BIOMARKERS FOR PREDICTING RESPONSIVENESS TO IMMUNE CHECKPOINT INHIBITOR THERAPY

CROSS REFERENCE

[0001] This application claims the benefit of US Provisional Application No. 63/302,941, filed January 25, 2022, the full disclosure of which is hereby incorporated by reference herein in its entirety.

FIELD

[0002] Provided herein, in certain aspects, are compositions and methods comprising biomarker genes for identifying subjects that are likely to respond to immune checkpoint inhibitor therapies.

BACKGROUND

[0003] Lung cancer remains the leading cause of cancer-related death worldwide among men and the second leading cause among women. Non-small-cell lung cancer (NSCLC), which is the most common type, accounts for 80% to 85% of all lung cancer diagnoses. It is frequently diagnosed in the advanced stage, with 5-year survival rates ranging from 0% to 5% with chemotherapy, the only systemic therapeutic strategy available for decades. In recent years, blockade of the programmed cell death-1 (PD-l)/programmed death ligand-1 (PD-L1) axis has opened new avenues in the lung cancer therapeutic landscape, increasing overall survival (OS) not only in patients with advanced NSCLC but also in patients with stage III NSCLC and extensive-stage small-cell lung cancer.

[0004] In particular, cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) and PD-1 have been successfully exploited as therapeutic targets to promote and reinvigorate immune responses against cancer. Both proteins are induced on T cells upon T-cell receptor (TCR) signaling activation, albeit with different kinetics. CTLA-4 is generally up-regulated during the initial stage of naive T-cell activation and competes with CD28 for the same ligands (CD86 and CD80) expressed on antigen presenting cells (APCs). CTLA-4 is constitutively expressed at high levels on regulatory T cells and constitutes one of the regulatory T cells’ immunosuppressive mechanisms. PD-1 is generally induced during the later phases of an immune response, controlling previously activated T cells, typically at the effector sites of immune responses.

[0005] The CTLA-4 and PD-1 immune checkpoints are particularly deregulated in tumorbearing hosts, where chronic ineffective immune responses routinely predominate and result in T-cell exhaustion and induction of regulatory T cells. These observations provided the impetus for developing interventions that inhibit CTLA-4 and PD-1 as cancer immunotherapy modalities.

[0006] Inhibition of PD-1 has achieved clinical success in chemotherapy-refractory advanced non-small cell lung cancer (NSCLC) patients, where it is currently being investigated in combination with CTLA-4 inhibition. Inhibition of PD-1 as a monotherapy or in combination with CTLA-4 inhibition can produce notable clinical benefits. However, there are no validated biomarkers guiding selection of subjects that are likely to benefit from checkpoint inhibitor therapy or for those subjects who are not likely to respond. The present disclosure addresses this need and provides related advantages.

SUMMARY

[0007] Compositions and methods are provided for identifying subjects that will respond to immune checkpoint inhibitor therapies.

[0008] In one aspect, the present disclosure provides a method comprising: (a) determining a genotype of one or more biomarker genes of a biomarker panel comprising APC, ARID 1 A, ARID2, ASXL1, ATM, ATRX, BAP1, BRCA1, BRCA2, CDKN2A, CHD2, CIC, CMTR2, CREBBP, CUL3, DICER1, DLC1, DUSP4, EP300, FAT1, FBXW7, KDM5C, KDM6A, KEAP1, KMT2C, KMT2D, KRASWT, LKB1, LRP1B, MG A, MSH2, MTAP, NCOA6, NF1, NF 2, P53, PALB2, PBRM1, PCNA, PTEN, PTPN13, PTPRD, PTPRS, RASA1, RBI, RB1CC1, RBM10, RNF43, SETD2, SHP2, SMAD2, SMAD4, SMARCA4, SMG1, STAG2, TET2, TGFBR2, TSC1, TSC2, USP15, and ZFHX3 in a biological sample of a subject afflicted with cancer; (b) determining a genotype of one or more biomarker genes in the biological sample; and (c) classifying a subject as sensitive or resistant to a therapy comprising an immune checkpoint inhibitor based on the genotype of each of the one or more biomarker genes in the biological sample.

[0009] In certain embodiments, the biomarker panel comprises APC, ARID2, ATM, ATRX, BRCA2, CDKN2A, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, STK11, TP53, and TSC1. In one aspect, the biomarker panel comprises APC, ARID2, ATM, ATRX, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, SIKH, and TSC1. In certain embodiments, the biomarker panel comprises BRCA2, CDKN2A, and TP53. In certain embodiments, the biomarker panel comprises APC, CMTR2, KEAP1, KMT2D, MGA, and NF J. In certain embodiments, the biomarker panel comprises ATM.

[0010] In certain embodiments, the genotype comprises a mutation in the one or more biomarker genes. In certain embodiments, the mutation inactivates the one or more biomarker genes.

[0011] In certain embodiments, the method further comprises comparing the genotype with a reference genotype. In certain embodiments, the reference is to an untreated population. In certain embodiments, the reference is to a treated population. In certain embodiments, the genotype is reported as a score.

[0012] In certain embodiments, determining the genotype comprises genomic profiling. In certain embodiments, determining the genotype comprises measuring gene expression. In certain embodiments, measuring gene expression comprises detection of ribonucleic acids (RNAs) or polypeptides.

[0013] In certain embodiments, the subject is classified as sensitive to an immune checkpoint inhibitor treatment. In certain embodiments, the subject is classified as resistant to an immune checkpoint inhibitor treatment.

[0014] In certain embodiments, the immune checkpoint inhibitor comprises an antibody or an antigen-binding fragment thereof. In certain embodiments, the immune checkpoint inhibitor inhibits programmed cell death protein 1 (PD-1). In certain embodiments, the immune checkpoint inhibitor is an anti -PD-1 antibody. In certain embodiments, the immune checkpoint inhibitor is selected from the group consisting of Nivolumab (Opdivo), Pembrolizumab (Keytruda), and Cemiplimab (Libtayo).

[0015] In certain embodiments, the immune checkpoint inhibitor inhibits cytotoxic T- lymphocyte-associated antigen 4 (CTLA-4). In certain embodiments, the immune checkpoint inhibitor is an anti-CTLA-4 antibody. In certain embodiments, the immune checkpoint inhibitor is Ipilimumab (Yervoy).

[0016] In certain embodiments, the immune checkpoint inhibitor inhibits programmed cell death protein 1 ligand (PD-L1). In certain embodiments, the immune checkpoint inhibitor is an anti-PD-Ll antibody. In certain embodiments, the immune checkpoint inhibitor is selected from the group consisting of Atezolizumab (Tecentriq), Avelumab (Bevencio), and Durvalumab (Imfinzi).

[0017] In certain embodiments, the methods of the present dislcousre further comprise administering an immune checkpoint inhibitor therapy to the subject. In certain embodiments, the methods comprise administering to the subject a combination therapy comprising two distinct checkpoint inhibitors. In certain embodiments, the methods comprise administering to the subject a combination therapy comprising a checkpoint inhibitor and a second therapy. In certain embodiments, the second therapy is chemotherapy. In certain embodiments, the chemotherapy comprises first-line platinum chemotherapy. In certain embodiments, the chemotherapy comprises taxane-based chemotherapy. In certain embodiments, the taxane is selected from the group consisting of docetaxel, paclitaxel, and cabazitaxel. In certain embodiments, the taxane is docetaxel.

[0018] In one aspect, the present disclosure provides a method of predicting resistance of tumor growth to inhibition by a therapy comprising an immune checkpoint inhibitor, comprising: (a) detecting in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting resistance of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating BRCA2, CDKN2A, or TP53 mutation, (ii) a decreased copy number of BRCA2, CDKN2A, or TP53, or (iii) a decreased expression of BRCA2, CDKN2A, or TP53 mRNA or protein. [0019] In one aspect, the present disclosure provides a method of predicting resistance of tumor growth to inhibition by a therapy comprising an immune checkpoint inhibitor, comprising: (a) detecting in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting resistance of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating ATM mutation, (ii) a decreased copy number of ATM, or (iii) a decreased expression of ATM mRNA or protein.

[0020] In one aspect, the present disclosure provides a method of predicting resistance of tumor growth to inhibition by a therapy comprising an immune checkpoint inhibitor, comprising: (a) detecting in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting resistance of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating TP53 mutation, (ii) a decreased copy number of TP53, or (iii) a decreased expression of TP53 mRNA or protein.

[0021] In one aspect, the present disclosure provides a method of predicting sensitivity of tumor growth to inhibition by a therapy comprising an immune checkpoint inhibitor, comprising: (a) detecting in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting sensitivity of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating APC, ARID2, ATM, ATRX, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, STK11, or TSC1 mutation, (ii) a decreased copy number of APC, ARID2, ATM, ATRX, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, STK11, or TSC1, or (iii) decreased expression oiAPC, ARID2, ATM, ATRX, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, SIKH, or TSC1 mRNA or protein.

[0022] In one aspect, the present disclosure provides a method of predicting sensitivity of tumor growth to inhibition by a therapy comprising an immune checkpoint inhibitor, comprising: (a) detecting in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting sensitivity of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating APC, CMTR2, KEAP1, KMT2D, MGA, or NF1 mutation, (ii) a decreased copy number of APC, CMTR2, KEAP1, KMT2D, MGA, or NF1, or (iii) decreased expression of APC, CMTR2, KEAP1, KMT2D, MGA, or NF1 mRNA or protein.

[0023] In one aspect, the present disclosure provides a method of predicting sensitivity of tumor growth to inhibition by a therapy comprising an immune checkpoint inhibitor, comprising: (a) detecting in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting sensitivity of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating APC mutation, (ii) a decreased copy number of APC, or (iii) decreased expression of APC mRNA or protein.

[0024] In one aspect, the present disclosure provides a method of predicting response of tumor growth to inhibition by a therapy comprising an immune checkpoint inhibitor, comprising: (a) determining in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting the response of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating APC, ARID2, ATM, ATRX, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, STK11, or TSC1 mutation, (ii) a decreased copy number of APC, ARID2, ATM, ATRX, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, SIKH, or TSC1, (iii) decreased expression of APC, ARID2, ATM, ATRX, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, SIKH, or TSC1 mRNA or protein; (iv) an inactivating BRCA2, CDKN2A, or TP53 mutation, (v) a decreased copy number of BRCA2, CDKN2A, or TP53, or (vi) a decreased expression of BRCA2, CDKN2A, or TP53 mRNA or protein. [0025] In one aspect, the present disclosure provides a method of predicting response of tumor growth to inhibition by a therapy comprising an immune checkpoint inhibitor, comprising: (a) determining in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting the response of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating APC, CMTR2, KEAP1, KMT2D, MGA, or NF1 mutation, (ii) a decreased copy number of APC, CMTR2, KEAP1, KMT2D, MGA, or NF1, (iii) decreased expression of APC, CMTR2, KEAP1, KMT2D, MGA, or NF1 mRNA or protein; (iv) an inactivating ATM mutation, (v) a decreased copy number of ATM, or (vi) a decreased expression of ATM mRNA or protein.

[0026] In one aspect, the present disclosure provides a method of treating non-small cell lung cancer (NSCLC) comprising the step of treating a subject with a immune checkpoint inhibitor when a tumor sample obtained from the subject comprises (i) an inactivating APC, CMTR2, KEAP1, KMT2D, MGA, or NF1 mutation, (ii) a decreased copy number of APC, CMTR2, KEAP1, KMT2D, MGA, or NF1, or (iii) decreased expression of APC, CMTR2, KEAP1, KMT2D, MGA, or NF1 mRNA or protein. In certain embodiments, the tumor sample obtained from the subject further comprises (iv) absence of an inactivating ATM mutation, (v) absence of a decreased copy number cT A TM, or (vi) absence of decreased expression cT A TM mRNA or protein.

[0027] In one aspect, the present disclosure provides a method of predicting resistance of tumor growth to inhibition by an immune checkpoint inhibitor therapy comprising a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor, the method comprising: (a) detecting in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting resistance of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating ATM mutation, (ii) a decreased copy number of ATM, or (iii) a decreased expression of ATM mRNA or protein.

[0028] In one aspect, the present disclosure provides a method of predicting sensitivity of tumor growth to inhibition by an immune checkpoint inhibitor therapy comprising a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor, the method comprising: (a) detecting in a tumor sample of a subject a genotype of one or more biomarker genes of a biomarker panel comprising; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting sensitivity of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating KEAP1 mutation, (ii) a decreased copy number of KEAP1, or (iii) decreased expression of KEAP1 mRNA or protein.

[0029] In one aspect, the present disclosure provides a method of predicting response of tumor growth to inhibition by an immune checkpoint inhibitor therapy comprising a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor, the method comprising: (a) determining in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting the response of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating KEAP1 mutation, (ii) a decreased copy number of KEAP1, (iii) decreased expression of KEAP1 mRNA or protein, (iv) an inactivating ATM mutation, (v) a decreased copy number of ATM, or (vi) a decreased expression of ATM mRNA or protein.

[0030] In one aspect, the present disclosure provides a method of enriching a prospective patient population for subjects likely to respond to an immune checkpoint inhibitor therapy, for example, a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor therapy, the method comprising performing the methods described herein of predicting sensitivity, resistance and/or response of tumor growth to inhibition by an immune checkpoint inhibitor therapy on two or more individual subjects within the prospective patient population.

[0031] In one aspect, the present disclosure provides a method of treating non-small cell lung cancer (NSCLC), comprising treating a subject with an immune checkpoint inhibitor when a tumor sample obtained from the subject comprises (i) an inactivating KEAP1 mutation, (ii) a decreased copy number of KEAP1, or (iii) decreased expression of KEAP1 mRNA or protein.

[0032] In one aspect, the present disclosure provides a method of treating non-small cell lung cancer (NSCLC), comprising treating a subject with a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor when a tumor sample obtained from the subject comprises (i) an inactivating KEAP1 mutation, (ii) a decreased copy number of KEAP1, or (iii) decreased expression of KEAP1 mRNA or protein.

[0033] In certain embodiments, the subject is already undergoing an immune checkpoint inhibitor therapy comprising a programmed cell death protein 1 (PD-1) inhibitor and a chemotherapy. In certain embodiments, the PD-1 inhibitor is selected from the group consisting of nivolumab (Opdivo), Pembrolizumab (Keytruda), and Cemiplimab (Libtayo). In certain embodiments, the chemotherapy comprises a taxane, for example, docetaxel, paclitaxel or cabazitaxel. In certain embodiments, the methods further comprise selecting a therapy for the subject. In certain embodiments, the therapy comprises a CTLA-4 inhibitor, for example, Ipilimumab (Yervoy).

