Cross-reference to Related Applications This application claims the benefit of U. S. Provisional Patent Application No. 60/509,782, filed October 8,2003, which application is incorporated by reference herein.

Field The invention relates to analysis of molecules present in biological fluids. More specifically, the invention relates to the use of biomarker attractant molecules to concentrate low abundance biomarkers such as peptides, proteins and protein fragments for analysis.

Background Biological fluids such as blood and lymph are the repositories of a vast number of molecules that are excreted or otherwise shed by cells. The molecules that are present in biological fluids reflect the physiological and pathological states of the cells that are contacted by the fluids or which were contacted by the fluids from which they are derived (for example, lymphatic fluid enters the blood through the thoracic duct). Therefore, a major goal of clinical diagnostics is to correlate the particular molecules (biomarkers) present in biological fluids with particular disease states. Although the biomarker approach to diagnostics holds great promise, the number of biomarkers that are reaching routine clinical use remains low.

The low molecular mass range (for example, between about 0.3 kDa and 12 kDa) of the plasma proteome remains largely uncharacterized, principally because no adequate method for discovering and utilizing biomarkers in this mass range currently exists. For example, the lower limit of effective resolution achieved by conventional 2-D gel electrophoresis (2D-GE) is about 10 kDa.

While mass spectrometry (MS) exhibits its optimal performance in the low molecular mass range, it is nonetheless inadequate for comprehensive analysis of plasma proteins because the dynamic range of concentrations of proteins in plasma is much greater than the dynamic range of the method.

Traditionally, analysis of complex protein mixtures by MS has involved a combination of chromatographic separation and enzymatic fragmentation prior to analysis by MS-MS. Enzymatic treatment may, however, destroy or mask the information content of the sample by cleaving disease biomarkers and by creating large quantities of interfering enzymatic fragments from high abundance proteins. To avoid this problem, recent applications of mass spectrometry for biomarker analysis have turned to the native undigested serum proteome as a launch point for biomarker discovery (see, for example, Petricoin et al.,"Use of Proteomic Patterns in Serum and Identification of Ovarian Cancer,"Lancet, 359: 572-577,2002). In this approach, chemical fractionation based on protein affinity for derivatized MS targets (surface-enhanced laser desorption/ionization or SELDI targets) is used to reduce sample complexity (Anderson and Anderson, "The Human Plasma Proteome," Molecular and Cellular Proteomics, 1 : 845-867, 2002). However, because many proteins of similar

mass are detected, the SELDI approach to analysis of whole samples of proteins does not directly provide a sequence-based identification of particular protein peaks. Furthermore, peaks due to high abundance proteins tend to mask underlying peaks due to low abundance molecules. Therefore, under the assumptions that low molecular mass biomarkers contain important diagnostic information and that protein biomarkers useful in disease detection are of very low abundance, the search for low molecular mass biomarkers typically begins with a separation step to remove the abundant high molecular mass proteins such as albumin. For example, Kantor (Kantor,"Comprehensive Phenotyping and Biological Marker Discovery, Disease Markers, 18: 91-97,2002) states that " [b] iological samples, like unfractionated serum are very complex and dominated by a few components present at very high concentration that can interfere with the detection of all other molecules by MS." Some biomarkers that were first identified as diagnostic for particular diseases have later been found to exist in chemically-stable complexes with other proteins. For example, prostate- specific antigen (PSA), first identified in prostatic tissue, was later found to be present in significant part as a complex with a,-antichymotrypsin (See, for example, Wang et al., Invest Urol., 17: 159-63, 1979 and Lilja et al., Clin. Chem., 37: 1618-1625,1991). The existence of these two forms of PSA has both complicated diagnostic assays for prostate cancer, and been exploited in such assays to provide better differentiation between benign prostatic hypertrophy and prostate cancer (see, for example, Peter et al., Clinical Chemistry, 46: 474-482,2000). Similarly, PSP 94 (prostate secretory protein of 94 amino acids) was isolated and later found to form chemically stable complexes with serum proteins (See, for example, Dube et al, J. Androl., 8: 182-189,1987 ; Wu et al., J. Cell Biochem., 76: 71-83,1999 ; and Guo et al. ,"Serum Bound Forms of PSP94 in Prostate Cancer Patients,"7. Cell Biochem., 76: 71-83,1999). Again, the existence of multiple forms of PSP94 has complicated the development of assays for this biomarker (See, for example, U. S. Patent No.

6,107, 103). In contrast to such later discoveries that known biomarkers exist as complexes in serum, it has been not reported that complexes of molecules adhered to each other in biological fluids can be separated from biological fluids and probed for the presence of potential biomarkers.

Summary The assumption that high molecular weight, high abundance proteins interfere with detection of biomarkers has now been found to diminish the chances of finding important low abundance, low molecular mass disease biomarkers. Surprisingly, it has been discovered by the inventors that much of the important diagnostic information present in serum is in the form of low molecular weight biomarkers (such as less than about 12 kDa) bound to high abundance, high molecular weight proteins such as albumin. For example, low molecular weight biomarkers that are introduced into the blood stream at surprisingly low rates, and that would otherwise be rapidly cleared from serum by filtration through the kidneys, are concentrated by binding to high abundance, high molecular weight proteins, making their detection possible. Molecules and substances which bind and concentrate low

abundance, low molecular weight biomarkers are referred to herein as"biomarker attractant molecules." A method is disclosed for discovering or identifying potential biomarkers by separating biomarker attractant molecules from biological fluids and detecting the molecules that are adhered to the biomarker attractant molecules, for example, by separating or isolating the biomarker from the biomarker attractant molecules and analyzing those molecules. This method is contrary to known methods of biomarker discovery in that high abundance molecules present in the biological fluid are not separated out of the biological fluid to improve analysis. Rather, the adherence of biomarkers to such high abundance molecules is exploited to increase detection sensitivity. In one embodiment, a naturally occurring biomarker attractant molecule is isolated from a biological fluid obtained from different populations of subjects exhibiting different biological states (such as the presence and absence of a type of cancer). Molecules differentially bound to the biomarker attractant molecule between the populations are then detected and identified as biomarkers that discriminate between the biological states. In another embodiment, a biomarker attractant molecule is added to the biological fluid of the subjects and allowed to concentrate low molecular weight molecules for some period of time (such as more than a day). After the period of time, the biomarker attractant molecule is isolated from the biological fluid of the subjects and analyzed for bound biomarkers that discriminate between the biological states.

Identification of low molecular mass peptides bound to naturally occurring high abundance biomarker attractant molecules, for example, by sequencing the peptides, has also unexpectedly revealed that many such peptides are fragments of known cellular proteins. In many instances, the cellular proteins that are the source of the fragments are themselves too large to pass through cell membranes (walls) and into biological fluids. Thus, the fragments provide a window into specific disease-related enzymatic cleavage patterns and/or post-translational modifications of the larger proteins that are occurring within cells. In addition, the cellular proteins that are the source of the fragments have, in some instances, already been correlated with a particular biological state (such as a disease state or pathological state) in a particular location (such as a particular organ, sub-anatomic structure, cell or organelle).

Furthermore, a diagnostic method is disclosed in which biomarker attractant molecules are separated from a biological fluid and biomarkers adhered to the biomarker attractant molecule are detected to determine the biological state of a subject. In some embodiments, proteins, fragments thereof, and combinations of such proteins and protein fragment that have been identified as biomarkers are used to provide a diagnosis for a subject. In other instances, molecules adhered to biomarker attractant molecules that are separated from a biological fluid are analyzed for the presence of fragments of particular cellular proteins that are known to correlate with particular biological states, such as biological states in particular anatomic or physiologic locations. Such fragments that might otherwise be cleared from the biological fluid and that would otherwise never reach detectable levels are surprisingly concentrated by adherence to biomarker attractant molecules and therefore can

be detected. For example, in one particular embodiment, biomarker attractant molecule bound fragments of proteins associated with hypoxia and/or apoptosis in the heart muscle are detected and used to diagnose myocardial ischemia or a myocardial infarction (heart attack). Concentration of such fragments by biomarker attractant molecules facilitates their detection.

Once identified, particular diagnostic fragment/biomarker attractant molecule complexes or biomarker/biomarker attractant molecule complexes may then be more sensitively detected through further amplification using, for example, enzyme-linked antibodies that recognize the fragment/biomarker attractant complex. In one embodiment, a capture surface that specifically binds one or more biomarker attractant molecules is used as a bait surface to separate the biomarker attractant molecule (s) from a biological fluid. Antibodies directed to one or more of the diagnostic fragments, biomarkers, or complexes thereof with the biomarker attractant molecule, are then applied to the captured complexes. Binding of the antibodies to the adhered complexes can be detected using any known antibody detection method including colorimetric methods, chemiluminescent methods, fluorescence methods, evanescent wave detection methods, electrochemical methods, magnetic methods or electrical methods. In another embodiment, the capture surface is used as a target for laser desorption and molecules bound to the separated biomarker attractant molecules are analyzed by mass spectrometry.

Brief Description of the Drawings FIG. 1 is a schematic diagram of a process by which proteins shed by cells are clipped into fragments small enough to pass into the blood stream.

FIG. 2 is a schematic diagram of a model of biomarker production and distribution between cellular and vascular compartments.

FIG. 3 is a series of graphs showing the results of a theoretical model of biomarker amplification/concentration according to the disclosed method.

FIG. 4 is a diagram illustrating a separation protocol used to generate the fractions (A-F) that were analyzed to generate the corresponding mass spectra of FIG. 5.

FIG. 5 is a diagram illustrating the mass spectra obtained for each of the fractions illustrated in FIG. 4. The letters A-F denote the spectra obtained for fractions A-F as illustrated in FIG. 4.

FIG. 6 is a series of SELDI-TOF mass spectra obtained for serum and fractions thereof that were derived from an ovarian cancer sera study set using the protocol shown in FIG. 4.

FIG. 7 is a series of mass spectra in which the spectral region between 5000-7500 m/z has been magnified, and comparing albumin associated ions normalized for sample concentration and amplitude.

FIG. 8 is series of SELDI-TOF mass spectra showing the dependence of ion amplitude on quantity of albumin captured (0.025-0. 175 mg) from the serum.

FIG. 9 is a schematic diagram of an exemplary process by which biomarker attractant molecules (denoted carrier with diagnostic cargo in the figure) may be isolated from biological fluids

by binding the biomarker attractant molecule to capture agents inside of a flow cell, and the biomarkers bound to the biomarker attractant molecule are separated therefrom.

FIG. 10 is a schematic diagram of a process by which the biomarkers bound to the biomarker attractant molecule immobilized by the process of FIG. 8 are eluted directly into a mass spectrometer for analysis, and the mass spectral data is used to detect previously sequenced protein fragments that are indicative of a subject's physiological and/or pathological profile.

FIG. 11 is a schematic diagram showing a process whereby a fluorescent labeled capture agent (in this case an antibody) immobilized to a silica substrate (A) is used to capture a biomarker attractant molecule (in this case a carrier protein) with biomarkers attached (B). Light (in this case UV light) is used to covalently attach the biomarker attractant molecule to the substrate through the immobilized antibody (C) and a laser pulse is used to desorb biomarkers directly from the biomarker attractant molecule, for example, after an energy absorbing molecule such as cinnapinic acid is used to coat the substrate with its bound molecules.

FIG. 12 is a schematic diagram showing a method of diagnosis utilizing the information obtained from analysis of biomarkers that bind to a biomarker attractant molecule.

Detailed Description of Several Disclosed Embodiments In order to facilitate an understanding of the embodiments presented, the following explanations are provided.

I. Abbreviations SELDI-surface-enhanced laser desorption/ionization MALDI-matric-enhanced laser desorption/ionization MS-mass spectrometry TOF-time-of-flight LMW-low molecular weight HMW-high molecular weight LMM-low molecular mass HMM-high molecular mass BAM-biomarker attractant molecule ESI-MS-electrospray ionization mass spectrometry m/z-mass-to-charge ratio II. Terms In order to facilitate an understanding of the embodiments presented, the following explanations are provided.

The singular terms"a, ""an,"and"the"include plural referents unless context clearly indicates otherwise. Similarly, the word"or"is intended to include"and"unless the context clearly <BR> <BR> indicates otherwise. The term"comprises"means"includes. "Hence, "comprising A or B"means including A or B, or A and B, unless the context clearly indicates otherwise. It is further to be

understood that all molecular weight or molecular mass values given for compounds are approximate, and are provided for description. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of this disclosure, suitable methods and materials are described below. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

The term"adhered to a biomarker attractant molecule"refers to physical or chemical association of another molecule (such as a known or potential biomarker) with the biomarker attractant molecule. The association can be either direct or indirect, in that molecules can be adhered in one or more layers to a surface (external or internal) of the biomarker attractant molecule. For example, molecules exhibiting hydrophilic and hydrophobic domains (an amphipathic molecule) on opposite sides of the molecule can adhere to a hydrophobic domain on a biomarker attractant molecule, thereby presenting the hydrophilic domain of the adhered molecule to the solution. A hydrophilic molecule can then adhere to the hydrophilic side of the amphipathic molecule, and thus become indirectly adhered to the biomarker attractant molecule. In some embodiments, adhered molecules are molecules that do not form covalent bonds with the biomarker attractant molecule. In other embodiments, the adhered molecule is bound non-specifically (that is, not to the substantial exclusion of other molecules) to one or more domains on the biomarker attractant molecule. In further embodiments, the molecule is adhered to the biomarker attractant molecule with an association constant (Ka) of less than about 108, for example, a Ka of less than about 107, such as less than about 106 or about 105. Adhered molecules also can be distinguished according to the strength of their association based upon the identity, strength (concentration), and or type of denaturant required to dissociate them from the biomarker attractant molecule.

The term"biological fluid"refers to a liquid into which biomarkers are released, or a liquid derived from the liquid into which biomarkers are initially released. Such derivation may occur either in vivo or in vitro. In some instances, the biological fluid is a circulating fluid such as blood or lymph, or a fraction thereof, such as serum or plasma. In other cases, the biological fluid remains substantially in a particular locus, for example, synovial fluid, cerebrospinal fluid or interstitial fluid.

In still further cases, the biological fluid is an excreted fluid, for example, urine, breast milk, saliva, sweat, tears, mucous, nipple aspirants, semen, vaginal fluid, pre-ejaculate and the like. A biological fluid also refers to a liquid in which cells are cultured in vitro such as a growth medium, or a liquid in which a cell sample is homogenized, such as a buffer.

The term"biomarker"refers to one or more molecules (or signals due to such molecules in an analytical method such as mass spectrometry) that are differentially released into a biological fluid by any means (including secretion or by leakage through the cell membrane) in one or more subjects in each of two or more populations that exhibit different biological states. Examples of biomarkers include proteins, lipids, lipoproteins, glycoproteins, nucleic acids (such as circulating nucleic acids, see, for example, Krishna et al., Jpn. J. Clin. Oncol., 34: 307-311,2004 ; Ngan et al., Ann. NYAcad.

Sci."û2t : 263-270,2004 ; Carstensen et al., Ann. NYAcad. Sci., 1022 : 210-210,2004 ; Watanagara et <BR> <BR> al. , Ann. NYAcad Sci., M22 : 90-99, 2004 ; Herman,"Circulating Methylated DNA,"Ann. NYAcad.

Sci., 1022: 33-39,2004 ; and Schwarzenbach, Ann. NYAcad. Sci, 1022: 25-32,2004), carbohydrates, lipopolysaccharides, small molecule metabolites, and fragments thereof. Typically, the presence and/or the concentration of a biomarker (or biomarkers, or pattern or patterns of biomarkers) in a biological fluid is discriminatory between physiological and pathological states of the cells from which they are released.

The tenn"biomarker ath actant molecule,"or"BAM,"refers to a molecule, or other substance to which biomarkers in a biological fluid adhere. In particular examples, biomarkers adhere to a BAM with a low binding affinity (for example, a binding affinity of less than 10-3, 10-4, 10-5, 10-6, 10-'or 10-8 L/mol-min). An antibody may be a BAM to the extent that it binds biomarkers, other than through the specific antigen antibody interaction that results from the immune response that stimulated its production. For example, biomarker binding to an antibody BAM may occur outside of the complementarity defining region (CDR), or outside of the variable region altogether, for example by binding to the Fc portion of the antibody. However, in certain embodiments of the disclosed methods, the BAM is not an antibody. Although a particular BAM may selectively bind a class of biomarkers, the binding affinities of the biomarkers in a particular class do not differ as significantly as the binding affinities of an antigen to a particular antibody compared to other non- recognized molecules. The less specific nature of biomarker binding may be illustrated in certain examples of the BAM in which more than one biomarker binds to the BAM, for example, at least 2, at least 5, at least 10, at least 20, or even 50 or more biomarkers bind to the BAM.

Typically, BAMs have a half-life of existence in a particular biological fluid (for example in the body) that is longer than the half-life of biomarkers that become adhered to the BAMs and thereby concentrate the biomarker in the biological fluid. For example, BAMs can have a half-life of greater than about 1 day, such as greater than 2,5, 10,20 or 50 days. In particular examples, the BAM has as size and/or shape such that it is not substantially filtered from the blood stream by the kidneys. In other particular examples, the BAM has a molecular weight of greater than 25 kDa, for example, greater than 30,50, 75,100, 150,200 or 300 kDa. In yet other particular examples, the BAM molecule has a molecular weight falling within a particular range, for example between 30 and 50 kDa, between 50 and 75 kDa, between 75 and 100 kDa, between 100 and 150 kDa, between 150 and 200 kDa, between 200 and 300 kDa, or any other range between 30 kDa and 300 kDa.

Biomarkers may adsorb to the surface or be absorbed into the interior of the BAM, or both.

Examples of BAMs include proteins (including natural and engineered proteins such as chimeric proteins proteins with modified amino acid composition, proteins modified postranslationally, nucleic acids, carbohydrate decorated molecules, and organic polymers), dendrimers and particles (such as microparticles and nanoparticles, including silica, metal, ceramic and carbohydrate microparticles and nanoparticles), and cellular microparticles (see, for example, Diamant et al, Cellular microparticles : new players in the field of vascular disease?, Eur J Clin Invest. 34: 392-401,2004).

BAMs may be produced or derivatized to provide ionic groups (such as carboxylate, protonated amine, quaternary ammonium, and sulfate groups), hydrogen-bond acceptors or hydrogen-bond donors, electron donors or electron acceptors, polar groups (such as amino, hydroxyl, ester,

sulfhydryl and nitrile groups), hydrophobic groups (such as alkyl, alkenyl and alkynyl groups or groups with specific partition coefficients), peptides, proteins, nucleic acids, carbohydrates, lipids or any combination thereof, on their surfaces or in their interiors. Where the BAM is a protein, such as a naturally occurring protein, it may also be referred to as a"carrier protein"to reflect its role in collecting and concentrating LMM biomarkers from biological fluids. Examples of carrier proteins include albumin, iron binding proteins (such as transferrin), fibrinogen, alpha-2-macroglobulin, immunoglobulins (such as IgA, IgE and IgG), complement, haptoglobulin, lipoproteins, prealbumin, alpha-l-acid glycoprotein, fibronectin, and ceruloplasmin, and fragments, combinations and chemical derivatives thereof.

The term"biological state"refers to any characterizing feature of an organism or a cell. The feature can be a biological trait such as a genotypic trait or a phenotypic trait. The feature can be a physiological or disease trait, such as the presence, stage or absence of a particular disease, including infectious disease. The feature also can be a condition (environmental, social, psychological, time- dependent, etc. ) to which the organism or cell has been exposed or not exposed, including exposure/non-exposure to a drug or exposure/non-exposure to a toxin. Examples of biological states include a pathologic diagnosis, a toxicity state, efficacy of a drug, prognosis of a disease, stage of a disease, condition of an organ, presence of a pathogen (such as a virus), or toxicity of one or more drugs. Particular examples of disease states include presence, absence or stage of cancers, such as carcinomas, melanomas, lymphomas, sarcomas, blastomas, leukemias, myelomas, and neural tumors.

Particular examples of cancers further include prostate cancer, breast cancer, pancreatic cancer, ovarian cancer, testicular cancer and lung cancer.

The term"capture agent"refers to an agent that preferentially binds or selects a particular molecule, substance or material from a sample to the substantial exclusion of others present in the sample. Examples of capture agents include antibodies, apatamers, and reactive groups that selectively react with a particular functional group present on the molecule, substance or material to be captured and not on others. Where the molecule, substance or material to be captured is magnetic, the capture agent may be a magnet. Where the molecule, substance or material to be captured has a molecular weight that is substantially different from others in a sample, the capture agent may be a molecular sieve or size exclusion material that either retains or passes the molecule, substance or material to be captured while passing or retaining others. Where the molecule, substance or material to be captured comprises a sugar, the capture agent may be lectin that binds the sugar.

