FIELD OF THE INVENTION

The present invention generally relates to the field of bioinformatics, genomic processing, proteomic processing, and related arts. More particularly, the present invention relates to a computer-implemented method for inferring activity of a JAK-STAT3 cellular signaling pathway in a subject performed by a digital processing device, wherein the inferring is based on expression levels of three or more target genes of the JAK-STAT3 cellular signaling pathway measured in a sample of the subject. The present invention further relates to an apparatus for inferring activity of a JAK-STAT3 cellular signaling pathway in a subject comprising a digital processor configured to perform the method, to a non-transitory storage medium for inferring activity of a JAK-STAT3 cellular signaling pathway in a subject storing instructions that are executable by a digital processing device to perform the method, and to a computer program for inferring activity of a JAK-STAT3 cellular signaling pathway in a subject comprising program code means for causing a digital processing device to perform the method, when the computer program is run on the digital processing device. The present invention further relates to a kit for measuring expression levels of three or more target genes of the JAK-STAT3 cellular signaling pathway in a sample of a subject, to a kit for inferring activity of a JAK-STAT3 cellular signaling pathway in a subject, and to uses of the kits in performing the method. BACKGROUND OF THE INVENTION

Genomic and proteomic analyses have substantial realized and potential promise for clinical application in medical fields such as oncology, where various cancers are known to be associated with specific combinations of genomic mutations/variations and/or high or low expression levels for specific genes, which play a role in growth and evolution of cancer, e.g., cell proliferation and metastasis.

STAT3 is an inducible transcription factor that regulates the expression of many genes involved in the immune response and in cancer. Biological processes that are crucial for cancer progression are mediated by the JAK signal transducer and activator of STAT3 signaling. In the nucleus, STAT3 binds to the promoters of genes and induces a genetic program that promotes various cellular processes that are required for cancer progression (see also Fig. 1, which is based on Yu H. et al., "STATs in cancer inflammation and immunity: a leading role for STAT3", Nature Reviews Cancer, Vol. 9, No. 11,

November 2009, pages 798 to 809).

With respect to the JAK-STAT3 signaling in e.g. cancer, it is important to be able to detect abnormal JAK-STAT3 signaling activity in order to enable the right choice of targeted drug treatment. Currently anti-JAK-STAT3 therapies are being developed (see Yue P. and Turkson J., "Targeting STAT3 in cancer: how successful are we?", Expert Opinion on Investigational Drugs, Vol. 18, No. 1, pages 45 to 56). However, today there is no clinical assay available to assess the functional state resp. activity of the JAK-STAT3 cellular signaling pathway, which in its active state indicates that it is, for instance, more likely to be tumor-promoting compared to its passive state. It is therefore desirable to be able to improve the possibilities of characterizing patients that have a disease, such as a cancer, e.g., a breast, cervical, endometrial, ovarian, pancreatic or prostate cancer, or an immune disorder, which is at least partially driven by an abnormal activity of the JAK-STAT3 cellular signaling pathway, and that are therefore likely to respond to inhibitors of the JAK-STAT3 cellular signaling pathway.

SUMMARY OF THE INVENTION

In accordance with a main aspect of the present invention, the above problem is solved by a computer-implemented method for inferring activity of a JAK-STAT3 cellular signaling pathway in a subject performed by a digital processing device, wherein the inferring comprises:

receiving expression levels of three or more, for example, three, four, five, six, seven, eight, nine, ten or more, target genes of the JAK-STAT3 cellular signaling pathway measured in a sample of the subject,

determining an activity level of a JAK-STAT3 transcription factor (TF) element in the sample of the subject, the JAK-STAT3 TF element controlling transcription of the three or more JAK-STAT3 target genes, the determining being based on evaluating a calibrated mathematical model pathway relating the expression levels of the three or more JAK-STAT3 target genes to the activity level of the JAK-STAT3 TF element, and inferring the activity of the JAK-STAT3 cellular signaling pathway in the subject based on the determined activity level of the JAK-STAT3 TF element in the sample of the subject,

wherein the three or more JAK-STAT3 target genes are selected from the group consisting of: AKT1, BCL2, BCL2L1, BIRC5, CCND1, CD274, CDKN1A, CRP, FGF2, FOS, FSCN1, FSCN2, FSCN3, HIF1A, HSP90AA1, HSP90AB1, HSP90B1, HSPA1A, HSPA1B, ICAM1, IFNG, IL10, JunB, MCL1, MMP1, MMP3, MMP9, MUC1, MYC, NOS2, POU2F1, PTGS2, SAA1, STAT1, TIMP1, TNFRSF1B, TWIST1, VIM, and ZEB1, preferably, either from the group consisting of: BCL2L1, BIRC5, CCND1, CD274, FOS, HIF 1 A, HSP90AA1 , HSP90AB 1 , MMP 1 , and MYC, or from the group consisting of: BCL2L1, CD274, FOS, HSP90B1, HSPA1B, ICAM1, IFNG, JunB, PTGS2, STAT1, TNFRSF1B, and ZEB1.

Herein, the "activity level" of a TF element denotes the level of activity of the TF element regarding transcription of its target genes.

The present invention is based on the innovation of the inventors that a suitable way of identifying effects occurring in the JAK-STAT3 cellular signaling pathway can be based on a measurement of the signaling output of the JAK-STAT3 cellular signaling pathway, which is - amongst others - the transcription of the target genes, which is controlled by a JAK-STAT3 transcription factor (TF) element that is controlled by the JAK- STAT3 cellular signaling pathway. This innovation by the inventors assumes that the TF activity level is at a quasi-steady state in the sample, which can be detected by means of - amongst others - the expression values of the JAK-STAT3 target genes. The JAK-STAT3 cellular signaling pathway targeted herein is known to control many functions in many cell types in humans, such as proliferation, differentiation and wound healing. Regarding pathological disorders, such as cancer (e.g., breast, cervical, endometrial, ovarian, pancreatic or prostate cancer), the abnormal JAK-STAT3 cellular signaling activity plays an important role, which is detectable in the expression profiles of the target genes and thus exploited by means of a calibrated mathematical pathway model.

The present invention makes it possible to determine the activity of the JAK- STAT3 cellular signaling pathway in a subject by (i) determining an activity level of a JAK- STAT3 TF element in the sample of the subject, wherein the determining is based on evaluating a calibrated mathematical model relating the expression levels of three or more target genes of the JAK-STAT3 cellular signaling pathway, the transcription of which is controlled by the JAK-STAT3 TF element, to the activity level of the JAK-STAT3 TF element, and by (ii) inferring the activity of the JAK-STAT3 cellular signaling pathway in the subject based on the determined activity level of the JAK-STAT3 TF element in the sample of the subject. This preferably allows improving the possibilities of characterizing patients that have a disease, such as cancer, e.g., a breast, cervical, endometrial, ovarian, pancreatic or prostate cancer, which is at least partially driven by an abnormal activity of the JAK-STAT3 cellular signaling pathway, and that are therefore likely to respond to inhibitors of the JAK-STAT3 cellular signaling pathway. In particular embodiments, treatment determination can be based on a specific JAK-STAT3 cellular signaling pathway activity. In a particular embodiment, the JAK-STAT3 cellular signaling status can be set at a cutoff value of odds of the JAK-STAT3 cellular signaling pathway being active of, for example, 10: 1, 5: 1, 4: 1, 2: 1, 1 : 1, 1 :2, 1 :4, 1 :5, or 1 : 10.

Herein, the term "JAK-STAT3 transcription factor element" or "JAK-STAT3 TF element" or "TF element" is defined to be a protein complex containing at least a STAT3 homodimer, which is capable of binding to specific DNA sequences, preferably the response elements with binding motif CTGGGAA, thereby controlling transcription of target genes. Preferably, the term refers to either a protein or protein complex transcriptional factor triggered by the binding of STAT3 inducing ligands such as interleukin-6 (IL-6) and IL-6 family cytokines to its receptor or an intermediate downstream signaling agent between the binding the ligand to its receptor and the final transcriptional factor protein or protein complex.

The calibrated mathematical pathway model may be a probabilistic model, preferably a Bayesian network model, based on conditional probabilities relating the activity level of the JAK-STAT3 TF element and the expression levels of the three or more JAK- STAT3 target genes, or the calibrated mathematical pathway model may be based on one or more linear combination(s) of the expression levels of the three or more JAK-STAT3 target genes. In particular, the inferring of the activity of the JAK-STAT3 cellular signaling pathway may be performed as disclosed in the published international patent application WO 2013/011479 A2 ("Assessment of cellular signaling pathway activity using probabilistic modeling of target gene expression") or as described in the published international patent application WO 2014/102668 A2 ("Assessment of cellular signaling pathway activity using linear combination(s) of target gene expressions"), the contents of which are herewith incorporated in their entirety. Further details regarding the inferring of cellular signaling pathway activity using mathematical modeling of target gene expression can be found in Verhaegh W. et al, "Selection of personalized patient therapy through the use of knowledge- based computational models that identify tumor-driving signal transduction pathways", Cancer Research, Vol. 74, No. 11, 2014, pages 2936 to 2945.

The term "subject", as used herein, refers to any living being. In some embodiments, the subject is an animal, preferably a mammal. In certain embodiments, the subject is a human being, preferably a medical subject. In still other embodiments, the subject is a cell line.

The term "target gene" as used herein, means a gene whose transcription is directly or indirectly controlled by a JAK-STAT3 transcription factor element. The "target gene" may be a "direct target gene" and/or an "indirect target gene" (as described herein). Moreover, the "target genes" may be "direct target genes" and/or "indirect target genes" (as described herein).

Particularly suitable JAK-STAT3 target genes are described in the following text passages as well as the examples below (see, e.g., Tables 1 to 3 below).

Thus, according to a preferred embodiment the JAK-STAT3 target genes are selected from the group consisting of the JAK-STAT3 target genes listed in Table 1, Table 2 or Table 3 below.

It has been found by the present inventors that the JAK-STAT3 target genes in the shorter lists become more probative for determining the activity of the JAK-STAT3 cellular signaling pathway.

Another aspect of the present invention relates to a method (as described herein), further comprising:

determining whether the JAK-STAT3 cellular signaling pathway is operating abnormally in the subject based on the inferred activity of the JAK-STAT3 cellular signaling pathway in the subject.

The present invention also relates to a method (as described herein), further comprising:

recommending prescribing a drug for the subject that corrects for the abnormal operation of the JAK-STAT3 cellular signaling pathway,

wherein the recommending is performed if the JAK-STAT3 cellular signaling pathway is determined to be operating abnormally in the subject based on the inferred activity of the JAK-STAT3 cellular signaling pathway.

The phrase "the cellular signaling pathway is operating abnormally" refers to the case where the "activity" of the pathway is not as expected, wherein the term "activity" may refer to the activity of the transcription factor complex in driving the target genes to expression, i.e., the speed by which the target genes are transcribed. "Normal" may be when it is inactive in tissue where it is expected to be inactive and active where it is expected to be active. Furthermore, there may be a certain level of activity that is considered "normal", and anything higher or lower maybe considered "abnormal".

The present invention also relates to a method (as described herein), wherein the abnormal operation of the JAK-STAT3 cellular signaling pathway is an operation in which the JAK-STAT3 cellular signaling pathway operates as a tumor promoter in the subject.