[0034] In one aspect, the present disclosure provides a method of treating non-small cell lung cancer (NSCLC), comprising treating a subject undergoing a therapy comprising a first immune checkpoint inhibitor and a chemotherapy with a second immune checkpoint inhibitor when a tumor sample obtained from the subject comprises (i) an inactivating KEAP1 mutation, (ii) a decreased copy number of KEAP1, or (iii) decreased expression of KEAP1 mRNA or protein. In certain embodiments, the first immune checkpoint inhibitor inhibits programmed cell death protein 1 (PD-1), for example, an anti -PD-1 antibody, e.g., nivolumab (Opdivo), Pembrolizumab (Keytruda), and Cemiplimab (Libtayo). In certain embodiments, the second immune checkpoint inhibitor comprises an cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor, for example, an anti-CTLA-4 antibody.

[0035] In one aspect, the present disclosure provides a method of determining effectiveness of an immune checkpoint inhibitor in reducing tumor size comprising: (a) treating a first inert tumor with a control therapy; (b) treating a second inert tumor with an immune checkpoint inhibitor, wherein the first and second inert tumors comprise identical genotypes; (c) treating a first mutant tumor with the control therapy; (d) treating a second mutant tumor with the immune checkpoint inhibitor, wherein the first and second mutant tumors comprise identical genotypes; (e) comparing sizes of the first and second inert tumors after the therapy; (f) comparing sizes of the first and second mutant tumors after completion of the therapy, and (g) identifying the mutant tumor genotype as sensitive to the immune checkpoint inhibitor if the change in tumor size between the first and second inert tumors after the therapy is less than the change in tumor size between the first and second mutant tumors after the therapy.

[0036] In one aspect, the present disclosure provides a method of determining effectiveness of an immune checkpoint inhibitor in reducing tumor size comprising: (a) treating a first inert tumor with a control therapy; (b) treating a second inert tumor with an immune checkpoint inhibitor, wherein the first and second inert tumors comprise identical genotypes; (c) treating a first mutant tumor with the control therapy; (d) treating a second mutant tumor with the immune checkpoint inhibitor, wherein the first and second mutant tumors comprise identical genotypes; (e) comparing sizes of the first and second inert tumors after the therapy; (f) comparing sizes of the first and second mutant tumors after completion of the therapy, and (g) identifying the mutant tumor genotype as resistant to the immune checkpoint inhibitor if the change in tumor size between the first and second inert tumors after the therapy is greater than the change in tumor size between the first and second mutant tumors after the therapy.

[0037] In certain embodiments of the methods disclosed herein, the cancer is a skin cancer, lung cancer, pancreatic cancer, breast cancer, or colorectal cancer. In certain embodiments, the cancer is lung cancer. In certain embodiments, the lung cancer is non-small cell lung cancer (NSCLC). In certain embodiments, the NSCLC is lung adenocarcinoma.

BRIEF DESCRIPTION OF THE DRAWINGS

[0038] FIG. 1 shows bootstrap confidence intervals for RTNscore for each of the twenty-two biomarker genes tested as described in Example 1. The left column shows RTNscore values when a minimal size cutoff of 500 cell was applied to the treated tumors, while the right columns show RTNscore when a large cutoff of 5000 cells was used. Each row gives the results of a different treatment study arm. Confidence intervals are displayed in the form of Letter-value plots: the central box shows the median and interquartile range of the bootstrap distribution, and every subsequent box edge shows the location of the 50 ^th percentile of bootstraps beyond the previous box edge. [0039] FIG. 2A - FIG. 2E shows multiplexed analysis of immunotherapy responses in an autothchonous mouse model of lung adenocarcinoma as described in FIG. 1. FIG. 2A shows an experimental schematic depicting the composition of the pool of barcoded Lenti-sgRNA/Cre vectors (Lenti-D2G^‘P° ⁰ Cre) used in Example 1. FIG. 2B shows mouse genotype, analysis time points, and readouts. FIG. 2C shows effects of each tumor genotype on growth. Tumors at the indicated percentiles of the tumor size distribution for each barcoded Lenti-sgRNA/Cre vector are shown. 95% confidence intervals are indicated. FIG. 2D. shows the treatment groups in this study. FIG. 2E shows overall responses to therapies, each dot represents a mouse. Antibodies, dosing, and number of mice are indicated.

[0040] FIG. 3A - FIG. 3D shows that Keapl -deficiency drives selective sensitivity to combination PD-1 inhibitor- and CTLA-4 inhibitor therapy in vivo. FIG. 3A and FIG. 3B show genotype specific responses to PD-1 inhibitor-treated relative to control -treated mice (FIG. 3A) and PD-1 inhibitor- and CTLA-4 inhibitor-treated mice relative to control treated mice (FIG. 3B). Significant sensitive effects are shown for Ape in FIG. 3 A, and Ape, Cmtr2, Keapl, Kmt2d, and Nfl in FIG. 3B. Significant resistant effects are shown for Atm in FIG. 3B. FIG. 3C shows genotype specific responses to PD-1 inhibitor-treated and CTLA-4 inhibitor-treated mice relative to PD-1 inhibitor-treated mice. Significant sensitive effects are shown for Keapl and significant resistant effects are shown for Atm in FIG. 3C. FIG. 3D shows a volcano plot of the magnitude of differential sensitivity to a PD-1 inhibitor treatment and a CTLA-4 inhibitor treatment relative to a PD-1 inhibitor treatment alone.

DETAILED DESCRIPTION

[0041] The present disclosure is based, at least in part, on the surprising discovery that the genotype of particular biomarker genes can be used to predict a subject’s response to an immune checkpoint inhibitor therapy. In certain embodiments, the genotype is predictive of sensitivity to a checkpoint inhibitor therapy. In certain embodiments, the genotype is predictive of resistance to a checkpoint inhibitor therapy. The present disclosure provides new and advantageous methods for determining whether a subject afflicted with a tumor (e.g., cancer) is a candidate for an immune checkpoint inhibitor therapy. [0042] The present disclosure is further based, in part, on the surprising discovery that KEAP /-deficiency drives selective sensitivity to a combination therapy comprising a PD-1 inhibitor and a CTLA-4 inhibitor, while A 7A/-deficiency drives selective resistance to a combination therapy comprising a PD-1 inhibitor and a CTLA-4 inhibitor. This discovery disclosed herein has important implications for the identification of subjects undergoing a PD-1 inhibitor therapy, for example, in combination with chemotherapy, who can benefit from the addition of a CTLA-4 inhibitor therapy to their treatment regimen.

[0043] In one aspect, the present disclosure provides a method comprising: (a) determining the genotype of one or more biomarker genes in a biological sample of a subject; (b) identifying whether the subject is likely to be sensitive to a therapy comprising an immune checkpoint inhibitor based on the genotype. In one aspect, the present disclosure provides a method comprising: (a) determining the genotype of one or more biomarker genes in a biological sample of a subject; (b) identifying whether the subject is likely to be sensitive to a therapy comprising an immune checkpoint inhibitor based on the genotype. In certain embodiments, the methods further comprise administering to the subject the immune checkpoint inhibitor if the subject is identified as likely to be sensitive to a therapy comprising an immune checkpoint inhibitor.

[0044] In certain embodiments, the biomarker genes useful in the methods of the present disclosure are ADAR, APC, ARID1A, ARID2, ASXL1, ATM, ATRX, BAP1, BRCA1, BRCA2, CDKN2A, CHD2, CIC, CMTR2, CREBBP, CUL3, DICER1, DLC1, DNMT1, DUSP4, EP300, FAT1, FBXW7, JAK1, KDM5C, KDM6A, KEAP1, KMT2C, KMT2D, KRAS, LRP1B, MET, MGA, MSH2, MTAP, NCOA6, NF1, NF2, PALB2, PBRM1, PCNA, PTEN, PTPN11, PTPN13, PTPRD, PTPRS, RASA1, RBI, RB1CC1, RBM10, RNF43, SETD2, SMAD2, SMAD4, SMARCA4, SMG1, STAG2, SIKH, TET2, TGFBR2, TREX1, TP53, TSC1, TSC2, USP15, and ZFHX3.

[0045] In certain embodiments, the biomarker genes useful in the methods of the present disclosure are ZPC, ARID2, ATM, ATRX, BRCA2, CDKN2A, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, STK11, TP53, and TSC1. [0046] In certain embodiments, the biomarker genes useful in the methods of the present disclosure are APC, ARID2, ATM, ATRX, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, SIKH, and TSC1.

[0047] In certain embodiments, the biomarker genes useful in the methods of the present disclosure are BRCA2, CDKN2A, and TP53.

[0048] It must be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural reference unless the context clearly dictates otherwise.

[0049] It should also be understood that the terms “about,” “approximately,” “generally,” “substantially,” and like terms, used herein when referring to a dimension or characteristic of a component of embodiments provided herein, indicate that the described dimension/characteristic is not a strict boundary or parameter and does not exclude minor variations therefrom that are functionally the same or similar, as would be understood by one having ordinary skill in the art. At a minimum, such references that include a numerical parameter would include variations that, using mathematical and industrial principles accepted in the art (for example, rounding, measurement or other systematic errors, manufacturing tolerances, efc.), would not vary the least significant digit. Unless otherwise stated, any numerical values, such as a concentration or a concentration range described herein, are to be understood as being modified in all instances by the term “about.”

[0050] Unless otherwise indicated, the term “at least” preceding a series of elements is to be understood to refer to every element in the series and any one or any and all combinations of the elements.

[0051] As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having,” “contains” or “containing,” or any other variation thereof, will be understood to imply the inclusion of a stated integer or group of integers but not the exclusion of any other integer or group of integers and are intended to be non-exclusive or open-ended. For example, a composition, a mixture, a process, a method, an article, or an apparatus that comprises a list of elements is not necessarily limited to only those elements but can include other elements not expressly listed or inherent to such composition, mixture, process, method, article, or apparatus. Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).

[0052] As used herein, the conjunctive term “and/or” between multiple recited elements is understood as encompassing both individual and combined options. For instance, where two elements are conjoined by “and/or,” a first option refers to the applicability of the first element without the second. A second option refers to the applicability of the second element without the first. A third option refers to the applicability of the first and second elements together. Any one of these options is understood to fall within the meaning, and therefore satisfy the requirement of the term “and/or” as used herein. Concurrent applicability of more than one of the options is also understood to fall within the meaning, and therefore satisfy the requirement of the term “and/or.”

[0053] As used herein, the term “consists of,” or variations such as “consist of’ or “consisting of,” as used throughout the specification and claims, indicate the inclusion of any recited integer or group of integers, but that no additional integer or group of integers can be added to the specified method, structure, or composition.

[0054] As used herein, the term “consists essentially of,” or variations such as “consist essentially of’ or “consisting essentially of,” as used throughout the specification and claims, indicate the inclusion of any recited integer or group of integers, and the optional inclusion of any recited integer or group of integers that do not materially change the basic or novel properties of the specified method, structure, or composition.

[0055] As used herein, the term “subject” refers to any organism, e.g., a mammal, for whom diagnosis, prognosis, or therapy is desired. Mammalian subjects include, but are not limited to, humans, domestic animals, farm animals, sports animals, and zoo animals including, for example, humans, non-human primates, dogs, cats, guinea pigs, rabbits, rats, mice, horses, and cattle. In certain embodiments, the subject has been diagnosed with cancer. In certain embodiments, the subject is a human afflicted with a tumor (e.g., cancer) who has been diagnosed with a need for treatment for a tumor (e.g., cancer). [0056] The terms “inhibit,” “block,” and “suppress” are used interchangeably and refer to any statistically significant decrease in a biological activity, including full blocking of the activity. An “inhibitor” is an active agent that inhibits, blocks, or suppresses biological activity in vitro or in vivo. Inhibitors include but are not limited to small molecule compounds; nucleic acids, such as siRNA and shRNA; polypeptides, such as antibodies or antigen-binding fragments thereof, dominant-negative polypeptides, and inhibitory peptides; and oligonucleotide or peptide aptamers.

[0057] An “immune checkpoint inhibitor” is any active agent that antagonizes the activity of an immune checkpoint protein or its ligand or reduces its production in a cell. Immune checkpoint proteins include, but are not limited to, cytotoxic T lymphocyte-associated antigen 4 (CTLA-4), also known as CD152, programmed cell death protein 1 (PD-1), also known as CD279, lymphocyte-activation gene 3 (LAG-3), also known as CD223, and T cell immunoglobulin mucin (TIM-3), also known as HAVcr2. For example, a “CTLA-4 inhibitor” is an active agent that antagonizes the activity of cytotoxic T lymphocyte-associated antigen 4 or reduces its production in a cell. Likewise, a “PD-1 inhibitor” is an active agent that antagonizes the activity of programmed cell death protein 1 or reduces its production in a cell. PD-1 inhibitors also include active agents that inhibit the PD-1 ligand (PD-L1).

[0058] Any suitable immune checkpoint inhibitor can be used in the methods described herein. In certain embodiments, the immune checkpoint inhibitor of PD-1 is an anti-PD-1 antibody. Non-limiting exemplary immune checkpoint inhibitors of programmed cell death protein 1 (PD-1) include nivolumab (Opdivo), Pembrolizumab (Keytruda), and Cemiplimab (Libtayo). In certain embodiments, the immune checkpoint inhibitor of CTLA-4 is an anti- CTLA-4 antibody. Non-limiting exemplary immune checkpoint inhibitors of CTLA-4 include Ipilimumab (Yervoy). In certain embodiments, the immune checkpoint inhibitor of PD-L1 is an anti-PD-Ll antibody. Non-limiting exemplary immune checkpoint inhibitors of PD-L1 include Atezolizumab (Tecentriq), avelumab (Bevencio), durvalumab (Imfinzi).

