transmembrane

219039_at SEMA4C domain (TM) and short cytoplasmic domain, (semaphorin) 4C 0.785394 0.002913 solute carrier family 25 (mitochondrial carrier: glutamate),

218725_at SLC25A22 member 22 0.785388 0.009577 excision repair cross-complementing rodent repair deficiency,

205162_at ERCC8 complementation group 8 0.785353 0.002648

216630_at ... 0.785298 0.000289

217521_at HAL Histidine ammonia-lyase 0.785231 0.009181

207460_at GZMM granzyme M (lymphocyte met-ase 1 ) 0.78516 0.006595

220003 at LRRC36 leucine rich repeat containing 36 0.785023 0.00475

208844_at VDAC3 voltage-dependent anion channel 3 0.785 0.007067

21 1010_s_at NCR3 natural cytotoxicity triggering receptor 3 0.784971 0.008985

207861 _at CCL22 chemokine (C-C motif) ligand 22 0.784835 0.00558

204951 _at RHOH ras homolog gene family, member H 0.784037 0.001379

219239_s_at ZNF654 zinc finger protein 654 0.783512 0.008124 peptidyl arginine deiminase, type I /// peptidyl arginine

220962_s_at PADM deiminase, type I 0.783287 0.001691

216102_at PHLDB1 pleckstrin homology-like domain, family B, member 1 0.782607 0.004162 cofactor required for Sp1 transcriptional activation, subunit 2,

215167_at CRSP2 15OkDa 0.78231 0.001092

221173_at USH1 C Usher syndrome 1 C (autosomal recessive, severe) 0.782221 0.000363

210952_at AP4S1 adaptor-related protein complex 4, sigma 1 subunit 0.78188 2.07E-05 dopachrome tautomerase (dopachrome delta-isomerase,

216512_s_at DCT tyrosine-related protein 2) 0.781505 0.000445 complement component 1 , q subcomponent, receptor 1 ///

202877 s at C1 QR1 complement component 1 , q subcomponent, receptor 1 0.781404 0.001327

210323_at TEKT2 tektin 2 (testicular) 0.781 101 0.005703

203996_s_at C21orf2 chromosome 21 open reading frame 2 0.780976 0.001676

200704_at LITAF lipopolysaccharide-induced TNF factor 0.780512 0.006062

216018_at RNF5 ring finger protein 5 0.779803 0.004741 calcium binding tyrosine-(Y)-phosphorylation regulated

219928_s_at CABYR (fibrousheathin 2) 0.77966 0.006812

217051_s_at SS18 Synovial sarcoma translocation, chromosome 18 0.778712 0.003873

207762_at LPAL2 lipoprotein, Lp(a)-like 2 0.778267 0.000605

213551_x_at PCGF2 polycomb group ring finger 2 0.778177 0.008632

204441 _s_at POLA2 polymerase (DNA directed), alpha 2 (7OkD subunit) 0.777742 0.006103

219019_at LRDD leucine-rich repeats and death domain containing 0.777704 0.004462

221385_s_at GPR41 /// GPR42 G protein-coupled receptor 41 /// G protein-coupled receptor 42 0.777162 0.001749

206916 x at TAT tyrosine aminotransferase 0.77691 7.20E-05

205297_s_at CD79B CD79B antigen (immunoglobulin-associated beta) 0.775791 0.001851

21 1188_at CD84 CD84 antigen (leukocyte antigen) 0.775755 0.000579 solute carrier family 7 (cationic amino acid transporter, y+

220135_s_at SLC7A9 system), member 9 0.775623 0.001369

216096 s at NRXN1 neurexin 1 0.775147 5.45E-05

217096_at PCLO piccolo (presynaptic cytomatrix protein) 0.774706 0.008978

220152_at C10orf95 chromosome 10 open reading frame 95 0.774477 0.000671

215247_at LOC339692 hypothetical protein LOC339692 0.774086 0.005562

206154 at RLBP1 retinaldehyde binding protein 1 0.773343 0.003012

213432_at MUC5B mucin 5, subtype B, tracheobronchial 0.77328 3.33E-05

208535_x_at COL13A1 collagen, type XIII, alpha 1 0.772756 0.000316

206480_at LTC4S leukotriene C4 synthase 0.772733 0.000977

218960 at TMPRSS4 transmembrane protease, serine 4 0.772683 0.008895

222169_x_at SH2D3A SH2 domain containing 3A 0.772376 0.005787

204789_at FMNL1 formin-like 1 0.771858 0.002657

220965_s_at RSHL1 radial spokehead-like 1 /// radial spokehead-like 1 0.771549 0.000651

214947 at FLJ11 127 Hypothetical protein FLJ 11 127 0.771387 0.001583

201759_at TBCD tubulin-specific chaperone d 0.770595 0.007336

206156_at GJ B5 gap junction protein, beta 5 (connexin 31 .1 ) 0.770371 0.001556

206672_at AQ P2 aquaporin 2 (collecting duct) 0.770297 0.006014

215040 at FLJ11712 Hypothetical protein FLJ 11712 0.770163 0.001169

potassium voltage-gated channel, subfamily G, member 1 ///

21 1053_at KCNG1 potassium voltage-gated channel, subfamily G, member 1 0.770058 0.003538

208278_s_at ... 0.769908 0.000496

217374_x_at TRGV5 T cell receptor gamma variable 5 0.769771 0.003118

213870 at COL11 A2 collagen, type Xl, alpha 2 0.768957 0.004164

216240_at ... 0.768727 2.27E-05

217969_at C11orf2 chromosome 11 open reading frame2 0.768471 0.00017

221108_at LOC51233 hypothetical protein LOC51233 0.767947 0.000851

220423_at PLA2G2D phospholipase A2, group MD 0.767944 0.005162

Finkel-Biskis-Reilly murine sarcoma virus (FBR-MuSV) ubiquitously expressed (fox derived); ribosomal protein S30 ///

Finkel-Biskis-Reilly murine sarcoma virus (FBR-MuSV)

200019_s_at FAU ubiquitously expressed (fox derived); ribosomal protein S30 0.767922 1.31 E-05

