WO2024178032A2 - Methods for diagnosing and treating ovarian cancer - Google Patents

Abstract

Disclosed herein is a method of treating cancer in a subject in need thereof, comprising classifying a tumor from the subject as a homologous recombination deficient (HRD) tumor and administering a therapeutically effective amount of at least one DNA damage repair inhibitor to the subject when the tumor is classified as an HRD tumor. Also disclosed herein are methods of classifying a tumor as an HRD or HRP tumor and kits for use in classifying a tumor as an HRD or HRP tumor.

Description

METHODS FOR DIAGNOSING AND TREATING OVARIAN CANCER

CROSS-REFEENCE TO RELATED APPLICATION

[0001] This application claims priority to U.S. Provisional Application No. 63/447,797, filed 23 February 2023, the contents of which arc hereby incorporated by reference in its entirety.

GOVERNMENT INTEREST

[0002] This invention was made with government support under HU0001-21-2-0027 awarded by the Uniformed Services University of the Health Sciences and W81XWH- 16-2-0010 awarded by the United States Army Medical Research and Development Command. The government has certain rights in the invention.

SEQUENCE LISTING

[0003] This application contains a Sequence Listing which has been submitted electronically in XML format and is hereby incorporated by reference in its entirety. Said XML copy, created on January 25, 2024, is named HMJ-184-PCT_SL.xml and is 691,181 bytes in size.

FIELD

[0004] The present disclosure relates to the field of targeted therapy for cancers, and more specifically relates to methods of selecting patients with cancer for targeted therapies. Described are molecular features that can be used for identifying cancer patients who may receive therapeutics specifically targeting cancer cells deficient in DNA-damage repair pathways.

BACKGROUND

[0005] The following discussion is merely provided to aid the reader in understanding the disclosure and is not admitted to describe or constitute prior art thereto.

[0006] Cancer is a leading cause of death worldwide, with the United States having an estimated more than 1,700,000 new cancer diagnoses and over 600,000 cancer fatalities in a single year. In particular, ovarian cancer is a highly diverse disease with a high rate of overall mortality. In the United Stated alone, over 19,000 women annually arc estimated to receive a new diagnosis of ovarian cancer, and over 13,000 women annually will die from the disease.

[0007] High-grade serous ovarian cancer (HGSOC) is the most common and aggressive type of epithelial ovarian cancer, exhibiting high levels of tumor heterogeneity and variable clinical outcomes. Several molecular abnormalities in HGSOC have been identified. For example, TP53 mutations are present in virtually all tumors, somatic or germline BRCA mutations are present in about 25% of cases, and extensive copy number changes and amplification of CCNE1 have been identified as well. The heterogeneity and apparent adaptability of the HGSOC genome under selective pressure by chemotherapy potentially explains the high rates of drug resistance. A thorough understanding of the molecular and cellular heterogeneity of ovarian cancer would provide new insights and may offer novel methods for treating this highly heterogenous malignant disease.

[0008] Over the past decade, much research has focused on mutations of cancer genes and their effects, including the identification of germline mutations having clinical utility that can be used in the prediction, management and treatment of cancers. For example, the Myriad MYRISK® Hereditary Cancer, INVITAE® Cancer Screen, Centogene’s CENTOCANCER® Comprehensive Cancer Panel, and Ambry Genetic’s CANCERNEXT® are all commercially-available products that use next-generation sequencing to predict the risk of cancer development, based on analysis of specific genes known or thought to be involved in carcinogenesis, including, for example, ovarian cancer. Nonetheless, a need exists to identify novel gene panels that can be used to predict, diagnose, or prognose cancer, or to identify and/or stratify cancer patients for targeted therapies. Therefore, new biomarkers and therapeutic markers that are specific for distinct cancer types (e.g. , ovarian cancer or HGSOC) and provide more accurate diagnostic and/or prognostic potential are needed.

SUMMARY

[0009] Disclosed herein arc methods of treating cancer in a subject in need thereof and methods of identifying a cancerous tumor as a homologous recombination deficient (HRD) tumor or a homologous recombination proficient (HRP) tumor. [00010] In one aspect, disclosed herein is a method of treating cancer, the method comprising first classifying a tumor from the subject as a homologous recombination deficient (HRD) tumor by (a) measuring expression levels of at least one, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, or 54 transcripts or proteins selected from the group consisting of: ACADSB, AGGF1, ARHGEF40, ARSK, ATF6B, BBS9, BMI1, BTF3, INTS15, CDKN1C, CKAP4, CLDND2, CWC27, DACH1, DAGLB, DESI1, DYNLL1, EEF1D, EIF2AK1, EPPK1, GFM2, IFT52, IWS1, KHDRBS1, KIAA0825, LEO1, LMF2, MARCKSL1, MARVELD2, MCUR1, MIEF1, NADSYN1, NDRG1, NDUFA3, NRBP2, NSL1, PHC2, PKIG, PLCB3, PPP1R16A, PYCR3, RAD17, TBC1D22A, TIMMDC1, TMEM167A, PACC1, TSG101, TSNARE1, TUBA1A, VPS13D, WDR41, XRCC4, ZBED3, and ZNF219; (b) comparing the expression levels of the transcripts or proteins of the tumor to expression levels of the same transcripts or proteins in a homologous recombinant proficient (HRP) tumor; and (c) classifying the tumor as HRD if the expression levels of the transcripts or proteins in the tumor are significantly different from the expression levels of the transcripts or proteins in the HRP tumor. The methods of treating disclosed herein further comprise administering a therapeutically effective amount of at least one DNA damage repair inhibitor to the subject when the tumor is classified as an HRD tumor, wherein the at least one DNA damage repair inhibitor is a poly ADP ribose polymerase (PARP) inhibitor.

[00011] In another aspect, disclosed herein is a method of classifying a tumor as a homologous recombinant deficient (HRD) tumor or a homologous recombination proficient (HRP) tumor, the method comprising (1) measuring expression levels in the tumor of at least one, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, or 54 transcripts or proteins selected from the group consisting of: ACADSB, AGGF1, ARHGEF40, ARSK, ATF6B, BBS9, BMI1, BTF3, INTS15, CDKN1C, CKAP4, CLDND2, CWC27, DACH1, DAGLB, DESI1, DYNLL1, EEF1D, EIF2AK1, EPPK1, GFM2, IFT52, IWS1, KHDRBS1, KIAA0825, LEO1, LMF2, MARCKSL1, MARVELD2, MCUR1, MIEF1, NADSYN1, NDRG1, NDUFA3, NRBP2, NSL1, PHC2, PKIG, PLCB3, PPP1R16A, PYCR3 RADU, TBC1D22A, TIMMDC1, TMEM167A, PACC1, TSG101, TSNARE1, TUBA1A, VPS13D, WDR41, XRCC4, ZBED3, and ZNF219; (2) comparing the expression levels of the transcripts or proteins of the tumor to expression levels of the same transcripts or proteins in a homologous recombination proficient (HRP) tumor; and (3) classifying the tumor as an HRD tumor if the expression levels of the transcripts or proteins in the tumor arc significantly different from the expression levels of the transcripts or proteins in the HRP tumor.

[00012] According to certain embodiments of the method of classifying a tumor as an HRD tumor, the tumor is from a subject who has been diagnosed with a cancer, and the method further comprises administering a therapeutically effective amount of at least one DNA damage repair inhibitor to the subject when the tumor is classified as an HRD tumor, wherein the at least one DNA damage repair inhibitor is a PARP inhibitor.

[00013] In certain embodiments, the expression levels of the transcripts or proteins are normalized to one or more control genes, such as one or more control genes selected from VCP, RTN4, CUL4A, PPP2R5D, RUFY1, ABCF1, and LARP1. According to certain embodiments, the expression levels of the transcripts or proteins in the tumor have a false-discovery rate expectation of adjusted p-value <0.05 when compared to the expression levels of the transcripts or proteins in the HRP tumor.

[00014] In certain embodiments, the cancer is selected from ovarian cancer, prostate cancer, breast cancer, endometrial cancer, gastric cancer, and lung cancer. In certain embodiments, the cancer is ovarian cancer, such as a high-grade serous ovarian cancer. In certain embodiments, the cancer is endometrial cancer, such as a high-grade serous endometrial cancer.

[00015] In certain embodiments, the transcript or protein is chosen from at least one, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the following transcripts or proteins: PYCR3, NADSYN1, NSL1, RAD17, EIFAK1, DAGLB, GFM2, BTF3, KIAA0825, PACC1, and CDKN1C. In certain embodiments, the transcript or protein is a transcript chosen from at least one, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the following transcripts: PYCR3, NADSYN1, NSL1, RAD17, EIFAK1, DAGLB, GFM2, BTF3, KIAA0825, PACC1, and CDKN1C. In certain embodiments, the transcript or protein is a protein chosen from at least one, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the following proteins: PYCR3, NADSYN1, NSL1, RAD 17, EIFAK1, DAGLB, GFM2, BTF3, KIAA0825, PACC1, and CDKN1C. In certain embodiments, the transcripts or proteins further comprise BMI1. [00016] In certain embodiments, the transcript or protein is chosen from at least one, such as at least 2, at least 3, at least 4, or 5 of the following transcripts or proteins: EPPK1, PYCR3, BMI1, WDR41, and KHDRBS1. In certain embodiments, the transcript or protein is a transcript chosen from at least one, such as at least 2, at least 3, at least 4, or 5 of the following transcripts: EPPK1, PYCR3, BMI1, WDR41, and KHDRBS1. In certain embodiments, the transcript or protein is a protein chosen from at least one, such as at least 2, at least 3, at least 4, or 5 of the following proteins: EPPK1, PYCR3, BMI1, WDR41, and KHDRBS1. In certain embodiments, the expression levels of at least one of EPPK1 and PYCR3 are over-expressed, and in certain embodiments, the expression levels of at least one of B Mil, WDR41, and KHDRBS1 are underexpressed, as compared to the expression of the same transcripts or proteins in the HRP tumor.

[00017] In certain embodiments, the PARP inhibitor comprises olaparib, rucaparib, talazoparib, or niraparib.

[00018] Also disclosed herein is a kit for use in classifying a tumor as a homologous recombination deficient (HRD) tumor or a homologous recombination proficient (HRP) tumor, the kit comprising a plurality of probes for detecting the expression of at least one, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, or 54 transcripts or proteins selected from the group consisting of: ACADSB, AGGF1, ARHGEF40, ARSK, ATF6B, BBS9, BMI1, BTF3, INTS15, CDKN1C, CKAP4, CLDND2, CWC27, DACH1, DAGLB, DESI1, DYNLL1, EEF1D, EIF2AK1, EPPK1, GFM2, IFT52, IWS1, KHDRBS1, KIAA0825, LEO1, LMF2, MARCKSL1, MARVELD2, MCUR1, MIEF1, NADSYN1, NDRG1, NDUFA3, NRBP2, NSL1, PHC2, PKIG, PLCB3, PPP1R16A, PYCR3, RAD17, TBC1D22A, TIMMDC1, TMEM167A, PACC1, TSG101, TSNARE1, TUBA1A, VPS13D, WDR41, XRCC4, ZBED3, and ZNF219, wherein the plurality of probes contains probes for detecting the expression of no more than 500 different genes. In certain embodiments, the plurality of probes contains probes for detecting expression of all 54 transcripts or proteins.

