Method for assessing microsatellite instability and homologous recombination repair deficiency in cancer

Abstract

The present disclosure discloses methods for determining the presence of microsatellite instability in a subject suffering from cancer and selecting treatments for the subject based on the presence or absence of microsatellite instability. The present disclosure also discloses methods for selecting a treatment and / or treating a subject based on the subject's determined presence or absence of homologous recombination repair deficiency / DNA damage.

Description

Attorney Docket No. GNCN-027 / 01WO 320289-2156 METHOD FOR ASSESSING MICROSATELLITE INSTABILITY AND HOMOLOGOUS RECOMBINATION REPAIR DEFICIENCY IN CANCER CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority from U.S. Provisional Application No. 63 / 574,971 filed April 5, 2024, which is incorporated by reference herein in its entirety for all purposes. FIELD

[0002] The present disclosure is directed to methods and systems for making and using an RNA- based microsatellite stable (MSS) predictive response signature (MSS-PRS) that selects tumors not identified with conventional MSI testing for patients suffering from colorectal cancer (CRC) or Uterine Corpus Endometrial Carcinoma (UCEC) based on their MSS-PRS as well as gene signatures for determining CMS groups in CRC. The present disclosure is also directed to methods and systems for making and using an RNA-based homologous recombination repair predictive response signature (HRR-PRS) in specific types of cancers (i.e., BRCA, PAAD, OV and PRAD). BACKGROUND

[0003] Microsatellites (MS) are tandem repeats of short DNA sequences, abundant throughout the human genome. Owing to their high mutation rates, MS have been widely used as polymorphic markers in population genetics and forensics. Microsatellite instability (MSI) is a hypermutator phenotype that occurs in tumors with impaired DNA mismatch repair (MMR) and is characterized by widespread length polymorphisms of MS repeats due to DNA polymerase slippage as well as by elevated frequency of single-nucleotide variants (SNVs) (Aaltonen, L. A. et al. Clues to the pathogenesis of familial colorectal cancer. Science 260, 812–816 (1993). MSI in sporadic cases is caused by inactivation of MMR genes (for example, MLH1, MSH2, MSH3, MSH6 and PMS2) through somatic mutations, with increased risk of cancer for those with inherited germline mutations (that is, Lynch syndrome) (Hendriks, Y. M. C. et al. Diagnostic approach and management of Lynch syndrome (hereditary nonpolyposis colorectal carcinoma): a guide for clinicians. CA. Cancer J. Clin. 56, 213–225 (2006). MSI also occurs by hypermethylation of the MLH1 promoter (for example, associated with the somatic BRAFV600E mutation), epigenetic inactivation of MSH2, or downregulation of MMR genes by microRNAs (Herman, J. G. et al. Incidence and functional consequences of hMLH1 promoter hypermethylation in colorectal carcinoma. Proc. Natl Acad. Sci. USA 95, 6870–6875 (1998); Ligtenberg, M. J. L. et al. Heritable somatic methylation and inactivation of MSH2 in families with Lynch syndrome due to deletion of the 3′ exons of TACSTD1. Nat. Genet. 41, 112–117 (2009) and Volinia, S. et al. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc. Natl Acad. Sci. USA 103, 2257–2261 (2006)). MSI events within coding regions can alter the reading frame, leading to truncated, functionally-impaired proteins (Jiricny, J. The multifaceted mismatch-repair system. Nat. Rev. Mol. Cell Biol. 7, 335–346 (2006)).

[0004] MSI has been reported in colorectal tumors, glioblastomas, lymphomas, stomach, urinary tract, ovarian and endometrial tumors (Dudley, J. C., Lin, M.-T., Le, D. T. & Eshleman, J. R. Microsatellite instability as a biomarker for PD-1 blockade. Clin. Cancer Res.22, 813–820 (2016)). Microsatellite instability and deficient mismatch repair proteins are key biomarkers to qualify for immuno-oncology (IO) treatments in colorectal cancer, but their prevalence is low.5- 15% of mCRC patients are indicated for immune checkpoint inhibitor (ICI) therapy (42% overall response rate; ORR) when their microsatellite instability is determined to be high (MSI-H) via polymerase chain reaction (PCR) and / or next generation sequencing (NGS) and / or mismatch repair deficiency (dMMR) is determined by immunohistochemistry (IHC). As such, MSI-H and mismatch repair deficient (dMMR) tumors are associated with favorable immune checkpoint inhibitor (ICI) responses (André T et al. N Engl J Med. 2020 Dec 3;383(23):2207-2218).

[0005] In contrast, 85-95% of metastatic colorectal cancer (mCRC) patients are considered microsatellite stable (MSI-low; designated MSS hereafter) based on negative MSI and / or MMR test results and MSS tumors are considered resistant to ICI monotherapy (~5% ORR). As such, MSS / proficient MMR (pMMR) patients often have poorer prognosis and treatment outcomes than MSI-H / dMMR patients (Le DT et al. N Engl J Med.2015 Jun 25;372(26):2509-20; Ribic CM et sl. N Engl J Med.2003 Jul 17;349(3):247-57). In MSS / pMMR metastatic CRC, multiple combination therapies are being investigated without biomarkers to guide therapy (Lizardo DY et al. Biochim Biophys Acta Rev Cancer.2020 Dec;1874(2):188447; Pecci F et al. Curr Treat Options Oncol. 2021 Jun 10;22(8):69).

[0006] Therefore, there is an unmet need in the art for means for identifying patients suffering from cancer (e.g., colorectal tumors, glioblastomas, lymphomas, stomach, urinary tract, ovarian and endometrial tumors) that might have high MSI or phenotypes that resemble high MSI and, thus, who might be candidates for specific cancer therapies based on their MSI status or CMS subtype. The systems, compositions and method provided herein address this need.

[0007] A wide range of approved and experimental drugs target elements of the homologous recombination HRD system (FIG. 32). Some tumor types have a high prevalence of homologous recombination deficiency / DNA damage repair (HRD / DDR) defects, particularly breast, prostate, pancreatic, and ovarian. Reliable biomarkers for any single tumor type have been elusive; in part due to differences in homologous recombination deficiency (HRD) within tumor biology (type) context. Narrow panels of HRD genes likely miss patients with HRD / DDR defect. While there are numerous diagnostic tools for assessing HRD across tumor types (see. FIG. 33), not all biomarker(+) patients respond, and some biomarker(-) patients do. Accordingly, there is a need for a tumor type-specific, more holistic functional assay for HRD / DDR defects.

[0008] The systems, compositions and method provided herein also address this need. SUMMARY

[0009] In one aspect, provided herein is a method of treating cancer in a subject, the method comprising: measuring the expression level of a plurality of biomarkers in a sample obtained from a subject suffering from cancer, wherein the plurality of biomarkers are selected from biomarkers listed in Table 3 or Table 5, wherein the measured expression levels of the plurality of biomarkers provide a microsatellite stability predictive response signature (MSS-PRS) for the sample; and administering an immuno-oncology therapeutic agent based on presence of a positive MSS-PRS or chemotherapeutic agent based on presence of a negative MSS-PRS. In some cases, the measuring the expression levels of the plurality of biomarkers is at a nucleic acid level by performing RNA sequencing, reverse transcriptase polymerase chain reaction (RT-PCR) or hybridization-based analyses. In some cases, the RT-PCR is quantitative real time reverse transcriptase polymerase chain reaction (qRT-PCR). In some cases, the RT-PCR is performed with primers specific to the biomarkers selected from the plurality of biomarkers of Table 3 or Table 5. In some cases, the hybridization analysis is a microarray-based hybridization analysis. In some cases, the sample is a formalin-fixed, paraffin-embedded (FFPE) tissue sample, fresh or a frozentissue sample, an exosome, or a bodily fluid obtained from the patient. In some cases, the bodily fluid is blood or fractions thereof, urine, saliva, cerebrospinal fluid (CSF) or sputum. In some cases, the immuno-oncology therapeutic agent is two or more immuno-oncology therapeutic agents used in combination. In some cases, the immuno-oncology therapeutic agent is used in combination in other therapeutic agents. In some cases, the other therapeutic agents are selected from Table 1. In some cases, the immuno-oncology therapeutic agent is an immune checkpoint inhibitor (ICI). In some cases, the chemotherapeutic agent is 5-FU chemotherapy. In some cases, the method further comprises comparing the expression levels of the plurality of biomarkers from Table 3 or Table 5 to an expression level of the plurality of biomarkers from Table 3 or Table 5 in at least one sample training set, wherein the at least one sample training set is from a reference microsatellite instability high / deficient MMR (MSI-H / dMMR)-containing cancer sample, or is from a reference MSI-low / proficient MMR cancer sample; and classifying the tumor sample as having a positive MSS predictive response signature (MMS-PRS (+)) or negative MSS predictive response signature (MMS-PRS (-)) based on the results of the comparing step. In some cases, the comparing comprises applying a statistical algorithm that comprises determining a correlation between the expression levels of the plurality of biomarkers from Table 3 or Table 5 obtained from the sample and the expression levels of the plurality of biomarkers from Table 3 or Table 5 from the at least one training set; and classifying the tumor sample as possessing a MSS-PRS (+) or MSS-PRS (-) based on the results of the statistical algorithm. In some cases, the at least one training set is from a reference MSI-H / dMMR cancer sample and the sample is classified as possessing the positive MSS-PRS if the expression levels of the plurality of biomarkers from Table 3 or Table 5 correlate with the expression levels of the plurality of biomarkers from Table 3 or Table 5 from the reference MSI-H / dMMR cancer sample. In some cases, the at least one training set is from a reference MSI-H / dMMR cancer sample and from a reference MSI- low / proficient MMR cancer sample and the sample is classified as being MSS-PRS (+) if the expression levels of the plurality of biomarkers from Table 3 or Table 5 correlate with the expression levels of the plurality of biomarkers from Table 3 or Table 5 from the reference MSI- H / dMMR cancer sample.

[0010] In some cases, the cancer the patient is suffering from is colon adenocarcinoma (COAD). In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 100 biomarkers or at least 110 biomarkers, at least 120 biomarkers or at least 130 biomarkers of Table 3. In some cases, the plurality of biomarkers selected from Table 3 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 3. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 3. In some cases, the method further comprises measuring the expression level of a plurality of biomarkers selected from biomarkers listed in Table 7 in the sample obtained from the subject suffering from cancer. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers or at least 40 biomarkers from Table 7. In some cases, the plurality of biomarkers selected from Table 7 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 7. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 7. In some cases, the method further comprises comparing the expression levels of the plurality of biomarkers from Table 7 to an expression level of the plurality of biomarkers from Table 7 in at least one sample training set, wherein the at least one sample training set is from a reference CMS1, CMS2, CMS3 and / or CMS4; and classifying the sample as having a CMS1, CMS2, CMS3 or CMS4 based on the results of the comparing step. In some cases, the comparing comprises applying a statistical algorithm that comprises determining a correlation between the expression levels of the plurality of biomarkers from Table 7 obtained from the sample and the expression levels of the plurality of biomarkers from Table 7 from the at least one training set; and classifying the tumor sample as possessing a CMS1, CMS2, CMS3 or CMS4 based on the results of the statistical algorithm.

[0011] In some cases, the cancer the patient is suffering from is Uterine Corpus Endometrial Carcinoma (UCEC). In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers or at least 60 biomarkers of Table 5. In some cases, the pluralityof biomarkers selected from Table 5 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 5. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 5.

[0012] In another aspect, provided herein is a method of detecting a biomarker in a sample obtained from a patient suffering from a cancer, the method comprising, consisting essentially of or consisting of measuring the expression level of a plurality of biomarkers selected from Table 3, Table 5 or Table 7 using an amplification, hybridization and / or sequencing assay. In some cases, the amplification, hybridization and / or sequencing assay comprises performing quantitative real time reverse transcriptase polymerase chain reaction (qRT-PCR), RNAseq, microarrays, gene chips, nCounter Gene Expression Assay, Serial Analysis of Gene Expression (SAGE), Rapid Analysis of Gene Expression (RAGE), nuclease protection assays, Northern blotting, or any other equivalent gene expression detection techniques. In some cases, the expression level is detected by performing qRT-PCR. In some cases, the detection of the expression level comprises using at least one pair of oligonucleotide primers per each biomarker from the plurality of biomarkers selected from Table 3, Table 5 or Table 7. In some cases, the sample is a formalin-fixed, paraffin- embedded (FFPE) tissue sample, fresh or a frozen tissue sample, an exosome, wash fluids, cell pellets, or a bodily fluid obtained from the patient. In some cases, the bodily fluid is blood or fractions thereof, urine, saliva, cerebrospinal fluid (CSF) or sputum. In some cases, the cancer the patient is suffering from is colon adenocarcinoma (COAD). In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 100 biomarkers or at least 110 biomarkers, at least 120 biomarkers or at least 130 biomarkers from Table 3. In some cases, the plurality of biomarkers selected from Table 3 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 3. In some cases, the plurality of biomarkers comprises, consistsessentially of or consists of all the biomarkers from Table 3. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers or at least 40 biomarkers from Table 7. In some cases, the plurality of biomarkers selected from Table 7 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 7. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 7. In some cases, the cancer the patient is suffering from is Uterine Corpus Endometrial Carcinoma (UCEC). In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers or at least 60 biomarkers of Table 5. In some cases, the plurality of biomarkers selected from Table 5 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 5. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 5.

[0013] In yet another aspect, provided herein is a method of treating cancer in a subject, the method comprising: measuring the expression level of a plurality of biomarkers in a sample obtained from a subject suffering from cancer, wherein the plurality of biomarkers are selected from biomarkers listed in Table 8, 9, 10, 11 or 12, wherein the measured expression levels of the plurality of biomarkers provide a homologous recombination repair deficiency predictive response signature (HRD-PRS) for the sample; and administering a therapeutic agent that is a PARP inhibitor based on the presence of a positive HRD-PRS or a therapeutic agent that is not a PARP inhibitor based on the presence of a negative HRD-PRS. In some cases, the measuring the expression levels of the plurality of biomarkers is at a nucleic acid level by performing RNA sequencing, reverse transcriptase polymerase chain reaction (RT-PCR) or hybridization-based analyses. In some cases, the RT-PCR is quantitative real time reverse transcriptase polymerase chain reaction (qRT-PCR). In some cases, the RT-PCR is performed with primers specific to the biomarkers selected from the plurality of biomarkers from Table 8, 9, 10, 11 or 12. In some cases, the hybridization analysis isa microarray-based hybridization analysis. In some cases, the sample is a formalin-fixed, paraffin- embedded (FFPE) tissue sample, fresh or a frozen tissue sample, an exosome, or a bodily fluid obtained from the patient. In some cases, the bodily fluid is blood or fractions thereof, urine, saliva, cerebrospinal fluid (CSF) or sputum. In some cases, the cancer the patient is suffering from is pancreatic adenocarcinoma (PAAD). In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers or at least 78 biomarkers from Table 8. In some cases, the plurality of biomarkers selected from Table 8 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 8. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 8. In some cases, the cancer the patient is suffering from is ovarian cancer (OV). In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers or at least 100 biomarkers from Table 9. In some cases, the plurality of biomarkers selected from Table 9 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 9. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 9. In some cases, the cancer the patient is suffering from is breast cancer (BRCA). In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 100 biomarkers, at least 110 biomarkers, at least 120 biomarkers, at least 130 biomarkers, at least 140 biomarkers, at least 150 biomarkers, or at least 151 biomarkers from Table 10. In some cases, the plurality of biomarkers selected from Table 10 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, atleast 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 10. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 10. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers or at least 93 biomarkers from Table 11. In some cases, the plurality of biomarkers selected from Table 11 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 11. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 11. In some cases, the cancer the patient is suffering from is prostate adenocarcinoma (PRAD). In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers or at least 100 biomarkers from Table 12. In some cases, the plurality of biomarkers selected from Table 12 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 12. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 12.