The sample(s) to be used in accordance with the present invention can be an extracted sample, that is, a sample that has been extracted from the subject. Examples of the sample include, but are not limited to, a tissue, cells, blood and/or a body fluid of a subject. If the subject is a medical subject that has or may have cancer, it can be, e.g., a sample obtained from a cancer lesion, or from a lesion suspected for cancer, or from a metastatic tumor, or from a body cavity in which fluid is present which is contaminated with cancer cells (e.g., pleural or abdominal cavity or bladder cavity), or from other body fluids containing cancer cells, and so forth, preferably via a biopsy procedure or other sample extraction procedure. The cells of which a sample is extracted may also be tumorous cells from hematologic malignancies (such as leukemia or lymphoma). In some cases, the cell sample may also be circulating tumor cells, that is, tumor cells that have entered the bloodstream and may be extracted using suitable isolation techniques, e.g., apheresis or conventional venous blood withdrawal. Aside from blood, a body fluid of which a sample is extracted may be urine, gastrointestinal contents, or an extravasate. The term "sample", as used herein, also encompasses the case where e.g. a tissue and/or cells and/or a body fluid of the subject have been taken from the subject and, e.g., have been put on a microscope slide, and where for performing the claimed method a portion of this sample is extracted, e.g., by means of Laser Capture Microdissection (LCM), or by scraping off the cells of interest from the slide, or by fluorescence-activated cell sorting techniques. In addition, the term "sample", as used herein, also encompasses the case where e.g. a tissue and/or cells and/or a body fluid of the subject have been taken from the subject and have been put on a microscope slide, and the claimed method is performed on the slide.

In accordance with another disclosed aspect, an apparatus for inferring activity of a JAK-STAT3 cellular signaling pathway in a subject comprises a digital processor configured to perform the method of the present invention as described herein. In accordance with another disclosed aspect, a non-transitory storage medium for inferring activity of a JAK-STAT3 cellular signaling pathway in a subject stores instructions that are executable by a digital processing device to perform the method of the present invention as described herein. The non-transitory storage medium may be a computer-readable storage medium, such as a hard drive or other magnetic storage medium, an optical disk or other optical storage medium, a random access memory (RAM), read only memory (ROM), flash memory, or other electronic storage medium, a network server, or so forth. The digital processing device may be a handheld device (e.g., a personal data assistant or smartphone), a notebook computer, a desktop computer, a tablet computer or device, a remote network server, or so forth.

In accordance with another disclosed aspect, a computer program for inferring activity of a JAK-STAT3 cellular signaling pathway in a subject comprises program code means for causing a digital processing device to perform the method of the present invention as described herein, when the computer program is run on the digital processing device. The digital processing device may be a handheld device (e.g., a personal data assistant or smartphone), a notebook computer, a desktop computer, a tablet computer or device, a remote network server, or so forth.

In accordance with another disclosed aspect, a kit for measuring expression levels of three or more, for example, three, four, five, six, seven, eight, nine, ten or more, target genes of the JAK-STAT3 cellular signaling pathway in a sample of a subject comprises:

one or more components for determining the expression levels of the three or more JAK-STAT3 target genes in the sample of the subject,

wherein the three or more JAK-STAT3 target genes are selected from the group consisting of: AKT1, BCL2, BCL2L1, BIRC5, CCND1, CD274, CDK 1A, CRP, FGF2, FOS, FSCN1, FSCN2, FSCN3, HIF1A, HSP90AA1, HSP90AB1, HSP90B1, HSPA1A, HSPA1B, ICAM1, IFNG, IL10, JunB, MCL1, MMP1, MMP3, MMP9, MUC1, MYC, NOS2, POU2F1, PTGS2, SAA1, STAT1, TIMP1, TNFRSF1B, TWIST1, VIM, and ZEB1, preferably, either from the group consisting of: BCL2L1, BIRC5, CCND1, CD274, FOS, HIF 1 A, HSP90AA1 , HSP90AB 1 , MMP 1 , and MYC, or from the group consisting of: BCL2L1, CD274, FOS, HSP90B1, HSPA1B, ICAM1, IFNG, JunB, PTGS2, STAT1, TNFRSF1B, and ZEB1.

The one or more components or means for measuring the expression levels of the three or more JAK-STAT3 target genes can be selected from the group consisting of: an DNA array chip, an oligonucleotide array chip, a protein array chip, an antibody, a plurality of probes, for example, labeled probes, a set of R A reverser-transcriptase sequencing components, and/or RNA or DNA, including cDNA, amplification primers. In an

embodiment, the kit includes a set of labeled probes directed to a portion of an mRNA or cDNA sequence of the three or more JAK-STAT3 target genes as described herein. In an embodiment, the kit includes a set of primers and probes directed to a portion of an mRNA or cDNA sequence of the three or more JAK-STAT3 target genes. In an embodiment, the labeled probes are contained in a standardized 96-well plate. In an embodiment, the kit further includes primers or probes directed to a set of reference genes. Such reference genes can be, for example, constitutively expressed genes useful in normalizing or standardizing expression levels of the target gene expression levels described herein.

In an embodiment, the kit for measuring the expression levels of three or more, for example, three, four, five, six, seven, eight, nine, ten or more, target genes of the JAK-STAT3 cellular signaling pathway in a sample of a subject comprises:

polymerase chain reaction primers directed to the three or more JAK-STAT3 target genes,

probes directed to the three or more JAK-STAT3 target genes, wherein the three or more JAK-STAT3 target genes are selected from the group consisting of: AKT1, BCL2, BCL2L1, BIRC5, CCND1, CD274, CDKN1A, CRP, FGF2, FOS, FSCN1, FSCN2, FSCN3, HIF1A, HSP90AA1, HSP90AB1, HSP90B1,

HSPA1A, HSPA1B, ICAM1, IFNG, IL10, JunB, MCL1, MMP1, MMP3, MMP9, MUC1, MYC, NOS2, POU2F1, PTGS2, SAA1, STAT1, TIMP1, TNFRSF1B, TWIST1, VIM, and ZEB1, preferably, either from the group consisting of: BCL2L1, BIRC5, CCND1, CD274, FOS, HIF1A, HSP90AA1, HSP90AB1, MMP1, and MYC, or from the group consisting of: BCL2L1, CD274, FOS, HSP90B1, HSPA1B, ICAM1, IFNG, JunB, PTGS2, STAT1, TNFRSF1B, and ZEB1.

In accordance with another disclosed aspect, a kit for inferring activity of a JAK-STAT3 cellular signaling pathway in a subject comprises:

the kit of the present invention as described herein, and

the apparatus of the present invention as described herein, the non-transitory storage medium of the present invention as described herein, or the computer program of the present invention as described herein. In accordance with another disclosed aspect, the kits of the present invention as described herein are used in performing the method of the present invention as described herein.

The present invention as described herein can, e.g., also advantageously be used in at least one of the following activities:

diagnosis based on the inferred activity of the JAK-STAT3 cellular signaling pathway in the subject;

prognosis based on the inferred activity of the JAK-STAT3 cellular signaling pathway in the subject;

drug prescription based on the inferred activity of the JAK-STAT3 cellular signaling pathway in the subject;

prediction of drug efficacy based on the inferred activity of the JAK-STAT3 cellular signaling pathway in the subject;

prediction of adverse effects based on the inferred activity of the JAK-STAT3 cellular signaling pathway in the subject;

monitoring of drug efficacy;

drug development;

assay development;

pathway research;

cancer staging;

enrollment of the subject in a clinical trial based on the inferred activity of the JAK-STAT3 cellular signaling pathway in the subject;

selection of subsequent test to be performed; and

selection of companion diagnostics tests.

Further advantages will be apparent to those of ordinary skill in the art upon reading and understanding the attached figures, the following description and, in particular, upon reading the detailed examples provided herein below.

It shall be understood that the method of claim 1, the apparatus of claim 7, the non-transitory storage medium of claim 8, the computer program of claim 9, the kits of claims 10 to 12, and the use of the kits of claim 13 have similar and/or identical preferred embodiments, in particular, as defined in the dependent claims.

It shall be understood that a preferred embodiment of the present invention can also be any combination of the dependent claims or above embodiments with the respective independent claim. These and other aspects of the invention will be apparent from and elucidated with reference to the embodiments described hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 shows schematically and exemplarily the JAK-STAT3 cellular signaling pathway. In the nucleus, STAT3 binds to the promoters of genes and induces a genetic program that promotes various cellular processes that are required for cancer progression (see also Fig. 1, which is based on Yu H. et al., "STATs in cancer inflammation and immunity: a leading role for STAT3", Nature Reviews Cancer, Vol. 9, No. 11, November 2009, pages 798 to 809; "UVR; S" = UV radiation or sunlight; "C" = carcinogen; "I" = infection; "ST" = stress; "SM" = smoke; "OA" = oncogene activation; "GFR" = growth factor receptor; "CR" = cytokine receptor; "T1R" = toll-like receptor; "AR" = adrenergic receptor; "NR" = nicotinic receptor; "OF, IF" = oncogenic and inflammatory factors).

Fig. 2 shows schematically and exemplarily a mathematical model, herein, a Bayesian network model, used to model the transcriptional program of the JAK-STAT3 cellular signaling pathway.

Fig. 3 shows a flow chart exemplarily illustrating a process for inferring activity of the JAK-STAT3 cellular signaling pathway in a subject based on expression levels of target genes of the JAK-STAT3 cellular signaling pathway measured in a sample of a subject.

Fig. 4 shows a flow chart exemplarily illustrating a process for obtaining a calibrated mathematical pathway model as described herein.

Fig. 5 shows a flow chart exemplarily illustrating a process for determining an activity level of a JAK-STAT3 transcription factor (TF) element in a sample of a subject as described herein.

Fig. 6 shows a flow chart exemplarily illustrating a process for inferring activity of a JAK-STAT3 cellular signaling pathway in a subject using discretized observables.

Fig. 7 shows a flow chart exemplarily illustrating a process for inferring activity of a JAK-STAT3 cellular signaling pathway in a subject using continuous observables.

Fig. 8 shows a flow chart exemplarily illustrating a process for determining Cq values from RT-qPCR analysis of the target genes of the JAK-STAT3 cellular signaling pathway. Fig. 9 shows calibration results of the Bayesian network model based on the evidence curated list of target genes (39 target genes list) from Table 1 and the methods as described herein using EGFR mutant cells of lung cancer from data set GSE57156.

Fig. 10 shows calibration results of the Bayesian network model based on the evidence curated list of target genes (39 target genes list) from Table 1 and the methods as described using a Sez-4 cell line which was derived from a cutaneous T-cell lymphoma from data set GSE8687.

Fig. 11 shows JAK-STAT3 cellular signaling pathway activity predictions of the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1 (data set GSE32975).

Fig. 12 shows JAK-STAT3 cellular signaling pathway activity predictions of the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1 (data set GSE20854).

Fig. 13 shows JAK-STAT3 cellular signaling pathway activity predictions of the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1 (data set GSE67051).

Fig. 14 shows JAK-STAT3 cellular signaling pathway activity predictions of the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1 (data set GSE52212).

Fig. 15 shows JAK-STAT3 cellular signaling pathway activity predictions of the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1 (data set GS64536).