[00019] In certain embodiments, the plurality of probes comprises probes for detecting expression of at least one, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 transcripts or proteins selected from the group consisting of: PYCR3, NADSYN1, NSL1, RAD17, EIFAK1, DAGLB, GFM2, BTF3, KIAA0825, PACC1, and CDKN1C. In certain embodiments, the plurality of probes contains probes for detecting expression of all 11 transcripts or proteins, and in certain embodiments, the plurality of probes further comprises a probe for detecting expression of BMI1.

[00020] In certain embodiments, the plurality of probes comprises probes for detecting expression of at least one, at least 2, at least 3, at least 4, or 5 transcripts or proteins selected from the group consisting of: EPPK1, PYCR3, BMI1, WDR41, and KHDRBS1. In certain embodiments, the plurality of probes contains probes for detecting expression of transcripts or proteins from EPPK1, PYCR3, BMI1, WDR41, and KHDRBS1.

[00021] In certain embodiments, the plurality of probes is selected from a plurality of oligonucleotide probes, a plurality of antibodies, or a plurality of polypeptide probes. In certain embodiments, the plurality of probes contains probes for detecting expression in no more than 250, 100, 75, 60, 54, 50, 40, 30, 25, 20, 15, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 different transcripts or proteins. In certain embodiments, the plurality of probes is attached to the surface of an array, and in certain embodiments, the array comprises no more than 250, 11, 75, 60, 50, 54, 40, 30, 25, 20, 15, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 different addressable elements. In certain embodiments, the plurality of probes is labeled.

BRIEF DESCRIPTION OF THE DRAWINGS

[00022] FIG. 1 is a heatmap showing the integrating germline and somatic mutation status for Breast cancer type 1 and 2 susceptibility protein (BRCA1 and BRCA2), as well as classification of tumors as being HRD or HRP and the probability of HRD classification (continuous) using the CHORD score and scarHRD, based on 69 patient samples as discussed in Example 1.

[00023] FIG. 2 is a plot showing the differential analysis of enriched proteome data (left) and transcriptome data (right) in HRD versus HRP patient tumors. Gene candidates having an adjusted p < 0.05 are noted as elevated (vertical lined circles) or decreased (diagonal lined circles) in HRD versus HRP tumors.

[00024] FIG. 3 is a graph showing the correlation analysis of 54 HRD candidates in transcriptome data for an independent cohort of HGSOC patients classified as HRD or HRP using CHORD score analysis, as discussed in Example 1.

[00025] FIG. 4 is a graph showing the classification of HRD versus HRP tumors using 54 protein and transcript features for an independent cohort of HGSOC patients, as described in Example 1. [00026] FIG. 5 is a graph showing the overall survival curves for HGSOC patients with high BMI1 transcript expression levels (BMIl_high) versus low BMI1 transcript expression levels (BMIl_low), where the BMI1 transcript levels are stratified by HRD and HRP status in a cohort as described in Example 1.

[00027] FIG. 6 is a graph showing the classification of HRD versus HRP tumors using 11 protein and transcript features for an independent cohort of HGSOC patients, as described in Example 1.

[00028] FIG. 7 is a graph showing the assessment of classification performance for determining HRD and HRP status in discovery (ART, n=69) or validation (MOCOG, n=126) cohorts based on the distance of HRD and HRP scores, as described in Example 1.

[00029] FIG. 8 is a graph showing the classification of HRD versus HRP tumors using 11 protein and transcript features plus BMH following consideration of control gene candidates and sample normalization for an independent cohort of HGSOC patients, as described in Example 1.

DETAILED DESCRIPTION

[00030] Reference will now be made in detail to various exemplary embodiments, examples of which are illustrated in the accompanying drawings. It is to be understood that the following detailed description is provided to give the reader a fuller understanding of certain embodiments, features, and details of aspects of the disclosure, and should not be interpreted as a limitation of the scope of the disclosure.

[00031] Disclosed herein are methods for treating cancer, such as ovarian cancer. Also disclosed herein are method of classifying a cancerous tumor as a homologous recombination deficient (HRD) tumor or a homologous recombination proficient (HRP) tumor. In certain embodiments, the methods disclosed herein include the use of a multi-omic, protein and transcript expression signature to allow classification of tumors collected from cancer patients as having HRD or HRP disease. As used herein, HRD refers to cells, such as cancer cells, that are deficient in their ability to repair double-stranded breaks in DNA due, for example, to a genetic mutation such as a mutation in a BRCA gene. As used herein, HRP refers to cells, such as cancer cells, that are effectively able to engage in homologous recombination DNA repair of damaged DNA.

[00032] The cancers or tumors identified as either HRD or HRP may include any type of cancers or carcinomas, for example epithelial carcinoma. Some examples of the cancers or tumors included in the embodiments disclosed herein may include, but are not limited to, ovarian cancer, for example high-grade serous ovarian cancer (HGSOC), pancreatic cancer, breast cancer, endometrial cancer, prostate cancer, colorectal cancer, liver cancer, gastric cancer, and/or lung cancer. Cancer patients with HRD disease are candidates for DNA damage response (DDR) targeted therapies such as poly ADP ribose polymerase (PARP) inhibitors, while cancer patients with HRP disease may not be adequate candidates for PARP inhibitors. Accordingly, the decision of whether to administer PARP inhibitors or pursue alternative therapies may depend, inter alia, on whether a cancer patient’s tumor is classified as HRD or HRP. PARP inhibitors may include, but are not limited to, olaparib (LYNPARZA®; AstraZeneca), rucaparib (RUBRACA®; Clovis Oncology), talazoparib (TALZENNA®), or niraparib (ZEJULA®; GlaxoSmithKline). In certain embodiments disclosed herein, the measurement of protein and transcript alterations in tumors (e.g., tumors collected by biopsy, at primary or interval debulking surgeries, or during prediagnostic laparotomy or other methods for tumor tissue sampling) that correlate with HRD or HRP status can function as a companion clinical diagnostic assay to prioritize patients for targeted DDR therapies, such as treatment with PARP inhibitors.

[00033] The methods disclosed herein are capable of dynamically quantifying transcript and protein expression alterations that directly correlate with functional homologous recombination repair. Previous diagnostic methodologies were only capable of reporting if homologous recombination activity was impaired in the history of the tumor, such as by providing evidence of genome scars, which could propagate in cells where homologous recombination deficiency may have since reverted to a proficient state. Additionally, in the methods disclosed herein, the HRD gene pane includes transcripts and/or proteins that are known drug targets (e.g., BMI1, which is a target of small molecule pharmacological inhibitors currently in clinical trials). Furthermore, because the HRD gene panel disclosed herein is transcript and/or protein based, novel spatial profiling techniques can be employed to identify spatial heterogeneity of HRD in tumor tissue. Clonal heterogeneity is increasingly recognized as a contributor in the evolutionary trajectory of advanced stage and/or recurrent cancer chemoresistance.

Definitions

[00034] In order that the present disclosure may be more readily understood, certain terms are first defined. Additional definitions are set forth throughout the detailed description. [00035] The term “detecting” or “detection” means any of a variety of methods known in the art for determining the presence or amount of a nucleic acid or a protein. As used throughout the specification, the term “detecting” or “detection” includes either qualitative or quantitative detection.

[00036] As used herein, the term “in some embodiments,” “in certain embodiments,” “in other embodiments,” “in some other embodiments,” or the like, refers to embodiments of all aspects of the disclosure, unless the context clearly indicates otherwise.

[00037] The term “therapeutically effective amount” refers to a dosage or amount that is sufficient for treating an indicated disease or condition, such as cancer.

[00038] The terms “polypeptide,” “peptide,” and “protein” are used interchangeably herein to refer to polymers of amino acids.

[00039] The term “polypeptide probe” as used herein refers to a labeled (e.g., isotopically labeled) polypeptide that can be used in a protein detection assay (e.g., mass spectrometry) to quantify a polypeptide of interest in a biological sample.

[00040] The term “primer” means a polynucleotide capable of binding to a region of a target nucleic acid, or its complement, and promoting nucleic acid amplification of the target nucleic acid. Generally, a primer will have a free 3' end that can be extended by a nucleic acid polymerase. Primers also generally include a base sequence capable of hybridizing via complementary base interactions either directly with at least one strand of the target nucleic acid or with a strand that is complementary to the target sequence. A primer may comprise targetspecific sequences and optionally other sequences that are non-complementary to the target sequence. These non-complementary sequences may comprise, for example, a promoter sequence or a restriction endonuclease recognition site.

[00041] The terms “prognosis” and “prognosing” as used herein mean predicting the likelihood of death from the cancer and/or recurrence or metastasis of the cancer within a given time period, with or without consideration of the likelihood that the cancer patient will respond favorably or unfavorably to a chosen therapy or therapies.

[00042] The term “gene expression profile” refers to the expression levels of a plurality of genes in a sample. As is understood in the art, the expression level of a gene can be analyzed by measuring the expression of a nucleic acid (e.g., genomic DNA or mRNA) or a polypeptide that is encoded by the nucleic acid. [00043] The term “gene panel” refers to one or more genes or groups of genes having a characteristic pattern of expression that may occur as a result of a pathological condition, such as cancer, or may identify a characteristic, such as HRD tumors or HRP tumors.

[00044] The term “54-gene panel” refers to the following 54 human genes: ACADSB, AGGF1, ARHGEF40, ARSK, ATF6B, BBS9, BMI1, BTF3, INTS15, CDKN1C, CKAP4, CLDND2, CWC27, DACH1, DAGLB, DESI1, DYNLL1, EEF1D, EIF2AK1, EPPK1, GFM2, IFT52, IWS1, KHDRBS1, KIAA0825, LEO1, LMF2, MARCKSL1, MARVELD2, MCUR1, MIEF1, NADSYN1, NDRG1, NDUFA3, NRBP2, NSL1, PHC2, PKIG, PLCB3, PPP1R16A, PYCR3, RADU, TBC1D22A, TIMMDC1, TMEM167A, PACC1, TSG101, TSNARE1, TUBA1A, VPS13D, WDR41, XRCC4, ZBED3, and ZNF219.

[00045] The term “11-gene panel” refers to the following 11 human genes: PYCR3, NADSYN1, NSL1, RADU, EIF2AK1, DAGLB, GFM2, BTF3, KIAA0825, PACC1, and CDKN1C.

[00046] The term “5-gene panel” refers to the following 5 human genes: EPPK1, PYCR3, BMI1, WDR41, and KHDRBS1.

[00047] In the specification, the term “biological sample” should be understood to mean any sample obtained from a subject, such as a patient, where gene expression levels can be detected, including tumor cells and non-tumor cells, such as epithelial cells, blood or blood derivatives (serum, plasma, etc.), saliva, semen or seminal fluid, urine, or cerebrospinal fluid.