[0014] In another aspect, provided herein is a method of detecting a biomarker in a sample obtained from a patient suffering from a cancer, the method comprising, consisting essentially of or consisting of measuring the expression level of a plurality of biomarkers selected from Table 8, 9, 10, 11 or 12 using an amplification, hybridization and / or sequencing assay. In some cases, the amplification, hybridization and / or sequencing assay comprises performing quantitative real time reverse transcriptase polymerase chain reaction (qRT-PCR), RNAseq, microarrays, gene chips, nCounter Gene Expression Assay, Serial Analysis of Gene Expression (SAGE), Rapid Analysis of Gene Expression (RAGE), nuclease protection assays, Northern blotting, or any otherequivalent gene expression detection techniques. In some cases, the expression level is detected by performing qRT-PCR. In some cases, the detection of the expression level comprises using at least one pair of oligonucleotide primers per each biomarker from the plurality of biomarkers selected from Table 8, 9, 10, 11 or 12. In some cases, the sample is a formalin-fixed, paraffin- embedded (FFPE) tissue sample, fresh or a frozen tissue sample, an exosome, wash fluids, cell pellets, or a bodily fluid obtained from the patient. In some cases, the bodily fluid is blood or fractions thereof, urine, saliva, cerebrospinal fluid (CSF) or sputum. In some cases, the cancer the patient is suffering from is pancreatic adenocarcinoma (PAAD). In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers or at least 78 biomarkers from Table 8. In some cases, the plurality of biomarkers selected from Table 8 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 8. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 8. In some cases, the cancer the patient is suffering from is ovarian cancer (OV). In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers or at least 100 biomarkers from Table 9. In some cases, the plurality of biomarkers selected from Table 9 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 9. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 9. In some cases, the cancer the patient is suffering from is breast cancer (BRCA). In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 100 biomarkers, at least 110 biomarkers, at least 120biomarkers, at least 130 biomarkers, at least 140 biomarkers, at least 150 biomarkers, or at least 151 biomarkers from Table 10. In some cases, the plurality of biomarkers selected from Table 10 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 10. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 10. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers or at least 93 biomarkers from Table 11. In some cases, the plurality of biomarkers selected from Table 11 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 11. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 11. In some cases, the cancer the patient is suffering from is prostate adenocarcinoma (PRAD). In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers or at least 100 biomarkers from Table 12. In some cases, the plurality of biomarkers selected from Table 12 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 12. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 12. BRIEF DESCRIPTION OF THE FIGURES

[0015] FIG. 1 illustrates the distribution of microsatellite instability (MSI) Mantis scores across TCGA cancer types. The dashed horizontal line depicts the MSI Mantis threshold of 0.4 where MSI Mantis >=0.4 is defined as MSI-high (Bonneville R, Krook MA, Kautto EA, Miya J, Wing MR, Chen HZ, Reeser JW, Yu L, Roychowdhury S. Landscape of Microsatellite Instability Across39 Cancer Types. JCO Precis Oncol. 2017;2017:PO.17.00073. doi: 10.1200 / PO.17.00073. Epub 2017 Oct 3. PMID: 29850653).

[0016] FIG. 2 illustrates the MSI Mantis scores and key gene alterations for colon adenocarcinoma (COAD) samples from The Cancer Genome Atlas (TCGA). The eight (8) genes scanned here for mutations in TCGA COAD samples have been reported in scientific literature as RNA-based signature markers of microsatellite instability and / or deficient mismatch repair (dMMR). These genes were evaluated for suitability as training labels for our RNA classifier training. Of the genes, only MLH1 is a canonical MMR gene whose protein is among the targets analyzed by immunohistochemistry to determine dMMR. The dashed horizontal line depicts the MSI Mantis threshold of 0.4 where MSI Mantis >=0.4 is defined as MSI-high. The dashed horizontal line depicts the MSI Mantis threshold of 0.4 where MSI Mantis >=0.4 is defined as MSI-high.

[0017] FIG. 3 illustrates selection of high mean and high variance genes in the RNA expression data resulted in 2621 genes for feature selection and development of the COAD model. Candidates for the model: 2621 genes with high mean and high variance in the log2(RSEM+1) training sample expression matrix (gene mean >2.75 and variance >1.75). Genes with high means and high variances, as defined by the threshold cutoffs, were used for classifier training.

[0018] FIG.4 illustrates two-fold cross validation curves using ClaNC software on TCGA COAD cancer training dataset (n=145) to guide the selection of the number of genes per MSI activation signature status (i.e., positive or negative) to include in the signature of Table 4 for ascertaining MSI / MMR activation status. ClaNC with cross-validation to fit a 112-gene nearest centroid classifier.

[0019] FIG. 5 illustrates box plots of COAD sample calls by MSI-PRS in the training and test sets.

[0020] FIG.6 illustrates heat maps using the 112 MSI-PRS classifier genes where n=268 COAD samples were arranged by train or test data set then ordered by ascending MSI Mantis score (left heat map) or n=268 COAD samples were allowed to cluster (right heat map).

[0021] FIGs 7A-7B illustrates the COAD MSI-PRS and MSS-PRS test configurations using the same 112-gene signature but depict results as either a binary “Yes / No” call (FIG. 7A) or a quaternary call (e.g., A / B / C / D; FIG.7B) that incorporates the MSI-PRS binary status and the MSIMantis binary status. MSS-PRS and MSI-PRS are sometimes used interchangeably, but commercially the prevailing use is MSS-PRS.

[0022] FIG. 8 illustrates a similar Distribution of MSS-PRS Scores between MSI-H and MSS Tumors Across Five Independent Colon Cancer RNA-seq Datasets

[0023] FIG.9 illustrates principal component analysis that shows that MSS-PRS groups A, B, and C are not globally identical. Although MSS-PRS groups are not globally identical, there are substantially more statistically significant differentially expressed genes between A and C (10533) and B and C (8797) than between A and B (3798) which suggests that groups A and B are more similar to each other than to group C. Note the p-value distributions across 20K genes is remarkable in the excess number of small raw p-values, and the number of FDR adjusted p-values < 0.05 are: A vs B: 3798; A vs C: 10533; B vs C: 8797

[0024] FIG. 10 shows that differentially expressed genes distinguish MSI-H / MSS-PRS (+) and MSS / MSS-PRS (-) (heat map and Table within FIG. 10) and that the MSS-PRS Signature Enriched with Immune Genes (protein-protein interaction mapping within FIG. 10). The heat- maps showed that the MSS-PRS identifies true MSI-H tumors and an MSS subset with an activation phenotype like MSI-H. The top differentially expressed genes from tumors classified at MSS-PRS positive (groups A and B in red and green, respectively) and negative (groups C and D in blue and black, respectively) suggest that group A and B are more similar to each other than to group C as shown on the heat map to the left. While neither group B or C are MSI-H, it is reasonable to ask whether group B is comprised of tumors bearing alterations in mismatch repair (MMR) genes that would make them deficient in MMR (dMMR). While there were more MMR alterations in group B than group C, 12% compared to 4%, this difference does account for the majority of tumors showing proficient MMR (pMMR) status (see Table within FIG.10).

[0025] FIG.11A-11B illustrates how the unique MSS-PRS biology points to its potential clinical utility. FIG. 11A shows a cluster analysis of common oncogenic mutations in TCGA COAD by MSS-PRS and CMS. FIG. 11B shows that MMR gene alterations are more common in MSI- H / MSS-PRS (+) than MSS Tumors and immune expression profiles separate MSS-PRS (+) and MSS-PRS (-).

[0026] FIGs 12A-12D illustrates that T cell receptor (TCR) repertoires independently confirm immune differences between MSS-PRS (+) and MSS-PRS (-) in terms of abundance (FIG. 12A), evenness (FIG.12B), diversity (FIG.12C) and richness (FIG.12D). Circulating TCR-β repertoireis a predictive biomarker of non-small cell lung cancer (NSCLC) response to pembrolizumab or pembrolizumab plus chemotherapy where patients receiving pembro + chemo showed significantly better progression-free survival if they had a higher number of unique clones, greater Shannon diversity, and lower evenness (Abed A, Beasley AB, Reid AL, Law N, Calapre L, Millward M, Lo J, Gray ES. Circulating pre-treatment T-cell receptor repertoire as a predictive biomarker in advanced or metastatic non-small-cell lung cancer patients treated with pembrolizumab alone or in combination with chemotherapy. ESMO Open.2023 Dec;8(6):102066. doi: 10.1016 / j.esmoop.2023.102066. Epub 2023 Nov 22. PMID: 37995426). Groups A and B show higher TCR-α and TCR-β abundance (FIG.12A) and diversity (FIG.12C) while displaying less evenness than group C (FIG. 12B). Greater TCR-β richness was also a predictor of durable clinical benefit from pembro in NSCLC (Dong N, Moreno-Manuel A, Calabuig-Fariñas S, Gallach S, Zhang F, Blasco A, Aparisi F, Meri-Abad M, Guijarro R, Sirera R, Camps C, Jantus-Lewintre E. Characterization of Circulating T Cell Receptor Repertoire Provides Information about Clinical Outcome after PD-1 Blockade in Advanced Non-Small Cell Lung Cancer Patients. Cancers (Basel). 2021 Jun 12;13(12):2950. doi: 10.3390 / cancers13122950. PMID: 34204662). Groups A and B show greater TCR-α and TCR-β richness than group C (FIG.12D).

[0027] FIG. 13 illustrates the approach used to develop the MSS-PRS that combines both MSI and MMR to capture tumors, independent of mutation status and overt MSI defects that may harbor MMR defects that facilitate an IO or other modality response.

[0028] FIG. 14 illustrates how the MSS-PRS may aid in the identification of colorectal cancer patients with an MSI-like phenotype who could benefit from treatment regimens containing IO.

[0029] FIG. 15 illustrates MSI Mantis scores and key gene alterations in TCGA UCEC samples. The dashed horizontal line depicts the MSI Mantis threshold of 0.4 where MSI Mantis >=0.4 is defined as MSI-high (Bonneville R, Krook MA, Kautto EA, Miya J, Wing MR, Chen HZ, Reeser JW, Yu L, Roychowdhury S. Landscape of Microsatellite Instability Across 39 Cancer Types. JCO Precis Oncol. 2017;2017:PO.17.00073. doi: 10.1200 / PO.17.00073. Epub 2017 Oct 3. PMID: 29850653).

[0030] FIG. 16 illustrates MSI-PRS UCEC signature training method and 60-gene output. Candidates for the model: 3068 genes with high mean and high variance in the log2(RSEM+1) training sample expression matrix (gene mean > median of all gene means and gene variance > median of all gene variances).

[0031] FIG.17 illustrates ClaNC with cross-validation to fit a 60-gene nearest centroid classifier.

[0032] FIGs 18A-18B illustrates box plots of UCEC sample calls by MSI-PRS (FIG. 18A) and 14-gene training label status (FIG. 18B) in the training and test sets on the Hiseq platform and second TCGA test set on the GA platform. Signature positive by ordinary nearest centroid call shown in orange and the lack of performance in TCGA UCEC Hiseq test was due to the culling of performers for training from the Hiseq cohort.

[0033] FIG. 19 illustrates a heat map using the 60 MSI-PRS genes where n=169 TCGA UCEC samples (Hiseq platform) were arranged by train or test data set then ordered by ascending MSI Mantis score. The samples for the heat map were comprised of the entire Hiseq cohort (i.e., pooled train and test) and heat map genes are the 60-genes of the classifier.

[0034] FIG. 20 illustrates the UCEC MSI-PRS and MSS-PRS test configurations using the same 60-gene signature but depict results as a quaternary call (e.g., A / B / C / D) that incorporates the MSI- PRS binary status and the MSI Mantis binary status.

[0035] FIG.21 illustrates a principal component analysis, which shows that TCGA UCEC MSS- PRS groups A, B, C, and D were not globally identical.

[0036] FIG. 22 illustrates the CMS1 groups as described in Guinney J et al., The consensus molecular subtypes of colorectal cancer. Nat Med. 2015 Nov;21(11):1350-6. doi: 10.1038 / nm.3967. Epub 2015 Oct 12. PMID: 26457759; PMCID: PMC4636487.

[0037] FIG.23 illustrates development of the CMS classification model from training set.

[0038] FIG.24 illustrates the centroid model for the 40-gene classifier found in Table 7.

[0039] FIG. 25 illustrates the training, testing, and performance of an RNA-based consensus molecular subtype (CMS) classifier (i.e., Table 7).

[0040] FIG.26 illustrates the prediction statistics in the testing set.

[0041] FIG.27 illustrates a heat map of immune signatures in TCGA COAD with MSS-PRS and CMS groups.

[0042] FIG. 28 illustrates a comparison of MSS-PRS and CMS groups in the TCGA COAD cohort.

[0043] FIG. 29 shows the distribution of common oncogenic mutations in the TCGA COAD cohort clustered by MSS-PRS and CMS groups.

[0044] FIG. 30 illustrates a heat map of therapeutic target pathway genes in the TCGA COAD cohort by clustered by concatenated MSS-PRS-CMS groups.

[0045] FIG. 31 illustrates boxplots of therapeutic target pathway genes in the TCGA COAD cohort by clustered by concatenated MSS-PRS-CMS groups.

[0046] FIG. 32 illustrates range of approved and experimental drugs target elements of the HRD system as described in Maresca L, Stecca B, Carrassa L. Novel Therapeutic Approaches with DNA Damage Response Inhibitors for Melanoma Treatment. Cells. 2022 Apr 26;11(9):1466. doi: 10.3390 / cells11091466. PMID: 35563772; PMCID: PMC9099918.

[0047] FIG. 33 illustrates approved diagnostic tools for HRD across tumor types as described in Stewart MD, Merino Vega D, Arend RC, Baden JF, Barbash O, Beaubier N, Collins G, French T, Ghahramani N, Hinson P, Jelinic P, Marton MJ, McGregor K, Parsons J, Ramamurthy L, Sausen M, Sokol ES, Stenzinger A, Stires H, Timms KM, Turco D, Wang I, Williams JA, Wong-Ho E, Allen J. Homologous Recombination Deficiency: Concepts, Definitions, and Assays. Oncologist. 2022 Mar 11;27(3):167-174. doi: 10.1093 / oncolo / oyab053. PMID: 35274707; PMCID: PMC8914493.

[0048] FIG.34 illustrates training of PAAD signature (78 gene signature).

[0049] FIG.35 illustrates training of OV signature (100 gene signature).

[0050] FIG.36 illustrates training of BRCA signature (151 gene signature).

[0051] FIG.37 illustrates training of BRCA_basal signature (93 gene signature).

[0052] FIG.38 illustrates specificity and sensitivity of HRD signature.

[0053] FIG.39 illustrates training of PRAD signature (100 gene signature). DETAILED DESCRIPTION Definitions

[0054] While the following terms are believed to be well understood by one of ordinary skill in the art, the following definitions are set forth to facilitate explanation of the presently disclosed subject matter.

[0055] As used herein, the singular forms "a", "an" and "the" may be intended to include the plural forms as well, unless the context clearly indicates otherwise. As such, the terms “a” or “an”, “one or more” and “at least one” can be used interchangeably herein. In addition, reference to “an element” by the indefinite article “a” or “an” does not exclude the possibility that more than one of the elements is present, unless the context clearly requires that there is one and only one of the elements. Additionally, the use of “or” is intended to include “and / or” unless the context clearly indicates otherwise. Furthermore, to the extent that the terms "including", "includes", "having","has", "with", or variants thereof are used in either the detailed description and / or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising". The term "about" as used herein can refer to a range that is 15%, 10%, 8%, 6%, 4%, or 2% plus or minus from a stated numerical value.

[0056] Unless the context requires otherwise, throughout the present specification and claims, the word “comprise” and variations thereof, such as, “comprises” and “comprising” are to be construed in an open, inclusive sense that is as “including, but not limited to”. The use of the alternative (e.g., "or") should be understood to mean either one, both, or any combination thereof of the alternatives. As used herein, the terms "about" and "consisting essentially of" mean + / - 20% of the indicated range, value, or structure, unless otherwise indicated.