Fig. 16 shows JAK-STAT3 cellular signaling pathway activity predictions of the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1 (data set GS8685).

Fig. 17 shows JAK-STAT3 cellular signaling pathway activity predictions of the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1 (data set GS8507).

Fig. 18 shows the correlation between the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1 and the 10 target gene shortlist for the JAK-STAT3 lung model from Table 2, respectively.

Fig. 19 shows the correlation between the trained exemplary blood Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1 and the 12 target gene shortlist for the JAK-STAT3 blood model from Table 3, respectively.

DETAILED DESCRIPTION OF EMBODIMENTS

The following examples merely illustrate particularly preferred methods and selected aspects in connection therewith. The teaching provided therein may be used for constructing several tests and/or kits, e.g., to detect, predict and/or diagnose the abnormal activity of the JAK-STAT3 cellular signaling pathway. Furthermore, upon using methods as described herein drug prescription can advantageously be guided, drug response prediction and monitoring of drug efficacy (and/or adverse effects) can be made, drug resistance can be predicted and monitored, e.g., to select subsequent test(s) to be performed (like a companion diagnostic test). The following examples are not to be construed as limiting the scope of the present invention. Example 1: Mathematical model construction

As described in detail in the published international patent application WO 2013/011479 A2 ("Assessment of cellular signaling pathway activity using probabilistic modeling of target gene expression"), by constructing a probabilistic model, e.g., a Bayesian network model, and incorporating conditional probabilistic relationships between the expression levels of three or more target genes of a cellular signaling pathway, herein, the JAK-STAT3 cellular signaling pathway, and the activity level of a transcription factor (TF) element, herein, the JAK-STAT3 TF element, the TF element controlling transcription of the three or more target genes of the cellular signaling pathway, such a model may be used to determine the activity of the cellular signaling pathway with a high degree of accuracy. Moreover, the probabilistic model can be readily updated to incorporate additional knowledge obtained by later clinical studies, by adjusting the conditional probabilities and/or adding new nodes to the model to represent additional information sources. In this way, the probabilistic model can be updated as appropriate to embody the most recent medical knowledge.

In another easy to comprehend and interpret approach described in detail in the published international patent application WO 2014/102668 A2 ("Assessment of cellular signaling pathway activity using linear combination(s) of target gene expressions"), the activity of a cellular signaling pathway, herein, the JAK-STAT3 cellular signaling pathway, may be determined by constructing and evaluating a linear or (pseudo-)linear model incorporating relationships between expression levels of three or more target genes of the cellular signaling pathway and the level of a transcription factor (TF) element, herein, the JAK-STAT3 TF element, the TF element controlling transcription of the three or more target genes of the cellular signaling pathway, the model being based on one or more linear combination(s) of expression levels of the three or more target genes.

In both approaches, the expression levels of the three or more target genes may preferably be measurements of the level of mR A, which can be the result of, e.g., (RT)-PCR and microarray techniques using probes associated with the target genes mRNA sequences, and of RNA-sequencing. In another embodiment, the expression levels of the three or more target genes can be measured by protein levels, e.g., the concentrations and/or activity of the protein(s) encoded by the target genes.

The aforementioned expression levels may optionally be converted in many ways that might or might not suit the application better. For example, four different transformations of the expression levels, e.g., microarray-based mRNA levels, may be: - "continuous data", i.e., expression levels as obtained after preprocessing of microarrays using well known algorithms such as MAS5.0 and fRMA,

"z-score", i.e., continuous expression levels scaled such that the average across all samples is 0 and the standard deviation is 1 ,

"discrete", i.e., every expression above a certain threshold is set to 1 and below it to 0 (e.g., the threshold for a probeset may be chosen as the (weighted) median of its value in a set of a number of positive and the same number of negative clinical samples),

"fuzzy", i.e., the continuous expression levels are converted to values between 0 and 1 using a sigmoid function of the following format: 1 / (1 + exp((thr - expr) I se)), with expr being the continuous expression levels, thr being the threshold as mentioned before and se being a softening parameter influencing the difference between 0 and 1.

One of the simplest linear models that can be constructed is a model having a node representing the transcription factor (TF) element, herein, the JAK-STAT3 TF element, in a first layer and weighted nodes representing direct measurements of the target genes expression levels, e.g., by one probeset that is particularly highly correlated with the particular target gene, e.g., in microarray or (q)PCR experiments, in a second layer. The weights can be based either on calculations from a training data set or based on expert knowledge. This approach of using, in the case where possibly multiple expression levels are measured per target gene (e.g., in the case of microarray experiments, where one target gene can be measured with multiple probesets), only one expression level per target gene is particularly simple. A specific way of selecting the one expression level that is used for a particular target gene is to use the expression level from the probeset that is able to separate active and passive samples of a training data set the best. One method to determine this probeset is to perform a statistical test, e.g., the t-test, and select the probeset with the lowest p-value. The training data set's expression levels of the probeset with the lowest p-value is by definition the probeset with the least likely probability that the expression levels of the (known) active and passive samples overlap. Another selection method is based on odds- ratios. In such a model, one or more expression level(s) are provided for each of the three or more target genes and the one or more linear combination(s) comprise a linear combination including for each of the three or more target genes a weighted term, each weighted term being based on only one expression level of the one or more expression level(s) provided for the respective target gene. If only one expression level is chosen per target gene as described above, the model may be called a "most discriminant probesets" model.

In an alternative to the "most discriminant probesets" model, it is possible, in the case where possibly multiple expression levels are measured per target gene, to make use of all the expression levels that are provided per target gene. In such a model, one or more expression level(s) are provided for each of the three or more target genes and the one or more linear combination(s) comprise a linear combination of all expression levels of the one or more expression level(s) provided for the three or more target genes. In other words, for each of the three or more target genes, each of the one or more expression level(s) provided for the respective target gene may be weighted in the linear combination by its own

(individual) weight. This variant may be called an "all probesets" model. It has an advantage of being relatively simple while making use of all the provided expression levels.

Both models as described above have in common that they are what may be regarded as "single- layer" models, in which the activity level of the TF element is calculated based on a linear combination of expression levels of the one or more probesets of the three or more target genes.

After the activity level of the TF element, herein, the JAK-STAT3 TF element, has been determined by evaluating the respective model, the determined TF element activity level can be thresholded in order to infer the activity of the cellular signaling pathway, herein, the JAK-STAT3 cellular signaling pathway. A preferred method to calculate such an appropriate threshold is by comparing the determined TF element activity levels wlc

(weighted linear combination) of training samples known to have a passive cellular signaling pathway and training samples with an active cellular signaling pathway. A method that does so and also takes into account the variance in these groups is given by using a threshold

? ? ? ? 4- 7 ? ? ?

^■ - wic _pas ^■ - wic _act ^{T ■} - wic _act ^■ - wic _pas

thr = — — (1)

^■ - wic _pas ^T - - wic _act

where σ and μ are the standard deviation and the mean of the determined TF element activity levels w/c for the training samples. In case only a small number of samples are available in the active and/or passive training samples, a pseudo count may be added to the calculated variances based on the average of the variances of the two groups: v =

X V 4 ^" { ^"" Ί-aet ^^wlcgut

w ^{lCaa =} x + n _act - l (2)

x V + (n _pBS - l)v _wlCp

^{Λ 1 ttr} p where v is the variance of the determined TF element activity levels wlc of the groups, x is a positive pseudo count, e.g., 1 or 10, and n _ac t and n _pas are the number of active and passive samples, respectively. The standard deviation σ can next be obtained by taking the square root of the variance v.

The threshold can be subtracted from the determined TF element activity levels wlc for ease of interpretation, resulting in a cellular signaling pathway's activity score in which negative values correspond to a passive cellular signaling pathway and positive values correspond to an active cellular signaling pathway.

As an alternative to the above-described "single- layer" models, a "two-layer" may also be used in an example. In such a model, a summary value is calculated for every target gene using a linear combination based on the measured intensities of its associated probesets ("first (bottom) layer"). The calculated summary value is subsequently combined with the summary values of the other target genes of the cellular signaling pathway using a further linear combination ("second (upper) layer"). Again, the weights can be either learned from a training data set or based on expert knowledge or a combination thereof. Phrased differently, in the "two-layer" model, one or more expression level(s) are provided for each of the three or more target genes and the one or more linear combination(s) comprise for each of the three or more target genes a first linear combination of all expression levels of the one or more expression level(s) provided for the respective target gene ("first (bottom) layer"). The model is further based on a further linear combination including for each of the three or more target genes a weighted term, each weighted term being based on the first linear combination for the respective target gene ("second (upper) layer").

The calculation of the summary values can, in a preferred version of the "two- layer" model, include defining a threshold for each target gene using the training data and subtracting the threshold from the calculated linear combination, yielding the target gene summary. Here the threshold may be chosen such that a negative target gene summary value corresponds to a down-regulated target gene and that a positive target gene summary value corresponds to an up-regulated target gene. Also, it is possible that the target gene summary values are transformed using, e.g., one of the above-described transformations (fuzzy, discrete, etc.), before they are combined in the "second (upper) layer".

After the activity level of the TF element has been determined by evaluating the "two-layer" model, the determined TF element activity level can be thresholded in order to infer the activity of the cellular signaling pathway, as described above.

In the following, the models described above are collectively denoted as "(pseudo-)linear" models. A more detailed description of the training and use of probabilistic models, e.g., a Bayesian network model, is provided in Example 3 below.

Example 2: Selection of target genes

A transcription factor (TF) is a protein complex (i.e., a combination of proteins bound together in a specific structure) or a protein that is able to regulate transcription from target genes by binding to specific DNA sequences, thereby controlling the transcription of genetic information from DNA to mRNA. The mRNA directly produced due to this action of the TF complex is herein referred to as a "direct target gene" (of the transcription factor). Cellular signaling pathway activation may also result in more secondary gene transcription, referred to as "indirect target genes". In the following, (pseudo-)linear models or Bayesian network models (as exemplary mathematical models) comprising or consisting of direct target genes as direct links between cellular signaling pathway activity and mRNA level, are preferred, however the distinction between direct and indirect target genes is not always evident. Herein, a method to select direct target genes using a scoring function based on available scientific literature data is presented. Nonetheless, an accidental selection of indirect target genes cannot be ruled out due to limited information as well as biological variations and uncertainties. In order to select the target genes, the MEDLINE database of the National Institute of Health accessible at "www.ncbi.nlm.nih.gov/pubmed" and herein further referred to as "Pubmed" was employed to generate a lists of target genes. Furthermore, two additional lists of target genes were selected based on the probative nature of their expression.

Publications containing putative JAK-STAT3 target genes were searched for by using queries such as ("JAK-STAT3" AND "target gene") in the period of the first and second quarter of 2017. The resulting publications were further analyzed manually following the methodology described in more detail below.

Specific cellular signaling pathway mRNA target genes were selected from the scientific literature, by using a ranking system in which scientific evidence for a specific target gene was given a rating, depending on the type of scientific experiments in which the evidence was accumulated. While some experimental evidence is merely suggestive of a gene being a direct target gene, like for example an mRNA increasing as detected by means of an increasing intensity of a probeset on a microarray of a cell line in which it is known that the JAK-STAT3 cellular signaling pathway is active, other evidence can be very strong, like the combination of an identified JAK-STAT3 cellular signaling pathway TF binding site and retrieval of this site in a chromatin immunoprecipitation (ChIP) assay after stimulation of the specific cellular signaling pathway in the cell and increase in mRNA after specific stimulation of the cellular signaling pathway in a cell line.