21 1153_s_at TNFSF11 tumor necrosis factor (ligand) superfamily, member 11 0.767237 0.003445

218429_s_at FLJ11286 hypothetical protein FLJ1 1286 0.767151 0.003267

220181 x at SLC30A5 solute carrier family 30 (zinc transporter), member 5 0.766698 0.005938

214576_at KRTHA6 keratin, hair, acidic, 6 0.76631 1 0.000486

207715_at CRYGB crystallin, gamma B 0.765847 7.38E-07

21 1332_x_at HFE hemochromatosis 0.765304 0.004529

219748_at TREML2 triggering receptor expressed on myeloid cells-like 2 0.765109 0.000312

220017_x_at CYP2C9 cytochrome P450, family 2, subfamily C, polypeptide 9 0.76507 0.006102

214626_s_at GANAB glucosidase, alpha; neutral AB 0.764832 0.004482

220466_at CCDC15 coiled-coil domain containing 15 0.764772 0.008919

217233_at ... 0.764632 0.001359

220533_at ... 0.764354 0.003745

221371_at TNFSF18 tumor necrosis factor (ligand) superfamily, member 18 0.764067 0.000419

207923_x_at PAX8 paired box gene 8 0.763831 0.003757 sparc/osteonectin, cwcv and kazal-like domains proteoglycan

206433_s_at SPOCK3 (testican) 3 0.76366 0.004355

216815_at ... 0.763609 0.000491

210938_at IPF1 insulin promoter factor 1 , homeodomain transcription factor 0.763097 0.000252

632_at GSK3A glycogen synthase kinase 3 alpha 0.763058 0.005278

217031 at KRTHB4 keratin, hair, basic, 4 0.763017 2.05E-05

220449 at MGC5566 Hypothetical protein MGC5566 0.761084 0.00779

X-ray repair complementing defective repair in Chinese hamster

205071 _x_at XRCC4 cells 4 0.76088 0.004838

208959_s_at TXN DC4 thioredoxin domain containing 4 (endoplasmic reticulum) 0.760576 0.00595

215788_at LOC339457 hypothetical protein LOC339457 0.760313 0.000587

213783 at MFNG Manic fringe homolog (Drosophila) 0.760231 0.002112

203069_at SV2A synaptic vesicle glycoprotein 2A 0.760159 0.002689

212750_at PPP1 R16B protein phosphatase 1 , regulatory (inhibitor) subunit 16B 0.760146 0.001868

GAGE1 ///

GAGE2 ///

GAGE3 ///

GAGE4 ///

GAGE5 ///

GAGE6 ///

GAGE7 /// G antigen 1 /// G antigen 2 /// G antigen 3 /// G antigen 4 /// G

GAGE7B /// antigen 5 /// G antigen 6 /// G antigen 7 /// G antigen 7B /// G

207739_s_at GAGE8 antigen 8 0.75952 9.99E-05

UDP-N-acetyl-alpha-D-galactosamine^N-acetylneuraminyl)- galactosylglucosylceramide N-acetylgalactosaminyltransferase

206435_at GALGT (GaINAc-T) 0.759372 0.003169

205900 at KRT1 keratin 1 (epidermolytic hyperkeratosis) 0.759183 0.003742

205148_s_at CLCN4 chloride channel 4 0.758576 0.005136

209791 _at PADI2 peptidyl arginine deiminase, type Il 0.758121 0.006275

220042_x_at HIVEP3 human immunodeficiency virus type I enhancer binding protein 3 0.757471 0.00162

219755_at CBX8 chromobox homolog 8 (Pc class homolog, Drosophila) 0.757315 0.005612

210921_at ... 0.757085 0.001124

205783_at KLK13 kallikrein 13 0.756988 0.008731 transient receptor potential cation channel, subfamily A, member

217590 s at TRPA1 1 0.756755 0.000977

206573_at KCNQ3 potassium voltage-gated channel, KQT-like subfamily, member 3 0.756527 0.009349

218153_at FLJ12118 hypothetical protein FLJ12118 0.756084 0.008173

203768_s_at STS steroid sulfatase (microsomal), arylsulfatase C, isozyme S 0.755637 0.001909

221373_x_at PSPN persephin 0.755625 2.28E-05

21 1825_s_at FLU Friend leukemia virus integration 1 0.755472 0.001633

201182_s_at CHD4 chromodomain helicase DNA binding protein 4 0.755267 0.001491

203257 s at MGC4707 MGC4707 protein 0.754948 0.001332

213834_at IQSEC3 IQ motif and Sec7 domain 3 0.75487 0.001324

214080_x_at PRKCSH protein kinase C substrate 80K-H 0.754731 0.000808

219879_s_at MGC3130 hypothetical protein MGC3130 0.753807 0.003639

215154_at ULK2 Unc-51 -like kinase 2 (C. elegans) 0.753204 0.004838

219072_at BCL7C B-cell CLL/lymphoma 7C 0.753019 0.006191

214846_s_at ALPK3 alpha-kinase 3 0.752754 0.005488

205130_at RAGE renal tumor antigen 0.750887 0.008519

58367_s_at ZNF419 zinc finger protein 419 0.750783 0.000734

208352 x at ANK1 ankyrin 1 , erythrocytic 0.750502 0.005223

206778_at CRYBB2 crystallin, beta B2 0.750164 0.002763

206927_s_at GUCY1 A2 guanylate cyclase 1 , soluble, alpha 2 0.750102 0.000677

21 1049_at TLX2 T-cell leukemia, homeobox 2 /// T-cell leukemia, homeobox 2 0.748417 0.003895

207354 at CCL16 chemokine (C-C motif) ligand 16 0.748113 1.99E-05

48580_at CXXC 1 CXXC finger 1 (PHD domain) 0.747662 0.003842

207509_s_at LAI R2 leukocyte-associated Ig-like receptor 2 0.746935 0.001224

208277_at PITX3 paired-like homeodomain transcription factor 3 0.746771 0.006171

209976_s_at CYP2E1 cytochrome P450, family 2, subfamily E, polypeptide 1 0.746595 0.002227