The term"denaturant"refers to an agent that disrupts the adherence of at least a portion of biomarkers adhered to a biomarker attractant molecule. Examples of denaturants include high (>1M) concentration salt solutions, solutions comprising a chaotropic agent such as urea, guanidinium chloride, and acetonitrile, solutions having low pH (for example, pH<5) or high pH (for example, pH>9), a high temperature solution (for example a solution having a temperature greater than 30 degrees celcius), an electrochemical perturbation, or application of electromagnetic radiation (for example, a laser pulse). A denaturant, may, in some embodiments, be a series of solutions with a continuous or discontinuous gradient of increasing or decreasing salt concentration, increasing or

decreasing concentration of a chaotropic agent, increasing or decreasing pH, increasing or decreasing temperature, or any combination thereof. In this instance, binding of different biomarkers may be sequentially disrupted as a function of the gradient. In some particular embodiments, a denaturant is other than a reducing agent such as (3-mercaptoethanol or an amino alcohol or diamine compound (such as ethanolamine) that has at least two nucleophilic groups, at least one of which is an amine group.

Conversely, the term"non-denaturant"refers to an agent that does not disrupt binding of a biomarker to a biomarker attractant molecule. Examples of non-denaturants include low concentration solutions of salts such as buffers and substantially pure water, such as distilled or reverse-osmosis purified water.

The term"non-denaturing conditions"refers to conditions do not significantly disrupt adherence of a biomarker to a biomarker attractant molecule. For example, such conditions include those under which biomarkers adhered to a biomarker attractant molecule with an association constant (Ka) of greater than 104, such as greater than 105, 106, 107 or 108 are remain substantially adhered to the biomarker attractant molecule. In other embodiments, biomarkers having a rate of association with the biomarker attractant molecule of greater than 10 L/mol-min, such as greater than 10-5, 104 or 10-3 L/mol-min remain adhered to the biomarker attractant molecule. Biomarker attractant molecules can be separated from a biological fluid under non-denaturing conditions to help reduce loss of biomarkers adhered to the biomarker attractant molecule during the separation.

The term"domain"refers to a portion of a molecule that exhibits a particular chemical or physical property. A domain can be as small as a particular amino acid residue on a peptide or as large as the entire surface of a biomarker attractant molecule, although more typically a domain will represent some portion of the surface of biomarker attractant molecule. Within a domain, a chemical or physical property can be substantially homogeneous, for example, substantially hydrophobic or substantially hydrophilic. Domains also can exhibit a set of chemical or physical properties that lead to adherence of particular classes of molecules, for example, phosphorylated or ubiquitinated protein fragments.

The term"eluting"refers to the act of disrupting intermolecular bonds (such as hydrogen bonds, dipole-dipole bonds, hydrophobic interactions and the like) between two molecules, substance, materials and the like, for example, a biomarker and a biomarker attractant molecule, and separating the two, for example, into separate containers or physical locations such as wells on a microtiter plate.

The term"identifiable molecule"refers to a molecule for which an analytical method is available for identifying its presence in a sample, such as substantially (for example, to a greater than 98% or 99% certainty) or unambiguously identifying its presence in a sample. Identifiable molecules can, for example, be identified according to their mass (such by a single or multiple peaks in a mass spectrum or an electrophoretogram), according to their immunochemical properties (such as by direct or sandwich antibody detection), or according to their sequence (such as using a cDNA array or RT- PCR). The erm"at least an identifiable fragment of a protein"refers to fragment of the protein that,

if detected, identifies (such as substantially or unambiguously) the protein from which the fragment was derived. An identifiable fragment of a protein can include, for example, one or more proteolytic peptide sequences (such as tryptic peptide sequences), the masses or sequences of which can be used to identify the protein with substantial certainty (for example, with >98% or >99% probability), for example unambiguously (such as accurate mass tag). An identifiable fragment of a protein can include an epitopic sequence of amino acids that is recognized by one or more antibodies that also recognize the whole protein from which the fragment is derived. Methods for using antibodies to immunochemically detect molecules are thoroughly discussed in Harlow and Lane, Using Antibodies : A Laboratory Manual, CSHL, New York, 1999 (incorporated by reference herein), and in Paul, Fundamental Immunology, 3rd ed. , 243-247 (Raven Press, 1993) and references cited therein.

The term"protein array analysis"refers to any technique in which samples comprising proteins or proteins fragments are placed on a substrate in particular locations and probed in a location specific manner, for example, with fluorescent antibodies to the proteins. Protein array analysis is discussed in detail in Liotta et al, Method and Devices for the isolation and analysis of cellular protein content, "U. S. Patent Application No. 09/913,667, which is incorporated by reference herein.

Additional examples of protein arrays are described in U. S. Patent No. 6,630, 358. Use of antibodies in such protein arrays as is discussed in these references is also generally applicable to detection/identification of molecules.

"Mass spectrometry"refers to any technique in which molecules are analyzed by converting them to gas phase ions and measuring the mass-to-charge ratio of the ions. Examples of mass spectrometric techniques include matrix-assisted laser desorption/ionization time-of-flight (MALDI- TOF), surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF), electrospray ionization mass spectrometry (ESI-MS), and Fourier-transform ion-cyclotron resonance (FT-ICR), and related techniques. Such techniques can further be coupled to a method of separation such as HPLC or capillary electrophoresis.

The term"population"refers to one or more subjects, but is more typically two or more subjects, for example, many more, such as more than 10,100, 500 or 1000.

The term"separating"refers to dividing a substance (such as a biomarker attractant molecule) from other substances (such as other molecules in a biological fluid) in order to obtain a <BR> <BR> purified form of the substance, such as an isolated form of the substance. In general, "purifying"is a relative term that does not require absolute purity and, for example, refers to separating a substance from other substances to provide a preparation wherein the separated substance represents at least 50% of the preparation. The term"isolating"refers to substantially separating a substance from other substances to provide an even more purified preparation, for example, a preparation in which the substance represents at least 90%, 95% or 98% or more of the preparation.

The term"size exclusion"refers to any technique that separates molecules based upon their size, including liquid chromatographic techniques such as column and high-pressure liquid chromatography (HPLC) and electrophoretic techniques such as denaturing polyacrylamide electrophoresis.

The term"subject"refers to an animal, and more particularly to a mammal (for example, a human or a veterinary animal such as a dog, a cat, a pig, a horse, a sheep, or a cow).

III. Overview Blood contains thousands of molecules shed from tissues into the blood stream. Similarly, molecules are shed into other biological fluids such as lymph fluid, interstitial fluid, synovial fluid, cerebrospinal fluid, saliva, seminal fluid and the like. In addition, molecules initially shed into one fluid may become part of another biological fluid. For example, lymph fluid is ultimately introduced into blood and blood is filtered by the kidneys to provide urine. Many of the molecules carried by these fluids are fragments or enzymatically modified forms of normal functioning proteins emanating from all levels of cellular and extra-cellular compartments. Every cell in the body can be viewed as leaving a record of its physiologic state in its waste products, or the products of its interactions with neighboring cells (FIG. 1). As shown in FIG. 1, such waste products may, for example, enter the bloodstream by passing through the vascular wall. To enter the bloodstream, however, large proteins are often be degraded into fragments small enough to go through or between the endothelial cells lining the blood vessels.

The lower molecular weight (LMW, such as less than about 12 kDa) proteome has not previously been systematically studied because this region of the proteome has been viewed as a non- interesting dumping ground of metabolic and proteolytic byproducts. Moreover, traditional protein- based discovery tools such as 2-D gel electrophoresis cannot discriminate in the LMW range.

Finally, and very importantly, previous methods for preparing serum for biomarker discovery efforts have destroyed, contaminated, or discarded, the LMW biomarker information archive by enzymatic treatment (e. g. trypsin digestion) or by removing the high abundance serum proteins, such as albumin. The inventors have found that virtually all of the LMW species are bound or complexed with the high molecular weight proteins, which act to accumulate and thereby amplify low abundance biomarkers. Biomarkers enter the circulation and adhere to long-half-life proteins such as albumin to provide an amplified and integrated history of biomarker production. Thus, the carrier proteins are a previously unappreciated storehouse of diagnostic information, which is exploited in certain of the disclosed methods.

Accordingly, a method is disclosed for identifying a biomarker in a biological fluid that includes separating a biomarker attractant molecule from samples of the biological fluid and detecting a biomarker that is differentially adhered to the biomarker attractant molecules between different populations of subjects that exhibit two or more different biological states. The detected biomarker that is adhered to the biomarker attractant molecule in the biological fluid discriminates between the different populations of subjects, and therefore can be used to discriminate between the biological states of subjects. For example, detection of the biomarker adhered to the biomarker attractant molecule in another subject can indicate that the subject exhibits a particular biological state such as a disease state.

In some embodiments, the biomarker attractant molecule is separated from the biological fluid using a capture agent that specifically or-ion-specifically binds the biomarker attractant

molecule (such as an antibody anchored to a substrate or a substrate that preferentially attracts the biomarker attractant molecule to its surface), and thereby removes at least a portion of the biomarker attractant molecule from the fluid. It is also possible to form a covalent bond between the biomarker attractant molecule and the capture agent to more firmly anchor the biomarker attractant molecule to the capture agent. In other embodiments, the biomarker attractant molecule can be separated from the biological fluid by size exclusion chromatography, for example, by cutoff at a particular molecular weight or by fractionation in to ranges of molecular weight. However, in general, any method that can concentrate the biomarker attractant molecule away from the biological fluid can be used to provide isolated biomarker attractant molecules for analysis of their adhered diagnostic cargo.

Typically, the biomarker attractant molecule that is separated from the biological fluid will have a half-life in the biological fluid that is longer than the half-life of biomarker that it amplifies and concentrates. For example, the biomarker attractant molecule can have a half-life in the biological fluid of greater than I day, for example, 3 days or more, such as 5 days, 10 days, 20 days, 30 days, 50 days, 100 days or more. In some instance, biomarker attractant molecules will have a molecular weight greater than about 25 kDa, for example, greater than about 40 kDa, such as greater than about 60 kDa or greater than about 80 kDa. In particular embodiments, the biomarker attractant molecule is selected from the group consisting of albumin, transferrin, fibrinogen, alpha-2- macroglobulin, an immunoglobulin, complement, haptoglobulin, a lipoprotein, prealbumin, alpha 1 acid glycoprotein, fibronectin, and ceruloplasmin, and fragments, chemical modifications and combinations thereof. Such biomarker attractant molecules can be obtained directly from a biological fluid, or can be Biomarker attractant molecules can be native to a subject's body or not native to a mammalian body (such as a human body) and introduced into the biological fluid of the subjects in the different populations to harvest diagnostic information from the biological fluid. Alternatively, native biomarker attractant molecules (such as blood proteins) can be extracted from a subject, stripped of bound molecules, and reintroduced to harvest a new constellation of bound molecules.

Examples of non-native biomarker attractant molecules include nanoparticles formed, for example, from silica, a ceramic, a magnetic metal oxide, or a biodegradable material. The biomarker attractant molecule also can include a dendrimer. Like other examples of biomarker attractant molecules, nanoparticles and dendrimers can be chemically modified to provide surfaces having particular chemical/physical properties. For example, nanoparticles can be modified to include at least one region (or domain) on their surface that is substantially hydrophobic and at least one region (or domain) on their surface that is substantially hydrophilic such that hydrophobic and hydrophilic biomarkers become adhered to the regions of the surface, respectively. Another way to modify the surface properties of a non-native biomarker attractant molecule is to conjugate it with a biological molecule, such as a protein or a protein fragment. Conjugated biomolecules can be chosen to specifically or non-specifically adhere particular classes of biomarkers. Since serum proteins (such as albumin, transferrin, fibrinogen, alpha 2 macroglobulin, immunoglobulins, complement, haptoglobulin, a lipoprotein, prealbumin, alpha 1 acid glycoprotein, fibronectin, and ceruloplasmin,

or fragments, chemical modifications and combinations thereof) often have domains on their surface exhibiting diverse chemical and physical properties that are capable of binding many different types of biomarker molecules, conjugation of such proteins to, for example, a nanoparticle or a dendrimer can provide a molecular"mop"that can be especially useful when the goal is to identify biomarkers in a biological fluid. Biomarker attractant molecules also can be produced to include properties or groups of atoms that facilitate their separation from a biological fluid, such as magnetism or an affinity tag such as a poly-histidine affinity tag or a sugar recognized by a lectin.

Biomarkers can be harvested from any biological fluid through adherence to biomarker attractant molecules. Non-limiting examples of biological fluids include blood, plasma, serum, lymph fluid, synovial fluid, cerebrospinal fluid, breast milk, nipple aspirants, sweat, tears, saliva, mucous, pre-ejaculate, semen, vaginal fluid, a cell culture medium, or interstitial fluid, or a fraction thereof, a combination thereof, or a fluid derived therefrom.

While it is possible to detect biomarkers by directly probing a separated biomarker attractant molecule for the presence of adhered molecules (for example, with panels of antibodies to known proteins), in some embodiments, it is advantageous to separate at least a portion of the molecules adhered to the biomarker attractant molecule from the biomarker attractant molecule prior to facilitate their detection. Molecules can be separated from a biomarker attractant molecule by dissociating them from the biomarker attractant molecule. This can be accomplished, for example, by contacting the biomarker attractant molecule with a denaturant to elute bound molecules from the biomarker attractant molecule. In this embodiment, it may be advantageous to immobilize the biomarker attractant molecule with a capture agent. Elution of biomarkers can be partial or complete, and can include a gradient elution with increasingly stronger denaturants and fractionation of the molecules eluted from the biomarker attractant molecule during the gradient elution. In other embodiments, molecules that are eluted from a biomarker attractant molecule are further separated from the biomarker attractant molecule by size exclusion chromatography. An alternative method of dissociating molecules from biomarker attractant molecules is to directly dissociate them using using a laser.

Molecules adhered to biomarker attractant molecules that are discriminatory between biological states and hence diagnostic for particular biological states can be identified according to the disclosed methods. There is virtually no limit to the biological states that can be discriminated using the methods. Examples of biological states include absence, presence and/or stage of a disease such as a cancer, exposure to a drug and non-exposure to the drug, exposure to a toxin and non- exposure to the toxin, and before, during and/or after a change in physiological, hormonal or developmental state (such as menopause) Molecules adhered to biomarker attractant molecules can be detected by any method, but some methods include mass spectrometric detection (which can be coupled to pattern recognition, especially for discovery of complex biomarkers comprising multiple molecule or identification of such complex biomarkers). Particular methods of mass spectrometric detection include MALDI- TOF, SELOI-TOF, FT-ICR, and ESI-MS. Other methods for detecting molecules adhered to

biomarker attractant molecules include protein array analysis, which also can be coupled to pattern recognition. Other methods include immunochemical analysis of molecules adhered to the biomarker attractant molecule, either with or without dissociation of the molecules from the biomarker attractant molecule.

The diverse methods of detection that can be used in the disclosed methods make it possible to identify and utilize biomarkers of diverse types. For example, in some embodiments, the biomarker comprises a protein, a lipid, a carbohydrate or a nucleic acid, or a fragment or combination thereof. In some embodiments, the biomarker includes two or more different molecules adhered to the biomarker attractant molecule, for example 5 or more, such as more than 10,20 or 50. In particular embodiments, the biomarker includes two or more different identifiable proteins or protein fragments.

In some embodiments the biomarker adheres to the biomarker attractant molecule non- covalently, for example, with a binding constant of less than 108 M-'. In other embodiments, the biomarker attractant molecule adheres and thereby concentrates the biomarker because the concentration of the biomarker attractant molecule in the biological fluid is at least 10 times greater (for example, at least 100 times greater, such as at least 103, 104, 105 or 106 times greater) than the concentration of the biomarker in the biological fluid. In still other embodiments, the biomarker attractant molecule further concentrates the biomarker because the biomarker attractant molecule has a half-life of greater than 1 day in the biological fluid.

In some embodiments, the biomarker attractant molecule circulates in the biological fluid and collects the biomarker as it is released into the biological fluid, and wherein the biomarker adheres specifically or non-specifically to one or more domains on the surface of the biomarker attractant molecule, the one or more domains on the surface of the biomarker further adhering additional molecules found in the biological fluid. In other embodiments, the biological fluid comprises serum or plasma and the biomarker, which would otherwise be filtered out of the serum or plasma at a rate equal to or greater than a rate of its introduction in the serum or plasma, is retained and concentrated over time in the serum or plasma because the biomarker attractant molecule to which it adheres is large enough and in sufficient excess concentration relative to the biomarker to prevent the biomarker from being filtered from the serum or plasma by normal kidney/glomerular function.

In still other embodiments, the biomarker adhered to the biomarker attractant molecule has a molecular weight of less than about 12 kDa (for example, less than about 10 kDa, such as less than about 8 kDa, 6 kDa or 5 kDa.).

In other embodiments, the biomarker attractant molecule includes two or more different biomarker attractant molecules. For example, a combination of two or more biomarker attractant molecules such as native carrier proteins can be used to provide more discrimination between biological states. In particular embodiments, the two or more different biomarker attractant molecules include all molecules naturally present in the biological fluid having a molecular weight of greater than about 25 kDa (for example, greater than about 30 kDa, such as greater than 40, kDa, 50

kDa, 70kDa or 90 kDa). And, the biomarker itself can include two or more different molecules differentially adhered between the two or more different biomarker attractant molecules.

Biomarkers identified (or discovered) by the disclosed method are contemplated.

Also disclosed is a method of detecting a biological state of a subject. The method includes separating a biomarker attractant molecule from a biological fluid obtained from the subject, wherein a biomarker of the biological state adheres to the biomarker attractant molecule. Detection of the biomarker adhered to the biomarker attractant molecule indicates that the subject exhibits the biological state. All of the permutations with regard to the separation of the biomarker attractant molecule from the biological fluid, detection of the biomarker, the biomarker itself, the biological fluids and the biomarker attractant molecules that were discussed above in regard to method of identifying a biomarker can be used in the method of detecting a biological state. However, in particular embodiments, a denaturant used to dissociate a biomarker from a biomarker attractant molecule does not include a reducing agent or a nucleophilic compound comprising at least two nucleophilic groups, wherein at least one of the at least two nucleophilic groups in the nucleophilic compound is an amine group.

In other embodiments, the biological state detected includes a pathological condition. For example, the pathological condition can include a disease or condition of a particular organ and the biomarker is a protein or a fragment thereof that is known to be produced in the particular organ (such as the heart) as a result of the disease or condition that becomes concentrated by the biomarker attractant molecule. In others, the pathological condition is the presence of a tumor, for example, of a particular type or a toxin exposure. Particular embodiments, wherein a particular pathological condition is detected using a particular biomarker attractant molecule by detecting at least an identifiable fragment of each of two or more particular proteins (previously identified by the disclosed discovery method to be discriminatory) adhered to the particular biomarker attractant molecule are disclosed in Examples 4-7.

The following examples are provided to illustrate certain particular features and/or embodiments, but these examples should not be construed to limit the invention to the particular features or embodiments described.

Example 1 This example demonstrates the method and provides experimental results that illustrate how low molecular mass (LMM) biomarkers reaching the blood may be significantly concentrated over time by binding (even with very low affinities such 104 L/mol min or lower, for example, 10-5 L/mol-min, or lower) to larger blood proteins having substantial half-lives (such as greater than 1 day, for example greater than 2,5, 10,20 or 50 days). Theoretical analysis of the binding kinetics, and experimental studies of human serum following molecular mass fractionation, demonstrates that the majority of low molecular mass biomarkers are bound to carrier proteins. Moreover, the pattern of LMM biomarkers bound specifically to albumin is distinct from those bound to non-albumin carriers.

Several surprising insights emerge from these results: a) accumulation of LMM biomarkers on

circulating carrier proteins greatly amplifies the total serum/plasma concentration of the biomarkers, making them more easily measured, b) the total serum/plasma biomarker concentration is largely determined by the carrier protein clearance rate, not the unbound biomarker clearance rate itself, and c) examination of the LMM species bound to a specific carrier protein contains important diagnostic information, as does examination of LMM species bound to differing carrier proteins. Carrier proteins and their biomarker content provide an unexpectedly superior means to detect biomarkers and sets of biomarkers for diagnostic purposes.

The following analytical and experimental approach was used to demonstrate the concept of biomarker amplification through the use of carrier protein binding. Amplification refers to an increase in the total plasma biomarker concentration due to the accumulation of biomarker molecules over time as they remain bound to carrier proteins. The proportion of low molecular mass species detectable by SELDI (surface enhanced laser desorption and ionization) that are associated with the higher molecular mass serum proteome was determined. Human serum was fractionated into high molecular mass and low molecular mass native fractions. Each fraction was assayed by SELDI to assess whether the preponderance of low molecular mass ions is found in the low or the high molecular mass fraction. The subpopulation of molecular species bound to albumin compared to the total carrier protein fraction was also determined. Further, it was determined whether SELDI-TOF identified biomarkers correlating with presence of ovarian cancer were associated with high molecular mass carrier proteins. The results demonstrate the surprising usefulness of carrier proteins as an affinity capture means for disease relevant biomarkers.