[0059] Other non-limiting exemplary immune checkpoint inhibitors presently under development and/or undergoing clinical trials include JTX-4014 (Jounce Therapeutics), Spartalizumab (PDR001) a PD-1 inhibitor developed by Novartis; Camrelizumab (SHR1210) an anti-PD-1 monoclonal antibody by Jiangsu HengRui Medicine Co., Ltd. that recently received conditional approval in China; Sintilimab (IBI308), a human anti-PD-1 antibody developed by Innovent and Eli Lilly; Tislelizumab (BGB-A317) a humanized IgG4 anti-PD-1 monoclonal antibody; Toripalimab (JS 001) a humanized IgG4 monoclonal antibody against PD-1; Dostarlimab (TSR-042, WBP-285) a humanized monoclonal antibody against PD-1 by GlaxoSmithKline; INCMGA00012 (MGA012) a humanized IgG4 monoclonal antibody developed by Incyte and MacroGenics; AMP -224 by AstraZeneca/Medlmmune and GlaxoSmithKline; and AMP-514 (MEDI0680) by AstraZeneca; Tremelimumab, as CTL-4 inhibitor and PD-L1 inhibitors KN035 and CK-301 Checkpoint Therapeutics; as well as AUNP12, a peptic PD-1/PD-L1 inhibitor developed by Aurigene and Laboratoires Pierre Fabre. While exemplary immune checkpoint inhibitors are described herein, the skilled person understands that the methods of the present invention encompass any active agent that antagonizes the activity of an immune checkpoint protein or its ligand or reduces its production in a cell.

[0060] In certain embodiments, the immune checkpoint inhibitor is a small molecule. In certain embodiments, the immune checkpoint inhibitor is a polypeptide. In certain embodiments, the immune checkpoint inhibitor is a polypeptide analog. In certain embodiments, the immune checkpoint inhibitor is a peptidomimetic. In certain embodiments, the immune checkpoint inhibitor is an aptamer.

[0061] In certain embodiments, an immune checkpoint inhibitor is an antibody or an antigenbinding fragment thereof. For example, the antibody or antigen-binding fragment thereof can be a humanized antibody, a recombinant antibody, a diabody, a chimerized or chimeric antibody, a monoclonal antibody, a deimmunized antibody, a fully human antibody, a single chain antibody, an F _v fragment, an Fa fragment, a Fab fragment, an Fab' fragment, or an F(ab')2 fragment.

[0062] Any compound which binds to and inhibits, or otherwise inhibits the activity, function and/or the expression of an immune checkpoint protein or its receptor can be utilized in accordance with the present disclosure. For example, an inhibitor of an immune checkpoint protein can be, for example, a small molecule, a nucleic acid or nucleic acid analog, a peptidomimetic, or a macromolecule that is not a nucleic acid or a protein. These agents include, but are not limited to, small organic molecules, RNA aptamers, L-RNA aptamers, Spiegelmers, antisense compounds, double stranded RNA, small interfering RNA, locked nucleic acid inhibitors, and peptide nucleic acid inhibitors. In certain embodiments, an immune checkpoint inhibitor is a protein or a protein fragment.

[0063] Other compounds which can be utilized include, but are not limited to, proteins, protein fragments, peptides, small molecules, RNA aptamers, L-RNA aptamers, spiegelmers, antisense compounds, serine protease inhibitors, molecules which can be utilized in RNA interference (RNAi) such as double stranded RNA including small interfering RNA (siRNA), locked nucleic acid (LNA) inhibitors, peptide nucleic acid (PNA) inhibitors, etc.

[0064] In certain embodiments, the immune checkpoint protein inhibitor is an antibody or an antigen-binding fragment thereof, which binds to an immune checkpoint protein.

[0065] As used herein, the term “antibody” is used in a broad sense and includes immunoglobulin or antibody molecules including human, humanized, composite and chimeric, single-chain, bi-specific and multi-specific antibodies and antibody fragments, in particular, antigen-binding fragments, that are monoclonal or polyclonal. In general, antibodies are proteins or peptide chains that exhibit binding specificity to a specific antigen. Antibody structures are well known. Immunoglobulins can be assigned to five major classes (specifically, IgA, IgD, IgE, IgG and IgM), depending on the heavy chain constant domain amino acid sequence. IgA and IgG are further sub-classified as the isotypes IgAl, IgA2, IgGl, IgG2, IgG3 and IgG4. Accordingly, the antibodies provided herein can be of any of the five major classes or corresponding sub-classes. In certain embodiments, the antibodies provided herein are IgGl, IgG2, IgG3 or IgG4. Antibody light chains of vertebrate species can be assigned to one of two clearly distinct types, namely kappa and lambda, based on the amino acid sequences of their constant domains.

[0066] An immune checkpoint inhibitor of can also be, for example, a small molecule, a polypeptide analog, a nucleic acid, or a nucleic acid analog. [0067] Small molecule” as used herein, is meant to refer to an agent, which has a molecular weight preferably of less than about 6 kDa and most preferably less than about 2.5 kDa. Many pharmaceutical companies have extensive libraries of chemical and/or biological mixtures comprising arrays of small molecules, often fungal, bacterial, or algal extracts, which can be screened with any of the assays of the application. It is within the scope of this application that such a library can be used to screen for agents that bind to a target antigen of interest (for example, an immune checkpoint protein). There are numerous commercially available compound libraries, such as the Chembridge DIVERSet® screening library. Libraries are also available from academic and governmental entities, such as the National Cancer Institute’s Developmental Therapeutics Program (DTP). Rational drug design can also be employed and can be achieved based on known compounds, for example, a known inhibitor of an immune checkpoint protein (for example, an antibody, or antigen-binding fragment thereof, that binds to an immune checkpoint protein).

[0068] In certain embodiments, the immune checkpoint inhibitor is a nucleic acid inhibitor. Nucleic acid inhibitors can be used to bind to and inhibit a target antigen of interest. The nucleic acid antagonist can be, for example, an aptamer. Aptamers are short oligonucleotide sequences that can be used to recognize and specifically bind almost any molecule, including cell surface proteins. The systematic evolution of ligands by exponential enrichment (SELEX) process is powerful and can be used to readily identify such aptamers. Aptamers can be made for a wide range of proteins of importance for therapy and diagnostics, such as growth factors and cell surface antigens. These oligonucleotides bind their targets affinities and specificities similar to those of antibodies.

[0069] In certain embodiments, the immune checkpoint inhibitor is a non-antibody scaffold protein. These proteins are, generally, obtained through combinatorial chemistry-based adaptation of pre-existing antigen-binding proteins. For example, the binding site of human transferrin for human transferrin receptor can be modified using combinatorial chemistry to create a diverse library of transferrin variants, some of which have acquired affinity for different antigens. The portion of human transferrin not involved with binding the receptor remains unchanged and serves as a scaffold, like framework regions of antibodies, to present the variant binding sites. The libraries are then screened, as an antibody library is, against a target antigen of interest to identify those variants having optimal selectivity and affinity for the target antigen. Non-antibody scaffold proteins, while similar in function to antibodies, are touted as having a number of advantages as compared to antibodies, which advantages include, among other things, enhanced solubility and tissue penetration, less costly manufacture, and ease of conjugation to other molecules of interest. Hey et al. (2005) TRENDS Biotechnol 23(10): 514- 522.

[0070] As used herein, the terms “inhibits” or “inhibiting” or “reducing,” as well grammatical variations of these terms, mean the decrease, limitation or blockage of, for example, a particular action, function, or interaction. For example, in the context of tumor growth, “inhibited” means terminated, reduced, delayed or prevented. Tumor growth is also “inhibited” if recurrence or metastasis of the cancer is reduced, slowed, delayed or prevented.

[0071] As used herein, “reducing the tumor,” means reducing the size, volume, or weight of the tumor, reducing the number of metastases, reducing the size or weight of a metastasis, or combinations thereof. In certain embodiments, a metastasis is cutaneous or subcutaneous. Thus, in certain embodiments, administration of the immune checkpoint inhibitor reduces the size or volume of the tumor by at least about 10%, at least about 20%, at least about 30%, at least about 40%, at least about 50%, at least about 60%, at least about 70%, at least about 75%, at least about 80%, at least about 90%, at least about 95%, at least about 98% or at least about 99%, for example, relative to a control drug in a subject of the same genotype. In certain embodiments, administration of the immune checkpoint inhibitor reduces the weight of the tumor by at least about 10%, at least about 20%, at least about 30%, at least about 40%, at least about 50%, at least about 60%, at least about 70%, at least about 75%, at least about 80%, at least about 90%, at least about 95%, at least about 98% or at least about 99%, for example, relative to a control drug in a subject of the same genotype. In certain embodiments, administration of the immune checkpoint inhibitor reduces the size or volume of a metastasis by at least about 10%, at least about 20%, at least about 30%, at least about 40%, at least about 50%, at least about 60%, at least about 70%, at least about 75%, at least about 80%, at least about 90%, at least about 95%, at least about 98% or at least about 99%, for example, relative to a control drug in a subject of the same genotype. In certain embodiments, administration of the immune checkpoint inhibitor reduces the number of metastases by at least about 10%, at least about 20%, at least about 30%, at least about 40%, at least about 50%, at least about 60%, at least about 70%, at least about 75%, at least about 80%, at least about 90%, at least about 95%, at least about 98% or at least about 99% for example, relative to a control drug in a subject of the same genotype. In certain embodiments, combinations of these effects are achieved.

[0072] A “biomarker gene” can be any gene having a genotype and/or expression level that can be determined, measured and/or evaluated as an indicator of a biologic process, pathogenic process, or pharmacologic response to a therapeutic intervention. A biomarker gene useful to practice the presently disclosed methods can be used as an indicator to determine whether a subject having a tumor (e.g., cancer) will be sensitive or resistant to a therapy comprising an immune checkpoint inhibitor and/or for monitoring response to a treatment with a therapy comprising an immune checkpoint protein inhibitor. In certain embodiments, a biomarker gene is a tumor suppressor gene. Sensitivity or resistance to a therapy with an immune checkpoint protein inhibitor can be determined by analyzing a nucleic acid molecule (DNA, mRNA, cDNA etc.) corresponding to a biomarker gene or the protein encoded by the biomarker gene.

Biomarker genes can include any gene whose genotype and/or level of expression in a tissue or cell can be used to predict response to an immune checkpoint inhibitor therapy. The detection, and in some cases the level, of one or more biomarker genes of the present disclosure permits the classification of a subject as sensitive or resistant to an immune checkpoint inhibitor therapy.

[0073] In certain embodiments, a biomarker gene is a tumor suppressor gene. In certain embodiments, a biomarker gene is part of a pathway or otherwise related to one of the biomarker genes disclosed herein. Additional biomarker genes useful in the methods disclosed herein can be identified by those skilled in the art based on the present disclosure. See, for example, Itatani et al., Int J Mol Sci., 20(23):5822, (2019)( TGF-P signaling pathway); Manning et al., Genes Dev., 19(15): 1773-1778, (2005) (PI3K-Akt pathway); Johannessen et al., Proc Natl Acad Sci, 102(24): 8573-8578, (2005) (PI3K-Akt pathway); Tsukiyama et al., Mol Cell Biol, 35:2007-2023, (2015)(Wnt signaling pathway); Zhang et al., Molecular Cancer, 17, 45 (2018)(c-Met signaling pathway); Mombach et al., BMC Genomics., 15 Suppl 7(Suppl 7): S7 (2014) (Gl/S checkpoint); Shain et al., PLoS ONE, 8(1): e55119, (2013)(SWI/SNF complex); Iyer et al., Oncogene, 23:4225-4231, (2004) (p300 and CBP); Villeneuve et al., Mol Cell., 51(1): 68-79, (2013)( USP15, KEAP1, and CUL3); Sahtoe etal., Nat Commun 7, 10292 (2016) (BAP1 and ASXL1); Harris et al., Oncogene, 24:2899-2908 (2005)(p53).

[0074] As used herein, the terms “determine,” “determine the genotype of a biomarker gene,” “determine the level of a biomarker gene,” “determine the amount of a biomarker gene,” “determine the biomarker gene level,” and the like are meant to encompass any technique that can be used to detect or measure the genotype, presence, or expression level of one or more biomarker genes. Such techniques can give qualitative or quantitative results. Biomarker gene levels can be determined by detecting the entire biomarker molecule or by detecting fragments or reaction products that are characteristic of the biomarker gene. The terms determining, measuring, or taking a measurement refer to a quantitative or qualitative determination of a property of an entity, for example, quantifying the amount or concentration of a molecule or the activity level of a molecule. Any known method of detecting or measuring the level of a biomarker can be used to practice the present invention, so long as the method detects the genotype, presence, absence, or expression level of the biomarker gene.

[0075] In certain embodiments, determining the genotype of a biomarker gene is performed at the nucleic acid level by performing RNA-seq, a reverse transcriptase polymerase chain reaction (RT-PCR) or a hybridization assay with oligonucleotides that are substantially complementary to portions of cDNA molecules of the at least one biomarker gene under conditions suitable for RNA-seq, RT-PCR or hybridization and obtaining expression levels of the at least one biomarker gene.

[0076] As used herein, the terms “cancer” or “tumor” refer to the presence of cells possessing characteristics typical of cancer-causing cells, such as uncontrolled proliferation, immortality, metastatic potential, rapid growth and proliferation rate, and certain characteristic morphological features. Cancer cells are often in the form of a tumor, but such cells can exist isolated within an animal, or can be non-tumorigenic, such as a leukemia cell. Cancers include, but are not limited to, B cell malignancies, for example, multiple myeloma, , the heavy chain diseases, such as, for example, alpha chain disease, gamma chain disease, and mu chain disease, benign monoclonal gammopathy, and immunocytic amyloidosis, skin cancer, breast cancer, lung cancer, bronchus cancer, colorectal cancer, prostate cancer, pancreatic cancer, stomach cancer, ovarian cancer, urinary bladder cancer, brain or central nervous system cancer, peripheral nervous system cancer, esophageal cancer, cervical cancer, uterine or endometrial cancer, cancer of the oral cavity or pharynx, liver cancer, kidney cancer, testicular cancer, biliary tract cancer, small bowel or appendix cancer, salivary gland cancer, thyroid gland cancer, adrenal gland cancer, osteosarcoma, chondrosarcoma, cancer of hematological tissues, and the like. Other non-limiting examples of types of cancers applicable to the methods encompassed by the present invention include human sarcomas and carcinomas, for example, fibrosarcoma, myxosarcoma, liposarcoma, chondrosarcoma, osteogenic sarcoma, chordoma, angiosarcoma, endotheliosarcoma, lymphangiosarcoma, lymphangioendotheliosarcoma, synovioma, mesothelioma, Ewing's tumor, leiomyosarcoma, rhabdomyosarcoma, colon carcinoma, colorectal cancer, pancreatic cancer, breast cancer, ovarian cancer, prostate cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinomas, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, renal cell carcinoma, hepatoma, bile duct carcinoma, liver cancer, choriocarcinoma, seminoma, embryonal carcinoma, Wilms' tumor, cervical cancer, bone cancer, brain tumor, testicular cancer, lung carcinoma, small cell lung carcinoma, bladder carcinoma, epithelial carcinoma, glioma, astrocytoma, medulloblastoma, craniopharyngioma, ependymoma, pinealoma, hemangioblastoma, acoustic neuroma, oligodendroglioma, meningioma, melanoma, neuroblastoma, retinoblastoma; leukemias, for example, acute lymphocytic leukemia and acute myelocytic leukemia (myeloblastic, promyelocytic, myelomonocytic, monocytic and erythroleukemia); chronic leukemia (chronic myelocytic (granulocytic) leukemia and chronic lymphocytic leukemia); and polycythemia vera, lymphoma (Hodgkin's disease and nonHodgkin's disease), multiple myeloma, Waldenstrom's macroglobulinemia, and heavy chain disease. In some embodiments, the cancer is an epithelial cancer such as, but not limited to, bladder cancer, breast cancer, cervical cancer, colon cancer, gynecologic cancers, renal cancer, laryngeal cancer, lung cancer, oral cancer, head and neck cancer, ovarian cancer, pancreatic cancer, prostate cancer, or skin cancer. In other embodiments, the cancer is breast cancer, prostate cancer, lung cancer, or colon cancer. In still other embodiments, the epithelial cancer is non-small-cell lung cancer, nonpapillary renal cell carcinoma, cervical carcinoma, ovarian carcinoma (for example, serous ovarian carcinoma), or breast carcinoma. The amount of a tumor in an individual is the “tumor burden” which can be measured as the number, volume, and/or weight of the tumor.