219309_at FLJ23584 hypothetical protein FLJ23584 0.745824 0.000907

220139_at DNMT3L DNA (cytosine-5-)-methyltransferase 3-like 0.745726 0.000366

210747_at HLA-DQB1 major histocompatibility complex, class II, DQ beta 1 0.74506 0.000739

216019_x_at PHLDB1 pleckstrin homology-like domain, family B, member 1 0.74487 0.006501 tumor necrosis factor receptor superfamily, member 10d, decoy

210654_at TNFRSF10D with truncated death domain 0.744763 0.001281

213448_at GBA Glucosidase, beta; acid (includes glucosylceramidase) 0.742498 0.001709

205710_at LRP2 low density lipoprotein-related protein 2 0.741673 0.00125

216991_at ZNF224 zinc finger protein 224 0.741594 0.000518 solute carrier family 6 (neurotransmitter transporter,

216611_s_at SLC6A2 noradrenalin), member 2 0.741249 0.00774

222100_at CYP2E1 Cytochrome P450, family 2, subfamily E, polypeptide 1 0.74094 0.000145

221914_at SYN 1 synapsin I 0.73969 0.004662

210451_at PKLR pyruvate kinase, liver and RBC 0.739627 0.001054

216275_at BUB1 BUB1 budding uninhibited by benzimidazoles 1 homolog (yeast) 0.739568 0.002039

206747_at KIAA0514 KIAA0514 0.739474 0.005984

21 1442 x at CYP3A43 cytochrome P450, family 3, subfamily A, polypeptide 43 0.739357 0.000429

213751_at LOC284352 hypothetical protein LOC284352 0.739197 0.004583

214178_s_at SOX2 SRY (sex determining region Y)-box 2 0.739108 0.001426

222300_at CAPZA1 Capping protein (actin filament) muscle Z-line, alpha 1 0.739085 0.005602

216141_at — 0.738963 0.004804 transient receptor potential cation channel, subfamily V, member

219632_s_at TRPV1 1 0.738926 0.004412

222374_at BTRC beta-transducin repeat containing 0.738846 0.003263

221969_at PAX5 Paired box gene 5 (B-cell lineage specific activator) 0.73839 5.62E-05

219704_at YBX2 Y box binding protein 2 0.738303 0.000803

217225_x_at NOMO2 NODAL modulator 2 0.737947 0.005815 potassium voltage-gated channel, shaker-related subfamily,

208564_at KCNA2 member 2 0.737875 0.000582

207404_s_at HTR1 E 5-hydroxytryptamine (serotonin) receptor 1 E 0.737621 0.002553

221439_at RBBP9 retinoblastoma binding protein 9 0.736647 3.29E-05

213550_s_at PRO1580 hypothetical protein PRO1580 0.736426 0.005736

205646_s_at PAX6 paired box gene 6 (aniridia, keratitis) 0.73633 0.003352

220384_at TXN DC3 thioredoxin domain containing 3 (spermatozoa) 0.735947 0.001248

219331_s_at FLJ10748 hypothetical protein FLJ10748 0.735735 0.001527

207716_at KRTHA8 keratin, hair, acidic, 8 0.735276 0.000705

220763_at HSPA12A heat shock 7OkDa protein 12A 0.734892 0.005677

220733_at SLC26A1 solute carrier family 26 (sulfate transporter), member 1 0.734381 0.007889

216157_at ... CDNA FLJ14169 fis, clone NT2RP2002056 0.734356 0.007663

214025_at DDX28 DEAD (Asp-Glu-Ala-Asp) box polypeptide 28 0.73419 0.001568

205812_s_at TMED9 transmembrane emp24 protein transport domain containing 9 0.734073 0.001812

218365_s_at DARS2 aspartyl-tRNA synthetase 2 (mitochondrial) 0.733985 0.001568

208434_at MDS1 myelodysplasia syndrome 1 0.733976 0.000405

220045_at NEUROD6 neurogenic differentiation 6 0.733942 0.002713

217172_at ... 0.73366 3.75E-05

216800_at ... CDNA: FLJ23416 fis, clone HEP20790 0.733561 0.000902

209840_s_at LRRN3 leucine rich repeat neuronal 3 0.733237 4.43E-05

215644_at ZNF518 zinc finger protein 518 0.732356 0.000236

217928_s_at SAPS3 SAPS domain family, member 3 0.732275 0.000443

216968_at MASP2 mannan-binding lectin serine peptidase 2 0.732254 0.008193

207841 at SPIN2 spindlin family, member 2 0.731694 0.001292

204561 _x_at APOC2 apolipoprotein C-Il 0.730618 0.005238 asparagine-linked glycosylation 3 homolog (yeast, alpha-1 ,3-

207396_s_at ALG3 mannosyltransferase) 0.729692 0.004543

208230_s_at NRG1 neuregulin 1 0.728905 0.005491

220146 at TLR7 toll-like receptor 7 0.728674 0.00041

220033_at ... 0.728663 0.002745

217611_at ERICH1 glutamate-rich 1 0.72831 0.002727

214380_at PRPF31 PRP31 pre-mRNA processing factor 31 homolog (yeast) 0.727856 0.006891

214407 x at GYPB glycophorin B (includes Ss blood group) 0.727724 0.000463

206076_at B7 B7 gene 0.727497 0.007895

215639_at SH2D3C SH2 domain containing 3C 0.72684 0.000244

221684_s_at NYX nyctalopin 0.72661 0.006153

Burkitt lymphoma receptor 1 , GTP binding protein (chemokine

206126_at BLR1 (C-X-C motif) receptor 5) 0.726313 0.007684

215005_at EFCBP2 EF-hand calcium binding protein 2 0.725687 0.003372

212850_s_at LRP4 low density lipoprotein receptor-related protein 4 0.725597 0.002745

213450_s_at ICOSLG inducible T-cell co-stimulator ligand 0.724751 0.008206

201995 at EXT1 exostoses (multiple) 1 0.724582 0.004546

48659_at IIP45 invasion inhibitory protein 45 0.724086 0.007762 olfactory receptor, family 7, subfamily E, member 156

