Prognostic glucocorticoid receptor transcriptomic signature for high-grade serous ovarian cancer

Abstract

Disclosed herein are methods and compositions for determining survival and treatment responsiveness of subjects with ovarian cancer. Specifically, the disclosure is directed to prognostic glucocorticoid receptor activity signatures for ovarian cancer. Also provided are methods for predicting responsiveness to certain cancer therapies (e.g., selective glucocorticoid receptor modulators (SGRMs)).

Description

Attorney Docket No. UTSDP4477WO- 1001385614TITLE PROGNOSTIC GLUCOCORTICOID RECEPTOR TRANSCRIPTOMIC SIGNATURE FOR HIGH-GRADE SEROUS OVARIAN CANCERCROSS REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to U.S. Provisional Patent Application Serial No.63 / 776,855, filed March 24, 2025, and titled “PROGNOSTIC GLUCOCORTICOID RECEPTOR TRANSCRIPTOMIC SIGNATURE FOR HIGH-GRADE SEROUS OVARIAN CANCER,” which is incorporated by reference herein in its entirety.ACKNOWLEDGEMENT OF GOVERNMENT SUPPORT

[0002] This invention was made with government support under Grant No. CA223426 awarded by the National Institutes of Health. The government has certain rights in the invention.BACKGROUND1. Field

[0003] The present disclosure is directed to prognostic methods for determining survival and treatment responsiveness of subjects with ovarian cancer. Specifically, the disclosure is directed to prognostic gene signatures for ovarian cancer.2. Discussion of Related Art

[0004] Ovarian cancer (OvCa) usually presents at a late stage. Once diagnosed, initial treatment consists of surgical resection followed by adjuvant chemotherapy (platinum and / or taxane-based). Nonetheless, approximately half of all subjects die within five years of diagnosis, highlighting the need for increased individualization in treatment plans. While next generation sequencing has provided some personalization of treatment in several cancer types, targetable DNA mutations in OvCa have been largely limited to BRCA1 or BRCA2. Recently, however, gene expression (mRNA) signatures have also become routine measurements in several molecular assays and may be valuable for OvCa prognosis. Additionally, if the gene signature expression represents the activation of a particular signaling pathway, this pathway (or its upstream regulator) may represent a therapeutic target. Several gene expression signatures have been published as prognostic biomarkers of subject outcome in OvCa, though none are specific to a receptor signaling pathway.

[0005] There is a need, therefore, for improved prognostics to predict ovarian cancer subject outcome and responsiveness to therapies.- 1 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614SUMMARY

[0006] Aspects of the present disclosure relate to methods of predicting survival, and / or disease progression, and / or responsiveness to a treatment in a subject having or suspected of having a cancer, the methods comprising determining a prognostic glucocorticoid receptor activity signature in a tumor sample isolated from the subject and predicting survival, and / or disease progression, and / or treatment responsiveness in the subject based on the prognostic glucocorticoid receptor activity signature.

[0007] In various aspects, the methods disclosed herein more accurately predicts survival, disease progression and / or responsiveness to the treatment in a subject than a corresponding prognostic signature based on glucocorticoid receptor levels and not activity output.

[0008] In various aspects, the prognostic glucocorticoid receptor activity signature is determined by analyzing the tumor sample for expression of one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or all ten genes selected from ANKRD1, CCNE1, CELSR3, NENE, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11 in comparison to control levels.

[0009] In some aspects, the subject is predicted to have decreased survival and / or increased disease progression if one or more, two or more, three or more, four or more, five or more, or all six genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2 have increased expression relative to control levels and / or one or more, two or more, three or more, or all four genes selected from CLDN14, DNAJA1, ENTPD61 and GDF11 have decreased expression relative to control levels.

[0010] In further aspects, the subject is predicted to have increased survival and / or decreased disease progression if one or more, two or more, three or more, or all four genes selected from CLDN14, DNAJA 1, ENTPD61 and GDF11 have increased expression relative to control levels and / or one or more, two or more, three or more, four or more, five or more, or all six genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2 have decreased expression relative to control levels.

[0011] In still further aspects, the subject is predicted to have increased responsiveness to a treatment and / or is identified for treatment with a selective glucocorticoid receptor transcriptomic signature if one or more, two or more, three or more, four or more, five or more, or all six genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2 have increased expression relative to control levels and / or one or more, two or more, three or more, or all four genes selected from CLDN14, DNAJA1, ENTPD61 and GDF11 have decreased expression relative to control levels.- 2 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614

[0012] In any of the methods herein, analyzing the tumor sample for expression of the one or more genes may comprise quantifying and / or detecting transcripts of each gene in one or more cells of the tumor sample. In some aspects, quantifying and / or detecting transcripts comprises digital transcript counting, high-density expression array, DNA microarray, polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), real-time quantitative reverse transcription PCR (qRT-PCR), digital droplet PCR (ddPCR), serial analysis of gene expression (SAGE), Spotted cDNA arrays, GeneChip, spotted oligo arrays, bead arrays, RNA Seq, tiling array, northern blotting, hybridization microarray, in situ hybridization, or any combination thereof. For example, quantifying and / or detecting transcripts may comprise using RNA Seq or a DNA microarray. As another example, quantifying and / or detecting transcripts may comprises using single cell RNA sequencing or a microarray.

[0013] In any of the methods herein, analyzing the tumor sample for expression of the one or more genes may comprise quantifying and / or detecting levels of one or more protein products of each gene in one or more cells of the tumor sample. In some aspects, quantifying and / or detecting levels of one or more protein products comprises Western blotting, enzyme-linked immunosorbent assay (ELISA), multi-analyte profiling (xMAP), mass spectrometry, HPLC, flow cytometry, fluorescence-activated cell sorting (FACS), liquid chromatography-mass spectrometry (LC / MS), immunoelectrophoresis, translation complex profile sequencing (TCP-seq), protein microarray, protein chip, capture arrays, reverse phase protein microarray (RPPA), two- dimensional gel electrophoresis or (2D-PAGE), functional protein microarrays, electrospray ionization (ESI), matrix-assisted laser desorption / ionization (MALDI), or a combination thereof.

[0014] In any of the methods herein, the control expression levels are derived from control tumor samples.

[0015] In any of the methods described herein, the treatment may comprise a glucocorticoid receptor modulator (e.g., a selective glucocorticoid receptor modulator (SGRM)).

[0016] In further aspects, the present disclosure is directed to methods for selecting a treatment for a subject having or suspected of having a cancer, the methods comprising determining a prognostic glucocorticoid receptor activity signature in a tumor sample isolated from the subject and selecting the treatment based on the prognostic glucocorticoid receptor activity signature.

[0017] In various aspects, the prognostic glucocorticoid receptor activity signature is determined by analyzing the tumor sample for expression of one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more,- 3 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614or all ten genes selected from ANKRD1, CCNE1, CELSR3, NENE, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11 in comparison to control levels.

[0018] In various treatment methods provided herein, a treatment selected may comprise an anti-cancer therapy comprising a glucocorticoid receptor modulator if one or more, two or more, three or more, four or more, five or more, or all six genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2 have increased expression relative to control levels and / or one or more, two or more, three or more, or all four genes selected from CLDN14, DNAJA1, ENTPD61 and GDF11 have decreased expression relative to control levels.

[0019] In various treatment methods provided herein, a treatment selected may comprise an anti-cancer therapy other than a glucocorticoid receptor modulator if one or more, two or more, three or more, or all four genes selected from CLDN14, DNAJA 1, ENTPD61 and GDF11 have increased expression relative to control levels and / or one or more, two or more, three or more, four or more, five or more, or all six genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2 have decreased expression relative to control levels.

[0020] In various aspects, the anti-cancer therapy other than a glucocorticoid receptor modulator can comprise chemotherapy, an immunotherapy, surgery, radiation, or any combination thereof.

[0021] In any of the methods of treatment provided herein, analyzing the tumor sample for expression of the one or more genes may comprise quantifying and / or detecting transcripts of each gene in one or more cells of the tumor sample. In some aspects, quantifying and / or detecting transcripts comprises digital transcript counting, high-density expression array, DNA microarray, polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), real-time quantitative reverse transcription PCR (qRT-PCR), digital droplet PCR (ddPCR), serial analysis of gene expression (SAGE), Spotted cDNA arrays, GeneChip, spotted oligo arrays, bead arrays, RNA Seq, tiling array, northern blotting, hybridization microarray, in situ hybridization, or any combination thereof. For example, in some aspects, quantifying and / or detecting transcripts comprises using RNA Seq and / or a microarray. In some aspects, quantifying and / or detecting transcripts comprises using single cell RNA sequencing.

[0022] In any of the methods of treatment provided herein, analyzing the tumor sample for expression of the one or more genes may comprise quantifying and / or detecting levels of one or more protein products of each gene in one or more cells of the tumor sample. In various aspects, quantifying and / or detecting levels of one or more protein products comprises Western blotting, enzyme-linked immunosorbent assay (ELISA), multi-analyte profiling (xMAP), mass spectrometry, HPLC, flow cytometry, fluorescence-activated cell sorting (FACS), liquid chromatography-mass spectrometry (LC / MS), immunoelectrophoresis,- 4 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614translation complex profile sequencing (TCP-seq), protein microarray, protein chip, capture arrays, reverse phase protein microarray (RPPA), two- dimensional gel electrophoresis or (2D-PAGE), functional protein microarrays, electrospray ionization (ESI), matrix-assisted laser desorption / ionization (MALDI), or a combination thereof.

[0023] In any of the methods of treatment provided herein, the control expression levels may be derived from control tumor samples.

[0024] Also provided herein is a method treating a subject with cancer, the method comprising administering a treatment to the subject, wherein the treatment is selected based on a prognostic glucocorticoid receptor activity signature of a tumor sample obtained from the subject.

[0025] In various aspects, the prognostic glucocorticoid receptor activity signature corresponds to expression of one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or all ten genes selected from ANKRD1, CCNE1, CELSR3, NENE, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11 in a tumor sample obtained from the subject.

[0026] In various aspects, the treatment comprises an anti-cancer therapy comprising a glucocorticoid receptor modulator when one or more, two or more, three or more, four or more, five or more, or all six genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2 have increased expression relative to control levels and / or one or more, two or more, three or more, or all four genes selected from CLDN14, DNAJA 1, ENTPD61 and GDF11 have decreased expression in the tumor sample relative to control levels. In various aspects, the glucocorticoid receptor modulator comprises a selective glucocorticoid receptor modulator (SGRM).

[0027] In various aspects, the treatment comprises an anti-cancer therapy other than a glucocorticoid receptor modulator when one or more, two or more, three or more, or all four genes selected from CLDN14, DNAJA1, ENTPD61 and GDF11 have increased expression relative to control levels and / or one or more, two or more, three or more, four or more, five or more, or all six genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2 have decreased expression relative to control levels. In various aspects, the anti-cancer therapy other than a glucocorticoid receptor modulator may comprise chemotherapy, an immunotherapy, surgery, radiation, or any combination thereof.

[0028] In any of the methods provided herein, the cancer may comprise an ovarian cancer. In some aspects, the ovarian cancer comprises epithelial ovarian carcinoma, a germ cell tumor, or a stromal tumor or any combination thereof.- 5 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614

[0029] In some aspects, the ovarian cancer comprises epithelial ovarian carcinoma. In some aspects, the epithelial ovarian carcinoma comprises serous epithelial ovarian carcinoma, a mucinous epithelial ovarian carcinoma, an endometroid epithelial ovarian carcinoma, clear cell epithelial ovarian carcinoma or any combination thereof.

[0030] In some aspects, the ovarian cancer comprises a germ cell tumor, optionally wherein the germ cell tumor comprises dysgerminoma, yolk sac tumor, malignant teratoma, mixed germ cell tumor, embryonal carcinoma, choriocarcinoma, or any combination thereof.

[0031] In some aspects, the ovarian cancer comprises a stromal tumor, optionally wherein the stromal tumor comprises granulosa cell tumors, Sertoli cell tumors, Sertoli-Leydig cell tumors or any combination thereof.

[0032] In various aspects, the ovarian cancer comprises a low-grade tumor. In various aspects, the ovarian cancer comprises a high-grade tumor. In various aspects, the ovarian cancer comprises high-grade serous epithelial ovarian carcinoma.

[0033] In various aspects, the present disclosure is also directed to kits for determining a prognostic glucocorticoid receptor activity signature in a sample, the kits comprising one or more components for analyzing expression of one or more genes selected from ANKRD1, CCNE1, CELSR3, NENE, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11 in a tumor sample. In various aspects, the one or more components are for use in an assay for quantifying and / or detecting transcripts of each gene in one or more cells of the tumor sample. In various aspects, the one or more components are for use in an assay for quantifying and / or detecting levels of one or more protein products of each gene in one or more cells of the tumor sample.

[0034] In various aspects, the kits provided herein further comprise an algorithm and software encoding the algorithm for calculating a prognostic glucocorticoid receptor activity signature from the expression of ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11 in a sample.

[0035] In various aspects, the present disclosure is also directed to a computer readable medium having software modules for performing a method comprising the acts of: (a) comparing glucocorticoid receptor activity data obtained from an ovarian tumor sample with a reference; and (b) providing an assessment of glucocorticoid receptor activity status to a physician for use in determining an appropriate therapeutic regimen for a subject, wherein glucocorticoid receptor activity data comprises one or more data points corresponding to expression of one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or all ten genes selected from ANKRD1, CCNE1,- 6 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11 in the tumor sample.

[0036] In various aspects, the present disclosure is also directed to a computer system, having a processor, memory, external data storage, input / output mechanisms, a display, for assessing glucocorticoid receptor activity, comprising: (a) a database; (b) logic mechanisms in the computer generating for the database a GR-responsive gene expression reference; and (c) a comparing mechanism in the computer for comparing the GR-responsive gene expression reference to expression data from an ovarian tumor sample using a comparison model to determine a GR gene expression profile of the ovarian tumor sample, wherein the GR-responsive gene expression comprises expression of one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or all ten genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11.

[0037] In further aspects, the present disclosure is also directed to an internet accessible portal for providing biological information constructed and arranged to execute a computer-implemented method for providing: (a) a comparison of gene expression data of one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or all ten GR-responsive genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11 in an ovarian tumor sample obtained from a subject with a calculated reporter index; and (b) providing an assessment of GR activity to a physician for use in determining an appropriate therapeutic regime for the subject.BRIEF DESCRIPTION OF THE DRAWINGS

[0038] FIG. 1 depicts a table showing subject and pathology characterizations of datasets in this study.

[0039] FIG. 2 is a table showing a gene GR-sig with coefficients and associated risk (HR). Forest plot reporting the Cox multivariate analysis of HRs and corresponding 95% confidence intervals for each GR-Sig gene.

[0040] FIGS. 3A-3B show Kaplan-Meier plots using a log-rank test to compare OS distributions for high- versus low-risk TCGA subjects using microarray datasets (FIG. 3A) or RNA-seq datasets (FIG. 3B).

[0041] FIG. 4 depicts a workflow for generating a glucocorticoid receptor transcriptomic signature of the present disclosure.- 7 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614

[0042] FIG. 5 is a plot showing Log2 fold-change gene expression by RNA-seq after dex treatment (6h) in OvCa cell lines. Significant p-values displayed with asterisks. Yellow bars indicate genes whose expression is associated with reduced OS in the training data. Blue bars indicate genes associated with improved OS.

[0043] FIGS. 6A-6D show boxplots displaying NR3C1 expression for each tumor in the TOGA microarray validation set (FIG. 6A) and TOGA RNA-seq validation set (FIG. 6B) where high-versus low- risk classification is based on the GR-Sig; and Kaplan-Meier survival curves reporting survival probability over time for subjects classified into high- and low-risk groups by median NR3C1 expression in the TOGA microarray validation set (FIG. 6C) and the TOGA RNA-seq validation set (FIG. 6D).

[0044] FIG. 7 is a boxplot showing NR3C1 expression in high- versus low-risk subjects in the GEO training set.

[0045] FIG. 8 is a bar graph showing p-values for HRs of each of the 10 GR-Sig genes generated by both univariate and multivariate analyses, compared to the p-value for the univariate NR3C1 HR.DETAILED DESCRIPTION

[0046] The following detailed description references the accompanying drawings that illustrate various aspects of the present inventive concept. The drawings and description are intended to describe aspects and aspects of the present inventive concept in sufficient detail to enable those skilled in the art to practice the present inventive concept. Other components can be utilized and changes can be made without departing from the scope of the present inventive concept. The following description is, therefore, not to be taken in a limiting sense. The scope of the present inventive concept is defined only by the appended claims, along with the full scope of equivalents to which such claims are entitled.

[0047] The present disclosure is based, in part, on the novel finding that determining a gene expression profile or protein abundance levels in a tumor sample obtained from a subject can be used to generate a prognostic glucocorticoid receptor activity signature for use in prediction of development and progression of ovarian cancer and treatment in the subject. Previously, it was reported that glucocorticoid receptor levels could predict therapeutic outcomes in breast cancer (see, for example, U.S. Patent No. 9,623,032, incorporated herein by reference in its entirety). As described further herein, the inventors have made the surprising discovery that glucocorticoid receptor activity, (and more particularly, activity reflected by gene expression of one or more key genes described below) more accurately predicts survival, disease progression and treatment responsiveness in patients with ovarian cancer as compared to prognostic signatures based primarily on glucocorticoid receptor levels. Accordingly, provided- 8 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614herein are methods for determining gene expression in a tissue and / or measuring protein abundance of a panel of proteins, determining a prognostic glucocorticoid receptor activity signature, and treating subjects with ovarian cancer according to their prognostic glucocorticoid receptor activity signature. Kits used in practicing the methods disclosed herein are also provided in the present disclosure.

[0048] In aspects, the present disclosure is directed to a method of predicting survival, and / or disease progression and / or treatment responsiveness in a subject having or suspected of having an ovarian cancer. In aspects, the methods comprise determining a prognostic glucocorticoid receptor transcriptomic signature in a tumor sample isolated from the subject and predicting survival and / or disease progression and / or treatment responsiveness in the subject based on the prognostic glucocorticoid receptor transcriptomic signature. In any of these aspects, the treatment may comprise a glucocorticoid receptor modulator. In some aspects, the treatment comprises a selective glucocorticoid receptor modulator (SGRM). Accordingly, methods are also provided for identifying a subject for treatment with a selective glucocorticoid receptor modulator (SGRM), the methods comprising determining a prognostic glucocorticoid receptor transcriptomic signature in a tumor sample isolated from the subject and identifying the subject for treatment with a selective glucocorticoid receptor modulator (SGRM) based on the prognostic glucocorticoid receptor transcriptomic signature.I. Terminology

[0049] The phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting. For example, the use of a singular term, such as, “a” is not intended as limiting of the number of items. Also, the use of relational terms such as, but not limited to, “top,” “bottom,” “left,” “right,” “upper,” “lower,” “down,” “up,” and “side,” are used in the description for clarity in specific reference to the figures and are not intended to limit the scope of the present inventive concept or the appended claims.

[0050] Further, as the present inventive concept is susceptible to aspects of many different forms, it is intended that the present disclosure be considered as an example of the principles of the present inventive concept and not intended to limit the present inventive concept to the specific aspects shown and described. Any one of the features of the present inventive concept may be used separately or in combination with any other feature. References to the terms “aspect,” “aspects,” and / or the like in the description mean that the feature and / or features being referred to are included in, at least, one aspect of the description. Separate references to the terms “aspect,” “aspects,” and / or the like in the description do not necessarily refer to the same aspect and are also not mutually exclusive unless so stated and / or except as will be readily apparent to those skilled in the art from the description. For example, a- 9 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614feature, structure, process, step, action, or the like described in one aspect may also be included in other aspects, but is not necessarily included. Thus, the present inventive concept may include a variety of combinations and / or integrations of the aspects described herein. Additionally, all aspects of the present disclosure, as described herein, are not essential for its practice. Likewise, other systems, methods, features, and advantages of the present inventive concept will be, or become, apparent to one with skill in the art upon examination of the figures and the description. It is intended that all such additional systems, methods, features, and advantages be included within this description, be within the scope of the present inventive concept, and be encompassed by the claim.

[0051] As used herein, the term “about,” can mean relative to the recited value, e.g., amount, dose, temperature, time, percentage, etc., ±10%, ±9%, ±8%, ±7%, ±6%, ±5%, ±4%, ±3%, ±2%, or ±1%.

[0052] The terms “comprising,” “including,” “encompassing” and “having” are used interchangeably in this disclosure. The terms “comprising,” “including,” “encompassing” and “having” mean to include, but not necessarily be limited to the things so described.

[0053] The terms “or” and “and / or,” as used herein, are to be interpreted as inclusive or meaning any one or any combination. Therefore, “A, B or C” or “A, B and / or C” mean any of the following: “A,” “B” or “C”; “A and B”; “A and C”; “B and C”; “A, B and C.” An exception to this definition will occur only when a combination of elements, functions, steps or acts are in some way inherently mutually exclusive.