[00048] As used herein, the term “fragment” means a part or portion of a polynucleotide sequence comprising about 10 or more contiguous nucleotides, about 15 or more contiguous nucleotides, about 20 or more contiguous nucleotides, about 30 or more, or even about 50 or more contiguous nucleotides. In certain embodiments, the polynucleotide probes will comprise 10 or more nucleic acids, 20 or more, 50 or more, or 100 or more nucleic acids. In order to confer sufficient specificity, the probe may have a sequence identity to a complement of the target sequence of about 90% or more, such as about 95% or more (e.g., about 98% or more or about 99% or more) as determined, for example, using the well-known Basic Local Alignment Search Tool (BLAST) algorithm (available through the National Center for Biotechnology Information (NCBI), Bethesda, Md.).

HRD and HRP Biomarkers [00049] Disclosed herein are multiple genes that may be used as biomarkers to classify a tumor, such as a malignant tumor from a patient, as being an HRD tumor or an HRP tumor. Homologous recombination is one of two main mechanisms in eukaryotic cells, including cancer cells, for repairing double- stranded DNA breaks, with the other mechanism being nonhomologous DNA end joining (NHEJ). A tumor that is classified as an HRD tumor indicates that the tumor lacks the ability to accurately repair double-stranded breaks in DNA via the mechanism of homologous recombination, leaving only the NHEJ mechanism as functional. A tumor that is classified as an HRP tumor is capable of repairing double-stranded breaks in DNA via both homologous recombination and NHEJ mechanisms. Without wishing to be bound by theory, it is believed that certain chemotherapeutic s, such as poly(ADP-ribose) polymerase (PARP) inhibitors, function by disabling the NHEJ mechanism of action in cells. Accordingly, if a tumor is classified as an HRD tumor, administering a PARP inhibitor may result in a cell that has both of the two main DNA damage repair pathways disabled, resulting in effective cell death. In contrast, if a tumor is classified as an HRP tumor, administrating a PARP inhibitor may result in disabling only one of the two main DNA damage repair pathways, resulting in less efficacy for cell death. Accordingly, biomarkers that serve to accurately classify tumors as HRD or HRP may be beneficial in the treatment and management of certain cancers.

[00050] Disclosed herein are 54 genes, including certain respective gene transcripts and variants, that may be used according to the methods disclosed herein to classify a tumor as an HRD tumor or an HRP tumor. As used herein, “gene transcript” or “transcript” refers to a molecule of RNA, e.g., messenger RNA (mRNA) that contains genetic information transcribed from a corresponding molecule of DNA. A single gene may contain multiple gene transcripts based, for example, on alternative splicing of exons during transcription. The various transcripts of a particular gene may be referred to as “transcript variants” or “variants,” and may encode the same or different amino acid sequences.

[00051] In certain embodiments disclosed herein, at least one of the 54 genes or transcripts or variants thereof in the 54-gene panel may be used as a biomarker to classify to classify a tumor as an HRD tumor or an HRP tumor. For example, in certain embodiments, at least 2, at least 3 at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, or 54 of the 54 genes or transcripts or variants thereof may be used as a biomarker to classify a tumor as an HRD tumor or an HRP tumor. In certain embodiments, the at least 2, at least 3 at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, or 54 biomarkers are transcripts of the genes.

[00052] The nucleic acid sequences of the 54 genes and their transcripts and variants and amino acid sequences encoded by the same are publicly available. The 54 genes and their transcripts and variants are described below in Table 1. Where available, HUGO Gene Nomenclature Committee (HGNC) annotations are used to describe the genes discussed herein, as well as Ensembl gene annotations. The following Table 1 lists the HGNC annotations, Ensemble gene annotations, UniProt numbers, and/or gene name descriptions for the genes discussed herein, where available.

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[00053] Table 1 - HGNC and Ensemble Gene Annotations

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Detecting Gene Expression

[00054] As used herein, measuring or detecting the expression of any of the foregoing genes or nucleic acids comprises measuring or detecting any nucleic acid transcript (e.g., mRNA or cDNA) corresponding to the gene of interest or the protein encoded thereby. If a gene is associated with more than one mRNA transcript (or isoform), the expression of the gene can be measured or detected by measuring or detecting one or more of the mRNA transcripts of the gene, or all of the mRNA transcripts associated with the gene.

[00055] Typically, gene expression can be detected or measured on the basis of mRNA or cDNA levels, although protein levels also can be used when appropriate. Any quantitative or qualitative method for measuring mRNA levels, cDNA, or protein levels can be used. Suitable methods of detecting or measuring mRNA or cDNA levels include, for example, Northern Blotting, microarray analysis, RNA-sequencing, or a nucleic acid amplification procedure, such as reverse-transcription PCR (RT-PCR) or real-time RT-PCR, also known as quantitative RT-PCR (qRT-PCR). Such methods are well known in the art. See e.g. , Sambrook et al., Molecular Cloning: A Laboratory Manual, 4^th Ed., Cold Spring Harbor Press, Cold Spring Harbor, N.Y., 2012. Other techniques include digital, multiplexed analysis of gene expression, such as the NCOUNTER® (NanoString Technologies, Seattle, WA) gene expression assays, which are further described in US20100112710 and US20100047924.

[00056] Detecting a nucleic acid of interest generally involves hybridization between a target (e.g. mRNA or cDNA) and a probe. Sequences of the genes used in various cancer gene expression profiles are known. Therefore, one of skill in the art can readily design hybridization probes for detecting those genes. See, e.g., Sambrook et al., Molecular Cloning: A Laboratory Manual, 4^th Ed., Cold Spring Harbor Press, Cold Spring Harbor, N.Y., 2012. For example, polynucleotide probes that specifically bind to the mRNA transcripts of the genes described herein (or cDNA synthesized therefrom) can be created using the nucleic acid sequences of the mRNA or cDNA targets themselves by routine techniques (e.g., PCR or synthesis).

[00057] Each probe may be substantially specific for its target, to avoid any crosshybridization and false positives. An alternative to using specific probes is to use specific reagents when deriving materials from transcripts (e.g., during cDNA production, or using target- specific primers during amplification). In both cases, specificity can be achieved by hybridization to portions of the targets that are substantially unique within the group of genes being analyzed, for example hybridization to the poly A tail would not provide specificity. If a target has multiple splice variants, it is possible to design a hybridization reagent that recognizes a region common to each variant and/or to use more than one reagent, each of which may recognize one or more variants.

[00058] Stringency of hybridization reactions is readily determinable by one of ordinary skill in the ail, and generally is an empirical calculation dependent upon probe length, washing temperature, and salt concentration. In general, longer probes may require higher temperatures for proper annealing, while shorter probes may require lower temperatures. Hybridization generally depends on the ability of denatured nucleic acid sequences to reanneal when complementary strands are present in an environment below their melting temperature. The higher the degree of desired homology between the probe and hybridizable sequence, the higher the relative temperature that can be used. As a result, it follows that higher relative temperatures would tend to make the reaction conditions more stringent, while lower temperatures less so.

[00059] “Stringent conditions” or “high stringency conditions,” as defined herein, are identified by, but not limited to, those that: (1) use low ionic strength and high temperature for washing, for example 0.015 M sodium chloride/0.0015 M sodium citrate/0.1% sodium dodecyl sulfate at 50°C; (2) use during hybridization a denaturing agent, such as formamide, for example, 50% (v/v) formamide with 0.1% bovine serum albumin/0.1% Ficoll/0.1% polyvinylpyrrolidone/50 mM sodium phosphate buffer at pH 6.5 with 750 mM sodium chloride, 75 mM sodium citrate at 42°C; or (3) use 50% formamide, 5XSSC (0.75 M NaCl, 0.075 M sodium citrate), 50 mM sodium phosphate (pH 6.8), 0.1% sodium pyrophosphate, 5X Denhardt's solution, sonicated salmon sperm DNA (50p.g/ml), 0.1% SDS, and 10% dextran sulfate at 42°C, with washes at 42°C in 0.2XSSC (sodium chloride/sodium citrate) and 50% formamide at 55°C, followed by a high- stringency wash of 0.1XSSC containing EDTA at 55°C. “Moderately stringent conditions” are described by, but not limited to, those in Sambrook et al., Molecular Cloning: A Laboratory Manual, New York: Cold Spring Harbor Press, 1989, and include the use of washing solution and hybridization conditions (e.g., temperature, ionic strength and % SDS) less stringent than those described above. An example of moderately stringent conditions is overnight incubation at 37°C in a solution comprising: 20% formamide, 5XSSC (150 mM NaCl, 15 mM trisodium citrate), 50 mM sodium phosphate (pH 7.6), 5X Denhardt's solution, 10% dextran sulfate, and 20 mg/mL denatured sheared salmon sperm DNA, followed by washing the filters in 1XSSC at about 37-50°C. The skilled artisan will recognize how to adjust the temperature, ionic strength, etc. as necessary to accommodate factors such as probe length and the like.

[00060] In certain embodiments, microarray analysis or a PCR-based method is used. In this respect, measuring the expression of the foregoing nucleic acids in a biological sample can comprise, for instance, contacting a sample containing or suspected of containing cancer cells with polynucleotide probes specific to the genes of interest, or with primers designed to amplify a portion of the genes of interest, and detecting binding of the probes to the nucleic acid targets or amplification of the nucleic acids, respectively. Detailed protocols for designing PCR primers are known in the art. See e.g., Sambrook et al., Molecular Cloning: A Laboratory Manual, 4^th Ed., Cold Spring Harbor Press, Cold Spring Harbor, N.Y., 2012. In certain embodiments, RNA obtained from a sample may be subjected to qRT-PCR. Reverse transcription may occur by any methods known in the art, such as through the use of an Omniscript RT Kit (Qiagen). The resultant cDNA may then be amplified by any amplification technique known in the art. Gene expression may then be analyzed through the use of, for example, control samples as described below. As described herein, the over- or under-expression of genes relative to controls may be measured to determine a gene expression profile for an individual biological sample. Similarly, detailed protocols for preparing and using microarrays to analyze gene expression are known in the art and described herein.