222327_x_at OR7E156P pseudogene 0.723604 0.005347

218070_s_at GMPPA GDP-mannose pyrophosphorylase A 0.722533 0.007225

214639_s_at HOXA1 homeo box A1 0.722029 6.38E-06

203268_s_at DRG2 developmentally regulated GTP binding protein 2 0.721389 5.99E-05 leukocyte immunoglobulin-like receptor, subfamily B (with TM

LILRB2 /// and ITIM domains), member 2 /// leukocyte immunoglobulin-like

21 1133 x at LILRB3 receptor, subfamily B (with TM and ITIM domains), member 3 0.721255 0.002849

216156_at RECK reversion-inducing-cysteine-rich protein with kazal motifs 0.720137 1.80E-07

206489_s_at DLGAP1 discs, large (Drosophila) homolog-associated protein 1 0.71899 0.007626

221164_x_at CHST5 carbohydrate (N-acetylglucosamine 6-0) sulfotransferase 5 0.718825 0.001232 thrombopoietin (myeloproliferative leukemia virus oncogene

21 1154 at THPO ligand, megakaryocyte growth and development factor) 0.717864 0.000856

216794_at ... CDNA clone IMAGE:30320498 0.7177 0.003651

21 1565 at SH3GL3 SH3-domain GRB2-like 3 0.717444 0.00335

202415 s at HSPBP1 hsp70-interacting protein 0.717136 0.000913

215374_at PAPOLA PoIy(A) polymerase alpha 0.716785 0.001155

208253_at SIGLEC8 sialic acid binding Ig-like lectin 8 0.716701 5.56E-05

212346_s_at MXD4 MAX dimerization protein 4 0.716128 0.006525

217295_at MUC8 mucin 8, tracheobronchial 0.716037 0.006062

31861_at IGHMBP2 immunoglobulin mu binding protein 2 0.716002 0.001943

221077_at ARMC4 armadillo repeat containing 4 0.715692 0.001454

221084_at HTR3B 5-hydroxytryptamine (serotonin) receptor 3B 0.715425 0.003807

207698 at C6orf123 chromosome 6 open reading frame 123 0.71537 0.003091

205177_at TNNM troponin I type 1 (skeletal, slow) 0.714778 0.009876

206166_s_at CLCA2 chloride channel, calcium activated, family member 2 0.714564 0.005892

1494_f_at CYP2A6 cytochrome P450, family 2, subfamily A, polypeptide 6 0.714226 0.006749

214539 at SERPINB10 serpin peptidase inhibitor, clade B (ovalbumin), member 10 0.714086 0.008971

202339_at SYMPK symplekin 0.713519 0.000504

221131_at A4GNT alpha-1 ,4-N-acetylglucosaminyltransferase 0.713416 0.000258

216561_x_at — 0.713341 0.002312 nuclear factor of kappa light polypeptide gene enhancer in B-

21 1524 at NFKB2 cells 2 (p49/p100) 0.71322 2.28E-05

217760_at TRIM44 tripartite motif-containing 44 0.713136 0.005436

220120_s_at EPB41 L4A erythrocyte membrane protein band 4.1 like 4A 0.713067 0.00041 1

208922_s_at NXF1 nuclear RNA export factor 1 0.71 1864 0.008214

Solute carrier family 28 (sodium-coupled nucleoside transporter),

216432_at SLC28A2 member 2 0.71 158 0.006507 solute carrier family 11 (proton-coupled divalent metal ion

210422_x_at SLC1 1A1 transporters), member 1 0.71 1371 0.004348

204639 at ADA adenosine deaminase 0.710666 0.000152

208465_at GRM2 glutamate receptor, metabotropic 2 0.710545 0.00449

210343_s_at SLC22A6 solute carrier family 22 (organic anion transporter), member 6 0.710136 0.009276

220421 _at BTNL8 butyrophilin-like 8 0.709998 0.001877

208516_at MTNR1 B melatonin receptor 1 B 0.7089 0.001154 transient receptor potential cation channel, subfamily M, member

221102_s_at TRPM6 6 0.708872 0.004025

21 1897 s at CRHR1 corticotropin releasing hormone receptor 1 0.708683 0.008851

208279 s at CDRT1 CMT1 A duplicated region transcript 1 0.708091 0.000593

214851_at HNF4A hepatocyte nuclear factor 4, alpha 0.707757 0.001902 21 1545_at GHRHR growth hormone releasing hormone receptor 0.707102 0.000342 205629_s_at CRH corticotropin releasing hormone 0.706933 0.003064 213565_s_at SMAD6 SMAD, mothers against DPP homolog 6 (Drosophila) 0.706596 0.007998 206561 _s_at AKR1 B10 aldo-keto reductase family 1 , member B10 (aldose reductase) 0.706432 0.000769 216390_at LCAT Lecithin-cholesterol acyltransferase 0.705841 0.001722

GTPase, IMAP family member 5 /// GTPase, IMAP family

218805_at GIMAP5 member 5 0.705832 0.007558

204579_at FGFR4 fibroblast growth factor receptor 4 0.705574 0.00644

219674_s_at PRO2900 hypothetical protein PRO2900 0.705443 0.000727

216677_at ZNF154 zinc finger protein 154 (pHZ-92) 0.70535 0.001091

204556_s_at DZIP1 DAZ interacting protein 1 0.705181 0.005691

205339_at SIL TAL1 (SCL) interrupting locus 0.704952 0.005112

21 1241_at ANXA2P3 annexin A2 pseudogene 3 0.70495 0.004679

221287_at RNASEL ribonuclease L (2',5'-oligoisoadenylate synthetase-dependent) 0.7045 0.000899

215097_at CAPZB capping protein (actin filament) muscle Z-line, beta 0.704142 0.00133

220505_at C9orf53 chromosome 9 open reading frame 53 0.703581 0.003953

210955_at CASP10 caspase 10, apoptosis-related cysteine peptidase 0.703033 0.004801

2101 18_s_at IL1 A interleukin 1 , alpha 0.7026 1.00E-06

216128_at TBCD tubulin-specific chaperone d 0.701967 0.003251

21 1259_s_at BMP7 bone morphogenetic protein 7 (osteogenic protein 1 ) 0.701912 0.007575