[0054] “Biomarker” as used herein refers to any biological molecules (e.g., nucleic acids, genes, peptides, proteins, lipids, hormones, metabolites, and the like) that, singularly or collectively, reflect the current or predict future state of a biological system. Thus, as used herein, the presence or concentration of one or more biomarkers can be detected and correlated with a known condition, such as a disease state. In some aspects, detecting the presence and / or concentration of one or more biomarkers herein may be an indication of an ovarian cancer risk in a subject. In some other aspects, detecting the presence and / or concentration of one or more biomarkers herein may be used in treating and / or preventing an ovarian cancer in a subject.

[0055] As used herein, the terms “treat”, “treating”, “treatment” and the like, unless otherwise indicated, can refer to reversing, alleviating, inhibiting the process of, or preventing the disease, disorder or condition to which such term applies, or one or more symptoms of such disease, disorder or condition and includes the administration of any of the compositions, pharmaceutical compositions, or dosage forms described herein, to prevent the onset of the- 10 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614symptoms or the complications, or alleviating the symptoms or the complications, or eliminating the condition, or disorder.

[0056] The term “biomolecule” as used herein refers to, but is not limited to, proteins, enzymes, antibodies, DNA, siRNA, and small molecules. “Small molecules” as used herein can refer to chemicals, compounds, drugs, and the like.

[0057] The term “nucleic acid” or “polynucleotide” refers to deoxyribonucleic acids (DNA) or ribonucleic acids (RNA) and polymers thereof in either single- or double-stranded form. Unless specifically limited, the term encompasses nucleic acids containing known analogues of natural nucleotides that have similar binding properties as the reference nucleic acid and are metabolized in a manner similar to naturally occurring nucleotides. Unless otherwise indicated, a particular nucleic acid sequence also implicitly encompasses conservatively modified variants thereof (e.g., degenerate codon substitutions), alleles, orthologs, SNPs, and complementary sequences as well as the sequence explicitly indicated. Specifically, degenerate codon substitutions may be achieved by generating sequences in which the third position of one or more selected (or all) codons is substituted with mixed-base and / or deoxyinosine residues (Batzeretal., Nucleic Acid Res. 19:5081 (1991); Ohtsuka et al., J. Biol. Chem. 260:2605-2608 (1985); and Rossolini et al., Mol. Cell. Probes 8:91-98 (1994)).

[0058] The terms “peptide,” “polypeptide,” and “protein” are used interchangeably, and refer to a compound comprised of amino acid residues covalently linked by peptide bonds. A protein or peptide must contain at least two amino acids, and no limitation is placed on the maximum number of amino acids that can comprise a protein’s or peptide’s sequence. Polypeptides include any peptide or protein comprising two or more amino acids joined to each other by peptide bonds. As used herein, the term refers to both short chains, which also commonly are referred to in the art as peptides, oligopeptides and oligomers, for example, and to longer chains, which generally are referred to in the art as proteins, of which there are many types. “Polypeptides” include, for example, biologically active fragments, substantially homologous polypeptides, oligopeptides, homodimers, heterodimers, variants of polypeptides, modified polypeptides, derivatives, analogs, fusion proteins, among others. A polypeptide includes a natural peptide, a recombinant peptide, or a combination thereof.

[0059] As used herein, a suitable subject includes a mammal, a human, a livestock animal, a companion animal, a lab animal, or a zoological animal. In some aspects, a subject may be a rodent, e.g., a mouse, a rat, a guinea pig, etc. In other aspects, a subject may be a livestock animal. Non-limiting examples of suitable livestock animals may include pigs, cows, horses, goats, sheep, llamas and alpacas. In yet other aspects, a subject may be a companion animal. Non-limiting examples of companion animals may include pets such as dogs, cats, rabbits,- 11 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614and birds. In yet other aspects, a subject may be a zoological animal. As used herein, a “zoological animal” refers to an animal that may be found in a zoo. Such animals may include non-human primates, large cats, wolves, and bears. In other aspects, the animal is a laboratory animal. Non-limiting examples of a laboratory animal may include rodents, canines, felines, and non-human primates. In some aspects, the animal is a rodent. Non-limiting examples of rodents may include mice, rats, guinea pigs, etc. In preferred aspects, the subject is a human.

[0060] It should also be understood that, unless clearly indicated to the contrary, in any methods claimed herein that include more than one step or act, the order of the steps or acts of the method is not necessarily limited to the order in which the steps or acts of the method are recited.II. Methods for Determining and Analyzing a Prognostic Glucocorticoid Receptor Activity Signature

[0061] In general, methods disclosed herein include determining a prognostic glucocorticoid receptor activity signature for a subject, wherein the determined prognostic glucocorticoid receptor activity signature can be used to predict ovarian cancer progression in the subject, the prognostic outcome for a subject having or suspected of having ovarian cancer, and / or providing a suitable treatment regimen to the subject. Standard procedures for diagnosing and monitoring ovarian cancer are difficult to apply in early stages. However ovarian cancer becomes significantly harder to treat as it matures and progresses. Therefore, the present disclosure provides novel methods of tracking the risk in a subject for ovarian cancer progression by determining a prognostic glucocorticoid receptor activity signature of the subject’s cancer.

[0062] As used herein, a suitable subject includes a mammal, a human, a livestock animal, a companion animal, a lab animal, or a zoological animal. In some aspects, a subject may be a rodent, e.g., a mouse, a rat, a guinea pig, etc. In other aspects, a subject may be a livestock animal. Non-limiting examples of suitable livestock animals may include pigs, cows, horses, goats, sheep, llamas and alpacas. In yet other aspects, a subject may be a companion animal. Non-limiting examples of companion animals may include pets such as dogs, cats, rabbits, and birds. In yet other aspects, a subject may be a zoological animal. As used herein, a “zoological animal” refers to an animal that may be found in a zoo. Such animals may include non-human primates, large cats, wolves, and bears. In other aspects, the animal is a laboratory animal. Non-limiting examples of a laboratory animal may include rodents, canines, felines, and non-human primates. In some aspects, the animal is a rodent. Non-limiting- 12 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614examples of rodents may include mice, rats, guinea pigs, etc. In preferred aspects, the subject is a human.

[0063] In some aspects, a suitable subject for the methods herein may have or be suspected of having ovarian cancer. In some aspects, a suitable subject for the methods herein may have or be suspected of having a risk factor that predisposes a subject to ovarian cancer. For example, a risk factor that may predispose the subject to ovarian cancer may be age, family history of ovarian cancer, BRCA1 or BRCA2 mutation, mutations in any DNA repair pathway, Lynch Syndrome diagnosis, endometriosis, estrogen usage (e.g., hormonal birth control), obesity, diabetes, or any combination thereof. In some aspects, a suitable subject for the methods herein may have or be suspected of having one or more risk factors that may predispose a subject to ovarian cancer.

[0064] In some aspects, a suitable subject for the methods herein may present with at least one clinical symptom associated with ovarian cancer. Non-limiting examples of clinical symptoms associated with ovarian cancer may include vaginal bleeding, pain or pressure in pelvic area, abdominal, shoulder, groin, or back pain, bloating, increased satiety, incontinence or urgency in urination, or any combination thereof.

[0065] In some aspects, a prognostic glucocorticoid receptor activity signature may be determined as disclosed herein from at least one sample collected from a subject. In some aspects, at least one sample can be obtained from a subject who has not been diagnosed with ovarian cancer or condition associated with ovarian cancer. In some aspects, at least one sample can be obtained from a subject who has not been diagnosed with an ovarian cancer or condition associated with ovarian cancer but is suspected of having ovarian cancer. In some other aspects, at least one sample can be obtained from a subject who has been diagnosed with an ovarian cancer or a condition associated with ovarian cancer. In some aspects, at least one sample can be obtained from a subject who may have or be suspected of having one or more risk factors that may predispose a subject to ovarian cancer.

[0066] In some aspects, a prognostic glucocorticoid receptor activity signature may be determined by obtaining a gene expression profile from a sample collected from a subject. As used herein, the term “gene expression profile” refers to a pattern of genes expressed in a sample at the transcription level. Non-limiting examples of methods of measuring gene expression in a sample suitable for use herein include digital transcript counting, high-density expression array, DNA microarray, polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), real-time quantitative reverse transcription PCR (qRT-PCR), digital droplet PCR (ddPCR), serial analysis of gene expression (SAGE), Spotted cDNA arrays, GeneChip, spotted oligo arrays, bead arrays, RNA Seq, tiling array, northern blotting, hybridization- 13 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614microarray, in situ hybridization, or any combination thereof. In some aspects, a gene expression profile as disclosed herein can be obtained by any known or future method suitable to assess gene expression.

[0067] In some aspects, a prognostic glucocorticoid receptor activity signature may be determined by obtaining a protein expression profile from a sample collected from a subject. As used herein, the term “protein expression profile” refers to a pattern of proteins expressed in a sample collected from the subject. Non-limiting examples of methods of measuring protein expression in a sample suitable for use herein include Western blotting, enzyme-linked immunosorbent assay (ELISA), multi-analyte profiling (xMAP), mass spectrometry, HPLC, flow cytometry, fluorescence-activated cell sorting (FACS), liquid chromatography-mass spectrometry (LC / MS), immunoelectrophoresis, translation complex profile sequencing (TCP-seq), protein microarray, protein chip, capture arrays, reverse phase protein microarray (RPPA), two- dimensional gel electrophoresis or (2D-PAGE), functional protein microarrays, electrospray ionization (ESI), matrix-assisted laser desorption / ionization (MALDI), or a combination thereof. In some aspects, a protein expression profile as disclosed herein can be obtained by any known or future method suitable to assess protein expression.

[0068] In some aspects, a sample obtained from a subject for determination of prognostic glucocorticoid receptor activity signature as disclosed in the methods herein may be any biological sample that contains or is suspected of containing cancerous cells, referred to herein as a “tumor sample”. The tumor sample may be obtained from any biological location that contains or is suspected of containing cancerous cells. In various aspects, the tumor sample comprises one or more cancerous cells. Suitable tumor sample may comprise a tissue or biopsy, especially tissue or biopsy of the female reproductive tract (e.g., ovarian tissue, peritoneal tissue, fallopian tube tissue, uterine tissue, endometrial tissue, cervical tissue, or vaginal tissue), blood, pleural fluid, ascitic fluid, plasma, hair, venous tissue, cartilage, amniotic fluid, skin, sputum, bone marrow, saliva, buccal, or any combination thereof. In some aspects, a sample obtained from a subject for determination of a prognostic glucocorticoid receptor activity signature as disclosed herein may be an ovarian tumor sample (e.g., obtained from an ovarian tumor or containing one or more ovarian tumor cells).

[0069] In some aspects, the ovarian tumor may comprise an epithelial ovarian carcinoma, a germ cell tumor, a fallopian tube epithelial tumor, or a stromal tumor or any combination thereof. In various aspects, the epithelial ovarian carcinoma comprises serous epithelial ovarian carcinoma, a mucinous epithelial ovarian carcinoma, an endometroid epithelial ovarian carcinoma, clear cell epithelial ovarian carcinoma or any combination thereof. In various aspects, the germ cell tumor comprises dysgerminoma, yolk sac tumor, malignant teratoma, mixed germ cell tumor, embryonal carcinoma, choriocarcinoma, or any combination - 14 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614thereof. In various aspects the stromal tumor comprises granulosa cell tumors, Sertoli cell tumors, Sertoli-Leydig cell tumors or any combination thereof.

[0070] In various aspects, the ovarian tumor may comprise a low-grade tumor (e.g., a low-grade serous, clear-cell, mucinous and / or transitional cell (Brenner) subtype tumor). In other aspects, the ovarian tumor may comprise a high-grade tumor e.g., high-grade serous). In various aspects, the ovarian tumor may comprise a high-grade serous tumor (e.g., high-grade serous ovarian epithelial carcinoma).

[0071] In some aspects, a sample obtained from a subject for determination of a prognostic glucocorticoid receptor activity signature as disclosed herein may be an ovarian tumor sample (e.g., a biopsy). Non-limiting methods suitable for use herein to collect tumor tissue include collection by fine needle aspirate, by removal of pleural or peritoneal fluid, and by excisional biopsy. In some aspects, an ovarian tumor sample can include a biopsy from a single site in the ovaries, the peritoneum, a biopsy from at least one tissue in the female reproductive tract and / or at least one tissue in contact with the ovaries can be from about 10 mg about 50 mg (e.g., about 10 mg, 15 mg, 20 mg, 25 mg, 30 mg, 35 mg, 40 mg, 45 mg, 50 mg) of tissue per sample.

[0072] In some aspects, a sample of ovarian cancer obtained from any primary or metastatic site for determination of a prognostic glucocorticoid receptor activity signature as disclosed herein may be stored at about 25°C to about -80°C for up to about 1 day to about 2 years, about 1 week to about 1 year, or about 1 month to about 6 months. In other aspects, a sample obtained from a subject may be processed to obtain a gene expression profile as disclosed herein. In some other aspects, a sample obtained from a subject may be processed to obtain a protein expression profile as disclosed herein. Nonlimiting examples of sample preparation methods can be found in art, for example in Gallagher & Wiley, (2012). CURRENT PROTOCOLS ESSENTIAL LABORATORY TECHNIQUES. Hoboken, N.J: Wiley-Blackwell, the disclosures of which are incorporated herein.A. Prognostic Glucocorticoid Receptor Activity Signature

[0073] In some aspects, a sample obtained from a subject for determination of a prognostic glucocorticoid receptor activity signature as disclosed herein consists of a gene expression profile. As used herein, a gene expression profile comprises a pattern of genes expressed in a sample at the transcription level. Non-limiting examples of methods of measuring gene expression in a sample suitable for use herein include high-density expression array, DNA microarray, polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), real-time quantitative reverse transcription PCR (qRT-PCR), digital droplet PCR (ddPCR), serial analysis of gene expression (SAGE), Spotted cDNA arrays, GeneChip, spotted oligo arrays,- 15 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614bead arrays, RNA Seq, tiling array, northern blotting, hybridization microarray, in situ hybridization, digital transcript counting, or any combination thereof. In some aspects, a gene expression profile as disclosed herein can be obtained by any known or future method suitable to assess gene expression. In some aspects a prognostic glucocorticoid receptor activity signature as disclosed herein can be determined from a gene expression profile of a tumor sample. In some aspects, a prognostic glucocorticoid receptor activity signature as disclosed herein can be determined from a gene expression profile expressed by cancerous cells in the tumor sample wherein the gene expression profile is comprised of a panel of genes associated with cancer progression (e.g., ovarian cancer progression).

[0074] In some aspects, a sample obtained from a subject for determination of a prognostic glucocorticoid receptor activity signature as disclosed herein consists of a protein expression profile. As used herein, the term “protein expression profile” refers to a pattern of proteins expressed in a sample collected from the subject. Non-limiting examples of methods of measuring protein expression in a sample suitable for use herein include Western blotting, enzyme-linked immunosorbent assay (ELISA), multi-analyte profiling (xMAP), mass spectrometry, HPLC, flow cytometry, fluorescence-activated cell sorting (FACS), liquid chromatography-mass spectrometry (LC / MS), immunoelectrophoresis, translation complex profile sequencing (TCP-seq), protein microarray, protein chip, capture arrays, reverse phase protein microarray (RPPA), two- dimensional gel electrophoresis or (2D-PAGE), functional protein microarrays, electrospray ionization (ESI), matrix-assisted laser desorption / ionization (MALDI), or a combination thereof. In some aspects, a protein expression profile as disclosed herein can be obtained by any known or future method suitable to assess protein expression. In some aspects a prognostic glucocorticoid receptor activity signature as disclosed herein can be determined from a protein expression profile of a tumor sample. In some aspects, a prognostic glucocorticoid receptor activity signature as disclosed herein can be determined from a protein expression profile expressed by cancerous cells in the tumor sample wherein the protein expression profile is comprised of protein products of a panel of genes associated with cancer progression (e.g., ovarian cancer progression).

[0075] In some aspects, a computational biology approach may be applied to identify a panel of genes associated with ovarian cancer progression. For example, ranked prioritized genes can be tested for their association with ovarian cancer progression against a plurality of matched control gene set. One or more regression models may be applied to a set of differentially expressed genes to further select for genes that are relevant to ovarian cancer, its progression and the risk associated thereof. In any of these aspects, computational approaches exemplified herein can identify panel of genes for prognostic prediction of ovarian cancer progression. As used herein, a “panel of genes” refers to one or more genes whose- 16 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614differential expression (i.e., over-expression or under-expression) is predictive of the risk for developing a pathological condition and / or having a pathological condition. In any of these aspects, the panel of genes may be derived from a gene expression profile or a protein expression profile as described herein. That is, the panel of genes may be analyzed or determined at the transcriptional level (e.g., as a gene expression profile) or at the translation level (e.g., as a protein expression profile). Accordingly, in some aspects, computational approaches exemplified herein can identify a panel of genes for prognostic prediction of ovarian cancer progression.

[0076] In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of one or more genes selected from: ANKRD1, CCNE1, CELSR3, NENE, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11. In various aspects, a panel of genes for ovarian cancer progression may comprise a combination of one or more genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2. In other aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of one or more genes selected from CLDN14, DNAJA 1, ENTPD61 and GDF11. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of two or more genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of two or more genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of two or more genes selected from CLDN14, DNAJA1, ENTPD61 and GDF11. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of three or more genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of three or more genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of three or more genes selected from CLDN14, DNAJA1, ENTPD61 and GDF11. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of four or more genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of four or more genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise all four genes selected from CLDN14, DNAJA1, ENTPD61 and GDF11 In some aspects, a panel of genes for ovarian- 17 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614cancer progression assessment may comprise a combination of five or more genes selected from ANKRD1, CCNE1, CELSR3, NENE, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of five or more genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of six or more genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise all six genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of seven or more genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of eight or more genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of nine or more genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of all ten genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11.

[0077] In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of one or more genes wherein at least one of the genes is a high-risk-associated gene. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of one or more, two or more, three or more, four or more, five or more, or all six high-risk-associated genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of one or more genes wherein at least one of the genes is a low-risk-associated gene. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of one or more, two or more, three or more, or all four low-risk associated genes selected from: CLDN14, DNAJA1, ENTPD61 and GDF11. In some aspects, a panel of genes for ovarian cancer progression assessment may comprise a combination of one or more, two or more, three or more, four or more, five or more, or all six high-risk-associated genes ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP36L2 or any combination thereof and one or more low-risk-associated genes of CLDN14, DNAJA1, ENTPD61, GDF11, or any combination thereof.- 18 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614B. Prognostic Glucocorticoid Receptor Signature Assays

[0078] In some aspects, a prognostic glucocorticoid receptor activity signature as disclosed herein may be used to determine a Risk Score. In some aspects, a Risk Score can be determined from one or more samples collected from a subject as described herein. In some aspects, a Risk Score can be determined from the results of a prognostic glucocorticoid receptor activity assay to measure expression of the panel of genes for ovarian cancer progression assessment, described above, at a transcriptional or translational level.a) Nucleic Acid Basedat

[0079] In some aspects, a sample collected from a subject as disclosed herein can be processed and used in a prognostic glucocorticoid receptor activity assay, wherein the assay comprises subjecting a sample to any method suitable for determining the level of gene expression of any one of the genes comprising the panel of genes for ovarian cancer progression assessment as disclosed herein. In some aspects, a prognostic glucocorticoid receptor activity assay may be a method of measuring gene expression of one or more genes within a prognostic glucocorticoid receptor activity signature for ovarian cancer progression assessment. In some aspects, a prognostic glucocorticoid receptor activity assay may be a method of measuring gene expression of one or more of ANKRD1, CCNE1, CELSR3, NENE, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, GDF11, or any combination thereof within a prognostic glucocorticoid receptor activity signature for ovarian cancer progression assessment.

[0080] In some aspects, a prognostic glucocorticoid receptor activity assay may comprise a method of measuring gene expression of one or more of ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, GDF11, or any combination thereof within a panel of genes for ovarian cancer progression assessment. For example, in some aspects a prognostic glucocorticoid receptor activity assay may comprise a method of measuring gene expression of one or more of ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP36L2 or any combination thereof within a panel of genes for ovarian cancer progression assessment. In another example, a prognostic glucocorticoid receptor activity assay may comprise a method of measuring gene expression of one or more of CLDN14, DNAJA1, ENTPD61 and GDF11 or any combination thereof within a panel of genes for ovarian cancer progression assessment. In some aspects, a prognostic glucocorticoid receptor activity assay may comprise a method of measuring gene expression of (a) one or more of ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP36L2 or any combination thereof and (b) one or more of CLDN14, DNAJA1, ENTPD61 and GDF11or any combination thereof within a panel of genes for ovarian cancer progression assessment.- 19 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614

[0081] In some aspects, a prognostic glucocorticoid receptor activity assay described herein may entail subjecting a sample from a subject herein to a gene expression profiling assay of one or more genes within a panel of genes for ovarian cancer progression assessment. As a non-limiting example, a gene expression profiling assay described herein may use labeled oligonucleotide probes to simultaneously measure multiple RNA transcripts in a sample in a single experiment. Non-limiting examples of gene expression profiling assays suitable for use herein may include digital transcript counting assays like the nCounter Analysis System (NanoString). Alternatively, the gene expression profiling assay may include a DNA microarray or RNA-Seq assay. In some aspects, the gene expression profiling assay may entail subjecting a sample collected from a subject herein to an FDA-approved clinical diagnostic digital transcript counting technology, nCounter platform.