A mathematical model of biomarker amplification by carrier proteins was developed in which the kinetics of biomarker production, binding to carrier protein (s), and clearance, was modeled as a deterministic compartmental model with first order kinetics. A schematic diagram of the compartmental model is shown in FIG. 2. Here, the tissue compartment generating the biomarker is composed of target cell (representing, for example, a tumor cell) and host cell (representing, for example, a normal cell) compartments, and the net rate of biomarker shed into the vascular compartment from the tissue compartment is represented by km, b. The biomarker in the vascular compartment exists either in the bound or free (unbound) state. The carrier protein and the biomarker leave the vascular compartment at different rates, represented by koutsb and kout. r. The binding state of the biomarker determines its rate of clearance from the vascular compartment.

Additional abbreviations used in the equations of the model that follows are shown below in Table 1.

Table 1: Theoretical Model Abbreviations SYMBOL UNITS Biomarker B Carrier Protein Complex of carrier protein and biomarker Br Level of Amplification A Total concentration of the biomarker CB mol/L Total concentration of the free unbound biomarker Cb mol/L Total concentration of the carrier protein Cr mol/L Total concentration of the complex Cbr mol/L Total blood volume V L Total mass of biomarker MB mol Total mass of free unbound biomarker Mb mol Total mass of carrier protein Mr mol Total mass of complex Mbr mol Biomarker production rate kin. b mol/min Carrier protein production rate ki., r mol/min Biomarker clearance rate kout,b l/min Carrier protein clearance rate kout, r 1/min Biomarker bound to carrier protein clearance rate knout, bu Affinity of the biomarker to carrier protein kn L/ (mol*min) Dissociation of the biomarker off the carrier protein kofr 1/min

Numerical integration of the equations that follow was performed using Runge-Kutta approximation and simulation by MATLAB software (The MathWorks Inc; Natick, MA).

In this model, biomarkers are considered to be molecules shed into the circulatory compartment from perfused tissue (See FIG. 1). The type and relative abundance of single or multiple biomarkers can reflect the diseased or physiologic state of the tissue source. Biomarkers arise either directly from the cellular target itself, or from an interaction between the target cell and its surrounding host cells. Exchange of cytokines, enzymes, ligands, and metabolic products at the interface between the target cell and the host can potentially generate a population of modified molecules. For example, in the case of tumor biomarkers, enzymes elaborated by the tumor cells can modify substrates donated by the host to ultimately generate cleavage products that enter the venous drainage of the tissue. The exchange of materials between compartments exists in equilibrium. In this model, the assumption that the biomarker is introduced into the vascular compartment at a low constant rate kin b is used. The biomarker enters the vascular compartment in a form that is not bound to a serum protein carrier, and becomes evenly distributed into the vascular compartment volume.

Circulating biomarkers cycle between the bound and free states, depending upon their affinity to the carrier. This is expressed by the associative and dissociative rate constants, kon and kofr respectively.

For a biomarker emanating from the tissue, it is assumed that the biomarker becomes evenly distributed within the circulation compartment. At any point in time the total concentration of the biomarker is dependent upon the biomarker production rate, the biomarker clearance/excretion rate, the binding of the biomarker to a circulating carrier protein, and the clearance/excretion rate of the carrier protein (see FIG. 2).

When the system is represented by a single compartment model, V is the volume of distribution or total blood volume, and CB is the total concentration of the biomarker b consisting of the sum of the bound and free forms of the biomarker. The rate of biomarker production is k, n, b, and the rate of its elimination, degradation, or excretion is knout Assuming first order kinetics, the total change in mass of the compartment by the biomarker can be represented through the principles of mass balance as: where MB is the mass of the total biomarker within the compartment.

An individual biomarker molecule can exist in the free form or it can become bound to a circulating carrier protein, "r."As a result, the system may be represented as Mb kpn Mb, 4 k. ff in which case the total mass of biomarker as a function of time is MB (t) = Mbr (t) + Mb (t) where MB is the mass of the total biomarker, Mb is the mass of the free form biomarker, M, is the mass of the carrier protein, Mbr is the mass of the biomarker bound to the carrier protein, k. n is the rate of biomarker associating with the carrier protein, and koff is the rate that the bound biomarker dissociates from the carrier protein.

Thus, where (Mr (t)-Mbr (t))/V represents the quantity of carrier proteins available to bind the free form of the biomarker.

Biomarker concentrations are represented as follows: Total CB (t), Free Cb (t), & Bound to Carrier Cb, (t): By the definition the concentration, Mb (t) = Cb (t) V and its change with respect to time is, and the change in free biomarker concentration can be expressed as the following non-linear equation, where Cb is the free form biomarker concentration, Cr is the concentration of the carrier protein and Cbr is the concentration of the biomarker bound to the carrier protein.

The change in mass of the biomarker bound to the carrier can be expressed as while the change in total concentration of the biomarker bound to the carrier protein is represented by the non-linear equation dcbr(t)=kon Cr(t) Cb(t)-(koff + kout,br + kon Cb(t)) Cbr(t) (5)<BR> dt where Cb is the free biomarker concentration, Cr is the concentration of the carrier protein and Cbr is the concentration of the biomarker bound to the carrier protein. The total concentration of biomarker can be expressed as the sum of the bound and free forms of the biomarker: thus, CB (t) = Cbr (t) + Cb (t) (6) and its change can be expressed as the sum of equations (3) and (5), or

For the carrier protein concentration, Cr (t): within the same single compartment model with volume V, where the concentration of the carrier protein r is defined as Cr, and where the rate of carrier protein production is k, n, r, and the rate of its elimination or removal is kout. r.

Then, the change in the carrier concentration can be expressed as the linear equation Using the LaPlace Transform, the concentration of the carrier protein, at time t becomes: The following equation represents amplification of biomarker concentration in the presence versus the absence of the carrier protein, under the assumption that the biomarker is continuously produced or shed at the tissue source, and leads to the conclusion that biomarker molecules can accumulate over time in a carrier-bound form. At steady state, the total concentration of a biomarker measured in a blood sample can therefore become elevated due to its association with the carrier protein. T he level of amplification (A) of the biomarker concentration at steady state, due to the presence of the carrier protein can be defined as the following ratio.

A = CB in the presence of carrier protein(t) (10) CB in the absence of carrier protein(t) When considering the special case solution for excess carrier protein compared to biomarker, which is reasonable because plasma biomarkers reflecting a physiologic or disease state of perfused tissues are expected to exist at concentrations many orders of magnitude below the concentration of large carrier proteins such as albumin and immunoglobulins.

Since the bound biomarker concentration is much greater than the free biomarker concentration (Cbr (t) » Cb (t) ), equation 8 simplifies to:

In other words, the majority of the biomarker molecules will exist in the bound state, Cb, (t), and since Cb (t) is negligible and equation (7) becomes Therefore, CB (t) =-Cb, (t), and At t Ooo or steady state, because the biomarker bound to the carrier protein acquires the clearance rate of the carrier protein, koutsbr = koutsrS and the total steady state concentration of biomarker in the plasma becomes a simple function of the biomarker production rate and the clearance, excretion rate of the carrier protein. and equation 1 can now be viewed as follows To illustrate the results predicted by these equations, some assumptions about rate constants are made. With respect to the biomarker production rate, the biomarker production rate from the tissue is likely to be less than an actively secreted hormone. Consequently, production rates ranging three orders of magnitude below the typical rate of hormone secretion were examined.

The association and dissociation rates for binding of biomarkers to carrier proteins are assumed to span the known range extending from peptides and small molecules binding to human serum albumin up to and including the affinity constants for hormone binding proteins and the binding of PSA (prostate specific antigen) to serum binding proteins.

The clearance rates for example serum carrier proteins listed in Table 2 are known. The clearance and excretion rate for free biomarkers was chosen to span the known range for small molecules.

The influence on total biomarker concentration over time of the carrier protein/biomarker complex clearance rate is illustrated in FIG. 3A, which is a graphical representation of the numerical solutions to Equation 8 for a series of total biomarker clearance rates (half-lives). The total biomarker concentration is shown as a function of time after commencement of biomarker production. The production rate of the biomarker is assumed to be continuous at 1.0 femtomole/day.

The biomarker mass is assumed to be 10 kDa, and possess a half-life of one hour. Even for the low level of biomarker production of one femtomole per day, steady state total biomarker concentrations are reached within 10 to 30 days. The steady state concentration of the biomarker is markedly influenced by the clearance rate of the carrier protein. For the example of FIG. 3A, if the lower limit of sensitivity of a biomarker assay is one femtomole per liter, and the production rate is one femtomole per day, then the biomarker must have a half-life of greater than several days in order to accumulate a measurable level in the blood. Thus, without amplification due to adherence to the carrier protein, small biomarkers that are quickly filtered from blood would be undetectable.

Influence of biomarker attractant molecule association is dramatic. Since a low molecular weight biomarker will be rapidly cleared by the kidney, its binding to high molecular weight carrier protein becomes the major mechanism for increasing the half-life of the biomarker in the circulation compartment. The amplification of biomarker molecules that are bound to the carrier protein at steady state is shown in FIG. 3B as a function of both the biomarker/biomarker attractant concentration and the carrier protein half-life. Numerical integration of equation 8 was conducted for a series of different ratios of biomarker/carrier protein as a function of the carrier protein half-life, assuming a very low affinity (10-4) between the biomarker and the carrier protein, a biomarker of mass 10 kDa with a half-life of one hour produced at a rate of 1.0 fmol/day. Under these assumptions, FIG 3B demonstrates that even for short half-life carrier proteins and relatively low affinity (kaon=10-4) between the biomarker and the carrier protein, high concentration ratios of the carrier protein to the biomarker can lead to dramatic amplification of the biomarker. This result shows that an excess in concentration of carrier proteins compared to the biomarker, and a carrier with a relatively long half-life compared to the biomarker, can both contribute to amplification of the biomarker.

FIG. 3C shows the effect that both biomarker affinity for a carrier protein and carrier protein concentration have on the percentage of biomarkers that are bound rather than free in a biological fluid. Numerical integration of equation 8 also shows that assuming a low affinity of 104 between the biomarker and the carrier protein, a biomarker of mass 10 kDa with a half-life of one hour produced at a rate of 1.0 fmol/day, half-life of the carrier protein equal to albumin (18 days), and a vast excess of the albumin, virtually all biomarker will exist in the bound form. That is, even if a putative biomarker possesses even a relatively weak affinity for a high abundance carrier protein such as albumin (half life 18 days), the majority of the total circulating biomarker will still exist bound to albumin and be concentrated (amplified) in the biological fluid. Detection of such biomarkers, thus advantageously focuses on detecting them adhered to the biomarker attractant molecules, rather than free in solution as in prior methods.

Table 2 below demonstrate the dramatic amplification of the biomarker concentration as a result of carrier protein binding. As shown in FIG. 3C and Table 2, the amplification A (Equation 10) can be several orders of magnitude. Carrier protein amplification thus becomes a major factor determining whether a low abundance biomarker can reach a threshold of concentration that is above the lower limits of detection. A corollary of this conclusion is embodied in the generalized solution to equation 13, and is displayed in the results of Table 2: if the concentration of the biomarker and the half-life of the carrier protein are known, then an estimate can be provided for the minimum rate of production of the biomarker emanating from the tissue.

Table 2* iomarker Biomarker Concentration (fmol/L) Biomarker Absence of Any Productio Carrier n Rate Protein Albumin Ig IgG IgA/M IgD IgE (fmol/da t/2 : 1/8 tl/2 : 18 tY2 : 21 tl/2 : 23 tV2 : 5 tV2 : 2.8 tY2 : 2 y) days days days days days days days 0.01 0.0005 0.07 0.08 0.09 0.02 0.01 0.01 0.10 0.005 0.69 0.80 0.88 0.19 0.11 0.08 1 0.05 6.88 8.02 8.79 1.91 1.07 0.76 10 0.5 68.8 80.2 87.9 19.1 10.7 7.6 100 5 688 802 879 191 107 76 1000 48 6877 8023 8788 1910 1070 764 10000 478 68773 80235 87876 19103 10698 7641 100000 4776 687725 802346 878760 191035 106980 76414 1000000 47759 6877254 8023463 8787602 1910348 1069795 764139 *Theoretical prediction based on equation 10 of measured total biomarker concentration for selected high abundance serum carrier proteins, as a function of biomarker production rate, and carrier protein half-life. The assumption is that due to the vast excess of carrier protein, and even for a weak association constant, the majority of the biomarker species exists in a state bound to the selected carrier protein.

With regard to mass spectrometry serum proteomics, the most significant result of the theoretical analysis is that the vast majority of low molecular mass serum/plasma biomarkers will

exist bound to large carrier proteins. This conclusion was tested experimentally using SELDI analysis of native serum fractions of high and low molecular mass.

Montage Albumin Deplete Kit, Centriplus centrifugal filters with average MWCO (molecular weight cutoff) of 30 kDa, and ZipTip C, 8 reversed-phase desalting columns (6 llm bed) were purchased from Millipore Corporation (Bedford, MA) ; human serum acquired and processed in house; sequencing grade modified trypsin purchased from Promega (Madison, WI); dithiothreitol (DTT) and iodoacetamide, (97%) were purchased from Aldrich Chemical Co (Milwaukee, WI); glacial acetic acid and formic acid (88%) were purchased from Maillinckrodt Baker Inc (Phillipsburg, NJ); trifluoroacetic acid (TFA, sequanal grade) purchased from Pierce (Rockford, IL); acetonitrile (ACN) spectrometric grade purchased from Merck (Darmstadt, GE); ammonium bicarbonate (NH4HC03) certified purchased from Fisher Scientific (Fair Lawn, NJ); urea, enzyme grade, purchased from BRL Life Tech (Gaithersburg, MD).

Serum samples were derived from the ovarian cancer clinical study set of the National Ovarian Cancer Early Detection Program, Northwestern University. The full characteristics of this study set has been described previously and bioinformatic serum proteomic pattern analysis was conducted as described therein (see, Petricon et al., Lancet, 359: 572-577,2002, the entirety of which is incorporated by reference herein).

Surface Enhanced Laser Desorption and Ionization (SELDI) was conducted using a PBSII (Ciphergen Systems) and human serum was collected and anonymized as previously reported in Bonk and Humeny, Neuroscientist, 7: 6-12,2001 and Weinberger et al., J. Chrom. B. Analyt. Technol Biomed. Life Sci., 782: 307-316. Analysis was conducted on a WCX2 (weak cationic exchange) chip.

The serum was fractionated into molecular mass classes under native conditions.

Mass fractionation was carried out as follows. Thirty microliters of unfractionated human native serum was introduced into a Sephadex G-25@ or a Sephadex G-50 molecular sieve spin column according to the manufacture's instructions. The column was centrifuged at 3000 x g for three minutes, and approximately 30 microliters of eluate containing the high molecular mass fraction was collected. The eluate was treated with 50% acetonitrile (w/w in water) to dissociate bound molecules for 30 minutes and was transferred to the inlet of a molecular filtration microcolumn. (Microcon YM-30 Millipore Centrifugal Filter Device) and the column was centrifuged at 1000 x g. The eluate containing the low molecular weight fraction was collected. All fractions at each stage were sampled and one microliter was analyzed by SELDI.

Segregation of albumin and its low molecular mass binding constituents was conducted using the Montage Albumin Deplete Kit. 100 uL of human serum was diluted one to one with Equilibration Buffer provided with the kit for a final volume of 200 uL and vortex sample. The column was rehydrated twice with 400 fiL of Equilibration Buffer and centrifuge through the column insert for 2 minutes at 2,000 rpm. 200 pL of diluted serum was introduced into the rehydrated albumin column and centrifuged for 2 minutes at 2,000 rpm. The eluate from the column contains the serum without albumin. The bound fraction contains the albumin and the low molecular weight

species bound to the albumin. 400 uL EAM solution were then added, composed of 50% acetonitrile and 0.1% TFA, to the column to strip the column and dissociate albumin from its bound species.

After 30 minutes, EAM solution was centrifuged through the column at 2,000 rpm for 3 minutes.

The eluate contained the dissociated albumin and low molecular weight species that binds to albumin.

Analysis of the proteins bound to the column using ion trap mass spectrometry was performed in line with an LCQ Classic MS (ThermoFinnigan, San Jose, CA) with a modified nanospray source. Dynamic exclusion of the three most abundant peptide hits from a full MS scan were selected for MS/MS analysis by collision induced dissociation with normalized collision energy of 35% and an activation time of 30 ms. Ion spray voltage was 2.00 kV with a capillary voltage of 26.20 V and a capillary temperature of 160 °C. Results for MS/MS scans were searched and compared with theoretical spectra in the Sequest Browser database specified for human proteins.

WCX2 protein arrays for SELDI-TOF analysis were processed in a bioprocessor (Ciphergen Biosystems, Inc). 100 ul of 10 mM HCL was applied to the protein arrays in the bioprocessor and allowed to incubate for 5 minutes. The HCL wash was aspirated and discarded and 100 ul of H20 was applied and allowed to incubate for one minute. The H2O was aspirated and discarded, then reapplied for another minute. 100 ul of 10 mM ammonium acetate with 0.1% TritonX was applied to the surface and allowed to incubate for 5 minutes. The ammonium acetate was aspirated and discarded. A second application of ammonium acetate was applied and allowed to incubate for 5 minutes. The chip surfaces were then dried using a vacuum to remove any excess amount of liquid.

Five ul of raw sera, or molecular mass fraction, or eluate was then applied to each chip surface and allowed to incubate for 55 minutes. Each protein chip was washed six times with 150 ul of PBS and H20 and then vacuum dried. Cross contamination was eliminated between spots by using a bioprocessor gasket. The gasket was removed and 1. 0 pi of a saturated solution of the energy absorbing molecule cinnamic acid (25% saturation) in 50% (v/v) acetonitrile, 0.5% trifluoroacetic acid was applied to each spot on the protein array twice allowing the solution to dry between applications.

A schematic of the fractionation scheme is shown in FIG. 4, and the mass spectrometry results following fractionation are shown in FIG. 5. The results demonstrate that most of the detectable ions by MS are derived from molecular species, putative biomarkers, that are bound to large carrier proteins. In fact, removal of the high molecular mass proteins under native conditions (FIG. 5B), a common method used for biomarker discovery, removes a significant proportion of the ions generated by SELDI-TOF. As shown, the preponderance of mass spectrometry detected low molecular weight ions are retained within the high molecular weight protein carrier fractions (FIGS.

SC-F), and do not exist in the free phase (FIG SB). FIG. SA is the spectrum obtained for a control of the SELDI matrix alone. Spectra generated from whole, native, unfractionated serum applied directly to the WCX2 chip surface. FIG 5B is the spectrum after removal of all species greater than 30,000 MW with a Microcon'> YM-30 Molecular Filter from the unfractionated serum. FIG. 5C is a direct analysis of high molecular weight native species by MS after fractionation through a Sephadex G-25 Molecular Sieve. The MS profile displays the low molecular weight species ionized and desorbed

away from the high molecular weight carriers. FIG. 5D shows the spectrum after the high molecular weight fraction eluted from the G-25 sieve was treated with a 50% acetonitrile solution to dissociate potential low molecular weight species bound to their high molecular weight carriers, and the dissociated eluate was then passed through the YM-30 Filter. Only the low molecular weight species previously bound to the G-25 sieved high molecular weight fraction should pass through the filter.

FIG SE is a spectrum of the ion species associated with HMW carrier species depleted of albumin and albumin binding partners. FIG 5F is a spectrum showing the ion species associated specifically with albumin only.

A comparison of the spectrum of FIG. 5B to that of FIG. 5C shows that the majority of ions generated from unfractionated serum are derived from species associated with larger carrier proteins.

FIG. 5D displays the spectrum of ion species previously bound, and then dissociated and separated from the higher molecular mass fraction. The intensity and number of ion species is augmented compared to FIG. SA and FIG 5C.

FIGS. 5E and 5F compare the proportion of SELDI-TOF generated ions associated with albumin compared to all the other serum carrier proteins. FIG. SE displays the ions associated with the non-albumin carrier proteins, and FIG. 5F displays the ions generated from species bound only to albumin. A significant proportion of the ions in the spectra appear to be derived from species associated with albumin.

Microcapillary LC MS/MS was used to verify that the albumin bound fraction acquired through stripping the Montage Albumin Deplete Column was entirely albumin and its bound low molecular mass species. Since there was no indication of other high molecular mass proteins bound to the albumin specific column, the low molecular mass species detected were specific to albumin.

Furthermore, a number of low molecular mass species that were dissociated from the albumin carrier were positively identified. Additional results characterizing the entire repertoire of low molecular mass species bound to serum carrier proteins by LC MS/MS were obtained.