Several types of experiments to find specific cellular signaling pathway target genes can be identified in the scientific literature:

1. ChIP experiments in which direct binding of a TF of the cellular signaling pathway of interest to its binding site on the genome is shown. Example: By using chromatin immunoprecipitation (ChIP) technology subsequently putative functional JAK-STAT3 TF binding sites in the DNA of cell lines with and without active induction of the JAK-STAT3 cellular signaling pathway, e.g., by stimulation with JAK-STAT3, were identified, as a subset of the binding sites recognized purely based on nucleotide sequence. Putative functionality was identified as ChlP-derived evidence that the TF was found to bind to the DNA binding site.

2. Electrophoretic Mobility Shift (EMS A) assays which show in vitro binding of a TF to a fragment of DNA containing the binding sequence. Compared to ChlP-based evidence EMSA-based evidence is less strong, since it cannot be translated to the in vivo situation.

3. Stimulation of the cellular signaling pathway and measuring mRNA expression using a microarray, RNA sequencing, quantitative PCR or other techniques, using JAK-STAT3 cellular signaling pathway-inducible cell lines and measuring mRNA profiles measured at least one, but preferably several time points after induction - in the presence of cycloheximide, which inhibits translation to protein, thus the induced mRNAs are assumed to be direct target genes.

4. Similar to 3, but alternatively measure the mRNAs expression further downstream with protein abundance measurements, such as western blot.

5. Identification of TF binding sites in the genome using a bio informatics approach. Example for the JAK-STAT3 TF element: Using the binding motif CTGGGAA, the potential binding sites were identified in gene promoter regions.

6. Similar as 3, only in the absence of cycloheximide.

7. Similar to 4, only in the absence of cycloheximide.

In the simplest form one can give every potential gene 1 point for each of these experimental approaches in which the gene was identified as being a target gene of the JAK-STAT3 family of transcription factors. Using this relative ranking strategy, one can make a list of most reliable target genes.

Alternatively, ranking in another way can be used to identify the target genes that are most likely to be direct target genes, by giving a higher number of points to the technology that provides most evidence for an in vivo direct target gene. In the list above, this would mean 7 points for experimental approach 1), 6 for 2), and going down to 1 point for experimental approach 7). Such a list may be called a "general list of target genes".

Despite the biological variations and uncertainties, the inventors assumed that the direct target genes are the most likely to be induced in a tissue-independent manner. A list of these target genes may be called an "evidence curated list of target genes". Such an evidence curated list of target genes has been used to construct computational models of the JAK-STAT3 cellular signaling pathway that can be applied to samples coming from different tissue sources.

The following will illustrate exemplary how the selection of an evidence curated target gene list specifically was constructed for the JAK-STAT3 cellular signaling pathway.

A scoring function was introduced that gave a point for each type of experimental evidence, such as ChIP, EMSA, differential expression, knock down/out, luciferase gene reporter assay, sequence analysis, that was reported in a publication. The same experimental evidence is sometimes mentioned in multiple publications resulting in a corresponding number of points, e.g., two publications mentioning a ChIP finding results in twice the score that is given for a single ChIP finding. Further analysis was performed to allow only for genes that had diverse types of experimental evidence and not only one type of experimental evidence, e.g., differential expression. Those genes that had more than one type of experimental evidence available were selected (as shown in Table 1).

A further selection of the evidence curated list of target genes (listed in Table 2) was made by the inventors. The target genes of the evidence curated list that were proven to be more probative in determining the activity of the JAK-STAT3 signaling pathway from the training samples were selected. Herein, available expression data sets of EGFR mutant cells of lung cancer from data set GSE57156 were used. The cells that were treated with Erlotinib were JAK-STAT3 inactive and cells that were treated with DMSO were JAK- STAT3 active. The gene expression values for the "evidence curated list of target genes" (39 target genes list) from Table 1 were compared between STAT3 active and inactive samples from the GSE57156 data set. If the expression level of a target gene was obviously differentiated between the pathway active and inactive groups, which signifies that the target gene can be used to distinguish between the pathway active and inactive groups, then the target gene was selected. This resulted in the "10 target genes shortlist for the JAK-STAT3 lung model" shown in Table 2. Regarding the JAK-STAT3 blood model, a Sez-4 cell line, which was derived from a cutaneous T-cell lymphoma in data set GSE8687, was adopted to select the target genes shortlist. The cells starved of IL-2 were JAK-STAT3 inactive and the cells cultured with IL-2 were JAK-STAT3 active. The gene expression values for the "evidence curated list of target genes" (39 target genes list) from Table 1 were compared between STAT3 active and inactive samples from the GSE8687 data set. If the expression level of a target gene was obviously differentiated between the pathway active and inactive groups, which signifies that the target gene can be used to distinguish between the pathway active and inactive groups, then the target gene was selected. This resulted in the "12 target genes shortlist for the JAK-STAT3 blood model" shown in Table 3.

Table 1 : "Evidence curated list of target genes" (39 target genes list) of the JAK- STAT3 cellular signaling pathway used in the JAK-STAT3 cellular signaling pathway models and associated probesets used to measure the mRNA expression level of the target genes.

Gene Probeset Gene Probeset

AKT1 207163_s_at HSPA1B 202581_at

BCL2 203685_at ICAM1 202637_s_at 203684_s_at 202638_s_at

232614_at 215485_s_at

232210_at IFNG 210354_at

244035_at JunB 201473_at

207004_at MCL1 200796_s_at

207005_s_at 200797_s_at

BCL2L1 212312_at 200798_x_at

206665_s_at 227175_at

215037_s_at MMP1 204475_at

BIRC5 202094_at MMP3 205828_at

202095_s_at MMP9 203936_s_at

210334_x_at MUC1 207847_s_at

CCND1 214019_at 213693_s_at

20871 l_s_at 211695_x_at

208712_at MYC 238381_x_at

CD274 223834_at 20243 l_s_at

227458_at 23993 l_at

CDK 1A 202284_s_at NOS2 210037_s_at

CRP 37020_at POU2F1 206789_s_at

205753_at 234649_at

FGF2 204422_s_at 1562280_at

20442 l_s_at 1564351_at

FOS 209189_at PTGS2 204748_at

FSCN1 201564_s_at 1554997_a_at

FSCN2 207204_at SAAl 214456_x_at

FSCN3 220379_at STAT1 200887_s_at

HIF1A 200989_at 232375_at

238869_at 209969_s_at

HSP90AA1 211968_s_at TIMP1 201666_at

211969_at TNFRSF1B 203508_at

21021 l_s_at TWIST 1 213943_at

HSP90AB1 200064_at VIM 201426_s_at

214359_s_at 1555938_x_at

HSP90B1 200598_s_at ZEB1 210875_s_at

200599_s_at 208078_s_at

23945 l_at 212758_s_at

HSPA1A 200799_at 212764_at

200800_s_at 239952_at

IL10 207433_at Table 2 : "10 target genes shortlist for the JAK-STAT3 lung model" of JAK-STAT3 target genes based on the evidence curated list of JAK-STAT3 target genes. (The associated probesets are the same as in Table 1.)

Target gene

BCL2L1

BIRC5

CCND1

CD274

FOS

HIF1A

HSP90AA1

HSP90AB1

MMP1

MYC

Table 3: "12 target genes shortlist for the JAK-STAT3 blood model" of JAK-STAT3 target genes based on the evidence curated list of JAK-STAT3 target genes. (The associated probesets are the same as in Table 1.)

Target gene

BCL2L1

CD274

FOS

HSP90B1

HSPA1B

ICAM1

IFNG

JunB

PTGS2

STAT1

TNFRSF1B

ZEB1

Example 3: Training and using the mathematical model

Before the mathematical model can be used to infer the activity of the cellular signaling pathway, herein, the JAK-STAT3 cellular signaling pathway, in a subject, the model must be appropriately trained. If the mathematical pathway model is a probabilistic model, e.g., a Bayesian network model, based on conditional probabilities relating the activity level of the JAK- STAT3 TF element and expression levels of three or more target genes of the JAK-STAT3 cellular signaling pathway measured in the sample of the subject, the training may preferably be performed as described in detail in the published international patent application WO 2013/01 1479 A2 ("Assessment of cellular signaling pathway activity using probabilistic modeling of target gene expression").

If the mathematical pathway model is based on one or more linear combination(s) of expression levels of three or more target genes of the JAK-STAT3 cellular signaling pathway measured in the sample of the subject, the training may preferably be performed as described in detail in the published international patent application WO

2014/102668 A2 ("Assessment of cellular signaling pathway activity using linear

combination(s) of target gene expressions").

Herein, an exemplary Bayesian network model as shown in Fig. 2 was used to model the transcriptional program of the JAK-STAT3 cellular signaling pathway in a simple manner. The model consists of three types of nodes: (a) a transcription factor (TF) element (with states "absent" and "present") in a first layer 1 ; (b) target genes TGi, TG ₂ , TG« (with states "down" and "up") in a second layer 2, and; (c) measurement nodes linked to the expression levels of the target genes in a third layer 3. These can be microarray probesets PSi.i, PSi,2, PSi,3, PS ₂ ,i, PS„,i, PS„, _m (with states "low" and "high"), as preferably used herein, but could also be other gene expression measurements such as RNAseq or RT-qPCR.

A suitable implementation of the mathematical model, herein, the exemplary Bayesian network model, is based on microarray data. The model describes (i) how the expression levels of the target genes depend on the activation of the TF element, and (ii) how probeset intensities, in turn, depend on the expression levels of the respective target genes. For the latter, probeset intensities may be taken from fRMA pre-processed Affymetrix HG- U133Plus2.0 microarrays, which are widely available from the Gene Expression Omnibus (GEO, www.ncbi.nlm.nih.gov/geo) and ArrayExpress (www. ebi.ac.uk/arrayexpress).

As the exemplary Bayesian network model is a simplification of the biology of a cellular signaling pathway, herein, the JAK-STAT3 cellular signaling pathway, and as biological measurements are typically noisy, a probabilistic approach was opted for, i.e., the relationships between (i) the TF element and the target genes, and (ii) the target genes and their respective probesets, are described in probabilistic terms. Furthermore, it was assumed that the activity of the oncogenic cellular signaling pathway which drives tumor growth is not transiently and dynamically altered, but long term or even irreversibly altered. Therefore the exemplary Bayesian network model was developed for interpretation of a static cellular condition. For this reason complex dynamic cellular signaling pathway features were not incorporated into the model.

Once the exemplary Bayesian network model is built and calibrated (see below), the model can be used on microarray data of a new sample by entering the probeset measurements as observations in the third layer 3, and inferring backwards in the model what the probability must have been for the TF element to be "present". Here, "present" is considered to be the phenomenon that the TF element is bound to the DNA and is controlling transcription of the cellular signaling pathway's target genes, and "absent" the case that the TF element is not controlling transcription. This probability is hence the primary read-out that may be used to indicate activity of the cellular signaling pathway, herein, the JAK- STAT3 cellular signaling pathway, which can next be translated into the odds of the cellular signaling pathway being active by taking the ratio of the probability of it being active vs. it being passive (i.e., the odds are given by p/(l-p), where p is the predicted probability of the cellular signaling pathway being active).