[0077] Non-limiting exemplary cancers in the embodiments of the present disclosure include skin cancer, lung cancer, pancreatic cancer, breast cancer, colorectal cancer bladder, liver cancer, kidney cancer, leukemia, and lymphoma. In certain embodiments, the cancer is lung cancer. In certain embodiments, the lung cancer is non-small cell lung cancer (NSCLC). In certain embodiments, NSCLC is lung adenocarcinoma. In certain embodiments, the cancer is skin cancer. In certain embodiments, skin cancer is melanoma.

[0078] As used herein, the term “lung cancer” refers to the collection of cancers affecting lung tissue. Non-small cell lung cancer (NSCLC) represents approximately accounts for 80% to 85% of all lung cancer diagnoses of all lung cancers. There are three main types of NSCLC: squamous cell carcinoma, large cell carcinoma, and adenocarcinoma.

[0079] The term “classifying” includes associating a sample with a response to an immune checkpoint inhibitor therapy. In certain instances, “classifying” is based on statistical evidence, empirical evidence, or both. In certain embodiments, the methods of classifying utilize a training set of samples having known genotypes. Once established, the training data set can serve as a basis, model, or template against which the features of an unknown sample are compared, to classify the sample.

[0080] The term “control” refers to any reference standard suitable to provide a comparison to the expression products in the test sample. A control can comprise a reference standard expression product level or genotype score from any suitable source, including but not limited to housekeeping genes, an expression product level range from normal tissue (or other previously analyzed control sample), a previously determined expression product level range within a test sample of a group of patients, or a set of patients with a certain outcome or receiving a certain therapy. It will be understood by those of ordinary skill in the art that such control samples and reference standard expression product levels can be used in combination as controls in the methods of the present invention. In various embodiments, the biomarker gene expression can be compared to a reference. A “reference” can be any value derived by art known methods for establishing a reference. [0081] The term “expression” as used herein, refers to the biosynthesis of a gene product. The term encompasses the transcription of a gene into RNA. The term also encompasses translation of RNA into one or more polypeptides, and further encompasses all naturally occurring post-transcriptional and post-translational modifications. The expressed protein can be within the cytoplasm of a host cell, into the extracellular milieu such as the growth medium of a cell culture or anchored to the cell membrane.

[0082] The term “gene product” as used herein, refers to RNA transcribed from a gene and to one or more proteins, polypeptides of fragments thereof that are the product of translation of the RNA transcribed from the gene, and further encompasses all naturally occurring post- transcriptional and post-translational modifications. The expressed protein can be within the cytoplasm of a host cell, into the extracellular milieu such as the growth medium of a cell culture or anchored to the cell membrane.

[0083] The terms “expression level” and “level of expression” as used herein refers to information regarding the relative or absolute level of expression of one or more biomarker genes in a cell or group of cells. The level of expression of a biomarker gene can be determined based on the level of RNA, such as mRNA, encoded by the gene. Alternatively, the level of expression can be determined based on the level of a polypeptide or fragment thereof encoded by the biomarker gene. Gene expression data can be acquired for an individual cell, or for a group of cells such as a tumor or biopsy sample. Gene expression data and gene expression levels can be stored on computer readable media, for example, the computer readable medium used in conjunction with a microarray or chip reading device. Such gene expression data can be manipulated to generate gene expression signatures.

[0084] The expression level of a biomarker gene can be determined using a reagent such as a probe, primer, or antibody and/or a method performed on a biological sample, for example a tumor sample of the subject, for ascertaining or measuring quantitatively, semi-quantitatively or qualitatively the amount of a of a polypeptide or mRNA (or cDNA derived therefrom) corresponding to one or more biomarker genes. For example, a level of a biomarker gene can be determined by a number of methods including for example immunoassays including for example immunohistochemistry, ELISA, Western blot, immunoprecipitation and the like, where a detection agent such as an antibody for example, a labeled antibody, specifically binds the encoded polypeptide and permits relative or absolute ascertaining of the amount of polypeptide encoded by the biomarker gene, hybridization and PCR protocols where a probe or primer or primer set are used to ascertain the amount of nucleic acid corresponding to the biomarker gene, including for example probe based and amplification based methods including for example microarray analysis, RT-PCR such as quantitative RT-PCR (qRT-PCR), gRT-PCR, serial analysis of gene expression (SAGE), Northern Blot, digital molecular barcoding technology, for example Nanostring Counter Analysis, and TaqMan quantitative PCR assays.

[0085] Other methods of mRNA detection and quantification can be applied, such as mRNA in situ hybridization in formalin-fixed, paraffin-embedded (FFPE) tissue samples or cells, which uses probe sets for each mRNA that bind specifically to an amplification system to amplify the hybridization signals; these amplified signals can be visualized using a standard fluorescence microscope or imaging system.

[0086] TaqMan probe-based gene expression analysis (PCR- based) can also be used for measuring biomarker gene expression levels in tissue samples, including mRNA levels in FFPE samples. TaqMan probe-based assays utilize a probe that hybridizes specifically to the mRNA target. This probe contains a quencher dye and a reporter dye (fluorescent molecule) attached to each end, and fluorescence is emitted only when specific hybridization to the mRNA target occurs. During the amplification step, the exonuclease activity of the polymerase enzyme causes the quencher and the reporter dyes to be detached from the probe, and fluorescence emission can occur. This fluorescence emission is recorded and signals are measured by a detection system; these signal intensities are used to calculate the abundance of a given transcript (gene expression) in a sample.

[0087] As used herein, a “nucleic acid” can generally refer to a polynucleotide sequence, or fragment thereof. A nucleic acid can comprise nucleotides. A nucleic acid can be exogenous or endogenous to a cell. A nucleic acid can exist in a cell-free environment. A nucleic acid can be a gene or fragment thereof. A nucleic acid can be DNA. A nucleic acid can be RNA. A nucleic acid can comprise one or more analogs (for example, altered backbone, sugar, or nucleobase). “Nucleic acid”, “polynucleotide, “target polynucleotide”, and “target nucleic acid” can be used interchangeably.

[0088] As used herein, the term “mRNA” or sometimes refer by “mRNA transcripts” include but is not limited to pre-mRNA transcript(s), transcript processing intermediates, mature mRNA(s) ready for translation and transcripts of the gene or genes, or nucleic acids derived from the mRNA transcript(s). Transcript processing can include splicing, editing and degradation. As used herein, a nucleic acid derived from an mRNA transcript refers to a nucleic acid for whose synthesis the mRNA transcript or a subsequence thereof has ultimately served as a template. Thus, a cDNA reverse transcribed from an mRNA, an RNA transcribed from that cDNA, a DNA amplified from the cDNA, an RNA transcribed from the amplified DNA, etc., are all derived from the mRNA transcript and detection of such derived products is indicative of the presence and/or abundance of the original transcript in a sample. Thus, mRNA derived samples include, but are not limited to, mRNA transcripts of the gene or genes, cDNA reverse transcribed from the mRNA, cRNA transcribed from the cDNA, DNA amplified from the genes, RNA transcribed from amplified DNA, and the like.

[0089] As used herein “sequencing” a nucleic acid molecule means determining the identity of at least one nucleotide in the molecule. In certain embodiments, the identity of less than all of the nucleotides in a molecule are determined. In certain embodiments, the identity of a majority or all of the nucleotides in the molecule is determined.

[0090] As used herein, the term “biological sample” refers to any sample obtained from a subject. A biological sample can be obtained from a subject prior to or subsequent to a diagnosis, at one or more time points prior to or following treatment or therapy, at one or more time points during which there is no treatment or therapy, or can be collected from a healthy subject. The biological sample can be a tissue sample or a fluid sample. In certain embodiments, the biological sample includes a tissue sample, a biopsy sample, a tumor aspirate, a bone marrow aspirate or a blood sample (or a fraction thereof, such as blood or serum). In certain embodiments, the biological sample includes a tumor cell or cancer cell, for example a circulating tumor cell present in a fluid sample, for example, blood or a fraction thereof. In certain embodiments, the biological sample includes a cell free nucleic acid present in a fluid sample, for example, blood or a fraction thereof. In one embodiment, the biological sample comprises a cell lysate (or lysate fraction) or cell extract; or a solution containing one or more molecules derived from a cell or cellular material (for example a polypeptide or nucleic acid). The cell lysate can include proteins, nuclear and/or mitochondrial fractions. In certain embodiments, the cell lysate includes a cytosolic fraction. In certain embodiments, the cell lysate includes a nuclear/mitochondrial fraction and a cytosolic fraction.

[0091] The source of a biological sample can be solid tissue as from a fresh, frozen and/or preserved organ, tissue sample, biopsy, or aspirate; blood or any blood constituents; bodily fluids such as cerebral spinal fluid, amniotic fluid, peritoneal fluid or interstitial fluid; or cells from any time in gestation or development of the subject. The biological sample can contain compounds that are not naturally intermixed with the tissue in nature such as preservatives, anticoagulants, buffers, fixatives, nutrients, antibiotics or the like. The biological sample can be preserved as a frozen sample or as formaldehyde- or paraformaldehyde-fixed paraffin- embedded (FFPE) tissue preparation. For example, the sample can be embedded in a matrix, for example, an FFPE block or a frozen sample. However, other tissue and sample types are amenable for use herein. In one embodiment, the other tissue and sample types can be fresh frozen tissue, wash fluids, or cell pellets, or the like. A biological sample can be a tumor sample, which contains nucleic acid molecules from a tumor or cancer. A biological sample that is a tumor sample can be DNA, for example, genomic DNA, or cDNA derived from RNA. In one embodiment, the tumor nucleic acid sample is purified or isolated (for example, it is removed from its natural state). In one embodiment, the sample is a tissue (for example, a tumor biopsy), a CTC or cell free nucleic acid.

[0092] In certain embodiments, a tumor sample is isolated from a human subject. In certain embodiments, the analysis is performed on a tumor biopsy embedded in paraffin wax. In one embodiment, the sample can be a fresh frozen tissue sample. In certain embodiments, the sample is a bodily fluid obtained from the subject. The bodily fluid can be blood or fractions thereof (specifically, serum, plasma), urine, saliva, sputum or cerebrospinal fluid (CSF). The sample can contain cellular as well as extracellular sources of nucleic acid. The extracellular sources can be cell-free nucleic acids and/or exosomes. The methods described herein, including the RT-PCR methods, are sensitive, precise and have multi- analyte capability for use with paraffin embedded samples. See, for example, Cronin et al., Am. J Pathol. 164(l):35-42 (2004).

[0093] General methods for mRNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, including Ausubel et al, ed., Current Protocols in Molecular Biology, John Wiley & Sons, New York 1987-1999. Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker (Lab Invest. 56:A67, 1987) and De Andres et al. (Biotechniques 18:42-44, 1995). In particular, RNA isolation can be performed using a purification kit, a buffer set and protease from commercial manufacturers according to the manufacturers’ instructions. RNA prepared from a tumor can be isolated, for example, by cesium chloride density gradient centrifugation.

[0094] “Likely to” or “increased likelihood,” as used herein, refers to an increased probability that an event will occur. Thus, in certain embodiments, a subject that is likely to respond to a treatment comprising an immune checkpoint inhibitor, such as a PD-1 inhibitor, has an increased probability of responding to a treatment comprising the immune checkpoint inhibitor relative to a reference subject or group of subjects.

[0095] “Unlikely to” refers to a decreased probability that an event, item, object, thing or person will occur with respect to a reference. Thus, a subject that is unlikely to respond to a treatment comprising an immune checkpoint inhibitor, such as a PD-1 inhibitor, has a decreased probability of responding to a treatment comprising the immune checkpoint inhibitor relative to a reference subject or group of subjects.

[0096] As used herein, “genomic profiling” means sequencing a part or all of the genome of a subject, such as to identify the nucleotide sequence of one or more genes in the subject, such as to identify genomic alterations (for example, mutations) in one or more biomarker genes that would identify the subject as a candidate to receive certain drugs or other therapeutic agents. Genomic profiling can be performed by a method described herein, such as by a next-generation sequencing method, or a massively parallel sequencing method.

[0097] The term “probe” refers to any molecule which is capable of selectively binding to a specifically intended target molecule, for example, a nucleotide transcript or protein encoded by or corresponding to a marker. Probes can be either synthesized by one skilled in the art, or derived from appropriate biological preparations. For purposes of detection of the target molecule, probes can be specifically designed to be labeled, as described herein. Examples of molecules that can be utilized as probes include, but are not limited to, RNA, DNA, proteins, antibodies, and organic molecules.

[0098] As used herein, the term “genotype” refers to the alleles at one or more specific biomarker genes. The genotype of a biomarker gene can be determined by methods that include nucleic acids (RNA, cDNA, and DNA) and proteins, and variants and fragments thereof.

[0099] As used herein, the term “sensitive” in the context of an immune checkpoint inhibitor therapy, means that the immune checkpoint inhibitor therapy is more effective at reducing the tumor relative to a control drug in a subject of the same genotype.

[00100] As used herein, the term “resistant” in the context of an immune checkpoint inhibitor therapy, means that the immune checkpoint inhibitor therapy is less effective at reducing the tumor relative to a control drug in a subject of the same genotype.