[0057] Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, structure or characteristic described in connection with the embodiment may be included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification may not necessarily all referring to the same embodiment. It is appreciated that certain features of the disclosure, which are, for clarity, described in the context of separate embodiments, may also be provided in combination in a single embodiment. Conversely, various features of the disclosure, which are, for brevity, described in the context of a single embodiment, may also be provided separately or in any suitable sub-combination.

[0058] Throughout this disclosure, various aspects of the methods and compositions provided herein can be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

[0059] Unless otherwise indicated, the methods and compositions provided herein can utilize conventional techniques and descriptions of organic chemistry, polymer technology, molecular biology (including recombinant techniques), cell biology, biochemistry, and immunology, whichare within the skill of the art. Such conventional techniques include polymer array synthesis, hybridization, ligation, and detection of hybridization using a label. Specific illustrations of suitable techniques can be had by reference to the example herein below. However, other equivalent conventional procedures can, of course, also be used. Such conventional techniques and descriptions can be found in standard laboratory manuals such as Genome Analysis: A Laboratory Manual Series (Vols. I-IV), Using Antibodies: A Laboratory Manual, Cells: A Laboratory Manual, PCR Primer: A Laboratory Manual, and Molecular Cloning: A Laboratory Manual (all from Cold Spring Harbor Laboratory Press), Gait, "Oligonucleotide Synthesis: A Practical Approach" 1984, IRL Press, London, Nelson and Cox (2000), Lehninger et al., (2008) Principles of Biochemistry 5th Ed., W.H. Freeman Pub., New York, N.Y. and Berg et al. (2006) Biochemistry, 6.sup.th Ed., W.H. Freeman Pub., New York, N.Y., all of which are herein incorporated in their entirety by reference for all purposes.

[0060] Conventional software and systems may also be used in the methods and compositions provided herein. Computer software products of the invention typically include computer readable medium having computer-executable instructions for performing the logic steps of the method of the invention. Suitable computer readable medium include floppy disk, CD- ROM / DVD / DVD-ROM, hard-disk drive, flash memory, ROM / RAM, magnetic tapes, etc. The computer-executable instructions may be written in a suitable computer language or combination of several languages. Basic computational biology methods are described in, for example, Setubal and Meidanis et al., Introduction to Computational Biology Methods (PWS Publishing Company, Boston, 1997); Salzberg, Searles, Kasif, (Ed.), Computational Methods in Molecular Biology, (Elsevier, Amsterdam, 1998); Rashidi and Buehler, Bioinformatics Basics: Application in Biological Science and Medicine (CRC Press, London, 2000) and Ouelette and Bzevanis Bioinformatics: A Practical Guide for Analysis of Gene and Proteins (Wiley & Sons, Inc., 2.sup.nd ed., 2001). See U.S. Pat. No.6,420,108.

[0061] The methods and compositions provided herein may also make use of various computer program products and software for a variety of purposes, such as probe design, management of data, analysis, and instrument operation. See, U.S. Pat. Nos. 5,593,839, 5,795,716, 5,733,729, 5,974,164, 6,066,454, 6,090,555, 6,185,561, 6,188,783, 6,223,127, 6,229,911 and 6,308,170. Computer methods related to genotyping using high density microarray analysis may also beused in the present methods, see, for example, US Patent Pub. Nos.20050250151, 20050244883, 20050108197, 20050079536 and 20050042654.

[0062] Additionally, the present disclosure may have preferred embodiments that include methods for providing genetic information over networks such as the Internet as shown in U.S. Patent Pub. Nos. 20030097222, 20020183936, 20030100995, 20030120432, 20040002818, 20040126840, and 20040049354.

[0063] As used herein, the terms “individual,” “patient,” and “subject” can refer to any single animal, more preferably a mammal (including such non-human animals as, for example, dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates) for which treatment is desired. In particular embodiments, the individual or patient herein is a human.

[0064] It will be appreciated that the term "healthy" as used herein, is relative to cancer status, as the term "healthy" cannot be defined to correspond to any absolute evaluation or status. Thus, an individual defined as healthy with reference to any specified disease or disease criterion, can in fact be diagnosed with any other one or more diseases, or exhibit any other one or more disease criterion, including one or more other cancers.

[0065] The term “tumor,” as used herein, can refer to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues. The terms “cancer,” “cancerous,” and “tumor” are not mutually exclusive and can be used interchangeably.

[0066] The term “detection” can include any means of detecting, including direct and indirect detection.

[0067] The terms “substantially” or “substantial” as used herein can mean substantially similar in function or capability or otherwise competitive to the products, items (e.g., type of cancer, nucleic acid complement), services or methods recited herein. Substantially similar products, items (e.g., type of cancer, nucleic acid complement), services or methods are at least 80%, 81%, 82%, 83%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.5% similar or the same as a product, item (e.g., type of cancer, nucleic acid complement), service or method recited herein.

[0068] As used herein, the term "determining an expression level" or "determining an expression profile" or “detecting an expression level” or “detecting an expression profile” as used in reference to a biomarker or classifier can mean the application of a biomarker specific reagent such as a probe, primer or antibody and / or a method applied to a sample, for example a sample of the subjector patient and / or a control sample, for ascertaining or measuring quantitatively, semi-quantitatively or qualitatively the amount of a biomarker or biomarkers, for example the amount of biomarker polypeptide or mRNA (or cDNA derived therefrom). The level of a biomarker as provided herein can be determined by any number of methods known in the art and / or provided herein. The methods can include for example immunoassays including for example immunohistochemistry, ELISA, Western blot, immunoprecipitation and the like, where a biomarker detection agent such as an antibody for example, a labeled antibody, specifically binds the biomarker and permits for example relative or absolute ascertaining of the amount of polypeptide biomarker, hybridization and PCR protocols where a probe or primer or primer set are used to ascertain the amount of nucleic acid biomarker, including for example probe based and amplification based methods including for example microarray analysis, RT-PCR such as quantitative RT-PCR (qRT-PCR), serial analysis of gene expression (SAGE), Northern Blot, digital molecular barcoding technology, for example Nanostring Counter Analysis, and TaqMan quantitative PCR assays. Other methods of mRNA detection and quantification can be applied, such as mRNA in situ hybridization in formalin-fixed, paraffin-embedded (FFPE) tissue samples or cells. This technology is currently offered by the QuantiGene ViewRNA (Affymetrix), which uses probe sets for each mRNA that bind specifically to an amplification system to amplify the hybridization signals; these amplified signals can be visualized using a standard fluorescence microscope or imaging system. This system for example can detect and measure transcript levels in heterogeneous samples; for example, if a sample has normal and tumor cells present in the same tissue section. As mentioned, TaqMan probe-based gene expression analysis (PCR-based) can also be used for measuring gene expression levels in tissue samples, and this technology has been shown to be useful for measuring mRNA levels in FFPE samples. In brief, TaqMan probe-based assays utilize a probe that hybridizes specifically to the mRNA target. This probe contains a quencher dye and a reporter dye (fluorescent molecule) attached to each end, and fluorescence is emitted only when specific hybridization to the mRNA target occurs. During the amplification step, the exonuclease activity of the polymerase enzyme causes the quencher and the reporter dyes to be detached from the probe, and fluorescence emission can occur. This fluorescence emission is recorded and signals are measured by a detection system; these signal intensities are used to calculate the abundance of a given transcript (gene expression) in a sample. Overview

[0069] Provided herein are systems, methods, kits and compositions for determining the presence or absence of microsatellite instability (MSI) or conversely, microsatellite stability (MSS) in subjects suffering from cancer. In one embodiment, the presence or absence of MSI or MSS is determined by determining the MSS predictive response signature (MSS-PRS) of a subject suffering from cancer. In some cases, the cancer is colon adenocarcinoma (COAD) and the MSS- PRS is the expression profile of a plurality of biomarkers comprising, consisting essentially of or consisting of biomarkers selected from the biomarkers listed in Table 3. In some cases, the cancer is uterine corpus endometrial carcinoma (UCEC) and the MSS-PRS is the expression profile of a plurality of biomarkers comprising, consisting essentially of or consisting of biomarkers selected from the biomarkers listed in Table 5. The MSS-PRS was developed to combine both MSI and mismatch repair (MMR) to capture tumors, independent of mutation status and overt MSI defects that may harbor MMR defects that facilitate an immuno-oncology (IO) or other modality response (see FIG.13). In some cases, the MSS-PRS provided herein can determine a subtype of a subject suffering from cancer (e.g., COAD or UCEC) that phenocopies MSI and / or MMR deficient (i.e., dMMR). In some cases, determining a subject’s MSS-PRS gene signature can inform whether or not the subject is a candidate or a potential responder for immuno-oncology (IO) treatment with or without combo treatments with therapeutics in clinical trials (e.g., see Table 2) if the subject’s MSS-PRS signature is MSS-PRS positive or is a candidate or a potential responder for 5-FU Chemotherapy treatment if the subject’s MSS-PRS signature is MSS-PRS negative. In some cases, the immuno-oncology treatment can include treatment with a combination of two or more immuno-oncology therapeutic agents. For example, the immuno-oncology treatment could include an anti-CTLA4 and an anti-PD-1 combination treatment. In one embodiment, provided herein is a method for treating a subject suffering from cancer comprising determining the subject’s MSS- PRS gene signature and administering a therapeutic agent to the subject based on the subject’s MSS-PRS signature. The determining step comprises measuring the expression of a plurality of biomarkers comprising, consisting essentially of or consisting of biomarkers selected from the biomarkers in Table 3 or Table 5 in a sample obtained from the subject. The expression pattern of said plurality of biomarkers comprising, consisting essentially of or consisting of biomarkers selected from the biomarkers in Table 3 reflects the subject’s COAD MSS-PRS. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, atleast 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 100 biomarkers or at least 110 biomarkers, at least 120 biomarkers or at least 130 biomarkers of Table 3. In some cases, the plurality of biomarkers selected from Table 3 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 3. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 3. The expression pattern of said plurality of biomarkers comprising, consisting essentially of or consisting of biomarkers selected from the biomarkers in Table 5 reflects the subject’s UCEC MSS-PRS. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers or at least 60 biomarkers of Table 5. In some cases, the plurality of biomarkers selected from Table 5 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 5. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 5. If the subject’s MSS-PRS signature is MSS-PRS positive, the subject is administered immune checkpoint inhibitor (ICI) therapy. If the subject’s MSS-PRS signature is MSS-PRS positive, the subject is administered immuno- oncology (IO) treatment with or without combo treatments with therapeutics in clinical trials (e.g., see Table 2). If the subject’s MSS-PRS signature is MSS-PRS negative, the subject is administered a chemotherapy agent such as, for example, 5-FU.

[0070] In one embodiment, the cancer is colon adenocarcinoma (COAD) and the systems, methods, kits and compositions further comprise determining the expression (e.g., nucleic acid expression) levels of a plurality of biomarkers comprising, consisting essentially of or consisting of biomarkers selected from the biomarkers in Table 7 in a sample obtained from the subject suffering from COAD. Determining the expression levels of the plurality of biomarkers comprising, consisting essentially of or consisting of biomarkers selected from the biomarkers in Table 7 indicates the subjects colorectal cancer CMS group each of whose attributes are described in FIG. 22. In some cases, determining the expression levels of the plurality of biomarkerscomprising, consisting essentially of or consisting of biomarkers selected from Table 3 as well as the expression levels of the plurality of biomarkers comprising, consisting essentially of or consisting of biomarkers selected from Table 7 in the sample obtained from the subject indicates if the subject possesses one of seven CMS / MSS-PRS subgroups (i.e., CMS1 / MSS-PRS (+); CMS2 / MSS-PRS (+) or (-); CMS3 / MSS-prs (+) or (-) or CMS4 / MSS-PRS (+) or (-); see FIG. 30). In some cases, the systems, methods, kits and compositions further comprise administering a therapeutic agent to the subject based on the subjects determined CMS / PSS-PRS signature. For example, if the subject is determined to possess a CMS4 subgroup as well as be PSS-PRS (+), then the subject is a candidate for treatment with an ICI agent. If the subject does not possess a CMS4 subtype, then the subject may be a candidate for a chemotherapeutic agent. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 100 biomarkers or at least 110 biomarkers, at least 120 biomarkers or at least 130 biomarkers of Table 3. In some cases, the plurality of biomarkers selected from Table 3 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 3. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 3. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers or at least 40 biomarkers from Table 7. In some cases, the plurality of biomarkers selected from Table 7 comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 7. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 7.

[0071] Exemplary immune checkpoint inhibitors for use in any method provided herein can be any checkpoint inhibitor known in the art and / or provided herein. The checkpoint inhibitors can be any checkpoint inhibitor provided herein such as, for example, a checkpoint inhibitor that targets PD-1, PD-LI or CTLA4.

[0072] Further to any of the embodiments provided herein, the cancer can include, but is not limited to, carcinoma, lymphoma, blastoma (including medulloblastoma and retinoblastoma), sarcoma (including liposarcoma and synovial cell sarcoma), neuroendocrine tumors (including carcinoid tumors, gastrinoma, and islet cell cancer), mesothelioma, schwannoma (including acoustic neuroma), meningioma, adenocarcinoma, melanoma, and leukemia or lymphoid malignancies. Examples of a cancer can also include, but are not limited to, a lung cancer (e.g., a non-small cell lung cancer (NSCLC) or small cell lung cancer), a kidney cancer (e.g., a kidney urothelial carcinoma or RCC), a bladder cancer (e.g., a bladder urothelial (transitional cell) carcinoma (e.g., locally advanced or metastatic urothelial cancer, including 1L or 2L+ locally advanced or metastatic urothelial carcinoma)), a breast cancer, a colorectal cancer (e.g., a colon adenocarcinoma), an ovarian cancer, a pancreatic cancer, a gastric carcinoma, an esophageal cancer, a mesothelioma, a melanoma (e.g., a skin melanoma), a head and neck cancer (e.g., a head and neck squamous cell carcinoma (HNSCC)), a thyroid cancer, a sarcoma (e.g., a soft-tissue sarcoma, a fibrosarcoma, a myxosarcoma, a liposarcoma, an osteogenic sarcoma, an osteosarcoma, a chondrosarcoma, an angiosarcoma, an endotheliosarcoma, a lymphangiosarcoma, a lymphangioendotheliosarcoma, a leiomyosarcoma, or a rhabdomyosarcoma), a prostate cancer, a glioblastoma, a cervical cancer, a thymic carcinoma, a leukemia (e.g., an acute lymphocytic leukemia (ALL), an acute myelocytic leukemia (AML), a chronic myelocytic leukemia (CML), a chronic eosinophilic leukemia, or a chronic lymphocytic leukemia (CLL)), a lymphoma (e.g., a Hodgkin lymphoma or a non-Hodgkin lymphoma (NHL)), a myeloma (e.g., a multiple myeloma (MM)), a mycosis fungoides, a Merkel cell cancer, a hematologic malignancy, a cancer of hematological tissues, a B cell cancer, a bronchus cancer, a stomach cancer, a brain or central nervous system cancer, a peripheral nervous system cancer, a uterine or endometrial cancer, a cancer of the oral cavity or pharynx, a liver cancer, a testicular cancer, a biliary tract cancer, a small bowel or appendix cancer, a salivary gland cancer, an adrenal gland cancer, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), a colon cancer, a myelodysplastic syndrome (MDS), a myeloproliferative disorder (MPD), a polycythemia Vera, a chordoma, a synovioma, a Ewing’s tumor, a squamous cell carcinoma, a basal cell carcinoma, a sweat gland carcinoma, a sebaceous gland carcinoma, a papillary carcinoma, a papillary adenocarcinoma, a medullary carcinoma, a bronchogenic carcinoma, a renal cell carcinoma, a hepatoma, a bile duct carcinoma, a choriocarcinoma, a seminoma, an embryonalcarcinoma, a Wilms' tumor, a bladder carcinoma, an epithelial carcinoma, a glioma, an astrocytoma, a medulloblastoma, a craniopharyngioma, an ependymoma, a pinealoma, a hemangioblastoma, an acoustic neuroma, an oligodendroglioma, a meningioma, a neuroblastoma, a retinoblastoma, a follicular lymphoma, a diffuse large B-cell lymphoma, a mantle cell lymphoma, a hepatocellular carcinoma, a thyroid cancer, a small cell cancer, an essential thrombocythemia, an agnogenic myeloid metaplasia, a hypereosinophilic syndrome, a systemic mastocytosis, a familiar hypereosinophilia, a neuroendocrine cancer, or a carcinoid tumor.