21 1258_s_at TGFA transforming growth factor, alpha 0.701745 0.002606

220388_at FER1 L4 fer-1 -like 4 (C. elegans) 0.701102 0.006934

203472_s_at SLCO2B1 solute carrier organic anion transporter family, member 2B1 0.701062 0.004284

207235_s_at GRM5 glutamate receptor, metabotropic 5 0.70104 5.94E-05

201482_at QSCN6 quiescin Q6 0.701013 0.008192

32699_s_at PVR poliovirus receptor 0.700801 2.20E-05

214228_x_at TNFRSF4 tumor necrosis factor receptor superfamily, member 4 0.700764 0.000871

216290_x_at CDNA FLJ10002 fis, clone HEMBA1000046 0.700045 0.000296

217508_s_at C18orf25 chromosome 18 open reading frame 25 0.699784 0.002469

206000_at MEP1 A meprin A, alpha (PABA peptide hydrolase) 0.699657 0.00511

207073_at CDKL2 cyclin-dependent kinase-like 2 (CDC2-related kinase) 0.699645 0.001776 hydroxyacyl-Coenzyme A dehydrogenase/3-ketoacyl-Coenzyme

208630 at HADHA A thiolase/enoyl-Coenzyme A hydratase (trifunctional protein), 0.698483 5.30E-05

alpha subunit

214099_s_at PDE4DIP phosphodiesterase 4D interacting protein (myomegalin) 0.698245 0.007284

210274_at MAGEA8 melanoma antigen family A, 8 0.697886 0.000904

210957_s_at AFF2 AF4/FMR2 family, member 2 0.696872 0.000701

207527_at KCNJ9 potassium inward Iy- rectifying channel, subfamily J, member 9 0.696617 0.006359

21 1855_s_at SLC25A14 solute carrier family 25 (mitochondrial carrier, brain), member 14 0.696571 0.003166

220508_at CESK1 T-complex protein 1 0.695884 0.002589

205491 _s_at GJ B3 gap junction protein, beta 3, 31 kDa (connexin 31 ) 0.695622 0.007144

58900 at LOC222070 hypothetical protein LOC222070 0.695468 0.003959

221388_at OR1 A1 olfactory receptor, family 1 , subfamily A, member 1 0.695239 0.00079

216208_s_at CREBL1 cAMP responsive element binding protein-like 1 0.693932 0.000465 similar to Aspartate aminotransferase, mitochondrial precursor

216545_at LOC441886 (Transaminase A) (Glutamate oxaloacetate transaminase-2) 0.693901 5.69E-06

210672_s_at C16orf35 chromosome 16 open reading frame 35 0.69376 0.006977

206671 _at SAG S-antigen; retina and pineal gland (arrestin) 0.693712 0.000763

216959_x_at NRCAM neuronal cell adhesion molecule 0.693669 0.003507 cholinergic receptor, nicotinic, alpha polypeptide 1 (muscle) ///

21 1039 at CHRNA1 cholinergic receptor, nicotinic, alpha polypeptide 1 (muscle) 0.693657 0.00497

207897_at CRHR2 corticotropin releasing hormone receptor 2 0.693251 0.001701

205149_s_at CLCN4 chloride channel 4 0.692806 0.001034

220871 _at ... 0.692677 0.000183

205372 at PLAG1 pleiomorphic adenoma gene 1 0.692605 0.006095

206774_at FRMPD1 FERM and PDZ domain containing 1 0.691499 0.001573

215784_at CD1 E CD1 E antigen, e polypeptide 0.691485 0.001671

221469_at GPR32 G protein-coupled receptor 32 0.691044 0.000573

210923 at SLC1 A7 solute carrier family 1 (glutamate transporter), member 7 0.69054 0.006606

206939_at DCC deleted in colorectal carcinoma 0.689678 0.002502

217180_at ... (clone 9F2L) Ig rearranged L-chain mRNA V-region, 5' end 0.689054 0.008912

207772_s_at HRMT1 L4 HMT1 hnRNP methyltransferase-like 4 (S. cerevisiae) 0.68874 0.000267

207427 at ACR acrosin 0.688695 0.006429

206867_at GCKR glucokinase (hexokinase 4) regulator 0.688571 0.005492 leukocyte immunoglobulin-like receptor, subfamily A (with TM

210313_at LILRA4 domain), member 4 0.6881 13 0.007393

201271 s at RALY RNA binding protein, autoantigenic (hnRNP-associated with 0.687496 0.00124

lethal yellow homolog (mouse))

203422_at POLD1 polymerase (DNA directed), delta 1 , catalytic subunit 125kDa 0.687435 0.004104

221175_at FLJ22173 hypothetical protein LOC8011 1 0.687281 4.03E-05 solute carrier family 6 (neurotransmitter transporter, GABA),

207184_at SLC6A13 member 13 0.686468 0.00648 solute carrier family 7, (cationic amino acid transporter, y+

207528_s_at SLC7A1 1 system) member 11 0.686428 0.007897

214901_at ZNF8 zinc finger protein 8 (clone HF.18) 0.686388 0.009915

220635 at PSORS1 C2 psoriasis susceptibility 1 candidate 2 0.686387 0.008048

215480_at KIAA0509 KIAA0509 protein 0.686352 0.000874

207084_at POU3F2 POU domain, class 3, transcription factor 2 0.685917 0.00014

219224_x_at ZNF408 zinc finger protein 408 0.6858 0.004119

205125 at PLCD1 phospholipase C, delta 1 0.685605 0.009962

208007_at ... 0.685302 0.004525

215449_at BZRPL1 benzodiazapine receptor (peripheral)-like 1 0.685249 0.006847

222098_s_at ZNF42 Zinc finger protein 42 (myeloid-specific retinoic acid-responsive) 0.685202 2.19E-05

207214_at SPINK4 serine peptidase inhibitor, Kazal type 4 0.684494 0.001924

214480_at ETV3 ets variant gene 3 0.68431 0.000781

214847_s_at GPSM3 G-protein signalling modulator 3 (AGS3-like, C. elegans) 0.68402 0.000718

221128_at ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 0.683503 0.004478

220507_s_at UPB1 ureidopropionase, beta 0.682742 0.009328

207663 x at GAGE3 G antigen 3 0.68228 0.003566

216946_at HLA-DOA major histocompatibility complex, class II, DO alpha 0.68216 0.000664