It was then shown that the ions generated from the species bound to albumin contained disease biomarker information. SELDI-TOF ion patterns, generated on the WCX2 chips, correlating with ovarian cancer were identified by methods described previously. The sample set included 91 controls and 162 ovarian cancers. The entire process of preparing the chips was done using a robotic instrument. The following selection of ion mass/charge (m/z) values generated a pattern that was 100% predictive in the training and blinded testing--2760. 6685,19643. 409,465. 56916,6631. 7043, 14051.976, 435.46452, and 3497.5508.

Randomly selected representative serum samples from this study set were analyzed by MALDI-TOF comparing the spectra generated by unfractionated sera to the spectra generated only from the species bound to albumin. As demonstrated in FIG. 6, the spectrum generated from the species bound to albumin is complex and exhibits a number of differences between the cancer and the unaffected ("normal") cases shown in the example. A comparison of the peak intensities between the unfractionated serum (FIGS. 6C and 6D) and the albumin-bound fraction (FIGS. 6A 6B) shows that a significant set of putative disease biomarkers may be associated with albumin. FIG. 8 displays the

ion spectra for an example serum sample in which the ion species bound only to albumin are compared for different amounts of albumin captured.

FIGS. 7A-D show the example of ion 6631.7043, a member of the ion pattern 100% correlated with ovarian cancer in this clinical study set. Matched for dilution and amplitude, the predicate ion is highly associated with albumin, and the ionization intensity is augmented in the albumin bound fraction. Finally the complexity and amplitude of the ion species of the SELDI-TOF spectra is directly related to the amount of albumin captured as shown in FIG. 8. The ion species increase in amplitude and complexity when they are derived from larger quantities of bound albumin.

This supports the concept that the biomarkers are quantitatively associated with the albumin The theoretical and experimental data support the concept that the vast majority of small mass ions detected by mass spectrometry of native human serum exist in association with circulating carrier proteins of higher molecular weight. This conclusion is important for biomarker physiology and biomarker measurement technology.

One result is that the concentration of a biomarker measured in serum or plasma is directly related to the clearance rate or half life of the carrier protein, not the biomarker clearance rate itself.

As shown above in equation 13, the concentration of the biomarker is a function of the ratio between the biomarker production rate from the tissue and the clearance rate of the carrier protein. This means that carrier protein binding amplifies the total biomarker concentration levels measured in serum or plasma. Amplification occurs because the carrier protein acts as a reservoir to accumulate the biomarker over time, as the tissue is continuously producing the biomarker. Thus, a biomarker produced by a small volume of tissue such as the ovary, prostate, or breast at a low concentration (for example, one femtomole per day) can accumulate to a concentration of one picomole in the serum because it binds with a carrier protein with much larger half-life. In this example, the existence of the carrier protein can raise the concentration of the biomarker to a range detectable by conventional assay technology. Without the carrier protein, the free biomarker would be rapidly cleared by the kidney and would therefore reside at a steady state concentration many fold below the detection limits of assay technology.

Another conclusion is that the results extend beyond current mass spectrometry detection technology. Small biomarkers are commonly not the province of two-site sandwich immunoassays, because it is difficult to develop two antibody-binding sites on the same small molecule. In contrast, if one half of the immunoassay sandwich is the carrier protein and the other half was the small biomarker, a sandwich immunoassay can be achieved.

The biomarker clearance rate becomes the carrier protein clearance rate because the carrier protein, even if it has low affinity for the biomarker, is in vast excess. Therefore, another implication is that if the clearance rate of a given carrier protein is known, and the serum/plasma concentration of the biomarker bound to that carrier protein is known; an estimate a lower limit on the continuous production rate by the tissue (Equation 13) may be made. For example, if the concentration of a generic, bound biomarker a is 3. 8 ng/mL (42. 22 fmol/mL, where the biomarker a with its carrier protein have a combined molecular weight of 90 kDa) and the carrier protein half-life is 2. 43 days,

then the production rate of a from the tissue is at least 45,000 femtomoles per day. If a is produced by a one cubic centimeter tumor composed of 109 cells, then each cell would produce approximately 16,000 molecules per day. This approximation is consistent with previous experimental findings.

As shown in the experimental data presented in FIGS. 5-7, the theoretical predictions of FIG. 3 have been demonstrated. Thus, in analyzing unfractionated native sera, the majority of ions generated by SELDI-TOF analysis are found to be associated with carrier proteins, rather than free in solution phase (compare FIG. 5B to FIGS. SC-F). Moreover, as shown in FIGS. 6 and 7, ion species altered in disease study sets may be those specifically captured on a single carrier protein. In the example, the carrier protein is albumin. In the past, extensive effort has been placed to separate and throw away the high abundance, large carrier proteins in the native plasma so that the remaining low abundance, disease-related markers can be discovered. The present results demonstrate that the search for biomarkers must be directed at those bound to the carrier proteins or other BAMs.

Removal of high abundance serum/plasma proteins prior to proteomic analysis should be conducted after, not before, dissociation from binding partners, since the results indicate that the low molecular mass proteome, existing within the range detectable by MALDI-TOF exists predominately in the bound phase. Thus, technologies that focus on efficient capture of the carrier proteins and specific elution of the low molecular weight biomarkers will likely yield the greatest amount of diagnostic information. Although the bound biomarkers may exist in concentrations ten to 500 times greater compared to their free counterparts, and since the carrier proteins exist in vast excess compared to the biomarkers, it is unlikely that the carrier proteins will become saturated with bound biomarkers.

Moreover, based on its unique affinity topology, each carrier protein may have its own constellation of bound biomarkers. Indeed, the distribution of biomarkers among specific plasma/serum carrier proteins may contains important diagnostic information.

Example 2-Identification of Proteins Having Fragments Bound to Natural Circulating BAMS Hundreds of peptides and clipped proteins bound to carrier proteins such as albumin have been sequenced by MS/MS. Following trypsin digestion of the carrier bound fragments, identification of the proteins/peptides was performed in line with an LCQ Classic MS (ThermoFinnigan, San Jose, CA) with a modified nanospray source. Dynamic exclusion of the three most abundant peptide hits from a full MS scan were selected for MS/MS analysis by collision induced dissociation with normalized collision energy of 35% and an activation time of 30 ms. Ion spray voltage was 2.00 kV with a capillary voltage of 26.20 V and a capillary temperature of 160 °C.

Results for MS/MS scans were searched and compared with theoretical spectra in the Sequest Browser database specified for human proteins.

The sequencing results for trypsin fragments of carrier bound peptides/proteins reveal molecules derived from all parts of cells, ranging from low concentration transcription factors from the nucleus, to membrane bound receptors, to secreted growth factors. Table 3 below illustrates the general types of proteins found.

Table 3 Receptors Cytoplasm Immune Epithelial Signal Transduction Cytoskeletal Antigens Cytokines Nuclear Coagulation Hypoxia Hormones Transcription Motility Mitochondria Viral and Adhesion Factors Apoptosis infectious Cell cycle E C M proteins Enzymes (all Novel Oncogenes Mucins classes)

Example 3-Discovery of ovarian cancer biomarkers bound to human serum albumin.

The Clementine Data Mining Bioinformatics Toolset (SPSS, Inc. , Chicago, IL) was used to discover patterns of ions representing biomarkers bound to albumin that discriminate between serum samples obtained from subjects with and without ovarian cancer. Prior to introducing the dataset to the Clementine system, a feature selection was performed by computing and then comparing the aggregate mean values of the total ion count (sum of amps) on a bucket by bucket (bin by bin) basis, and selecting the 100 buckets (bins) with the largest difference in mean values between normal and cancer samples. The Clementine system was then trained using un-normalized total ion count per bin values. Using a decision tree analysis, a set of ion species bound to albumin were found to discriminate between 127 non cancer and 115 ovarian cancer cases with 100% specificity and 100% sensitivity.

A Qstar mass spectrometer (described in Example 1) was used to analyze albumin bound proteins (and protein fragments) in the mass to charge regions of 1000-7760 and 7762-11,000. These ranges excluded the matrix effect area and the region of Transferrin. The ion amplitude and density of ion peaks was ten fold greater using the albumin eluted fraction, compared to direct analysis of whole unfractionated sera, making detection of the discriminatory patterns of ions surprisingly reliable. Although several groupings of ions were found to be discriminatory, the key ions in one highly discriminatory group had mass to charge ratios as follows: 3884,1033, 2366,4033, 4070, 3157,7953, 6678,7837, 2375, in order of importance.

Within the molecular mass regions identified by the decision tree analysis, fragments of a number of previously unknown serum biomarkers were also identified as being bound to albumin in the ovarian cancer patients. The molecular masses of the fragments were validated using Western Blotting with polyclonal antibodies directed against the candidate proteins. The new ovarian cancer biomarker proteins, identified as being bound to albumin are listed in Table 4 below. These proteins are considered to be low abundance cellular proteins, and were not previously expected or reported to be found in serum.

Table 4 Zona Pellucida Receptor Tyrosine Kinase CTCL tumor antigen se2-1 MDM2 P53 binding protein LYST interacting protein Disco Interacting Protein 2 ARF binding protein 3 Protein Kinase Cyclic AMP dependent MLL2 leukemia protein DJ-1 protein Phosphocholine cytidylyltransferase Lymphoid transcription factor LYF-1 Cleavage Stimulation Factor Bombesin receptor RHAMM hyaluron motility receptor Steroidogenic Acute Regulatory Protein Mitochondrial translation initiation factor Death Domain Protein RIP Estrogen Sulfotransferase Rai ShcC Cysteinyl TRNA synthetase Huamm KIAA1587 Hepatocellar carcinoma Zinc Finger protein 195 associated protein MAGE-E1 S17564 alpha 2 glycoprotein

In conclusion, the mass spectrometric analysis of molecules bound to a BAM (in this case a serum carrier protein, specifically albumin) circulating in the blood of the women test subjects who either had biopsy proven ovarian cancer, or were disease free for 5 years, in combination with pattern recognition, revealed a large number set of previously unknown biomarkers associated with ovarian cancer. In this case the BAM is natural albumin. Sequencing and identification of the carrier protein associated molecules revealed that they are primarily derived from low abundance cellular and tissue sources, and represent all major classes of cellular compartments, including membrane, cytosol, nucleus, and extracellular matrix.

Example 4-Detection of Stage Specific Ovarian Cancer Biomarkers and Biomarkers Indicating Lack of Ovarian Cancer in a Subject.

In this example, albumin bound molecules were analyzed to detect protein and protein fragment biomarkers that discriminate between different populations of subjects having no evidence of ovarian cancer, Stage I ovarian cancer or Stage III ovarian cancer. Biomarkers adhered to albumin were analyzed and identified by mass spectrometry.

Serum Samples Serum was collected prior to physical evaluation, diagnosis and treatment and was stored at - 80 centigrade. The study set used consisted of 98 unaffected"high risk"patient samples, and 146 cancer patients. Serum samples were obtained from the National Ovarian Cancer Early Detection Program (NOCEDP) and gynecologic oncology clinic at Northwestern University (Chicago, Illinois).

Specimens from women enrolled in the NOCEDP who had no evidence of any cancer for 5 years

were evaluated as healthy (no cancer) women. Similarly, only preoperative specimens were used from women who were surgically staged and found to have epithelial ovarian carcinoma. Two hundred and forty eight samples were prepared using a Biomek 2000 robotic liquid handler <BR> <BR> (Beckman Coulter, Inc. , Palo Alto, California). All analyses were performed using ProteinChip weak<BR> cation exchange interaction chips (WCX2, Ciphergen Biosystems Inc. , Fremont, California). A control reference sample was randomly applied to one spot on each protein array as a quality control for overall process integrity, sample preparation and mass spectrometer function. The control sample, SRM 1951 A, which is comprised of pooled normal human sera, was provided by the National Institute of Standards and Technology (Gaithersburg, Maryland).

Application of Quality Control Analysis The 248 mass spectra acquired by the QqTOF MS were analyzed with a wide variety of statistical tools to evaluate the spectral quality [i. e. record count and mean amplitude] and statistical variances greater than the population norm. Mean spectral amplitude and the file record count (i. e. the total number of data points within a mass spectrum) were selected as global parameters for statistical analysis. Using these parameters, the mass spectra from serum albumin elutions of unaffected individuals and cancer patients were tracked over several days of analysis and over different lots of ProteinChips. The total record count of each mass spectrum was compared to that recorded for the reference sample.

QqTOFMS Analysis ProteinChip arrays were analyzed using a hybrid quadrupole time-of-flight mass <BR> <BR> spectrometer (QSTAR pulsar i, Applied Biosystems Inc. , Framingham, Massachusetts) fitted with a<BR> ProteinChip array interface (Ciphergen Biosystems Inc. ). Samples were ionized with a 337 nm pulsed nitrogen laser (ThermoLaser Sciences model VSL-337-ND-S, Waltham, Massachusetts) operating at 30 Hz. Approximately 20 mTorr of nitrogen gas was used for collisional ion cooling.

Each spectrum represents 100 multi-channel averaged scans (1.667 min acquisition/spectrum). The mass spectrometer was externally calibrated using a mixture of known peptides.

Elution and sequencing of albumin bound proteins Chemicals: acetonitrile (ACN) (HPLC grade), ammonium bicarbonate (NH4HCO3), iodoacetamide (97%), methanol (99+%) and dithiothreitol (DTT) were purchased from Sigma- Aldrich Co. (St. Louis, MO). Trifluoroacetic acid (TFA) (sequanal grade) purchased from Pierce (Rockford, II). Formic acid (88%), acetic acid (glacial) purchased from Mallinckrodt Baker (Phillipsburg, NJ). H2O was doubly distilled in house with Kontes High Purity Water System.

Porcine sequencing grade modified trypsin was purchased from Promega (Madison, WI). Coomassie Brilliant Blue R 250 was purchased from Fluka Chemical (Switzerland).

Sequencing of albumin associated proteins was conducted on groups of 30 stage 1 cancers, 30 stage 3 cancers, and 30 high high risk ovarian human serum, obtained as described above.

Vivaspin 500 centrifugal membrane filters were purchased from VivaScience. ZipTip C, 8 (6 um bed) desalting columns and Montage Albumin Depletion Columns were purchased from Millipore. Elution buffer was replaced with a 70% ACN/30% dd H20/0. 2% TFA mixture. Pre cast

gels, sample, and running buffers were purchased from Invitrogen Co. Fused silica is from Polymicro Technologies (Phoenix, AZ).

Albumin and bound peptide purification : 40 uL of human stage specific (pooled) ovarian cancer serum (-5 mg protein) was diluted to 200 uL with Equilibration Buffer (Millipore) and run through a (Montage) albumin specific affinity column twice. The bound protein was washed thoroughly, and eluted from the column by equilibrating with 70% ACN/30% H2O/0. 2% TFA for 30 minutes, followed by a slow spin-through of the elution mixture. The eluate was lyophilized to <1 0 gL in a HetoVac rotofor (CT 110) and reconstituted in a 95% HZOI 5% ACN/0.1% formic acid buffer (Buffer A). Samples were sometimes desalted with a ZipTip cleanup or with Vivaspin 500 centrifugal membranes and always reconstituted in a 1: 1 mixture of water and SDS sample buffer (20 uL total volume).

ID gel separation and digestion : 20 uL of sample in SDS sample buffer (40 uL of original serum) was boiled for 5 minutes at 95 °C and run on a 1D pre-cast gel (16% Tricene or 4-20% Tris-Glycine) to separate albumin from the proteins/peptides/fragments of interest. The gel was stained with Coomassie Blue for one hour and destained overnight in 30% methanol/10 % acetic acid solution. The entire lane containing stage specific serum proteins was excised from the gel and finely sliced into very small molecular weight regions (-40 slices/lane). Gel bands were reduced and alkylated with 10 mM DTT and 55 mM iodoacetamide, incubated at 4 °C for 1 hour in trypsin (20 ng/uL) and allowed to digest overnight at 37°C in 25 mM NH4HCO3. The following morning, proteins were extracted from the gel with a 70% ACN/5% formic acid solution.

FLC/MS/MS analysis : Samples were lyophilized to near dryness and reconstituted in 6. 5 uL of Buffer A for mass spec analysis. Microcapillary reverse phase LC/MS/MS analysis was performed with Dionex's LC Packings liquid chromatography system coupled online to a ThermoFinnigan LCQ Classic ion trap mass spectrometer (San Jose, CA) with a nanospray source. Reverse phase separations were performed with an in-house, slurry packed capillary column. The C, 8 silica-bonded column is 75 um <BR> <BR> i. d. , 360 um o. d. , 10 cm long fused silica packed with 5 um beads with 300 Angstrom pores (Vydac, Hesperia, CA). A u-precolumn PepMap Cl8 cartridge (Dionex) acts as a desalting column. Sample is injected in RL pick-up mode and washed with Buffer A for five minutes prior to a linear gradient elution with Buffer B (95% ACN/5% H2O/0. 1% formic acid) up to 85% over 95 minutes at a flow rate of 200 nL/minute. Full MS scans are followed by three MS/MS scans of the most abundant peptide ions (in a data dependant mode) and collision induced dissociation is performed at a collision energy of 38%.

Data analysis : Data analysis was performed by searching MS/MS spectra against the European Bioinformatics Institute of the non-redundant proteome set of Swiss-Prot, TrEMBL and Ensembl entries through the Sequest Bioworlcs Browser (ThermoFinnigan). Peptides were considered

legitimate hits after filtering the correlation scores (refer below) and manual inspection of the MS/MS data. The criteria used to filter data were at least as stringent as most reported in the literature.

Charge 3ccorr DeltaCNSp Ions Rsp +1 >1.9 >0.1 >500 >50% <5 +2 >2.5 >0.1 >500 >50% <5 +3 >3.5 >0.1 >500 >50% <5 Accepted peptide hits were required to have an Short ranking = 1 relative to all other peptides in the database. The albumin extraction, gel electrophoresis, protein digestion/extraction, and LC/MS/MS analysis was repeated in more than five distinct trials-each time yielding diminishing returns of new identifications for low abundance peptide hits. Repetitive sequencing of peptides in multiple trials further validated the experimental procedure-both within a stage and between ovarian cancer stages.

All of the proteins identified adhered to albumin, and their presence or absence in one or more of the no-cancer high risk, stage I cancer and stage III cancer populations are shown in Table 5 below. Biomarkers useful for providing diagnoses of ovarian cancer can be chosen from Table 5 as follows: a) Biomarkers that indicate a subject exhibits no cancer can be chosen to include at least an identifiable fragment of each of two or more proteins in Table 5 (such as 3 or more, or 5 or more) that are were identified only in the no cancer population. b) Biomarkers that indicate a subject exhibits ovarian cancer regardless of its stage can be chosen to include at least an identifiable fragment of each of two or more proteins in Table 5 (such as 3 or more, or 5 or more) that were identified only in either or both of Stage I and Stage III populations, and can further include fragments of proteins in Table 5. c) Biomarkers that indicate a subject exhibits Stage I ovarian cancer can be chosen to include at least an identifiable fragment of each of two or more proteins in Table 5 (such as 3 or more, or 5 or more) that were identified only in the Stage I population. d) Biomarkers that indicate a subject exhibits Stage III ovarian cancer can be chosen to include at least an identifiable fragment of each of two or more proteins in Table 5 (such as 3 or more, or 5 or more) that were identified only in the Stage III population.