[00061] As used herein, RNA- sequencing (RNA-seq), also called Whole Transcriptome Shotgun Sequencing, refers to any of a variety of high-throughput sequencing techniques used to detect the presence and quantity of RNA transcripts in real time. See Wang, Z., M. Gerstein, and M. Snyder, RNA-Seq: a revolutionary tool for transcriptomics, NAT REV GENET, 2009. 10(1): p. 57-63. RNA-seq can be used to reveal a snapshot of a sample’s RNA from a genome at a given moment in time. In certain embodiments, RNA is converted to cDNA fragments via reverse transcription prior to sequencing, and, in certain embodiments, RNA can be directly sequenced from RNA fragments without conversion to cDNA. Adaptors may be attached to the 5’ and/or 3’ ends of the fragments, and the RNA or cDNA may optionally be amplified, for example by PCR. The fragments are then sequenced using high-throughput sequencing technology, such as, for example, those available from Roche (e.g., the 454 platform), Illumina, Inc., and Applied Biosystem (e.g., the SOLiD system). [00062] Alternatively or additionally, expression levels of genes can be determined at the protein level, meaning that levels of proteins encoded by the genes discussed herein arc measured. Several methods and devices are known for determining levels of proteins including immunoassays, such as described, for example, in U.S. Pat. Nos. 6,143,576; 6,113,855; 6,019,944; 5,985,579; 5,947,124; 5,939,272; 5,922,615; 5,885,527; 5,851,776; 5,824,799; 5,679,526; 5,525,524; 5,458,852; and 5,480,792, each of which is hereby incorporated by reference in its entirety. These assays may include various sandwich, competitive, or non-competitive assay formats, to generate a signal that is related to the presence or amount of a protein of interest. Any suitable immunoassay may be utilized, for example, lateral flow, enzyme-linked immunoassays (ELISA), radioimmunoassays (RIAs), competitive binding assays, and the like. Numerous formats for antibody arrays have been described. Such arrays may include different antibodies having specificity for different proteins intended to be detected. For example, at least 50 different antibodies are used to detect at least 50 different protein targets, each antibody being specific for one target. Other ligands having specificity for a particular protein target can also be used, such as the synthetic antibodies disclosed in WO 2008/048970, which is hereby incorporated by reference in its entirety. Other compounds with a desired binding specificity can be selected from random libraries of peptides or small molecules. U.S. Pat. No. 5,922,615, which is hereby incorporated by reference in its entirety, describes a device that uses multiple discrete zones of immobilized antibodies on membranes to detect multiple target antigens in an array. Microtiter plates or automation can be used to facilitate detection of large numbers of different proteins.

[00063] One type of immunoassay, called nucleic acid detection immunoassay (NADIA), combines the specificity of protein antigen detection by immunoassay with the sensitivity and precision of the polymerase chain reaction (PCR). This amplified DNA-immunoassay approach is similar to that of an enzyme immunoassay, involving antibody binding reactions and intermediate washing steps, except the enzyme label is replaced by a strand of DNA and detected by an amplification reaction using an amplification technique, such as PCR. Exemplary NADIA techniques are described in U.S. Patent No. 5,665,539 and published U.S. Application 2008/0131883, both of which are hereby incorporated by reference in their entirety. Briefly, NADIA uses a first (reporter) antibody that is specific for the protein of interest and labelled with an as say- specific nucleic acid. The presence of the nucleic acid does not interfere with the binding of the antibody, nor does the antibody interfere with the nucleic acid amplification and detection. Typically, a second (capturing) antibody that is specific for a different epitope on the protein of interest is coated onto a solid phase (c.g., paramagnetic particles). The reporter antibody/nuclcic acid conjugate is reacted with sample in a microtiter plate to form a first immune complex with the target antigen. The immune complex is then captured onto the solid phase particles coated with the capture antibody, forming an insoluble sandwich immune complex. The microparticles are washed to remove excess, unbound reporter antibody/nucleic acid conjugate. The bound nucleic acid label is then detected by subjecting the suspended particles to an amplification reaction (e.g. PCR) and monitoring the amplified nucleic acid product.

[00064] Although immunoassays have been used for the identification and quantification of proteins, recent advances in mass spectrometry (MS) techniques have led to the development of sensitive, high-throughput MS protein analyses. The MS methods can be used to detect low abundant proteins in complex biological samples. For example, it is possible to perform targeted MS by fractionating the biological sample prior to MS analysis. Common techniques for carrying out such fractionation prior to MS analysis include, for example, two-dimensional electrophoresis, liquid chromatography, and capillary electrophoresis. Selected reaction monitoring (SRM), also known as multiple reaction monitoring (MRM), has also emerged as a useful high-throughput MSbased technique for quantifying targeted proteins in complex biological samples, including biomarkers that are encoded by gene fusion.

Biological Samples

[00065] The methods described herein involve analysis of gene expression profiles in biological samples obtained from a subject, such as a cancer patient. Cancer cells may be found in a biological sample, such as a tumor, a tissue, or blood. Nucleic acids or polypeptides may be isolated from the sample prior to detecting gene expression. In one embodiment, the biological sample comprises tumor tissue and is obtained through a biopsy. The methods disclosed herein can be used with biological samples collected from a variety of mammals, and in certain embodiments, the methods disclosed herein may be used with biological samples obtained from a human subject.

Controls

[00066] In certain embodiments, the control may be any suitable reference that allows evaluation of the expression level of the genes in the biological sample as compared to the expression of the same genes in a sample comprising control cells. In certain embodiments, the control cells may be non-cancerous cells, such as cells obtained from a patient or pool of patients who have not been diagnosed with cancer. Thus, for instance, the control can be a sample that is analyzed simultaneously or sequentially with the test sample, or the control can be the average expression level of the genes of interest in a pool of samples known to be non-cancerous. In certain embodiments, the control is a predetermined “cut-off” or threshold value of absolute expression. Thus, the control can be embodied, for example, in a pre-prepared microarray used as a standard or reference, or in data that reflects the expression profile of relevant genes in a sample or pool of samples known to be non-cancerous, such as might be part of an electronic database or computer program.

[00067] Overexpression and decreased expression (under-expression) of a gene can be determined by any suitable method, such as by comparing the expression of the genes in a test sample with a control gene or threshold value. In certain embodiments, the control gene is one or more housekeeping genes, such as ACTB, GAPDH, HMBS, GUSB, or RPLPO, that can be used to normalize gene expression levels.

[00068] In certain embodiments, the expression level of a gene may be normalized against one or more control genes. In certain embodiments, the one or more control genes comprise one or more of the following 7 genes that can be used to normalize gene expression: VCP, RTN4, CUL4A, PPP2R5D, RUFY1, ABCF1, and LARP1. The nucleic acid sequences of these 7 control genes and the amino acid sequences encoded by the same are publicly available. The 7 control genes are described below in Table 2. Where available, HUGO Gene Nomenclature Committee (HGNC) annotations are used to describe the 7 control genes discussed herein, as well as Ensembl gene annotations. The following Table 2 lists the HGNC annotations, Ensemble gene annotations, UniProt numbers, and/or gene name descriptions for the genes discussed herein, where available. [00069] Table 2 - Control Genes for Normalization

[00070] Regardless of the method used, overexpression and under-expression can be defined as any level of expression greater than or less than the level of expression of a control gene or threshold value. By way of further illustration, overexpression can be defined as expression that is at least about 1.2-fold, 1.5-fold, 2-fold, 2.5-fold, 4-fold, 5-fold, 10-fold, 20-fold, 50-fold, 100- fold higher or even greater expression as compared to a control gene or threshold value, and underexpression can similarly be defined as expression that is at least about 1.2-fold, 1.5-fold, 2-fold, 2.5-fold, 4-fold, 5-fold, 10-fold, 20-fold, 50-fold, 100-fold lower or even lower expression as compared to a control gene or threshold value.

[00071] In certain embodiments, the difference in expression level between the genes measured in an HRD tumor and the genes measured in an HRP tumor may be statistically significant, as measured according to any appropriate statistical method known in the art. In certain embodiments, the expression levels of the transcripts or proteins in the tumor have a false- discovery rate (“FDR”) expectation of adjusted p-value <0.05 when compared to the expression levels of the transcripts or proteins in the HRP tumor. As used herein, a FDR expectation indicates the expected ratio of the number of false positive classifications to the total number of positive classifications.

Cancer Types and Staging

[00072] In various embodiments, the cancer may be selected from testicular, prostate, colorectal, breast, endometrial, pancreatic, ovarian, cervical, uterine, bone (e.g., osteosarcoma, chondrosarcoma, Ewing’s tumor, and chordoma), bladder, skin (e.g., melanoma, squamous cell carcinoma and basal cell carcinoma), blood (e.g., leukemia, lymphoma, and myeloma), lung (e.g., squamous cell carcinoma, adenocarcinoma, large cell carcinoma, small cell carcinoma, and carcinoid tumors), central nervous system, and kidney cancer. [00073] In some embodiments, the cancers or tumors are pancreatic cancers. In some embodiments, the pancreatic cancer is a pancreatic ductal adenocarcinoma (PDAC). Defects in DNA damage response genes causing HRD may identify a clinically relevant subgroup of patients with pancreatic cancer with therapeutic implications.

[00074] In certain embodiments, the cancer is ovarian cancer, such as a serous ovarian cancer, e.g., a high-grade serous ovarian cancer. Ovarian cancer is a cancer that originates in the ovaries or the fallopian tubes. Ovarian tumors may originate from one of three types of cells: epithelial cells, germ cells, and structural tissue (stromal) cells, wherein epithelial cell tumors are the most common. Malignant epithelial cell tumors, or carcinomas, may be further classified by histology into the following four types: serous carcinomas, clear cell carcinomas, mucinous carcinomas, and endometrioid carcinomas, wherein serous carcinomas are the most common. Ovarian cancers may be either low grade (Grade 1 or Grade 2), wherein the majority of cells appear more like normal tissue cells, or high grade (Grade 3), wherein the majority of cells look less like normal tissue cells.

[00075] In certain embodiments, the cancer is endometrial cancer, such as serous endometrial cancer, e.g., a high-grade serous endometrial cancer. Endometrial cancer is a cancer that originates in the endometrium lining of the uterus and can be divided into multiple histological types, including adenocarcinoma, uterine carcinosarcoma, squamous cell carcinoma, small cell carcinoma, transitional carcinoma, and serous carcinoma. Endometrial cancer may be graded based on the amount of cancer cells that are organized into glands. In low grade (Grade 1 or Grade 2) endometrial cancer, more of the cancer cells (i.e., about 50% or more) form glands, whereas in high grade (Grade 3) endometrial cancer, more of the cancer cells (i.e., about 50% or more) are disorganized and do not form glands. Grade 3 cancers tend to be more aggressive and have a worse prognosis than Grade 1 or Grade 2 cancers.

[00076] Many cancers, including breast and ovarian cancers, may be further diagnosed and classified based on the TNM staging system. In the TNM staging system, a tumor stage (T stage), lymph node stage (N stage) and metastases stage (M stage) can be assessed. As used herein, TO indicates no evidence of tumor; T1 indicates the tumor is limited to the tissue of origin (e.g., ovaries); T2 indicates the tumor extends beyond the ovaries (e.g., into the pelvis, such as the uterus and/or fallopian tubes); T3 indicates the tumor has metastasis outside the pelvis region and/or there is lymph node involvement. For lymph node staging, NO indicates the cancer is not present in any regional lymph nodes; and N1 indicates the cancer has spread to retroperitoneal lymph nodes. For metastasis staging, MO indicates there is no spread of the cancer outside of the site of origin, and Ml indicates there is spread to at least one distant organ.