214780_s_at MYO9B myosin IXB 0.682094 0.005034

207368_at HTR1 D 5-hydroxytryptamine (serotonin) receptor 1 D 0.68205 0.002086

ATXN7L1 ///

214342_at ATXN7L4 ataxin 7-like 1 /// ataxin 7-like 4 0.681833 0.000681

218406_x_at NENF neuron derived neurotrophic factor 0.681533 0.009775

217458_at ... 0.681019 0.004983

215845 x at ... CDNA: FLJ22840 fis, clone KAIA4709 0.680687 0.004799

207773_x_at CYP3A43 cytochrome P450, family 3, subfamily A, polypeptide 43 0.679836 0.004517

215507_x_at ... 0.678624 9.67E-07

206979_at C8B complement component 8, beta polypeptide 0.678523 0.001294

214950 at IL9R /// interleukin 9 receptor /// similar to interleukin 9 receptor 0.678421 0.005302

LOC400481

48612_at N4BP1 Nedd4 binding protein 1 0.677876 0.006656

216015_s_at CIAS1 cold autoinflammatory syndrome 1 0.677614 0.003641

215340_at ADCY1 adenylate cyclase 1 (brain) 0.677261 0.008956

215129 at PIK3C2G phosphoinositide-3-kinase, class 2, gamma polypeptide 0.676962 0.001151

206706_at NTF3 neurotrophin 3 0.676784 0.001065

205742_at TNNI3 troponin I type 3 (cardiac) 0.676275 0.000732 solute carrier family 6 (neurotransmitter transporter, taurine), member 6 /// solute carrier family 6 (neurotransmitter transporter,

21 1030_s_at SLC6A6 taurine), member 6 0.675893 0.004243

208458 at SCNN1 D sodium channel, nonvoltage-gated 1 , delta 0.67583 0.006252

207091 _at P2RX7 purinergic receptor P2X, ligand-gated ion channel, 7 0.675672 0.004169

220813_at CYSLTR2 cysteinyl leukotriene receptor 2 0.675225 0.000809 fibroblast growth factor receptor 2 (bacteria-expressed kinase, keratinocyte growth factor receptor, craniofacial dysostosis 1 ,

Crouzon syndrome, Pfeiffer syndrome, Jackson-Weiss

208234_x_at FGFR2 syndrome) 0.675055 0.002147

208006_at FOXM forkhead box 11 0.674062 0.006671

208409_at SLC14A2 solute carrier family 14 (urea transporter), member 2 0.673791 0.00052 similar to b24o18.3 (POM121 membrane glycoprotein (rat

216582_at LOC441 135 homolog)-like 2) 0.673689 0.003291

210411_s_at GRIN2B glutamate receptor, ionotropic, N-methyl D-aspartate 2B 0.673602 0.001328

207310_s_at NOS1 nitric oxide synthase 1 (neuronal) 0.673555 6.66E-05

218399_s_at CDCA4 cell division cycle associated 4 0.673371 0.002387

216519_s_at PROSC Proline synthetase co-transcribed homolog (bacterial) 0.673158 9.50E-05

216599_x_at SLC22A6 solute carrier family 22 (organic anion transporter), member 6 0.672989 0.009312

210885_s_at TRIM15 tripartite motif-containing 15 0.672987 0.005886

216866_s_at COL14A1 collagen, type XIV, alpha 1 (undulin) 0.672729 0.002016

216201_at ... CDNA: FLJ21586 fis, clone COL06920 0.672581 0.000223

220531_at FLJ14126 hypothetical protein FLJ14126 0.672223 0.002803 transglutaminase 1 (K polypeptide epidermal type I, protein-

206008 at TGM1 glutamine-gamma-glutamyltransferase) 0.671929 0.000167

212173_at AK2 adenylate kinase 2 0.671486 0.004892

218939 at LETM1 leucine zipper-EF-hand containing transmembrane protein 1 0.671473 0.009256

207299_s_at GRM1 glutamate receptor, metabotropic 1 0.670659 0.005754

205999_x_at CYP3A4 cytochrome P450, family 3, subfamily A, polypeptide 4 0.67042 0.004553

210773_s_at FPRL1 formyl peptide receptor-like 1 /// formyl peptide receptor-like 1 0.67022 0.002169

204680_s_at RAPGEF5 Rap guanine nucleotide exchange factor (GEF) 5 0.670041 0.006308

208521 _at OR5I1 olfactory receptor, family 5, subfamily I, member 1 0.67003 0.00512

212092_at PEG10 paternally expressed 10 0.669851 0.002749

207921 _x_at PAX8 paired box gene 8 0.669082 0.006122 cytochrome P450, family 2, subfamily A, polypeptide 7

216340 s at CYP2A7P1 pseudogene 1 0.668582 0.002646

214405_at CUGBP2 CUG triplet repeat, RNA binding protein 2 0.667683 0.000104

216270_at ILVBL ilvB (bacterial acetolactate synthase)-like 0.667477 0.007231

201815_s_at TBC1 D5 TBC1 domain family, member 5 0.667378 0.009907

204705_x_at ALDOB aldolase B, fructose-bisphosphate 0.666853 0.009626

208483_x_at KRTHA3A keratin, hair, acidic, 3A 0.666273 0.000862

216970_at RGS7 regulator of G-protein signalling 7 0.665812 0.000289

214628_at NHLH1 nescient helix loop helix 1 0.665803 0.007019

208365 s at GRK4 G protein-coupled receptor kinase 4 0.664082 0.003763

217490_at ... 0.663949 0.004796

208585_at BTN2A3 butyrophilin, subfamily 2, member A3 0.662932 0.009763

214135_at CLDN18 claudin 18 0.662862 0.009927

ADAM metallopeptidase with thrombospondin type 1 motif, 12 ///

221421_s_at ADAMTS12 ADAM metallopeptidase with thrombospondin type 1 motif, 12 0.662405 0.008913

222370_x_at LOC388906 Hypothetical gene supported by BC039496 0.661134 0.000577

207429_at SLC22A2 solute carrier family 22 (organic cation transporter), member 2 0.6611 15 0.005383