[0082] In some aspects, a prognostic glucocorticoid receptor activity assay may entail subjecting a sample from a subject herein to a multi-analyte gene expression profiling assay for gene expression of one or more genes (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10) within a gene panel of ANKRD1, CCNE1, CELSR3, NENE, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, GDF11 or any combination thereof. In some aspects, a prognostic glucocorticoid receptor activity assay may entail subjecting a sample from a subject herein to a multi-analyte gene expression profiling assay for gene expression of one or more genes (e.g., 1, 2, 3, 4, 5, or 6) within a gene panel of ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP36L2, or any combination thereof. In some aspects, a prognostic glucocorticoid receptor activity assay may entail subjecting a sample from a subject herein to a multi-analyte gene expression profiling assay for gene expression of one or more genes (e.g., 1, 2, 3, or 4) within a gene panel of CLDN14, DNAJA1, ENTPD61, GDF11 or any combination thereof. In some aspects, a prognostic glucocorticoid receptor activity assay may entail subjecting a sample from a subject herein to a multi-analyte gene expression profiling assay for gene expression of ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11.

[0083] In some aspects, a prognostic glucocorticoid receptor activity assay may entail subjecting a sample from a subject herein to a multi-analyte gene expression profiling assay for gene expression of one or more genes (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10) within a gene panel of ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, GDF11 or any combination thereof and normalizing the gene expression measurements (e.g., to gene expression in a control sample). One of skill in the art will appreciate that the method of normalizing gene expression measurements will depend upon the specifics of the multianalyte gene expression profiling assay used. In accordance with some of the aspects herein, a prognostic glucocorticoid receptor activity assay may entail subjecting a sample from a subject herein to a multi-analyte gene expression profiling assay for gene expression of one- 20 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614or more genes (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10) within a gene panel of ANKRD1, CCNE1, CELSR3, NENE, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, GDF11 or any combination thereof and normalizing the gene expression measurements to gene expression levels of the corresponding genes (e.g., ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, GDF11 or any combination thereof) in a control sample. In various aspects, normalizing the gene expression measurements comprises quantifying raw expression of each gene as a digital count of its transcript and then dividing each digital count by the geometric mean of the raw counts of the control genes.

[0084] Screening methods based on differentially expressed gene products are well known in the art. In accordance with one aspect of the present invention, the differential expression patterns of the panel of genes for ovarian cancer progression assessment can be determined by measuring the levels of RNA transcripts of these genes, or genes whose expression is modulated by these genes, in the subject’s ovarian cancer cells. As a non-limiting example, this gene expression profiling assay may use labeled oligonucleotide probes to simultaneously measure multiple RNA transcripts in a sample in a single experiment. Suitable methods for this purpose include, but are not limited to, RT-PCR, Northern Blot, in situ hybridization, Southern Blot, slot-blotting, nuclease protection assay and oligonucleotide arrays. Other suitable methods include digital transcript counting assays such as nCounter Analysis System (NanoString). Accordingly, in some aspects, a prognostic glucocorticoid receptor activity assay may entail subjecting a sample collected from a subject herein to an FDA-approved clinical diagnostic digital transcript counting technology, nCounter platform.

[0085] In certain aspects, RNA isolated from ovarian cancer cells can be amplified to cDNA or cRNA before detection and / or quantitation. The isolated RNA can be either total RNA or mRNA. The RNA amplification can be specific or non-specific. Suitable amplification methods include, but are not limited to, reverse transcriptase PCR, isothermal amplification, ligase chain reaction, and Qbeta replicase. The amplified nucleic acid products can be detected and / or quantitated through hybridization to labeled probes. In some aspects, detection may involve fluorescence resonance energy transfer (FRET) or some other kind of quantum dots.

[0086] Amplification primers or hybridization probes for a panel of genes for ovarian cancer progression assessment can be prepared from the gene sequence or obtained through commercial sources, such as Affymatrix. In certain aspects the gene sequence is identical or complementary to at least 8 contiguous nucleotides of the coding sequence.

[0087] Sequences suitable for making probes / primers for the detection of their corresponding genes for ovarian cancer progression assessment include those that are identical or- 21 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614complementary to all or part of genes described herein. These sequences are all nucleic acid sequences of ovarian cancer biomarkers.

[0088] The use of a probe or primer of between 13 and 100 nucleotides, preferably between 17 and 100 nucleotides in length, or in some aspects of the invention up to 1-2 kilobases or more in length, allows the formation of a duplex molecule that is both stable and selective. Molecules having complementary sequences over contiguous stretches greater than 20 bases in length are generally preferred, to increase stability and / or selectivity of the hybrid molecules obtained. One will generally prefer to design nucleic acid molecules for hybridization having one or more complementary sequences of 20 to 30 nucleotides, or even longer where desired. Such fragments may be readily prepared, for example, by directly synthesizing the fragment by chemical means or by introducing selected sequences into recombinant vectors for recombinant production.

[0089] In one aspect, each probe / primer comprises at least 15 nucleotides. For instance, each probe can comprise at least or at most 20, 25, 50, 75, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 400 or more nucleotides (or any range derivable therein). They may have these lengths and have a sequence that is identical or complementary to a gene described herein. Preferably, each probe / primer has relatively high sequence complexity and does not have any ambiguous residue (undetermined “n” residues). The probes / primers preferably can hybridize to the target gene, including its RNA transcripts, under stringent or highly stringent conditions. In some aspects, because each of the biomarkers has more than one human sequence, it is contemplated that probes and primers may be designed for use with each on of these sequences. For example, inosine is a nucleotide frequently used in probes or primers to hybridize to more than one sequence. It is contemplated that probes or primers may have inosine or other design implementations that accommodate recognition of more than one human sequence for a particular biomarker.

[0090] For applications requiring high selectivity, one will typically desire to employ relatively high stringency conditions to form the hybrids. For example, relatively low salt and / or high temperature conditions, such as provided by about 0.02 M to about 0.10 M NaCI at temperatures of about 50°C to about 70°C. Such high stringency conditions tolerate little, if any, mismatch between the probe or primers and the template or target strand and would be particularly suitable for isolating specific genes or for detecting specific mRNA transcripts. It is generally appreciated that conditions can be rendered more stringent by the addition of increasing amounts of formamide.

[0091] In another aspect, the probes / primers for a gene are selected from regions which significantly diverge from the sequences of other genes. Such regions can be determined by- 22 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614checking the probe / primer sequences against a human genome sequence database, such as the Entrez database at the NCBI. One algorithm suitable for this purpose is the BLAST algorithm. This algorithm involves first identifying high scoring sequence pairs (HSPs) by identifying short words of length W in the query sequence, which either match or satisfy some positive-valued threshold score T when aligned with a word of the same length in a database sequence. T is referred to as the neighborhood word score threshold. These initial neighborhood word hits act as seeds for initiating searches to find longer HSPs containing them. The word hits are then extended in both directions along each sequence to increase the cumulative alignment score. Cumulative scores are calculated using, for nucleotide sequences, the parameters M (reward score for a pair of matching residues; always >0) and N (penalty score for mismatching residues; always <0). The BLAST algorithm parameters W, T, and X determine the sensitivity and speed of the alignment. These parameters can be adjusted for different purposes, as appreciated by one of ordinary skill in the art.

[0092] In one aspect, quantitative RT-PCR (such as TaqMan, ABI) is used for detecting and comparing the levels of RNA transcripts in ovarian cancer samples. Quantitative RT-PCR involves reverse transcription (RT) of RNA to cDNA followed by relative quantitative PCR (RT-PCR). The concentration of the target DNA in the linear portion of the PCR process is proportional to the starting concentration of the target before the PCR was begun. By determining the concentration of the PCR products of the target DNA in PCR reactions that have completed the same number of cycles and are in their linear ranges, it is possible to determine the relative concentrations of the specific target sequence in the original DNA mixture. If the DNA mixtures are cDNAs synthesized from RNAs isolated from different tissues or cells, the relative abundances of the specific mRNA from which the target sequence was derived may be determined for the respective tissues or cells. This direct proportionality between the concentration of the PCR products and the relative mRNA abundances is true in the linear range portion of the PCR reaction. The final concentration of the target DNA in the plateau portion of the curve is determined by the availability of reagents in the reaction mix and is independent of the original concentration of target DNA. Therefore, the sampling and quantifying of the amplified PCR products preferably are carried out when the PCR reactions are in the linear portion of their curves. In addition, relative concentrations of the amplifiable cDNAs preferably are normalized to some independent standard, which may be based on either internally existing RNA species or externally introduced RNA species. The abundance of a particular mRNA species may also be determined relative to the average abundance of all mRNA species in the sample.

[0093] In one aspect, the PCR amplification utilizes one or more internal PCR standards. The internal standard may be an abundant housekeeping gene in the cell, or it can specifically be- 23 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614GAPDH, GLISB and p-2 microglobulin. These standards may be used to normalize expression levels so that the expression levels of different gene products can be compared directly. A person of ordinary skill in the art would know how to use an internal standard to normalize expression levels.

[0094] A problem inherent in clinical samples is that they are of variable quantity and / or quality. This problem can be overcome if the RT-PCR is performed as a relative quantitative RT-PCR with an internal standard in which the internal standard is an amplifiable cDNA fragment that is similar or larger than the target cDNA fragment and in which the abundance of the mRNA encoding the internal standard is roughly 5-100 fold higher than the mRNA encoding the target. This assay measures relative abundance, not absolute abundance of the respective mRNA species.

[0095] In another aspect, the relative quantitative RT-PCR uses an external standard protocol. Under this protocol, the PCR products are sampled in the linear portion of their amplification curves. The number of PCR cycles that are optimal for sampling can be empirically determined for each target cDNA fragment. In addition, the reverse transcriptase products of each RNA population isolated from the various samples can be normalized for equal concentrations of amplifiable cDNAs.

[0096] Nucleic acid arrays can also be used to detect and compare the differential expression patterns of a panel of genes for ovarian cancer progression assessment in ovarian cancer cells. The probes suitable for detecting the corresponding ovarian cancer biomarkers can be stably attached to known discrete regions on a solid substrate. As used herein, a probe is “stably attached” to a discrete region if the probe maintains its position relative to the discrete region during the hybridization and the subsequent washes. Construction of nucleic acid arrays is well known in the art. Suitable substrates for making polynucleotide arrays include, but are not limited to, membranes, films, plastics and quartz wafers.

[0097] A nucleic acid array of the present invention can comprise at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 150, 200, 250 or more different polynucleotide probes, which may hybridize to different and / or the same biomarkers. Multiple probes for the same gene can be used on a single nucleic acid array. Probes for other disease genes can also be included in the nucleic acid array. The probe density on the array can be in any range. In some aspects, the density may be 50, 100, 200, 300, 400, 500 or more probes / cm2.

[0098] Specifically contemplated by the present inventors are chip-based nucleic acid technologies such as those described by Hacia et al. (1996) and Shoemaker et al. (1996). Briefly, these techniques involve quantitative methods for analyzing large numbers of genes- 24 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614rapidly and accurately. By tagging genes with oligonucleotides or using fixed probe arrays, one can employ chip technology to segregate target molecules as high density arrays and screen these molecules on the basis of hybridization (see also, Pease et al., 1994; and Fodor etal, 1991). It is contemplated that this technology may be used in conjunction with evaluating the expression level of one or more genes from a panel of genes for ovarian cancer progression assessment with respect to diagnostic, prognostic, and treatment methods of the invention.

[0099] The present invention may involve the use of arrays or data generated from an array. Data may be readily available. Moreover, an array may be prepared in order to generate data that may then be used in correlation studies.

[0100] An array generally refers to ordered macroarrays or microarrays of nucleic acid molecules (probes) that are fully or nearly complementary or identical to a plurality of mRNA molecules or cDNA molecules and that are positioned on a support material in a spatially separated organization. Macroarrays are typically sheets of nitrocellulose or nylon upon which probes have been spotted. Microarrays position the nucleic acid probes more densely such that up to 10,000 nucleic acid molecules can be fit into a region typically 1 to 4 square centimeters. Microarrays can be fabricated by spotting nucleic acid molecules, e.g., genes, oligonucleotides, etc., onto substrates or fabricating oligonucleotide sequences in situ on a substrate. Spotted or fabricated nucleic acid molecules can be applied in a high density matrix pattern of up to about 30 non-identical nucleic acid molecules per square centimeter or higher, e.g. up to about 100 or even 1000 per square centimeter. Microarrays typically use coated glass as the solid support, in contrast to the nitrocellulose-based material of filter arrays. By having an ordered array of complementing nucleic acid samples, the position of each sample can be tracked and linked to the original sample. A variety of different array devices in which a plurality of distinct nucleic acid probes are stably associated with the surface of a solid support are known to those of skill in the art. Useful substrates for arrays include nylon, glass and silicon. Such arrays may vary in a number of different ways, including average probe length, sequence or types of probes, nature of bond between the probe and the array surface, e.g. covalent or non-covalent, and the like. The labeling and screening methods of the present invention and the arrays are not limited in its utility with respect to any parameter except that the probes detect expression levels; consequently, methods and compositions may be used with a variety of different types of genes.

[0101] Representative methods and apparatus for preparing a microarray have been described, for example, in U.S. Patent Nos. 5,143,854; 5,202,231; 5,242,974; 5,288,644; 5,324,633; 5,384,261; 5,405,783; 5,412,087; 5,424,186; 5,429,807; 5,432,049; 5,436,327; 5,445,934; 5,468,613; 5,470,710; 5,472,672; 5,492,806; 5,525,464; 5,503,980; 5,510,270;- 25 - 302397143Attorney Docket No. UTSDP4477WO- 10013856145,525,464; 5,527,681; 5,529,756; 5,532,128; 5,545,531; 5,547,839; 5,554,501; 5,556,752; 5,561,071; 5,571,639; 5,580,726; 5,580,732; 5,593,839; 5,599,695; 5,599,672; 5,610;287; 5,624,711; 5,631,134; 5,639,603; 5,654,413; 5,658,734; 5,661,028; 5,665,547; 5,667,972; 5,695,940; 5,700,637; 5,744,305; 5,800,992; 5,807,522; 5,830,645; 5,837,196; 5,871,928; 5,847,219; 5,876,932; 5,919,626; 6,004,755; 6,087,102; 6,368,799; 6,383,749; 6,617,112; 6,638,717; 6,720,138, as well as WO 93 / 17126; WO 95 / 11995; WO 95 / 21265; WO 95 / 21944; WO 95 / 35505; WO 96 / 31622; WO 97 / 10365; WO 97 / 27317; WO 99 / 35505; WO 09923256; WO 09936760; W00138580; WO 0168255; WO 03020898; WO 03040410; WO 03053586; WO 03087297; WO 03091426; W003100012; WO 04020085; WO 04027093; EP 373 203; EP 785 280; EP 799 897 and UK 8 803 000; the disclosures of which are all herein incorporated by reference.

[0102] It is contemplated that the arrays can be high density arrays, such that they contain 100 or more different probes. It is contemplated that they may contain 1000, 16,000, 65,000, 250,000 or 1,000,000 or more different probes. The probes can be directed to targets in one or more different organisms. The oligonucleotide probes range from 5 to 50, 5 to 45, 10 to 40, or 15 to 40 nucleotides in length in some aspects. In certain aspects, the oligonucleotide probes are 20 to 25 nucleotides in length.

[0103] The location and sequence of each different probe sequence in the array are generally known. Moreover, the large number of different probes can occupy a relatively small area providing a high density array having a probe density of generally greater than about 60, 100, 600, 1000, 5,000, 10,000, 40,000, 100,000, or 400,000 different oligonucleotide probes per cm2. The surface area of the array can be about or less than about 1 , 1.6, 2, 3, 4, 5, 6, 7, 8, 9, or 10 cm2.

[0104] Moreover, a person of ordinary skill in the art could readily analyze data generated using an array. Such protocols include information found in WO 9743450; WO 03023058; WO 03022421; WO 03029485; WO 03067217; WO 03066906; WO 03076928; WO 03093810; WO 03100448A1, all of which are specifically incorporated by reference.

[0105] In one aspect, nuclease protection assays are used to quantify RNAs derived from the ovarian cancer samples. There are many different versions of nuclease protection assays known to those practiced in the art. The common characteristic that these nuclease protection assays have is that they involve hybridization of an antisense nucleic acid with the RNA to be quantified. The resulting hybrid double-stranded molecule is then digested with a nuclease that digests single-stranded nucleic acids more efficiently than double-stranded molecules. The amount of antisense nucleic acid that survives digestion is a measure of the amount of the target RNA species to be quantified. An example of a nuclease protection assay that is- 26 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614commercially available is the RNase protection assay manufactured by Ambion, Inc. (Austin, Tex.)Protein or Peptide Based at Translational

[0106] In some aspects, a prognostic glucocorticoid receptor activity assay may comprise a method of measuring levels of one or more protein products encoded by one or more genes selected from ANKRD1, CCNE1, CELSR3, NENE, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11 within a panel of genes for ovarian cancer progression assessment, as described herein. For example, in some aspects a prognostic glucocorticoid receptor activity assay may comprise a method of measuring levels of one or more protein products encoded by one or more genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX, and ZFP36L2 within a panel of genes for ovarian cancer progression assessment, as described herein. In another example, a prognostic glucocorticoid receptor activity assay may comprise a method of measuring levels of one or more protein products encoded by one or more genes selected from CLDN14, DNAJA1, ENTPD61 and GDF11 within a panel of genes for ovarian cancer progression assessment, as described herein. In some aspects, a prognostic glucocorticoid receptor activity assay may comprise a method of measuring levels of one or more protein products encoded by (a) one or more ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP36L2 or any combination thereof and (b) one or more of CLDN14, DNAJA1, ENTPD61, GDF11 or any combination thereof within a panel of genes for ovarian cancer progression assessment, as described herein.

[0107] In some aspects, a sample collected from a subject as disclosed herein can be processed and used in a prognostic glucocorticoid receptor activity assay, wherein the assay comprises subjecting a sample to any method suitable for determining the level of protein expression of any one of the proteins encoded by the panel of genes for ovarian cancer progression assessment as disclosed herein. In some aspects, the assay may comprise a method of measuring protein abundance of one or more proteins encoded by the panel of genes for ovarian cancer progression assessment. Accordingly, in some aspects, a prognostic glucocorticoid receptor activity assay may comprise a method of measuring protein abundance of one or more of: ankyrin repeat domain-containing protein 1 (ANKRD1), G1 / S-specific cyclin-E1 (CCNE1), cadherin EGF l_AG seven-pass G-type receptor 3 I (CELSR3), neudesin neutrophic factor (NENF), thymocyte selection-associated high mobility group box protein (TOX), ZFP3612, claudin 14(CLDN14), DnaJ heat shock protein family (Hsp40) member A1 (DNAJA1), ectonucleoside triphosphate diphosphohydrolase 6 (ENTPD6), growth differentiation factor 11 (GDF11) or any combination thereof for ovarian cancer progression assessment. In some aspects, a prognostic glucocorticoid receptor activity assay may- 27 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614comprise a method of measuring protein abundance of one or more of: ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, or any combination thereof for ovarian cancer progression assessment. In some aspects, a prognostic glucocorticoid receptor activity assay may comprise a method of measuring protein abundance of one or more of: CLDN14, DNAJA1, ENTPD6, GDF11 or any combination thereof for ovarian cancer progression assessment. In some aspects, a prognostic glucocorticoid receptor activity assay may comprise a method of measuring protein abundance of one or more of: ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, or any combination thereof and protein abundance of one or more of CLDN14, DNAJA1, ENTPD6, GDF11 or any combination thereof for ovarian cancer progression assessment. In some aspects, a prognostic glucocorticoid receptor activity assay may be a method of measuring protein abundance of one or more protein products from a panel of genes for ovarian cancer progression assessment, wherein the protein products may be a protein panel of ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11.