[0073] In one embodiment, the cancer is selected from kidney renal papillary cell carcinoma (KIRP); breast invasive carcinoma (BRCA); thyroid cancer (THCA); bladder urothelial carcinoma (BLCA); prostate adenocarcinoma (PRAD); kidney chromophobe (KICH); cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC); kidney renal clear cell carcinoma (KIRC); liver hepatocellular carcinoma (LIHC); low grade glioma (LGG); sarcoma (SARC); lung adenocarcinoma (LUAD); colon adenocarcinoma (COAD); head and neck squamous cell carcinoma (HNSC); uterine corpus endometrial carcinoma (UCEC); glioblastoma multiforme (GBM); esophageal carcinoma (ESCA); stomach adenocarcinoma (STAD); ovarian serous cystadenocarcinoma (OV); rectum adenocarcinoma (READ); adrenocortical carcinoma (ACC); uveal melanoma (UVM); mesothelioma (MESO); pheochromocytoma and paraganglioma (PCPG); skin cutaneous melanoma (SKCM); uterine carcinosarcoma (UCS); lung squamous cell carcinoma (LUSC); testicular germ cell tumors (TGCT); cholangiocarcinoma (CHOL); pancreatic adenocarcinoma (PAAD); thymoma (THYM); Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC); and Acute Myeloid Leukemia [LAML]. In another embodiment, the cancer is selected from kidney renal papillary cell carcinoma (KIRP); breast invasive carcinoma (BRCA); thyroid cancer (THCA); bladder urothelial carcinoma (BLCA); prostate adenocarcinoma (PRAD); kidney chromophobe (KICH); cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC); kidney renal clear cell carcinoma (KIRC); liver hepatocellular carcinoma (LIHC); low grade glioma (LGG); sarcoma (SARC); lung adenocarcinoma (LUAD); colon adenocarcinoma (COAD); head and neck squamous cell carcinoma (HNSC); uterine corpus endometrial carcinoma (UCEC); glioblastoma multiforme (GBM); esophageal carcinoma (ESCA); stomach adenocarcinoma (STAD); ovarian serous cystadenocarcinoma (OV); rectum adenocarcinoma (READ); adrenocortical carcinoma (ACC); uveal melanoma (UVM); mesothelioma (MESO); pheochromocytoma and paraganglioma (PCPG); skin cutaneousmelanoma (SKCM); uterine carcinosarcoma (UCS); lung squamous cell carcinoma (LUSC); testicular germ cell tumors (TGCT); cholangiocarcinoma (CHOL); pancreatic adenocarcinoma (PAAD); thymoma (THYM); and Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC).

[0074] Also provided herein is a kit, method or system for treating cancer in a subject, the kit, method or system comprising determining a subject’s homologous recombination repair deficiency predictive response signature (HRD-PRS) and administering a therapeutic agent based on said subject’s HRD-PRS. In some cases, if the subject has a positive HRD-PRS, they are administered a PARP inhibitor. In some cases, if the subject has a negative HRD-PRS, they are administered a therapeutic agent that is not a PARP inhibitor. The HRD-PRS can comprise a panel of two or more biomarkers whose expression level profile for any one subject marks said subject as having a positive or negative HRD-PRS. In some cases, the panel of two or more biomarkers can comprise the biomarkers listed in Table 8, 9, 10, 11 or 12. In some cases, the subject is suffering from or is suspected of suffering from cancer. In some cases, the determining the HRD- PRS comprises measuring the expression level of the panel of two or more biomarkers in a sample obtained from the subject. In one embodiment, provided herein is a kit, method or system for treating cancer in a subject, the kit, method or system comprising: measuring the expression level of a plurality of biomarkers in a sample obtained from a subject suffering from cancer, wherein the plurality of biomarkers comprise, consist essentially of or consist of biomarkers selected from biomarkers listed in Table 8, 9, 10, 11 or 12, wherein the measured expression levels of the plurality of biomarkers provide a homologous recombination repair deficiency predictive response signature (HRD-PRS) for the sample; and administering a therapeutic agent that is a PARP inhibitor based on the presence of a positive HRD-PRS or a therapeutic agent that is not a PARP inhibitor based on the presence of a negative HRD-PRS. In some cases, the measuring the expression levels of the plurality of biomarkers is at a nucleic acid level by performing RNA sequencing, reverse transcriptase polymerase chain reaction (RT-PCR) or hybridization-based analyses. In some cases, the RT-PCR is quantitative real time reverse transcriptase polymerase chain reaction (qRT-PCR). In some cases, the RT-PCR is performed with primers specific to the biomarkers selected from the plurality of biomarkers from Table 8, 9, 10, 11 or 12. In some cases, the hybridization analysis is a microarray-based hybridization analysis. In some cases, the cancer the patient is suffering from is pancreatic adenocarcinoma (PAAD). In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers or at least 78 biomarkers of Table 8. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 8. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 8. In some cases, the cancer the patient is suffering from is ovarian cancer (OV). In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers or at least 100 biomarkers from Table 9. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 9. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 9. In some cases, the cancer the patient is suffering from is breast cancer (BRCA). In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 100 biomarkers, at least 110 biomarkers, at least 120 biomarkers, at least 130 biomarkers, at least 140 biomarkers, at least 150 biomarkers, or at least 151 biomarkers from Table 10. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 10. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 10. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, atleast 80 biomarkers, at least 90 biomarkers or at least 93 biomarkers from Table 11. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 11. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 11. In some cases, the cancer the patient is suffering from is prostate adenocarcinoma (PRAD). In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers or at least 100 biomarkers from Table 12. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 12. In some cases, the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 12.

[0075] Generation of the PRSs or subtypes provided herein can be accomplished using training data sets and use of the ordinary nearest centroid classifier (e.g., CLaNC) to generate a fitted classifier can be as described in the Examples provided herein and / or Dabney A.R. Classification of microarrays to nearest centroids, Bioinformatics, 2005, vol. 21 (pg. 4148-4154) or Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, Davies S, Fauron C, He X, Hu Z, Quackenbush JF, Stijleman IJ, Palazzo J, Marron JS, Nobel AB, Mardis E, Nielsen TO, Ellis MJ, Perou CM, Bernard PS. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009 Mar 10;27(8):1160-7. Use of the training data sets and the ordinary nearest centroid classifier (e.g., CLaNC) to generate the fitted classifier can be computer-implemented methods or systems as provided herein.

[0076] In one embodiment, the sample used herein is obtained from an individual and comprises formalin-fixed paraffin-embedded (FFPE) tissue. However, other tissue and sample types are amenable for use herein. In one embodiment, the other tissue and sample types can be fresh frozen tissue, wash fluids, or cell pellets, or the like. In one embodiment, the sample can be a bodily fluid or a liquid biopsy obtained from the individual. The bodily fluid can be blood or fractions thereof(e.g., serum, plasma), urine, sputum, saliva or cerebrospinal fluid (CSF). A biomarker nucleic acid as provided herein can be extracted from a cell or can be cell free or extracted from an extracellular vesicular entity such as an exosome.

[0077] Methods are known in the art for the isolation of RNA from FFPE tissue. In one embodiment, total RNA can be isolated from FFPE tissues as described by Bibikova et al. (2004) American Journal of Pathology 165:1799-1807, herein incorporated by reference. Likewise, the High Pure RNA Paraffin Kit (Roche) can be used. Paraffin is removed by xylene extraction followed by ethanol wash. RNA can be isolated from sectioned tissue blocks using the MasterPure Purification kit (Epicenter, Madison, Wis.); a DNase I treatment step is included. RNA can be extracted from frozen samples using Trizol reagent according to the supplier's instructions (Invitrogen Life Technologies, Carlsbad, Calif.). Samples with measurable residual genomic DNA can be resubjected to DNaseI treatment and assayed for DNA contamination. All purification, DNase treatment, and other steps can be performed according to the manufacturer's protocol. After total RNA isolation, samples can be stored at -80 ºC until use.

[0078] General methods for mRNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, including Ausubel et al., ed., Current Protocols in Molecular Biology, John Wiley & Sons, New York 1987-1999. Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker (Lab Invest. 56:A67, 1987) and De Andres et al. (Biotechniques 18:42-44, 1995). In particular, RNA isolation can be performed using a purification kit, a buffer set and protease from commercial manufacturers, such as Qiagen (Valencia, Calif.), according to the manufacturer's instructions. For example, total RNA from cells in culture can be isolated using Qiagen RNeasy mini-columns. Other commercially available RNA isolation kits include MasterPureTM. Complete DNA and RNA Purification Kit (Epicentre, Madison, Wis.) and Paraffin Block RNA Isolation Kit (Ambion, Austin, Tex.). Total RNA from tissue samples can be isolated, for example, using RNA Stat-60 (Tel-Test, Friendswood, Tex.). RNA prepared from a tumor can be isolated, for example, by cesium chloride density gradient centrifugation. Additionally, large numbers of tissue samples can readily be processed using techniques well known to those of skill in the art, such as, for example, the single- step RNA isolation process of Chomczynski (U.S. Pat. No. 4,843,155, incorporated by reference in its entirety for all purposes).

[0079] In one embodiment, a sample comprises cells harvested from a tissue sample. Cells can be harvested from a biological sample using standard techniques known in the art. For example, in one embodiment, cells are harvested by centrifuging a cell sample and resuspending the pelleted cells. The cells can be resuspended in a buffered solution such as phosphate-buffered saline (PBS). After centrifuging the cell suspension to obtain a cell pellet, the cells can be lysed to extract nucleic acid, e.g., messenger RNA. All samples obtained from a subject, including those subjected to any sort of further processing, are considered to be obtained from the subject.

[0080] The sample, in one embodiment, is further processed before the detection of the biomarker levels of the combination of biomarkers set forth herein. For example, mRNA in a cell or tissue sample can be separated from other components of the sample. The sample can be concentrated and / or purified to isolate mRNA in its non-natural state, as the mRNA is not in its natural environment. For example, studies have indicated that the higher order structure of mRNA in vivo differs from the in vitro structure of the same sequence (see, e.g., Rouskin et al. (2014). Nature 505, pp.701-705, incorporated herein in its entirety for all purposes).

[0081] mRNA from the sample in one embodiment, is hybridized to a synthetic DNA probe, which in some embodiments, includes a detection moiety (e.g., detectable label, capture sequence, barcode reporting sequence). Accordingly, in these embodiments, a non-natural mRNA-cDNA complex is ultimately made and used for detection of the biomarker. In another embodiment, mRNA from the sample is directly labeled with a detectable label, e.g., a fluorophore. In a further embodiment, the non-natural labeled-mRNA molecule is hybridized to a cDNA probe and the complex is detected.

[0082] In one embodiment, once the mRNA is obtained from a sample, it is converted to complementary DNA (cDNA) prior to the hybridization reaction or is used in a hybridization reaction together with one or more cDNA probes. cDNA does not exist in vivo and therefore is a non-natural molecule. Furthermore, cDNA-mRNA hybrids are synthetic and do not exist in vivo. Besides cDNA not existing in vivo, cDNA is necessarily different than mRNA, as it includes deoxyribonucleic acid and not ribonucleic acid. The cDNA is then amplified, for example, by the polymerase chain reaction (PCR) or other amplification method known to those of ordinary skill in the art. For example, other amplification methods that may be employed include the ligase chain reaction (LCR) (Wu and Wallace, Genomics, 4:560 (1989), Landegren et al., Science, 241:1077 (1988), incorporated by reference in its entirety for all purposes, transcriptionamplification (Kwoh et al., Proc. Natl. Acad. Sci. USA, 86:1173 (1989), incorporated by reference in its entirety for all purposes), self-sustained sequence replication (Guatelli et al., Proc. Nat. Acad. Sci. USA, 87:1874 (1990), incorporated by reference in its entirety for all purposes), incorporated by reference in its entirety for all purposes, and nucleic acid based sequence amplification (NASBA). Guidelines for selecting primers for PCR amplification are known to those of ordinary skill in the art. See, e.g., McPherson et al., PCR Basics: From Background to Bench, Springer- Verlag, 2000, incorporated by reference in its entirety for all purposes. The product of this amplification reaction, i.e., amplified cDNA is also necessarily a non-natural product. First, as mentioned above, cDNA is a non-natural molecule. Second, in the case of PCR, the amplification process serves to create hundreds of millions of cDNA copies for every individual cDNA molecule of starting material. The numbers of copies generated are far removed from the number of copies of mRNA that are present in vivo.

[0083] In one embodiment, cDNA is amplified with primers that introduce an additional DNA sequence (e.g., adapter, reporter, capture sequence or moiety, barcode) onto the fragments (e.g., with the use of adapter-specific primers), or mRNA or cDNA biomarker sequences are hybridized directly to a cDNA probe comprising the additional sequence (e.g., adapter, reporter, capture sequence or moiety, barcode). Amplification and / or hybridization of mRNA to a cDNA probe therefore serves to create non-natural double stranded molecules from the non-natural single stranded cDNA, or the mRNA, by introducing additional sequences and forming non-natural hybrids. Further, as known to those of ordinary skill in the art, amplification procedures have error rates associated with them. Therefore, amplification introduces further modifications into the cDNA molecules. In one embodiment, during amplification with the adapter-specific primers, a detectable label, e.g., a fluorophore, is added to single strand cDNA molecules. Amplification therefore also serves to create DNA complexes that do not occur in nature, at least because (i) cDNA does not exist in vivo, (i) adapter sequences are added to the ends of cDNA molecules to make DNA sequences that do not exist in vivo, (ii) the error rate associated with amplification further creates DNA sequences that do not exist in vivo, (iii) the disparate structure of the cDNA molecules as compared to what exists in nature, and (iv) the chemical addition of a detectable label to the cDNA molecules.

[0084] In some embodiments, the expression of a biomarker of interest is detected at the nucleic acid level via detection of non-natural cDNA molecules.

[0085] In one embodiment, the method and systems provided herein for generating any of the predictive response signatures (PRS) or sub-typers provided herein are computer-implemented methods or system. Conventional software and systems may also be used in the methods and systems provided herein. Computer software products of the invention typically include computer readable medium (e.g., non-transitory computer readable medium) having computer- executable instructions for performing the logic steps (e.g., determining a predictive response signature provided herein by receiving expression level data for any of the plurality of biomarkers provided herein, and selecting a treatment for a test subject based on the determined predictive response signature) of the method of the inventions. Suitable computer readable medium include floppy disk, CD-ROM / DVD / DVD-ROM, hard-disk drive, flash memory, ROM / RAM, magnetic tapes, etc. The computer-executable instructions may be written in a suitable computer language or combination of several languages. Basic computational biology methods are described in, for example, Setubal and Meidanis et al., Introduction to Computational Biology Methods (PWS Publishing Company, Boston, 1997); Salzberg, Searles, Kasif, (Ed.), Computational Methods in Molecular Biology, (Elsevier, Amsterdam, 1998); Rashidi and Buehler, Bioinformatics Basics: Application in Biological Science and Medicine (CRC Press, London, 2000) and Ouelette and Bzevanis Bioinformatics: A Practical Guide for Analysis of Gene and Proteins (Wiley & Sons, Inc., 2.sup.nd ed., 2001). See U.S. Pat. No. 6,420,108.