[00101] As described herein, responses to an immune checkpoint inhibitor include sensitivity and resistance compared to the response to a control drug in a subject of the same genotype. Such genotype-specific therapeutic responses (GSTRs) that can be characterized based on the relative numbers of tumors above a certain size after treatment (ScoreRTN - Relative Tumor Number) and the geometric mean of tumors from the full distribution of tumor sizes (ScoreRGM - Relative Geometric Mean) as described in Li, C., Lin, W.-Y et al. Quantitative in vivo analyses reveal a complex pharmacogenomic landscape in lung adenocarcinoma. bioRxiv, 2020.01.28.923912 (doi.org/10.1101/2020.01.28.923912).

[00102] In certain embodiments, the resistance and/or sensitivity profiles of one or more biomarker genes for an immune checkpoint inhibitor can be compared to the corresponding resistance and/or sensitivity scores for a standard of care therapy in order to determine whether a subject is likely to benefit from an immune checkpoint inhibitor therapy. For example, a PD-1 inhibitor therapy can be compared to standard of care (SoC) therapy for a particular cancer to determine which genotypes are sensitive or resistant relative to the SoC therapy using the described herein. Then, by excluding resistant patients and enrichment for sensitive patients within a prospective patient population, the performance of a PD-1 inhibitor relative to SoC can be improved.

[00103] For example, KEAP1 -deficiency drives selective sensitivity to a combination therapy comprising a PD-1 inhibitor and CTLA-4 inhibitor as compared to a PD-1 inhibitor therapy, while A T -deficiency drives selective resistance to combination therapy comprising a PD-1 inhibitor and CTLA-4 inhibitor as compared to a PD-1 inhibitor therapy. This discovery has important implications for the identification of subjects undergoing a PD-1 inhibitor therapy, for example, in combination with chemotherapy, who can benefit from the addition of a CTLA-4 inhibitor therapy to their treatment regimen.

[00104] In certain embodiments, the resistance and/or sensitivity profiles of one or more biomarker genes for an immune checkpoint inhibitor can be compared to the corresponding resistance and/or sensitivity scores for a standard of care therapy in order to determine whether a subject is likely to benefit from the immune checkpoint inhibitor therapy. For example, if there are four biomarker genes predictive of resistance to an immune checkpoint inhibitor therapy and two of the four biomarker genes show a lower resistance to an immune checkpoint inhibitor therapy relative to the standard of care therapy, while the other two show a higher resistance relative to the standard of care therapy, only the former two biomarker genes can be used for selecting the subject for an immune checkpoint inhibitor therapy over the standard of care therapy.

[00105] As used herein, the term “polynucleotide,” synonymously referred to as “nucleic acid molecule,” “nucleotides” or “nucleic acids,” refers to any polyribonucleotide or polydeoxyribonucleotide, which can be unmodified RNA or DNA or modified RNA or DNA. “Polynucleotides” include, without limitation single- and double-stranded DNA, DNA that is a mixture of single- and double-stranded regions, single- and double-stranded RNA, and RNA that is mixture of single- and double-stranded regions, hybrid molecules comprising DNA and RNA that can be single-stranded or, more typically, double-stranded or a mixture of single- and double-stranded regions. In addition, “polynucleotide” refers to triple-stranded regions comprising RNA or DNA or both RNA and DNA. The term polynucleotide also includes DNAs or RNAs containing one or more modified bases and DNAs or RNAs with backbones modified for stability or for other reasons. “Modified” bases include, for example, tritylated bases and unusual bases such as inosine. A variety of modifications can be made to DNA and RNA; thus, “polynucleotide” embraces chemically, enzymatically or metabolically modified forms of polynucleotides as typically found in nature, as well as the chemical forms of DNA and RNA characteristic of viruses and cells. “Polynucleotide” also embraces relatively short nucleic acid chains, often referred to as oligonucleotides.

[00106] A nucleic acid molecule corresponding to a biomarker gene of the present disclosure can be isolated using standard molecular biology techniques and the sequence information in the database records described herein. Using all or a portion of such nucleic acid sequences, nucleic acid molecules of the present invention can be isolated using standard hybridization and cloning techniques (for example, as described in Sambrook et al., ed., Molecular Cloning: A Laboratory Manual, 2 ^nd ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N. Y., 1989).

[00107] A nucleic acid molecule of the present disclosure can be amplified using cDNA, mRNA, or genomic DNA as a template and appropriate oligonucleotide primers according to standard PCR amplification techniques.

[00108] As used herein, the terms “peptide,” “polypeptide,” or “protein” can refer to a molecule comprised of amino acids and can be recognized as a protein by those of ordinary skill in the art. The conventional one-letter or three-letter code for amino acid residues is used herein. The terms “peptide,” “polypeptide,” and “protein” can be used interchangeably herein to refer to polymers of amino acids of any length. The polymer can be linear or branched, it can comprise modified amino acids, and it can be interrupted by non-amino acids. The terms also encompass an amino acid polymer that has been modified naturally or by intervention; for example, disulfide bond formation, glycosylation, lipidation, acetylation, phosphorylation, or any other manipulation or modification, such as conjugation with a labeling component. Also included within the definition are, for example, polypeptides containing one or more analogs of an amino acid (including, for example, unnatural amino acids, etc.), as well as other modifications known in the art. [00109] In the case of measuring protein levels to determine biomarker gene expression, any method known in the art is suitable provided it results in adequate specificity and sensitivity. For example, protein levels can be measured by binding to an antibody or an antibody fragment specific for the protein and measuring the amount of antibody -bound protein. Antibodies can be labeled by radioactive, fluorescent or other detectable reagents to facilitate detection. Methods of detection include, without limitation, enzyme-linked immunosorbent assay (ELISA) and immunoblot techniques.

[00110] In certain embodiments, the methods described herein include determining the genotype of one or more biomarker genes such as ADAR, APC, ARID 1 A, ARJD2, ASXL1, ATM, ATRX, BAP1, BRCA1, BRCA2, CDKN2A, CHD2, CIC, CMTR2, CREBBP, CUL3, DICER1, DLC1, DNMT1, DUSP4, EP 300, FAT1, FBXW7, JAK1, KDM5C, KDM6A, KEAP1, KMT2C, KMT2D, KRAS, LRP1B, MET, MGA, MSH2, MTAP, NCOA6, NF1, NF2, PALB2, PBRM1, PCNA, PTEN, PTPN11, PTPN13, PTPRD, PTPRS, RASA1, RBI, RB1CC1, RBM10, RNF43, SETD2, SMAD2, SMAD4, SMARCA4, SMG1, STAG2, STK11, TET2, TGFBR2, TREX1, TP53, TSC1, TSC2, USP15, and ZFHX3. In certain embodiments, the one or more biomarker genes comprise APC, ARID2, ATM, ATRX, BRCA2, CDKN2A, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, SIKH, TP53, and TSC1. In one embodiment, the one or more biomarker genes comprise APC, ARID2, ATM, ATRX, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, STK11, and TSC1. In certain embodiments, the one or more biomarker genes comprise BRCA2, CDKN2A, and TP53.

[00111] As exemplified herein, Keapl -deficiency drives selective sensitivity to combination therapy comprising a PD-1 inhibitor and a CTLA-4 inhibitor in vivo, while 4//77-deficiency drives selective resistance to a combination therapy comprising a PD-1 inhibitor and a CTLA-4 inhibitor in vivo. The discovery that /-deficiency drives selective sensitivity to a combination therapy comprising a PD-1 inhibitor and a CTLA-4 inhibitor in vivo, while ATM- deficiency drives selective resistance to combination therapy comprising a PD-1 inhibitor and a CTLA-4 inhibitor in vivo. This discovery has important implications for the identification of subjects undergoing a PD-1 inhibitor therapy, for example, in combination with chemotherapy, who can benefit from the addition of a CTLA-4 inhibitor therapy to their treatment regimen. [00112] In one aspect, the present disclosure provides a method of predicting response of tumor growth to inhibition by an immune checkpoint inhibitor therapy comprising a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor, the method comprising: (a) determining in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting the response of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating KEAP1 mutation, (ii) a decreased copy number of KEAP1, (iii) decreased expression of KEAP1 mRNA or protein, (iv) an inactivating ATM mutation, (v) a decreased copy number of ATM, or (vi) a decreased expression of ATM mRNA or protein.

[00113] In one aspect, the present disclosure provides a method of predicting resistance of tumor growth to inhibition by an immune checkpoint inhibitor therapy comprising a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor, the method comprising: (a) detecting in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting resistance of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating ATM mutation, (ii) a decreased copy number of ATM, or (iii) a decreased expression of ATM mRNA or protein.

[00114] In one aspect, the present disclosure provides a method of predicting sensitivity of tumor growth to inhibition by an immune checkpoint inhibitor therapy comprising a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor, the method comprising: (a) detecting in a tumor sample of a subject a genotype of one or more biomarker genes of a biomarker panel comprising; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting sensitivity of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating KEAP1 mutation, (ii) a decreased copy number of KEAP1, or (iii) decreased expression of KEAP1 mRNA or protein.

[00115] In one aspect, the present disclosure provides a method of treating non-small cell lung cancer (NSCLC), comprising treating a subject with an immune checkpoint inhibitor when a tumor sample obtained from the subject comprises (i) an inactivating KEAP1 mutation, (ii) a decreased copy number of KEAP1, or (iii) decreased expression of KEAP1 mRNA or protein.

[00116] In one aspect, the present disclosure provides a method of treating non-small cell lung cancer (NSCLC), comprising treating a subject with a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor when a tumor sample obtained from the subject comprises (i) an inactivating KEAP1 mutation, (ii) a decreased copy number of KEAP1, or (iii) decreased expression of KEAP1 mRNA or protein. In certain embodiments, the tumor sample further comprises, (iv) absence of an inactivating ATM mutation, (v) absence of a decreased copy number of ATM, or (vi) absence of a decreased expression of ATM mRNA or protein.

[00117] In one aspect, the present disclosure provides a method of treating non-small cell lung cancer (NSCLC), comprising treating a subject undergoing a therapy comprising a first immune checkpoint inhibitor and a chemotherapy with a second immune checkpoint inhibitor when a tumor sample obtained from the subject comprises (i) an inactivating KEAP1 mutation, (ii) a decreased copy number of KEAP1, or (iii) decreased expression of KEAP1 mRNA or protein.

[00118] In certain embodiments, the tumor sample further comprises (iv) absence of an inactivating ATM mutation, (v) absence of a decreased copy number of ATM, or (vi) absence of a decreased expression of ATM mRNA or protein.

[00119] In certain embodiments, the subject is already undergoing a therapy comprising one or more immune checkpoint inhibitors and a chemotherapy. In certain embodiments, the subject is already undergoing an immune checkpoint inhibitor therapy comprising a programmed cell death protein 1 (PD-1) inhibitor and a chemotherapy. In certain embodiments, the methods further comprise selecting a therapy for the subject. In certain embodiments, the therapy comprise a CTLA-4 inhibitor (e.g., a CTLA-4 therapy), for example, an anti-CTLA-4 antibody, e.g., Ipilimumab (Yervoy). In certain embodiments, the PD-1 inhibitor is an anti-PD-1 antibody. In certain embodiments, the PD-1 inhibitor is selected from the group consisting of nivolumab (Opdivo), Pembrolizumab (Keytruda), and Cemiplimab (Libtayo). In certain embodiments, the chemotherapy comprises a taxane, for example, docetaxel, paclitaxel or cabazitaxel. In certain embodiments, the chemotherapy comprises platinum-based agents, for example, cisplatin, carboplatin, oxaliplatin, nedaplatin, and lobaplatin. In certain embodiments, the chemotherapy is selected from Cisplatin, Carboplatin, Paciltaxel (Taxol), Docetaxel (Taxotere), Albuminbound paclitaxel (nab-paclitaxel, Abraxane), Gemcitabine (Gemzar), Vinorelbine (Navelbine), Etoposide (VP- 16), and Pemetrexed (Alimta), or a combination of one or more of these agents. Combinations of agents are routinely used to treat early-stage lung cancer. If a combination is used, it can include a platinum-based agent plus one other drug. Sometimes other combinations that do not include these drugs, such as gemcitabine with vinorelbine or paclitaxel, can also be used.

[00120] The present disclosure provides, in part, methods for accurately classifying a subject afflicted with cancer as likely to be sensitive for a therapy comprising an immune checkpoint inhibitor. The methods comprise obtaining a tumor sample of the subject and determining a genotype of one or more biomarker genes. In certain embodiments, the biological sample (for example, tumor sample) comprises polypeptides encoded by the one or more biomarker genes. Alternatively, the biological sample can comprise mRNA molecules or genomic DNA corresponding to the one or more biomarker genes. In certain embodiments, the methods involve obtaining a tumor sample of the subject and contacting the tumor sample with a reagent capable of determining the genotype by detecting, for example, a polypeptide or a nucleic acid that encodes the biomarker gene or fragments thereof.

[00121] In certain embodiments, the present disclosure provides methods for selecting a subject for treatment with a combination therapy comprising an immune checkpoint inhibitor and a chemotherapy if it is likely that the subject will respond to the combination therapy comprising an immune checkpoint inhibitor and a chemotherapy, wherein the likelihood of response is determined by performing any of the disclosed methods for predicting resistance, sensitivity, or response of tumor growth to inhibition by the therapy on a biological sample obtained from the subject. In certain embodiments, the determination is based on results of an existing biological sample of the subject.

[00122] In certain embodiments, the present disclosure provides methods for selecting a subject for a therapy comprising a immune checkpoint inhibitor therapy if it is likely that the subject will respond to the immune checkpoint inhibitor therapy, wherein the likelihood of response is determined by performing any of the disclosed methods for predicting resistance, sensitivity, or response of tumor growth to inhibition by the therapy on a biological sample obtained from the subject

[00123] In certain embodiments, the present disclosure provides methods for selecting a subject undergoing a combination therapy comprising a first immune checkpoint inhibitor and a chemotherapy for a treatment comprising a second immune checkpoint inhibitor if it is likely that the subject will respond to the combination therapy comprising two immune checkpoint inhibitors and a chemotherapy, wherein the likelihood of response is determined by performing any of the disclosed methods for predicting resistance, sensitivity, or response of tumor growth to inhibition by the therapy on a biological sample obtained from the subject. In some embodiments, the determination is based on results of an existing biological sample of the subject.

[00124] In some embodiments, the present disclosure provides methods for selecting a subject undergoing treatment with a combination therapy comprising a first immune checkpoint inhibitor and a second immune checkpoint inhibitor if it is likely that the subject will respond to the combination therapy comprising two immune checkpoint inhibitors, wherein the likelihood of response is determined by performing any of the disclosed methods for predicting resistance, sensitivity, or response of tumor growth to inhibition by the therapy on a biological sample obtained from the subject. In some embodiments, the determination is based on results of an existing biological sample of the subject.