In the exemplary Bayesian network model, the probabilistic relations have been made quantitative to allow for a quantitative probabilistic reasoning. In order to improve the generalization behavior across tissue types, the parameters describing the probabilistic relationships between (i) the TF element and the target genes have been carefully hand- picked. If the TF element is "absent", it is most likely that the target gene is "down", hence a probability of 0.95 is chosen for this, and a probability of 0.05 is chosen for the target gene being "up". The latter (non-zero) probability is to account for the (rare) possibility that the target gene is regulated by other factors or that it is accidentally observed as being "up" (e.g. because of measurement noise). If the TF element is "present", then with a probability of 0.70 the target gene is considered "up", and with a probability of 0.30 the target gene is considered "down". The latter values are chosen this way, because there can be several causes why a target gene is not highly expressed even though the TF element is present, e.g., because the gene's promoter region is methylated. In the case that a target gene is not up- regulated by the TF element, but down-regulated, the probabilities are chosen in a similar way, but reflecting the down-regulation upon presence of the TF element. The parameters describing the relationships between (ii) the target genes and their respective probesets have been calibrated on experimental data. For the latter, in this example, microarray data was used from patients samples which are known to have an active JAK-STAT3 cellular signaling pathway whereas normal, healthy samples from the same data set were used as passive JAK-STAT3 cellular signaling pathway samples, but this could also be performed using cell line experiments or other patient samples with known cellular signaling pathway activity status. The resulting conditional probability tables are given by:

A: for upregulated target genes

In these tables, the variables ALij, AHij, PLij, and PHij indicate the number of calibration samples with an "absent" (A) or "present" (P) transcription complex that have a "low" (L) or "high" (H) probeset intensity, respectively. Dummy counts have been added to avoid extreme probabilities of 0 and 1.

To discretize the observed probeset intensities, for each probeset PSij a threshold Uj was used, below which the observation is called "low", and above which it is called "high". This threshold has been chosen to be the (weighted) median intensity of the probeset in the used calibration data set. Due to the noisiness of microarray data, a fuzzy method was used when comparing an observed probeset intensity to its threshold, by assuming a normal distribution with a standard deviation of 0.25 (on a log2 scale) around the reported intensity, and determining the probability mass below and above the threshold.

If instead of the exemplary Bayesian network described above, a (pseudo- linear model as described in Example 1 above was employed, the weights indicating the sign and magnitude of the correlation between the nodes and a threshold to call whether a node is either "absent" or "present" would need to be determined before the model could be used to infer cellular signaling pathway activity in a test sample. One could use expert knowledge to fill in the weights and the threshold a priori, but typically the model would be trained using a representative set of training samples, of which preferably the ground truth is known, e.g., expression data of probesets in samples with a known "present" transcription factor complex (= active cellular signaling pathway) or "absent" transcription factor complex (= passive cellular signaling pathway).

Known in the field are a multitude of training algorithms (e.g., regression) that take into account the model topology and changes the model parameters, here, the weights and the threshold, such that the model output, here, a weighted linear score, is optimized. Alternatively, it is also possible to calculate the weights directly from the observed expression levels without the need of an optimization algorithm.

A first method, named "black and white"-method herein, boils down to a ternary system, in which each weight is an element of the set {-1, 0, 1} . If this is put in a biological context, the -1 and 1 correspond to target genes or probesets that are down- and up-regulated in case of cellular signaling pathway activity, respectively. In case a probeset or target gene cannot be statistically proven to be either up- or down-regulated, it receives a weight of 0. In one example, a left-sided and right-sided, two sample t-test of the expression levels of the active cellular signaling pathway samples versus the expression levels of the samples with a passive cellular signaling pathway can be used to determine whether a probe or gene is up- or down-regulated given the used training data. In cases where the average of the active samples is statistically larger than the passive samples, i.e., the p-value is below a certain threshold, e.g., 0.3, the target gene or probeset is determined to be up-regulated. Conversely, in cases where the average of the active samples is statistically lower than the passive samples, the target gene or probeset is determined to be down-regulated upon activation of the cellular signaling pathway. In case the lowest p-value (left- or right-sided) exceeds the aforementioned threshold, the weight of the target gene or probeset can be defined to be 0.

A second method, named "log odds"-weights herein, is based on the logarithm (e.g., base e) of the odds ratio. The odds ratio for each target gene or probeset is calculated based on the number of positive and negative training samples for which the probeset/target gene level is above and below a corresponding threshold, e.g., the (weighted) median of all training samples. A pseudo-count can be added to circumvent divisions by zero. A further refinement is to count the samples above/below the threshold in a somewhat more probabilistic manner, by assuming that the probeset/target gene levels are e.g. normally distributed around its observed value with a certain specified standard deviation (e.g., 0.25 on a 2-log scale), and counting the probability mass above and below the threshold. Herein, an odds ratio calculated in combination with a pseudo-count and using probability masses instead of deterministic measurement values is called a "soft" odds ratio.

Further details regarding the inferring of cellular signaling pathway activity using mathematical modeling of target gene expression can be found in Verhaegh W. et al., "Selection of personalized patient therapy through the use of knowledge-based computational models that identify tumor-driving signal transduction pathways", Cancer Research, Vol. 74, No. 11, 2014, pages 2936 to 2945.

Herein, we have used publically available mRNA expression data from

Affymetrix U133Plus2.0 on two data sets from the GEO database. Because the STAT3 pathway activation of solid cancer cells and blood cells has slightly different effects on the target gene expression levels, two different calibration data sets were used, representative for STAT3 activation in solid cancer cell and blood cell. One data set has EGFR mutant cells from non- small cell lung cancers. EGFR mutant cells treated with Erlotinib formed the JAK- STAT3 inactive group, and EGFR mutant cells treated with DMSO were taken as JAK- STAT3 active calibration samples. Another data set had a Sez-4 cell line which was derived from a cutaneous T-cell lymphoma. Cells that were starved of IL-2 were taken as the JAK- STAT3 inactive group, and cells cultured with IL-2 were taken as JAK-STAT3 active calibration samples. Hence, two different models were calibrated separately on calibration samples with lung cancer cells and blood cells, respectively, using the same target gene list (see Table 1).

In the following, calibration results of the Bayesian network model on data sets with lung cancer cells and blood cells, respectively, are shown in Figs. 9 and 10.

Fig. 9 shows calibration results of the Bayesian network model based on the evidence curated list of target genes (39 target genes list) from Table 1 and the methods as described herein using EGFR mutant cells of lung cancer from data set GSE57156. The cells that were treated with Erlotinib (group 1) were JAK-STAT3 inactive and the cells treated with DMSO (group 2) were considered JAK-STAT3 active. In the diagram, the vertical axis indicates the odds that the TF element is "present" resp. "absent", which corresponds to the JAK-STAT3 cellular signaling pathway being active resp. passive, wherein values above the horizontal axis correspond to the TF element being more likely„present"/active and values below the horizontal axis indicate that the odds that the TF element is "absent'Vpassive are larger than the odds that it is„present"/active. The JAK-STAT3 model (lung model) was able to separate clearly the inactive from the active calibration samples.

Fig. 10 shows calibration results of the Bayesian network model based on the evidence curated list of target genes (39 target genes list) from Table 1 and the methods as described using a Sez-4 cell line which was derived from a cutaneous T-cell lymphoma from data set GSE8687. The cells starved of IL-2 (group 1) were JAK-STAT3 inactive and have been used as control group. The training group included 3 samples with cells cultured with IL-2, which were STAT3 active. The model was tested on other samples treated with pan-Jak inhibitor (group 3) and Jak3 inhibitor (group 4). In the diagram, the vertical axis indicates the odds that the TF element is "present" resp. "absent", which corresponds to the JAK-STAT3 cellular signaling pathway being active resp. passive, wherein values above the horizontal axis correspond to the TF element being more likely„present"/active and values below the horizontal axis indicate that the odds that the TF element is "absent'Vpassive are larger than the odds that it is„present"/active. The JAK-STAT3 model (blood model) was able to separate clearly the inactive from the active calibration samples.

In the following, validation results of the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) are shown in Figs. 11 to 15.

Fig. 11 shows JAK-STAT3 cellular signaling pathway activity predictions of the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1. Epithelial cells from HaCaT cell lines were stimulated with epidermal growth factor (EGF) in data set GSE32975. Each group represents one replica from the cell line. In the diagram, the vertical axis indicates the odds that the TF element is "present" resp. "absent", which corresponds to the JAK-STAT3 cellular signaling pathway being active resp. passive, wherein values above the horizontal axis correspond to the TF element being more likely„present"/active and values below the horizontal axis indicate that the odds that the TF element is "absent'Vpassive are larger than the odds that it is „present"/active. The JAK-STAT3 lung model correctly predicts higher STAT3 activity in the samples which were stimulated with EGF (second bar of each group), and inactive STAT3 in the unstimulated control group (first of each group). In group 6 and group 7, the samples were treated with gefitinib, and JAK-STAT3 lung model can predict the decreased STAT3 pathway activity (third bar of group 6 and group 7).

Fig. 12 shows JAK-STAT3 cellular signaling pathway activity predictions of the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1. Ishikawa H cells (derived endometrial carcinomas) were dosed with either EGF (epidermal growth factor) or Iressa (gefitinib) for 12 or 24 hours in data set GSE20854. In the diagram, the vertical axis indicates the odds that the TF element is "present" resp. "absent", which corresponds to the JAK-STAT3 cellular signaling pathway being active resp. passive, wherein values above the horizontal axis correspond to the TF element being more likely„present"/active and values below the horizontal axis indicate that the odds that the TF element is "absent'Vpassive are larger than the odds that it is

„present"/active. The JAK-STAT3 lung model correctly predicts higher STAT3 activity in the samples which were stimulated with EGF for 12 hours (group 2) and 24 hours (group 5), compared to undosed samples and harvested at 12 hours (group 1) and at 24 hours (group 4). Group 3 and group 6 were dosed with iressa for 12 hours and 24 hours, respectively, and the JAK-STAT3 lung model predicts decreased STAT3 pathway activity.

Fig. 13 shows JAK-STAT3 cellular signaling pathway activity predictions of the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1. PC9 or HCC827 cells are GFR-mutant NSCLC (Non-small cell lung cancer) cells, and they were treated with erlotinib or DMSO for 8 days (data set GSE67051). In the diagram, the vertical axis indicates the odds that the TF element is "present" resp. "absent", which corresponds to the JAK-STAT3 cellular signaling pathway being active resp. passive, wherein values above the horizontal axis correspond to the TF element being more likely„present"/active and values below the horizontal axis indicate that the odds that the TF element is "absent'Vpassive are larger than the odds that it is

„present"/active. The JAK-STAT3 lung model correctly predicts higher STAT3 activity in the PC 9 (group 1) and HCC827 (group 3) cells that were treated with DMSO, compared to PC 9 (group 2) and HCC827 (group 4) cells that were treated with erlotinib.