[00077] Based on the TNM staging, a cancer may be staged in a range of 0 to IV, wherein stage IV indicates the cancer has metastases; in general, the higher the stage, the poorer the prognosis. Thus, cancers with a high stage (Stage III and Stage IV) have a poorer prognosis for overall survival than cancers with a lower stage (Stage I and Stage II). In general, the lower the stage, the less aggressive the cancer and the better the prognosis (outlook for cure or long-term survival). The higher the stage, the more aggressive the cancer and the poorer the prognosis for long-term, metastases-free survival.

[00078] Cancer may also be graded on a scale of G1 to G4, wherein the higher the grade, the more likely the cancer is to grow and spread. G1 indicates that the cells of the biopsied cancerous tissue are well-differentiated, i.e., most like the cells of the tissue of origin (e.g., breast or ovarian tissue), and therefore less likely to spread, and G2 indicates that the cells of the biopsied cancerous tissue are moderately differentiated. G3 and G4 indicate that the cells of the biopsied cancerous tissue are poorly differentiated, and therefore the most likely to spread.

Arrays

[00079] A convenient way of measuring RNA transcript levels for multiple genes in parallel is to use an array (also referred to as microarrays in the art). A useful array may include multiple polynucleotide probes (such as DNA) that are immobilized on a solid substrate (e.g., a glass support such as a microscope slide, or a membrane) in separate locations (e.g., addressable elements) such that detectable hybridization can occur between the probes and the transcripts to indicate the amount of each transcript that is present. The arrays disclosed herein can be used in methods of detecting the expression of a desired combination of genes, which combinations are discussed herein.

[00080] In one embodiment, the array comprises (a) a substrate and (b) at least one, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, or 54 different addressable elements that each comprise at least one polynucleotide probe for detecting the expression of an mRNA transcript (or cDNA synthesized from the mRNA transcript) that is specific for one of the genes in the 54-gene signature, such that the array can be used to simultaneously detect the expression of these at least 1 , at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, or 54 genes.

[00081] In another embodiment, the substrate comprises at least 1, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 different addressable elements, wherein each different addressable element is specific for one of the genes in the 11 -gene signature, such that the array can be used to simultaneously detect expression of these at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at 11 genes. In certain embodiments, the addressable element is specific for a transcript chosen from at least one, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the following transcripts: PYCR3, NADSYN1, NSL1, RAD17, EIFAK1, DAGLB, GFM2, BTF3, KIAA0825, PACC1, and CDKN1C. In certain embodiments, the transcripts further comprise BMI1. In certain embodiments, the transcript or protein is a protein and is chosen from at least one, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the following proteins: PYCR3, NADSYN1, NSL1, RAD 17, EIFAK1, DAGLB, GFM2, BTF3, KIAA0825, PACC1, and CDKN1C. In certain embodiments, the proteins further comprise BMI1.

[00082] In certain embodiments, the array further comprises one or more different addressable elements comprising at least one oligonucleotide probe for detecting the expression of an mRNA transcript (or cDNA synthesized from the mRNA transcript) of a control gene.

[00083] As used herein, the term “addressable element” means an element that is attached to the substrate at a predetermined position and specifically binds a known target molecule, such that when target-binding is detected (e.g., by fluorescent labeling), information regarding the identity of the bound molecule is provided on the basis of the location of the element on the substrate. Addressable elements are “different” for the purposes of the present disclosure if they do not bind to the same target gene. The addressable element comprises one or more polynucleotide probes specific for an mRNA transcript of a given gene, or a cDNA synthesized from the mRNA transcript. The addressable element can comprise more than one copy of a polynucleotide or can comprise more than one different polynucleotide, provided that all of the polynucleotides bind the same target molecule. Where a gene is known to express more than one mRNA transcript, the addressable element for the gene can comprise different probes for different transcripts, or probes designed to detect a nucleic acid sequence common to two or more (or all) of the transcripts. Alternatively, the array can comprise an addressable clement for the different transcripts. The addressable element also can comprise a detectable label, suitable examples of which are well known in the art.

[00084] The array can comprise addressable elements that bind to mRNA or cDNA other than that of the above-referenced 54 genes or the above-referenced 11 genes. However, an array capable of detecting a vast number of targets (e.g., mRNA or polypeptide targets), such as arrays designed for comprehensive expression profiling of a cell line, chromosome, genome, or the like, may not be economical or convenient for collecting data to use in diagnosing and/or prognosing cancer. Thus, the array typically comprises no more than about 1000 different addressable elements, such as no more than about 500 different addressable elements, no more than about 250 different addressable elements, or even no more than about 100 different addressable elements, such as about 75 or fewer different addressable elements, about 60 or fewer different addressable elements, about 50 or fewer different addressable elements, about 40 or fewer different addressable elements, about 30 or fewer different addressable elements, about 20 or fewer different addressable elements, about 15 or fewer, about 10 or fewer, or about 5 different addressable elements.

[00085] It is also possible to distinguish these diagnostic arrays from the more comprehensive genomic arrays and the like by limiting the number of polynucleotide probes on the array. Thus, in one embodiment, the array has polynucleotide probes for no more than 1000 genes immobilized on the substrate. In other embodiments, the array has oligonucleotide probes for no more than 500, no more than 250, no more than 100, no more than 75, no more than 60, or no more than 50 genes. In certain embodiments, the array has oligonucleotide probes for no more than 40 genes, and in certain embodiments, the array has oligonucleotide probes for no more than 30 genes, no more than 20 genes, or no more than 15 genes.

[00086] The substrate can be any rigid or semi-rigid support to which polynucleotides can be covalently or non-covalently attached. Suitable substrates include membranes, filters, chips, slides, wafers, fibers, beads, gels, capillaries, plates, polymers, microparticles, and the like. Materials that are suitable for substrates include, for example, nylon, glass, ceramic, plastic, silica, aluminosilicates, borosilicates, metal oxides such as alumina and nickel oxide, various clays, nitrocellulose, and the like. [00087] The polynucleotides of the addressable elements (also referred to as “probes”) can be attached to the substrate in a prc-dctcrmincd 1- or 2-dimcnsional arrangement, such that the pattern of hybridization or binding to a probe is easily correlated with the expression of a particular gene. Because the probes are located at specified locations on the substrate (i.e., the elements are “addressable”), the hybridization or binding patterns and intensities create a unique expression profile, which can be interpreted in terms of expression levels of particular genes and can be correlated with cancer in accordance with the methods described herein.

[00088] The array can comprise other elements common to polynucleotide arrays. For instance, the array also can include one or more elements that serve as a control, standard, or reference molecule, such as one or more control genes or portion thereof, to assist in the normalization of expression levels or the determination of nucleic acid quality and binding characteristics, reagent quality and effectiveness, hybridization success, analysis thresholds and success, etc. These other common aspects of the arrays or the addressable elements, as well as methods for constructing and using arrays, including generating, labeling, and attaching suitable probes to the substrate, consistent with the invention are well-known in the art. Other aspects of the array are as described with respect to the methods disclosed herein.

[00089] An array can also be used to measure protein levels of multiple proteins in parallel. Such an array comprises one or more supports bearing a plurality of ligands that specifically bind to a plurality of proteins, wherein the plurality of proteins comprises no more than 500, no more than 250, no more than 100, no more than 75, no more than 60, no more than 50, no more than 40, no more than 30, no more than 20, no more than 15, no more than 10, or no more than 5 different proteins. The ligands are optionally attached to a planar support or beads. In one embodiment, the ligands are antibodies. The proteins that are to be detected using the array correspond to the proteins encoded by the nucleic acids of interest, as described above, including the specific gene expression profiles disclosed. Thus, each ligand (e.g. antibody) is designed to bind to one of the target proteins (e.g., polypeptide sequences encoded by the genes disclosed herein). As with the nucleic acid arrays, each ligand may be associated with a different addressable element to facilitate detection of the different proteins in a sample.

[00090] In certain embodiments, disclosed herein are methods of obtaining a gene expression profile in a biological sample, such as a tumor sample, the method comprising: a) incubating an array as disclosed herein with the biological sample; and b) measuring the expression level of the genes of interest.

Patient Treatment

[00091] Disclosed herein are methods of treating cancer in a patient, the method comprising administering a cancer treatment regimen to the patient, wherein prior to the administering step, the patient has been identified as having an HRD tumor, according to the methods disclosed herein. As discussed above, the presence of an HRD tumor may confer an enhanced lethal response to therapies that induce DNA damage and/or apoptosis, thereby enhancing the sensitivity of cancer cells in HRD tumors. Such DNA damage control system therapies may include, for example, radiation, poly(ADP ribose) polymerase (PARP) inhibitors, and platinum-based therapeutics, as discussed below.

[00092] Cancer treatment options include, but are not limited to, surgery, radiation therapy, hormone therapy, chemotherapy, biological therapy, or high intensity focused ultrasound. Drugs for cancer treatment include, but are not limited to: melphalan (ALKERAN®), bevacizumab (ALYMSYS®, AVASTIN®, MVASI®, ZIRABEV®), carboplatin (PARAPLATIN®), cisplatin, cyclophosphamide, doxorubicin hydrochloride, doxorubicin hydrochloride liposome (DOXIL®), mirvetuximab soravtansine-gynx (ELAHERE®), gemcitabine hydrochloride (GEMZAR®, INFUGEM®), topotecan hydrochloride (HYCAMTIN®, olaparib (LYNPARZA®), talazoparib (TALZENNA®), niraparib tosylate monohydrate (ZEJULA®), paclitaxel, rucaparib camsylate (RUBRACA®), thiotepa (TEPADINA®), topotecan hydrochloride, abiraterone acetate, cabazitaxel (JEVTANA®), degarelix, enzalutamide (XTANDI®), prednisone, sipuleucel-T (PROVENGE®), or docetaxel.

[00093] Additional drugs that may be used to treat cancer include poly(ADP ribose) polymerase (PARP) inhibitors, immune checkpoint inhibitors, and platinum-based agents. PARP inhibitors may include, for example, olaparib, rucaparib, talazoparib, and niraparib. PARP1 is a protein that functions to repair single- stranded nicks in DNA. Drugs that inhibit PARP1 (PARP inhibitors) result in DNA containing multiple double stranded breaks during replication, which can lead to cell death. Immune checkpoint inhibitors work by blocking certain checkpoint proteins from binding with their partner proteins, allowing T cells to kill cancer cells. Immune checkpoint inhibitors may include, for example, pembrolizumab, nivolumab, and cemiplimab. Platinum-based agents are chemical complexes comprising platinum and cause crosslinking of DNA. Crosslinked DNA inhibits DNA repair and synthesis in cancerous cells. Exemplary platinum-based agents may include cisplatin, oxaliplatin, and carboplatin. As discussed above, HRD tumors may have increased sensitivity as compared to HRP tumors to certain chemotherapeutic s, such as PARP inhibitors. Accordingly, in certain embodiments of the methods disclosed herein, after detecting expression of at least one gene as disclosed herein and classifying a tumor as being an HRD tumor, a patient who has been diagnosed with cancer is treated by administration of a PARP inhibitor and/or an immune checkpoint inhibitor. In certain embodiments, the cancer is ovarian cancer, and in certain embodiments, the cancer is endometrial cancer.