[0086] The methods and systems provided herein may also make use of various computer program products and software for a variety of purposes, such as probe design, management of data, analysis, and instrument operation. See, U.S. Pat. Nos. 5,593,839, 5,795,716, 5,733,729, 5,974,164, 6,066,454, 6,090,555, 6,185,561, 6,188,783, 6,223,127, 6,229,911 and 6,308,170. Computer methods related to genotyping using high density microarray analysis may also be used in the present methods, see, for example, US Patent Pub. Nos. 20050250151, 20050244883, 20050108197, 20050079536 and 20050042654. EXAMPLES

[0087] The present disclosure is further illustrated by reference to the following Examples. However, it should be noted that these Examples, like the embodiments described above, are illustrative and are not to be construed as restricting the scope of the invention in any way.Example 1- Development of a novel RNA-based microsatellite stable predictive response signature (MSS-PRS) to identify MSS colorectal cancer (CRC) patients with a microsatellite instability-high (MSI-H) molecular phenotype. Objective

[0088] MSI-H and mismatch repair deficient (dMMR) tumors are associated with favorable immune checkpoint inhibitor (ICI) responses (PMID: 3326454). However, up to 95% of CRC tumors are MSS / proficient MMR (pMMR) leading to poorer prognosis and treatment outcomes than MSI-H / dMMR patients (PMID: 12867608, 26028255). In MSS / pMMR metastatic CRC, multiple combination therapies are being investigated despite a lack of biomarkers to guide therapy (see Table 1). Therefore, the goal of this Example was to develop an RNA-based MSS-PRS with a primary objective to identify MSS COAD tumors that have MSI-H / dMMR molecular features indicative of ICI response.

[0089] Table 1. The unmet need for effective treatments for MSS CRC is so great that numerous clinical trials, mostly combinations, are being held (mostly without biomarkers).Methods

[0090] The MSS-PRS was developed using the TCGA CRC cohort (COAD; n=268). As shown in Table 2, training labels were assigned using the MSI Mantis score and mutation status from 8 genes (i.e., MLH1, EPM2AIP1, TTC30A, SMAP1, RNLS, RPL22L1, LYG1, and TNNT1) with reported MSI association (Li L et al. Comput Struct Biotechnol J.2020 Mar 19; 18:668-675; Sorokin M et al. Front Mol Biosci.2021 Nov 23; 8:737821). MSI Mantis is a proxy for MSI that is widely available in genomic databases (PMID: 29850653; PMID: 27980218). A cutoff of 0.4 has been established and samples with an MSI Mantis score above 0.4 are considered MSI-high (MSI-H). Samples called “activated” had both one or more of the training label gene mutations and an MSI Mantis score > 0.4 (i.e., MSI-H), and samples called “non-activated” were wild type for all training label genes and had MSI Mantis score < 0.4. All other samples were designated “ambiguous” and excluded from training. Two thirds of the non-ambiguous samples were assigned to the classifier training set and all remaining samples were assigned to the test set. Using ClaNC software (PMID: 16269418) and cross-validation in the training set, a nearestcentroid classifier was developed from a set of high mean, high variance candidate genes to select an optimal gene set to separate activated (PRS score > 0) and non-activated groups (PRS score < 0) (see FIGs 3-4). The classifier performance was evaluated in the test set as well as 4 additional separate CRC RNA-seq datasets accessed through GEO (GSE24551 and GSE39084) or cBioPortal (coad_silu_2022 and coad_cptac_2019) (see FIG.5).

[0091] Table 2. Sample allocation of TCGA COAD cohort in Training and Testing sets after application of training label definitions.Results

[0092] The MSS-PRS (see Table 3) contained 112-genes enriched for mismatched DNA binding, DNA damage repair, PD-L1, innate and adaptive immune response, cellular immunity, and cytokines. Cross validation for its ability to correctly call MSI-H samples showed high agreement and exhibited comparable performance in both the TCGA COAD test and train set. Further, MSS- PRS produced a similar distribution in four additional CRC cohorts (FIG. 8), validating its consistency in classifying colon adenocarcinoma samples. The biological basis of the MSS-PRS was explored by dividing TCGA COAD into 4 groups defined by the intersection of MSI Mantis (MSI-H or MSS) and MSS-PRS (+ or -): Groups A, B, C, and D (FIG. 10). Heat map display of the top 256 differentially expressed genes between Groups A, B, and C showed a clear distinction between Groups A and C, with Group B having an intermediate phenotype like Group A (FIG.10, left panel). Protein-protein interaction network mapping in String (Szklarczyk et al., 2023; https: / / string-db.org / ) revealed a core set of immune genes related to chemotaxis, natural killer cells, and T-lymphocytes as well as several unlinked one-off genes (FIG.10, right panel).

[0093] Some gene alterations (e.g., BRAF) were more prevalent in MSI-H / MSS-PRS (+) tumors (Group A) whereas others (e.g., TP53, APC) were more frequent in MSS (Groups B and C) regardless of MSS-PRS status (FIG. 11A). High TMB values were predominantly associated with MSI-H tumors. The Consensus Molecular Subtype 1 (CMS1) accounted for about two- thirds of MSI-H tumors whereas CMS2 was most prevalent in MSS Group C and about half the tumors of MSS Group B were CMS4 and the remainder was mixed.

[0094] The mutation status and TMB of MSS Group B and C tumors were similar at the aggregate level, but notable differences were observed for CMS classification and KRAS and BRAF alterations. Group D, MSI-H / MSS-PRS (-), could not be characterized because it only contained 2 tumors.

[0095] MMR genes (MLH1, MLH3, MSH2, MSH3, MSH6, PMS1, PMS2) were examined in cBioPortal to address whether MSS tumors harbored MMR alterations (Table in FIG.10). Of the MMR genes, only MLH1 was included as a training label for MSS-PRS.

[0096] As expected, Group A (MSI-H / MSS-PRS+) carried the most MMR alterations with 48% of tumors having at least one alteration. While Group B (MSS / MSS-PRS+) tumors had more MMR alterations, 12%, than Group C (MSS / MSS-PRS-), 4%, these differences did not explain the difference in molecular phenotype between these two MSS groups for the majority of tumors (Table in FIG.10)

[0097] In contrast, gene expression-based immune profiles in MSS tumors called activated (Group B) were markedly more like MSI-H tumors (Group A) compared to MSS tumors called non- activated (Group C; FIG. 11B).

[0098] Examination of T cell receptor (TCR) α / β repertoires confirmed differences in adaptive immunity between MSS-PRS+ (Groups A and B) and MSS-PRS- (Group C; FIG.12A-12D). TCR clonal expansion is an epigenomic event, thus the differences in TCR repertoire offer an independent indication that the immune molecular state of Groups A and B are more similar to each other than to Group C.

[0099] In the test set, the classifier called 44 of 46 MSI-H samples activated; of the remaining 77 samples that were MSS tumors, 38 were called activated and 39 were called not activated. Visualinspection of heat maps of tumor by MSI / MSS-PRS status and their association with TMB and CRC-related mutations suggest greater differences between MSS tumors called activated and MSI- H tumors than between MSS tumors called activated and MSS tumors called not activated (see FIG. 6). In contrast, immune marker expression profiles in MSS tumors called activated were markedly more similar to MSI-H tumors compared to MSS tumors called non-activated. Summary and Conclusions

[0100] Herein, the development of a novel MSS-PRS that captures an MSI-H / dMMR-like molecular phenotype in a subset of patients with MSS tumors despite their lack of high TMB or overt MSI defects is described. As such, the MSS-PRS selects tumors not identified with conventional MSI testing (e.g., MSI-H / dMMR) but have molecular characteristics consistent with microsatellite instability, thus making them a potential target for ICI. This MSS-PRS overcomes many of the shortcomings found in previously known methods of assessing mismatch repair (MMR) and MSI (see FIG. 13). Accordingly, the MSS-PRS developed in this Example and described herein, can aid in the identification of colorectal cancer patients with an MSI-like phenotype who could benefit from treatment regimens containing IO (see FIG. 14). The MSS- PRS test potentially selects for 2-3x as many patients as MSI / MMR status; differential expression of genes for MMR, DNA damage, immune checkpoint, cytokine, and cellular immunity genes distinguish MSS-PRS positive and negative groups. Based upon these initial findings, further development of the MSS-PRS and its clinical validation as a tool to select patients with MSS tumors who may benefit from ICI-containing treatment regimens is warranted. Table 3. Gene Centroids of 112 Classifier genes in COAD MSI-PRS.References

[0101] 1. Tian J, Chen JH, Chao SX, Pelka K, Giannakis M, Hess J, Burke K, Jorgji V, Sindurakar P, Braverman J, Mehta A, Oka T, Huang M, Lieb D, Spurrell M, Allen JN, Abrams TA, Clark JW, Enzinger AC, Enzinger PC, Klempner SJ, McCleary NJ, Meyerhardt JA, Ryan DP, Yurgelun MB, Kanter K, Van Seventer EE, Baiev I, Chi G, Jarnagin J, Bradford WB, Wong E, Michel AG, Fetter IJ, Siravegna G, Gemma AJ, Sharpe A, Demehri S, Leary R, Campbell CD, Yilmaz O, Getz GA, Parikh AR, Hacohen N, Corcoran RB. Combined PD-1, BRAF and MEK inhibition in BRAFV600Ecolorectal cancer: a phase 2 trial. Nat Med.2023 Feb;29(2):458-466. doi: 10.1038 / s41591-022-02181-8. Epub 2023 Jan 26. PMID: 36702949; PMCID: PMC9941044.

[0102] 2. Lemech C, Dredge K, Bampton D, Hammond E, Clouston A, Waterhouse NJ, Stanley AC, Leveque-El Mouttie L, Chojnowski GM, Haydon A, Pavlakis N, Burge M, Brown MP, Goldstein D. Phase Ib open-label, multicenter study of pixatimod, an activator of TLR9, in combination with nivolumab in subjects with microsatellite-stable metastatic colorectal cancer, metastatic pancreatic ductal adenocarcinoma and other solid tumors. J Immunother Cancer. 2023 Jan;11(1):e006136. doi: 10.1136 / jitc-2022-006136. PMID: 36634920; PMCID: PMC9843174.

[0103] 3. Fukuoka S, Hara H, Takahashi N, Kojima T, Kawazoe A, Asayama M, Yoshii T, Kotani D, Tamura H, Mikamoto Y, Hirano N, Wakabayashi M, Nomura S, Sato A, Kuwata T, Togashi Y, Nishikawa H, Shitara K. Regorafenib Plus Nivolumab in Patients With Advanced Gastric or Colorectal Cancer: An Open-Label, Dose-Escalation, and Dose-Expansion Phase Ib Trial (REGONIVO, EPOC1603). J Clin Oncol. 2020 Jun 20;38(18):2053-2061. doi: 10.1200 / JCO.19.03296. Epub 2020 Apr 28. PMID: 32343640.

[0104] 4. Saunders MP, Graham J, Cunningham D, Plummer R, Church D, Kerr R, Cook S, Zheng S, La Thangue N, Kerr D. CXD101 and nivolumab in patients with metastatic microsatellite-stable colorectal cancer (CAROSELL): a multicentre, open-label, single-arm, phase II trial. ESMO Open.2022 Dec;7(6):100594. doi: 10.1016 / j.esmoop.2022.100594. Epub 2022 Oct 27. PMID: 36327756; PMCID: PMC9808483.

[0105] 5. Meltzer S, Negård A, Bakke KM, Hamre HM, Kersten C, Hofsli E, Guren MG, Sorbye H, Flatmark K, Ree AH. Early radiologic signal of responsiveness to immune checkpoint blockade in microsatellite-stable / mismatch repair-proficient metastatic colorectal cancer. Br J Cancer. 2022 Dec;127(12):2227-2233. doi: 10.1038 / s41416-022-02004-0. Epub 2022 Oct 13. PMID: 36229579; PMCID: PMC9726864.

[0106] 6. Palmer CD, Rappaport AR, Davis MJ, Hart MG, Scallan CD, Hong SJ, Gitlin L, Kraemer LD, Kounlavouth S, Yang A, Smith L, Schenk D, Skoberne M, Taquechel K, Marrali M, Jaroslavsky JR, Nganje CN, Maloney E, Zhou R, Navarro-Gomez D, Greene AC, Grotenbreg G, Greer R, Blair W, Cao MD, Chan S, Bae K, Spira AI, Roychowdhury S, Carbone DP, Henick BS, Drake CG, Solomon BJ, Ahn DH, Mahipal A, Maron SB, Johnson B, Rousseau R, Yelensky R, Liao CY, Catenacci DVT, Allen A, Ferguson AR, Jooss K. Individualized, heterologous chimpanzee adenovirus and self-amplifying mRNA neoantigen vaccine for advanced metastaticsolid tumors: phase 1 trial interim results. Nat Med. 2022 Aug;28(8):1619-1629. doi: 10.1038 / s41591-022-01937-6. Epub 2022 Aug 15. PMID: 35970920.

[0107] 7. Hubbard JM, Tőke ER, Moretto R, Graham RP, Youssoufian H, Lőrincz O, Molnár L, Csiszovszki Z, Mitchell JL, Wessling J, Tóth J, Cremolini C. Safety and Activity of PolyPEPI1018 Combined with Maintenance Therapy in Metastatic Colorectal Cancer: an Open- Label, Multicenter, Phase Ib Study. Clin Cancer Res. 2022 Jul 1;28(13):2818-2829. doi: 10.1158 / 1078-0432.CCR-22-0112. PMID: 35472243; PMCID: PMC9365360.

[0108] 8. Martini G, Ciardiello D, Dallio M, Famiglietti V, Esposito L, Corte CMD, Napolitano S, Fasano M, Gravina AG, Romano M, Loguercio C, Federico A, Maiello E, Tuccillo C, Morgillo F, Troiani T, Di Maio M, Martinelli E, Ciardiello F. Gut microbiota correlates with antitumor activity in patients with mCRC and NSCLC treated with cetuximab plus avelumab. Int J Cancer. 2022 Aug 1;151(3):473-480. doi: 10.1002 / ijc.34033. Epub 2022 Apr 29. PMID: 35429341; PMCID: PMC9321613.

[0109] 9. Morano F, Raimondi A, Pagani F, Lonardi S, Salvatore L, Cremolini C, Murgioni S, Randon G, Palermo F, Antonuzzo L, Pella N, Racca P, Prisciandaro M, Niger M, Corti F, Bergamo F, Zaniboni A, Ratti M, Palazzo M, Cagnazzo C, Calegari MA, Marmorino F, Capone I, Conca E, Busico A, Brich S, Tamborini E, Perrone F, Di Maio M, Milione M, Di Bartolomeo M, de Braud F, Pietrantonio F. Temozolomide Followed by Combination With Low-Dose Ipilimumab and Nivolumab in Patients With Microsatellite-Stable, O6-Methylguanine-DNA Methyltransferase-Silenced Metastatic Colorectal Cancer: The MAYA Trial. J Clin Oncol. 2022 May 10;40(14):1562-1573. doi: 10.1200 / JCO.21.02583. Epub 2022 Mar 8. PMID: 35258987; PMCID: PMC9084437.

[0110] 10. Mettu NB, Ou FS, Zemla TJ, Halfdanarson TR, Lenz HJ, Breakstone RA, Boland PM, Crysler OV, Wu C, Nixon AB, Bolch E, Niedzwiecki D, Elsing A, Hurwitz HI, Fakih MG, Bekaii-Saab T. Assessment of Capecitabine and Bevacizumab With or Without Atezolizumab for the Treatment of Refractory Metastatic Colorectal Cancer: A Randomized Clinical Trial. JAMA Netw Open. 2022 Feb 1;5(2):e2149040. doi: 10.1001 / jamanetworkopen.2021.49040. PMID: 35179586; PMCID: PMC8857687.