[0108] In some aspects, prognostic glucocorticoid receptor activity assay described herein may entail subjecting a sample from a subject herein to a multi-analyte profiling assay for protein quantification of one or more proteins encoded by a panel of genes associated with ovarian cancer progression. A multi-analyte profiling assay (xMAP; also known as a multiplex assay) is a type of immunoassay that uses magnetic beads to simultaneously measure multiple analytes in a single experiment. A multiplex assay is a derivative of an ELISA using beads for binding the capture antibody. Non-limiting examples of multi-analyte profiling (xMAP) assays suitable for use herein may include Myriad RBM MAP LuminexxMAP, and / or bead array assays performed on either multi-use flow cytometers (such as the commonly available clinical cytometers from Becton Dickinson, Beckman-Coulter, Dako- Cytomation, or Partec). In some aspects, a prognostic glucocorticoid receptor activity assay may entail subjecting a sample collected from a subject herein to FDA-approved multiplex clinical diagnostic technology, xMAP platform (e.g., Luminex). Other methods suitable for this analysis include immunoassays such as ELISA, RIA, FACS, dot blot, Western Blot, immunohistochemistry, 2-dimensional SDS-polyacrylamide gel electrophoresis, and antibodybased radioimaging. These procedures may be used to recognize any of the polypeptides encoded by the panel of genes for ovarian cancer progression assessment, as described further below.

[0109] In some aspects, a prognostic glucocorticoid receptor activity assay may entail subjecting a sample from a subject herein to a multi-analyte profiling assay for protein quantification of one or more proteins (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, 9, or 10) within a protein panel of ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, GDF11,- 28 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614or any combination thereof. In some aspects, a prognostic glucocorticoid receptor activity assay may entail subjecting a sample from a subject herein to a multi-analyte profiling assay for protein quantification of ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11.

[0110] In some aspects, an prognostic glucocorticoid receptor activity assay may entail subjecting a sample from a subject herein to a multi-analyte profiling assay for protein quantification of one or more proteins (e.g., 1 , 2, 3, 4, 5, 6, 7, 8, 9, or 10) within a protein panel of ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, GDF11, or any combination thereof and normalizing the protein quantification measurements. One of skill in the art will appreciate that the method of normalizing the protein quantification measurements will depend upon the specifics of the multi-analyte profiling assay used. In accordance with some of the aspects herein, an prognostic glucocorticoid receptor activity assay may entail subjecting a sample from a subject herein to a multi-analyte profiling assay for protein quantification of one or more proteins (e.g., 1, 2, 3, 4, 5, 6, 7) within a protein panel of e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10) within a protein panel of ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, GDF11 , or any combination thereof, and normalizing the protein quantification measurements to median fluorescent intensity in a control tumor sample.

[0111] One example of a method suitable for detecting the levels of target proteins is ELISA. In an exemplifying ELISA, antibodies capable of binding to the target proteins encoded by one or more ovarian cancer biomarker genes are immobilized onto a selected surface exhibiting protein affinity, such as wells in a polystyrene or polyvinylchloride microtiter plate. Then, ovarian cancer cell samples to be tested are added to the wells. After binding and washing to remove non-specifically bound immunocomplexes, the bound antigen(s) can be detected. Detection can be achieved by the addition of a second antibody which is specific for the target proteins and is linked to a detectable label. Detection may also be achieved by the addition of a second antibody, followed by the addition of a third antibody that has binding affinity for the second antibody, with the third antibody being linked to a detectable label. Before being added to the microtiter plate, cells in the peripheral blood samples can be lysed using various methods known in the art. Proper extraction procedures can be used to separate the target proteins from potentially interfering substances.

[0112] In another ELISA aspect, the ovarian cancer cell samples containing the target proteins are immobilized onto the well surface and then contacted with the antibodies of the invention. After binding and washing to remove non-specifically bound immunocomplexes, the bound antigen is detected. Where the initial antibodies are linked to a detectable label, the immunocomplexes can be detected directly. The immunocomplexes can also be detected - 29 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614using a second antibody that has binding affinity for the first antibody, with the second antibody being linked to a detectable label.

[0113] Another typical ELISA involves the use of antibody competition in the detection. In this ELISA, the target proteins are immobilized on the well surface. The labeled antibodies are added to the well, allowed to bind to the target proteins, and detected by means of their labels. The amount of the target proteins in an unknown sample is then determined by mixing the sample with the labeled antibodies before or during incubation with coated wells. The presence of the target proteins in the unknown sample acts to reduce the amount of antibody available for binding to the well and thus reduces the ultimate signal.

[0114] Different ELISA formats can have certain features in common, such as coating, incubating or binding, washing to remove non-specifically bound species, and detecting the bound immunocomplexes. For instance, in coating a plate with either antigen or antibody, the wells of the plate can be incubated with a solution of the antigen or antibody, either overnight or for a specified period of hours. The wells of the plate are then washed to remove incompletely adsorbed material. Any remaining available surfaces of the wells are then “coated” with a nonspecific protein that is antigenically neutral with regard to the test samples. Examples of these nonspecific proteins include bovine serum albumin (BSA), casein and solutions of milk powder. The coating allows for blocking of nonspecific adsorption sites on the immobilizing surface and thus reduces the background caused by nonspecific binding of antisera onto the surface.

[0115] In ELISAs, a secondary or tertiary detection means can also be used. After binding of a protein or antibody to the well, coating with a non-reactive material to reduce background, and washing to remove unbound material, the immobilizing surface is contacted with the control and / or clinical or biological sample to be tested under conditions effective to allow immunocomplex (antigen / antibody) formation. These conditions may include, for example, diluting the antigens and antibodies with solutions such as BSA, bovine gamma globulin (BGG) and phosphate buffered saline (PBS)ZTween and incubating the antibodies and antigens at room temperature for about 1 to 4 hours or at 49°C overnight. Detection of the immunocomplex then requires a labeled secondary binding ligand or antibody, or a secondary binding ligand or antibody in conjunction with a labeled tertiary antibody or third binding ligand.

[0116] After all of the incubation steps in an ELISA, the contacted surface can be washed so as to remove non-complexed material. For instance, the surface may be washed with a solution such as PBS / Tween, or borate buffer. Following the formation of specific immunocomplexes between the test sample and the originally bound material, and subsequent washing, the occurrence of the amount of immunocomplexes can be determined.- 30 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614

[0117] To provide a detecting means, the second or third antibody can have an associated label to allow detection. In one aspect, the label is an enzyme that generates color development upon incubating with an appropriate chromogenic substrate. Thus, for example, one may contact and incubate the first or second immunocomplex with a urease, glucose oxidase, alkaline phosphatase or hydrogen peroxidase-conjugated antibody for a period of time and under conditions that favor the development of further immunocomplex formation (e.g., incubation for 2 hours at room temperature in a PBS-containing solution such as PBS-Tween).

[0118] After incubation with the labeled antibody, and subsequent to washing to remove unbound material, the amount of label is quantified, e.g., by incubation with a chromogenic substrate such as urea and bromocresol purple or 2,2’-azido-di-(3-ethyl)-benzhiazoline-6-sulfonic acid (ABTS) and hydrogen peroxide, in the case of peroxidase as the enzyme label. Quantitation can be achieved by measuring the degree of color generation, e.g., using a spectrophotometer.

[0119] Another suitable method is RIA (radioimmunoassay). An example of RIA is based on the competition between radiolabeled-polypeptides and unlabeled polypeptides for binding to a limited quantity of antibodies. Suitable radiolabels include, but are not limited to, I125. In one aspect, a fixed concentration of l125-labeled polypeptide is incubated with a series of dilution of an antibody specific to the polypeptide. When the unlabeled polypeptide is added to the system, the amount of the l125-polypeptide that binds to the antibody is decreased. A standard curve can therefore be constructed to represent the amount of antibody-bound l125-polypeptide as a function of the concentration of the unlabeled polypeptide. From this standard curve, the concentration of the polypeptide in unknown samples can be determined. Various protocols for conducting RIA to measure the levels of polypeptides in ovarian cancer cell samples are well known in the art.

[0120] Suitable antibodies for this invention include, but are not limited to, polyclonal antibodies, monoclonal antibodies, chimeric antibodies, humanized antibodies, single chain antibodies, Fab fragments, and fragments produced by a Fab expression library.

[0121] Antibodies can be labeled with one or more detectable moieties to allow for detection of antibody-antigen complexes. The detectable moieties can include compositions detectable by spectroscopic, enzymatic, photochemical, biochemical, bioelectronic, immunochemical, electrical, optical or chemical means. The detectable moieties include, but are not limited to, radioisotopes, chemiluminescent compounds, labeled binding proteins, heavy metal atoms, spectroscopic markers such as fluorescent markers and dyes, magnetic labels, linked- 31 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614enzymes, mass spectrometry tags, spin labels, electron transfer donors and acceptors, and the like.

[0122] Protein array technology is discussed in detail in Pandey and Mann (2000) and MacBeath and Schreiber (2000), each of which is herein specifically incorporated by reference. These arrays typically contain thousands of different proteins or antibodies spotted onto glass slides or immobilized in tiny wells and allow one to examine the biochemical activities and binding profiles of a large number of proteins at once. To examine protein interactions with such an array, a labeled protein is incubated with each of the target proteins immobilized on the slide, and then one determines which of the many proteins the labeled molecule binds. In certain aspects such technology can be used to quantitate a number of proteins in a sample, such as proteins encoded by the panel of genes for ovarian cancer progression assessment described herein.

[0123] The basic construction of protein chips has some similarities to DNA chips, such as the use of a glass or plastic surface dotted with an array of molecules. These molecules can be DNA or antibodies that are designed to capture proteins. Defined quantities of proteins are immobilized on each spot, while retaining some activity of the protein. With fluorescent markers or other methods of detection revealing the spots that have captured these proteins, protein microarrays are being used as powerful tools in high-throughput proteomics and drug discovery.

[0124] The earliest and best-known protein chip is the ProteinChip by Ciphergen Biosystems Inc. (Fremont, Calif.). The ProteinChip is based on the surface-enhanced laser desorption and ionization (SELDI) process. Known proteins are analyzed using functional assays that are on the chip. For example, chip surfaces can contain enzymes, receptor proteins, or antibodies that enable researchers to conduct protein-protein interaction studies, ligand binding studies, or immunoassays. With state-of-the-art ion optic and laser optic technologies, the ProteinChip system detects proteins ranging from small peptides of less than 1000 Da up to proteins of 300 kDa and calculates the mass based on time-of-flight (TOF).

[0125] The ProteinChip biomarker system is the first protein biochip-based system that enables biomarker pattern recognition analysis to be done. This system allows researchers to address important clinical questions by investigating the proteome from a range of crude clinical samples ( / .e., laser capture microdissected cells, biopsies, tissue, urine, and serum). The system also utilizes biomarker pattern software that automates pattern recognition-based statistical analysis methods to correlate protein expression patterns from clinical samples with disease phenotypes.- 32 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614

[0126] In other aspects, the levels of polypeptides in samples can be determined by detecting the biological activities associated with the polypeptides. If a biological function / activity of a polypeptide is known, suitable in vitro bioassays can be designed to evaluate the biological function / activity, thereby determining the amount of the polypeptide in the sample.Control

[0127] In various aspects, the prognostic glucocorticoid receptor activity signature is determined by comparing gene expression (or protein expression) in a sample (i.e., a tumor sample) relative to gene expression in a control sample. In various aspects, the control sample may comprise a control tumor sample. In various aspects, the control tumor sample is of the same tumor type as the sample being analyzed for the prognostic glucocorticoid receptor activity signature. In some aspects, the gene expression profile of the control sample {e.g., the control tumor sample) is obtained from database. Suitable databases providing gene expression data (including transcript and / or protein expression levels) for control tumor samples (especially, ovarian tumor samples) include GSE14764 obtained from Tumor Bank Ovarian Cancer Network, GSE26193 obtained from Institut Curie, GSE26712 obtained from Memorial Sloan Kettering Cancer Center, GSE30161 obtained from UVA Cancer Registry and Biorepository Tissue Research Facility, GSE32062 obtained from Niigata University Graduate School of Medical and Dental Sciences, GSE63885 obtained from Maria Skodowska-Curie Memorial Cancer Center and Institute of Oncology in Warsaw, GSE9891 obtained from the Royal Brisbane Hospital, Wesmead Hospital, and Netherlands Cancer Institute, or TCGA obtained from the TCGA Consortium.C. Risk Scores

[0128] In any of the foregoing aspects, normalized gene expression measurements produced by a prognostic glucocorticoid receptor activity assay herein may be used to generate a “Risk Score”. As used herein, the term “Risk Score” is a quantification of the relative gene expression of the panel of genes for ovarian cancer progression assessment, where an elevated Risk Score is directly proportional to the expression of one or more genes selected from ANKRD1, CCNE1, CELSR3, NENE, TOX and ZFP3612 (relative to a control sample) and inversely proportional to the expression of one or more genes selected from CLDN14, DNAJA1, ENTPD6, and GDF11 (relative to a control sample). In some non-limiting aspects, the Risk score may be generated by considering the hazard ratio (HR) of one or more genes in the panel of genes for ovarian cancer progression, as provided herein. The hazard ratio for each gene corresponds to a risk each gene has in predicting negative disease prognosis, specifically as measured by overall survival. Genes with a higher hazard ratio were found to correlate with decreased overall survival (OS). Genes with a lower hazard ratio (HR) were- 33 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614found to correlate with increased overall survival (OS). As described further in the Examples herein, each of the genes within a panel of genes for ovarian cancer progression assessment (e.g., ANKRD1, CCNE1, CELSR3, NENE, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11) were found to have an individual hazard ratio (HR) and these HRs can be used to generate a Risk Score for a given sample. In some aspects, therefore, a Risk Score may be calculated by the following Equation:Risk Score =(1 ,171*ANKRD1)+(1 ,249*CCNE1)+(1 ,229*CELSR3)+(1.132*NENF)+(1.120*TOX)+ (1.237*ZFP36L2)+(0.848*CLDN14)+(0.820*DNAJA1)+(0.835*ENTPD6)+(0.830*GDF11)

[0129] Therefore, in various aspects, the Risk Scores herein are provided as a measure of a risk of an individual for having decreased survival and / or increased disease progression (e.g., “negative disease prognosis”). In various aspects, the Risk Scores herein are provided as a measure of a risk of an individual for having increased survival and / or decreased disease progression (e.g., “positive disease prognosis”). In various aspects, the Risk Scores herein are provided as a measure of a predicted responsiveness of an individual to a cancer therapy (e.g., a therapy that modulates or targets glucocorticoid receptors). In some aspects, an individual having a Risk Score greater than or equal to 1.02 is predicted to have decreased survival and / or increased disease progression (e.g., “negative disease prognosis”) and / or be more responsive to a cancer therapy (e.g., a therapy that modulates or targets glucocorticoid receptors). In some aspects, an individual having a Risk Score less than 1.02 is predicted to have increased survival and / or decreased disease progression (e.g., “positive disease prognosis”) and / or be less responsive to a cancer therapy (e.g., a therapy that modulates or targets glucocorticoid receptors). Applications of this prognostic glucocorticoid receptor activity Signature, Assays and Risk Scores in treating subjects with ovarian cancer are described further below.III. Methods of Treatment

[0130] Various aspects of the present disclosure are also related to improved methods of treating cancer in a subject in need thereof based on the prognostic glucocorticoid receptor activity signature described herein. For example, the prognostic glucocorticoid receptor activity signature (and associated “Risk Score” described herein) allows for an improved and more accurate determination of a subject’s prognosis (e.g., overall survival). In addition, the prognostic glucocorticoid receptor activity signature (and associated “Risk Score” described herein) can also be used to predict subject responsiveness to certain cancer therapies, especially therapies that target the glucocorticoid receptor.

[0131] In general, methods disclosed herein include predicting overall survival, ovarian cancer progression, and / or treatment responsiveness in a subject based on a prognostic - 34 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614glucocorticoid receptor activity signature in a tumor sample obtained from the subject. Further methods are provided for treating a subject having or suspected of having ovarian cancer with a therapy selected based on a prognostic glucocorticoid receptor activity signature in a tumor sample obtained from the subject. Together, these methods improve overall subject outcomes in ovarian cancer.

[0132] The methods disclosed herein comprise determining a prognostic glucocorticoid receptor activity signature in a tumor sample isolated from a subject. In some aspects, the prognostic glucocorticoid receptor activity signature is determined by measuring protein abundance and / or gene expression of one or more genes in the panel of genes for ovarian cancer progression assessment as disclosed herein. In further aspects, this prognostic glucocorticoid receptor activity signature can be used to (a) make predictions of the subject’s overall survival, cancer progression and / or treatment responsiveness, and / or (b) select an effective anti-cancer therapy to administer to the subject.

[0133] As described above, the prognostic glucocorticoid receptor activity signature may be quantified as a Risk Score where an elevated Risk Score (e.g., greater than or equal to 1.02) is predictive of more aggressive cancer progression and decreased survival but higher responsiveness to certain anti-cancer therapies (e.g., glucocorticoid receptor modulators) and a lower Risk Score (e.g., less than 1.02) is predictive of less aggressive cancer progression and increased survival and lower responsiveness to certain anti-cancer therapies (e.g., glucocorticoid receptor modulators).

[0134] Further methods disclosed herein include predicting survival and / or disease progression in a subject having or suspected of having an ovarian cancer by performing a prognostic glucocorticoid receptor activity assay (e.g., a nucleic acid based assay) to measure gene expression of one or more genes in the panel of genes for ovarian cancer progression assessment as disclosed herein, further obtaining a Risk Score from the assay results, and predicting survival and / or disease progression in the subject based on the Risk Score. In some aspects, the subject is predicted to have reduced survival and / or increased disease progression if the Risk Score exceeds a threshold (e.g., greater than or equal to 1.02). In some aspects, the subject is predicted to have increased survival and / or decreased disease progression if the Risk Score is below a threshold (e.g., less than 1.02).

[0135] Further methods disclosed herein include treating a subject having or suspected of having ovarian cancer by performing a prognostic glucocorticoid receptor activity assay (e.g., a protein based assay) to measure protein abundance of one or more protein products encoded by the genes in the panel of genes for ovarian cancer progression assessment as disclosed herein, obtaining a Risk Score from the assay results, and predicting survival and / or- 35 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614disease progression in the subject based on the Risk Score. In some aspects, the subject is predicted to have reduced survival and / or increased disease progression if the Risk Score exceeds a threshold (e.g., is greater than or equal to 1.02). In some aspects, the subject is predicted to have increased survival and / or decreased disease progression if the Risk Score is below a threshold (e.g., less than 1.02).

[0136] Further methods disclosed herein include treating a subject having or suspected of having ovarian cancer by performing a prognostic glucocorticoid receptor activity assay (e.g., a nucleic acid based assay) to measure gene expression of one or more genes in the panel of genes for ovarian cancer progression assessment as disclosed herein, obtaining a Risk Score from the assay results, and administering the appropriate treatment based on the Risk Score. In some aspects, the appropriate treatment selected after determining the Risk Score as disclosed herein may depend on if the Risk Score is indicative of high responsiveness to the treatment (e.g., greater than or equal to 1.02) or lowered responsiveness to the treatment (e.g., less than 1.02).

[0137] Further methods disclosed herein include treating a subject having or suspected of having ovarian cancer by performing a prognostic glucocorticoid receptor activity assay (e.g., a protein based assay) to measure protein abundance of one or more protein products encoded by the genes in the panel of genes for ovarian cancer progression assessment as disclosed herein, further obtaining an Risk Score from the assay results, and administering the appropriate treatment based on the Risk Score. In some aspects, the appropriate treatment selected after determining the Risk Score as disclosed herein may depend on if the Risk Score is indicative of high responsiveness to the treatment (e.g., greater than or equal to 1.02) or lowered responsiveness to the treatment (e.g., less than 1.02).

[0138] A suitable tailored treatment approach for ovarian cancer progression as used herein may be selected based on the subject’s diagnosis and / or classification of the ovarian cancer or condition associated with ovarian cancer. In some aspects, a subject can be diagnosed with ovarian cancer progression based on increased or decreased protein abundance of one or more protein products encoded by a panel of genes for ovarian cancer progression assessment as disclosed herein. In some aspects, a subject can be diagnosed with ovarian cancer progression based on increased or decreased gene expression of one or more genes that make up a panel of genes for ovarian cancer progression assessment as disclosed herein. In some aspects, a subject can be predicted to have a high or low risk for ovarian cancer progression based on increased or decreased protein abundance of one or more protein products encoded by a panel of genes for ovarian cancer progression assessment as disclosed herein. In some aspects, a subject can be predicted to have a high or low risk for ovarian cancer progression based on increased or decreased gene expression of one or more - 36 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614genes that make up a panel of genes for ovarian cancer progression assessment as disclosed herein.

[0139] In some aspects, a subject can be diagnosed and / or predicted to have high or low risk for ovarian cancer progression based on methods of determining a prognostic glucocorticoid receptor activity signature and / or a Risk Score as disclosed herein and be further diagnosed with ovarian cancer via an additional method. In some aspects, an additional method of diagnosing ovarian cancer that can be used in addition to determination of prognostic glucocorticoid receptor activity signature and / or a Risk Score may include pelvic exams, imaging tests (e.g., ultrasound or CT scans), blood tests (e.g., CA-125 blood test), surgery, or genetic testing.