[00094] A method as described in this application may include a further therapy step, e.g., surgery, radiation therapy, hormone therapy, chemotherapy, biological therapy, or high intensity focused ultrasound. In certain embodiments, the therapy step comprises administering a DNA damage control system therapy, such as radiation, a PARP inhibitor, or a platinum-based agent.

Kits

[00095] The polynucleotide probes and/or primers or antibodies or polypeptide probes that can be used in the methods described herein can be arranged in a kit. Thus, one embodiment is directed to a kit for classifying a tumor as an HRD tumor or an HRP tumor, the kit comprising a plurality of polynucleotide probes for detecting expression levels of at least 1, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the genes in the 11-gene panel, wherein the plurality of polynucleotide probes contains polynucleotide probes for no more than 500, 250, 100, 75, 60, 54, 50, 45, 40, 35, 30, 25, 20, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 genes. In one embodiment, the plurality of polynucleotide probes comprises polynucleotide probes for detecting all 11 of the aforementioned genes.

[00096] In yet another embodiment, there is provided a kit for classifying a tumor as an HRD tumor or an HRP tumor comprising a plurality of polynucleotide probes for detecting expression of at least 1, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, or 54 of the genes in the 54-gene panel, wherein the plurality of polynucleotide probes contains polynucleotide probes for no more than 500, 250, 100, 75, 60, 55, 54, 50, 40, 35, 30, 25, 20, 15, 10, 5, 4, 3, 2, or 1 genes. In one embodiment, the plurality of polynucleotide probes comprises polynucleotide probes for detecting all 54 of the aforementioned genes. [00097] In certain embodiments, the kit comprises at least one polynucleotide probe for detecting expression of BMI1.

[00098] In one embodiment, the kit comprises at least one polynucleotide probe for detecting the expression of at least one control gene. In certain embodiments, at least one control gene is selected from VCP, RTN4, CUL4A, PPP2R5D, RUFY1, ABCF1, and LARP1.

[00099] The polynucleotide probes may be optionally labeled.

[000100] The kit may optionally include polynucleotide primers for amplifying a portion of the mRNA transcripts from at least 1, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the genes in the 11-gene panel. In one embodiment, the kit optionally includes polynucleotide primers for amplifying a portion of the mRNA transcripts from all 11 of the aforementioned genes. In one embodiment, the kit comprises polynucleotide primers for amplifying a portion of the mRNA transcripts from at least one control gene, including, for example, one or more of VCP, RTN4, CUL4A, PPP2R5D, RUFY1, ABCF1, and LARP1.

[000101] In another embodiment, the kit optionally includes polynucleotide primers for amplifying a portion of the mRNA transcripts from at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, or 54 of the genes in the 54-gene panel. In one embodiment, the kit comprises polynucleotide primers for amplifying a portion of the mRNA transcripts from at least one control gene, including, for example, one or more of VCP, RTN4, CUL4A, PPP2R5D, RUFY1, ABCF1, and LARP1.

[000102] The kit for classifying a tumor as an HRD tumor or an HRP tumor may also comprise antibodies. Thus, in one embodiment, the kit for classifying a tumor as an HRD tumor or an HRP tumor comprises a plurality of antibodies for detecting at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the polypeptides encoded by genes in the 11-gene panel, wherein the plurality of antibodies contains antibodies for detecting no more than 500, 250, 100, 75, 60, 55, 54, 50, 45, 40, 35, 30, 25, 20, 15, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 polypeptides.

[000103] In another embodiment, the kit for classifying a tumor as an HRD tumor or an HRP tumor comprises a plurality of antibodies for detecting at least 1, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, or 54 of the polypeptides encoded by genes in the 54-gcnc panel, wherein the plurality of antibodies contains antibodies for detecting no more than 500, 250, 100, 75, 60, 55, 54, 50, 40, 35, 30, 25, 20, 15, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 polypeptides.

[000104] As noted above, the polynucleotide or polypeptide probes and antibodies described herein may be optionally labeled with a detectable label. Any detectable label used in conjunction with probe or antibody technology, as known by one of ordinary skill in the art, can be used. As described herein, the labelled polynucleotide probes or labelled antibodies are not naturally occurring molecules; that is the combination of the polynucleotide probe coupled to the label or the antibody coupled to the label do not exist in nature. In certain embodiments, the probe or antibody is labeled with a detectable label selected from the group consisting of a fluorescent label, a chemiluminescent label, a quencher, a radioactive label, biotin, mass tags and/or gold.

[000105] In one embodiment, a kit includes instructional materials disclosing methods of use of the kit contents in a disclosed method. The instructional materials may be provided in any number of forms, including, but not limited to, written form (e.g., hardcopy paper, etc.), in an electronic form (e.g., external drive, computer diskette or compact disk) or may be visual (e.g., video files). The kits may also include additional components to facilitate the particular application for which the kit is designed. Thus, for example, the kits may additionally include other reagents routinely used for the practice of a particular method, including, but not limited to buffers, enzymes, labeling compounds, and the like. The kit can also include a reference or control sample. The reference or control sample can be a biological sample or a data base.

[000106] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. Although methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present embodiments, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

EXAMPLES

[000107] Unless indicated otherwise in these Examples, the methods involving commercial kits were done following the instructions of the manufacturers. Example 1

[000108] Materials'. HGSOC blood and fresh-frozen tumor tissues collected from 69 patients diagnosed with HGSOC were obtained from the Gynecologic Cancer Center of Excellence (GYN- COE). Fresh-frozen tumors were embedded in optimal cutting temperature (OCT) and were scrolled or sectioned onto polyethylene naphthalate (PEN) membrane slides (Leica Microsystems). [000109] Molecular extraction to generate purified DNA, RNA and peptide digests from tumor tissues: DNA was extracted from tumor tissue scrolls according to manufacturer’s protocol (DNA Purification from Tissues) using the QiAamp DNA Mini Kit (Qiagen Sciences LLC, Germantown, MD) as previously described (7). RNA was extracted using the RNeasy Micro Kit (Qiagen Sciences LLC, Germantown, MD), and peptide digests were generated as previously described (in Bateman, N.W. et al., Proteogenomic landscape of uterine leiomyomas from hereditary leiomyomatosis and renal cell cancer patients, Scientific Reports 2021; 11:9371) for tumor cell populations collected from PEN membrane tissue sections by laser microdissection using an LMD7 laser microdissection microscope (Leica Microsystems).

[000110] Whole Genome Sequencing (WGS): WGS analysis was performed on germline DNA extracted from blood and on tumor tissues as previously described (7), achieving >30x coverage of nucleobases for germline DNA and >90x coverage for tumor DNA. Briefly, purified DNA underwent library preparation using the TruSeq DNA PCR-free Library Preparation Kit (Illumina, San Diego, CA). Paired-end sequencing was performed on resulting libraries with the HiSeq X HD SBS Kit (300 cycles) on the Illumina HiSeq X. WGS sample raw reads were aligned to the hg38 human reference genome and further processed through the Resequencing workflow within Illumina’s HiSeq Analysis Software (HAS; Isis version 2.5.55.1311).

[000111] Transcriptome analysis (mRNAseq): Transcriptome analysis was performed on total RNA extracted from LMD enriched tumor cell populations as previously described (7). Briefly, sequencing libraries were prepared from 500 ng of total RNA input using the TruSeq Stranded mRNA Library Preparation Kit (Illumina) with index barcoded adapters. Clustering and sequencing was performed on the HiSeq 500 (Illumina) using a High Output 150 cycle kit for paired-end reads of 75 bp length and an intended depth of 50 million reads per sample. FASTQ files were aligned to hg38 by MapSplice aligner (v 2.2.2). The transcript abundance level was quantified using HTSEq (v 0.9.1) package, and read counts underwent VST normalization using DESeq2 (3.14). [000112] Quantitative, multiplexed (TMT-11 ) proteomic analysis: Global proteome analysis was performed on peptide digests generated from LMD enriched tumor cell populations as previously described (Bateman 2021). Briefly, equivalent amounts of peptide digests were labelled with tandem-mass tag (TMT) isobaric labels (TMT-11 Isobaric Label Reagent Set, Thermo Fisher Scientific) for each tissue sample according to the manufacturer’s protocol. TMT-labelled samples were combined, and multiplexes were pooled and fractionated by basic reversed-phase liquid chromatography (bRPLC, 1260 Infinity II liquid chromatographer, Agilent). Fractions were concatenated and underwent global proteomic analysis by liquid chromatography, tandem mass spectrometry employing a nanoflow LC system (EASY-nLC 1200, Thermo Fisher Scientific) coupled online with an Q-Exactive HF-X mass spectrometer (Thermo Fisher Scientific). Peptide identifications and protein quantitation for TMT multiplexes included searching. RAW data files were compared against a publicly-available, nonredundant human proteome database (Uniprot, 12/01/17) using Mascot (v2.6.0, Matrix Science) and Proteome Discoverer (v2.2.0.388, Thermo Fisher Scientific).

[000113] Bioinformatic Analysis: Germline and somatic mutation analysis of WGS data was performed to identify mutations in BRCA1 and BRCA2 tumor suppressor genes. Homologous recombination deficiency was determined by assessment of WGS data using the CHORD (4) and scarHRD (5) packages in R using default parameters. Differentially expressed features by CHORD-defined HRD or HRP status for n=69 patient samples in the enriched tumor transcriptome and proteome datasets were determined using limma (ver 3.40.6) (6) in RStudio (ver 3.6.0). Proteins and transcripts were considered significant if they passed a Benjamini-Hochberg adjusted p-value < 0.05. Sparse Partial Lease Squares Discriminant analysis (sPLS-DA) was performed using mixOmics (ver 6.8.5) (7) using the 54 defined optimal features in the independently z-score normalized transcriptome data to further select optimal features by a variable importance greater than 1.

[000114] The resultant 31 optimized features were further refined by considering possible gene combinations and panel sizes to minimize the number of features in the final model, maximize the area under the receiver operating curve (AUROC) in the testing data set by predicting on the mahalanobis distance from the sPLS-DA model (pROC ver 1.16.2), and further maximize the classification accuracy in the training and testing data sets. The AUROC reflected in the training data for the final 11 optimized candidates and for the MOCOG validation data was calculated over 1000 iterations on the second component by averaging the HRD and HRP predicted values respectively. The classification accuracy was calculated from a feature attribution algorithm (Equation 1 below) that predicts HRD or HRP status for a sample based on the abundance of signature candidates.

[000115] These equations use a sample’s z-score normalized expression, weigh it by the variable importance as used in the sPLS-DA training model, and average it to balance the number of model- selected HRD and HRP genes. A sample’ s class is determined by whether the HRD score or HRP score is larger. A sample’s reliability for the HRD or HRP classification is determined by the magnitude of difference between the respective feature attribution scores. The sample’s reliability can be calculated using Equation 2, below, wherein the larger the difference, the more accurately the classification as HRD or HRP can be made. Equation 1 and Equation 2 are defined as follows:

[000116] Equation 1:

[000117] Equation 2: Distance = abs(HRDScore - HRPScore)

[000118] Results: A total of 18 tumors out of 69 tumors were classified as HRD using

CHORD scores that also exhibited significantly higher scarHRD scores than tumors classified as HRP (MWU p < 0.0001). As shown in FIG. 1, it was further observed that many tumors classified as HRD harbor a germline or somatic alteration in Breast cancer type 1 and 2 susceptibility protein (BRCA1 and BRCA2) genes, genetic alterations known to underlie HRD.