Table 5 Protein Stage I Stage III Ascession # Protein NO Cancer Cancer Cancer P02763 Alpha-1-acid glycoprotein 1 precursor X X X P 19652 Alpha-1-acid glycoprotein 2 precursor X X X Alpha-1-antichymotrypsin precursor P01011 fragment x x x P01009 A1 ha-1-anti sin recursor fra ment x x x P02760 AMBP protein precursor x x x NP 000020 Angiotensinogen precursor fragment x x x P01008 Antithrombin-III precursor fragment x x x NP 000030 A oli o rotein A-I recursor fragment x x x NP 000030 Apolipoprotein A-11 precursor fragmentxxx P04114 Apolipoprotein B-100 precursor X X X P02654 Apolipoprotein C-I precursor x x xX X PO2655 Apolipoprotein C-II precursor x x x NP 001638 Apolipoprotein D precursor X X X P02649 Apolipoprotein E precursor X X X C13790 Apolipoprotein F precursor X X X Q014791 Apolipoprotein L1 precursor X X X AAH20683 Apolipoprotein M x x x Cadherin EGF LAG seven pass G-type Q9NYQ6 receptor 1 recursor x x x Calgranulin B (Migration inhibitory factor) x x x cAMP-dependent protein kinase, alpha- P 17612 catal ic subunit x x x Cation channel of sperm X X X 043866 CD5 antigen-like precursor fragment x x x P00450 Cerulo lasmin recursor fra ment x x x A41386 Clusterin precursor fragment X X X P01024 Complement C3 precursor X X X P01028 Complement C4 precursor x x x P01031 Complement C5 precursor X X X P13671 Complement component C6 precursor X X X Complement component C8 gamma P07360 chain precursor X X X P00751 Complement factor B precursor X X X Complement factor H-related protein 1 C03591 precursor X X X P05156 Complement factor I precursor X X X P82279 Crumbs protein homolog 1 precursor X X X PO 1034 Cystatin C precursor X X X P02751 Fibronectin precursor X X X 075636 Ficolin 3 Precursor x x x P14136 Glial fibrillary acidic protein, astrocyte X X X Haptoglobin-1 precursor X X X P00739 Ha to lobin-related rotein recursor x x x P01922 Hemoglobin alpha chain X X X P02023 Hemoglobin beta chain X X X Hemopexin precursor (Beta-lB- P02790 glycoprotein) x x x NP 002103 Histidine decarboxylase X X X P01876 Ig alpha-1 chain C region x x x P01857 Ig gamma-1 chain C region X X X G2HU Ig gamma-2 chain C region x x x Ig gamma-3 chain C region (Heavy chain P01860 disease protein) x x x P01766 Ig heavy chain V-III region BRO x x x P01834 Ig kappa chain C region x x x K 1 HUAG Ig kappa chain V-I region AG x x x K 1 HUAR Ig kappa chain V-I region CAR x x x K1HUDEIg kappa chain V-I region DEE x x x K 1 HUOU Ig kappa chain V-1 region OU x x x K2HUCM Ig kappa chain V-11 region Cum x x x K2HUML Ig kappa chain V-II region MIL x x x K3HUSI Ig kappa chain V-III region SIE x x x Ig kappa chain V-III region VG precursor K3HUVG (Fragment) x x x L2HU Ig lambda chain C regions x x x P80748 Ig lambda chain V-III region LOI x x x P01717 Ig lambda chain V-IV region Hil x x x Ig mu heavy chain disease protein x x x Inter-alpha-trypsin inhibitor heavy chain P19827 H1 Precursor x x x Inter-alpha-trypsin inhibitor heavy chain P 19823 H2 Precursor x x x Inter-alpha-trypsin inhibitor heavy chain O 14624 H4 Precursor x x x 096FN5 Kinesin-like protein KIF12 x x x Kininogen Precursorxxx NP 000230 Lysozyme C precursor x x x Mediator of RNA polymerase II 096G25 transcription subunit 8 homolog x x x mitochondrial trifunctional protein beta AAB09424 subunit x x x Myosin-reactive immunoglobulin heavy AAD56279 chain variable region x x x Myosin-reactive immunoglobulin light AAD56280 chain variable region x x x S33414 NuMA protein x x x BAA95316 Organic anion transporter 4 x x x Pigment epithelium-derived factor P36955precursor (PEDF) x x x P02753 Plasma retinol-binding protein Precursor x x x P02775 Platelet basic protein precursor (PBP) x x x Ql 6378 Proline-rich protein 4 precursor x x x 016513Protein kinase C-like 2 x x x P00734 Prothrombin precursor x x x P02787 Serotransferrin precursor (Transferrin) x x x P02768 Serum albumin Precursor x x x P35542 Serum amyloid A-4 protein precursor x x x Q15165 Serum paraoxonase/arylesterase 1 x x x P04278 Sex hormone-binding globulin Precursor x x x Similar to RIKEN cDNA 9230116B18 AAH29526 gene x x x P05452 Tetranectin precursor x x x P02766 Transthyretin recursor (Prealbumin) x x x AAK28066 Truncated beta-globin x x x P02774 Vitamin D-Bindin rotein Precursor x x x P25311 Zinc-alpha-2-glycoprotein Precursor x x x 130-kDa phosphatidylinositol 4,5- Q2E biphosphate-dependent ARFI x 6-phosphofructo-2, 6-bisphosphatase 3 x Q9H2U9 ADAM 7 precursor x CAC17767 Adaptor molecule-1 x P55196 AF-6 rotein x AAH35025 ALMS1protein x Alpha 1C adrenergic receptor isoform 2 BAA06901 (band #23) x Alpha-fetoprotein enhancer binding O15911 protein x Ankyrin repeat and SOCS box containing Q8WXK3 protein 13 x AAC99992 Anthracycline-associated resistance ARX x APOC protein x Atrial natriuretic peptide clearance P17342 receptor precursor x A-type potassium channel modulator AAF33682 protein 1 x CAC26840 BA489M19. 1 (Retinal cadherin 4 type 1) x P53004 Biliverdin reductase A precursor x Brain tumor associated protein NAG14 x Breast cancer type 2 susceptibility protein x AAL76077 BRG1-binding protein ELD/OSAI x NP 005095 Bromodomain-containing protein 2 x CAB56188 C380A1.2.1 P05109 Calgranulin A x AAN86031 CDC42-binding protein kinase beta x CDK5 regulatory subunit associated protein 1 P06493 Cell division control protein 2 homolog x AAH29760 CGI-77 protein x AAL37427 Ciliary dynein heavy chain 7 x P53618 Coatomer beta subunit x NP 000055 Complement component 3 precursor x 075881 Cytochrome P450 7B1 x AAC19129 Cytohesin binding protein HE x NP 065097 Deleted in azoospermia 3x AAL18349 Dermcidin precursor 0 Q9BY27 DGCR6L protein x NP 004708 Diacylglycerol kinase, iota x AAN11306 DNA strand-exchange protein SEP1 x DNA (cyotsin-5)-methyltransferase 3B x NP 003965 Docking protein 2 x AAH11755 Down syndrome critical region protein 2 x CAA07243 DRIM protein x AAH62545 Dual Specificity protein phosphatase 5 x Dynamin-like 120kDa protein, mitochondrial precursor x NP 003557 Early endosome antigen 1x NP 001979 Envoplakin x F-box/LRR-repeat protein 2-like x F-box/WD-repeat protein 2-like x BAB84982 FLJ00229 protein x BAC03400 FLJ00327 protein (band #17) x AAC50405 FRAP-related protein x Gag protein x NP 004868 Glia derived nexin precursor x NP 004868 Glia maturation factor gamma x Q9NZWO Golgi autoantigen, golgin-67 x NP 006588 Heat shock 70kDa protein 8 isoform 1 x PI 1 142 Heat shock cognate 71 kDa protein x HECT domain protein x P02753 Human Plasma retinol-binding protein x Hydroxymethylglutaryl-CoA synthase, mitochondrial precursor x S69339 Ig heavy chain V region precursor x S16833 kappa chain V-III region x Ig lambda chain V-I region x S14683 Ig mu chain precursor, membrane-bound x AAQ 13404 Importinalpha-3subunitx Inflammation-related G protein-coupled NP 065103 receptor EX33x Insulin-like growth factor binding protein P17936 3 precursor x Insulinoma-glucagonoma protein 20 pro- AAL40266 poptotic splice variant x 96P 7 Kelch-like protein 5 x NP 660358 Lactation elevated 1 x P11047 Laminin amma-1 chain precursor x Leucine-rich transmembrane protein FLRT3 precursor x NP 663731 Lipin 1 x AAD41257 LRR FLI-I interacting protein 2 x P11117 Lysosomal acid phosphatase precursor x Minor capsid protein L2 x Mitochondrial 28S ribosomal protein S29 x Mitogen-activated protein kinase kinase Q9Y6R4 kinase 4 x AAB71758 Multidrug resistance-associated protein 5 x Myosin VA (heavy polypeptide 12, NP 000250 myosin) x NP 115997 Myosin XVIIIB x AAB01380 NADP-dependent malic enzyme x Q9ULB1 Neurexin 1-alpha precursor x AAG 17027 Ninein centrosomal protein x AAF22014 PRO 1659 x Probable phosphodiesterase I x Q 15084 Protein disulfide isomerase A6 precursor x Protein tyrosine phosphatase, non- AAH10674 receptor type 4 x QY513 Protocadherin alpha 1 precursor x Protocadherin gamma All short form AAD43766 protein x Putative ATP-binding cassette Q9NSE7 transporter C13 x Pyruvate dehydrogenase E1 component P29803 alpha subunit, testis specific x Rho-associated, coiled-coil containing AAB02814 protein kinase l60ROCK x CAA09009 Ribosomal protein kinase B x Q9BZR9 RING finger protein 27 x AA065167 Sarcoma antigen NY-SAR-24 x 9UDX4 SEC14-like protein 3 Sialic acid binding Ig-like lectin-like 1 Q96PQ1 precursor x Signal transducer and activator of P42224transcription 1-alpha/betax Similar to Riken cDNA 4921522K05 gene x Similar to sperm specific antigen 2 x Similar to synaptotagmin x Q92673 Sortilin-related receptor precursor x 095718 Steroid hormone receptor ERR2 x SWI/SNF-related, matrix associated, actin-dependent regulator x Q9BT88 Synaptotagmin XI x AAB62679 Talin-related protein x Talloid-like protein Tast receptor type 2 member 16 x NP 005870 TRAF-interacting protein x P07477 T sin I recursor x P35030 T sin III recursor x P02248 Ubiquitin x Ubiquitin carboxyl-terminal hydrolase P09936 isozyme L1 x Ubi uitin-con'u atin enz e x WeRNer syndrome related WRN-I (human) x Williams-Beuren syndrome chromosome Q9NP71 region 14 protein x AAH63818 Zinc finger protein 273 x 006730 Zinc finger protein 33A x 1-phosphatidylinositol-4, 5-bisphosphate P16885phosphodiesterase gammax P35998 26S protease regulatory subunit 7 x Actin-related protein 2/3 complex Q92747 subunit 1A (SOP2-like protein) x P25054 Adenomatous polyposis coli protein x NP 056234 Adlican x Q9UM73 ALK tyrosine kinase receptor precursor x P35475 Alpha-L-iduronidase precursor x AAC26019 Alpha-tectorin x P20073 Annexin A7 x P46013 Antigen KI-67 x Apolipoprotein A-IV precursor (Apo- P06727 AIV) x NP 877956 Apolipoprotein B48 receptor x ATP-binding cassette, sub-family A NP 001080 member 3 x CAC78758 BA1821. 21. 1 x Baculoviral IAP repeat-containing Q13075 protein 1 x AAC04509 BAF57 x BAG-family molecular chaperone 99933 re ulator-1 x 013323 Bc1-2 interacting killer x Beta-2-glycoprotein I precursor P02749 (Apolipoprotein H) x Q9H1M4 Beta-defensin 127 precursor x AAM81211 BLOCK 18 x Brain specific angiogenesis inhibitor 1 NP 001693 precursorx Brain-muscle-ARNT-like transcription AAL, 50340 factor 2b x Breast cancer antigen NY-BR-1 x Bromodomain adjacent to zinc finger O9UTF9 domain 2A x Q9NPP4 Caspase recruitment domain protein 12 x 09YSK6 CD2-associated protein x CDK5 regulatory subunit associated Q96SN8 protein 2 x AAC02429 Cell matrix adhesion regulator variant x Q9BV73 Centrosomal protein 2 x S69275 cGMP-gated cation channel beta subunit x CHORD containing protein-1 x 96RL7 Chorein x Chromodomain helicase-DNA-binding 012873 protein 3 x Q9NYC9 Ciliary dynein heavy chain 9 x P00451 Coagulation factor VIII precursor x Collagen alpha 1 (V) chain precursor x 099715 Collagen alpha 1 (XII) chain precursor x Complement Clq tumor necrosis factor- Q9BXJ1 related protein 1 precursor x P 10643 Complement component C7 precursor x Contactin associated protein-like 3 09BZ76precursorx AAF01498 CoREST rotein x Cystic fibrosis transmembrane P 13569 conductance regulator x P05181 Cytochrome P450 2E1 P98187 Cytochrome P450 4F8 x CAA76730 C okeratin e II x NP 079216 Dedicator of cyotkinesis protein 5 x P15924 Desmoplakin x 9P1A6 Disks large-associated protein 2 x AAF91408 Disrupter of silencing SAS10 x CAC34532 DJ1099D15. 3. 1 x CAC13165 DJ927M24. 2 x DNA replication licensing factor MCM6 x AAN11306 DNA strand-exchange protein SEP1 x P11532 Dystrophin x 09Y6C2 EMILIN precursorx NP 009208 Exocyst complex component Sec6x BAB86868 Fat3 x BAB84905 FLJ00150 protein x BAB84908 FLJ00153 protein x NP 071441 Fructosamine-3-kinase x XP 039515 G2 rotein x NP 000144 Galactocerebrosidase precursor x P48058 Glutamate receptor 4 precursor x NP 005042 Glutaminyl-tRNA synthetase x Glycylpeptide N-tetradecanoyltransferase NP 004799 2 x 075343 Guanylate cyclase soluble, beta-2 chain x P00738 Haptoglobin precursor x HBV pX associated protein 8 large AAK57515 isoform x P34932 Heat shock 70kDa protein 4 x AAF66640 Heat shock protein HSP60 x AAK37554 Hemoglobin alpha-1 globin chain x P02042 Hemoglobin delta chain x P02100 Hemoglobin epsilon chain x Hemoglobin gamma-A and gamma-G P02096 chains x P50219 Homeobox protein HB9 x HSV1 binding protein x P38567 Hyaluronidase PH-20 precursor x P01825 Ig heavy chain V-11 region NEWM x P01765 Ig heavy chain V-III region TIL x P01602 Ig kappa chain V-I region HK102 x Kl l-TKA Ig kappa chain V-I region Ka x Insulin-like growth factor binding protein P35858 complex acid labilechain x 075578 Integrin alpha-10 precursor x Intercellular adhesion molecule-5 AAH26338 precursor x NP 055154 InterIeultin-17 recursor x Interleukin-5 receptor alpha chain 001344precursorx AAF37278 Intestinal cell kinase x Q9HDC5 Junctophilin 1 x BAB21822 KIAA1731 rotein x Kinase suppresor of ras-1 x P48634 Large proline-rich protein BAT2 x Leucine-rich repeat transmembrane Q9NZU1 protein FLRT1 precursor x Leukemia inhibitory factor receptor P42702 precursor x P08547 LINE-1 reverse transcriptase homolog x AAC26102 Liprin-alpha4 x P12524 L-myc-1 proto-oncogene protein x Loss of heterozygosity 11 chromosomal 000534 region 2 gene A protein x Low-density lipoprotein receptor-related 007954protein 1x AAD41258 LRR FLI-I interacting protein 1 x Q9698 Lysosomal trafficking regulator x 015151Mdm4 protein (p53 binding protein) x 015479 Melanoma-associated antigen B2 x BAB39741 Membrane-type mosaic serine protease Molybdenum cofactor biosynthesis Q9NZB8 protein 1 B x NP 705832 Myocardin x Q06413 Myocyte-specific enhancer factor 2C x Myosin heavy chain, skeletal muscle, Q9UKX3 extraocular x Myosin heavy chain, skeletal muscle, Q9Y623 fetal x Q92614 Myosin XVIIIA x Neural Wiskott-Aldrich syndrome 000401 protein x P22894 Neutrophil collagenase precursor x AA088243 NIMA-related kinase 12a x G02453 NN8-4AG (Fragment) x AAG44474 NPD013 x CAA58676 Nuclear protein x 075376Nuclear receptor co-repressor 1x Q8NH79 Olfactory receptor 6X1 x 09LJNA1OIigophrenin-1 like proteinx Osmotic stress protein 94 (Heat Shock 095757 70-related protein) x P400 SW12/SNF2-related protein x NP 443071 Pannexin2x 095613 Pericentrin 2 x Q9Y6VOPiccolo protein (Aczonin) x BAA09435 Plakoglobin x Platelet basic protein precursor (PBP) P02775 (Small inducible cytokine B7) x Potassium-transporting ATPase alpha P20648 chain 1 x Q96SM3 potential carboxydase X precursor x Potential phospholipids-transporting Q9Y2QO ATPase IAx Presynaptic protein SAP102 (synapse 92796 associated protein 102) x AAF69651 PR02760 x Probable G protein-coupled receptor x NP 006337 Progesterone-induced blocking factor 1 x 096MY7 Protein C14orf44 x Protein C21 orf63 precursor (Portein P58658 PRED34) (SUE21) x AAH65258 Protein KIAA0052 x Protein kinase C and casein kinase FO substrate in neurons protein 2 x 002156Protein kinase C, epsilon type x 094855 Protein transport protein Sec24D x AAK51611 Protocadherin-psi2 x Proton/amino acid transporter 2 x AAB60393 Proto-oncogene tyrosine-protein kinase x CAB97249 putative organic anion transporter x putative RNA binding protein x pyruvate carboxylase, mitochondrial P 11498precursor x Rab proteins geranylgeranyltransferase component x Receptin x CAC86116 Retinitis pigmentosa GTPase regulator Nu 065816 retinoblastoma-associated factor 600 x P52565 Rho GDP-dissociation inhibitor 1 x P78345 Ribonuclease P protein subunit p38 x AAH24208 RNA-binding region containing protein 4 x ryanodine receptor 3 (Brain type NP 001027 ryanodine receptor) x Sak x 000506 Serine/threonine protein kinase 25 x Serine/threonine protein phosphatase 014830 with EF-hands-2 x Signal transducer and activator of 14765 transcri tion 4 x Similar to alanine-glyoxylate aminotransferase 2-like 1 x XP 351299 similar to calcium binding XP 295263 similar to growth arrest-specific 2 x Similar to h othetical rotein FLJ10204 x Similar to nuclear mitotic apparatus protein 1 x similar to outer dense fiber of sperm tails 2 x similar to RIKEN cDNA 1110058L19 x Similar to RIKEN cDNA 1200015K23 gene x Similar to RIKEN cDNA 1500041N16 x Similar to RIKEN cDNA 4921508011 gene x Similar to RIKEN cDNA 6430514L14 gene x Similar to SET domain and mariner transposase fusion gene x AAH44948 Similar to synaptotagmin 6 x AAK31796 SLIT1 isoform B x small muscular protein (Stretch 091JHP9 responsive skeletal muscle protein) x 12766 SMF rotein (Fragment x 9UHW9 solute carrier family 12 member 6 x Q9H3E2 Sorting nexin 25 x squamous cell carcinoma antigen AAF00087 recognized by T cell x AAH01958 SRp25 nuclear protein 060224 SSX4 protein x Sterol regulatory element binding protein O 12770 cleavage-activating x Q8WXE9 Stonin 2 (stoned B) x P48061 Stromal cell-derived factor 1 precursor x CAA91947 Tenacin-R (restrictin) x Q9UQP3Tenascin N precursor x AAK96045 Testicular soluble adenylyl cyclase x AAK31980 Testis protein TEX14 x P42166 Thymopoietin, isoform alpha (TP alpha) x CAD 12456 Titin x BAA25914 Trad x Transcription elongation factor B polypeptide 1 (RNA polymerase II O 15369 transcrption) _ x P41225 Transcription factor SOX-3 x Transcription factor-like protein 4 (Max- Q9UH92 like bHLHZip protein) x Transforming growth factor-beta induced 15582 rotein IG-H3 recursor x AAC67491 Transmembrane receptor UNC5Cx P22102 Trifunctional purine biosynthetic protein x adenosine-3 AAG50168 Triparite motif protein TRIM29 beta x P19429 troponin I, cardiac muscle x NP 036275 Tuftelin-interacting protein 11 x Tumor associated calcium signal transducer 2 precursor (Pancreatic P09758 carcinoma) x Tumor associated hydroquinone (NADH) 16206 oxidase tNOX x Tumor-associated calcium signal transducer 1 precursor (major P16422 gastrointestinal) x Tumor-associated hydroquinone oxidase x Tyrosine-protein kinase CSK x P08579 U2 small nuclear ribonucleoprotein B"x NP 695012 Ubiquitin-activating enzyme E1 x UDP-glucuronosyltransferase 1-1 P22309 Precursor, microsomal x P06132 Uroporphyrinogen decarboxylase x Vascular non-inflamatory molecule 2 095498 recursor x P07225 Vitamin K-dependent protein S precursor x P04275 Von Willebrand factor precursor x AAK38745 WD repeat membrane protein x O9HBG6 WD repeat protein 10x WUGSC: H 2G3A. 1 protein x WUGSC: H DJ1086D14. 2 proteinx AAG02123 XPMC2H x zinc finger and BTB domain containing 9Y2K1 protein 1 x zinc finger homeobox protein lb (small 060315interacting protein 1) x Q9UJW8 Zinc finger protein 180 x NP 066966 Zinc finger protein 20x O9NYT6 zinc finger protein 226 x 094822 Zinc finger protein 294 x NP 065127 Zinc finger protein SBZF3 x 26S proteasome-associated padl NP 005796 homolog x 39S ribosomal protein L40, 09NO50 rnitochondrial precursor x NP 036387 5'-3'exoribonuclease 2 x 01553 5'-TG-3'interacting factor x 5-methyltetrahydrofolate-homocysteine methyltransferase x NP 000977 60S Ribosomal protein L24 x 9BYD2 60S ribosomal protein L9, mitochondrial x precursor AAP13576 ABC A13 x P03996 Actin, aortic smooth muscle x Q9UKQ2 ADAM 28 precursor x 01718 Adrenocorticotro ic hormone receptor x Amyloid lambda 6 light chain variable AAK58586 region PIP (Fragment) x Q96Q91 Anion exchange protein 4 x Ankyrin 2 x Ankyrin repeat and SOCS containing NP 057234 Protein 2x Attractin precursor x Autoantigen (fragment) x CAC08001 BA13B9. 3 x Beta-1,4 N- NP 703147 acetylgalactosaminyltransferase x P42025 Beta-centractin x 005682 Caldesmon x Cancer associated SCM-recognition AAB24775 IMMUNEDEFENSE-suppressing and x NP 001729 Carbonic anhydrase I x cAMP/cGMP cyclic nucleotide CAC15567 phospodiesterase 11A3 x Complement Clq subcomponent, C P02747 chain precursor x P00746 Complement factor D precursor x P02741 C-reactive protein precursor x Q9UER7 Death domain-associated protein 6 x Q9BZ29 Dedicator of cytokinesis 9 x NP 056111 Dedicator of cytokinesis protein 4 x DGCR6L protein (DiGeorge syndrome Q2BY27 critical region 6-like) x 075417 DNA polymerase theta x 13202 Dual s ecifici rotein hos hatase 8 x Q14203 Dynactin 1 x 060832 Dyskerin (Nucleolar protein NAP57) x Electron transfer flavorpotein alpha- P 13804 subunit, mitochondrial precursor x Eukaryotic translation initiation factor 3 NP 003744 subunit 7 x 096Al 4 Far upstream element binding protein 1 x P35556 Fibrillin 2 precursor x 15485 Ficolin 2 precursor x BAB15716 FLJ00015 protein x BAB 15748 FLJ00050 protein x BAB84910 FLJ00155 protein x AAN64909 Forkhead box protein D4 x Gamma-aminobutyric-acid receptor P48169 alpha-4 subunit precursor x 14392 GARP Protein precursor x P09211 Glutathione S-transferase P x Golgi autoantigen, golgin subfamily A 08378 member 3 x AAK13251 GTX x 043593 Hairless protein x Q8WUI4 Histone deacetylase 7a x P28001 Histone H2A. a x HLA class II histocompatibility antigen, P01907 DQ(5) alpha chain precursor x Q14774 Homeobox protein HLX1 x 075330 Hyaluronan mediated motility receptor x PN0446 Ig kappa chain V-11 region precursor x Ig kappa chain V-1 region Walker P04431 precursor x Ig kappa chain V-11 region NEWM x P01617 Ig kappa chain V-II region TEW x P01714 I lambda chain V-III region SH x P06889 Ig lambda chain V-IV region MOL x NP 653247 Immunoglobulin J chain x Inter-alpha-trypsin inhibitor heavy chain 006033 H3 precursor x Interleukin-13 receptor alpha-1 chain P78552 precursor x Kinael x P25391 Laminin alpha-1 chain precursor x P29536 Leiomodin 1 x AAM21562 LTRPC6 channel kinase 2 x 08N3R9 MAGUK p55 subfamily member 2 x Mannan-binding serine protease 2 000187 precursor x 015481 Melanoma-associated antigen B4 x Metaloprotease 1 x AAM77722 Midasin x AAH25404 MRG2 x 09Y411 Myosin 5a (heavy chain) x Myosin heavy chain, cardiac muscle P 13533 alpha isoform x Myosin heavy chain, skeletal muscle, P12882 adult 1 x Myosin light chain alkali x P51606 N-acylglucosamine 2-epimerase x 015259 Ne hrocystin 1 x P56730 Neurotrypsin precursor x Neutrophil gelatinase-associated P80188 lipocalin precursor x CAA09496 NPIK-C protein x Q8N448 Numb-binding protein 2 x CAD92564 NYD-TSPG protein x CAC85755 Obscurin x ovarian cancer related tumor marker AAL65133 CA125 x AAL32459 PARX protein Phosphatidylinositol 3-kinase regulatory 000459 beta subunit x P22352 Plasma glutathione peroxidase precursor x Potential phospholipids-transporting ATPase VD x PRO x Q9NOXl PR-domain zinc finger protein 5 x P20742 Pregnancy zone protein precursor x 09HD20 Probable cation-transporting ATPase 2 x AAH57828 Profilin I x 016378Proline-rich protein 4x Protein associated with Mycx Q8WUT4 protein C20orf75 precursor x 9Y5E3 Protocadherin beta 6 precursor x Q9Y5G6 Protocadherin gamma A7 precursor x putative T7-like mitochondrial DNA AAK69558 helicase x putative zinc finger protein x AAL93215 Rabconnectin x 013671Ras and Rab interactor 1 x Renal Sodium-dependent phosphate Q transport protein 2 x AAN86963 Retinitis pigmentosa l-like 1 protein x NP 004155 Retinol-binding protein II, cellular x Rotavirus'X'associated non-structural protein x Q14690 RRP5 protein homolog x Q8WXD2 Secretogranin III precursor x Serine/threonine protein phosphatase 2A, Q14738 56 kDa regulatory subunit x NP 036422 Serine/threonine-proteinkinaseChkl x P02735 Serum amyloid A protein precursor x Q 15165 Serum paraoxonase/arylesterase 2 x NP 116024 Seventransmembrane-domain protein x BAC 11707 SF21 protein Similar to NICE-5 protein x Similar to nuclear factor x Similar to RIKEN cDNA 2010109H09 AAH40620 gene x Similar to RIKEN cDNA A330102H22 gene x Similar to threonine synthase x Similar to tudor repeat association with PCTAIRE 2 x Stress-70 protein, mitochondrial P38646 precursor x Stress-activated map kinase interacting AAR07950 protein 1 x Structural maintenance of chromosome Q8NDV3 1-like 2 protein x Sucl-associated neurotrophic factor target x AAP35597 Sulfotransferase 1CI x AAC50693 SWI/SNF complex 155 kDa subunit x Q15431 Synaptonemal complex protein 1 x XP 166453 Tau-tubulin kinase x T-cell receptor alpha chain V region P01737 PY14 precursor x P07996 Thrombospondin I precursor x 014106 Tob2 protein x Q9UBU8 Transcription factor-like protein MRG15 x 000403Transcription initiation factor IIB x P19429 Troponin I, cardiac muscle x 015350 Tumor Protein p73 x 0911267 Vacolar sorting 33B x Vascular endothelial growth factor receptor 1 precursor x Vasopressin-activated calcium- AAH63306 mobilizing receptor x AAL30811 Very large G protein-coupled receptor lb x XP 091895 Zinc finger protein x allograft Inflamatory factor-1 isoform G I x x P01023 Alpha 2 macroglobulin precursor x x AAK92217 Axonemal dynein heavy chain DNAH5 x x NP 060009 Axonemal dynein heavy chain type 3 x x P01884 Beta-2-microglobulin precursor x x 9Q 6R11 Bile acid receptor x x P22792 Carboxypeptidase N 83 kDa chain x x NP 055625 centrosome-associated protein 350 x x P06396 Gelsolin precursor, plasma x x Golgi autoantigen, golgin subfamily A O 13439 member 4 x x Guanine nucleotide-binding protein Q14344 alpha-13 x x P01861 Ig gamma-4 chain C region x x KIHULY Ig kappa chain V-I region Lay x x K 1 HUNY Ig kappa chain V-I region Nix x K3HUB6 Ig kappa chain V-III region B6 x x Ig kappa chain V-III region NG9 K3HUNG precursor xx Ig kappa chain V-IV region precursor P06312 (Fragment) x x P35568 Insulin receptor substrate 1 x x AAB09089 megakaryocyte stimulating factor x x AAL33801 Nesprin 2 x x P05155 Plasma protease Cl inhibitor precursor x x Probable ribosome biogenesis protein O92979 NEP I x x 096MU7 Putative splicing factor YT521 x x Replication protein x x NP 061918 Similar to rab l-binding protein x x V (D) J recombination activating protein 1 x x P04004 Vitronectin precursor x x CAB97494 Zinc finger protein Cezanne x x A54906 Afamin precursor x x WOHU Alpha-2-HS-glycoprotein precursor x x P01023 Alpha-2-macroglobulin precursor x x P06727Apolipoprotein A-IV precursorxx Baculoviral IAP repeat-containing Q9NR09 protein 6 x x P02749 Beta-2-glycoprotein I precursor x x KFHU5 Coagulation factor V precursor x x 1361G Cullin homolo 1 x x Extracellular glycoprotein lacritin AAG32949 precursor x x AAH63579 FLJ11457 protein x x NP 071441 Fructosamine-3-kinase x x P01625 Ig kappa chain V-IV region Len x x P01700 Ig lambda chain V-I region HA x x Leucine-rich alpha-2-glycoprotein P02750 precursor x x AAF62402 Microfibrillar protein 2 x x BAA77625 Organic-cation transporter like 3 x x P00747 Plasminogen precursor x x 000888Pregnancy-specific beta-1-glycoprotein 4xx precursor CAC32991 Putative protein kinase WNK4 x x Receptor-type protein-tyrosine Q13332 phosphatase S precursor x x AAH07058 Serum amyloid P-component precursor x x Similar to FLJ00226 protein x x 16206 Tumor associated hydroquinone oxidase x x P08631 Tyrosine-protein kinase HCK x x UDP-glucuronosyltransferase 1-4 P22310 Precursor, microsomal x x CAA75068 1 C7f x x P02570 actin, cytoplasmic 1 x x P02656 Apolipoprotein C-III precursor x x Betaine-homocysteine S- 093088 methyltransferase x x Canalicular multispecific organic anion Q92887 transporter 1 x x 02224 Centromeric rotein E x x Q8TCT0 Ceramide kinase x x Complement Clq subcomponent, B Tao67338 chain precursor x x P08603 Complement factor H precursor x x Complement factor H-related protein 2 P36980 precursor x x BAB15722 FLJ00022 protein x x BAC03445 FLJ00385 protein x x Glyceraldehyde 3-phosphate NP 055179 dehydrogenase, testis specific x x NP 004481 Growth factor receptor-bound protein 14 x x Ha to lobin-2 recursor x x P02278 Histone x P01611 Ig kappa chain V-I region Wes x x P06312 kappa chain V-IV region precursor x x 15811 Intersectin 1 x x Kinesin-like protein K1F13B x x LIM/homeobox protein Lhx3 x x mRNA clone with similarity to L- glycerol-3-phosphate : NAD oxidoreducer x x Multiple inositol polyphosphate AAD02438 phosphatase x x Q9BYT8 Neurolysin, mitochondrial precursor x x Oxidored-nitro domain-containing NP 659484 protein x x 013371 Phosducin-like protein x x Plexin-B1/SEP receptor precursor x x Potassium channel subfamily K member 09H427 5 x x 9Y2J8 Protein-ar inine deiminase e II x x BAC05705 Seven transmembrane helix receptor x x Similar to serine/threonine kinase x x AAF86287 Tolloid-like rotein x x Transcription elongation factor B Q 15369 polypeptide I x x P01375 Tumor necrosis factor precursor x x 043829Zinc finger protein 161 homolog x x