[0140] In some aspects, prognostic glucocorticoid receptor activity signature and / or a Risk Score may be obtained using the methods herein to determine one or more treatment options for ovarian cancer progression in a subject. In some aspects, prognostic glucocorticoid receptor activity signature and / or a Risk Score may be obtained using the methods herein to determine one or more treatment options for ovarian cancer progression in a subject in conjunction with one or more additional factors. In some aspects, treatment options for ovarian cancer progression in a subject herein may depend on a Risk score as disclosed herein and one or more of the following additional factors: age of patient, family history of ovarian cancer and treatment thereof, gene mutations in the ovarian cancer, or any combination thereof. These treatment options may also consider other risk factors such as Lynch Syndrome diagnosis, endometriosis, estrogen usage (e.g., hormonal birth control), obesity, diabetes, or any combination thereof.

[0141] In some aspects, prognostic glucocorticoid receptor activity signature and / or a Risk score may be obtained using the methods herein to determine one or more treatment options for ovarian cancer progression in a subject wherein the one or more treatments may include any standard cancer therapy that does not specifically target glucocorticoid receptors (e.g., chemotherapy, surgery, radiation, immunotherapy) or a therapy that specifically targets glucocorticoid receptors (e.g., a selective glucocorticoid receptor modulator (SGRM)). In various aspects, the subject is treated with a therapy that targets glucocorticoid receptors (e.g., selective glucocorticoid receptor modulator (SGRM)) when the prognostic glucocorticoid receptor activity signature and / or a Risk score is elevated (e.g., a Risk score greater than or equal to 1.02). In various aspects, the subject is treated with a conventional cancer therapy that does not target glucocorticoid receptors (e.g., selective glucocorticoid receptor modulator (SGRM)) when the prognostic glucocorticoid receptor activity signature and / or a Risk score is reduced (e.g., a Risk score less than 1.02).- 37 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614

[0142] Exemplary therapies are described further below.A. Cancer Therapies

[0143] As noted, the methods herein may further comprise treating a subject with one or more cancer therapies which may be selected based on the outcome of the prognostic glucocorticoid receptor signature.that Target Glucocorticoid

[0144] In some aspects, the therapy that targets glucocorticoid receptors comprises a glucocorticoid receptor modulator. In some aspects, the glucocorticoid receptor modulator is a selective glucocorticoid receptor modulator (SGRM). In some aspects, the selective glucocorticoid receptor modulator (SGRM) is a selective glucocorticoid receptor antagonist, as set forth in Clark, Curr. Top. Med. Chem. 8(9):813-838, 2008., which is hereby incorporated by reference. In other aspects, the glucocorticoid receptor modulator is a non-selective glucocorticoid receptor antagonist, such as mifepristone. In certain aspects, the glucocorticoid receptor modulator is steroidal. In other aspects, the glucocorticoid receptor modulator is nonsteroidal. A glucocorticoid receptor modulator (e.g., a glucocorticoid receptor antagonist) includes those in the following classes of chemical compounds: octahydrophenanthrenes, spirocyclic dihydropyridines, triphenylmethanes and diaryl ethers, chromenes, dibenzyl anilines, dihydroisoquinolines, pyrimidinediones, azadecalins, and aryl pyrazolo azadecalins, and which are described in more detail in Clark, 2008 supra. Some aspects of steroidal modulators (i.e., antagonists) from Clark, 2008 are: Rll-486, RU-43044, 11-monoaryl and 11,21 bisaryl steroids (including lip-substituted steroids), 10p-substituted steroids, 11 p-aryl conjugates of mifepristone, and phosphorous-containing mifepristone analogs. Further aspects of nonsteroidal modulators (i.e., antagonists) from Clark, 2008 are: octahydrophenanthrenes, spirocyclic dihydropyridines, triphenylmethanes and diaryl ethers, chromenes, dibenzyl anilines, dihyrdroquinolines, pyrimidinediones, azadecalins, aryl pyrazolo azadecalins (including 8a-benzyl isoquinolones, N-substituted derivatives, bridgehead alcohol and ethers, bridgehead amines). Additional specific examples include, but are not limited to the following specific antagonists: beclometasone, betamethasone, budesonide, ciclesonide, flunisolide, fluticasone, mifepristone, mometasone, and triamcinolone. Other examples include those described and / or depicted in U.S. Patent Application Publication 2010 / 0135956, which is hereby incorporated by reference. Even further examples include ORG-34517 (Merck), RU-43044, dexamethasone mesylate (Dex-Mes), dexamethasone oxetanone (Dex-Ox), deoxycorticosterone (DOC) (Peeters et al., Ann. NY Acad. Sci., 1148:536-41, 2008, which is hereby incorporated by reference in its entirety and Cho et al. Biochemistry, 44(9): 3547-61, 2005, which is hereby incorporated by reference- 38 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614in its entirety). In additional aspects, glucocorticoid receptor modulator (e.g., selective glucocorticoid receptor modulator (SGRM)) may be CORT 0113083 or CORT 00112716, described in Belanoff et al., Eur. J. Pharmacol., 655(1 -3): 117-20, 2011, which is hereby incorporated by reference. In additional aspects, glucocorticoid receptor modulator (e.g., selective glucocorticoid receptor modulator (SGRM)) may be CORT125134 (Relacorilant) described in Pivonello R, et al., Front Endocrinol (Lausanne). 2022 Jan 4;12:793262, which is incorporated by reference. In various aspects, the glucocorticoid receptor modulator (e.g., selective glucocorticoid receptor modulator (SGRM)) may be CORT 0113083, CORT 00112716, CORT 125134 or any combination thereof. In various aspects, the glucocorticoid receptor modulator (e.g., selective glucocorticoid receptor modulator (SGRM)) may be CORT 0113083. In various aspects, the glucocorticoid receptor modulator (e.g., selective glucocorticoid receptor modulator (SGRM)) may be CORT 00112716. In various aspects, the glucocorticoid receptor modulator (e.g., selective glucocorticoid receptor modulator (SGRM)) may be CORT 125134. In various aspects, the glucocorticoid receptor modulator (e.g., selective glucocorticoid receptor modulator (SGRM)) may be CORT 0113083 and CORT 00112716. In various aspects, the glucocorticoid receptor modulator (e.g., selective glucocorticoid receptor modulator (SGRM)) may be CORT 0113083 and CORT 125134. In various aspects, the glucocorticoid receptor modulator (e.g., selective glucocorticoid receptor modulator (SGRM)) may be CORT 00112716 and CORT 125134. In various aspects, the glucocorticoid receptor modulator (e.g., selective glucocorticoid receptor modulator (SGRM)) may be CORT 0113083, CORT 00112716, and CORT 125134. It is specifically contemplated that one or more of the modulators and / or antagonists discussed herein or in the incorporated references may be excluded in aspects of the invention. It is also contemplated that in some aspects, more than one glucocorticoid receptor modulator and / or antagonist is employed, while in other aspects, only one is employed as part of the therapeutic method (though it may be administered multiple times). It is contemplated that the second one may be administered concurrently with the first one or they may be administered at different times.Conventional Cancer

[0145] Conventional cancer therapies include one or more selected from the group of chemotherapies, immunotherapies, oncolytic viruses, polysaccharides, neoantigens, targeted therapies, hormone therapies, and surgery.(a) Immunotherapies

[0146] In some aspects, the conventional anti-cancer therapy comprises a cancer immunotherapy. Cancer immunotherapy (sometimes called immuno-oncology, abbreviated IO) is the use of the immune system to treat cancer. Immunotherapies can be categorized as- 39 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614active, passive or hybrid (active and passive). These approaches exploit the fact that cancer cells often have molecules on their surface that can be detected by the immune system, known as tumor-associated antigens (TAAs); they are often proteins or other macromolecules (e.g. carbohydrates). Active immunotherapy directs the immune system to attack tumor cells by targeting TAAs. Passive immunotherapies enhance existing anti-tumor responses and include the use of monoclonal antibodies, lymphocytes and cytokines. Immumotherapies are known in the art, and some are described below.(i) Inhibition of co-stimulatory molecules

[0147] In some aspects, the immunotherapy comprises an inhibitor of a co-stimulatory molecule. In some aspects, the inhibitor comprises an inhibitor of B7-1 (CD80), B7-2 (CD86), CD28, ICOS, 0X40 (TNFRSF4), 4-1 BB (CD137; TNFRSF9), CD40L (CD40LG), GITR (TNFRSF18), and combinations thereof. Inhibitors include inhibitory antibodies, polypeptides, compounds, and nucleic acids.(ii) Dendritic cell therapy

[0148] The immunotherapy may comprise dendritic cells. Dendritic cell therapy provokes antitumor responses by causing dendritic cells to present tumor antigens to lymphocytes, which activates them, priming them to kill other cells that present the antigen. Dendritic cells are antigen presenting cells (APCs) in the mammalian immune system. In cancer treatment they aid cancer antigen targeting. One example of cellular cancer therapy based on dendritic cells is sipuleucel-T.

[0149] One method of inducing dendritic cells to present tumor antigens is by vaccination with autologous tumor lysates or short peptides (small parts of protein that correspond to the protein antigens on cancer cells). These peptides are often given in combination with adjuvants (highly immunogenic substances) to increase the immune and anti-tumor responses. Other adjuvants include proteins or other chemicals that attract and / or activate dendritic cells, such as granulocyte macrophage colony-stimulating factor (GM-CSF).

[0150] Dendritic cells can also be activated in vivo by making tumor cells express GM-CSF. This can be achieved by either genetically engineering tumor cells to produce GM-CSF or by infecting tumor cells with an oncolytic virus that expresses GM-CSF.

[0151] Another strategy is to remove dendritic cells from the blood of a subject and activate them outside the body. The dendritic cells are activated in the presence of tumor antigens, which may be a single tumor-specific peptide / protein or a tumor cell lysate (i.e. a solution of broken down tumor cells). These cells (with optional adjuvants) are infused and provoke an immune response.- 40 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614

[0152] Dendritic cell therapies include the use of antibodies that bind to receptors on the surface of dendritic cells. Antigens can be added to the antibody and can induce the dendritic cells to mature and provide immunity to the tumor. Dendritic cell receptors such as TLR3, TLR7, TLR8 or CD40 have been used as antibody targets.(iii) CAR-T cell therapy

[0153] The immunotherapy may comprise a chimeric antigen receptor (CAR). CARs, also known as chimeric immunoreceptors, chimeric T cell receptors or artificial T cell receptors are engineered receptors that combine a new specificity with an immune cell to target cancer cells. Typically, these receptors graft the specificity of a monoclonal antibody onto a T cell. The receptors are called chimeric because they are fused of parts from different sources. CAR-T cell therapy refers to a treatment that uses such transformed cells for cancer therapy.

[0154] The basic principle of CAR-T cell design involves recombinant receptors that combine antigen-binding and T-cell activating functions. The general premise of CAR-T cells is to artificially generate T-cells targeted to markers found on cancer cells. Scientists can remove T-cells from a person, genetically alter them, and put them back into the subject for them to attack the cancer cells. Once the T cell has been engineered to become a CAR-T cell, it acts as a “living drug”. CAR-T cells create a link between an extracellular ligand recognition domain to an intracellular signaling molecule which in turn activates T cells. The extracellular ligand recognition domain is usually a single-chain variable fragment (scFv). An important aspect of the safety of CAR-T cell therapy is how to ensure that only cancerous tumor cells are targeted, and not normal cells. The specificity of CAR-T cells is determined by the choice of molecule that is targeted.(iv) Cytokine therapy

[0155] The immunotherapy may comprise cytokines. Cytokines are proteins produced by many types of cells present within a tumor. They can modulate immune responses. The tumor often employs them to allow it to grow and reduce the immune response. These immune-modulating effects allow them to be used as drugs to provoke an immune response. Two commonly used cytokines are interferons and interleukins.

[0156] Interferons are produced by the immune system. They are usually involved in anti-viral response, but also have use for cancer. They fall in three groups: type I (IFNa and I FN p) , type II (IFNy) and type III (IFNA).

[0157] Interleukins have an array of immune system effects. IL-2 is an exemplary interleukin cytokine therapy.- 41 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614(v) Adoptive T-cell therapy

[0158] The immunotherapy may comprise adoptive T cell therapy. Adoptive T cell therapy is a form of passive immunization by the transfusion of T-cells (adoptive cell transfer). They are found in blood and tissue and usually activate when they find foreign pathogens. Specifically, they activate when the T-cell’s surface receptors encounter cells that display parts of foreign proteins on their surface antigens. These can be either infected cells, or antigen presenting cells (APCs). They are found in normal tissue and in tumor tissue, where they are known as tumor infiltrating lymphocytes (TILs). They are activated by the presence of APCs such as dendritic cells that present tumor antigens. Although these cells can attack the tumor, the environment within the tumor is highly immunosuppressive, preventing immune-mediated tumor death.

[0159] Multiple ways of producing and obtaining tumor targeted T-cells have been developed. T-cells specific to a tumor antigen can be removed from a tumor sample (TILs) or filtered from blood. Subsequent activation and culturing is performed ex vivo, with the results reinfused. Activation can take place through gene therapy, or by exposing the T cells to tumor antigens.(vi) Checkpoint Inhibitors and Combination Treatment

[0160] In some aspects, the immunotherapy comprises immune checkpoint inhibitors. Certain aspects are further described below.

[0161] PD-1 can act in the tumor microenvironment where T cells encounter an infection or tumor. Activated T cells upregulate PD-1 and continue to express it in the peripheral tissues. Cytokines such as IFN-gamma induce the expression of PDL1 on epithelial cells and tumor cells. PDL2 is expressed on macrophages and dendritic cells. The main role of PD-1 is to limit the activity of effector T cells in the periphery and prevent excessive damage to the tissues during an immune response. Additional anti-cancer therapies of the disclosure may block one or more functions of PD-1 and / or PDL1 activity.

[0162] Alternative names for “PD-1” include CD279 and SLEB2. Alternative names for “PDL1” include B7-H1, B7-4, CD274, and B7-H. Alternative names for “PDL2” include B7-DC, Btdc, and CD273. In some aspects, PD-1, PDL1, and PDL2 are human PD-1, PDL1 and PDL2.

[0163] In some aspects, the PD-1 inhibitor is a molecule that inhibits the binding of PD-1 to its ligand binding partners. In a specific aspect, the PD-1 ligand binding partners are PDL1 and / or PDL2. In another aspect, a PDL1 inhibitor is a molecule that inhibits the binding of PDL1 to its binding partners. In a specific aspect, PDL1 binding partners are PD-1 and / or B7-1. In another aspect, the PDL2 inhibitor is a molecule that inhibits the binding of PDL2 to its binding partners. In a specific aspect, a PDL2 binding partner is PD-1. The inhibitor may be- 42 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614an antibody, an antigen binding fragment thereof, an immunoadhesin, a fusion protein, or oligopeptide. Exemplary antibodies are described in U.S. Patent Nos. 8,735,553, 8,354,509, and 8,008,449, all incorporated herein by reference. Other PD-1 inhibitors for use in the methods and compositions provided herein are known in the art such as described in U.S. Patent Application Nos. US2014 / 0294898, US2014 / 022021, and US2011 / 0008369, all incorporated herein by reference.

[0164] In some aspects, the PD-1 inhibitor is an anti-PD-1 antibody (e.g., a human antibody, a humanized antibody, or a chimeric antibody). In some aspects, the anti-PD-1 antibody is selected from the group consisting of nivolumab, pembrolizumab, and pidilizumab. In some aspects, the PD-1 inhibitor is an immunoadhesin (e.g., an immunoadhesin comprising an extracellular or PD-1 binding portion of PDL1 or PDL2 fused to a constant region (e.g., an Fc region of an immunoglobulin sequence)). In some aspects, the PDL1 inhibitor comprises AMP-224. Nivolumab, also known as MDX-1106-04, MDX-1106, ONO-4538, BMS-936558, and OPDIVO®, is an anti-PD-1 antibody described in W02006 / 121168. Pembrolizumab, also known as MK-3475, Merck 3475, lambrolizumab, KEYTRUDA®, and SCH-900475, is an anti-PD-1 antibody described in W02009 / 114335. Pidilizumab, also known as CT-011, hBAT, or hBAT-1, is an anti-PD-1 antibody described in W02009 / 101611. AMP-224, also known as B7-DCIg, is a PDL2-Fc fusion soluble receptor described in WO2010 / 027827 and WO2011 / 066342. Additional anti-cancer PD-1 inhibitors include MEDI0680, also known as AMP-514, and REGN2810.

[0165] In some aspects, the immune checkpoint inhibitor is a PDL1 inhibitor such as Durvalumab, also known as MEDI4736, atezolizumab, also known as MPDL3280A, avelumab, also known as MSB00010118C, MDX-1105, BMS-936559, or combinations thereof. In certain aspects, the immune checkpoint inhibitor is a PDL2 inhibitor such as rHlgM12B7.

[0166] In some aspects, the inhibitor comprises the heavy and light chain CDRs or VRs of nivolumab, pembrolizumab, or pidilizumab. Accordingly, in one aspect, the inhibitor comprises the CDR1, CDR2, and CDR3 domains of the VH region of nivolumab, pembrolizumab, or pidilizumab, and the CDR1, CDR2 and CDR3 domains of the VL region of nivolumab, pembrolizumab, or pidilizumab. In another aspect, the antibody competes for binding with and / or binds to the same epitope on PD-1, PDL1, or PDL2 as the above- mentioned antibodies. In another aspect, the antibody has at least about 70, 75, 80, 85, 90, 95, 97, or 99% (or any derivable range therein) variable region amino acid sequence identity with the above-mentioned antibodies.- 43 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614

[0167] Another immune checkpoint that can be targeted in the methods provided herein as a conventional anti-cancer therapy is the cytotoxic T-lymphocyte-associated protein 4 (CTLA-4), also known as CD152. The complete cDNA sequence of human CTLA-4 has the Genbank accession number L15006. CTLA-4 is found on the surface of T cells and acts as an “off” switch when bound to B7-1 (CD80) or B7-2 (CD86) on the surface of antigen-presenting cells. CTLA4 is a member of the immunoglobulin superfamily that is expressed on the surface of Helper T cells and transmits an inhibitory signal to T cells. CTLA4 is similar to the T-cell costimulatory protein, CD28, and both molecules bind to B7-1 and B7-2 on antigen-presenting cells. CTI-A-4 transmits an inhibitory signal to T cells, whereas CD28 transmits a stimulatory signal. Intracellular CTLA-4 is also found in regulatory T cells and may be important to their function. T cell activation through the T cell receptor and CD28 leads to increased expression of CTI_A-4, an inhibitory receptor for B7 molecules. Inhibitors of the disclosure may block one or more functions of CTLA-4, B7-1, and / or B7-2 activity. In some aspects, the inhibitor blocks the CTLA-4 and B7-1 interaction. In some aspects, the inhibitor blocks the CTLA-4 and B7-2 interaction.

[0168] In some aspects, the immune checkpoint inhibitor is an anti-CTLA-4 antibody (e.g., a human antibody, a humanized antibody, or a chimeric antibody), an antigen binding fragment thereof, an immunoadhesin, a fusion protein, or oligopeptide.

[0169] Anti-human-CTLA-4 antibodies (or VH and / or VL domains derived therefrom) suitable for use in the present methods can be generated using methods well known in the art. Alternatively, art recognized anti-CTLA-4 antibodies can be used. For example, the anti-CTLA-4 antibodies disclosed in: US 8,119,129, WO 01 / 14424, WO 98 / 42752; WO 00 / 37504 (CP675.206, also known as tremelimumab; formerly ticilimumab), U.S. Patent No. 6,207,156; Hurwitz et al., 1998; can be used in the methods disclosed herein. The teachings of each of the aforementioned publications are hereby incorporated by reference. Antibodies that compete with any of these art-recognized antibodies for binding to CTLA-4 also can be used. For example, a humanized CTLA-4 antibody is described in International Patent Application No. W02001 / 014424, W02000 / 037504, and U.S. Patent No. 8,017,114; all incorporated herein by reference.

[0170] A further anti-CTLA-4 antibody useful as a checkpoint inhibitor in the methods and compositions of the disclosure is ipilimumab (also known as 10D1, MDX- 010, MDX- 101, and Yervoy®) or antigen binding fragments and variants thereof (see, e.g., WOO 1 / 14424).