[0111] 11. Cohen SM. 13C and 31P NMR studies of hepatic metabolism in two experimental models of diabetes. Ann N Y Acad Sci. 1987;508:109-29. doi: 10.1111 / j.1749- 6632.1987.tb32899.x. PMID: 3326454.

[0112] 12. Ribic CM, Sargent DJ, Moore MJ, Thibodeau SN, French AJ, Goldberg RM, Hamilton SR, Laurent-Puig P, Gryfe R, Shepherd LE, Tu D, Redston M, Gallinger S. Tumor microsatellite-instability status as a predictor of benefit from fluorouracil-based adjuvant chemotherapy for colon cancer. N Engl J Med. 2003 Jul 17;349(3):247-57. doi: 10.1056 / NEJMoa022289. PMID: 12867608

[0113] 13. Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, Skora AD, Luber BS, Azad NS, Laheru D, Biedrzycki B, Donehower RC, Zaheer A, Fisher GA, Crocenzi TS, Lee JJ, Duffy SM, Goldberg RM, de la Chapelle A, Koshiji M, Bhaijee F, Huebner T, Hruban RH, Wood LD, Cuka N, Pardoll DM, Papadopoulos N, Kinzler KW, Zhou S, Cornish TC, Taube JM, Anders RA, Eshleman JR, Vogelstein B, Diaz LA Jr. PD-1 Blockade in Tumors with Mismatch- Repair Deficiency. N Engl J Med.2015 Jun 25;372(26):2509-20. doi: 10.1056 / NEJMoa1500596. Epub 2015 May 30. PMID: 26028255

[0114] 14. Bonneville R, Krook MA, Kautto EA, Miya J, Wing MR, Chen HZ, Reeser JW, Yu L, Roychowdhury S. Landscape of Microsatellite Instability Across 39 Cancer Types. JCO Precis Oncol. 2017;2017:PO.17.00073. doi: 10.1200 / PO.17.00073. Epub 2017 Oct 3. PMID: 29850653.

[0115] 15. Kautto EA, Bonneville R, Miya J, Yu L, Krook MA, Reeser JW, Roychowdhury S. Performance evaluation for rapid detection of pan-cancer microsatellite instability with MANTIS. Oncotarget. 2017 Jan 31;8(5):7452-7463. doi: 10.18632 / oncotarget.13918. PMID: 27980218.

[0116] 16. Dabney AR. ClaNC: point-and-click software for classifying microarrays to nearest centroids. Bioinformatics. 2006 Jan 1;22(1):122-3. doi: 10.1093 / bioinformatics / bti756. Epub 2005 Nov 2. PMID: 16269418.

[0117] 17. Li L et al. Comput Struct Biotechnol J. 2020 Mar 19; 18:668-675.

[0118] 18. Sorokin M et al. Front Mol Biosci.2021 Nov 23; 8:737821.

[0119] 19. André T et al. N Engl J Med. 2020 Dec 3;383(23):2207-2218.

[0120] 20. Lizardo DY et al. Biochim Biophys Acta Rev Cancer.2020 Dec;1874(2):188447.

[0121] 21. Pecci F et al. Curr Treat Options Oncol. 2021 Jun 10;22(8):69.

[0122] 22. Szklarczyk D et al. Nucleic Acids Res.2023 Jan 6;51(D1):D638-D646. Example 2- Development of a novel RNA-based microsatellite stable predictive response signature (MSS-PRS) to identify MSS Uterine Corpus Endometrial Carcinoma (UCEC) patients with a microsatellite instability-high (MSI-H) molecular phenotype. Objective

[0123] The goal of this Example was to develop an RNA-based MSS-PRS with a primary objective to identify MSS UCEC tumors that have MSI-H / dMMR molecular features indicative of ICI response. Methods

[0124] The UCEC MSS-PRS was developed using two cohorts from TGCA: GA expression platform and HiSeq platform.

[0125] Table 4. Training and Test sets for developing and validating UCEC MSS-PRS.

[0126] For the training set, there was an n=169 with complete data (i.e., HiSeq expression, MSI mantis scores and gene mutation status. Training labels were assigned using theMSI Mantis score and mutation status from 14 genes (i.e., MLH1, EPM2AIP1, TTC30A, SMAP1, RNLS, SHROOM4, RPL22L1, ARID1A, ARID5B, MSH2, MSH6, PMS2, POLE and POLD1) with reported MSI association. MSI Mantis is a proxy for MSI that is widely available in genomic databases (PMID: 29850653; PMID: 27980218). A cutoff of 0.4 has been established and samples with an MSI Mantis score above 0.4 are considered MSI-high (MSI-H). Samples called “activated” had both one or more of the training label gene mutations and an MSI Mantis score > 0.4 (i.e., MSI-H), and samples called “non-activated” were wild type for all training label genes and had MSI Mantis score < 0.4. All other samples were designated “ambiguous” and excluded from training. Using ClaNC software (PMID: 16269418) and cross-validation in the training set, a nearest centroid classifier was developed from a set of high mean, high variance candidate genes in the RNA expression data to select an optimal gene set to separate activated (PRS score > 0) and non-activated groups (PRS score < 0), which resulted in 3068 genes for feature selection and development of the UCEC model (see FIGs 16-17). The classifier performance was evaluated in HiSeq and GA test sets (see Table 4 above as well as FIG.18A and FIG. 18B). Results and Conclusions

[0127] Herein, the development of a novel MSS-PRS that captures an MSI-H / dMMR-like molecular phenotype in a subset of patients with UCEC MSS tumors despite their lack of high TMB or overt MSI defects is described. As such, the MSS-PRS selects tumors not identified with conventional MSI testing (e.g., MSI-H / dMMR) but have molecular characteristics consistent with microsatellite instability, thus making them a potential target for ICI (see FIGs 18A-18B, FIG. 19, FIG.20 and FIG.21)). Accordingly, the MSS-PRS developed in this Example and described herein, can aid in the identification of UCEC cancer patients with an MSI-like phenotype who could benefit from treatment regimens containing IO. Based upon these initial findings, further development of the MSS-PRS and its clinical validation as a tool to select patients with MSS tumors who may benefit from ICI-containing treatment regimens is warranted. Table 5. Gene Centroids of 60 Classifier genes in UCEC MSI-PRS.Example 3- Development of a Colorectal Cancer Consensus Molecular Subtyper Background

[0128] In 2015, an international consortium published the results of their efforts to combine and harmonize 6 separate classifiers into a single classifier that produced four consensus molecular subtypes (CMS) for colorectal cancer (CRC) (Guinney J, et al., The consensus molecular subtypes of colorectal cancer. Nat Med. 2015 Nov;21(11):1350-6. doi: 10.1038 / nm.3967. Epub 2015 Oct 12). Kaplan-Meier survival analysis was conducted in the aggregated cohort for overall survival (n = 2,129), relapse-free survival (n = 1,785) and survival after relapse (n = 405) and showed key survival distinctions in CMS1 and CMS4 groups (see FIG. 22; Guinney J, et al., The consensus molecular subtypes of colorectal cancer. Nat Med. 2015 Nov;21(11):1350-6. doi: 10.1038 / nm.3967. Epub 2015 Oct 12). Moreover, CMS groups have different immune profiles (see PMID: 33035640, 32210966, 32104582, 36684057 and 34110510) and data from trials evaluating CMS groups as biomarkers has given an indication of their prognostic and predictive utility (see Table 6), but said studies are limited. Objective

[0129] The goal of this Example was to develop a gene expression classifier that accurately assigns colorectal cancer Consensus Molecular Subtypes (CMS) (i.e., CMS1: MSI Immune; CMS2: Canonical; CMS3: Metabolic; and CMS4: Mesenchymal as shown in FIG.22). Materials and Methods

[0130] The computer-implemented method for developing the CMS subtyper for colorectal cancer generally comprised: (1) transforming TCGA colorectal data set, comprised of COAD and READ cancer types, by log2(x+1); (2) removing low expression and low variance genes; (3) splitting samples into training and testing sets (i.e., split into 2 / 3 training and 1 / 3 test); (4) removing “No Label (NOLBL)” samples from training set; (5) selecting genes that are significantly differ for each subtype vs all other samples (Wilcoxon test); and (6) generating a centroid based predictive model from 40 genes (top 10 of each ranked Wilcoxon result) (see Dabney et al., ClaNC: point-and-click software for classifying microarrays to nearest centroids. Bioinformatics. 2006 Jan 1;22(1):122-3. doi: 10.1093 / bioinformatics / bti756. Epub 2005 Nov 2. PMID: 16269418) to predict the 4 CMS subtypes. Results

[0131] FIG.23 shows the development of the CMS subtyper including threshold selection, while FIG. 24 shows the centroid model developed in this Example and reflects the 40 gene classifier found in Table 7. FIGs 25 and 26 showed that the RNA-based CMS subtyper accurately recapitulated the CMS classifications previously defined in Guinney J, et al., The consensus molecular subtypes of colorectal cancer. Nat Med. 2015 Nov;21(11):1350-6. doi: 10.1038 / nm.3967. Epub 2015 Oct 12 (see FIG.22). Like previously described, CMS1 and CMS4 are hot tumors, while CMS2 and CMS3 are cold tumors.

[0132] As shown in FIG.27, many group B members (Mantis (-) / MSI-PRS (+)) had higher immune signature scores, particularly CMS4. This may signal potential sensitivity to ICI and indicate predictive utility for a combined MSS-PRS / CMS classifier. Accordingly, CMS subtypes may provide further potential for subdividing COAD according to their molecular characteristics (see FIG.28). Distinct mutation patterns were observed in MSS-PRS group A versus other groups (e.g., more BRAF mutations, fewer TP53 mutations, fewer APC mutations; FIG. 29). Moreover, distinct gene expression profiles for key therapeutic target areas suggested that the information provided by the combined MSS-PRS and CMS groups could help guide therapy selection and inform combination therapy selection. Although CMS has been published, the surprising discovery shown in this Example (see FIG. 30) was how CMS subtype, paired with MSS-PRS yielded 7 subgroups (i.e., CMS1 / MSI-PRS(+); CMS2 / MSI-PRS (+) / (-); CMS3 / MSI-PRS (+) / (-) and CMS4 / MSI-PRS (+) / (-). As the heat map in FIG. 30 suggests, some of those subgroups have expression profile characteristics that suggested they may be more responsive to particular therapies than other subgroups. Conclusions

[0133] MSI / MMR testing is ground truth in CRC, yet 85-95% of patients are MSS. MSS tumors are resistant to ICI monotherapy and efforts to “heat up” MSS tumors underlie more than half of the ongoing combination trials. However, there is a dearth of prognostic and predictive biomarkers in the MSS space but, so far, this has not held back clinical trials.

[0134] Unlike established MSI / MMR testing, as shown in Example 1 and here, MSS-PRS (with or without the overlay of CMS subtypes) appeared to parse out the MSS population into useful subgroups. MSS-PRS (+) has hallmarks of being immune active.

[0135] As one example, CMS4 MSS Hi has an immune gene expression pattern closely mirroring that of the ICI responsive MSI-H group. Moreover, there are other MSS-CMS subgroups with compelling suggestions of therapeutic pathway targets (e.g., Wnt, FGFR1 / 4, VEGF).

[0136] Table 6. Review of recent clinical trials where CMS subtypes have displayed prognostic and predictive utility.

[0137] Table 7.40-gene CMS subtyper.References

[0138] 1. www.onclive.com / view / nivolumab-plus-soc-misses-pfs-end-point-of- checkmate-9x8-trial-but-provides-notable-benefit-in-select-mcrc-subsets The addition of theimmunotherapy did produce encouraging efficacy in subsets of patients with baseline CMS1 and CMS3 status, according to data from the phase 2 / 3 CheckMate-9X8 trial (NCT03414983).

[0139] 2. Kawazoe A, Kuboki Y, Shinozaki E, Hara H, Nishina T, Komatsu Y, Yuki S, Wakabayashi M, Nomura S, Sato A, Kuwata T, Kawazu M, Mano H, Togashi Y, Nishikawa H, Yoshino T. Multicenter Phase I / II Trial of Napabucasin and Pembrolizumab in Patients with Metastatic Colorectal Cancer (EPOC1503 / SCOOP Trial). Clin Cancer Res. 2020 Nov 15;26(22):5887-5894. doi: 10.1158 / 1078-0432.CCR-20-1803. Epub 2020 Jul 21. PMID: 32694160.

[0140] 3. Nusrat M. Response to Anti-PD-1 in Microsatellite-Stable Colorectal Cancer: A STAT Need. Clin Cancer Res.2020 Nov 15;26(22):5775-5777. doi: 10.1158 / 1078-0432.CCR-20- 2901. Epub 2020 Sep 21. PMID: 32958701.

[0141] 4. Stahler A, Heinemann V, Schuster V, Heinrich K, Kurreck A, Gießen-Jung C, Fischer von Weikersthal L, Kaiser F, Decker T, Held S, Graeven U, Schwaner I, Denzlinger C, Schenk M, Neumann J, Kirchner T, Jung A, Kumbrink J, Stintzing S, Modest DP. Consensus molecular subtypes in metastatic colorectal cancer treated with sequential versus combined fluoropyrimidine, bevacizumab and irinotecan (XELAVIRI trial). Eur J Cancer.2021 Nov;157:71- 80. doi: 10.1016 / j.ejca.2021.08.017. Epub 2021 Sep 8. PMID: 34507244.

[0142] 5. Stahler A, Hoppe B, Na IK, Keilholz L, Müller L, Karthaus M, Fruehauf S, Graeven U, Fischer von Weikersthal L, Goekkurt E, Kasper S, Kind AJ, Kurreck A, Alig AHS, Held S, Reinacher-Schick A, Heinemann V, Horst D, Jarosch A, Stintzing S, Trarbach T, Modest DP. Consensus Molecular Subtypes as Biomarkers of Fluorouracil and Folinic Acid Maintenance Therapy With or Without Panitumumab in RAS Wild-Type Metastatic Colorectal Cancer (PanaMa, AIO KRK 0212). J Clin Oncol. 2023 Jun 1;41(16):2975-2987. doi: 10.1200 / JCO.22.02582. Epub 2023 Apr 5. PMID: 37018649.

[0143] 6. Stintzing S, Wirapati P, Lenz HJ, Neureiter D, Fischer von Weikersthal L, Decker T, Kiani A, Kaiser F, Al-Batran S, Heintges T, Lerchenmüller C, Kahl C, Seipelt G, Kullmann F, Moehler M, Scheithauer W, Held S, Modest DP, Jung A, Kirchner T, Aderka D, Tejpar S, Heinemann V. Consensus molecular subgroups (CMS) of colorectal cancer (CRC) andfirst-line efficacy of FOLFIRI plus cetuximab or bevacizumab in the FIRE3 (AIO KRK-0306) trial. Ann Oncol.2019 Nov 1;30(11):1796-1803. doi: 10.1093 / annonc / mdz387. PMID: 31868905; PMCID: PMC6927316.

[0144] 7. Guinney J, Dienstmann R, Wang X, de Reyniès A, Schlicker A, Soneson C, Marisa L, Roepman P, Nyamundanda G, Angelino P, Bot BM, Morris JS, Simon IM, Gerster S, Fessler E, De Sousa E Melo F, Missiaglia E, Ramay H, Barras D, Homicsko K, Maru D, Manyam GC, Broom B, Boige V, Perez-Villamil B, Laderas T, Salazar R, Gray JW, Hanahan D, Tabernero J, Bernards R, Friend SH, Laurent-Puig P, Medema JP, Sadanandam A, Wessels L, Delorenzi M, Kopetz S, Vermeulen L, Tejpar S. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015 Nov;21(11):1350-6. doi: 10.1038 / nm.3967. Epub 2015 Oct 12. PMID: 26457759; PMCID: PMC4636487.