Fig. 14 shows JAK-STAT3 cellular signaling pathway activity predictions of the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1. EGFR-mutant lung cancer cells HCC827 were treated with luM erlotinib (EGFR inhibitor) and DMSO in data set GSE51212. In the diagram, the vertical axis indicates the odds that the TF element is "present" resp. "absent", which corresponds to the JAK-STAT3 cellular signaling pathway being active resp. passive, wherein values above the horizontal axis correspond to the TF element being more likely „present"/active and values below the horizontal axis indicate that the odds that the TF element is "absent'Vpassive are larger than the odds that it is„present"/active. The JAK- STAT3 lung model correctly predicts higher STAT3 activity in the cells that were treated with DMSO for 6 hours (group 1) and 24 hours (group 2), compared to cells that were treated with erlotinib for 3 hours (group 3), 6 hours(group 4), 12 hours (group 5) and 24 hours (group 6).

Fig. 15 shows JAK-STAT3 cellular signaling pathway activity predictions of the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1. siSTAT3 knockdown of a tamoxifen initiated, transformation inducible, breast cancer model system (data set GSE64536) with associated controls of ethanol (EtOH) and si EG treatments. In the diagram, the vertical axis indicates the odds that the TF element is "present" resp. "absent", which corresponds to the JAK- STAT3 cellular signaling pathway being active resp. passive, wherein values above the horizontal axis correspond to the TF element being more likely„present"/active and values below the horizontal axis indicate that the odds that the TF element is "absent'Vpassive are larger than the odds that it is„present"/active. The JAK-STAT3 lung model correctly predicts higher JAK-STAT3 activity in the cells that were treated with EtOH for 4 hours (group 1) and 24 hours (group 2), compared to cells that were initiated with tamoxifen for 4 hours (group 3) and 24 hours (group 4).

In the following, validation results of the trained exemplary blood Bayesian network model using the evidence curated list of target genes (39 target genes list) are shown in Figs. 16 and 17.

Fig. 16 shows JAK-STAT3 cellular signaling pathway activity predictions of the trained exemplary blood Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1. A Sez-4 cell line which was derived from a cutaneous T-cell lymphoma (data set GSE8685). The cells starved of IL-2 for 16 hours (group 1), followed by addition of IL-2 (200U) resp. IL-15 (20ng/mL). In the diagram, the vertical axis indicates the odds that the TF element is "present" resp. "absent", which corresponds to the JAK-STAT3 cellular signaling pathway being active resp. passive, wherein values above the horizontal axis correspond to the TF element being more likely „present"/active and values below the horizontal axis indicate that the odds that the TF element is "absent'Vpassive are larger than the odds that it is„present"/active. The JAK- STAT3 blood model correctly predicts that JAK-STAT3 is active in the cells that were treated with IL-2 (group 2) and IL-15 (group 3) compared to the control group (group 1).

Fig. 17 shows JAK-STAT3 cellular signaling pathway activity predictions of the trained exemplary blood Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1. Peripheral blood mononuclear cells (PBMC) were isolated from whole blood from patients, who had JAK-STAT3 mutations and a resulting immune disease (hyper-IgE syndrome), and healthy control subjects (data set GSE8507). In the diagram, the vertical axis indicates the odds that the TF element is "present" resp.

"absent", which corresponds to the JAK-STAT3 cellular signaling pathway being active resp. passive, wherein values above the horizontal axis correspond to the TF element being more likely„present"/active and values below the horizontal axis indicate that the odds that the TF element is "absent'Vpassive are larger than the odds that it is„present"/active. The JAK- STAT3 blood model correctly predicts that JAK-STAT3 is inactive in healthy control groups (group 1), that JAK-STAT3 activity is increased for the control group (no latex beads) after 180 minutes (group 2), and that STAT3 is highly active in cells treated with IgG-coated latex beads for 180 minutes.

Further validation results of the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1 and the 10 target gene shortlist for the JAK-STAT3 lung model from Table 2 are shown in Fig. 18. Here, the evidence curated list of target genes (39 target genes list) of Table 1 is compared with the 10 target gene shortlist for the JAK-STAT3 lung model for the same data sets for the JAK-STAT3 lung model.

Fig. 18 shows the correlation between the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1 and the 10 target gene shortlist for the JAK-STAT3 lung model from Table 2, respectively. In the diagram, the horizontal axis indicates the odds (on a log2 scale) that the TF element is "present" resp. "absent", which corresponds to the JAK-STAT3 cellular signaling pathway being active resp. passive, as predicted by the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1. The vertical axis indicates the same information, as predicted by the trained exemplary lung Bayesian network model using the 10 target gene shortlist for the JAK- STAT3 lung model (data sets GSE57156, GSE32975, GSE20854, GSE67051, GSE51212, GSE64536). The two models are significantly correlated with a p-value of 2.2e-16 and a correlation coefficient of 0.866.

Further validation results of the trained exemplary lung Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1 and the 12 target gene shortlist for the JAK-STAT3 blood model from Table 3 are shown in Fig. 19. Here, the evidence curated list of target genes (39 target genes list) of Table 1 is compared with the 12 target gene shortlist for the JAK-STAT3 blood model for the same data sets for the JAK-STAT3 blood model.

Fig. 19 shows the correlation between the trained exemplary blood Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1 and the 12 target gene shortlist for the JAK-STAT3 blood model from Table 3, respectively. In the diagram, the horizontal axis indicates the odds (on a log2 scale) that the TF element is "present" resp. "absent", which corresponds to the JAK-STAT3 cellular signaling pathway being active resp. passive, as predicted by the trained exemplary blood Bayesian network model using the evidence curated list of target genes (39 target genes list) from Table 1. The vertical axis indicates the same information, as predicted by the trained exemplary blood Bayesian network model using the 10 target gene shortlist for the JAK- STAT3 blood model (data sets GSE8687, GSE8685, GSE8507). The two models are significantly correlated with a p-value of 2.2e-16 and a correlation coefficient of 0.963.

Further experiments with respect to the predictability of diseases, e.g.

rheumatoid arthritis, and/or therapy response, e.g., to JAK-STAT inhibitors based on JAK- STAT3 activity are described. In a public data set GSE65010 memory and naive T effector (i.e., mature and, unlike activated or memory T cells, in a state where its cognate antigen has not encountered within the periphery) and T-Reg (CD4+-CD25+) cells were isolated from peripheral blood from healthy individuals and patients with rheumatoid arthritis (RA). RNA was isolated and an Affymetrix HG-U133Plus2.0 microarray was performed. The JAK-

STAT3 blood-based pathway model was used to analyze the Affymetrix data, and the JAK- STAT3 pathway activity was determined on a log2odds scale for each individual sample. The results clearly indicated that in samples from patients with rheumatoid arthritis the JAK- STAT3 pathway is more active in activated memory and T-Reg cells, compared to healthy individuals (Wilcox test p-value 0.04 between activated memory cells from healthy controls vs. RA patients; Wilcox test p-value 0.065 between T-Reg cells from healthy controls vs. RA patients; combined Wilcox test p-value 0.0045 between activated memory plus T-Reg cells from healthy controls vs. RA patients). Measuring JAK-STAT3 pathway activity using the JAK-STAT3 pathway model can therefore enable diagnosis of rheumatoid arthritis and prediction of response to anti-STAT therapy, and monitoring of therapy response, correct dosing of the drug and compliance checking.

Instead of applying the calibrated mathematical model, e.g., the exemplary Bayesian network model, on mRNA input data coming from microarrays or RNA

sequencing, it may be beneficial in clinical applications to develop dedicated assays to perform the sample measurements, for instance on an integrated platform using qPCR to determine mRNA levels of target genes. The RNA/DNA sequences of the disclosed target genes can then be used to determine which primers and probes to select on such a platform.

Validation of such a dedicated assay can be done by using the microarray- based mathematical model as a reference model, and verifying whether the developed assay gives similar results on a set of validation samples. Next to a dedicated assay, this can also be done to build and calibrate similar mathematical models using RNA sequencing data as input measurements.

The set of target genes which are found to best indicate specific cellular signaling pathway activity, e.g., Tables 1 to 3, based on microarray/RNA sequencing based investigation using the calibrated mathematical model, e.g., the exemplary Bayesian network model, can be translated into a multiplex quantitative PCR assay to be performed on a sample of the subject and/or a computer to interpret the expression measurements and/or to infer the activity of the JAK-STAT3 cellular signaling pathway. To develop such a test (e.g., FDA- approved or a CLIA waived test in a central service lab or a laboratory developed test for research use only) for cellular signaling pathway activity, development of a standardized test kit is required, which needs to be clinically validated in clinical trials to obtain regulatory approval.

The present invention relates to a computer-implemented method for inferring activity of a JAK-STAT3 cellular signaling pathway in a subject performed by a digital processing device, wherein the inferring is based on expression levels of three or more target genes of the JAK-STAT3 cellular signaling pathway measured in a sample of the subject. The present invention further relates to an apparatus for inferring activity of a JAK-STAT3 cellular signaling pathway in a subject comprising a digital processor configured to perform the method, to a non-transitory storage medium for inferring activity of a JAK-STAT3 cellular signaling pathway in a subject storing instructions that are executable by a digital processing device to perform the method, and to a computer program for inferring activity of a JAK-STAT3 cellular signaling pathway in a subject comprising program code means for causing a digital processing device to perform the method, when the computer program is run on the digital processing device.

The method may be used, for instance, in diagnosing an (abnormal) activity of the JAK-STAT3 cellular signaling pathway, in prognosis based on the inferred activity of the JAK-STAT3 cellular signaling pathway, in the enrollment of a subject in a clinical trial based on the inferred activity of the JAK-STAT3 cellular signaling pathway, in the selection of subsequent test(s) to be performed, in the selection of companion diagnostics tests, in clinical decision support systems, or the like. In this regard, reference is made to the published international patent application WO 2013/011479 A2 ("Assessment of cellular signaling pathway activity using probabilistic modeling of target gene expression"), to the published international patent application WO 2014/102668 A2 ("Assessment of cellular signaling pathway activity using linear combination(s) of target gene expressions"), and to Verhaegh W. et al, "Selection of personalized patient therapy through the use of knowledge-based computational models that identify tumor-driving signal transduction pathways", Cancer Research, Vol. 74, No. 11, 2014, pages 2936 to 2945, which describe these applications in more detail.

Example 4: Further information for illustrating the present invention

(1) Measuring Levels of gene expression

Data derived from the unique set of target genes described herein is further utilized to infer an activity of the JAK-STAT3 cellular signaling pathway using the methods described herein.

Methods for analyzing gene expression levels in extracted samples are generally known. For example, methods such as Northern blotting, the use of PCR, nested PCR, quantitative real-time PCR (qPCR), RNA-seq, or microarrays can all be used to derive gene expression level data. All methods known in the art for analyzing gene expression of the target genes are contemplated herein.

Methods of determining the expression product of a gene using PCR based methods may be of particular use. In order to quantify the level of gene expression using PCR, the amount of each PCR product of interest is typically estimated using conventional quantitative real-time PCR (qPCR) to measure the accumulation of PCR products in real time after each cycle of amplification. This typically utilizes a detectible reporter such as an intercalating dye, minor groove binding dye, or fluorogenic probe whereby the application of light excites the reporter to fluoresce and the resulting fluorescence is typically detected using a CCD camera or photomultiplier detection system, such as that disclosed in U.S. Pat. No. 6,713,297 which is hereby incorporated by reference.