Example 5-Prostate Cancer Biomarkers This example demonstrates that the disclosed method can detect serum biomarkers for prostate cancer. The sera of subjects in each of a prostate cancer population and a no cancer population (hospital control) were pooled and analyzed as in Example 4, except that potential biomarkers adhered to all serum proteins of MW > 25 kDa were separated from the pooled sera samples, and potential protein and protein fragment biomarkers were eluted therefrom and identified.

The results of this analysis are shown in Table 6. Useful biomarkers for prostate cancer can be selected to include at least an identifiable fragment of each of two or more proteins in Table 6 (such as 3 or more, or 5 or more) that were found only in the prostate cancer population. Useful biomarkers for the absence of prostate cancer can be selected to include at least an identifiable fragment of each of two or more proteins in Table 6 (such as 3 or more, or 5 or more) that were found only in the no prostate cancer population.

Table 6 Prostate Cancer prostate cancer hospital control Afamin precursorX X Alpha-1-acid glycoprotein 1 precursor X X Alpha-1-acid glycoprotein 2 precursor X X Alpha-l-antichymotrypsin precursor X X Alpha-l-antitrypsin precursorXX Alpha-IB-glycoprotein precursor XX Alpha-2-antiplasmin precursor X X Alpha-2-HS-glycoprotein precursor X X Alpha-2-macroglobulin precursor X X Angiotensinogen precursor XX Antithrombin-III precursorX X Apolipoprotein A-Iprecursor X X A oli o rotein A-II recursor X X Apolipoprotein A-IV precursor X X Apolipoprotein B-100 precursor x x A oli o rotein C-I recursor X X Apolipoprotein C-II precursor Apolipoprotein C-III precursor Apolipoprotein D precursor x x Apolipoprotein E precursor X X A oli o rotein L1 recursor X X Apolipoprotein M X X Beta-2-glycoprotein I precursorX X Beta-2-microglobulin precursor x x CDK5 regulatory subunit associated protein 2 X X Cerulo lasmin recursor x x Clusterin precursor x x Complement C3 precursor x x Complement C4 precursor x x Complement C5 precursor xx Complement component C6 precursor x x Complement component C7precursor x x Complement component C8 alpha chain precursor x x Complement factor B precursor X X Complement factor H precursor X X Complement factor I precursor X X Fibronectin precursor X X Gelsolin precursor, plasma X X Ha to lobin recursor X X Haptoglobin-related protein precursor X X Hemoglobin alpha chain X X Hemoglobin beta chain X X Hemoglobin delta chain X X Hemopexin precursor X X Ig alpha-l chain C region x x Ig gamma-1 chain C region X X Ig gamma-2 chain C region X X Ig gamma-3 chain C region X X Ig gamma-4 chain C regions X Ig heavy chain V-III region BROXX Ig kappa chain C region X X Ig kappa chain V-1 region AG X X Ig kappa chain V-1 region CAR X X Ig kappa chain V-I region OU X X Ig kappa chain V-1 region Wes X X Ig kappa chain V-II region Cum X X Ig kappa chain V-III region SIEXX Ig lambda chain C regions Ig lambda chain V-III region SHXX Insulin-like growth factor binding protein complex acid labile chain precursor x x Inter-alpha-trypsin inhibitor heavy chain H2 precursor x x Inter-alpha-trypsin inhibitor heavy chain H4 precursor x x Kininogen precursor Leucine-rich repeat-containing G protein-coupled receptor 6 Lumican precursor x Mitochondrial trifunctional protein beta subunit (Fragment) x x MSTP155 X X Myosin-reactive immunoglobulin heavy chain variable region (Fragment) X X Myosin-reactive immunoglobulin light chain variable region (Fragment) x x N-ace lmuramo 1-L-alanine amidase recursor x x Plasma protease C1 inhibitor precursor x x Plasma retinol-binding protein precursor x x Plasminogen precursor x x Prothrombin precursor x x Serotransferrin precursor x x Serum albumin precursor x x Serum amyloid A-4 protein precursor x x Serumparaoxonase/arylesterase x x I Transthyretin precursor x x Truncated beta-globin x x Vitamin D-binding protein precursor x x Zinc-al ha-2-1 co rotein recursor X X Ig mu chain C region X X Ig mu heavy chain disease protein x x Pigment epithelium-derived factor precursor x x Platelet basic protein precursor | X X Vitronectin precursor x x 24-dehydrocholesterol reductase precursor X Amyloid lambda 6 light chain variable region PIP (Fragment) X ATP-dependent RNA helicase DDX19 x Betaine--homocysteine S-methyltransferase x C4b-binding protein alpha chain precursor x C4b-binding protein beta chain precursor X Calpain 3 x Cancer-associated SCM-recognition IMMUNEDEFENSE-suppressing and serine protease-protecting peptide, CRISPP peptide X Carboxypeptidase N 83 kDa x CD5 antigen-like precursor x Coagulation factor XII precursor x Complement Cl subcomponent, B chain precursor x Crumbs rotein homolo 1 recursor X CUG-BP and ETR-3 like factor 4 X Cystatin C precursory Endothelin-converting enzyme-like 1X F-box/LRR-repeat protein 2-like X Ficolin 3 precursory G protein-coupled receptor kinase GRK4X Hypothetical protein X Hypothetical protein (Fragment) X Hypothetical protein KIAA0350 (Fragment) X l kappa chain V-1 region BAN Ig kappa chain V-1 region DEE X Ig kappa chain V-1 region HK102 precursor (Fragment) X Ig kappa chain V-I region Mev X Ig kappa chain V-II region MIL X Ig kappa chain V-II region TEW X Ig kappa chain V-III region CLL precursor X Ig kappa chain V-III region NG9 precursor (Fragment) X Ig kappa chain V-III region VG precursor................................................... ............. (Fragment) X Ig kappa chain V-IV region Len X Ig kappa chain V-IV region precursor (Fragment) X Ig lambda chain V-III region LOI X Ig kappa chain V-IV region MOL X Immunoglobulin J chain X Immunoglobulin lambda-like polypeptide 1 precursor X Importin beta-3 subunit X Inositol polyphosphate 1-phosphatase X Interleukin-6 precursor X Intersectin 1 X Kinesin-like protein KIF2C X Multiple inositol polyphosphate phosphatase X M otubularin-related rotein 1 X Neuroblast differentiation associated protein AHNAK X P53 inducible protein X Platelet factor 4 variant precursory Pol A) ol erase amma X Protocadherin amma A11 recursor X RNA helicase-DEAD box protein RH 116 X TBC1 domain family member 5 X Vitamin K-dependent protein S precursor X Zinc finger protein Cezanne X Ig heavy chain V-III region TRO X Estradiol 17 beta-dehydrogenase 7 X Hypothetical protein FLJ22169 X Hypothetical protein KIAA0205X Hypothetical protein KIAA1064 (Fragment) X Ig heavy chain V-III region HIL X Ig heavy chain V-III region TIL X Ig lambda chain V-I region HA X Ig lambda chain V-VI region WLT X Protein C6orfl30 X Sex hormone-binding globulin precursor X

Example 6-Breast Cancer This example demonstrates that the disclosed method can detect serum biomarkers for breast cancer. The sera of subjects in each of a breast cancer population and a no breast cancer population (hospital control) were pooled and analyzed as in Example 4. The results of this analysis are shown in Table 7. Useful biomarkers for breast cancer can be selected to include at least an identifiable fragment of each of two or more proteins in Table 7 (such as 3 or more, or 5 or more) that were found only in the breast cancer population. Useful biomarkers for the absence of breast cancer can be selected to include at least an identifiable fragment of each of two or more proteins in Table 7 (such as 3 or more, or 5 or more) that were found only in the no breast cancer population.