[0145] 8. Lizardo DY, Kuang C, Hao S, Yu J, Huang Y, Zhang L. Immunotherapy efficacy on mismatch repair-deficient colorectal cancer: From bench to bedside. Biochim Biophys Acta Rev Cancer. 2020 Dec;1874(2):188447. doi: 10.1016 / j.bbcan.2020.188447. Epub 2020 Oct 6. PMID: 33035640; PMCID: PMC7886024.

[0146] 9. Picard E, Verschoor CP, Ma GW, Pawelec G. Relationships Between Immune Landscapes, Genetic Subtypes and Responses to Immunotherapy in Colorectal Cancer. Front Immunol. 2020 Mar 6;11:369. doi: 10.3389 / fimmu.2020.00369. PMID: 32210966; PMCID: PMC7068608.

[0147] 10. Huyghe N, Baldin P, Van den Eynde M. Immunotherapy with immune checkpoint inhibitors in colorectal cancer: what is the future beyond deficient mismatch-repair tumours? Gastroenterol Rep (Oxf). 2019 Nov 25;8(1):11-24. doi: 10.1093 / gastro / goz061. PMID: 32104582; PMCID: PMC7034232.

[0148] 11. Li DD, Tang YL, Wang X. Challenges and exploration for immunotherapies targeting cold colorectal cancer. World J Gastrointest Oncol. 2023 Jan 15;15(1):55-68. doi: 10.4251 / wjgo.v15.i1.55. PMID: 36684057; PMCID: PMC9850757.

[0149] 12. Pecci F, Cantini L, Bittoni A, Lenci E, Lupi A, Crocetti S, Giglio E, Giampieri R, Berardi R. Beyond Microsatellite Instability: Evolving Strategies Integrating Immunotherapyfor Microsatellite Stable Colorectal Cancer. Curr Treat Options Oncol.2021 Jun 10;22(8):69. doi: 10.1007 / s11864-021-00870-z. PMID: 34110510; PMCID: PMC8192371.

[0150] 13. Dabney AR. ClaNC: point-and-click software for classifying microarrays to nearest centroids. Bioinformatics. 2006 Jan 1;22(1):122-3. doi: 10.1093 / bioinformatics / bti756. Epub 2005 Nov 2. PMID: 16269418. Example 4- Homologous Recombination Repair Deficiency / DNA Damage Predictive Response RNA Gene Signature (HRD-PRS)

[0151] A wide range of approved and experimental drugs target elements of the HRD system (FIG. 32). Some tumor types have a high prevalence of HRD / DDR defects, particularly breast, prostate, pancreatic, and ovarian. Reliable biomarkers for any single tumor type have been elusive; in part due to differences in HRD within tumor biology (type) context. Narrow panels of HRD genes likely miss patients with HRD / DDR defect. Not all biomarker(+) patients respond, and some biomarker(-) patients do. Accordingly, there is a need for a tumor type-specific, more holistic functional assay for HRD / DDR defects. Objective

[0152] To develop disease specific homologous recombination repair deficiency / DNA damage predictive response RNA gene signature (HRD-PRS) for breast cancer (BRCA), BRCA basal, PRAD, OV, and PAAD Materials and Methods

[0153] Recognize distinct tumor or subtype biology to build tumor specific HRD signatures using TCGA expression and mutation data from cbioportal and firehose; TCGA HRD_Score and DDR gene set from Cell 2018 "Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas"; ISPY2 data from GSE194040 and manuscript supplemental files (www.sciencedirect.com / science / article / pii / S1535610822002161) elastic net. Signature training strategy: identify and create consensus of HRD, independent of response data through the use of a curated HRD / DDR gene set and an approach for repelling noise (false positive / false negatives) while enriching for signal (true positives and true negatives).Results and Conclusions

[0154] The inventors have found that within any individual tumor type, various HRD biomarkers (individual gene mutation status, DNA-derived HRD signatures, etc) aren't markedly correlated. As such, training HRD gene expression signatures using individual biomarkers as labels or even bookend labels based on multiple biomarkers hasn't yielded satisfactory results, with test set signature gene expression profiles not aligning well with patterns in the training set. As an alternative, in this Example, the inventors developed a signature training approach that didn't directly model biomarker-based labels but rather used a curated HRD / DDR gene set (Knijnenburg TA, Wang L, Zimmermann MT, et al. Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas. Cell Reports. 2018 Apr;23(1):239-254.e6. DOI: 10.1016 / j.celrep.2018.03.076. PMID: 29617664; PMCID: PMC5961503) for principal component analysis (PCA) and identified a single top-ranked principal component (PC) as an HRD PC by correlating individual PCs with a DNA-derived biomarker (HRD_Score) from a highly-cited paper (Knijnenburg TA, Wang L, Zimmermann MT, et al. Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas. Cell Reports. 2018 Apr;23(1):239- 254.e6. DOI: 10.1016 / j.celrep.2018.03.076. PMID: 29617664; PMCID: PMC5961503) (see FIGs . The HRD PC was then modeled as a continuous variable using the glmnet package in R with features selected from the HRD / DDR gene set to get a portable HRD signature that was a weighted average of gene expression values. Success was determined by whether or not the association between the signature and the HRD_Score was similar in the training and test set (TCGA was split 2 / 3 training and 1 / 3 test). In the end, 5 HRD / DDR signatures, comprising: pancreatic (PAAD; Table 8), Ovarian (OV; Table 9), Breast (BRCA; Tables 10 & 11), and Prostate (PRAD; Table 12).

[0155] Additionally, the BRCA and BRCA basal signatures were applied to the ISPY2 data set, which contained pre-treatment RNAseq expression profiles and binary treatment response data (pCR) for approximately 1000 breast cancer patients, including 71 patients treated with the FGFR inhibitor Velatonib plus Carboplatin (VC). A similar performance was found to predict pCR in VC-treated patients compared to 3 signatures in the paper (see the AUC plot for the BRCA basal HRD signature). Importantly, the signature provided herein does not predict pCR in the standard of care control group.

[0156] The 3 signatures in the ISPY2 paper were all trained using response data: "PARPi7_score" (PMID:22875744 PMID:28948212),"PARPi7_plus_MP2" (PMID:28948212), and a third "VCpred_TN" trained in this data set using TN breast cancer.

[0157] There have been many proposed / utilized biomarkers for Homologous Recombination Repair Deficiency in breast, pancreatic, prostate, and ovarian cancer patients. However, HRD is a complex and, to some extent, a tumor specific defect that is difficult to capture with a single DDR alteration panel or genomic scarring algorithms. As shown in FIGs 34-39, the HRD-PRS gene signatures are tumor-type specific and “surgically” trained to define clear HRD defects and avoid spurious, non-penetrant HRD associated genomic signals by detecting an HRD / DDR phenotype rather than a narrow set of biomarkers. Accordingly, the HRD-PRS developed and described herein will add clinical value by better identifying patients likely to respond to PARP inhibitors.

[0158] Table 8. PAAD Gene Signature.

[0159] Table 9. OV Gene Signature.

[0160] Table 10. BRCA Gene Signature.

[0161] Table 11. BRCA_Basal Gene Signature.

[0162] Table 12. PRAD Gene Signature.Further Numbered Embodiments of the Disclosure

[0163] Other subject matter contemplated by the present disclosure is set out in the following numbered embodiments:

[0164] 1. A method of treating cancer in a subject, the method comprising: measuring the expression level of a plurality of biomarkers in a sample obtained from a subject suffering from cancer, wherein the plurality of biomarkers are selected from biomarkers listed in Table 3 or Table 5, wherein the measured expression levels of the plurality of biomarkers provide a microsatellite stability predictive response signature (MSS-PRS) for the sample; and administering an immuno- oncology therapeutic agent based on presence of a positive MSS-PRS or chemotherapeutic agent based on presence of a negative MSS-PRS.

[0165] 2. The method of embodiment 1, wherein the measuring the expression levels of the plurality of biomarkers is at a nucleic acid level by performing RNA sequencing, reverse transcriptase polymerase chain reaction (RT-PCR) or hybridization-based analyses.

[0166] 3. The method of embodiment 2, wherein the RT-PCR is quantitative real time reverse transcriptase polymerase chain reaction (qRT-PCR).

[0167] 4. The method of embodiment 3, wherein the RT-PCR is performed with primers specific to the biomarkers selected from the plurality of biomarkers of Table 3 or Table 5.

[0168] 5. The method of embodiment 4, wherein the hybridization analysis is a microarray- based hybridization analysis.

[0169] 6. The method of any one of the above embodiments, wherein the sample is a formalin-fixed, paraffin-embedded (FFPE) tissue sample, fresh or a frozen tissue sample, an exosome, or a bodily fluid obtained from the patient.

[0170] 7. The method of embodiment 6, wherein the bodily fluid is blood or fractions thereof, urine, saliva, cerebrospinal fluid (CSF) or sputum.

[0171] 8. The method of any one of the above embodiments, wherein the immuno- oncology therapeutic agent is two or more immuno-oncology therapeutic agents used in combination.

[0172] 8.1 The method of any one of the above embodiments, wherein the immuno- oncology therapeutic agent is used in combination in other therapeutic agents.

[0173] 9. The method of embodiment 8.1, wherein the other therapeutic agents are selected from Table 1.

[0174] 10. The method of any one of the above embodiments, wherein the immuno- oncology therapeutic agent is an immune checkpoint inhibitor (ICI).

[0175] 11. The method of any one of the above embodiments, wherein the chemotherapeutic agent is 5-FU chemotherapy.

[0176] 12. The method of any one of the above embodiments, further comprising comparing the expression levels of the plurality of biomarkers of Table 3 or Table 5 to an expression level of the plurality of biomarkers of Table 3 or Table 5 in at least one sample training set, wherein the at least one sample training set is from a reference microsatellite instability high / deficient MMR (MSI-H / dMMR)-containing cancer sample, or is from a reference MSI- low / proficient MMR cancer sample; and classifying the tumor sample as having a positive MSS predictive response signature (MMS-PRS (+)) or negative MSS predictive response signature (MMS-PRS (-)) based on the results of the comparing step.

[0177] 13. The method of embodiment 12, wherein the comparing comprises applying a statistical algorithm that comprises determining a correlation between the expression levels of the plurality of biomarkers of Table 3 or Table 5 obtained from the sample and the expression levels of the plurality of biomarkers of Table 3 or Table 5 from the at least one training set; and classifying the tumor sample as possessing a MSS-PRS (+) or MSS-PRS (-) based on the results of the statistical algorithm.

[0178] 14. The method of embodiment 13, wherein the at least one training set is from a reference MSI-H / dMMR cancer sample and the sample is classified as possessing the positive MSS-PRS if the expression levels of the plurality of biomarkers of Table 3 or Table 5 correlate with the expression levels of the plurality of biomarkers of Table 3 or Table 5 from the reference MSI-H / dMMR cancer sample.

[0179] 15. The method of embodiment 13, wherein the at least one training set is from a reference MSI-H / dMMR cancer sample and from a reference MSI-low / proficient MMR cancer sample and the sample is classified as being MSS-PRS (+) if the expression levels of the plurality of biomarkers of Table 3 or Table 5 correlate with the expression levels of the plurality of biomarkers of Table 3 or Table 5 from the reference MSI-H / dMMR cancer sample.

[0180] 16. The method of any one of the above embodiments, wherein the cancer the patient is suffering from is colon adenocarcinoma (COAD).

[0181] 17. The method of embodiment 16, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 100 biomarkers or at least 110 biomarkers, at least 120 biomarkers or at least 130 biomarkers from Table 3.

[0182] 18. The method of embodiment 16, wherein the plurality of biomarkers comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 3.

[0183] 19. The method of embodiment 16, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 3.

[0184] 20. The method of any one of embodiments 16-19, further comprising measuring the expression level of a plurality of biomarkers selected from biomarkers listed in Table 7 in the sample obtained from the subject suffering from cancer.

[0185] 21. The method of embodiment 20, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers or at least 40 biomarkers from Table 7.

[0186] 22. The method of embodiment 20, wherein the plurality of biomarkers selected from Table 7 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 7.

[0187] 23. The method of embodiment 20, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 7.

[0188] 24. The method of any one of embodiments 20-23, further comprising comparing the expression levels of the plurality of biomarkers of Table 7 to an expression level of the plurality of biomarkers of Table 7 in at least one sample training set, wherein the at least one sample training set is from a reference CMS1, CMS2, CMS3 and / or CMS4; and classifying the sample as having a CMS1, CMS2, CMS3 or CMS4 based on the results of the comparing step.

[0189] 25. The method of embodiment 24, wherein the comparing comprises applying a statistical algorithm that comprises determining a correlation between the expression levels of the plurality of biomarkers of Table 7 obtained from the sample and the expression levels of the plurality of biomarkers of Table 7 from the at least one training set; and classifying the tumor sample as possessing a CMS1, CMS2, CMS3 or CMS4 based on the results of the statistical algorithm.

[0190] 26. The method of any one of embodiments 1-15, wherein the cancer the patient is suffering from is Uterine Corpus Endometrial Carcinoma (UCEC).

[0191] 27. The method of embodiment 26, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers or at least 60 biomarkers from Table 5.

[0192] 28. The method of embodiment 26, wherein the plurality of biomarkers selected from Table 5 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 5.

[0193] 29. The method of embodiment 26, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 5.

[0194] 30. A method of detecting a biomarker in a sample obtained from a patient suffering from a cancer, the method comprising, consisting essentially of or consisting of measuring the expression level of a plurality of biomarkers selected from Table 3, Table 5 or Table 7 using an amplification, hybridization and / or sequencing assay.

[0195] 31. The method of embodiment 30, wherein the amplification, hybridization and / or sequencing assay comprises performing quantitative real time reverse transcriptase polymerase chain reaction (qRT-PCR), RNAseq, microarrays, gene chips, nCounter Gene Expression Assay, Serial Analysis of Gene Expression (SAGE), Rapid Analysis of Gene Expression (RAGE), nuclease protection assays, Northern blotting, or any other equivalent gene expression detection techniques.

[0196] 32. The method of embodiment 30, wherein the expression level is detected by performing qRT-PCR.

[0197] 33. The method of any one of embodiments 30-32, wherein the detection of the expression level comprises using at least one pair of oligonucleotide primers per each biomarker from the plurality of biomarkers selected from Table 3, Table 5 or Table 7.

[0198] 34. The method of any one of embodiments 30-33, wherein the sample is a formalin-fixed, paraffin-embedded (FFPE) tissue sample, fresh or a frozen tissue sample, an exosome, wash fluids, cell pellets, or a bodily fluid obtained from the patient.

[0199] 35. The method of embodiment 34, wherein the bodily fluid is blood or fractions thereof, urine, saliva, cerebrospinal fluid (CSF) or sputum.

[0200] 36. The method of any one of embodiments 30-34, wherein the cancer the patient is suffering from is colon adenocarcinoma (COAD).

[0201] 37. The method of embodiment 36, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 100 biomarkers or at least 110 biomarkers, at least 120 biomarkers or at least 130 biomarkers from Table 3.

[0202] 38. The method of embodiment 36, wherein the plurality of biomarkers selected from Table 3 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 3.

[0203] 39. The method of embodiment 36, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 3.

[0204] 40. The method of any one of embodiments 30-39, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers or at least 40 biomarkers from Table 7.

[0205] 41. The method of any one of embodiments 30-39, wherein the plurality of biomarkers selected from Table 7 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 7.

[0206] 42. The method of any one of embodiments 30-39, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 7.

[0207] 43. The method of any one of embodiments 30-35, wherein the cancer the patient is suffering from is Uterine Corpus Endometrial Carcinoma (UCEC).

[0208] 44. The method of embodiment 43, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers or at least 60 biomarkers from Table 5.

[0209] 45. The method of embodiment 43, wherein the plurality of biomarkers selected from Table 5 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 5.

[0210] 46. The method of embodiment 43, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 5.