[000119] As discussed above, proteins and transcripts significantly (LIMMA adjusted p < 0.05) altered in HRD (n=18) versus HRP (n=51) tumors were prioritized from enriched tumor collections, and 67 total proteins and transcript candidates were identified passing an adjusted p- value of p<0.05. The results are shown in FIG. 2, wherein proteins candidates are shown on the left, and transcript candidates arc shown on the right.

[000120] Comparison of the protein and transcript alterations between HRD versus HRP tumors identified the following 54 unique gene candidates corresponding to proteins or transcripts mapping to protein-coding genes: ACADSB, AGGF1, ARHGEF40, ARSK, ATF6B, BBS9, BMI1, BTF3, INTS15, CDKN1C, CKAP4, CLDND2, CWC27, DACH1 , DAGLB, DESI1 , DYNLL1 , EEF1D, EIF2AK1, EPPK1, GFM2, IFT52, IWS1, KHDRBS1, KIAA0825, LE01, LMF2, MARCKSL1, MARVELD2, MCUR1, MIEF1, NADSYN1, NDRG1, NDUFA3, NRBP2, NSL1, PHC2, PKIG, PLCB3, PPP1R16A, PYCR3, RAD17, TBC1D22A, TIMMDC1, TMEM167A, PACC1, TSG101, TSNARE1, TUBA1A, VPS13D, WDR41, XRCC4, ZBED3, andZNF219.

[000121] Further investigation identified 5 of these 54 candidates that were significantly coaltered (LIMMA adjusted p < 0.05) at protein and transcript levels and exhibited concordant abundance trends. The 5 identified candidates included: EPPK1 and PYCR3, both of which were elevated in HRD versus HRP tumors, and BMI1, WDR41, and KHDRBS1, all 3 of which were decreased in HRD versus HRP tumors. Thirty-six gene candidates were co-quantified at the protein and transcript levels and exhibited significantly correlated abundance trends in HRD versus HRP tumors (Spearman Rho = 0.813, P<lE-4).

[000122] The performance of this 54-gene panel was investigated to classify HRD and HRP tumors using a sparse Partial Least Squares for Discrimination Analysis (sPLS-DA) approach (6), and it was found that assessment of the abundance of these candidates were classified as HRD (n=18) from HRP (n=51) tumors with high sensitivity and specificity. The performance of these candidates was then assessed in transcript-level data (RNA-seq) for a recently described cohort of HGSOC tumors classified as HRD (n=69) and HRP (n=57) by CHORD score analysis of companion WGS data (Garsed D et al, 2021). A correlation analysis was performed of 54 HRD candidates in transcriptome data for the independent cohort of HGSOC patients in Garsed D et al. 2021 that were classified as HRD (n=69) or HRP (n=57) using CHORD score analysis, and the results are shown in FIG. 3 (Spearman Rho= R = 0.856, p-value <lE-3). It was found that the abundance of these 54 gene candidates in the cohort of 69 HRD patient tumors exhibited a high quantitative correlation between HRD versus HRP tumors within this independent cohort of HGSOC tumors, as shown in FIG. 4, wherein the HRD versus HRP tumors were classified with high sensitivity and specificity using this features set (average AUC=0.81 following 1,000 iterations in sPLS-DA, average p-value < 3.35E-9).

[000123] The 54-gene panel was further optimized by prioritizing those genes with a variable importance greater >1 in the discovery cohort of 18 HRD and 51 HRP HGSOC patient tumors through iterative assessment of potential candidate panels at various sizes with the goal of minimizing the number of features necessary to classify samples as HRD or HRP while maximizing performance in the validation dataset of HRD (n=69) and HRP (n=57) HGSOC tumors by both AUC and by model calculated testing error.

[000124] 11 gene candidates were then identified as follows: PYCR3, NADSYN1, NSL1,

RAD17, EIF2AK1, DAGLB, GFM2, BTF3, KIAA0825, PACC1, and CDKN1C. The 11 candidates classified HRD and HRP tumors within the discovery cohort with high sensitivity and specificity (average AUC=0.93 following 1,000 iterations in sPLS-DA, average p-value < 1.0E- 7). Table 3 below provides the 11 prioritized gene candidates, as well as their fold-change and results of significance testing comparing HRD tumors (n=18) and HRP tumors (n=51).

[000125] Table 3 - 11 -gene panel for identifying patients with HRD tumor

[000126] It was also found that the abundance of these 11 gene candidates in the validation cohort of 69 patient tumors exhibited a high quantitative correlation between HRD versus HRP tumors within this independent cohort of 126 HGSOC tumors (Pearson Rho=0.954, P<lE-5), and further classified HRD versus HRP tumors with high sensitivity and specificity (AUC=0.87, p- value = 4.96E-13). See FIG. 6. Feature attribution algorithms (Equations 1 and 2) were further derived by weighting expression abundance of signature candidates elevated in HRD or HRP tumors by the magnitude of variable importance for a given candidate optimized during model development to enable calculation of a sample specific HRD and HRP score. It was found that the overall accuracy to classify a sample as HRD or HRP was correlated with the magnitude of difference between HRD and HRP scores for the discovery (n=69) and validation cohorts (n=126) and achieved an accuracy of

80.2% with =5 5.0 % difference in the magnitude of HRD and HRP scores, as shown in FIG. 7. Thus, identified herein is an expression signature that enables classification of tumors as having HRD or HRP status independent of mutational analysis.

[000127] To best position utility of the homologous recombination repair expression signature for deployment as a companion diagnostic assay, optimized control genes were identified that allow normalization of unknown samples for direct analysis in the feature attribution algorithm described Equation 3, below. Control genes were prioritized based on four aspects: low percent coefficient of variation at read count and RNAseq or microarray normalized gene expression, high proteome and transcriptome feature correlation, sufficient coverage by spectral counts at the protein level, and well-documented across multiple tissue sites in proteomic and transcriptomic datasets.

[000128] By exhibiting a coefficient of variation (CV) below the first quartile of read counts and VST normalized gene expression in the training and testing datasets and by low percent CV in The Cancer Genome Atlas’ companion microarray expression dataset for HGSOC (2), control genes were prioritized for downstream analysis. The following 7 control genes were identified: VCP, RTN4, CUL4A, PPP2R5D, RUFY1, ABCF1, and LARP1. Control genes further exhibited high correlation between expression-level proteome and transcriptome data for matched training samples in LMD enriched tumor and bulk tissue collections (Spearman > 0.7). To use the feature attribution model with the control genes, the biomarker panel gene counts are divided by the average read count of the control genes as the normalized expression value (CGExpression) in the formula (Equation 3 below).

[000129] Equation 3:

(1) HRD Score =

(2) HRP Score

[000130] Accordingly, a companion diagnostic assay is contemplated to include measurement of control genes in concert with gene signatures of interest, e.g., the 54-gene panel and/or the 11-gene panel. Prior to the assessment of gene in the feature attribution algorithm for an unknown sample, the abundance of signature genes may be divided by the average abundance of control genes to normalize expression and adjust for batch effects. Performance of the 11-gene panel including BMI1 abundance following consideration of control gene abundances was assessed, and the method continued to classify HRD and HRP tumors with high sensitivity and specificity, as shown in FIG. 8 (AUC=0.86, p-value = 1.95E-12).

Example 2

[000131] Investigation of the 54 gene candidates as discussed in Example 1 resulted in the identification of one protein, polycomb complex protein BMI-1 (BMI1), as significantly elevated in HRP versus HRD tumors.

[000132] Analysis of the relationship between BMI1 transcript abundance and overall survival (OS) was performed, wherein OS was defined as the time from diagnosis until death from any cause using Kaplan-Meier methods, where high versus low expression and correlation with BMI1 abundance was defined by the median cut-point. Adjusted analysis further considered patient age (continuous variable), disease stage (III vs. IV), and residual disease status (residual vs. no residual) and was performed using SASSurvival in SAS (version 9.4).

[000133] As shown in FIG. 5, it was noted that elevated transcript- level abundances of BMI1 (BMI_high) was significantly correlated with an increased risk of poor OS in HRP (n=57, adjusted hazard ratio (aHR) = 2.47, p = 0.02), but not HRD (n=69, aHR = 1.6, p = 0.153) HGSOC patients from a cohort that included exceptionally long survivors (Garsed 2021), as well as in HGSOC patients with wild-type BRCA1 or BRCA2 (n=379, aHR = 1.36, p = 0.02) compared to patients harboring mutations in these genes (n=61, aHR = 1.00, p = 0.997) (2) following adjustment for patient age, disease stage and residual disease status.

[000134] Additionally, UWB 1.289 (CRL-2945) and UWB 1.289 + BRCA1 (CRL-2946) cell lines were purchased from a commercial source, and response to BMI1 inhibitors PTC-028 (#S8662, Selleckchem, Houston, TX, USA) or PTC596 (# S8820, Selleckchem, Houston, TX, USA) was assessed by colony survival assays (8). The data reflects three independent biological replicate experiments. It was discovered that ovarian cancer cells expressing wildtype BRCA1, UWB 1.289 & BRCA1, a model of HRP disease, are significantly more sensitive to pharmacologic inhibitors of BMH, i.e. PTC-028 and PTC-596 (3), than ovarian cancer cells with mutant BRCA1, UWB 1.289 cells. These data suggest that assessment of BMH abundance in the context of the homologous recombination expression signature disclosed herein may represent a pharmacodynamic marker of response to BMH inhibitors for HRP, but not HRD tumors. References

[000135] 1. Garscd, D.W. ct al., The genomic and immune landscape of long-term survivors of high-grade serous ovarian cancer, Nat. Genet. 2022; 54(12): 1854-64.

[000136] 2. Cancer Genome Atlas Research Network, Integrated genomic analysis of ovarian carcinoma, Nature 2011; 54(12):609-15.

[000137] 3. Dey, A. et al., Evaluating the Mechanism and Therapeutic Potential of PTC-028, a Novel Inhibitor ofBMI-1 Function in Ovarian Cancer, Mol Cancer Ther. 2018; 17(l):39-49.

[000138] 4. Nguyen, L. et al., Pan-cancer landscape of homologous recombination deficiency, Nat. Commun. 2020; 11(1):5584.

[000139] 5. Sztupinski, Z. et al., Migrating the SNP array-based homologous recombination deficiency measures to next generation sequencing data of breast cancer, NPJ Breast Cancer 2018; 4:16.

[000140] 6. Ritchie, M.E. et al., Limma powers differential expression analyses for RNA- sequencing and microarray studies, Nucleic Acids Res. 2015; 43(7):e47.