Table 7 Breast cancer breast cancer hospital control Afamin precursor X X Alpha-1-acid glycoprotein 1 precursor X X Alpha-1-acid glycoprotein 2 precursor X X Alpha-1-antichymotrypsin precursor X X Alpha-1-antitrypsin precursor X X Alpha-1B-glycoprotein precursor XX Alpha-2-antiplasmin precursor X X Alpha-2-HS-glycoprotein precursor X X Alpha-2-HS-glycoprotein precursor (Fetuin-A) X X Alpha-2-macroglobulin precursor X X Antithrombin-III precursor X X Apolipoprotein A-I precursor X X Apolipoprotein A-II precursor X X Apolipoprotein A-IV precursor X X Apolipoprotein B-100 precursor X X Apolipoprotein C-I precursor X X Aprolipoprotein C-II precursor X X Apolipoprotein C-III recursor X X Apolipoprotein D precursor X X Apolipoprotein E precursor X X Apolipoprotein F precursor X X A oli o rotein L1 recursor X X A oli o rotein M X X Baculoviral IAP repeat-containing protein 6X X Beta-2-glycoprotein I precursorX X Calgranulin BXX Carboxypeptidase N 83 kDa chain _ X X Cerulo lasmin recursor X X Clusterin precursor X X Complement C3 precursorX X Complement C4 precursor X X Complement C5 precursor X X Complement component C6 precursor X X Complement factor B precursor X X Complement factor I precursor X X Crumbs protein homolog 1 precursor X X Cystatin C precursor X X Dedicator of c okinesis rotein S Fra ment X X Dermcidin precursor X X Desmoglein 1 precursor XX Desmoplakin X X Gelsolin precursor, plasma X X Glial fibrilla acidic protein, astroc e X X Haptoglobin precursorXX Haptoglobin-1 Precursor X X Ha toglobin-related rotein recursor X X Hemoglobin alpha chain X X Hemoglobin beta chain X X Hemopexin precursor X X Ig alpha-1 chain C region x x Ig gamma-1 chain C region X X Ig gamma-2 chain C region X X Ig gamma-3 chain C region X X Ig heavy chain V-III region BROXX Ig kappa chain C region x x Ig kappa chain V-I region AG X X Ig kappa chain V-I region CARXX Ig kappa chain V-I region DEEXX Ig kappa chain V-I region OUX X Ig kappa chain V-II region MIL X X Ig kappa chain V-III region SIE XX Ig kappa chain V-IV region Precursor X X Ig lambda chain C regions X X Ig mu chain C region x x Ig mu heavy chain disease protein X X Immunoglobulin J chain X X Insulin-like growth factor binding protein complex acid labile chain precursorX X Inter-alpha-trypsin inhibitor heavy chain H1 Precursor X X Inter-alpha-trypsin inhibitor heavy chain H2 X X Inter-alpha-trypsin inhibitor heavy chain H4 precursor X X Kininogen precursor X X Lactotransferrin precursor X X Leucine-rich alpha-2-glycoprotein precursor X X Long palate, lung and nasal epithelium carcinoma associated protein 1 precursor X X Mediator of RNA polymerase II transcription subunit 8 homolog X X Mitochondrial trifunctional protein beta subunit X X Myosin-reactive immunoglobulin heavy chain variable region X X Myosin-reactive immunoglobulin light chain variable region X X N-acetylmuramoyl-L-alanine amidase precursor X X Pigment epithelium-derived factor precursor X X Plasma protease C1 inhibitor precursor X X Plasma retinol-binding protein precursor X X Plasminogen preCursor Platelet basic protein precursor X X Serotransferrin precursor X X Serum albumin precursorX X Serum amyloid A-4 protein precursor X X Serum amyloid P-component precursor X X Serum paraoxonase/arylesterase 1X X Sex hormone-binding globulin precursorX X Spectrin alpha chain, brain X X Transcription elongation factor B polypeptide 1 X X Transthyretin precursor X X Truncated beta-lobin X X Vitamin D-binding protein precursorX X Vitronectin precursor X X Zinc-al ha-2-1 co rotein recursor X X Angiotensinogen precursor X X Complement Clq subcomponent, B chain precursor X X Complement component C8 gamma chain precursor X X Complement component C9 precursor X X Complement factor H precursor X X Complement factor H-related protein 1 Precursor X X Complement factor H-related protein 3 precursorX X Fibronectin precursor XX Hemoglobin delta chain X X Ig kappa chain V-1 region Lay X X Ig kappa chain V-1 region Ni X X Ig kappa chain V-11 region Cum X X Ig kappa chain V-III region x x Ig kappa chain V-III region NG9 precursor X X Ig kappa chain V-III region VG precursor X X Ig lambda chain V-I region HAXX Ig lambda chain V-III region LOIXX Ig lambda chain V-IV region Hil X X Prothrombin precursor X X 130-kDa phosphatidylinositol 4,5-biphosphate-dependent X 14-3-3 protein beta/alpha X 14-3-3 protein sigma (stratifin) _ X 160S ribosomal protein L24 (L30) X 60S ribosomal protein L6 X Actin, aortic smooth muscle X Actin, cytoplasmic I X ADAM 7 precursor X Adaptor molecule-1 X AF-6 protein Allograft INFLAMATORY factor-1 isoform G1 X ALMS1 protein X Alpha 1C adrenergic receptor isoform 2 (band #23) X A1 ha-feto rotein enhancer binding protein X AMBP protein precursor X AMPK-related protein kinase 5 X Angiopoietin-related protein 4 precursor X Anion exchange protein 4 X Ankyrin 2X Ankyrin repeat and SOCS box containing protein 13 X Ankyrin repeat and SOCS box containing protein 2 X Annexin A2 X Anthracycline-associated resistance ARX X AP endonuclease XTH2 X Apolipoprotein (a) precursor X Atrial natriuretic peptide clearance receptor precursor X Attractin preucursor X A-type potassium channel modulatory protein 1 X Axonemal dynein heavy chain DNAH5 X Axonemal dynein heavy chain type 3 X BA489M19. 1 (Retinal cadherin 4 type 1) x Basic helix-loop-helix protein 5 X Bcl-2 interacting killer (Apoptosis inducer NBK) X Beta-2-microglobulin Precursor X Bile acid receptor X Biliverdin reductase A precursor X Brain tumor associated protein NAG14 X Breast cancer type 2 susceptibility protein X Brefeldin A-inhibited guanine nucleotide-exchange protein X BRGI-binding protein ELD/OSA1 X Bromodomain-containing protein 2X C380A1. 2.1 (novelprotein (isoform 1)) X Cadherin EGF LAG seven-pass G-type receptor 1 precursor X Calgranulin A X Calmodulin-like skin protein X cAMP-dependent protein kinase, alpha-catalytic subunit X Cancer-associated SCM-recognition IMMUNEDEFENSE- suppressing and serine protease-protecting peptide, CRISPP peptide X Cation channel of sperm (band #23) X Cd 5 antigen-like Precursor X CDC42-binding protein kinase beta X CDK5 regulatory subunit associated protein 1 X Cell division control protein 2 homolog X Centromeric protein E X Centrosome-associated protein 350 X CGI-145 protein X CGI-77 protein X Cholinephosphate cytidylyltransferase A X Cholinesterase precursor X Chromodomain helicase DNA binding protein 5 X Ciliary dynein heavy chain 7 X Coagulation factor V precursorX Coatomer beta subunit X Cornifm B X Corticosteroid-binding globulin precursor X Cullin homolog 1X Cystatin M precursory Cytochrome c oxidase polypeptide Vb, mitochondrial precursor (EC 1.9. 3. 1) X Cytochrome P450 4F8X Cytochrome P450 7B1 X Cytohesin binding protein HE X Cytoskeleton-like bicaudal D protein homolog 2 X Deleted in azoospermia 3 X DGCR6L protein X Diacylglycerol kinase, iota X Disheveled associated activator of morphogenesis 2 X DJ75H8.2 protein x DNA strand exchange protein SEP1 X DNA (cyotsin-5)-methyltransferase 3B X DNA-dependent protein kinase catalytic subunit X Docking protein 2 X Down syndrome critical region protein 2 X DRIM protein x Dual Specificity protein phosphatase 5X Dynamin-like 120kDa protein, mitochondrial precursor X Early endosome antigen 1X Elongation factor 1-alpha 1X Exocyst complex component Sec5 X Exocyst complex component Sec6 X Extracellular glycoprotein lacritin precursory Fatty acid-binding protein, epidermal X F-box/LRR-repeat protein 2-like X F-box/WD-repeat protein 2-like X Fibrillin 2 precursorX Fibrinogen alpha/alpha-E chain precursor [Contains: Fibrinopeptide A] X Fibulin-1 precursory Ficolin 3 precursor X FLJ00327 protein (band #17) X FLJ00385 protein X FLJ11457 protein X FRAP-related protein X Fructosamine-3-kinase X Galectin-7 X Galectin-8 X GC-rich sequence DNA-binding factor X Glia derived nexin precursory Glia maturation factor gamma X Glyceraldehyde 3-phosphate dehydrogenase, liver X Golgi autoantigen, golgin subfamily A member 4 X Golgi autoantigen, golgin-67 X Guanine nucleotide-binding protein alpha-13 X Hairless protein X Heat shock cognate 71 kDa protein X HEJ 1 X Hepatocyte growth factor activator precursor X Histidine decarboxylase X Histone H2A. aX Histone H2B. a/g/k X Histone H4 X HNK-1 sulfotransferase X Ig gamma-4 chain C region X Ig kappa chain V-1 region WEAX Ig kappa chain V-III region B6 X Ig kappa chain V-IV region Len X Importin alpha-3 subunit X Inflammation-related G protein-coupled receptor EX33 X Insulin-like growth factor binding protein 3 precursor X Insulinoma-glucagonoma protein 20 pro-poptotic splice variant X Interleukin-5 receptor alpha chain precursor X Interleukin-6 precursor X IRLB (fragment) X Junction plakoglobin X Kelch-like protein 5 X Kinesin-like protein KIF12 X Lactation elevated 1 X Laminin alpha-4 chain precursor X Laminin gamma-1 chain precursor X Leucine-rich glioma-inactivated protein 1 precursor (Epitempin 1 X Leucine-rich transmembrane protein FLRT3 precursor X Lipin 1 X Liprin-alpha 2 X Liprin-betal X LRR FLI-I interacting protein 2 X Lumican precursor (Keratan sulfate proteoglycan lumican) X Lysosomal acid phosphatase precursor X MAC-2 binding protein precursor X Megakaryocyte stimulating factor X Melanoma-associated antigen 9 (MAGE-9) X Melastatin 1 X Methyl-CpG-binding protein 2 X Microfibrillar protein 2 X Mina53 form-3 X Mitochondrial 28S ribosomal protein S29 X Mitogen-activated protein kinase kinase kinase 4 X Multidrug resistance-associated protein 5 X Myosin XVIIIB X NADP-dependent malic enzyme X Nebulin X Nesprin 2 X Neurexin 1-alpha precursor X Neurobeachin protein (Lysosomal trafficking regulator 2) X Neurcblast differentiation associated protein AHNAK (Desmoyokin) (Fragments) X Ninein centrosomal protein X Nitric oxide synthase, inducible IID X Nuclear pore complex protein Nup214 X NuMA protein X Organic anion transporter 4 X Organic-cation transporter like 3 X Paraneoplastic antigen Ma2 (Onconeuronal antigen MA2) X Phosducin-like protein X Phosphatidylinositol-glycan-specific phospholipase D 1 precursor X Plako hilin 1 X Plasma glutathione peroxidase precursor X Plasma kallikrein precursor X Polycystic kidney disease 1-related protein X Polymeric-immunoglobulin receptor precursor X Potassium channel subfamily K member 5 X Pregnancy zone protein precursor X Pregnancy-specific beta-1-glycoprotein 4 Precursor X PRO ! 068X PRO1659 X Probable ribosome biogenesis protein NEP1 X Proline-rich protein 4 precursor X Proteasome subunit alpha type 3 X Protein disulfide isomerase A6 precursor X Protein phosphatase 2C gamma isoform X Protein tyrosine phosphatase, non-receptor type 4 X Protocadherin alpha 1 precursor X Protocadherin gamma All short form protein X Putative adenosylhomocysteinase 3 X Putative ATP-binding cassette transporter C13 X Putative protein kinase WNK4X Putative splicing factor YT521X Pyruvate dehydrogenase El component alpha subunit, testis specific X Pyruvate kinase, Ml isozyme X Receptor-type protein-tyrosine phosphatase S precursory Renal Sodium-dependent phosphate transport protein 2 X Reticulon 1 (Neuroendocrine-specific protein) X Rho guanine nucleotide exchange factor 11 (PDZ-RhoGEF) X Rho-associated, coiled-coil containing protein kinase 160ROCK X Ribosomal protein kinase B X Ribosomal protein S6 kinase alpha 3 X RING finger protein 27 X Rod cGMP-specific 3', 5'-cyclic phosphodiesterase alpha- subunit X Sarcoma antigen NY-SAR-24X SEC14-like Drotein 3 X Secretogranin III precursory Selenoprotein P precursor (SeP) X Sentrin-specific protease 5 X Sialic acid binding Ig-like lectin-like 1 precursor X Signal transducer and activator of transcri tion 1-al ha/beta X Sortilin-related receptor precursor X Spectrin beta chain, erythrocyte (Beta-I spectrin) X Steroid hormone receptor ERR2 X Structural maintenance of chromosome 1-like 2 X Sulfotransferase 1C1 X Synaptotagmin XI X Synaptotagmin-like protein 2 X Talin 1 X Talin-related protein X Tetranectin precursor X Thyroid receptor interacting protein 13 X Thyroxine-binding globulin precursor (T4-binding globulin) X TRABID protein X TRAF-interacting protein X Tropomyosin I alpha chain A1 ha-tro omyosin X Trypsin I precursor X Trypsin III precursor X Tumor associated hydroquinone oxidase X Tumor necrosis factor precursor X Tyrosine-protein kinase HCK (band #17) X Ubiquitin X Ubi uitin carboxyl-terminal hydrolase isozyme Ll X Ubiquitin-conjugating enzyme E2-18 kDa UbcH8 X UDP-glucuronosyltransferase 1-4 Precursor, microsomal X V (D) J recombination activating protein 1 X Vault poly (ADP-ribose) polymerase X Voltage-dependent P/Q-type calcium channel X WD-repeat protein 9X Williams-Beuren syndrome chromosome region 14 protein X Wiskott-Aldrich syndrome protein family member 1 X-linked retinitis pigmentosa GTPase regulator X Zinc finger protein 273 X Zinc finger protein 33A X Zinc finger protein Cezanne X Zona pellucida sperm-binding protein 2 precursor X Alpha-amylase 2B precursor X Alpha-amylase, salivary precursor X Alpha-mannosidase IIx X CDK5 regulatory subunit associated protein 2 X Cellular repressor of ElA-stimulated genes CREG X Complement component C7 precursor X C statin A X Cystatin A X Gag-ro-ol rotein Fra ment X Glucosamine-6-hos hate isomerase X Hypothetical protein x Glucosamine-6-phosphate isomerase X Hypothetical protein X Hypothetical protein FLJ13305 (Fragment) X Hypothetical protein FLJ20420 X Hypothetical protein FLJ33718 X Hypothetical protein KIAA1301 (Fragment) X Ig heavy chain V-III region GAL X Intersectin 1X Kallistatin precursor X Lysozyme C precursorX Myeloid/lymphoid or mixed-lineage leukemia protein 4 X Myosin heavy chain, cardiac muscle alpha isofbrmX Myosin heavy chain, skeletal muscle, adult 1X Olfactory receptor 9Q1X Phosphatidylinositol 3-kinase regulatory beta subunit X Prolactin-inducible protein precursorX Protein PIunc precursorX Short palate, lung and nasal epithelium carcinoma associated I heav chain V-III re ion TIL X Ig kappa chain V-I region Wes X Intersectin 1 x Kallistatin precursor X Lysozyme C precursor X Myeloid lymphoid or mixed-lineage leukemia protein 4 X Myosin heavy chain, cardiac muscle alpha isoform X Myosin heavy chain, skeletal muscle, adult 1 X Olfacto rece tor 9 1 X Phosphatidylinositol 3-kinase regulatory beta subunit X Prolactin-inducible protein precursor X Protein Plunc precursor X Short palate, lung and nasal epithelium carcinoma associated protein 2 precursor X Similar to calcium binding protein 1 X Solute carrier family 13, member 3 X Tumor rotein 73 X Von Ebner's gland protein precursor X

Example 7-Lung Cancer Biomarkers This example demonstrates that the disclosed method can detect serum biomarkers for lung cancer, and biomarkers that differentiate the histopathological type of the lung cancer. The sera of subjects in each of an adenocarcinoma population, a squamous cell carcinoma population and a no cancer population (hospital control) were pooled and analyzed as in Example 4. The results of this analysis are shown in Table 8. Useful biomarkers for lung cancer (of either type) can be selected to

include at least an identifiable fragment of each of two or more proteins in Table 8 (such as 3 or more, or 5 or more) that were found only in one or both of the adenocarcinoma population and the squamous cell carcinoma population. Useful biomarkers for adenocarcinoma can be selected to include at least an identifiable fragment of each of two or more proteins in Table 8 (such as 3 or more, or 5 or more) found only in the adenocarcinoma population. Useful biomarkers for squamous cell carcinoma can be selected to include at least an identifiable fragment of each of two or more proteins in Table 8 (such as 3 or more, or 5 or more) found only in the squamous cell carcinoma population. Useful biomarkers for the absence of lung cancer can be selected to include at least an identifiable fragment of each of two or more proteins in Table 8 (such as 3 or more, or 5 or more) found only in the no cancer population.

Table 8 Lung Cancer Adeno-Squamous Hospital carcinoma control Alpha-1-acid glycoprotein I recursor X X X Alpha-1-antichymotrypsin precursorXXX Alpha-1-antitrypsin precursor X X X Alpha-2-macroglobulin precursorXXX Angiotensinogen precursor x x x A oli o rotein A-I recursor X X X Apolipoprotein A-IV precursor x x x Apolipoprotein B-100 precursor x x x Apolipoprotein C-III precursor X X X Beta-2-glycoprotein I precursor X Ceruloplasmin precursor x x x Clusterin precursor x x x Complement C3 precursor x x x Complement C4 precursor x x x Complement 5 precursor x x x Complement component C6 precursor x x x Complement factor I precursor x x x Fibronectin precursor x x x Ha toglobin recursor X X X Haptoglobin-related protein precursor X X X Hemopexin precursor x x x Ig alpha-1 chain C region X X X Ig gamma-1 chain C region X X X Ig gamma-2 chain C region X X X I gamma-3 chain C re ion X X Ig heavy chain V-III region BROX XX Ig kappa chain C regionXXX Ig kappa chain V-1 region AG X X X Ig kappa chain V-1 region CARXXX Ig kappa chain V-1 region OUX XX Ig kappa chain V-III region SIEX XX Ig lambda chain C regionsXXX Inter-alpha-trypsin inhibitor heavy chain H4 precursor x x x Kininogen precursor. X X X Myosin-reactive immunoglobulin heavy chain variable region (Fragment) X X X N-acetylmuramoyl-L-alanine amidase precursor x x x Plasma protease Cl inhibitor precursor x x x Plasminogen precursor x x x Serotransferrin precursor x x x Serum albumin precursor X 0 X X Serum amyloid A-4 protein precursor x x x Transthyretin precursorXXX Vitamin D-binding protein precursor x x x Zinc-al ha-2-1 co rotein recursor X X X Afamin precursor x x x Gelsolin precursor, plasma x x x Apolipoprotein C-11 precursorXXX Apolipoprotein MXXX Ig heavy chain V-III region HILX XX Ig heavy chain V-III region TILX XX Ig heavy chain V-III region TROX XX Insulin-like growth factor binding protein complex acid labile chain precursor X. X X Sex hormone-binding globulin precursor x x x AI ha-1-acid 1 co rotein 2 precursor x x x Alpha-2-HS-glycoprotein precursor X X X AMBP protein precursor x x x Antithrombin-III precursor x x x Apoli oprotein A-II recursor X X X A olipoprotein E precursor x x x Complement component C8 gamma chain precursor x x x Complement component C9 precursor X _ X X Complement factor B precursor X X X Hemoglobin alpha chain X X X Hemoglobin beta chain X X X Hypothetical protein x x x Ig gamma-4 chain C region x x x Ig kappa chain V-III region NG9 precursor (Fragment) X X X Ig kappa chain V-III region VG precursor Fragment X X X Ig kappa chain V-IV region precursor (Fragment) X X X Inter-alpha-trypsin inhibitor heavy chain H I precursor X X X Inter-alpha-trypsin inhibitor heavy chain H2 precursor x x x Mediator of RNA polymerase II transcription subunit 8 homolog X X X Pi ment e ithelium-derived factor recursor X X X Plasma retinol-binding protein precursor X X X Platelet basic protein precursor X X X Serum paraoxonase/arylesterase tX X X Truncated beta-globin X X X 28S ribosomal protein S17, mitochondrial precursory ADAM 12 precursory ADAMTS-2 precursory Alpha-mannosidase IIx X BA489M19. 1 (Retinal cadherin 4 type 1 (R- cadherin)) (Fragment) X BAG-family molecular chaperone regulator-1 X C4b-binding protein alpha chain precursorX CD5 antigen-tike precursor X Coagulation factor XI precursor X Complement component C7 precursor X Crumbs protein homolog 1 precursor X BAG-famil molecular chaperone re ulator-1 X C4b-binding protein alpha chain precursor X precursorX Coagulation factor XI precursor x Complement component C7 precursor X Crumbs protein homolog 1 precursor X DNA topoisomerase II, beta isoz e X Glutamate dehydrogenase 2, mitochondrial precursor X Hypothetical protein CGI-96 X Hypothetical protein FLJ90575 X Ig kappa chain V-I region DEE x Ig kappa chain V-I region WEA X Inositol 1, 4, 5-trisphosphate 3-kinase C X Intersectin 1 X Leucine-rich alpha-2-glycoprotein precursor X Leucine-rich repeat LGI family member 2 precursory Peroxiredoxin 2 X Phosphatidylinositol-glycan-specific X phospholipase D 1 precursor Protein kinase C, beta type X Prothrombin precursory Similar to RIKEN cDNA 4933436G17 gene (Fragment) X Similar to RIKEN cDNA 9230116B 18 gene x SmcY protein X Smoothelin X Solute carrier family 12 member 6 X Tau-tubulin kinase X Tetrodotoxin-resistant voltage-gated sodium channel X Transcription elongation factor B polypeptide 1X Transcription intermediary factor 1-beta X Acidic nucleoplasmic DNA-binding protein 1X Activin receptor type IIprecursor X ADAM 2 precursor X Apolipoprotein D precursory A oli o rotein Li recursor X Baculoviral IAP repeat-containing protein 6X Band 4.1-like protein 2 X Complement factor H precursory Cortactin-binding protein 2 X Cytochrome P450 24A1, mitochondrial precursor X Exoc st com lex co onent Sec3 X Ficolin 2 precursory Hypothetical protein KIAA1332 (Fragment) X Ig heavy chain V-1 region EUX Ig kappa chain V-11 region MIL X Ig kappa chain V-III region B6 X Ig lambda chain V-IV region Hil X Ig mu heavy chain disease protein (BOT) X Interleukin-6 precursory Kinesin-like protein KIF12 (Fragment) X Microfibrillar protein 2 (Fragment) X Myosin-reactive immunoglobulin light chain variable region (Fragment) X N2B-titin isoform X Nido en-2 recursor X Polycystic kidney disease 1-related protein X Protocadherin gamma All precursorX Rabaptin-5 X Ras-related protein Rab-33A X RING finger protein 9X Serum amyloid A protein precursory A1 ha-1B-gl co rotein recursor X CDK5 regulatory subunit associated protein 2X Estradiol 17 beta-dehydrogenase 7X Hypothetical protein FLJ22169 X Hypothetical protein KIAA0205 X Hypothetical protein KIAA1064 (Fragment) X Ig kappa chain V-1 region Wes X Ig kappa chain V-II region Cum ; X Ig lambda chain V-1 region HAX Ig lambda chain V-III region SHX Ig lambda chain V-VI region WLT X Mitochondrial trifunctional protein beta subunit (Fragment) X MSTP155 X Protein C6orfl30 X

Example 8-High Throughput Chamber for Collection of Biomarkers from BAMs Collection of BAMs and subsequent elution of their bound biomarker cargo may be accomplished in a high throughput flow chamber. With reference to FIG. 9, the sample body fluid containing the carrier molecule and its associated diagnostic molecule analytes are passed into a column or passed along a surface which contains immobilized antibodies or other ligand directed against a BAM, such as the illustrated carrier protein. In one embodiment, immobilized antibodies that also comprise a means to couple (for example a means to covalently couple) the captured BAMs are used. Following capture of the BAM (and possibly covalent coupling of the BAM to the capture agent) and its analyte cargo, the analytes can be dissociated off the carrier molecule using a denaturation or suitable elution buffer. Because the carrier protein itself is covalently attached to the solid phase through the antibody/ligand it will not be eluted. Thus, only the analytes previously associated with the carrier molecule will be eluted and can be analyzed for the diagnostic information they contain based on their biochemical or physiologic characteristics.