[0211] 47. A method of treating cancer in a subject, the method comprising: measuring the expression level of a plurality of biomarkers in a sample obtained from a subject suffering from cancer, wherein the plurality of biomarkers are selected from biomarkers listed in Table 8, 9, 10, 11 or 12, wherein the measured expression levels of the plurality of biomarkers provide a homologous recombination repair deficiency predictive response signature (HRD-PRS) for the sample; and administering a therapeutic agent that is a PARP inhibitor based on the presence of a positive HRD-PRS or a therapeutic agent that is not a PARP inhibitor based on the presence of a negative HRD-PRS.

[0212] 48. The method of embodiment 47, wherein the measuring the expression levels of the plurality of biomarkers is at a nucleic acid level by performing RNA sequencing, reverse transcriptase polymerase chain reaction (RT-PCR) or hybridization-based analyses.

[0213] 49. The method of embodiment 48, wherein the RT-PCR is quantitative real time reverse transcriptase polymerase chain reaction (qRT-PCR).

[0214] 50. The method of embodiment 49, wherein the RT-PCR is performed with primers specific to the biomarkers selected from the plurality of biomarkers of Table 8, 9, 10, 11 or 12.

[0215] 51. The method of embodiment 50, wherein the hybridization analysis is a microarray-based hybridization analysis.

[0216] 52. The method of any one of embodiments 47-51, wherein the sample is a formalin-fixed, paraffin-embedded (FFPE) tissue sample, fresh or a frozen tissue sample, an exosome, or a bodily fluid obtained from the patient.

[0217] 53. The method of embodiment 52, wherein the bodily fluid is blood or fractions thereof, urine, saliva, or sputum.

[0218] 54. The method of any one of embodiments 47-51, wherein the cancer the patient is suffering from is pancreatic adenocarcinoma (PAAD).

[0219] 55. The method of embodiment 54, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers or at least 78 biomarkers from Table 8.

[0220] 56. The method of embodiment 54, wherein the plurality of biomarkers selected from Table 8 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 8.

[0221] 57. The method of embodiment 54, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 8.

[0222] 58. The method of any one of embodiments 47-51, wherein the cancer the patient is suffering from is ovarian cancer (OV).

[0223] 59. The method of embodiment 58, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers or at least 100 biomarkers from Table 9.

[0224] 60. The method of embodiment 58, wherein the plurality of biomarkers selected from Table 9 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 9.

[0225] 61. The method of embodiment 58, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 9.

[0226] 62. The method of any one of embodiments 47-51, wherein the cancer the patient is suffering from is breast cancer (BRCA).

[0227] 63. The method of embodiment 62, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 100 biomarkers, at least 110 biomarkers, at least 120 biomarkers, at least 130 biomarkers, at least 140 biomarkers, at least 150 biomarkers, or at least 151 biomarkers from Table 10.

[0228] 64. The method of embodiment 62, wherein the plurality of biomarkers selected from Table 10 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 10.

[0229] 65. The method of embodiment 62, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 10.

[0230] 66. The method of embodiment 62, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers or at least 93 biomarkers from Table 11.

[0231] 67. The method of embodiment 62, wherein the plurality of biomarkers selected from Table 11 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 11.

[0232] 68. The method of embodiment 62, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 11.

[0233] 69. The method of any one of embodiments 47-51, wherein the cancer the patient is suffering from is prostate adenocarcinoma (PRAD).

[0234] 70. The method of embodiment 69, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers or at least 100 biomarkers from Table 12.

[0235] 71. The method of embodiment 69, wherein the plurality of biomarkers selected from Table 12 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 12.

[0236] 72. The method of embodiment 69, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 12. * * * * * * *

[0237] The various embodiments described above can be combined to provide further embodiments. All of the U.S. patents, U.S. patent application publications, U.S. patent application, foreign patents, foreign patent application and non-patent publications referred to in this specification and / or listed in the Application Data Sheet are incorporated herein by reference, in their entirety. Aspects of the embodiments can be modified, if necessary to employ concepts of the various patents, application and publications to provide yet further embodiments.

[0238] These and other changes can be made to the embodiments in light of the above- detailed description. In general, in the following claims, the terms used should not be construed to limit the claims to the specific embodiments disclosed in the specification and the claims, but should be construed to include all possible embodiments along with the full scope of equivalents to which such claims are entitled. Accordingly, the claims are not limited by the disclosure. INCORPORATION BY REFERENCE

[0239] All references, articles, publications, patents, patent publications, and patent applications cited herein are incorporated by reference in their entireties for all purposes. However, mention of any reference, article, publication, patent, patent publication, and patent application cited herein is not, and should not be taken as an acknowledgment or any form of suggestion that they constitute valid prior art or form part of the common general knowledge in any country in the world.

Claims

CLAIMS What is claimed:

1. A method of treating cancer in a subject, the method comprising: measuring the expression level of a plurality of biomarkers in a sample obtained from a subject suffering from cancer, wherein the plurality of biomarkers are selected from biomarkers listed in Table 3 or Table 5, wherein the measured expression levels of the plurality of biomarkers provide a microsatellite stability predictive response signature (MSS-PRS) for the sample; and administering an immuno- oncology therapeutic agent based on presence of a positive MSS-PRS or chemotherapeutic agent based on presence of a negative MSS-PRS.

2. The method of claim 1, wherein the measuring the expression levels of the plurality of biomarkers is at a nucleic acid level by performing RNA sequencing, reverse transcriptase polymerase chain reaction (RT-PCR) or hybridization-based analyses.

3. The method of claim 2, wherein the RT-PCR is quantitative real time reverse transcriptase polymerase chain reaction (qRT-PCR).

4. The method of claim 3, wherein the RT-PCR is performed with primers specific to the biomarkers selected from the plurality of biomarkers from Table 3 or Table 5.

5. The method of claim 4, wherein the hybridization analysis is a microarray-based hybridization analysis.

6. The method of any one of the above claims, wherein the sample is a formalin-fixed, paraffin-embedded (FFPE) tissue sample, fresh or a frozen tissue sample, an exosome, or a bodily fluid obtained from the patient.

7. The method of claim 6, wherein the bodily fluid is blood or fractions thereof, urine, saliva, cerebrospinal fluid (CSF) or sputum.

8. The method of any one of the above claims, wherein the immuno-oncology therapeutic agent is used in combination in other therapeutic agents.

9. The method of claim 8, wherein the other therapeutic agents are selected from Table 1.

10. The method of any one of the above claims, wherein the immuno-oncology therapeutic agent is an immune checkpoint inhibitor (ICI).

11. The method of any one of the above claims, wherein the chemotherapeutic agent is 5-FU chemotherapy.

12. The method of any one of the above claims, further comprising comparing the expression levels of the plurality of biomarkers from Table 3 or Table 5 to an expression level of the plurality of biomarkers from Table 3 or Table 5 in at least one sample training set, wherein the at least one sample training set is from a reference microsatellite instability high / deficient MMR (MSI- H / dMMR)-containing cancer sample, or is from a reference MSI-low / proficient MMR cancer sample; and classifying the tumor sample as having a positive MSS predictive response signature (MMS-PRS (+)) or negative MSS predictive response signature (MMS-PRS (-)) based on the results of the comparing step.

13. The method of claim 12, wherein the comparing comprises applying a statistical algorithm that comprises determining a correlation between the expression levels of the plurality of biomarkers from Table 3 or Table 5 obtained from the sample and the expression levels of the plurality of biomarkers from Table 3 or Table 5 from the at least one training set; and classifying the tumor sample as possessing a MSS-PRS (+) or MSS-PRS (-) based on the results of the statistical algorithm.

14. The method of claim 13, wherein the at least one training set is from a reference MSI- H / dMMR cancer sample and the sample is classified as possessing the positive MSS-PRS if the expression levels of the plurality of biomarkers from Table 3 or Table 5 correlate with the expression levels of the plurality of biomarkers from Table 3 or Table 5 from the reference MSI- H / dMMR cancer sample.

15. The method of claim 13, wherein the at least one training set is from a reference MSI- H / dMMR cancer sample and from a reference MSI-low / proficient MMR cancer sample and the sample is classified as being MSS-PRS (+) if the expression levels of the plurality of biomarkers from Table 3 or Table 5 correlate with the expression levels of the plurality of biomarkers from Table 3 or Table 5 from the reference MSI-H / dMMR cancer sample.

16. The method of any one of the above claims, wherein the cancer the patient is suffering from is colon adenocarcinoma (COAD).

17. The method of claim 16, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 100 biomarkers or at least 110 biomarkers, at least 120 biomarkers or at least 130 biomarkers from Table 3.

18. The method of claim 16, wherein the plurality of biomarkers selected from Table 3 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 3.

19. The method of claim 16, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 3.

20. The method of any one of claims 16-19, further comprising measuring the expression level of a plurality of biomarkers selected from biomarkers listed in Table 7 in the sample obtained from the subject suffering from cancer.

21. The method of claim 20, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers or at least 40 biomarkers from Table 7.

22. The method of claim 20, wherein the plurality of biomarkers selected from Table 7 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 7.

23. The method of claim 20, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 7.

24. The method of any one of claims 20-23, further comprising comparing the expression levels of the plurality of biomarkers from Table 7 to an expression level of the plurality of biomarkers from Table 7 in at least one sample training set, wherein the at least one sample training set is from a reference CMS1, CMS2, CMS3 and / or CMS4; and classifying the sample as having a CMS1, CMS2, CMS3 or CMS4 based on the results of the comparing step.

25. The method of claim 24, wherein the comparing comprises applying a statistical algorithm that comprises determining a correlation between the expression levels of the plurality of biomarkers from Table 7 obtained from the sample and the expression levels of the plurality of biomarkers from Table 7 from the at least one training set; and classifying the tumor sample as possessing a CMS1, CMS2, CMS3 or CMS4 based on the results of the statistical algorithm.

26. The method of any one of claims 1-15, wherein the cancer the patient is suffering from is Uterine Corpus Endometrial Carcinoma (UCEC).

27. The method of claim 26, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers or at least 60 biomarkers from Table 5.

28. The method of claim 26, wherein the plurality of biomarkers selected from Table 5 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 5.

29. The method of claim 26, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 5.

30. A method of detecting a biomarker in a sample obtained from a patient suffering from a cancer, the method comprising, consisting essentially of or consisting of measuring the expression level of a plurality of biomarkers selected from Table 3, Table 5 or Table 7 using an amplification, hybridization and / or sequencing assay.

31. The method of claim 30, wherein the amplification, hybridization and / or sequencing assay comprises performing quantitative real time reverse transcriptase polymerase chain reaction (qRT- PCR), RNAseq, microarrays, gene chips, nCounter Gene Expression Assay, Serial Analysis of Gene Expression (SAGE), Rapid Analysis of Gene Expression (RAGE), nuclease protection assays, Northern blotting, or any other equivalent gene expression detection techniques.

32. The method of claim 30, wherein the expression level is detected by performing qRT-PCR.

33. The method of any one of claims 30-32, wherein the detection of the expression level comprises using at least one pair of oligonucleotide primers per each biomarker from the plurality of biomarkers selected from Table 3, Table 5 or Table 7.

34. The method of any one of claims 30-33, wherein the sample is a formalin-fixed, paraffin- embedded (FFPE) tissue sample, fresh or a frozen tissue sample, an exosome, wash fluids, cell pellets, or a bodily fluid obtained from the patient.

35. The method of claim 34, wherein the bodily fluid is blood or fractions thereof, urine, saliva, cerebrospinal fluid (CSF) or sputum.

36. The method of any one of claims 30-34, wherein the cancer the patient is suffering from is colon adenocarcinoma (COAD).

37. The method of claim 36, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 100 biomarkers or at least 110 biomarkers, at least 120 biomarkers or at least 130 biomarkers from Table 3.

38. The method of claim 36, wherein the plurality of biomarkers selected from Table 3 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 3.

39. The method of claim 36, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 3.

40. The method of any one of claims 30-39, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers or at least 40 biomarkers of Table 7.

41. The method of any one of claims 30-39, wherein the plurality of biomarkers selected from Table 7 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 7.

42. The method of any one of claims 30-39, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 7.

43. The method of any one of claims 30-35, wherein the cancer the patient is suffering from is Uterine Corpus Endometrial Carcinoma (UCEC).

44. The method of claim 43, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers or at least 60 biomarkers from Table 5.

45. The method of claim 43, wherein the plurality of biomarkers selected from Table 5 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 5.

46. The method of claim 43, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 5.

47. A method of treating cancer in a subject, the method comprising: measuring the expression level of a plurality of biomarkers in a sample obtained from a subject suffering from cancer, wherein the plurality of biomarkers are selected from biomarkers listed in Table 8, 9, 10, 11 or 12, wherein the measured expression levels of the plurality of biomarkers provide a homologous recombination repair deficiency predictive response signature (HRD-PRS) for the sample; and administering a therapeutic agent that is a PARP inhibitor based on the presence of a positive HRD-PRS or a therapeutic agent that is not a PARP inhibitor based on the presence of a negative HRD-PRS.

48. The method of claim 47, wherein the measuring the expression levels of the plurality of biomarkers is at a nucleic acid level by performing RNA sequencing, reverse transcriptase polymerase chain reaction (RT-PCR) or hybridization-based analyses.

49. The method of claim 48, wherein the RT-PCR is quantitative real time reverse transcriptase polymerase chain reaction (qRT-PCR).

50. The method of claim 49, wherein the RT-PCR is performed with primers specific to the biomarkers selected from the plurality of biomarkers from Table 8, 9, 10, 11 or 12.

51. The method of claim 50, wherein the hybridization analysis is a microarray-based hybridization analysis.

52. The method of any one of claims 47-51, wherein the sample is a formalin-fixed, paraffin- embedded (FFPE) tissue sample, fresh or a frozen tissue sample, an exosome, or a bodily fluid obtained from the patient.

53. The method of claim 52, wherein the bodily fluid is blood or fractions thereof, urine, saliva, cerebrospinal fluid (CSF) or sputum.

54. The method of any one of claims 47-51, wherein the cancer the patient is suffering from is pancreatic adenocarcinoma (PAAD).

55. The method of claim 54, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers or at least 78 biomarkers from Table 8.

56. The method of claim 54, wherein the plurality of biomarkers selected from Table 8 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 8.

57. The method of claim 54, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 8.

58. The method of any one of claims 47-51, wherein the cancer the patient is suffering from is ovarian cancer (OV).

59. The method of claim 58, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers or at least 100 biomarkers from Table 9.

60. The method of claim 58, wherein the plurality of biomarkers selected from Table 9 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 9.

61. The method of claim 58, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 9.

62. The method of any one of claims 47-51, wherein the cancer the patient is suffering from is breast cancer (BRCA).

63. The method of claim 62, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 80 biomarkers, at least 90 biomarkers, at least 100 biomarkers, at least 110 biomarkers, at least 120 biomarkers, at least 130 biomarkers, at least 140 biomarkers, at least 150 biomarkers, or at least 151 biomarkers from Table 10.

64. The method of claim 62, wherein the plurality of biomarkers selected from Table 10 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 10.

65. The method of claim 62, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 10.

66. The method of claim 62, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers or at least 93 biomarkers from Table 11.

67. The method of claim 62, wherein the plurality of biomarkers selected from Table 11 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 11.

68. The method of claim 62, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 11.

69. The method of any one of claims 47-51, wherein the cancer the patient is suffering from is prostate adenocarcinoma (PRAD).

70. The method of claim 69, wherein the plurality of biomarkers comprises, consists essentially of or consists of at least 10 biomarkers, at least 20 biomarkers, at least 30 biomarkers, at least 40 biomarkers, at least 50 biomarkers, at least 60 biomarkers, at least 70 biomarkers, at least 80 biomarkers, at least 90 biomarkers or at least 100 biomarkers from Table 12.

71. The method of claim 69, wherein the plurality of biomarkers selected from Table 12 comprises at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95% or at least 99% of the biomarkers from Table 12.

72. The method of claim 69, wherein the plurality of biomarkers comprises, consists essentially of or consists of all the biomarkers from Table 12.

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