In some embodiments, the probes used in the detection of PCR products in the quantitative real-time PCR (qPCR) assay can include a fluorescent marker. Numerous fluorescent markers are commercially available. For example, Molecular Probes, Inc.

(Eugene, Oreg.) sells a wide variety of fluorescent dyes. Non- limiting examples include Cy5, Cy3, TAMRA, R6G, Rl 10, ROX, JOE, FAM, Texas Red™, and Oregon Green™.

Additional fluorescent markers can include IDT ZEN Double-Quenched Probes with traditional 5' hydrolysis probes in qPCR assays. These probes can contain, for example, a 5' FAM dye with either a 3' TAMRA Quencher, a 3' Black Hole Quencher (BHQ, Biosearch Technologies), or an internal ZEN Quencher and 3' Iowa Black Fluorescent Quencher (IBFQ).

Fluorescent dyes useful according to the invention can be attached to oligonucleotide primers using methods well known in the art. For example, one common way to add a fluorescent label to an oligonucleotide is to react an N-Hydroxysuccinimide (NHS) ester of the dye with a reactive amino group on the target. Nucleotides can be modified to carry a reactive amino group by, for example, inclusion of an allyl amine group on the nucleobase. Labeling via allyl amine is described, for example, in U.S. Pat. Nos. 5,476,928 and 5,958,691, which are incorporated herein by reference. Other means of fluorescently labeling nucleotides, oligonucleotides and polynucleotides are well known to those of skill in the art.

Other fluorogenic approaches include the use of generic detection systems such as SYBR-green dye, which fluoresces when intercalated with the amplified DNA from any gene expression product as disclosed in U.S. Pat. Nos. 5,436,134 and 5,658,751 which are hereby incorporated by reference.

Another useful method for determining target gene expression levels includes RNA-seq, a powerful analytical tool used for transcriptome analyses, including gene expression level difference between different physiological conditions, or changes that occur during development or over the course of disease progression.

Another approach to determine gene expression levels includes the use of microarrays for example RNA and DNA microarray, which are well known in the art.

Microarrays can be used to quantify the expression of a large number of genes

simultaneously.

(2) Generalized workflow for determining the activity of JAK-STAT3 cellular signaling

A flowchart exemplarily illustrating a process for inferring the activity of JAK-STAT3 cellular signaling from a sample isolated from a subject is shown in Fig. 3. First, the mRNA from a sample is isolated (11). Second, the mRNA expression levels of a unique set of at least three or more JAK-STAT3 target genes, as described herein, are measured (12) using methods for measuring gene expression that are known in the art. Next, an activity level of a JAK-STAT3 transcription factor (TF) element (13) is determined using a calibrated mathematical pathway model (14) relating the expression levels of the three or more JAK-STAT3 target genes to the activity level of the JAK-STAT3 TF element. Finally, the activity of the JAK-STAT3 cellular signaling pathway in the subject is inferred (15) based on the determined activity level of the JAK-STAT3 TF element in the sample of the subject. For example, the JAK-STAT3 cellular signaling pathway is determined to be active if the activity is above a certain threshold, and can be categorized as passive if the activity falls below a certain threshold.

(3) Calibrated mathematical pathway model

As contemplated herein, the expression levels of the unique set of three or more JAK-STAT3 target genes described herein are used to determine an activity level of a JAK-STAT3 TF element using a calibrated mathematical pathway model as further described herein. The calibrated mathematical pathway model relates the expression levels of the three or more JAK-STAT3 target genes to the activity level of the JAK-STAT3 TF element.

As contemplated herein, the calibrated mathematical pathway model is based on the application of a mathematical pathway model. For example, the calibrated

mathematical pathway model can be based on a probabilistic model, for example, a Bayesian network model, or a linear or pseudo-linear model.

In an embodiment, the calibrated mathematical pathway model is a probabilistic model incorporating conditional probabilistic relationships relating the JAK- STAT3 TF element and the expression levels of the three or more JAK-STAT3 target genes. In an embodiment, the probabilistic model is a Bayesian network model.

In an alternative embodiment, the calibrated pathway mathematical model can be a linear or pseudo-linear model. In an embodiment, the linear or pseudo-linear model is a linear or pseudo-linear combination model as further described herein.

A flowchart exemplarily illustrating a process for generating a calibrated mathematical pathway model is shown in Fig. 4. As an initial step, the training data for the mRNA expression levels is collected and normalized. The data can be collected using, for example, microarray probeset intensities (101), real-time PCR Cq values (102), raw RNAseq reads (103), or alternative measurement modalities (104) known in the art. The raw expression level data can then be normalized for each method, respectively, by normalization using a normalization algorithm, for example, frozen robust military analysis (fRMA) or MAS5.0 (111), normalization to average Cq of reference genes (112), normalization of reads into reads/fragments per kilobase of transcript per million mapped reads (RPKM/FPKM) (113), or normalization w.r.t. reference genes/proteins (114). This normalization procedure leads to a normalized probeset intensity (121), normalized Cq values (122), normalized RPKM/FPKM (123), or normalized measurement (124) for each method, respectively, which indicate target gene expression levels within the training samples.

Once the training data has been normalized, a training sample ID or IDs (131) is obtained and the training data of these specific samples is obtained from one of the methods for determining gene expression (132). The final gene expression results from the training sample are output as training data (133). All of the data from various training samples are incorporated to calibrate the model (including for example, thresholds, CPTs, for example in the case of the probabilistic or Bayesian network, weights, for example, in the case of the linear or pseudo-linear model, etc) (144). In addition, the pathway's target genes and measurement nodes (141) are used to generate the model structure for example, as described in Fig. 2 (142). The resulting model structure (143) of the pathway is then incorporated with the training data (133) to calibrate the model (144), wherein the gene expression levels of the target genes is indicative of the transcription factor element activity. As a result of the TF element determination in the training samples, a calibrated pathway model (145) is generated, which assigns the JAK-STAT3 cellular signaling pathway activity for a subsequently examined sample of interest, for example from a subject with a cancer, based on the target gene expression levels in the training samples.

(4) TF element determination

A flowchart exemplarily illustrating a process for determining an activity level of a TF element is shown in Fig. 5. The expression level data (test data) (163) from a sample extracted from a subject is input into the calibrated mathematical pathway model (145). The mathematical pathway model may be a probabilistic model, for example, a Bayesian network model, a linear model, or a pseudo-linear model.

The mathematical pathway model may be a probabilistic model, for example, a Bayesian network model, based on conditional probabilities relating the JAK-STAT3 TF element and expression levels of the three or more target genes of the JAK-STAT3 cellular signaling pathway measured in the sample of the subject, or the mathematical model may be based on one or more linear combination(s) of expression levels of the three or more target genes of the JAK-STAT3 cellular signaling pathway measured in the sample of the subject. In particular, the determining of the activity of the JAK-STAT3 cellular signaling pathway may be performed as disclosed in the published international patent application WO

2013/011479 A2 ("Assessment of cellular signaling pathway activity using probabilistic modeling of target gene expression"), the contents of which are herewith incorporated in their entirety. Briefly, the data is entered into a Bayesian network (BN) inference engine call (for example, a BNT toolbox) (154). This leads to a set of values for the calculated marginal BN probabilities of all the nodes in the BN (155). From these probabilities, the transcription factor (TF) node's probability (156) is determined and establishes the TF element's activity level (157).

Alternatively, the mathematical model may be a linear model. For example, a linear model can be used as described in the published international patent application WO 2014/102668 A2 ("Assessment of cellular signaling pathway activity using linear

combination(s) of target gene expressions"), the contents of which are herewith incorporated in their entirety. Further details regarding the calculating/determining of cellular signaling pathway activity using mathematical modeling of target gene expression can also be found in Verhaegh W. et al, "Selection of personalized patient therapy through the use of knowledge- based computational models that identify tumor-driving signal transduction pathways", Cancer Research, Vol. 74, No. 11, 2014, pages 2936 to 2945. Briefly, the data is entered into a calculated weighted linear combination score (w/c) (151). This leads to a set of values for the calculated weighted linear combination score (152). From these weighted linear combination scores, the transcription factor (TF) node's weighted linear combination score (153) is determined and establishes the TF's element activity level (157).

(5) Procedure for discretized observables

A flowchart exemplarily illustrating a process for inferring activity of a JAK-

STAT3 cellular signaling pathway in a subject as a discretized observable is shown in Fig. 6. First, the test sample is extracted and given a test sample ID (161). Next, the test data for the mRNA expression levels is collected and normalized (162). The test data can be collected using the same methods as discussed for the training samples in Fig. 5, using microarray probeset intensities (101), real-time PCR Cq values (102), raw RNAseq reads (103), or an alternative measurement modalities (104). The raw expression level data can then be normalized for each method, respectively, by normalization using an algorithm, for example fRMA or MAS5.0 (111), normalization to average Cq of reference genes (112),

normalization of reads into RPKM/FPKM (113), and normalization w.r.t. reference genes/proteins (114). This normalization procedure leads to a normalized probeset intensity

(121) , normalized Cq values (122), normalized RPKM/FPKM (123), or normalized measurement (124) for each method, respectively.

Once the test data has been normalized, the resulting test data (163) is analyzed in a thresholding step (164) based on the calibrated mathematical pathway model (145), resulting in the thresholded test data (165). In using discrete observables, in one non- limiting example, every expression above a certain threshold is, for example, given a value of 1 and values below the threshold are given a value of 0, or in an alternative embodiment, the probability mass above the threshold as described herein is used as a thresholded value. Based on the calibrated mathematical pathway model, this value represents the TF element's activity level (157), which is then used to calculate the cellular signaling pathway's activity (171). The final output gives the cellular signaling pathway's activity (172) in the subject.

(6) Procedure for continuous observables

A flowchart exemplarily illustrating a process for inferring activity of a JAK-

STAT3 cellular signaling pathway in a subject as a continuous observable is shown in Fig. 7. First, the test sample is extracted and given a test sample ID (161). Next, the test data for the mRNA expression levels is collected and normalized (162). The test data can be collected using the same methods as discussed for the training samples in Figure 5, using microarray probeset intensities (101), real-time PCR Cq values (102), raw RNAseq reads (103), or an alternative measurement modalities (104). The raw expression level data can then be normalized for each method, respectively, by normalization using an algorithm, for example fRJVIA (111), normalization to average Cq of reference genes (112), normalization of reads into RPKM/FPKM (113), and normalization to w.r.t. reference genes/proteins (114). This normalization procedure leads to a normalized probeset intensity (121), normalized Cq values

(122) , normalized RPKM/FPKM (123), or normalized measurement (124) for each method, respectively.

Once the test data has been normalized, the resulting test data (163) is analyzed in the calibrated mathematical pathway model (145). In using continuous observables, as one non-limiting example, the expression levels are converted to values between 0 and 1 using a sigmoid function as described in further detail herein. The TF element determination as described herein is used to interpret the test data in combination with the calibrated mathematical pathway model, the resulting value represents the TF element's activity level (157), which is then used to calculate the cellular signaling pathway's activity (171). The final output gives the cellular signaling pathway's activity (172) in the subject.