[00125] The presently disclosed methods detect mRNA, polypeptide, genomic DNA, or fragments thereof, in a biological sample in vitro as well as in vivo. For example, in vitro techniques for detection of mRNA or a fragment thereof include Northern hybridizations and in situ hybridizations. In vitro techniques for detection of polypeptide include enzyme linked immunosorbent assays (ELISAs), Western blots, immunoprecipitations, and immunofluorescence. In vitro techniques for detection of biomarker genomic DNA or a fragment thereof include Southern hybridizations. Furthermore, in vivo techniques for detection of one or more polypeptides or fragments thereof include labeled antibodies. For example, the antibody can be labeled with a radioactive marker whose presence and location in a subject can be detected by standard imaging techniques.

[00126] In some embodiments, the genotype, presence or level of at least one, two, three, four, five, six, seven, eight, nine, ten, fifty, sixty, or more biomarker genes of the invention is determined in the tumor sample. In some embodiments, the presently disclosed methods employ a statistical algorithm and/or empirical data (for example, the presence or level of one or biomarker genes described herein). In certain instances, a single learning statistical classifier system can be used to classify a sample. The use of a single learning statistical classifier system typically classifies the sample accurately with a sensitivity, specificity, positive predictive value, negative predictive value, and/or overall accuracy of at least about 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[00127] Other suitable statistical algorithms are well known to those of skill in the art. For example, learning statistical classifier systems include a machine learning algorithmic technique capable of adapting to complex data sets (for example, panel of markers of interest) and making decisions based upon such data sets. In some embodiments, a single learning statistical classifier system such as a classification tree (for example, random forest) is used. In other embodiments, a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, or more learning statistical classifier systems are used, preferably in tandem. Examples of learning statistical classifier systems include, but are not limited to, those using inductive learning (for example, decision/classification trees such as random forests, classification and regression trees (C&RT), boosted trees, etc.), Probably Approximately Correct (PAC) learning, connectionist learning (for example, neural networks (NN), artificial neural networks (ANN), neuro fuzzy networks (NFN), network structures, perceptrons such as multi-layer perceptrons, multi-layer feed-forward networks, applications of neural networks, Bayesian learning in belief networks, etc.), reinforcement learning (for example, passive learning in a known environment such as naive learning, adaptive dynamic learning, and temporal difference learning, passive learning in an unknown environment, active learning in an unknown environment, learning action-value functions, applications of reinforcement learning, and genetic algorithms and evolutionary programming. Other learning statistical classifier systems include support vector machines (for example, Kernel methods), multivariate adaptive regression splines (MARS), Levenberg-Marquardt algorithms, Gauss- Newton algorithms, mixtures of Gaussians, gradient descent algorithms, and learning vector quantization (LVQ). In certain embodiments, the method of the present invention further comprises sending the cancer classification results to a clinician, for example, an oncologist or hematologist.

[00128] In certain embodiments, determining the genotype of one or more biomarker genes comprises genomic profiling to directly determine the genotype of the one or more biomarker genes. In certain embodiments, genomic profiling comprises contacting the biological sample with reagents, including, probes and/or primers, for sequencing the one or more biomarker genes or a portion thereof. In certain embodiments, probes or primers can be designed to detect a mutation in the one or more biomarker genes. In certain embodiments, the mutation is an inactivating mutation. In certain embodiments, the mutation results in decreased gene expression.

[00129] In certain embodiments, the methods include directly determining the genotype of one or more biomarker genes by genomic profiling to detect the presence or absence of a genetic alteration characterized by at least one alteration affecting the integrity of a gene encoding one or more biomarkers polypeptide, or the mis-expression of the biomarker (for example, mutations and/or splice variants). For example, such genetic alterations can be detected by ascertaining the existence of at least one of a deletion of one or more nucleotides from one or more biomarker genes; an addition of one or more nucleotides to one or more biomarker genes; a substitution of one or more nucleotides of one or more biomarker genes; a chromosomal rearrangement of one or more biomarker genes; an alteration in the level of a mRNA transcript of one or more biomarker genes; aberrant modification of one or more biomarker genes; such as of the methylation pattern of the genomic DNA; the presence of a non-wild type splicing pattern of a messenger RNA transcript of one or more biomarker genes; a non-wild type level of one or more biomarkers polypeptide; allelic loss of one or more biomarker genes, and inappropriate post-translational modification of one or more biomarkers polypeptide. As described herein, there are a large number of assays known in the art which can be used for detecting alterations in one or more biomarker genes. [00130] In certain embodiments, detection of the alteration involves the use of a probe/primer in a polymerase chain reaction (PCR), such as anchor PCR or RACE PCR, or, alternatively, in a ligation chain reaction (LCR) (see, for example, Landegran et al. (1988) Science 241:1077- 1080; and Nakazawa et al. (1994) Proc. Natl. Acad. Sci. USA 91 :360-364), the latter of which can be particularly useful for detecting point mutations in one or more biomarker genes (see Abravaya et al. (1995) Nucleic Acids Res. 23:675-682). This method can include collecting a sample of cells from a subject, isolating nucleic acid from the cells of the sample, contacting the nucleic acid sample with one or more primers which specifically hybridize to one or more biomarker genes of the present disclosure, or fragments thereof, under conditions such that hybridization and amplification of the biomarker gene (if present) occurs, and detecting the presence or absence of an amplification product, or detecting the size of the amplification product and comparing the length to a control sample. It is anticipated that PCR and/or LCR can be desirable to use as a preliminary amplification step in conjunction with any of the techniques used for detecting mutations described herein.

[00131] Alternative amplification methods include self-sustained sequence replication (Guatelli, J. C. et al. (1990) Proc. Natl. Acad. Sci. USA 87: 1874-1878), transcriptional amplification system (Kwoh, D. Y. et al. (1989) Proc. Natl. Acad. Sci. USA 86: 1173-1177), Q- Beta Replicase (Lizardi, P. M. et al. (1988) Bio-Technology 6: 1197), or any other nucleic acid amplification method, followed by the detection of the amplified molecules using techniques well known to those of skill in the art. These detection schemes are especially useful for the detection of nucleic acid molecules if such molecules are present in very low numbers.

[00132] In certain embodiments, mutations in the presently disclosed one or more biomarker genes, or a fragment thereof, from a sample cell can be identified by alterations in restriction enzyme cleavage patterns. For example, sample and control DNA is isolated, amplified (optionally), digested with one or more restriction endonucleases, and fragment length sizes are determined by gel electrophoresis and compared. Differences in fragment length sizes between sample and control DNA indicates mutations in the sample DNA. Moreover, the use of sequence specific ribozymes can be used to score for the presence of specific mutations by development or loss of a ribozyme cleavage site. [00133] In certain embodiments, genetic mutations in the presently disclosed one or more biomarker genes, or a fragment thereof, can be identified by hybridizing a nucleic acid to high density arrays containing hundreds or thousands of oligonucleotide probes (Cronin, M. T. et al. (1996) Hum. Mutat. 7:244-255; Kozal, M. J. et al. (1996) Nat. Med. 2:753-759).

[00134] In certain embodiments, any of a variety of sequencing methods known in the art can be used to directly sequence the presently disclosed one or more biomarker genes, or a fragment thereof, and detect mutations by comparing the sequence of the sample biomarker gene with the corresponding wild-type (control) sequence. Non-limiting examples of sequencing reactions include next-generation sequencing to determine the nucleotide sequence of either individual nucleic acid molecules (for example, in single molecule sequencing) or clonally expanded proxies for individual nucleic acid molecules in a highly parallel fashion.

[0001] Next generation sequencing methods are known in the art, and are described, for example, in Metzker, M. (2010) Nature Biotechnology Reviews 11 :31-46. Next generation sequencing collectively refers to several DNA/RNA sequencing technologies that vary according to the input material, length of read, and portion of the genome to be sequenced. Broadly, the two major next generation sequencing technologies are short-read sequencing and long-read sequencing. Short-read sequencing generally refers to reads that are shorter than 300 bp, whereas long-read sequencing refers to reads that are longer than 2.5 Kb. Short-read sequencing is a relatively inexpensive option (low costs per Gb) that has a high level of accuracy and is used more frequently in clinical practice for the detection of specific mutation hotspots. Moreover, based on the initial input material, different sequencing approaches can be used (for example, genomic DNA [DNA-seq], messenger or noncoding RNA [RNA-seq], or any nucleic or ribonucleic material obtained following the use of certain procedures).

[0002] Current next generation sequencing approaches also differ based on the extent of target enrichment and sequencing involved, with the 3 major types being whole genome sequencing (WGS), whole-exome sequencing (WES), and targeted gene panels. WGS refers to sequencing the entire genome, including coding and noncoding regions. It allows detection of several types of genetic aberrations, including single nucleotide variants and/or such structural alterations as insertions or deletions (also called indels), copy number variations involving duplications or deletions of long stretches of a chromosomal region, and rearrangements involving gross alterations in chromosomes or large chromosomal regions. WES involves sequencing only the coding regions of the genome and is limited in its ability to detect rearrangements between genes with breakpoints that frequently occur in intronic regions. RNA-based whole-transcriptome approaches can be another strategy to identify gene rearrangements.

[0003] Another next generation sequencing strategy, which is currently the most commonly used approach to cancer genotyping in clinical use, is targeted gene panels that interrogate a discrete number of genes. This approach has the advantage of being able to focus on clinically relevant targets with deeper sequencer and focused analyses. Targeted gene panels can be performed with either amplicon-based or hybrid-capture enrichment strategies and can range from small, hotspot-only panels focusing on less than fifty genes to larger, more comprehensive panels that include hundreds to greater than a thousand genes with selected intronic tiling coverage. In addition to lower costs, the advantages of targeted gene panels include greater analytic sensitivity because of the greater depth of coverage, less complex data analysis and interpretation than would be necessary for WES and WGS, and greater flexibility that allows for tailoring the testing to genomic regions relevant to cancer. Any of the known Next generation sequencing approaches can be practiced for the methods described herein and a skilled person will be able to select the best sequencing strategy to practice the methods described herein.

[00135] In certain embodiments, determining the genotype of one or more biomarker genes comprises measuring the expression level of one or more biomarker genes. The expression level can be measured in a number of ways, including, but not limited to measuring the mRNA encoded by the biomarker genes; measuring the amount of protein encoded by the biomarker genes; and measuring the activity of the protein encoded by the biomarker genes. In certain embodiments, a genotype of a biomarker gene is determined by measuring RNA, cDNA, protein or any combination thereof. When a genotype is determined by measuring RNA, the RNA can be reverse transcribed to produce cDNA (such as by RT-PCR), and the produced cDNA expression level can be detected. The expression level of a biomarker gene can be detected by forming a complex between a nucleic acid corresponding to a biomarker gene and a labeled probe or primer. When the nucleic acid is RNA or cDNA, the RNA or cDNA can be detected by forming a complex between the RNA or cDNA and a labeled nucleic acid probe or primer. The complex between the RNA or cDNA and the labeled nucleic acid probe or primer can be a hybridization complex. [00136] Another method of determining the genotype of a biomarker gene at the nucleic acid level is the use of an amplification method such as, for example, RT-PCR or quantitative RT- PCR (qRT- PCR). Methods for determining the level of mRNA in a sample can involve the process of nucleic acid amplification, for example, by RT-PCR, ligase chain reaction or any other nucleic acid amplification method, followed by the detection of the amplified molecules using techniques well known to those of skill in the art. Numerous different PCR or qRT-PCR protocols are known in the art and can be directly applied or adapted for use using the presently described compositions for the detection and/or quantification of expression of biomarker genes in a sample.

[00137] Isolated mRNA can be used in hybridization or amplification assays that include, but are not limited to, Southern or Northern analyses, PCR analyses and probe arrays. One method for the detection of mRNA levels involves contacting the isolated mRNA or synthesized cDNA with a nucleic acid molecule (probe) that can hybridize to the mRNA encoded by the gene being detected. The nucleic acid probe can be, for example, a cDNA, or a portion thereof, such as an oligonucleotide of at least about 7, about 15, about 30, about 50, about 100, about 250, or about 500 nucleotides in length and sufficient to specifically hybridize under stringent conditions to the non-natural cDNA or mRNA.

[00138] As described herein, biomarker gene expression can be assessed by any of a wide variety of well-known methods for detecting expression of a transcribed molecule or protein. Non-limiting examples of such methods include immunological methods for detection of secreted, cell-surface, cytoplasmic, or nuclear proteins, protein purification methods, protein function or activity assays, nucleic acid hybridization methods, nucleic acid reverse transcription methods, and nucleic acid amplification methods.

[00139] In certain embodiments, activity of a particular biomarker gene is characterized by a measure of gene transcript (for example mRNA), by a measure of the quantity of translated protein, or by a measure of gene product activity. Biomarker gene expression can be monitored in a variety of ways, including by detecting mRNA levels, protein levels, or protein activity, any of which can be measured using standard techniques. Detection can involve quantification of the level of gene expression (for example, genomic DNA, cDNA, mRNA, protein, or enzyme activity), or, alternatively, can be a qualitative assessment of the level of gene expression, in particular in comparison with a control level. The type of level being detected will be clear to the skilled person from the context.

[00140] In certain embodiments, detecting or determining expression levels of a biomarker gene and functionally similar homologs thereof, including a fragment or genetic alteration thereof (for example, in regulatory or promoter regions thereof) comprises detecting or determining RNA levels for the biomarker marker gene. In certain embodiments, one or more cells from the subject to be tested are obtained and RNA is isolated from the cells.

[00141] General methods for mRNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, including Ausubel et al, ed., Current Protocols in Molecular Biology, John Wiley & Sons, New York 1987-1999. Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker (Lab Invest. 56:A67, 1987) and De Andres et al. (Biotechniques 18:42-44, 1995). In particular, RNA isolation can be performed using a purification kit, a buffer set and protease from commercial manufacturers according to the manufacturers’ instructions. RNA prepared from a tumor can be isolated, for example, by cesium chloride density gradient centrifugation.

[00142] The population of RNA can optionally be enriched, and further be amplified. For example, where RNA is mRNA, an amplification process such as RT-PCR can be utilized to amplify the mRNA, such that a signal is detectable or detection is enhanced. Such an amplification process is beneficial particularly when the biological, tissue, or tumor sample is of a small size or volume. Various amplification and detection methods can be used. For example, it is within the scope of the present invention to reverse transcribe mRNA into cDNA followed by polymerase chain reaction (RT-PCR); or, to use a single enzyme for both steps, or reverse transcribe mRNA into cDNA followed by symmetric gap ligase chain reaction (RT-AGLCR).