217183_at LDLR low density lipoprotein receptor (familial hypercholesterolemia) 0.660799 0.00832

210888 s at mm inter-alpha (globulin) inhibitor H1 0.660776 0.000405

38487_at STAB1 stabilin 1 0.659954 0.002558

207316_at HAS1 hyaluronan synthase 1 0.659204 0.000687

219535_at HUNK hormonally upregulated Neu-associated kinase 0.658977 0.001516

216725_at DCAMKL2 Doublecortin and CaM kinase-like 2 0.65883 0.002664

220599_s_at CARD14 caspase recruitment domain family, member 14 0.658331 0.006057

207373_at HOXD10 homeo box DI O 0.657965 0.001713

206873_at CA6 carbonic anhydrase Vl 0.657297 0.000275

203564 at FANCG Fanconi anemia, complementation group G 0.656636 0.00802

207803 s at CSN3 casein kappa 0.656344 0.000671

222245_s_at FER1 L4 fer-1 -like 4 (C. elegans) 0.65633 0.001014

220208_at ADAMTS13 ADAM metallopeptidase with thrombospondin type 1 motif, 13 0.65628 0.000316

Sema domain, transmembrane domain (TM), and cytoplasmic

215479 at SEMA6A domain, (semaphorin) 6A 0.655939 0.000832

219452_at DPEP2 dipeptidase 2 0.65569 0.007827

219369_s_at OTUB2 OTU domain, ubiquitin aldehyde binding 2 0.655527 0.001941

210240_s_at CDKN2D cyclin-dependent kinase inhibitor 2D (p19, inhibits CDK4) 0.655421 0.001936

219144 at DUSP26 dual specificity phosphatase 26 (putative) 0.655364 0.007142

207143_at CDK6 cyclin-dependent kinase 6 0.65494 0.003864 leukocyte immunoglobulin-like receptor, subfamily B (with TM

LILRB2 /// and ITIM domains), member 2 /// leukocyte immunoglobulin-like

210784_x_at LILRB3 receptor, subfamily B (with TM and ITIM domains), member 3 0.654114 0.008924

39318_at TCL1 A T-cell leukemia/lymphoma 1A 0.65396 0.000332

216937_s_at RS1 retinoschisis (X-linked, juvenile) 1 0.653486 0.001534

205985_x_at CLCNKB chloride channel Kb 0.653162 0.005956 pleckstrin homology domain containing, family G (with RhoGef

212821_at PLEKHG3 domain) member 3 0.652689 0.004061

220683 at RDH8 retinol dehydrogenase 8 (all-trans) 0.651726 0.003389

215128_at --- CDNA FLJ1 1682 fis, clone HEMBA1004880 0.651235 0.004292 leukocyte immunoglobulin-like receptor, subfamily A (with TM domain), member 1 /// leukocyte immunoglobulin-like receptor,

210660_at LILRA1 subfamily A (with TM domain), member 1 0.651 118 7.02E-05

207203_s_at NR1 I2 nuclear receptor subfamily 1 , group I, member 2 0.651057 0.008407

210349 at CAMK4 calcium/calmodulin-dependent protein kinase IV 0.650668 0.002781

217272_s_at SERPINB13 serpin peptidase inhibitor, clade B (ovalbumin), member 13 0.650189 5.23E-05

207433_at IL10 interleukin 10 0.649524 0.009087

216203_at SPTLC2 serine palmitoyltransferase, long chain base subunit 2 0.648129 0.006588

21 1127 x at EDA ectodysplasin A 0.647757 3.94E-05

221106_at SLC22A17 solute carrier family 22 (organic cation transporter), member 17 0.647437 0.009469

214535_s_at ADAMTS2 ADAM metallopeptidase with thrombospondin type 1 motif, 2 0.647426 0.006177 intercellular adhesion molecule 4, Landsteiner-Wiener blood

207194_s_at ICAM4 group 0.647368 0.006822

ATPase, H+ transporting, lysosomal 56/58kDa, V1 subunit B,

205473 at ATP6V1 B1 isoform 1 (Renal tubular acidosis with deafness) 0.647213 0.001514

220025_at TBR1 T-box, brain, 1 0.647042 0.000433

215015_at LOC92558 hypothetical protein LOC92558 0.647 0.008419

21 1807_x_at PCDHGB5 protocadherin gamma subfamily B, 5 0.645647 0.000406 solute carrier family 6 (neurotransmitter transporter,

206058_at SLC6A12 betaine/GABA), member 12 0.645204 0.003786

210318_at RBP3 retinol binding protein 3, interstitial 0.644939 0.00617

213796_at SPRR1A small proline-rich protein 1 A 0.644378 0.002666 thyroid hormone receptor, alpha (erythroblastic leukemia viral (v-

214883_at THRA erb-a) oncogene homolog, avian) 0.644145 0.001489

203861 _s_at ACTN2 actinin, alpha 2 0.644141 0.009782

216663_s_at ZMYND10 zinc finger, MYND-type containing 10 0.643923 0.005071

217568_at FAM 12A family with sequence similarity 12, member A 0.642689 0.001106

217280 x at GABRA5 gamma-aminobutyric acid (GABA) A receptor, alpha 5 0.64247 0.000332

217686_at PTPN1 protein tyrosine phosphatase, non-receptor type 1 0.642061 0.006585

204585_s_at L1 CAM L1 cell adhesion molecule 0.641976 0.005439

214393_at LOC284062 hypothetical protein LOC284062 0.641908 0.002076 potassium voltage-gated channel, Shal-related subfamily,

21 1301_at KCND3 member 3 0.641723 0.006252

210841_s_at NRP2 neuropilin 2 0.64116 0.006339

221446_at ADAM30 ADAM metallopeptidase domain 30 0.641056 0.001598 fascin homolog 1 , actin-bundling protein (Strongylocentrotus

210933_s_at FSCN1 purpuratus) 0.639664 0.004596

213985_s_at C19orf6 Chromosome 19 open reading frame 6 0.639385 0.009485

21 1624_s_at DRD2 dopamine receptor D2 /// dopamine receptor D2 0.63907 0.008961

222260 at ... 0.638943 0.004649

2161 17_at EXOSC2 exosome component 2 0.638391 0.009761

216651_s_at GAD2 glutamate decarboxylase 2 (pancreatic islets and brain, 65kDa) 0.638329 0.00349