[0171] In some aspects, the inhibitor comprises the heavy and light chain CDRs or VRs of tremelimumab or ipilimumab. Accordingly, in one aspect, the inhibitor comprises the CDR1, CDR2, and CDR3 domains of the VH region of tremelimumab or ipilimumab, and the CDR1,- 44 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614CDR2 and CDR3 domains of the VL region of tremelimumab or ipilimumab. In another aspect, the antibody competes for binding with and / or binds to the same epitope on PD-1, B7-1, or B7-2 as the above- mentioned antibodies. In another aspect, the antibody has at least about 70, 75, 80, 85, 90, 95, 97, or 99% (or any derivable range therein) variable region amino acid sequence identity with the above-mentioned antibodies.(b) Oncolytic virus

[0172] In some aspects, the conventional anti-cancer therapy comprises an oncolytic virus. An oncolytic virus is a virus that preferentially infects and kills cancer cells. As the infected cancer cells are destroyed by oncolysis, they release new infectious virus particles or virions to help destroy the remaining tumor. Oncolytic viruses are thought not only to cause direct destruction of the tumor cells, but also to stimulate host anti-tumor immune responses for longterm immunotherapy.(c) Polysaccharides

[0173] In some aspects, the conventional anti-cancer therapy comprises polysaccharides. Certain compounds found in mushrooms, primarily polysaccharides, can up-regulate the immune system and may have anti-cancer properties. For example, beta-glucans such as lentinan have been shown in laboratory studies to stimulate macrophage, NK cells, T cells and immune system cytokines and have been investigated in clinical trials as immunologic adjuvants.(d) Neoantigens

[0174] In some aspects, the conventional anti-cancer therapy comprises neoantigen administration. Many tumors express mutations. These mutations potentially create new targetable antigens (neoantigens) for use in T cell immunotherapy. The presence of CD8+ T cells in cancer lesions, as identified using RNA sequencing data, is higher in tumors with a high mutational burden. The level of transcripts associated with cytolytic activity of natural killer cells and T cells positively correlates with mutational load in many human tumors.(e) Targeted Therapies

[0175] In some aspects, the conventional anti-cancer therapy comprises a targeted therapy. Targeted therapies are drugs or other substances that block the growth and spread of cancer by interfering with specific molecules (“molecular targets”) that are involved in the growth, progression, and / or spread of cancer. Targeted cancer therapies are sometimes called “molecularly targeted drugs,” “molecularly targeted therapies,” “precision medicines,” or similar names. Non-limiting examples of targeted therapies include hormone therapies, signal transduction inhibitors, gene expression modulators, apoptosis inducers, angiogenesis- 45 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614inhibitors, immunotherapies, toxin delivery molecules, and the like. In particular aspects, the targeted therapy may be a poly ADP ribose polymerase (PARP) inhibitor (e.g., niraparib). PARP (e.g., PARP-1 and / or PARP-2) inhibitors are well known in the art (e.g., Olaparib, ABT-888, BSI-201, BGP-15, INO-1001, PJ34, 3-aminobenzamide, 4-amino-1,8- naphthalimide, 6(5H)-phenanthridinone, benzamide, NLI1025).(f) Hormone Therapy

[0176] In some aspects, the conventional anti-cancer therapy comprises hormone therapy. In some aspects, one or more anticancer therapies may be hormonal therapy, Hormonal therapeutic treatments can comprise, for example, hormonal agonists, hormonal antagonists (e.g., flutamide, bicalutamide, tamoxifen, raloxifene, leuprolide acetate (LLIPRON), LH-RH antagonists), inhibitors of hormone biosynthesis and processing, and steroids (e.g., dexamethasone, retinoids, deltoids, betamethasone, cortisol, cortisone, prednisone, dehydrotestosterone, glucocorticoids, mineralocorticoids, estrogen, testosterone, progestins), vitamin A derivatives (e.g., all-trans retinoic acid (ATRA)); vitamin D3 analogs; antigestagens (e.g., mifepristone, onapristone), or antiandrogens (e.g., cyproterone acetate).(g) Chemotherapies

[0177] In some aspects, the conventional anti-cancer therapy comprises a chemotherapy. Suitable classes of chemotherapeutic agents include (a) Alkylating Agents, such as nitrogen mustards (e.g., mechlorethamine, cylophosphamide, ifosfamide, melphalan, chlorambucil), ethylenimines and methylmelamines (e.g., hexamethylmelamine, thiotepa), alkyl sulfonates (e.g., busulfan), nitrosoureas (e.g., carmustine, lomustine, chlorozoticin, streptozocin) and triazines (e.g., dicarbazine), (b) Antimetabolites, such as folic acid analogs (e.g., methotrexate), pyrimidine analogs (e.g., 5-fluorouracil, floxuridine, cytarabine, azauridine) and purine analogs and related materials (e.g., 6-mercaptopurine, 6-thioguanine, pentostatin), (c) Natural Products, such as vinca alkaloids (e.g., vinblastine, vincristine), epipodophylotoxins (e.g., etoposide, teniposide), antibiotics (e.g., dactinomycin, daunorubicin, doxorubicin, bleomycin, plicamycin and mitoxanthrone), enzymes (e.g., L-asparaginase), and biological response modifiers (e.g., Interferon-a), and (d) Miscellaneous Agents, such as platinum coordination complexes (e.g., cisplatin, carboplatin), substituted ureas (e.g., hydroxyurea), methylhydiazine derivatives (e.g., procarbazine), and adreocortical suppressants (e.g., taxol and mitotane). In some aspects, cisplatin is a particularly suitable chemotherapeutic agent.

[0178] Cisplatin has been widely used to treat cancers such as, for example, metastatic testicular or ovarian carcinoma, advanced bladder cancer, head or neck cancer, cervical cancer, lung cancer or other tumors. Cisplatin is not absorbed orally and must therefore be delivered via other routes such as, for example, intravenous, subcutaneous, intratumoral or- 46 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614intraperitoneal injection. Cisplatin can be used alone or in combination with other agents, with efficacious doses used in clinical applications including about 15 mg / m2 to about 20 mg / m2 for 5 days every three weeks for a total of three courses being contemplated in certain aspects. In some aspects, the amount of cisplatin delivered to the cell and / or subject in conjunction with the construct comprising an Egr-1 promoter operably linked to a polynucleotide encoding the therapeutic polypeptide is less than the amount that would be delivered when using cisplatin alone.

[0179] Other suitable chemotherapeutic agents include antimicrotubule agents, e.g., Paclitaxel (“Taxol”) and doxorubicin hydrochloride (“doxorubicin”). The combination of an Egr-1 promoter / TNFa construct delivered via an adenoviral vector and doxorubicin was determined to be effective in overcoming resistance to chemotherapy and / or TNF-a, which suggests that combination treatment with the construct and doxorubicin overcomes resistance to both doxorubicin and TNF-a.

[0180] Doxorubicin is absorbed poorly and is preferably administered intravenously. In certain aspects, appropriate intravenous doses for an adult include about 60 mg / m2 to about 75 mg / m2 at about 21-day intervals or about 25 mg / m2 to about 30 mg / m2 on each of 2 or 3 successive days repeated at about 3 week to about 4 week intervals or about 20 mg / m2 once a week. The lowest dose should be used in elderly subjects, when there is prior bone-marrow depression caused by prior chemotherapy or neoplastic marrow invasion, or when the drug is combined with other myelopoietic suppressant drugs.

[0181] Nitrogen mustards are another suitable chemotherapeutic agent useful in the methods of the disclosure. A nitrogen mustard may include, but is not limited to, mechlorethamine (HN2), cyclophosphamide and / or ifosfamide, melphalan (L-sarcolysin), and chlorambucil. Cyclophosphamide (CYTOXAN®, Mead Johnson) and NEOSTAR® (Adria) are also suitable chemotherapeutic agents. Suitable oral doses for adults include, for example, about 1 mg / kg / day to about 5 mg / kg / day, intravenous doses include, for example, initially about 40 mg / kg to about 50 mg / kg in divided doses over a period of about 2 days to about 5 days or about 10 mg / kg to about 15 mg / kg about every 7 days to about 10 days or about 3 mg / kg to about 5 mg / kg twice a week or about 1.5 mg / kg / day to about 3 mg / kg / day. Because of adverse gastrointestinal effects, the intravenous route is preferred. The drug also sometimes is administered intramuscularly, by infiltration or into body cavities.

[0182] Additional suitable chemotherapeutic agents include pyrimidine analogs, such as cytarabine (cytosine arabinoside), 5-fluorouracil (fluouracil; 5-Fll) and floxuridine (fluorodeoxyuridine; FudR). 5-Fll may be administered to a subject in a dosage of anywhere between about 7.5 to about 1000 mg / m2. Further, 5-Fll dosing schedules may be for a variety of time- 47 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614periods, for example up to six weeks, or as determined by one of ordinary skill in the art to which this disclosure pertains.

[0183] Gemcitabine diphosphate (GEMZAR®, Eli Lilly & Co., “gemcitabine”), another suitable chemotherapeutic agent, is recommended for treatment of advanced and metastatic pancreatic cancer, and will therefore be useful in the present disclosure for these cancers as well.

[0184] The amount of the chemotherapeutic agent delivered to the subject may be variable. In one suitable aspect, the chemotherapeutic agent may be administered in an amount effective to cause arrest or regression of the cancer in a host, when the chemotherapy is administered with the construct. In other aspects, the chemotherapeutic agent may be administered in an amount that is anywhere between 2 to 10,000 fold less than the chemotherapeutic effective dose of the chemotherapeutic agent. For example, the chemotherapeutic agent may be administered in an amount that is about 20 fold less, about 500 fold less or even about 5000 fold less than the chemotherapeutic effective dose of the chemotherapeutic agent. The chemotherapeutics of the disclosure can be tested in vivo for the desired therapeutic activity in combination with the construct, as well as for determination of effective dosages. For example, such compounds can be tested in suitable animal model systems prior to testing in humans, including, but not limited to, rats, mice, chicken, cows, monkeys, rabbits, etc. In vitro testing may also be used to determine suitable combinations and dosages, as described in the examples.(h) Radiotherapy

[0185] In some aspects, the conventional anti-cancer therapy comprises radiation, such as ionizing radiation. As used herein, “ionizing radiation” means radiation comprising particles or photons that have sufficient energy or can produce sufficient energy via nuclear interactions to produce ionization (gain or loss of electrons). An exemplary and preferred ionizing radiation is an x-radiation. Means for delivering x-radiation to a target tissue or cell are well known in the art.

[0186] In some aspects, the amount of ionizing radiation is greater than 20 Gy and is administered in one dose. In some aspects, the amount of ionizing radiation is 18 Gy and is administered in three doses. In some aspects, the amount of ionizing radiation is at least, at most, or exactly 2, 4, 6, 8, 10, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 18, 19, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, or 40 Gy (or any derivable range therein). In some aspects, the ionizing radiation is administered in at least, at most, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 does (or any derivable range therein). When more than one dose is administered, the does may be about 1, 4, 8, 12, or 24 hours or 1, 2, 3, 4, 5, 6, 7,- 48 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614or 8 days or 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, or 16 weeks apart, or any derivable range therein.

[0187] In some aspects, the amount of IR may be presented as a total dose of IR, which is then administered in fractionated doses. For example, in some aspects, the total dose is 50 Gy administered in 10 fractionated doses of 5 Gy each. In some aspects, the total dose is 50-90 Gy, administered in 20-60 fractionated doses of 2-3 Gy each. In some aspects, the total dose of I R is at least, at most, or about 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 125, 130, 135, 140, or 150 (or any derivable range therein). In some aspects, the total dose is administered in fractionated doses of at least, at most, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 20, 25, 30, 35, 40, 45, or 50 Gy (or any derivable range therein). In some aspects, at least, at most, or exactly 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 fractionated doses are administered (or any derivable range therein). In some aspects, at least, at most, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 (or any derivable range therein) fractionated doses are administered per day. In some aspects, at least, at most, or exactly 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 (or any derivable range therein) fractionated doses are administered per week. (i) Surgery

[0188] The conventional anti-cancer therapy may comprise surgery. Approximately 60% of persons with cancer will undergo surgery of some type, which includes preventative, diagnostic or staging, curative, and palliative surgery. Curative surgery includes resection in which all or part of cancerous tissue is physically removed, excised, and / or destroyed and may be used in conjunction with other therapies, such as the treatment of the present aspects, chemotherapy, radiotherapy, hormonal therapy, gene therapy, immunotherapy, and / or alternative therapies. Tumor resection refers to physical removal of at least part of a tumor. In addition to tumor resection, treatment by surgery includes laser surgery, cryosurgery, electrosurgery, and microscopically-controlled surgery (Mohs’ surgery).

[0189] Upon excision of part or all of cancerous cells, tissue, or tumor, a cavity may be formed in the body. Treatment may be accomplished by perfusion, direct injection, or local application- 49 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614of the area with a conventional anti-cancer therapy. Such treatment may be repeated, for example, every 1, 2, 3, 4, 5, 6, or 7 days, or every 1, 2, 3, 4, and 5 weeks or every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 months. These treatments may be of varying dosages as well.B. Combination Therapies and Dosing

[0190] As noted, the prognostic glucocorticoid receptor activity signature described herein may inform the treatment selection for a subject with ovarian cancer. That is, a subject may be selected for treatment with a cancer therapy that targets glucocorticoid receptors (like a selective glucocorticoid receptor modulator). In these aspects, a subject may be further administered another anticancer agent or compound (or a combination of such agents and / or compounds). In some aspects, the subject may not be further administered another anticancer agent or compound (or a combination of such agents and / or compounds). In various aspects, any of the following treatment regimen may be followed where the glucocorticoid receptor modulator is “A” and the anticancer agent or compound (or a combination of such agents and / or compounds) given as part of an anticancer therapy regime, is “B”: A / B / A, B / A / B, B / B / A, A / A / B, A / B / B, B / A / A, A / B / B / B, B / A / B / B, B / B / B / A, B / B / A / B, A / A / B / B, A / B / A / B, A / B / B / A, B / B / A / A B / A / B / A, B / A / A / B, A / A / A / B, B / A / A / A, A / B / A / A, A / A / B / A.

[0191] One of skill in the art will appreciate that dosing regimens can vary and require optimization for a subject to be treated based on the various factors such as that subject’s age, weight, gender, cancer stage, and the like. In some aspects, any of the methods disclosed herein can further include monitoring occurrence of one or more adverse effects in the subject having prognostic glucocorticoid receptor activity signature and / or a Risk score indicative of a high-risk for ovarian cancer progression. Adverse effects may include, but are not limited to, hematologic toxicity, hepatic toxicity, neurologic toxicity, cutaneous toxicity, gastrointestinal toxicity, or a combination thereof. When one or more adverse effects are observed, the methods disclosed herein can further include reducing or increasing the dose of one or more of the treatment regimens depending on the adverse effect or effects in the subject. For example, when a moderate to severe hepatic impairment is observed in a subject after treatment, compositions of use to treat the subject can be reduced in concentration or frequency of dosing with one or more disclosed drugs.

[0192] In certain aspects, the prognostic glucocorticoid receptor activity signature and / or a Risk score may be monitored in an individual before and after treatment. In some cases, changes in the prognostic glucocorticoid receptor activity signature and / or a Risk score may lead to continuing or discontinuing the treatment. For example, if the prognostic glucocorticoid receptor activity signature and / or a Risk score in a subject decreases after treatment, the treatment may be continued. If the prognostic glucocorticoid receptor activity signature and / or- 50 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614a Risk score in a subject increases or doesn’t change after treatment, the treatment may be discontinued.C. Therapeutic Outcomes

[0193] In some aspects, treatment of a subject after determining the prognostic glucocorticoid receptor activity signature and / or a Risk score as disclosed herein, may prevent ovarian cancer progression. In some aspects, treatment of a subject after determining the prognostic glucocorticoid receptor activity signature and / or a Risk score as disclosed herein, may ameliorate one or more symptoms associated with ovarian cancer. In still other aspects, treatment of a subject after determining the prognostic glucocorticoid receptor activity signature and / or a Risk score as disclosed herein, may reduce risk of ovarian cancer in the subject. In other aspects, treatment of a subject after determining the prognostic glucocorticoid receptor activity signature and / or a Risk score as disclosed herein, may slow ovarian cancer progression in the subject.

[0194] In some aspects, methods of treatment disclosed herein can impair ovarian cancer progression compared to ovarian cancer progression in an untreated subject with identical disease condition and predicted outcome. In some aspects, ovarian cancer progression can be stopped following treatments according to the methods disclosed herein. In other aspects, ovarian cancer progression can be impaired at least about 5% or greater to at least about 100%, at least about 10% or greater to at least about 95% or greater, at least about 20% or greater to at least about 80% or greater, at least about 40% or greater to at least about 60% or greater compared to an untreated subject with identical disease condition and predicted outcome. In other words, ovarian tumors in subject treated according to the methods disclosed herein grow at least 5% less (or more as described above) when compared to an untreated subject with identical disease condition and predicted outcome. In some aspects, ovarian cancer progression can be impaired at least about 5% or greater, at least about 10% or greater, at least about 15% or greater, at least about 20% or greater, at least about 25% or greater, at least about 30% or greater, at least about 35% or greater, at least about 40% or greater, at least about 45% or greater, at least about 50% or greater, at least about 55% or greater, at least about 60% or greater, at least about 65% or greater, at least about 70% or greater, at least about 75% or greater, at least about 80% or greater, at least about 85% or greater, at least about 90% or greater, at least about 95% or greater, at least about 100% compared to an untreated subject with identical disease condition and predicted outcome. In some aspects, ovarian cancer progression can be impaired at least about 5% or greater to at least about 10% or greater, at least about 10% or greater to at least about 15% or greater, at least about 15% or greater to at least about 20% or greater, at least about 20% or greater to- 51 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614at least about 25% or greater, at least about 25% or greater to at least about 30% or greater, at least about 30% or greater to at least about 35% or greater, at least about 35% or greater to at least about 40% or greater, at least about 40% or greater to at least about 45% or greater, at least about 45% or greater to at least about 50% or greater, at least about 50% or greater to at least about 55% or greater, at least about 55% or greater to at least about 60% or greater, at least about 60% or greater to at least about 65% or greater, at least about 65% or greater to at least about 70% or greater, at least about 70% or greater to at least about 75% or greater, at least about 75% or greater to at least about 80% or greater, at least about 80% or greater to at least about 85% or greater, at least about 85% or greater to at least about 90% or greater, at least about 90% or greater to at least about 95% or greater, at least about 95% or greater to at least about 100% compared to an untreated subject with identical disease condition and predicted outcome.

[0195] In some aspects, treatment of ovarian cancers according to the methods disclosed herein can result in a shrinking of an ovarian tumor in comparison to the starting size of the ovarian tumor. In some aspects, ovarian tumor shrinking may be at least about 5% or greater to at least about 10% or greater, at least about 10% or greater to at least about 15% or greater, at least about 15% or greater to at least about 20% or greater, at least about 20% or greater to at least about 25% or greater, at least about 25% or greater to at least about 30% or greater, at least about 30% or greater to at least about 35% or greater, at least about 35% or greater to at least about 40% or greater, at least about 40% or greater to at least about 45% or greater, at least about 45% or greater to at least about 50% or greater, at least about 50% or greater to at least about 55% or greater, at least about 55% or greater to at least about 60% or greater, at least about 60% or greater to at least about 65% or greater, at least about 65% or greater to at least about 70% or greater, at least about 70% or greater to at least about 75% or greater, at least about 75% or greater to at least about 80% or greater, at least about 80% or greater to at least about 85% or greater, at least about 85% or greater to at least about 90% or greater, at least about 90% or greater to at least about 95% or greater, at least about 95% or greater to at least about 100% (meaning that the ovarian tumor is completely gone after treatment) compared to the starting size of the ovarian tumor.

[0196] In various aspects, treatments administered according to the methods disclosed herein can improve subject life expectancy compared to the life expectancy of an untreated subject with identical disease condition and predicted outcome. As used herein, “subject life expectancy” is defined as the time at which 50 percent of subjects are alive and 50 percent have passed away. In some aspects, subject life expectancy can be indefinite following treatment according to the methods disclosed herein. In other aspects, subject life expectancy can be increased at least about 5% or greater to at least about 100%, at least about 10% or- 52 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614greater to at least about 95% or greater, at least about 20% or greater to at least about 80% or greater, at least about 40% or greater to at least about 60% or greater compared to an untreated subject with identical disease condition and predicted outcome. In some aspects, subject life expectancy can be increased at least about 5% or greater, at least about 10% or greater, at least about 15% or greater, at least about 20% or greater, at least about 25% or greater, at least about 30% or greater, at least about 35% or greater, at least about 40% or greater, at least about 45% or greater, at least about 50% or greater, at least about 55% or greater, at least about 60% or greater, at least about 65% or greater, at least about 70% or greater, at least about 75% or greater, at least about 80% or greater, at least about 85% or greater, at least about 90% or greater, at least about 95% or greater, at least about 100% compared to an untreated subject with identical disease condition and predicted outcome. In some aspects, subject life expectancy can be increased at least about 5% or greater to at least about 10% or greater, at least about 10% or greater to at least about 15% or greater, at least about 15% or greater to at least about 20% or greater, at least about 20% or greater to at least about 25% or greater, at least about 25% or greater to at least about 30% or greater, at least about 30% or greater to at least about 35% or greater, at least about 35% or greater to at least about 40% or greater, at least about 40% or greater to at least about 45% or greater, at least about 45% or greater to at least about 50% or greater, at least about 50% or greater to at least about 55% or greater, at least about 55% or greater to at least about 60% or greater, at least about 60% or greater to at least about 65% or greater, at least about 65% or greater to at least about 70% or greater, at least about 70% or greater to at least about 75% or greater, at least about 75% or greater to at least about 80% or greater, at least about 80% or greater to at least about 85% or greater, at least about 85% or greater to at least about 90% or greater, at least about 90% or greater to at least about 95% or greater, at least about 95% or greater to at least about 100% compared to an untreated subject with identical disease condition and predicted outcome.D. Ovarian Cancers

[0197] In any of the foregoing aspects, the subject may have or be suspected of having an ovarian cancer. In various aspects, the ovarian cancer can comprise an epithelial ovarian carcinoma, a germ cell tumor, or a stromal tumor or any combination thereof.