An example means for coupling the antibody ligand to the captured BAM is the use of light activated coupling described by Holden and Cramer (JACS, 125: 8074-8075,2003). This method couples fluorescently labeled molecules to binding partners using a photo-bleaching reaction that initiates a reactive singlet oxygen to covalently couple with highly localized electron rich sites on the binding partner. Using this method to capture albumin-associated analytes a serum sample is first passed through a column containing immobilized fluorescein (or Alexa dye) labeled antibodies to albumin. The albumin is captured on the antibodies. A photo-bleaching illumination is then

provided to couple the antibodies to the albumin through the fluorescein moiety. This is then followed by an elution step which elutes the albumin associated analytes but leaves the albumin itself bound to the column. The whole process can be in-line and continuous, with sample introduction followed by pulses of light energy to couple the carrier molecule and followed by subsequent elutions of the analytes. The capture itself can take place in a filter format, a microwell chamber, etched channels, packed nozzels, in-solution on particles which are subsequently filtered out or captured by another means or in a microfluidic device. Further separation and processing can be done before or after this capture and elution step. The output can even go directly into a mass spectrometer, or into a chemical and enzymatic step (for example, a trypsin or cyanogen bromide cleavage) and then be analyzed by MS as illustrated in FIGS. 10.

FIG. 11 illustrates an embodiment in which capture and covalent coupling of a BAM to an immobilized antibody is followed directly by laser desorption into a mass spectrometer. In this embodiment, an antibody labeled with fluorescein is immobilized on a silica surface through a linker molecule (FIG. 11A). A biological fluid comprising a BAM that is bound by the particular antibody (such as the illustrated carrier protein) is passed through the device and the BAM with its diagnostic cargo is captured by the immobilized antibody (FIG. 11B). LJV (or visible) light is then used to excite the fluorescein moiety, which then catalyzes covalent coupling between antibody and the BAM (FIG. 11C). Once the BAM is immobilized, a laser pulse is used to desorb the biomarkers, such as peptides, from the BAM and into a mass spectrometer.

Any method known in the art may be used to immobilize a capture agent (such as an antibody or protein A) to a surface in the devices of FIGS. 9-11, and the device itself may be constructed from a wide variety of materials, including, for example, a metal, silicon or a plastic.

Examples of methods for attaching capture agents to particular substrate materials include the photoactivated polystyrene method of Bora et al. (J. Immunol. Methods, 268: 171-7, 2002), the method for immobilizing proteins to modified silicon surfaces described by Yakovleva et al. (Anal.

Chem., 74: 2994-3004,2002), the use of microporous membranes made of poly (2- hydroxyethylmethacrylate) carrying protein A as described by Denizli and Arica (J. Biomater. Sci.

Polm. Ed., 11: 367-382,2000), the method for immobilizing antibodies to a metal coated with polyimide described by Wessa et al. (Biosens Bioelectron. 14: 93-98,1999), or the method for coupling immunoglobulin to a graft polymer of poly (vinyl) alcohol-poly (acrylic acid) described by Disley et al., (Biosens. Bioelectron, 13: 383-396,1998). Methods for attaching antibodies to a metal surface are described in Storri et al., Biosens. Bioelectron., 13: 347-357,1998. A method of immobilizing proteins such as antibodies using heterobifunctional crosslinkers is described by Shriver-Lake et al., Biosens. Bioelectron., 12: 1101-1106,1997. Photochemical linkage of antibodies to silicon chips is described by Sundarababu et al, Photochem. Photobiol, 61: 540-546,1995. A variety of methods for immobilization of protein monolayers are reviewed in Williams and Blanch, Biosens. Bioelectron., 9: 159-167,1994. Chelating peptides may also be used to immobilize antiobodies on a solid support (see, for example, Loetscher et al., J. Chromatogr., 595 : 113-119, 1992). Antibodies may also be immobilized on a polystyrene copolymer surface, such as onto

styrene copolymer beads, which could be used in a column format for the disclosed device where a sample is passed through a column of such beads to remove BAMs, from which biomarkers may later be eluted (see, for example, Bale-Oenick et al., Ann BioL Clin, 48: 651-654,1990). A combination of thiol-terminated silanes and heterobifunctional crosslinkers may be used to immobilize antiobodies on silica surfaces (see, for example, Bhatia et al., Anal. Biochem., 178: 408-413,1989). Additional methods exist and are contemplated.

If the BAM is a magnetic particle, a magnet may be included in the device of FIGS. 9-11, and used to capture the BAM, and retain it as the biomarkers are eluted therefrom.

If the BAM is a dendrimer, either antibodies directed to the type of dendrimer, or a chemical reaction between the surface functional groups of the dendrimer and the surface of the device, may be used to retain the BAM while the biomarkers are eluted therefrom. Alternatively, the dendrimer BAM could be derivatized, for example, with biotin and captured by streptavidin covalently bound to the surface of the device.

The device itself may be constructed using nanofabrication methods as is described, for example, in Marrian and Tennant, J. Vac. Sci. Technol. A, 21: S207-S215, 2003 (incorporated by reference herein) to provide a microfluidic device.

Example 9-Artificial BAMs In addition to using natural circulating proteins to collect molecules of diagnostic importance, it is possible to use engineered molecules and materials to collect biomarkers from biological fluids. Advances in nanotechnology and microfabrication provide a means to mass- produce"nanoharvesting"BAMs designed to specifically capture and amplify different classes of LMW biomarkers by altering, for example, their surface chemistry. For example, BAMs tailored to monitor specific diseases developing in target tissues may be produced using existing targeting technologies. Such BAMs agents may be instilled into the blood of a patient at one point in time, and then collected at a later time point with their bound diagnostic cargo. In addition, nanoharvesting BAMs, filtered and arrayed in a high throughput platform may be directly queried with mass spectroscopy to obtain an individual global health profile rendered at an affordable cost, all from a drop of blood.

Examples of suitable artificial BAMs include chimeric proteins and proteins altered to enhance separation of the BAM from a biological fluid following exposure to the fluid. For example, native protein sequences having an added polyhistidine tag may be used to enhance separation of the altered protein from the biological fluid using a metal-affinity column. Metal affinity columns are described, for example, in U. S. Patent No. 5,962, 641.

Further examples of artificial BAMs include silica-coated nanoparticles having, for example, a magnetic core that facilitates separation of the BAM from a biological fluid. Functional groups, such as carboxyl and amine groups, may be derivatized to the surface of the silica shell by any known method. Such functional groups may also comprise proteins, enzymes, biotin, streptavidin, nucleic acids, and chemosensors such as fluorescent probes, that function to assist separation of the BAM

from a biological fluid, permit detection of the BAM, or alter the types of LMM biomarkers that accumulate on the BAM when exposed to the biological fluid. Silica-coated nanoparticles, their preparation, and derivatization are described in detail in U. S. Patent No. 6,548, 264.

Another class of artificial BAM useful for the disclosed methods are the ultrafine lightly coated superparamagnetic particles described in U. S. Patent No. 6,207, 134. These particles comprise a metal oxide core, such as an iron oxide core and a shell comprising a polyelectrolyte such as a structural polysaccharide or a synthetic polymer such as a polyaminoacid.

Micorparticles and nanoparticles comprising cross-linked monosaccharides and oligosaccharides may also be utilized as BAMs. Examples of such particles are described in U. S.

Patent No. 6,197, 757. Such particles are biodegradable and may, for example, be separated from biological fluids with lectins specific for the sugar moities found on their surface. In one embodiment, the particles comprise cyclodextrins that may be chosen to be of a size that captures biomarkers of particular mass ranges. The surface of such particles may be derivatized with, for example, amino or carboxylate groups according to known methods.

Biodegradable, injectable nanoparticles that are not rapidly cleared from blood, and may be modified to target specific cells or organs, and to change their lipophilicity and hydrophilicity, are described in U. S. Patent No. 5,543, 159.

A wide variety of microspheres and nanospheres that may be used directly or derivatized for use as BAMs are available from Bangs Laboratories, Inc. (Fishers, IN). Examples of such microspheres include polystyrene, latex, silica, and polymethylmethacrylate particles. Covalent coupling of various biomolecules (such as Protein-A) and derivatization of the surface of such spheres are discussed in TechNotes 201 and 205 published by Bangs Laboratories (TechNote 201, October 25,2002/TechNote 205, March 30,2002).

Another class of BAMs that may be used in the disclosed methods and apparatus are dendrimers. Examples of dendrimers, their synthesis, and surface derivatization are discussed in U. S.

Patent Nos. 4,587, 329 and 5,338, 532. Dendrimers may also be derivatized with antibodies to provide targeted access to particular types of tissue, and collection of biomarkers therefrom. Both polyamidoamine and polyalkylenimine dendrimers may be used. Propylenimine dendrimers with primary amino surface groups, polyamidoamine dendrimers with primary amino surface groups, polyamidoamine dendrimers with carboxylate surface groups and polyamidoamine dendrimers with hydroxyl surface groups are available from Aldrich (Milwaukee, WI). Over 200 modifications of the surface groups of dendrimers are described in Dvomic et al, Poly. Prepr., 40: 408,1999.

Example 10-Pattern Recognition Methods Any known pattern recognition method may be used to identify BAM-associated molecules that discriminate between biological states, and to provide a probable diagnosis of a subject's biological state based on BAM-associated molecules. Statistical methods of pattern recognition may be used and are reviewed in Jain et al., IEEE Transactions on Pattern Analysis and Machine Intelligence, 22: 4-37,2000, which is incorporated by reference herein. Artificial intelligence methods may also be used (see, for example, Zupan and Gasteiger, Neural Networks for Chemists, VCH, 1993).

In general, pattern recognition relates to the discovery of hidden patterns that characterize subsets of data within a larger data set, for example, data patterns that distinguish between objects of different classifications. Pattern recognition methods may be divided into three major components: clustering or classification, association rules, and sequential analysis. In classification or clustering, a set of data is analyzed to generate a set of grouping rules that may, for example, be used to classify diseases. An association rule is a rule that implies certain relationships between objects in a database.

For example, sets of symptoms often occurring together with certain kinds of diseases may be identified. In sequential analysis, patterns that occur in sequence are discovered. For example, patterns that correlate with progression of disease may be detected.

Pattern recognition involves a training (learning) mode and a classification (testing) mode.

During the training mode, data for a set of objects (such as mass spectra obtained for a number of samples) is used to establish patterns of data that characterize various subsets of objects present amongst all objects (such as patterns of mass spectral data that are characteristic of those samples from subjects with cancer). Class (subset) labels may be assigned to the objects before patterns are detected, or after objects are first grouped into subsets based on their similar patterns of data. In the former case, the pattern discovery process is known as supervised learning, and in the latter, unsupervised learning. In both supervised and unsupervised learning, the goal is to extract patterns of data that may be used to classify future data.

In the classification mode, data for objects of known (validation) or unknown (testing) class is compared to the patterns detected during the training mode. For example, a diagnostic pattern of mass spectral data may be validated by using the pattern to classify the spectra of samples known to be from subjects with or without cancer. If the diagnostic pattern is"robust, "it will provide the correct classification of the known samples. Often this process is repeated for each of the known samples used during the training mode in what is called a leave-one-out (LOO) cross-validation.

Once the pattern is determined to be effective for classification, testing is performed to assign a class to an object of unknown class.

Both the training mode and the classification mode may involve preprocessing of raw data to separate the desired pattern (s) from background and/or noise. Preprocessing may also include normalization of the data, or any other process that ultimately leads to a compact representation of the pattern (s) within the data. For example, mass spectral data may be normalized such that the highest intensity signal is given an intensity value of 100 and all other signals are given an intensity value

equal to their intensity as a percentage of that seen for the highest intensity signal. Alternatively, an even simpler way of preprocessing mass spectral data is to convert the data to a series of ones and zeroes for each mass value (or mass/charge ratio), where a one means that a signal (regardless of intensity) is present at a particular mass value and a zero represents the absence of a signal at a particular mass value.

Feature extraction/selection is performed during training to identify a subset of features within the data (such as mass spectral signals at particular mass values) that may be used to classify objects into their respective classes without having to consider all the available features. More specifically, feature selection refers to using an algorithm to select the most discriminatory subset of features within the input feature set, and feature extraction refers to applying an algorithm that creates new features based on transformations or combinations of the original feature set. Feature selection may be performed after feature extraction to select the most discriminatory set of extracted features.

Feature extraction/selection may involve an exhaustive search for the optimal subset of features (i. e. all combinations are tried), or when the number of features is so large that an exhaustive search is computationally infeasible, a heuristic search is used. In either case, feature extraction/selection leads to greater computational efficiency in learning.

In the training mode, learning involves applying a classifier to partition the feature space into regions characteristic of each class of object within the training set. In other words, combinations of features that are characteristic of objects of a particular class are identified. Since objects may be represented by points in multidimensional feature space with coordinates corresponding to the values of their features, objects of the same class will tend to appear close to each other (cluster) in the feature space. The classifier tries to draw boundaries between the portions of the feature space where objects of the same class tend to appear together. In the classification mode, an object is represented by a point in the feature space and assigned a class according to the class of objects that are nearest to it in the feature space.

A feedback process may also be employed to refine the preprocessing and feature/extraction steps in order to provide data subsets that supply patterns that best separate objects into their known classes. Pattern recognition methods that involve iterative identification of feature subsets are sometimes called"wrapper"methods. Feature subsets that provide improved diagnostic patterns are retained and used to generate new feature subsets, which are then tested for improved diagnostic ability. This process is repeated until an optimal subset of features is identified. If, however, the feedback process is not employed, the technique is known as a"filter"method, and feature selection is performed independently of the learning algorithm.

Examples of classifier methods that may be used for pattern recognition during learning include decision tree methods, statistical methods, neural networks and evolutionary algorithms.

These same methods may also be used to extract and select appropriate subsets of features.

Combinations of these methods are also possible. For example, one type of algorithm may be used for feature extraction/selection and a second algorithm used as the classifier, in either a filter or wrapper method.

Decision tree methods classify objects using a series of tests based on data features. For example, in the classification and regression tree (CART) method, the algorithm considers all the possible tests (e. g. presence, absence or range of values of each feature) that can split the objects of the data set into two subsets. The test that provides the"best"separation of the objects into known classes (or subsets of similar pattern) is then selected. The remaining tests are then explored to find the test that best separates each of these subsets into two smaller subsets with increasing homogeneity of class membership, and the process is repeated until each subset hopefully contains objects of only one class. The resulting scheme may be represented by a tree-like structure (dendrogram) with branches corresponding to each test that was used to partition the data into separate classes.

Unknown objects are classified by applying the series of feature tests used to partition the training data, and assigned the class membership of the subset into which it falls.

In statistical pattern recognition, each object in a data set is represented as a vector in multi- dimensional space where the space is defined by the features or combinations of the features used to describe the objects. Statistical decision concepts are then used to establish boundaries that partition the feature space into regions characteristic of different classes of objects. Examples of statistical pattern recognition methods include principal component analysis (PCA) and discriminant analysis.

Artificial neural networks mimic the pattern-finding capacity of the human brain and may be viewed as a parallel computing system consisting of a large number of simple processors with many interconnections. Neural networks relate particular input patterns to a classification through these interconnections. A principal difference between neural networks and other approaches to pattern recognition are that neural networks have the ability to learn complex non-linear relationships between data and the classes of objects they represent. Neural networks also have the characteristic of adapting themselves to the data. Examples of neural networks include the Self-Organizing Map (SOM) and multilayer feed-forward neural networks.

Evolutionary algorithms, such as genetic algorithms, are optimization techniques that mimic the processes of natural selection, and in the case of genetic algorithms, genetic mutation and recombination. In genetic algorithms, individuals represent candidate solutions to the optimization problem being solved. For example, in the feature subset selection problem, each individual would represent a feature subset. In each"generation,"the most"fit"individuals (feature subsets that are best for classifying objects) are retained and undergo mutation and crossover (of the features within the subsets) to yield the next generation. Successive generations undergo mutation and crossover until a satisfactory solution is found.

"Cluster analysis"is a broad term used to describe methods that group, or cluster, objects into subsets based on their data patterns, often in an unsupervised manner. Since each object may be represented as a point in multidimensional feature space with coordinates corresponding to its values for the features, a cluster analysis will group objects based on their similar positions in the feature space. Generally, clustering techniques employ some measure of similarity to decide cluster membership of objects. Common similarity measures include distances between objects in the feature space (e. g. Euclidian or Mahalanobis distances) and affinity of the objects in the feature space

(e. g. density or neighborhood). For example, clusters of objects in the feature space may be selected in a manner that minimizes the distances between objects in the same cluster and maximizes the distances between objects in different clusters. The K-means algorithm and the Self-Organizing Map are examples of cluster analyses.

Particular examples of pattern recognition techniques that may be used in the disclosed methods including the methods described in U. S. Patent Application Publication No. 20030004402, which is incorporated by reference herein, and the methods described in PCT Publication WO 02/42733, which is also incorporated by reference herein. Additional methods for pattern recognition are described in U. S. Patent Publication Nos. 20020193950 and 20030134304.

Examples of statistical pattern recognition software that is commercially available include <BR> <BR> SPSS (SPSS Inc. , Chicago, IL), JMP (SAS Inc. , Cary NC), Stata (Stata Inc. , College Station, TX) and Cluster. Artificial neural network software is available from, among other sources, Neurodimension, Inc. , Gainesville, FL (Neurosolutions) and The Mathworks, Inc. , Natick, MA (MATLAB Neural Network Toolbox).

Example 11-Diagnostic Method Based on Identity of BAM-associated Biomarkers This example describes a particular embodiment of a method for diagnosing the biological state of a subject. In this method the molecules bound to one or more biomarker attractant molecules are analyzed for the presence of previously identified biomarkers. With reference to FIG. 12, biomarkers shed, for example, by a tumor cell pass into the blood stream where they encounter and bind to a circulating carrier protein such as albumin. Over time, such biomarkers accumulate on the carrier protein along with other molecules. After isolating the carrier protein from the blood (typically from serum), laser desorption of the bound molecules may be used to introduce them into a mass spectrometer which provides an ion spectrum readout of the molecules bound to the carrier protein. Based on the masses observed in the mass spectrum, it is possible to make a direct identification of the ions comprising the mass spectral pattern by reference to a database of previously identified biomarkers. In some cases, ions appearing at particular masses are fragments of known proteins that are correlated with a biological state of a particular anatomic of physiologic location. Thus, identification of particular ions in a mass spectrum of the molecules bound to a particular BAM is correlated with the presence of a particular biological state. For example, identification of a particular fragment from a particular protein (such as a protein known to be associated with growth or metastasis of a tumor or a particular type of tumor) may be used to make a specific diagnosis. Similarly, BAM associated fragments of proteins associated with hypoxia or apoptosis of the heart muscle may be detected to provide a diagnosis of myocardial infarction (heart attack).

It should be understood that the foregoing relates only to particular embodiments and that numerous modifications or alterations may be made without departing from the true scope and spirit of the invention as defined by the following claims.