(7) Target gene expression level determination procedure

A flowchart exemplary illustrating a process for deriving target gene expression levels from a sample extracted from a subject is shown in Fig. 8. In an exemplary embodiment, samples are received and registered in a laboratory. Samples can include, for example, Formalin-Fixed, Paraffin-Embedded (FFPE) samples (181) or fresh frozen (FF) samples (180). FF samples can be directly lysed (183). For FFPE samples, the paraffin can be removed with a heated incubation step upon addition of Proteinase K (182). Cells are then lysed (183), which destroys the cell and nuclear membranes which makes the nucleic acid (NA) available for further processing. The nucleic acid is bound to a solid phase (184) which could for example, be beads or a filter. The nucleic acid is then washed with washing buffers to remove all the cell debris which is present after lysis (185). The clean nucleic acid is then detached from the solid phase with an elution buffer (186). The DNA is removed by DNAse treatment to ensure that only R A is present in the sample (187). The nucleic acid sample can then be directly used in the RT-qPCR sample mix (188). The RT-qPCR sample mixes contains the RNA sample, the RT enzyme to prepare cDNA from the RNA sample and a PCR enzyme to amplify the cDNA, a buffer solution to ensure functioning of the enzymes and can potentially contain molecular grade water to set a fixed volume of concentration. The sample mix can then be added to a multiwell plate (i.e., 96 well or 384 well plate) which contains dried RT-qPCR assays (189). The RT-qPCR can then be run in a PCR machine according to a specified protocol (190). An example PCR protocol includes i) 30 minutes at 50°C; ii) 5 minutes at 95°C; iii) 15 seconds at 95°C; iv) 45 seconds at 60°C; v) 50 cycles repeating steps iii and iv. The Cq values are then determined with the raw data by using the second derivative method (191). The Cq values are exported for analysis (192).

(8) JAK-STAT3 Mediated diseases and disorders and methods of treatment

As contemplated herein, the methods and apparatuses of the present invention can be utilized to assess JAK-STAT3 cellular signaling pathway activity in a subject, for example, a subject suspected of having, or having, a disease or disorder wherein the status of the JAK-STAT3 signaling pathway is probative, either wholly or partially, of disease presence or progression. In an embodiment, provided herein is a method of treating a subject comprising receiving information regarding the activity status of a JAK-STAT3 cellular signaling pathway derived from a sample extracted from the subject using the methods described herein and administering to the subject a JAK-STAT3 inhibitor if the information regarding the activity of the JAK-STAT3 cellular signaling pathway is indicative of an active JAK-STAT3 signaling pathway. In a particular embodiment, the JAK-STAT3 cellular signaling pathway activity indication is set at a cutoff value of odds of the JAK-STAT3 cellular signaling pathway being active of 10: 1, 5: 1, 4: 1, 2: 1, 1 : 1, 1 :2, 1 :4, 1 :5, 1 : 10.

The JAK-STAT3 pathway plays a role in a large number of diseases, such as in various cancer types like, for example, pancreatic cancer, colon cancer, breast cancer, head and neck cancer, osteosarcoma, multiple myeloma, follicular lymphoma, prostate cancer, cervical dysplasia, laryngeal papilloma, Peritoneal cavity carcinoma, ovarian cancer, cervical cancer, non-small cell lung cancer, bladder cancer, melanoma, oesophageal cancer, thyroid cancer, gastric cancer; lymphomas, prostate cancer, rhabdomyosarcoma, gastric cancer, melanoma, low-grade gliomas, Hodgkin's lymphoma; Hepatocellular carcinoma, head and neck squamous cell carcinoma, kidney cancer, liver cancer, glioblastoma multiforme

Neuroendocrine carcinoma, multiple myeloma, Chronic lymphocytic leukaemia, squamous cell lung cancer, and other cancer types and cancer subtypes that have an active STAT3 signaling pathway as a cancer driving pathway, in immune system-mediated diseases like inflammatory bowel disease, rheumatoid arthritis, psoriasis, SLE, multiple sclerosis, et cetera, and in inflammatory diseases like asthma, atherosclerosis, diabetes, psychiatric diseases like depression and schizophrenia, acne, endometriosis, et cetera. With such diseases, measuring the JAK-STAT3 pathway activity profile in immune cell types in tissue and blood is expected to be helpful to diagnose, subtype, and predict and/or monitor response to immunomodulatory, especially immunosuppressive and targeted immunosuppressive, therapy and monitoring immune response status. For example, especially for rheumatoid arthritis and psoriasis. Prediction of response to drugs can be used to match an anti-STAT3 pathway drug to a patient, like for example STA-21 for treatment of psoriasis, curcumin for treatment of Pancreatic cancer (Phase II/III clinical trial), colon cancer (Phase 1/ II/III), breast cancer (Phase II), head and neck cancer (Phase 0), osteosarcoma (Phase I/II), multiple myeloma (Phase II), atopic asthma (phase not provided), dermatitis (Phase II/III), type 2 diabetes (Phase IV), schizophrenia (Phase I/II), Alzheimer's disease (Phase I/II), multiple sclerosis (Phase II), rheumatoid arthritis (Phase 0), AZD for treatment of Hepatocellular carcinoma, lung carcinoma and gastric cancer (Phase I), essential thrombocythaemia myelofibrosis and post-polycythaemia vera (Phase I), Oligodeoxy-nucleotide decoy for treatment of head and neck cancer (Phase O), Tofacitinib for treatment of Rheumatoid arthritis (Phase I/II/III), juvenile idiopathic arthritis (Phase I/II/III), psoriasis (Phase I/II/III), ankylosing spondylitis (Phase II), keratoconjunctivitis sicca (Phase II), ulcerative colitis (Phase III), capsaicin for treatment of Chronic obstructive pulmonary disease (Phase O/I/II), psoriasis (Phase IV), chronic neck pain (Phase II), rhinitis (Phase I/II/IV), pulmonary hypertension (Phase II), HIV infections (Phase II/III), peripheral nervous system diseases

(Phase II/III), migraine (Phase I), burning mouth syndrome (Phase 0), curcumin for treatment of Pancreatic cancer (Phase II/III), colon cancer (Phase 1/ II/III), breast cancer (Phase II), head and neck cancer (Phase 0), osteosarcoma (Phase I/II), multiple myeloma (Phase II), atopic asthma (phase not provided), dermatitis (Phase II/III), type 2 diabetes (Phase IV), schizophrenia (Phase I/II), Alzheimer's disease (Phase I/II), multiple sclerosis (Phase II), rheumatoid arthritis (Phase 0), resveratrol for treatment of Colorectal cancer (Phase I), follicular lymphoma (Phase II), cardiovascular diseases (Phase I/II), type 2 diabetes (Phase I/II/III), obesity (Phase II), Alzheimer's disease (Phase II/III), memory impairment (phase not provided), WithaferinA for treatment of schizophrenia, 3,3"-diindolyl-methane for treatment of Breast cancer (Phase I/II/III), prostate cancer (Phase I/II), cervical dysplasia (Phase III), laryngeal papilloma (Phase II), thyroid disease (Phase 0), Emodin for treatment of polycysitic kidney disease, paclitaxel for treatment of Peritoneal cavity carcinoma (Phase I/II/III), breast cancer (Phase I/II/III/IV), ovarian cancer (Phase I/II/III/ IV), cervical cancer (Phase I/II/III), non-small cell lung cancer (Phase I/II/III/IV), bladder cancer (Phase I/II/III), melanoma (Phase I/II/III), oesophageal cancer (Phase I/II/III), thyroid cancer (Phase I/II/III), gastric cancer (Phase I/II/III), Oleanolic acid/CDDO-Me for treatment of Solid tumours and lymphomas (Phase I), chronic kidney disease and type 2 diabetes (Phase I/II/III), diabetic nephropathy (Phase II), hepatic dysfunction (Phase I/II), vinorelbine for treatment of Non- small cell lung cancer (Phase I/II/III/IV), breast cancer (Phase I/II/III/IV), prostate cancer (Phase 1/ II), rhabdomyosarcoma (Phase I/II/III), gastric cancer (Phase II), melanoma (Phase II), low-grade gliomas (Phase II), Hodgkin's lymphoma (Phase I/II/III), Cryptotanshinone for treatment of Polycystic ovary syndrome, cinnamon bark for treatment of Polycystic ovary syndrome (Phase I), hypercholesterolaemia and type 2 diabetes (Phase II), sorafenib for treatment of Hepatocellular carcinoma (Phase I/II/III/IV), head and neck squamous cell carcinoma (Phase I/II), gastric cancer (Phase I/II), breast cancer (Phase I/II/III), prostate cancer (Phase I/II), thyroid cancer (Phase II/ III), non-small cell lung cancer (Phase I/II/III), pancreatic cancer (Phase I/II/III), bladder cancer (Phase I/II), colorectal cancer (Phase I/II), kidney cancer (Phase I/II/III/IV), liver cancer (Phase I/II/III), glioblastoma multiforme (Phase I/II), leukaemia (Phase I/II/III), melanoma (Phase I/II/III), Atiprimod for treatment of Neuroendocrine carcinoma (Phase II), multiple myeloma (Phase I/II), Auranofm for treatment of Chronic lymphocytic leukaemia (Phase II), squamous cell lung cancer (Phase II), ovarian cancer (phase not provided), and Oligodeoxy-nucleotide decoy to treat head and neck cancer (Phase O) (see also Miklossy G. et al., "Therapeutic modulators of STAT signaling for human diseases", Nature Reviews Drug Discovery, Vol. 12, No. 8, August 2013, pages 611 to 629).

This application describes several preferred embodiments. Modifications and alterations may occur to others upon reading and understanding the preceding detailed description. It is intended that the application is construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

In the claims, the word "comprising" does not exclude other elements or steps, and the indefinite article "a" or "an" does not exclude a plurality.

A single unit or device may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

Calculations like the determination of the risk score performed by one or several units or devices can be performed by any other number of units or devices.

A computer program may be stored/distributed on a suitable medium, such as an optical storage medium or a solid-state medium, supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

Example 5: Sequence Listings Used in Application

SEQUENCE LISTING:

Seq. No. Gene:

Seq. 1 AKT1

Seq. 2 BCL2

Seq. 3 BCL2L1

Seq. 4 BIRC5

Seq. 5 CCND1 Seq. 6 CD274 Seq. 7 CDK 1A Seq. 8 CRP

Seq. 9 FGF2 Seq. 10 FOS Seq. 11 FSCN1 Seq. 12 FSCN2 Seq. 13 FSCN3 Seq. 14 HIF1A Seq. 15 HSP90AA1 Seq. 16 HSP90AB1 Seq. 17 HSP90B1 Seq. 18 HSPA1A Seq. 19 HSPA1B Seq. 20 ICAM1 Seq. 21 IFNG Seq. 22 IL10 Seq. 23 JunB Seq. 24 MCL1 Seq. 25 MMP1 Seq. 26 MMP3 Seq. 27 MMP9 Seq. 28 MUC1 Seq. 29 MYC Seq. 30 NOS2 Seq. 31 POU2F1 Seq. 32 PTGS2 Seq. 33 SAA1 Seq. 34 STAT1 Seq. 35 TIMP1 Seq. 36 TNFRSFIB Seq. 37 TWIST 1 Seq. 38 VIM Seq. 39 ZEB1