[0198] In various aspects, the ovarian cancer can comprise an epithelial ovarian carcinoma. In some aspects, the epithelial ovarian carcinoma comprises a serous epithelial ovarian carcinoma, a mucinous epithelial ovarian carcinoma, an endometroid epithelial ovarian carcinoma, clear cell epithelial ovarian carcinoma or any combination thereof.- 53 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614

[0199] In various aspects, the ovarian cancer can comprise a germ cell tumor. In some aspects, the germ cell tumor comprises dysgerminoma, yolk sac tumor, malignant teratoma, mixed germ cell tumor, embryonal carcinoma, choriocarcinoma, or any combination thereof.

[0200] In various aspects, the ovarian cancer can comprise a stromal tumor. In some aspects, the stromal tumor comprises granulosa cell tumors, Sertoli cell tumors, Sertoli-Leydig cell tumors or any combination thereof.

[0201] The ovarian cancer may be a low grade or a high-grade ovarian cancer which correspond to the two broad categories used by the WHO to categorize tumors of the female reproductive organs (i.e., Type 1 (low-grade) and Type 2 (high-grade)). The Type 1 neoplasms typically develop along a step-wise progression from pre-malignant or borderline lesions in a manner common to many other epithelial cancers. From the genetic perspective, these tumors display frequent oncogenic alterations to many cellular signaling pathways such as RAS-MAPK and PI3K-AKT but are otherwise genomically stable and P53 wild type. From a clinical perspective, these tumors typically present as large, unilateral, cystic neoplasms that grow in an indolent fashion and when confined to the ovary they have an excellent prognosis. This category includes low-grade serous, clear-cell, mucinous and transitional cell (Brenner) subtypes. In contrast, the Type 2 category is marked by a far more aggressive pattern of disease behavior. Tumors develop rapidly and usually are disseminated widely at the time of presentation, resulting in poor overall prognosis. From a genetic viewpoint, these tumors are characterized by P53 mutations and genomic instability due to defects in pathways contributing to DNA repair. The prototypical Type II neoplasm, High grade serous ovarian cancer (HGSOC), is by far the dominant subtype diagnosed clinically and accounts for 70-80% of deaths from all forms of ovarian cancer.

[0202] Therefore, in various aspects, the ovarian cancer may comprise a low-grade cancer (e.g., a low-grade serous, clear-cell, mucinous and / or transitional cell (Brenner) subtype cancer). In other aspects, the ovarian cancer may comprise a high-grade cancer {e.g., highgrade serous). In various aspects, the ovarian cancer may comprise a high-grade serous cancer (e.g., high-grade serous ovarian epithelial carcinoma).

[0203] In some aspects, the subject has high-grade serous epithelial ovarian carcinoma. IV. Kits

[0204] Certain aspects of the present disclosure also encompass kits for performing the diagnostic and prognostic methods of the disclosure. Such kits can be prepared from readily available materials and reagents. For example, such kits can comprise any one or more of the following materials: enzymes, reaction tubes, buffers, detergent, primers, probes, antibodies. In a preferred aspect, these kits allow a practitioner to obtain samples of neoplastic cells in - 54 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614blood, tears, semen, saliva, urine, tissue, serum, stool, sputum, cerebrospinal fluid and supernatant from cell lysate. In another preferred aspect these kits include the needed apparatus for performing RNA extraction, RT-PCR, and gel electrophoresis. In another preferred aspect these kits include the needed apparatus for performing protein purification and analysis. Instructions for performing the assays can also be included in the kits.

[0205] In a particular aspect, these kits may comprise a plurality of agents for assessing the differential expression of a panel of genes for ovarian cancer progression assessment as disclosed herein, wherein the kit is housed in a container. The kits may further comprise instructions for using the kit for assessing expression, means for converting the expression data into expression values and / or means for analyzing the expression values to generate prognosis. The agents in the kit for measuring gene expression may comprise a plurality of PCR probes and / or primers for qRT-PCR and / or a plurality of antibody or fragments thereof for assessing expression of protein products encoded by the genes. In another aspect, the agents in the kit for measuring gene expression may comprise an array of polynucleotides complementary to the mRNAs of the genes in the panel of genes for ovarian cancer progression assessment as disclosed herein. Possible means for converting the expression data into expression values and for analyzing the expression values to generate scores that predict survival or prognosis may be also included.

[0206] Kits may comprise a container with a label. Suitable containers include, for example, bottles, vials, and test tubes. The containers may be formed from a variety of materials such as glass or plastic. The container may hold a composition which includes a probe that is useful for prognostic or non-prognostic applications, such as described above. The label on the container may indicate that the composition is used for a specific prognostic or non-prognostic application, and may also indicate directions for either in vivo or in vitro use, such as those described above. The kit of the invention will typically comprise the container described above and one or more other containers comprising materials desirable from a commercial and user standpoint, including buffers, diluents, filters, needles, syringes, and package inserts with instructions for use.V. Computer Systems

[0207] In certain aspects, any of the methods described herein can relate to a system for performing such methods, the system comprising (a) apparatus or device for storing data expression level of at least one marker gene or activity; (b) apparatus or device for determining the expression level of at least one marker gene or activity; (c) apparatus or device for comparing the expression level of at least one first marker gene or activity with a predetermined first threshold value; (d) apparatus or device for determining the expression- 55 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614level of at least one second marker gene or activity; and (e) computing apparatus or device programmed to provide a unfavorable or poor prognosis if the data indicates an increased or decreased expression level of the at least one first marker gene or activity with the predetermined first threshold value and, alternatively, the expression level of the at least one second marker gene is above or below a predetermined second threshold level.

[0208] In various aspects, a computer readable medium is provided, the computer readable medium having software modules for performing a method comprising the acts of: (a) comparing glucocorticoid receptor activity data obtained from an ovarian tumor sample with a reference; and (b) providing an assessment of glucocorticoid receptor activity status to a physician for use in determining an appropriate therapeutic regimen for a subject, wherein glucocorticoid receptor activity data comprises one or more data points corresponding to expression of one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or all ten genes selected from ANKRD1, CCNE1, CELSR3, NENE, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11 in the tumor sample.

[0209] Also provided is a computer system, having a processor, memory, external data storage, input / output mechanisms, a display, for assessing glucocorticoid receptor activity, comprising: (a) a database; (b) logic mechanisms in the computer generating for the database a GR-responsive gene expression reference; and (c) a comparing mechanism in the computer for comparing the GR-responsive gene expression reference to expression data from an ovarian tumor sample using a comparison model to determine a GR gene expression profile of the ovarian tumor sample, wherein the GR-responsive gene expression comprises expression of one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or all ten genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11.

[0210] Also provided is an internet accessible portal for providing biological information constructed and arranged to execute a computer-implemented method for providing: (a) a comparison of gene expression data of one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or all ten GR-responsive genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11 in an ovarian tumor sample obtained from a subject with a calculated reporter index; and (b) providing an assessment of GR activity to a physician for use in determining an appropriate therapeutic regime for the subject.*********- 56 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614

[0211] Having described several aspects, it will be recognized by those skilled in the art that various modifications, alternative constructions, and equivalents may be used without departing from the spirit of the present inventive concept. Additionally, a number of well-known processes and elements have not been described in order to avoid unnecessarily obscuring the present inventive concept. Accordingly, this description should not be taken as limiting the scope of the present inventive concept.

[0212] Those skilled in the art will appreciate that the presently disclosed aspects teach by way of example and not by limitation. Therefore, the matter contained in this description or shown in the accompanying drawings should be interpreted as illustrative and not in a limiting sense. The following claims are intended to cover all generic and specific features described herein, as well as all statements of the scope of the method and assemblies, which, as a matter of language, might be said to fall there between.EXAMPLESExample 1 : Overview of the Examples

[0213] Background:

[0214] Epithelial ovarian cancer (EOC), commonly known as ovarian cancer (OvCa), is the deadliest gynecological malignancy. High tumor expression of either NR3C1 mRNA (encoding the glucocorticoid receptor or GR) orGR protein (IHC) are associated with poor overall survival (OS) in subjects (pts) with advanced stage OvCa. It was hypothesized that high GR expression and associated GR transcriptional activity favor OvCa cell survival and chemotherapy resistance, thereby reducing OS. Experiments were completed testing whether a GR transcriptome-based gene signature (GR-sig) would be a better prognosticator than tumor NR3C1 (GR) mRNA expression alone.

[0215] Methods:

[0216] mRNAs up- or downregulated >1.5 fold after glucocorticoid treatment (Dex) in one or both cultured OvCa cell lines (OVSAHO and HEYA8) were defined as differentially expressed genes (DEGs). DEGs were then identified in n=1247 unique OvCa microarrays from 15 publicly available Gene Expression Omnibus (GEO) datasets with matched pt OS (training set). Overall survival (OS) data was used to train a Cox proportional hazards model. The “best subset selection” (BeSS) algorithm and bootstrap sampling identified those GR-regulated DEGs whose tumor mRNA expression correlated most significantly with pt OS, creating a GR-sig “risk score.” Subsequently, all n=522 microarray and n=374 RNA-seq publicly available samples from the OvCa TCGA were used as two independent validation sets. Microarray and RNA-seq data were normalized using the MAS5 and FPKM-UQ algorithms respectively. The- 57 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614two validation sets were independently stratified relative to the median training set risk score into low- and high-risk score groups. To examine the prognostic ability of NR3C1 mRNA expression alone, the median training set NR3C1 expression value was used to stratify validation set pts into low- and high-expression groups. In all cases, OS was estimated by the Kaplan-Meier method.Results:

[0217] n=2288 protein-coding DEGs were identified in one or both cell lines. Of these genes, n=1251 were expressed in each GEO microarray sample (training set). A 10-gene GR-signature (GR-Sig) was then identified using the BeSS algorithm and bootstrap sampling, yielding a median training set risk score of 1.02. In both the microarray and RNA-seq validation datasets, the 10-gene GR-sig risk score was associated with significant OS differences between low- and high-risk score groups (microarray p = 0.018; RNA-seq p = 0.00049). In contrast, the OS difference between low- and high- NR3C1 mRNA expression groups did not reach significance (microarray p = 0.12; RNA-seq p = 0.28).Conclusion:

[0218] In this retrospective analysis, a 10-gene GR-sig derived from OvCa GR-regulated DEGs performed better as a prognosticator than NR3C1 mRNA expression alone. Prospective application of the GR-sig may be useful in stratifying subjects for treatment response with novel agents in OvCa clinical trials.Example 2: Introduction to Examples 3-8

[0219] Ovarian cancer (OvCa) usually presents at a late stage. Once diagnosed, initial treatment consists of surgical resection followed by adjuvant chemotherapy (platinum and / or taxane-based). Nonetheless, approximately half of all subjects die within five years of diagnosis, highlighting the need for increased individualization in treatment plans. While next generation sequencing has provided some personalization of treatment in several cancer types, targetable DNA mutations in OvCa have been largely limited to BRCA1 or BRCA2. Recently, however, gene expression (mRNA) signatures have also become routine measurements in several molecular assays and may be valuable for OvCa prognosis. Additionally, if the gene signature expression represents the activation of a particular signaling pathway, this pathway (or its upstream regulator) may represent a therapeutic target. Several gene expression signatures have been published as prognostic biomarkers of subject outcome in OvCa, though none that we know of are specific to a receptor signaling pathway.

[0220] It is known that platinum-based chemotherapy resistance is strongly associated with poor OS in subjects in the Cancer Genome Atlas (TCGA). It has been hypothesized that high- 58 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614glucocorticoid receptor (GR) activity may reduce chemotherapy-induced tumor cell death, possibly contributing to chemotherapy resistance. For example, in vitro experiments in GR-positive OvCa cell lines reveal that GR-activation inhibits tumor cell cytotoxicity in response to carboplatin (carbo) and gemcitabine (gem), which can be reversed by treatment with a GR-antagonist. Likewise, a xenograft model of intraperitoneal OvCa cells showed that when cotreated with carbo / gem and a GR-antagonist, tumor burden is significantly reduced compared to chemotherapy alone. A subsequent three-arm, phase II clinical trial (NCT03776812) in recurrent high-grade serous OvCa, found that subjects receiving intermittent GR-antagonism with nab-paclitaxel had improved progression-free survival (PFS) compared to subjects receiving nab-paclitaxel alone. Thus, it is not surprising that GR protein and gene expression were found to correlate with reduced PFS in a study of 481 OvCa samples that were collected during initial debulking surgery and prior to chemotherapy. However, no significant association with OS was observed. In a separate study, a Cox proportional hazard model was used to compare tumor NR3C1 (GR) mRNA expression with subject OS, and once again, only a borderline significant (p=0.068) association was observed with OS when GR was considered as an independent factor. When GR was considered among other variables, such as age, race, histologic subtype, grade, etc, a more significant association was observed (p=0.0251).

[0221] Acknowledging this association between NR3C1 expression and OS while also understanding the role of GR as a transcription factor, experiments, described in the following Examples, were undertaken to develop an improved GR-associated prognostic signature for OvCa OS. As described herein below, the signature focused on GR transcriptome rather than simply evaluating GR or NR3C1 expression as a single predictor. Specifically, a GR gene signature using GR-mediated gene expression was derived from synthetic glucocorticoid (dexamethasone) treatment of two OvCa cell lines, which we then applied to subject tumor gene expression. Specifically, n=158 GR-target genes that were positively correlated with OS were then subjected to repeated bootstrap sampling that ultimately resulted in a 10-gene GR signature (GR-Sig). It is further shown below that this GR-Sig is a more effective prognosticator than NR3C1 expression alone in publicly available OvCa microarray and RNA-seq samples. Because GR antagonism is currently being studied in a HGS OvCa therapeutic clinical trial, the GR-Sig may also be useful in predicting those subjects with high GR tumor activity who are most likely to benefit from GR antagonism.Example 3: Materials and Methods of ExamplesOvCa cell line culture and RNA sequencing

[0222] Two high-grade serous adenocarcinoma OvCa cell lines (HEYA8 and CAOV3) were grown in 10% charcoal-stripped serum and DM EM for 48 hours and then treated for six hours- 59 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614with dexamethasone (dex; 100nM) or vehicle (0.1% v / v ETOH). RNA samples were extracted using Qiagen kits as previously described, and sequencing libraries with single-end 50bp read length were prepared using two biological replicates. Sequencing was done on the Illumina platforms at The University of Chicago Genomics Facility.

[0223] Raw FASTQ files were processed through the Mayo Clinic’s internal MAP-RSeq pipeline (Version 3.0), a customized compilation of publicly available bioinformatics tools. The read alignment was performed using the Spliced Transcripts Alignment to a Reference (STAR) aligner against the hg38 reference genome. The gene and exon counts were generated by the FeatureCounts program, using the gene definition files from Ensembl v78. Quality control was carried out using RSeqQC and some additional metrics as described previously. Differential expression between the two treatments was analyzed in edgeR (version 3.20.1). GR-mediated differentially expressed genes (GR-DEGs) were identified using the following criteria: average expression >0.5 counts per million, absolute fold-change >1.5 (Dex vs. Vehicle), and adjusted p-value <0.05. All statistical analysis was carried out in R version 3.6.2.Selecting and annotating publicly available OvCa datasets

[0224] Jetset probes were selected for each GR-DEG and gene expression was then examined in the OvCa microarray datasets found in Gene Expression Omnibus (GEO) and TCGA. There were n=1,247 publicly available primary OvCa microarrays on GEO and n=565 available in TCGA, which we evaluated for gene expression. There were also 379 TCGA RNA-seq samples. Of these 379 RNA-seq samples, 364 came from tumors who also had microarray data, which was included in the n=565 TCGA microarrays analyzed for GR-DEGs. The microarray expression values were normalized using the MAS5 algorithm (which is the Affymetrix default normalization) and the RNA-seq was normalized using FPKM-UQ.

[0225] A list of GR-DEGs was compiled based on their expression in all OvCa GEO microarrays. Tumors treated with neoadjuvant chemotherapy, and subjects for whom OS data were not available, were excluded. Tumor histology, grade, stage, and subject OS were available for most GEO and all TCGA samples. To fill in missing clinical characteristics / subject histories for GEO samples, we referenced the primary papers published for each database and contacted study authors. The hazard ratios (HRs) pertaining to OS and corresponding 95% confidence intervals (Cis) were estimated for each gene in the final model. Additionally, the proportional hazards assumption was assessed for expression of each gene in the Cox regression model using Schoenfeld residuals and found to be valid, indicating that the HRs were consistent over time.- 60 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614Example 4: High-grade serous ovarian cancer cell line gene expression and subject dataset characteristics

[0226] The six-hour dex treatment and subsequent RNA-seq analysis of the cell lines revealed that n=2,859 genes were differentially expressed in at least one OvCa cell line. Of these genes, n=2, 288 were protein-coding. Of the protein-coding genes, n=478were downregulated relative to vehicle after six hours of dex treatment in both cell lines, n=705 were upregulated, and n=677 genes were differentially regulated between the two cell lines, likely representing differential timing of altered gene expression following GR activation. The remaining n=428 genes were up- or down-regulated in response to GR activation in only one of the two cell lines. Probing publicly available microarrays for the 2,288 protein coding genes revealed that almost all (n=2,284) GR-DEGs were present in each TCGA microarray sample. In contrast, only n=1,251 of the 2,288 GR-DEGs were present in each GEO microarray, perhaps due to the relatively low tumor cellularity permitted in the GEO samples (see FIG. 1).

[0227] Because most samples had associated subject overall survival (OS) data, OS was used rather than progression-free survival as a surrogate for clinical outcome. After excluding neoadjuvant samples, duplicate entries, and those without accompanying OS data, we evaluated n=778 GEO (Table S3) and n=522 TCGA microarrays. Additionally, n=374 TCGA subjects had RNA-seq samples available which met the criteria for the study. FIG. 1 shows clinical and pathological characteristics of GEO (GSE) and TCGA datasets.Example 5: Development and validation of an OvCa GR-Sig

[0228] After correcting for batch effects (using the ComBat method as described by Yuqing Zhang, et al., “ComBat-seq: batch effect adjustment for RNA-seq count data”, NAR Genomics and Bioinformatics, Volume 2, Issue 3, September 2020, incorporated herein by reference in its entirety, implemented in R package sva as described in Leek, J.T., et al., “The sva package for removing batch effects and other unwanted variation in high-throughput experiments.” Bioinformatics, 2012. 28(6): p. 882-3, also incorporated by reference in its entirety), a Cox proportional hazards model as described by Ludwig Fahrmeir, T.K., et al., (“Regression: Models, Methods and Applications” 2013, Berlin: Springer Science & Business Media. 1., incorporated herein by reference in its entirety) was used to examine the correlation of tumor GR-DEG expression (n=1,251 genes from dex vs veh treated cell lines) with corresponding subject OS in the GEO training set. Using this method, it was found that n=246 DEGs were significantly correlated with OS (p-value <0.05, Wald test). To stabilize the model and ensure reliability, highly intercorrelated genes were removed, leaving n=158 GR-Sig candidate genes. The z-score was then calculated as z-score = (x - / z) - s, where x is the normalized microarray expression of a gene, is the mean gene expression across training samples, and- 61 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614is the standard deviation for each gene’s expression. Using the Best Subset Algorithm (BeSS) (described in Wen, C., et al., BeSS: An R Package for Best Subset Selection in Linear, Logistic and Cox Proportional Hazards Models. Journal of Statistical Software, 2020. 94(4): p.1 - 24 and incorporated herein by reference in its entirety), the n=158 genes were then sampled to achieve a smaller gene subset that accurately represented the larger gene population. An optimal Cox model with minimized Akaike information criterion (AIC) was obtained. The genes returned by the algorithm were recorded and returned to the starting gene population for additional sampling runs. This sampling (bootstrapping) was performed 1 ,001 times, rendering 1 ,001 gene subsets, each of which represented the greater population. Genes that appeared in >900 of these gene subsets were selected to create the final ten-gene GR-Sig consisting ofANKRDI, CCNE1, CELSR3, NENE, TOX, ZFP36L2, CLDN14, DNAJA1, ENTPD6, and GDF11 (FIG. 2).

[0229] The ten-gene GR-Sig was validated by applying it to the TCGA microarray and RNA-seq datasets. First, the data were scaled by determining the z-score of the normalized gene expression values for each of the 10 genes in both these TCGA datasets. Considering the relationships between the expression of the 10 genes and OS, multivariate analysis was used to estimate the HRs for each gene. In the final model, HRs and corresponding 95% confidence intervals (Cis) were estimated for each gene by multivariate analysis (FIG. 2).