[000141] 7. Rohart, F. et al., mixOmics: An R package for ‘omics feature selection and multiple data integration, PLoS Comput Biol. 2017; 13(l l):el005752.

[000142] 8. Feoktistova, M. et al., Crystal Violet Assay for Determining Viability of Cultured

Cells, Cold Spring Harb. Protoc. 2016; 2016(4):pdb prot087379.

Claims

What is claimed:

1. A method of treating cancer in a subject in need thereof, comprising:

(1) classifying a tumor from the subject as a homologous recombination deficient (HRD) tumor by

(a) measuring expression levels of at least one, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, or 54 transcripts or proteins selected from the group consisting of: ACADSB, AGGF1, ARHGEF40, ARSK, ATF6B, BBS9, BMI1, BTF3, INTS15, CDKN1C, CKAP4, CLDND2, CWC27, DACH1, DAGLB, DESI1, DYNLL1, EEF1D, EIF2AK1, EPPK1, GFM2, IFT52, IWS1, KHDRBS1, KIAA0825, LEO1, LMF2, MARCKSL1, MARVELD2, MCUR1, MIEF1, NADSYN1, NDRG1, NDUFA3, NRBP2, NSL1, PHC2, PKIG, PLCB3, PPP1R16A, PYCR3, RAD17, TBC1D22A, TIMMDC1, TMEM167A, PACC1, TSG101, TSNARE1, TUBA1A, VPS13D, WDR41, XRCC4, ZBED3, and ZNF219,

(b) comparing the expression levels of the transcripts or proteins of the tumor to expression levels of the same transcripts or proteins measured in step (1) in a homologous recombinant proficient (HRP) tumor, and

(c) classifying the tumor as HRD if the expression levels of the transcripts or proteins in the tumor are significantly different from the expression levels of the transcripts or proteins in the HRP tumor and have a false-discovery rate expectation of adjusted p- value < 0.05 when compared to the HRP tumor; and

(2) administering a therapeutically effective amount of at least one DNA damage repair inhibitor to the subject when the tumor is classified as an HRD tumor, wherein the at least one DNA damage repair inhibitor is a poly ADP ribose polymerase (PARP) inhibitor.

2. The method according to claim 1, wherein the expression levels of the transcripts or proteins are normalized to one or more control genes.

3. The method according to claim 2, wherein the one or more control genes are selected from VCP, RTN4, CUL4A, PPP2R5D, RUFY1, ABCF1, and LARP1.

4. The method according to any one of the preceding claims, wherein the expression levels of the transcripts or proteins in the tumor have a false-discovery rate expectation of adjusted p- value <0.05 when compared to the expression levels of the transcripts or proteins in the HRP tumor.

5. The method according to any one of the preceding claims, wherein the cancer is selected from ovarian cancer, prostate cancer, breast cancer, endometrial cancer, gastric cancer, and lung cancer.

6. The method according to according to any one of the preceding claims, wherein the cancer is an ovarian cancer.

7. The method according to claim 6, wherein the ovarian cancer is a high-grade serous ovarian cancer.

8. The method according to any one of claims 1-5, wherein the cancer is an endometrial cancer.

9. The method according to claim 8, wherein the endometrial cancer is a high-grade serous endometrial cancer.

10. The method according to any of the preceding claims, wherein the transcripts or proteins are chosen from at least one, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the following transcripts or proteins: PYCR3, NADSYN1, NSL1, RAD17, EIFAK1, DAGLB, GFM2, BTF3, KIAA0825, PACC1, and CDKN1C.

11. The method according to claim 10, wherein the transcripts or proteins further comprise BMI1.

12. The method according to any one of claims 1 -9, wherein the transcripts or proteins are chosen from at least one, such as at least 2, at least 3, at least 4, or 5 of the following transcripts or proteins: EPPK1, PYCR3, BMI1, WDR41, and KHDRBS1.

13. The method according to claim 12, wherein the expression levels the transcripts or proteins of at least one of EPPK1 and PYCR3 in the tumor are over-expressed compared to the expression levels of the same transcripts or proteins in the HRP tumor.

14. The method according to claim 12, wherein the expression levels of the transcripts or proteins of at least one of BMI1, WDR41, and KHRBS1 are under-expressed compared to the expression levels of the same transcripts or proteins in the HRP tumor.

15. The method according to any of the preceding claims, wherein the PARP inhibitor comprises olaparib, rucaparib, talazoparib, or niraparib.

16. A method of classifying a tumor as a homologous recombinant deficient (HRD) tumor or a homologous recombination proficient (HRP) tumor, the method comprising:

(1) measuring expression levels in the tumor of at least one, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, or 54 transcripts or proteins selected from the group consisting of: ACADSB, AGGF1, ARHGEF40, ARSK, ATF6B, BBS9, BMI1, BTF3, INTS15, CDKN1C, CKAP4, CLDND2, CWC27, DACH1, DAGLB, DESI1, DYNLL1, EEF1D, EIF2AK1, EPPK1, GFM2, IFT52, IWS1, KHDRBS1, KIAA0825, LEO1, LMF2, MARCKSL1, MARVELD2, MCUR1, MIEF1, NADSYN1, NDRG1, NDUFA3, NRBP2, NSL1, PHC2, PKIG, PLCB3, PPP1R16A, PYCR3, RAD17, TBC1D22A, TIMMDC1, TMEM167A, PACC1, TSG101, TSNARE1, TUBA1A, VPS13D, WDR41, XRCC4, ZBED3, and ZNF219;

(2) comparing the expression levels of the transcripts or proteins of the tumor to expression levels of the same transcripts or proteins measured in step (1) in a homologous recombinant proficient (HRP) tumor; and (3) classifying the tumor as an HRD tumor if the expression levels of the transcripts or proteins in the tumor arc significantly different from the expression levels of the transcripts or proteins in the HRP tumor.

17. The method according to claim 16, wherein the expression levels of the transcripts or proteins are normalized to one or more control genes.

18. The method according to claim 17, wherein the one or more control genes are selected from VCP, RTN4, CUL4A, PPP2R5D, RUFY1, ABCF1, and LARP1.

19. The method according to any one of claims 16-18, wherein the expression levels of the transcripts or proteins in the tumor have a false-discovery rate expectation of adjusted p-value <0.05 when compared to the expression levels of the transcripts or proteins in the HRP tumor.

20. The method according to any one of claims 16-19, wherein the tumor is from a subject who has been diagnosed with a cancer, and wherein the method further comprises administering a therapeutically effective amount of at least one DNA damage repair inhibitor to the subject when the tumor is classified as an HRD tumor, wherein the at least one DNA damage repair inhibitor is a poly ADP ribose polymerase (PARP) inhibitor.

21. The method according to claim 20, wherein the PARP inhibitor comprises olaparib, rucaparib, talazoparib, or niraparib.

22. The method according to any one of claims 16-21, wherein the tumor is from a cancer selected from ovarian cancer, prostate cancer, breast cancer, endometrial cancer, gastric cancer, and lung cancer.

23. The method according to any one of claims 16-23, wherein the cancer is ovarian cancer.

24. The method according to claim 23, wherein the ovarian cancer is a high-grade serous ovarian cancer.

25. The method according to any one of claims 16-22, wherein the cancer is an endometrial cancer.

26. The method according to claim 25, wherein the endometrial cancer is a high-grade serous endometrial cancer.

27. The method according to any of claims 16-26, wherein the transcripts or proteins are chosen from at least one, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 of the following transcripts or proteins: PYCR3, NADSYN1, NSL1, RAD17, EIFAK1, DAGLB, GFM2, BTF3, KIAA0825, PACC1, and CDKN1C.

28. The method according to claim 27, wherein the transcripts or proteins further comprise BMI1.

29. The method according to any one of claims 16-26, wherein the transcripts or proteins are chosen from at least one, such as at least 2, at least 3, at least 4, or 5 of the following transcripts or proteins: EPPK1 , PYCR3, BMI1 , WDR41 , and KHDRBS1.

30. The method according to claim 29, wherein the expression levels the transcripts or proteins of at least one of EPPK1 and PYCR3 in the tumor are over-expressed compared to the expression levels of the same transcripts or proteins in the HRP tumor.

31. The method according to claim 29, wherein the expression levels of the transcripts or proteins of at least one of BMI1, WDR41, and KHRBS1 are under-expressed compared to the expression levels of the same transcripts or proteins in the HRP tumor.

32. A kit for use in classifying a tumor as a homologous recombination deficient (HRD) tumor or a homologous recombination proficient (HRP) tumor, the kit comprising a plurality of probes for detecting the expression of at least one, such as at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, or 54 transcripts or proteins selected from the group consisting of: ACADSB, AGGF1, ARHGEF40, ARSK, ATF6B, BBS9, BMI1, BTF3, INTS15, CDKN1C, CKAP4, CLDND2, CWC27, DACH1, DAGLB, DESI1, DYNLL1, EEF1D, EIF2AK1, EPPK1, GFM2, IFT52, IWS1, KHDRBS1, KIAA0825, LEO1, LMF2, MARCKSL1, MARVELD2, MCUR1, MIEF1, NADSYN1, NDRG1, NDUFA3, NRBP2, NSL1, PHC2, PKIG, PLCB3, PPP1R16A, PYCR3, RAD 17, TBC1D22A, TIMMDC1, TMEM167A, PACC1, TSG101, TSNARE1, TUBA1A, VPS13D, WDR41, XRCC4, ZBED3, and ZNF219, wherein the plurality of probes contains probes for detecting the expression of no more than 500 different genes.

33. The kit of claim 32, wherein the plurality of probes comprises probes for detecting expression of at least one, at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, or 11 transcripts or proteins selected from the group consisting of: PYCR3, NADSYN1, NSL1, RAD17, EIFAK1, DAGLB, GFM2, BTF3, KIAA0825, PACC1, and CDKN1C.

34. The kit of claim 33, wherein the plurality of probes further comprises a probe for detecting expression of BMI1.

35. The kit of claim 32, wherein the plurality of probes contains probes for detecting expression of all 54 transcripts or proteins.

36. The kit of claim 33, wherein the plurality of probes contains probes for detecting expression of all 11 transcripts or proteins.

37. The kit of any one of claims 32-36, wherein the plurality of probes is selected from a plurality of oligonucleotide probes, a plurality of antibodies, or a plurality of polypeptide probes.

38. The kit of any one of claims 32-37, wherein the plurality of probes contains probes for detecting expression in no more than 250, 100, 75, 60, 50, 40, 30, 25, 20, 19, 16, 15, 13, 9, 10, 8, or 6 different transcripts or proteins.

39. The kit of any one of claims 32-38, wherein the plurality of probes is attached to the surface of an array.

40. The kit of claim 39, wherein the array comprises no more than 250, 100, 75, 60, 50, 40, 30, 25, 20, 19, 16, 15, 13, 9, 10, 8, or 6 different addressable elements.

41. The kit of any one of claims 32-40, wherein the plurality of probes is labeled.