[00143] Many techniques are known in the state of the art for determining absolute and relative levels of gene expression, commonly used techniques suitable for use in the present invention include Northern analysis, RNase protection assays (RPA), microarrays and PCR- based techniques, such as quantitative PCR and differential display PCR. For example, Northern blotting involves running a preparation of RNA on a denaturing agarose gel, and transferring it to a suitable support, such as activated cellulose, nitrocellulose or glass or nylon membranes. Radiolabeled cDNA or RNA is then hybridized to the preparation, washed, and analyzed by autoradiography.

[00144] In situ hybridization visualization can also be employed, wherein a radioactively labeled antisense RNA probe is hybridized with a thin section of a biopsy sample, washed, cleaved with RNase and exposed to a sensitive emulsion for autoradiography. The samples can be stained with hematoxylin to demonstrate the histological composition of the sample, and dark field imaging with a suitable light filter shows the developed emulsion. Non-radioactive labels such as digoxigenin can also be used.

[00145] Alternatively, mRNA expression can be detected on a DNA array, chip or a microarray. Labeled nucleic acids of a test sample obtained from a subject can be hybridized to a solid surface comprising biomarker DNA. Positive hybridization signal is obtained with the sample containing biomarker transcripts. In one embodiment, gene expression can be detected by microarray analysis. Differential gene expression can also be identified or confirmed using a microarray technique. The expression levels of one or more biomarker genes can be measured in either fresh or fixed tissue, using microarray technology. In this method, polynucleotide sequences of interest (including cDNAs and oligonucleotides) are plated, or arrayed, on a microchip substrate. The arrayed sequences are then hybridized with specific DNA probes from cells or tissues of interest. Fluorescently labeled cDNA probes can be generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest. Labeled cDNA probes applied to the chip hybridize with specificity to each spot of DNA on the array. After stringent washing to remove non-specifically bound probes, the microarray chip is scanned by a device such as, confocal laser microscopy or by another detection method. Quantitation of hybridization of each arrayed element allows for assessment of corresponding mRNA abundance. Microarray analysis can be performed by commercially available equipment, following manufacturer's protocols.

[00146] Types of probes that can be used in the methods described herein include cDNA, riboprobes, synthetic oligonucleotides and genomic probes. The type of probe used will generally be dictated by the particular situation, such as riboprobes for in situ hybridization, and cDNA for Northern blotting, for example. In one embodiment, the probe is directed to nucleotide regions unique to the RNA. The probes can be as short as is required to differentially recognize marker mRNA transcripts, and can be as short as, for example, about 15 bases; however, probes of at least 17, 18, 19 or 20 or more bases can be used. In one embodiment, the primers and probes hybridize specifically under stringent conditions to a DNA fragment having the nucleotide sequence corresponding to the marker. As herein used, the term “stringent conditions” means hybridization will occur only if there is at least about 95% identity in nucleotide sequences. In another embodiment, hybridization under “stringent conditions” occurs when there is at least 97% identity between the sequences.

[00147] The activity, level or presence of a protein encoded by a biomarker gene can be detected and/or quantified by detecting or quantifying the expressed polypeptide. The polypeptide can be detected and quantified by any of a number of means well known to those of skill in the art. Any method known in the art for detecting polypeptides can be used. Such methods include, but are not limited to, immunodiffusion, immunoelectrophoresis, radioimmunoassay (RIA), enzyme-linked immunosorbent assays (ELISAs), immunofluorescent assays, Western blotting, binder-ligand assays, immunohistochemical techniques, agglutination, complement assays, high performance liquid chromatography (HPLC), thin layer chromatography (TLC), hyperdiffusion chromatography, and the like (for example, Basic and Clinical Immunology, Sites and Terr, eds., Appleton and Lange, Norwalk, Conn, pp 217-262, 1991 which is incorporated by reference).

[00148] ELISA and RIA procedures can be conducted such that a desired protein standard is labeled (with a radioisotope such as ¹²⁵ I or ³⁵ S, or an assayable enzyme, such as horseradish peroxidase or alkaline phosphatase), and, together with the unlabelled sample, brought into contact with the corresponding antibody, whereon a second antibody is used to bind the first, and radioactivity or the immobilized enzyme assayed (competitive assay). Alternatively, the protein in the sample is allowed to react with the corresponding immobilized antibody, radioisotope- or enzyme-labeled antibody is allowed to react with the system, and radioactivity or the enzyme assayed (ELISA-sandwich assay). Other conventional methods can also be employed as suitable. [00149] Enzymatic and radiolabeling of a protein encoded by a biomarker gene and/or the antibodies can be effected by conventional means. It is possible to immobilize the enzyme itself on a support, but if solid-phase enzyme is required, then this is generally best achieved by binding to antibody and affixing the antibody to a support, models, and systems for which are well-known in the art.

[00150] Other techniques can be used to detect protein corresponding to a biomarker gene according to a practitioner's preference based upon the present disclosure. One such technique is Western blotting (Towbin et al., Proc. Nat. Acad. Sci. 76:4350 (1979)), wherein a suitably treated sample is run on an SDS-PAGE gel before being transferred to a solid support, such as a nitrocellulose filter. Antibodies specific for the protein (unlabeled) are then brought into contact with the support and assayed by a secondary immunological reagent, such as labeled protein A or anti-immunoglobulin (suitable labels including ¹²⁵ I, horseradish peroxidase and alkaline phosphatase). Chromatographic detection can also be used.

[00151] Immunohistochemistry can be used to detect expression of a protein corresponding to a biomarker gene , for example, in a biopsy sample. A suitable antibody is brought into contact with, for example, a thin layer of cells, washed, and then contacted with a second, labeled antibody. Labeling can be by fluorescent markers, enzymes, such as peroxidase, avidin, or radiolabelling. The assay is scored visually, using microscopy. Any other art-known method can be used to detect a protein corresponding to a biomarker gene.

EMBODIMENTS

[00152] The present disclosure provides the following non-limiting embodiments.

[00153] In one set of embodiments, provided are:

1. A method comprising: (a) determining the genotype of one or more biomarker genes in a biological sample of a subject; and (b) identifying whether the subject is likely to be sensitive to a therapy comprising an immune checkpoint inhibitor based on the genotype. A method comprising: (a) determining a genotype of one or more biomarker genes of a biomarker panel comprising ADAR, APC, ARID 1 A, ARID2, ASXL1, ATM, ATRX, BAP1, BRCA1, BRCA2, CDKN2A, CHD2, CIC, CMTR2, CREBBP, CUL3, DICER1, DLC1, DNMT1, DUSP4, EP 300, FAT1, FBXW7, JAK1, KDM5C, KDM6A, KEAP1, KMT2C, KMT2D, KRAS, LRP IB, MET, MG A, MSH2, MTAP, NC0A6, NF1, NF2, PALB2, PBRM1, PCNA, PTEN, PTPN11, PTPN13, PTPRD, PTPRS, RASA1, RBI, RB1CC1, RBM10, RNF43, SETD2, SMAD2, SMAD4, SMARCA4, SMG1, STAG2, SIKH, TET2, TGFBR2, TREX1, TP53, TSC1, TSC2, USP15, and ZFHX3 in a biological sample of a subject afflicted with cancer; (b) determining a genotype of each of the one or more biomarker genes in the biological sample; and (c) classifying a subject as sensitive or resistant to a therapy comprising an immune checkpoint inhibitor based on the genotype of each of the one or more biomarker genes in the biological sample. The method of embodiment 1 or 2, wherein the biomarker panel comprises APC, ARID2, ATM, ATRX, BRCA2, CDKN2A, CMTR2, KEAP1, KMT2D, MG A, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, STK11, TP53, and TSC1. The method of any one of embodiments 1-3, wherein the biomarker panel comprises APC, CMTR2, KEAP1, KMT2D, MGA, and NF 1. The method of any one of embodiments 1-3, wherein the biomarker panel comprises ATM. The method of any one of embodiments 1-5, further comprising an initial step of obtaining a biological sample from the subject. The method of any one of embodiments 1-6, wherein the biological sample is a tumor sample. The method of any one of embodiments 1-7, wherein the genotype comprises a mutation in the one or more biomarker genes. The method of embodiment 7, wherein the mutation inactivates the one or more biomarker genes. 10. The method of any one of embodiments 1-9, further comprising comparing the genotype with a reference genotype.

11. The method of any one of embodiments 1-10, wherein the genotype is reported as a score.

12. The method of any one of embodiments 1-11, wherein determining the genotype comprises genomic profiling.

13. The method of any one of embodiments 1-11, wherein determining the genotype comprises measuring gene expression.

14. The method of embodiment 13, wherein measuring gene expression comprises detection of ribonucleic acids (RNAs) or polypeptides.

15. The method of any one of any one of embodiments 1-14, wherein the subject is classified as sensitive to an immune checkpoint inhibitor treatment.

16. The method of any one of any one of embodiments 1-14, wherein the subject is classified as resistant to an immune checkpoint inhibitor treatment.

17. The method of any one of embodiments 2-16, wherein the cancer is selected from the group consisting of skin cancer, lung cancer, pancreatic cancer, breast cancer, and colorectal cancer.

18. The method of embodiment 17, wherein the cancer is lung cancer.

19. The method of embodiment 18, wherein the lung cancer is non-small cell lung cancer (NSCLC). 0. The method of embodiment 19, wherein the NSCLC is lung adenocarcinoma. 1. The method of embodiment 17, wherein the cancer is skin cancer. 2. The method of embodiment 21, wherein the skin cancer is melanoma. 23. The method of any one of embodiments 1-22, wherein the immune checkpoint inhibitor comprises an antibody or an antigen-binding fragment thereof.

24. The method of any one of embodiments 1-22, wherein the immune checkpoint inhibitor inhibits programmed cell death protein 1 (PD-1).

25. The method of embodiment 24, wherein the immune checkpoint inhibitor is an anti-PD- 1 antibody, optionally wherein the immune checkpoint inhibitor is selected from the group consisting of nivolumab (Opdivo), Pembrolizumab (Keytruda), and Cemiplimab (Libtayo).

26. The method of any one of embodiments 1-22, wherein the immune checkpoint inhibitor inhibits cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4).

27. The method of embodiment 26, wherein the immune checkpoint inhibitor is an anti- CTLA-4 antibody, optionally wherein the immune checkpoint inhibitor is Ipilimumab (Yervoy).

28. The method of any one of embodiments 1-22, wherein the immune checkpoint inhibitor inhibits programmed cell death protein 1 ligand (PD-1L).

29. The method of embodiment 28, wherein the immune checkpoint inhibitor is an anti-PD- L1 antibody, optionally wherein the immune checkpoint inhibitor is selected from the group consisting of Atezolizumab (Tecentriq), avelumab (Bevencio), and durvalumab (Imfinzi).

30. The method of any one of embodiments 1-29, wherein the therapy further comprises a chemotherapy.

31. The method of embodiment 30, wherein the chemotherapy comprises docetaxel, paclitaxel or cabazitaxel.

32. The method of embodiment 30, wherein the chemotherapy comprises platinum-based chemotherapy. 33. The method of any one of embodiments 1-32, further comprising contacting the biological sample with reagents for determining a genotype of the one or more biomarker genes.

34. The method of embodiment 33, wherein the reagents specifically bind to the one or more biomarker genes or a gene product of to the one or more biomarker genes.

35. The method of embodiment 33, wherein the reagents are sequencing reagents.

36. The method of embodiment 33, wherein the reagents comprise a probe or primer.

37. The method of embodiment 33, wherein the reagents comprise an antibody or an antigen-binding fragment thereof.

38. The method of any one of embodiments 1-37, further comprising administering an immune checkpoint inhibitor therapy to the subject.

39. A method of predicting resistance of tumor growth to inhibition by a therapy comprising an immune checkpoint inhibitor, comprising: (a) detecting in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting resistance of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating BRCA2, CDKN2A, or TP53 mutation, (ii) a decreased copy number of BRCA2, CDKN2A, or TP53, or (iii) a decreased expression of BRCA2, CDKN2A, or TP53 mRNA or protein.

40. A method of predicting sensitivity of tumor growth to inhibition by a therapy comprising an immune checkpoint inhibitor, comprising: (a) detecting in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting sensitivity of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating APC, ARID2, ATM, ATRX, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, STK11, or TSC1 mutation, (ii) a decreased copy number of APC, ARID 2, ATM, ATRX, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, SIKH, or TSC1, or (iii) decreased expression of APC, ARID2, ATM, ATRX, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, SIKH, or TSC1 mRNA or protein. A method of predicting response of tumor growth to inhibition by a therapy comprising an immune checkpoint inhibitor, comprising: (a) determining in a tumor sample of a subject a genotype of one or more biomarker genes of a biomarker panel comprising APC, ARID2, ATM, ATRX, BRCA2, CDKN2A, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, SIKH, TP53, and TSCF, (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting the response of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating APC, ARID2, ATM, ATRX, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, SIKH, or TSC1 mutation, (ii) a decreased copy number of APC, ARID2, ATM, ATRX, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, SIKH, or TSC1, (iii) decreased expression of APC, ARID2, ATM, ATRX, CMTR2, KEAP1, KMT2D, MGA, NF1, PTEN, PTPRD, RBI, RBM10, RNF43, SETD2, SMAD4, STAG2, STK11, or TSC1 mRNA or protein; (iv) an inactivating BRCA2, CDKN2A, or TP53 mutation, (v) a decreased copy number of BRCA2, CDKN2A, or TP53, or (vi) a decreased expression of BRCA2, CDKN2A, or TP53 mRNA or protein. A method of determining effectiveness of an immune checkpoint inhibitor in reducing tumor size, comprising: (a) treating a first inert tumor with a control therapy; (b) treating a second inert tumor with an immune checkpoint inhibitor, wherein the first and second inert tumors comprise identical genotypes; (c) treating a first mutant tumor with the control therapy; (d) treating a second mutant tumor with the immune checkpoint inhibitor, wherein the first and second mutant tumors comprise identical genotypes; (e) comparing sizes of the first and second inert tumors after the therapy; (f) comparing sizes of the first and second mutant tumors after completion of the therapy, and (g) identifying the mutant tumor genotype as sensitive to the immune checkpoint inhibitor if the change in tumor size between the first and second inert tumors after the therapy is less than the change in tumor size between the first and second mutant tumors after the therapy.