[0230] These HRs were then used to calculate a risk score for each subject in the TCGA validation datasets using the following formula:Risk Score =(1.171*ANKRD1)+(1.249*CCNE1)+(1.229*CELSR3)+(1.132*NENF)+(1.120*TOX)+(1.237*ZFP36L2)+(0.848*CLDN14)+(0.820*DNAJA 1)+(0.835*ENTPD6)+(0.830*GDF11)

[0231] In this formula, each scaled gene expression value was multiplied by its corresponding HR. These 10 values (one for each gene) were then summed to yield an absolute OS risk score for each subject. Each subject from the validation set was thereby stratified into a higher low-risk group based on whether their risk score was greater or less than the median risk score (1.02) as calculated from the GEO training set. The probability of each subject’s OS at a given time point was computed using a Kaplan-Meier estimate and then a logrank test was performed on the high- versus low-risk TCGA subjects (FIGS. 3A-3B). In both TCGA datasets, these results revealed a significantly increased survival probability for the low-risk subjects compared to high-risk subjects (log-rank test: p = 0.018 for TCGA microarray dataset; p=0.00049 for TCGA RNA-seq dataset).

[0232] The workflow for generating the GR-Sig is summarized in FIG. 4.- 62 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614Example 6: GR-Sig gene expression in the OvCa cell lines

[0233] Because GR-mediated gene expression is associated with HGS OvCa cell line survival and chemotherapy resistance, it was hypothesized that the GR-Sig genes identified above would be up-regulated if the risk score was high and down-regulated if the risk score was low. We therefore examined the HEYA8 and CAOV3 individual GR-Sig gene RNA-seq expression levels using SGK1 expression as a positive GR target gene (FIG. 5).

[0234] While all 10 genes were differentially expressed in one or both cell lines (a criterion required for the GR-Sig analysis, FIG. 4), it was found that four of the six genes associated with poor OS (FIG. 5, yellow bars: ANKRD1, CELSR3, TOX, and ZFP36L2) were significantly upregulated in one or both cell lines at the six-hour time point. However, CCNE1 and NENE were downregulated in the cell lines, despite being associated with a poor subject prognosis. Of the four genes associated with reduced risk, three were repressed (FIG. 5, blue bars: CDLN14, DNAJA1 and GDF1), while one (ENTPD6) was upregulated by GR-activation at the six-hour time point. Though it was predicted that the expression of CCNE1, NENF, and ENTPD6 would be regulated in the opposite direction, these findings may be due to measuring gene expression at a single time point, whereas the GR regulated genes associated with outcome are likely the result of consistent differential GR activity occurring over the evolution of the tumor before resection.Example 7: OS is better prognosticated by the GR-Sig than NR3C1 (GR) expression alone

[0235] It was then tested whether the GR-Sig was a better prognostic indicator than NR3C1 expression alone. In a first experiment, tumor NR3C1 expression was measured in the validation sets between the high- and low- risk subject groups and a trend of increased NR3C1 expression in the high-risk groups was observed, though this difference was insignificant (p=0.1 , p=0.083, FIG. 6A and 6B). However, in the training set there was significantly greater tumor NR3C1 expression in the high-risk group (p=0.019, FIG. 7). Some degree of increased NR3C1 expression was expected for all high-risk groups classified by the GR-Sig, given its association with GR activity. To further characterize the prognostic capabilities of NR3C1 expression, univariate analysis was used to generate an OS HR for NR3C1 in the training set, and the p-value was only borderline significant (p = 0.067, FIG. 7). Conversely, when HRs for each independent gene of the GR-Sig were generated using the same method, they were all significantly associated with OS (FIG. 7, univariate analysis). The association of the 10 genes with OS was even more significant when they were considered together as a gene signature (FIG. 7, multivariate analysis). Thus, these experiments suggested that the GR-Sig was a more effective indicator of OS than NR3C1.- 63 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614

[0236] To test this idea, Kaplan-Meier curves were generated using a Cox proportional hazards model which analyzed NR3C1 expression against subject OS within our validation sets. This allowed for the direct comparison between the prognostic capabilities of the GR-Sig and NR3C1. Using median NR3C1 expression to divide high- and low- risk subjects, no significant difference in OS was seen between the two groups (FIG. 6C and FIG. 6D; p = 0.12, p = 0.28). However, despite lack of significance, there was a trend of reduced OS for high risk (high NR3C1 tumor expression) subjects. This implicates GR as a prognostic indicator, as well as the trends of NR3C1 expression observed in the high- versus low-risk subjects in these datasets. Nonetheless, the highly significant differences between the high and low risk groups determined by the GR-Sig in the same datasets (FIGS. 3A-3B; p = 0.018, p = 0.00049). Thus, the GR-Sig was found to be far more effective at identifying high and low risk subjects than NR3C1 expression alone. Therefore, the GR-Sig provides a more accurate OS.Example 8: Discussion of Examples

[0237] The foregoing examples describe the development of a 10-gene prognostic signature that reflects the GR-transcriptome in HGS OvCa. Expression of each GR-Sig gene was found to be significantly associated with OS in two independent subject datasets and was chosen from a set of putative GR target genes based on in vitro HGS OvCa cell line exposure to dexamethasone. After the 10-gene GR-Sig was determined, HRs measuring the risk of death associated with each gene were calculated in a multivariate analysis (FIG. 2). Of the 10 GR-Sig genes, four (CLDN14, ENTPD6, DNAJA1, and GDF11) had hazard ratios <1.0, indicating their gene expression was associated with increased OS. Expression of each of the remaining six GR-Sig genes (ANKRD1, CCNE1, CELSR3, NENE, TOXand ZFP36L2) had hazard ratios >1.0, suggesting that higher tumor gene expression is associated with increased risk of death following OvCa diagnosis. Taken together, these data support the hypothesis that tumor GR transcriptomic activity is associated with subject outcome.

[0238] Each GR-Sig gene had an improved prognostic capability compared to NR3C1 (GR) expression alone (FIG. 7) using a univariate gene analysis. Greater GR expression (NR3C1) was found in tumors from subjects who were classified by the GR-Sig as high-risk versus low-risk for shortened OS (FIG. 6A, FIG. 6B and FIG. 8). This was consistent with expectations, as the GR-Sig is derived from genes representing the GR-transcriptome.

[0239] Validation datasets showed that the GR-Sig effectively divided high- and-low risk subjects based on OS probability. This was significant using both tumor microarray and RNA-seq gene expression data (FIGS. 3A-3B). When NR3C1 expression alone was used in the same datasets to accomplish the same task, it performed dramatically worse (FIG. 6), further demonstrating that our approach to measure GR-transcriptome activity using a gene signature- 64 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614is as a far more accurate prognosticator of subject risk than NR3C1 expression. The most significant p-value was seen in the GR-Sig RNA-seq data. This is not surprising given that RNA-seq data are thought to be a more accurate representation of gene expression than microarrays. RNA-sequencing of tumors is becoming increasingly available to clinicians because of commercial platforms routinely providing these data in their assays. For future studies, we plan to examine the GR-Sig prospectively. One avenue may be using gene expression values that are normalized using a platform’s reference panel to calculate a z-score and determine a subject’s risk. For example, the GR-Sig can be employed to stratify participants in a GR antagonist clinical trial to help determine which subjects will benefit most from GR antagonist therapy.

[0240] Limitations of this retrospective study include the use of only two HGS OvCa cell lines to generate the GR-associated gene expression data (GR transcriptome), and the use of only one time point following dex treatment of cells. Thus, genes regulated earlier or later than six hours would have been excluded from the GR-Sig. Despite these weaknesses, this study suggests that the GR-Sig identified here may help identify OvCa subjects with a poor prognosis and those who may particularly benefit from added treatment with GR-antagonism or a related GR pathway-targeted therapy.

[0241] In all, these findings demonstrate that a GR transcriptome-derived 10-gene signature outperforms the prognostic capabilities of NR3C1 (GR) expression alone. Given its association with subject OS and GR pathway activity, this GR-Sig may be able to better predict which subjects benefit from the addition of a GR-pathway antagonist to their treatment.- 65 - 302397143

Claims

Attorney Docket No. UTSDP4477WO- 1001385614CLAIMSWhat is claimed is:

1. A method of predicting survival, and / or disease progression, and / or responsiveness to a treatment in a subject having or suspected of having a cancer, the method comprising determining a prognostic glucocorticoid receptor activity signature in a tumor sample isolated from the subject and predicting survival, and / or disease progression, and / or responsiveness to a treatment in the subject based on the prognostic glucocorticoid receptor activity signature.

2. The method of claim 1 , wherein the method more accurately predicts survival, disease progression and / or responsiveness to the treatment in a subject than a corresponding prognostic signature based on glucocorticoid receptor levels and not activity output.

3. The method of claim 1 or 2, wherein the prognostic glucocorticoid receptor activity signature is determined by analyzing the tumor sample for expression of one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or all ten genes selected from ANKRD1, CCNE1, CELSR3, NENE, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11 in comparison to control levels.

4. The method of claim 3, wherein the subject is predicted to have decreased survival and / or increased disease progression if one or more, two or more, three or more, four or more, five or more, or all six genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2 have increased expression relative to control levels and / or one or more, two or more, three or more, or all four genes selected from CLDN14, DNAJA1, ENTPD61 and GDF11 have decreased expression relative to control levels.

5. The method of claim 3, wherein the subject is predicted to have increased survival and / or decreased disease progression if one or more, two or more, three or more, or all four genes selected from CLDN14, DNAJA1, ENTPD61 and GDF11 have increased expression relative to control levels and / or one or more, two or more, three or more, four or more, five or more, or all six genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2 have decreased expression relative to control levels.

6. The method of claim 3, wherein the subject is predicted to have increased responsiveness to a treatment and / or is identified for treatment with a selective glucocorticoid receptor transcriptomic signature if one or more, two or more, three or more, four or more, five or more, or all six genes selected from ANKRD1, CCNE1, CELSR3,- 66 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614NENF, TOX and ZFP36L2 have increased expression relative to control levels and / or one or more, two or more, three or more, or all four genes selected from CLDN14, DNAJA1, ENTPD61 and GDF11 have decreased expression relative to control levels.

7. The method of any one of claims 3 to 6, wherein analyzing the tumor sample for expression of the one or more genes comprises quantifying and / or detecting transcripts of each gene in one or more cells of the tumor sample.

8. The method of claim 7, wherein quantifying and / or detecting transcripts comprises digital transcript counting, high-density expression array, DNA microarray, polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), real-time quantitative reverse transcription PCR (qRT-PCR), digital droplet PCR (ddPCR), serial analysis of gene expression (SAGE), Spotted cDNA arrays, GeneChip, spotted oligo arrays, bead arrays, RNA Seq, tiling array, northern blotting, hybridization microarray, in situ hybridization, or any combination thereof.

9. The method of claim 8, wherein quantifying and / or detecting transcripts comprises using RNA Seq or a DNA microarray.

10. The method of any one of claims 7 to 9, wherein quantifying and / or detecting transcripts comprises using single cell RNA sequencing or a microarray.

11. The method of any one of claims 3 to 6, wherein analyzing the tumor sample for expression of the one or more genes comprises quantifying and / or detecting levels of one or more protein products of each gene in one or more cells of the tumor sample.

12. The method of claim 11 , wherein quantifying and / or detecting levels of one or more protein products comprises Western blotting, enzyme-linked immunosorbent assay (ELISA), multi-analyte profiling (xMAP), mass spectrometry, HPLC, flow cytometry, fluorescence-activated cell sorting (FACS), liquid chromatography-mass spectrometry (LC / MS), immunoelectrophoresis, translation complex profile sequencing (TCP-seq), protein microarray, protein chip, capture arrays, reverse phase protein microarray (RPPA), two-dimensional gel electrophoresis or (2D-PAGE), functional protein microarrays, electrospray ionization (ESI), matrix-assisted laser desorption / ionization (MALDI), ora combination thereof.

13. The method of any one of claims 3 to 12, wherein the control expression levels are derived from control tumor samples.

14. The method of any one of claims 1 to 13, wherein the treatment comprises a glucocorticoid receptor modulator.- 67 - 302397143Attorney Docket No. UTSDP4477WO- 100138561415. The method of claim 14, wherein the glucocorticoid receptor modulator comprises a selective glucocorticoid receptor modulator (SGRM).

16. A method for selecting a treatment for a subject having or suspected of having a cancer, the method comprising determining a prognostic glucocorticoid receptor activity signature in a tumor sample isolated from the subject and selecting the treatment based on the prognostic glucocorticoid receptor activity signature.

17. The method of claim 16, wherein the prognostic glucocorticoid receptor activity signature is determined by analyzing the tumor sample for expression of one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or all ten genes selected from ANKRD1, CCNE1, CELSR3, NENE, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11 in comparison to control levels.

18. The method of claim 17, wherein the treatment selected comprises an anti-cancer therapy comprising a glucocorticoid receptor modulator if one or more, two or more, three or more, four or more, five or more, or all six genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2 have increased expression relative to control levels and / or one or more, two or more, three or more, or all four genes selected from CLDN14, DNAJA1, ENTPD61 and GDF11 have decreased expression relative to control levels.

19. The method of claim 17, wherein the treatment selected comprises an anti-cancer therapy other than a glucocorticoid receptor modulator if one or more, two or more, three or more, or all four genes selected from CLDN14, DNAJA1, ENTPD61 and GDF11 have increased expression relative to control levels and / or one or more, two or more, three or more, four or more, five or more, or all six genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2 have decreased expression relative to control levels.

20. The method of claim 19, wherein the anti-cancer therapy other than a glucocorticoid receptor modulator comprises chemotherapy, an immunotherapy, surgery, radiation, or any combination thereof.

21. The method of any one of claims 17 to 20, wherein analyzing the tumor sample for expression of the one or more genes comprises quantifying and / or detecting transcripts of each gene in one or more cells of the tumor sample.

22. The method of claim 21, wherein quantifying and / or detecting transcripts comprises digital transcript counting, high-density expression array, DNA microarray, polymerase chain reaction (PCR), reverse transcriptase PCR (RT-PCR), real-time quantitative reverse transcription PCR (qRT-PCR), digital droplet PCR (ddPCR), serial analysis of gene expression (SAGE), Spotted cDNA arrays, GeneChip, spotted oligo arrays, bead arrays, - 68 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614RNA Seq, tiling array, northern blotting, hybridization microarray, in situ hybridization, or any combination thereof.

23. The method of claim 22, wherein quantifying and / or detecting transcripts comprises using RNA Seq and / or a microarray.

24. The method of any one of claims 21 to 23, wherein quantifying and / or detecting transcripts comprises using single cell RNA sequencing.

25. The method of any one of claims 17 to 20, wherein analyzing the tumor sample for expression of the one or more genes comprises quantifying and / or detecting levels of one or more protein products of each gene in one or more cells of the tumor sample.

26. The method of claim 25, wherein quantifying and / or detecting levels of one or more protein products comprises Western blotting, enzyme-linked immunosorbent assay (ELISA), multi-analyte profiling (xMAP), mass spectrometry, HPLC, flow cytometry, fluorescence-activated cell sorting (FACS), liquid chromatography-mass spectrometry (LC / MS), immunoelectrophoresis, translation complex profile sequencing (TCP-seq), protein microarray, protein chip, capture arrays, reverse phase protein microarray (RPPA), two-dimensional gel electrophoresis or (2D-PAGE), functional protein microarrays, electrospray ionization (ESI), matrix-assisted laser desorption / ionization (MALDI), ora combination thereof.

27. The method of any one of claims 17 to 26, wherein the control expression levels are derived from control tumor samples.

28. A method of treating a subject with cancer, the method comprising administering a treatment to the subject, wherein the treatment is selected based on a prognostic glucocorticoid receptor activity signature of a tumor sample obtained from the subject.

29. The method of claim 28, wherein the prognostic glucocorticoid receptor activity signature corresponds to expression of one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or all ten genes selected from ANKRD1, CCNE1, CELSR3, NENE, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11 in a tumor sample obtained from the subject.

30. The method of claim 29, wherein the treatment comprises:(a) an anti-cancer therapy comprising a glucocorticoid receptor modulator when one or more, two or more, three or more, four or more, five or more, or all six genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2 have increased expression relative to control levels and / or one or more, two or more, three or more, or all four genes- 69 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614selected from CLDN14, DNAJA1, ENTPD61 and GDF11 have decreased expression in the tumor sample relative to control levels; and(b) an anti-cancer therapy other than a glucocorticoid receptor modulator when one or more, two or more, three or more, or all four genes selected from CLDN14, DNAJA1, ENTPD61 and GDF11 have increased expression relative to control levels and / or one or more, two or more, three or more, four or more, five or more, or all six genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX and ZFP36L2 have decreased expression relative to control levels.

31. The method of claim 30, wherein the glucocorticoid receptor modulator comprises a selective glucocorticoid receptor modulator (SGRM).

32. The method of any one of claims 30 or 31 , wherein the anti-cancer therapy other than a glucocorticoid receptor modulator comprises chemotherapy, an immunotherapy, surgery, radiation, or any combination thereof.

33. The method of any one of claims 1 to 32, wherein the cancer comprises an ovarian cancer.

34. The method of claim 33, wherein the ovarian cancer comprises epithelial ovarian carcinoma, a germ cell tumor, or a stromal tumor or any combination thereof.

35. The method of any one of claims 33 or 34, wherein the ovarian cancer comprises epithelial ovarian carcinoma, optionally wherein the epithelial ovarian carcinoma comprises serous epithelial ovarian carcinoma, a mucinous epithelial ovarian carcinoma, an endometroid epithelial ovarian carcinoma, clear cell epithelial ovarian carcinoma or any combination thereof.

36. The method of any one of claims 33 to 35, wherein the ovarian cancer comprises a germ cell tumor, optionally wherein the germ cell tumor comprises dysgerminoma, yolk sac tumor, malignant teratoma, mixed germ cell tumor, embryonal carcinoma, choriocarcinoma, or any combination thereof.

37. The method of any one of claims 33 to 36, wherein the ovarian cancer comprises a stromal tumor, optionally wherein the stromal tumor comprises granulosa cell tumors, Sertoli cell tumors, Sertoli-Leydig cell tumors or any combination thereof.

38. The method of any one of claims 33 to 37, wherein the ovarian cancer comprises a low-grade tumor.- 70 - 302397143Attorney Docket No. UTSDP4477WO- 100138561439. The method of any one of claims 33 to 37, wherein the ovarian cancer comprises a high-grade tumor.

40. The method of any one of claims 33 to 39, wherein the ovarian cancer comprises high-grade serous epithelial ovarian carcinoma.

41. A kit for determining a prognostic glucocorticoid receptor activity signature in a sample, the kit comprising one or more components for analyzing expression of one or more genes selected from ANKRD1, CCNE1, CELSR3, NENE, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11 in a tumor sample.

42. The kit of claim 41 , wherein the one or more components are for use in an assay for quantifying and / or detecting transcripts of each gene in one or more cells of the tumor sample.

43. The kit of claim 41 , wherein the one or more components are for use in an assay for quantifying and / or detecting levels of one or more protein products of each gene in one or more cells of the tumor sample.

44. The kit of any one of claims 41 to 43, further comprising an algorithm and software encoding the algorithm for calculating a prognostic glucocorticoid receptor activity signature from the expression ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11 in a sample.

45. A computer readable medium having software modules for performing a method comprising the acts of:(a) comparing glucocorticoid receptor activity data obtained from an ovarian tumor sample with a reference; and(b) providing an assessment of glucocorticoid receptor activity status to a physician for use in determining an appropriate therapeutic regimen for a subject,wherein glucocorticoid receptor activity data comprises one or more data points corresponding to expression of one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or all ten genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11 in the tumor sample.

46. A computer system, having a processor, memory, external data storage, input / output mechanisms, a display, for assessing glucocorticoid receptor activity, comprising:(a) a database;- 71 - 302397143Attorney Docket No. UTSDP4477WO- 1001385614(b) logic mechanisms in the computer generating for the database a GR-responsive gene expression reference; and(c) a comparing mechanism in the computer for comparing the GR-responsive gene expression reference to expression data from an ovarian tumor sample using a comparison model to determine a GR gene expression profile of the ovarian tumor sample, wherein the GR-responsive gene expression comprises expression of one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or all ten genes selected from ANKRD1, CCNE1, CELSR3, NENE, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11.

47. An internet accessible portal for providing biological information constructed and arranged to execute a computer-implemented method for providing:(a) a comparison of gene expression data of one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, or all ten GR-responsive genes selected from ANKRD1, CCNE1, CELSR3, NENF, TOX, ZFP3612, CLDN14, DNAJA1, ENTPD6, and GDF11 in an ovarian tumor sample obtained from a subject with a calculated reporter index; and(b) providing an assessment of GR activity to a physician for use in determining an appropriate therapeutic regime for the subject.- 72 - 302397143

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