214655_at GPR6 G protein-coupled receptor 6 0.638125 0.001913

214478_at SP P2 secreted phosphoprotein 2, 24kDa 0.637981 0.009715

220488_s_at BCAS3 breast carcinoma amplified sequence 3 0.637797 0.001 15

21 1190_x_at CD84 CD84 antigen (leukocyte antigen) 0.637131 0.007378

220263_at DAMS SMAD in the antisense orientation 0.636198 0.001032

21 1222_s_at HAP1 huntingtin-associated protein 1 (neuroan 1 ) 0.635069 0.007545

215858 at CDNA FLJ12301 fis, clone MAMMA1001858 0.635059 0.002254

217444 at C20orf112 Chromosome 20 open reading frame 1 12 0.635005 0.002125

205445_at PRL prolactin 0.634649 0.008128

217667_at ... 0.63437 0.003206

203811_s_at DNAJB4 DnaJ (Hsp40) homolog, subfamily B, member 4 0.633838 0.00937

210141_s_at INHA inhibin, alpha 0.632752 0.007205

209995_s_at TCL1 A T-cell leukemia/lymphoma 1A /// T-cell leukemia/lymphoma 1A 0.632732 0.006924 potassium voltage-gated channel, shaker-related subfamily, beta

221413_at KCNAB3 member 3 0.632716 0.00132

220350_at ZNF235 zinc finger protein 235 0.632367 0.003665

213008_at FLJ10719 hypothetical protein FLJ10719 0.632358 0.002705

207612_at WNT8B wingless-type MMTV integration site family, member 8B 0.631405 0.003129

215406_at TRIO Triple functional domain (PTPRF interacting) 0.631368 3.81 E-05

205587 at FGFR1 OP FGFR1 oncogene partner 0.631259 0.006694

220185_at SPTBN4 spectrin, beta, non-erythrocytic 4 0.630963 0.008977

V-set and immunoglobulin domain

217035_at containing 6 Similar to Ig heavy chain V-I region HG3 precursor 0.630534 0.002426 transmembrane 7 superfamily member 4 /// transmembrane 7

221266_s_at TM7SF4 superfamily member 4 0.63045 0.00336

210821 x at CENPA centromere protein A, 17kDa 0.630091 0.00604

222015_at CSNK1 E Casein kinase 1 , epsilon 0.629986 0.006464

221742_at CUGBP1 CUG triplet repeat, RNA binding protein 1 0.62962 0.003909 leukocyte immunoglobulin-like receptor, subfamily B (with TM

LILRB2 /// and ITIM domains), member 2 /// leukocyte immunoglobulin-like

21 1135_x_at LILRB3 receptor, subfamily B (with TM and ITIM domains), member 3 0.629619 0.004875

221384_at UCP1 uncoupling protein 1 (mitochondrial, proton carrier) 0.629528 0.003518

216054_x_at MYL4 myosin, light polypeptide 4, alkali; atrial, embryonic 0.629321 0.0095

206198_s_at CEACAM7 carcinoembryonic antigen-related cell adhesion molecule 7 0.628859 0.000456

214615_at P2RY10 purinergic receptor P2Y, G-protein coupled, 10 0.628501 0.001264

219635_at ZNF606 zinc finger protein 606 0.628266 0.005787 tumor necrosis factor receptor superfamily, member 11 b

204933_s_at TNFRSF1 1 B (osteoprotegerin) 0.628073 0.009766

21 1863_x_at HFE hemochromatosis 0.62797 0.001587

220593 s at FLJ20753 hypothetical protein FLJ20753 0.627656 0.007883

217086_at CHRNB3 cholinergic receptor, nicotinic, beta polypeptide 3 0.62761 0.002219

206709_x_at GPT glutamic-pyruvate transaminase (alanine aminotransferase) 0.627482 0.001786

221343_at OR1 1A1 olfactory receptor, family 1 1 , subfamily A, member 1 0.627306 0.00126

208321 _s_at CABP1 calcium binding protein 1 (calbrain) 0.626533 0.003268

205789_at CD1 D CD1 D antigen, d polypeptide /// CD1 D antigen, d polypeptide 0.626018 0.00363

219404_at EPS8L3 EPS8-like 3 0.625934 0.002602

210647_x_at PLA2G6 phospholipase A2, group Vl (cytosolic, calcium-independent) 0.625339 0.006877

221292_at PTCH2 patched homolog 2 (Drosophila) 0.625336 0.001284

213257_at SARM1 sterile alpha and TIR motif containing 1 0.62507 0.004291

213723_s_at IDUA Iduronidase, alpha-L- 0.624433 0.005599

206062_at GUCA1 A guanylate cyclase activator 1 A (retina) 0.624433 0.005125

220616_at ... 0.62411 1 0.004223

203299_s_at AP1 S2 adaptor-related protein complex 1 , sigma 2 subunit 0.62261 1 0.003786

205936_s_at HK3 hexokinase 3 (white cell) 0.622537 0.001779 natriuretic peptide receptor A/guanylate cyclase A

32625_at NPR1 (atrionatriuretic peptide receptor A) 0.621574 0.005781

216191 s at TRA@ /// TRD@ T cell receptor alpha locus /// T cell receptor delta locus 0.621343 0.001205

221668_s_at DNAI2 dynein, axonemal, intermediate polypeptide 2 0.620678 0.006114

214317_x_at RPS9 Ribosomal protein S9 0.62026 0.004392

213866_at LOC201 191 hypothetical protein LOC201191 0.620043 0.001367

210637_at TAC R 1 tachykinin receptor 1 0.61959 0.003206

217723_x_at ... 0.619563 0.001015

215841_at GUCA1 B guanylate cyclase activator 1 B (retina) 0.618949 0.005487

65635_at FLJ21865 endo-beta-N-acetylglucosaminidase 0.618868 0.004338

207270 x at CD300C CD300C antigen 0.618761 0.00465