43. A method of determining effectiveness of an immune checkpoint inhibitor in reducing tumor size, comprising: (a) treating a first inert tumor with a control therapy; (b) treating a second inert tumor with an immune checkpoint inhibitor, wherein the first and second inert tumors comprise identical genotypes; (c) treating a first mutant tumor with the control therapy; (d) treating a second mutant tumor with the immune checkpoint inhibitor, wherein the first and second mutant tumors comprise identical genotypes; (e) comparing sizes of the first and second inert tumors after the therapy; (f) comparing sizes of the first and second mutant tumors after completion of the therapy, and (g) identifying the mutant tumor genotype as resistant to the immune checkpoint inhibitor if the change in tumor size between the first and second inert tumors after the therapy is greater than the change in tumor size between the first and second mutant tumors after the therapy.

44. A method of predicting resistance of tumor growth to inhibition by a therapy comprising an immune checkpoint inhibitor, comprising: (a) detecting in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting resistance of tumor cell growth to inhibition by the therapy comprising the immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating ATM mutation, (ii) a decreased copy number of ATM, or (iii) a decreased expression of ATM mRNA or protein.

45. A method of predicting sensitivity of tumor growth to inhibition by a therapy comprising an immune checkpoint inhibitor, comprising: (a) detecting in a tumor sample of a subject a genotype of one or more biomarker genes of a biomarker panel comprising; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting sensitivity of tumor cell growth to inhibition by the therapy comprising the immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating APC, CMTR2, KEAP1, KMT2D, MGA, or NF1 mutation, (ii) a decreased copy number oiAPC, CMTR2, KEAP1, KMT2D, MGA, or NF 1, or (iii) decreased expression of APC, CMTR2, KEAP1, KMT2D, MGA, or NF1 mRNA or protein.

46. A method of predicting response of tumor growth to inhibition by a therapy comprising an immune checkpoint inhibitor, comprising: (a) determining in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting the response of tumor cell growth to inhibition by the therapy comprising the immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating APC, CMTR2, KEAP1, KMT2D, MGA, or NF1 mutation, (ii) a decreased copy number of APC, CMTR2, KEAP1, KMT2D, MGA, or NF1, (iii) decreased expression of APC, CMTR2, KEAP1, KMT2D, MGA, or NF 1 mRNA or protein; (iv) an inactivating ATM mutation, (v) a decreased copy number of ATM, or (vi) a decreased expression of ATM mRNA or protein.

47. The method of any one of embodiments 39-46, further comprising selecting a therapy for the subject.

48. A method of enriching a prospective patient population for subjects likely to respond to an immune checkpoint inhibitor therapy, comprising performing the method of any one of any one of embodiments 39 to 47 on two or more individual subjects within the prospective patient population.

49. The method of any one of embodiments 39-48, wherein the tumor sample corresponds to a cancer selected from the group consisting of skin cancer, lung cancer, pancreatic cancer, breast cancer, and colorectal cancer.

50. The method of embodiment 49, wherein the cancer is lung cancer.

51. The method of embodiment 50, wherein the lung cancer is non-small cell lung cancer (NSCLC).

52. The method of embodiment 51, wherein the NSCLC is lung adenocarcinoma. 53. The method of embodiment 49, wherein the cancer is skin cancer.

54. The method of embodiment 53, wherein the skin cancer is melanoma.

55. The method of any one of embodiments 39-54, wherein the immune checkpoint inhibitor comprises an antibody or an antigen-binding fragment thereof.

56. The method of any one of embodiments 39-55, wherein the immune checkpoint inhibitor inhibits programmed cell death protein 1 (PD-1)

57. The method of embodiment 56, wherein the immune checkpoint inhibitor is an anti-PD- 1 antibody, optionally wherein the immune checkpoint inhibitor is selected from the group consisting of nivolumab (Opdivo), Pembrolizumab (Keytruda), and Cemiplimab (Libtayo).

58. The method of any one of embodiments 39-55, wherein the immune checkpoint inhibitor inhibits cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4).

59. The method of embodiment 58, wherein the immune checkpoint inhibitor is an anti- CTLA-4 antibody, optionally wherein the immune checkpoint inhibitor is Ipilimumab (Yervoy).

60. The method of any one of embodiments 39-55, wherein the immune checkpoint inhibitor inhibits programmed cell death protein 1 ligand (PD-1L).

61. The method of embodiment 60, wherein the immune checkpoint inhibitor is an anti-PD- L1 antibody, optionally wherein the immune checkpoint inhibitor is selected from the group consisting of Atezolizumab (Tecentriq), avelumab (Bevencio), and durvalumab (Imfinzi).

62. The method of any one of any one of embodiments 39-61, wherein the therapy further comprises a chemotherapy.

63. The method of embodiment 62, wherein the chemotherapy comprises docetaxel, paclitaxel or cabazitaxel. 64. The method of embodiment 62, wherein the chemotherapy comprises platinum-based chemotherapy.

65. A method of treating non-small cell lung cancer (NSCLC), comprising treating a subject with a therapy comprising an immune checkpoint inhibitor when a tumor sample obtained from the subject comprises the genotype of any one of any one of embodiments 39-64.

66. A method of predicting resistance of tumor growth to inhibition by an immune checkpoint inhibitor therapy comprising a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor, the method comprising: (a) detecting in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting resistance of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating ATM mutation, (ii) a decreased copy number of ATM, or (iii) a decreased expression of ATM mRNA or protein.

67. A method of predicting sensitivity of tumor growth to inhibition by an immune checkpoint inhibitor therapy comprising a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor, the method comprising: (a) detecting in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting sensitivity of tumor cell growth to inhibition by the therapy comprising an immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating KEAP1 mutation, (ii) a decreased copy number of KEAP1, or (iii) decreased expression of KEAP1 mRNA or protein.

68. A method of predicting response of tumor growth to inhibition by an immune checkpoint inhibitor therapy comprising a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor, the method comprising: (a) determining in a tumor sample of a subject a genotype of one or more biomarker genes; (b) analyzing the genotype of the one or more biomarker genes in the tumor sample; and (c) predicting the response of tumor cell growth to inhibition by the therapy comprising the immune checkpoint inhibitor, if the tumor sample comprises (i) an inactivating KEAP1 mutation, (ii) a decreased copy number of KEAP1, (iii) decreased expression of KEAP1 mRNA or protein, (iv) an inactivating ATM mutation, (v) a decreased copy number o ATM, or (vi) a decreased expression of ATM mRNA or protein.

69. The method of any one of any one of embodiments 66-68, wherein subject is undergoing a first immune checkpoint inhibitor therapy comprising a programmed cell death protein- 1 (PD-1) inhibitor and a chemotherapy.

70. The method of any one of any one of embodiments 66-69, further comprising selecting a second immune checkpoint inhibitor therapy for the subject.

71. The method of embodiment 70, wherein the second immune checkpoint inhibitor therapy comprises a CTLA-4 inhibitor.

72. The method of embodiment 71, wherein the CTLA-4 inhibitor is anti-CTLA-4 antibody, optionally wherein the CTLA-4 inhibitor is Ipilimumab (Yervoy).

73. A method of enriching a prospective patient population for subjects likely to respond to an immune checkpoint inhibitor therapy comprising a cytotoxic T-lymphocyte- associated antigen 4 (CTLA-4) inhibitor, the method comprising performing the method of embodiment 68 on two or more individual subjects within the prospective patient population.

74. The method of any one of embodiments 66-73, wherein the tumor sample corresponds to a cancer selected from the group consisting of skin cancer, lung cancer, pancreatic cancer, breast cancer, and colorectal cancer.

75. The method of embodiment 74, wherein the cancer is lung cancer.

76. The method of embodiment 75, wherein the lung cancer is non-small cell lung cancer (NSCLC).

77. The method of embodiment 76, wherein the NSCLC is lung adenocarcinoma.

78. The method of embodiment 74, wherein the cancer is skin cancer. 79. The method of embodiment 78, wherein the skin cancer is melanoma.

80. The method of embodiment 69, wherein the PD-1 inhibitor is anti-PD-1 antibody, optionally wherein the PD-1 inhibitor is selected from the group consisting of nivolumab (Opdivo), Pembrolizumab (Keytruda), and Cemiplimab (Libtayo).

81. The method of embodiment 69, wherein the chemotherapy comprises a taxane-based chemotherapy.

82. The method of embodiment 81, wherein the taxane is selected from the group consisting of docetaxel, paclitaxel, and cabazitaxel.

83. The method of embodiment 69, wherein the comprises platinum-based chemotherapy.

84. A method of treating non-small cell lung cancer (NSCLC), comprising treating a subject with an immune checkpoint inhibitor when a tumor sample obtained from the subject comprises (i) an inactivating KEAP1 mutation, (ii) a decreased copy number of KKAP1, or (iii) decreased expression of KEAP1 mRNA or protein.

85. The method of embodiment 84, wherein the immune checkpoint inhibitor is a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor.

86. The method of embodiment 85, wherein the CTLA-4 inhibitor is an anti-CTLA-4 antibody, optionally wherein the CTLA-4 inhibitor is Ipilimumab (Yervoy).

87. The method of any one of embodiments 66-86, wherein the tumor sample further comprises, (iv) absence of an inactivating ATM mutation, (v) absence of a decreased copy number of ATM, or (vi) absence of a decreased expression of ATM mRNA or protein.

88. The method of any one of embodiments 66-87, wherein the subject is already undergoing a therapy comprising one or more immune checkpoint inhibitors and a chemotherapy. 89. The method of embodiment 88, wherein the immune checkpoint inhibitor inhibits programmed cell death protein 1 (PD-1).

90. The method of embodiment 89, wherein the immune checkpoint inhibitor is an anti-PD- 1 antibody, optionally wherein the immune checkpoint inhibitor is selected from the group consisting of nivolumab (Opdivo), Pembrolizumab (Keytruda), and Cemiplimab (Libtayo).

91. A method of treating non-small cell lung cancer (NSCLC), comprising treating a subject undergoing a therapy comprising a first immune checkpoint inhibitor and a chemotherapy with a second immune checkpoint inhibitor when a tumor sample obtained from the subject comprises (i) an inactivating KEAP1 mutation, (ii) a decreased copy number of KEAP1, or (iii) decreased expression of KEAP1 mRNA or protein.

92. The method of embodiment 91, wherein the tumor sample further comprises (iv) absence of an inactivating ATM mutation, (v) absence of a decreased copy number of ATM, or (vi) absence of a decreased expression of ATM mRNA or protein.

93. The method of embodiment 91 or 92, wherein the first immune checkpoint inhibitor inhibits programmed cell death protein 1 (PD-1)

94. The method of embodiment 93, wherein the first immune checkpoint inhibitor is an anti- PD-1 antibody, optionally wherein the first immune checkpoint inhibitor is selected from the group consisting of nivolumab (Opdivo), Pembrolizumab (Keytruda), and Cemiplimab (Libtayo).

95. The method of any one of embodiments 91-94, wherein the chemotherapy comprises a taxane-based chemotherapy.

96. The method of any one of embodiments 91-94, wherein the chemotherapy comprises a platinum-based chemotherapy.

97. The method of any one of embodiments 91-96, wherein the second immune checkpoint inhibitor is a cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) inhibitor. 98. The method of embodiment 97, wherein the CTLA-4 inhibitor is an anti-CTLA-4 antibody, optionally wherein the CTLA-4 inhibitor is Ipilimumab (Yervoy).

[00154] It is to be understood that the present disclosure is not limited to the particular methodologies, protocols, cell lines, vectors, and reagents described, as these can vary. It is also understood that the terminology used herein is for the purpose of describing particular embodiments only and is not to limit the scope of the present disclosure.

[00155] Various publications, articles and patents are cited or described in the background and throughout the specification; each of these references is herein incorporated by reference in its entirety. Discussion of documents, acts, materials, devices, articles and the like which has been included in the present specification is for the purpose of providing context for the invention. Such discussion is not an admission that any or all of these matters form part of the prior art with respect to any disclosures disclosed or claimed.

[00156] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood to one of ordinary skill in the art to which this invention pertains. Otherwise, certain terms used herein have the meanings as set forth in the specification.

[00157] Particular embodiments of the present disclosure are described herein. Those skilled in the art will recognize or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments described herein. Such equivalents are intended to been compassed by the present disclosure. Accordingly, it is intended that the present disclosure be practiced otherwise than as specifically described herein, and that the present disclosure includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the abovedescribed elements in all possible variations thereof is encompassed by the invention unless otherwise indicated herein or otherwise clearly contradicted by context. A number of embodiments of the present disclosure have been described. Nevertheless, it will be understood that various modifications can be made without departing from the spirit and scope of the present disclosure. Accordingly, the descriptions in the Examples section are intended to illustrate but not limit the scope of present disclosure described in the claims. EXAMPLES

[00158] Example 1. Biomarkers of Responsiveness to Immune Checkpoint Inhibitor Therapies

[00159] This example describes the identification of tumor suppressor genes that are biomarkers of response to immune checkpoint inhibitor therapies through a method that integrates CRISPR/Cas9-based somatic genome engineering and molecular barcoding into established Cre/Lox-based genetically engineered mouse models of oncogenic Kras-driven lung cancer. This example shows, inter alia, that Keapl -deficiency drives selective sensitivity to a combination therapy comprising a PD-1 inhibitor and a CTLA-4 inhibitor in vivo, while Atm- deficiency drives selective resistance to a combination therapy comprising a PD-1 inhibitor and a CTLA-4 inhibitor in vivo.

[00160] Generation of Barcoded Lenti-sgRNA/Cre vector pool

[00161] 1. Design and Generation of sgRNAs

[00162] Lentiviral vectors carrying Cre as well as an sgRNA targeting each of 22 known and putative lung adenocarcinoma tumor suppressors: Ape, Arid2, Atm, Atrx, Brca2, Cdkn2a, Cmtr2, Keapl, Kmt2d, Lkbl, Mga, Nfl, p53, Pten, Ptprd, Rbl, RbmlO, Rnf43, Setd2, Smad4, Stag2, Tscl, Fbxw7, Kdm6a, Kras WT, Msh2, Nf2, Palb2, Pena, Shp2, Jakl, Adar, Pbrml, Dnmtl, and Trexl, were generated. Vectors were also generated carrying inert guides: sgRosa26-l, sgRosa26-2, sgRosa26-3, sgNT-1, sgNT-2, and sgNT-3. All possible 20-bp sgRNAs (using an NGG PAM) targeting each tumor suppressor gene of interest were identified and scored for predicted on-target cutting efficiency using an available sgRNA design/scoring algorithm (Doench et al., Nat Biotechnol 34, 184-191 (2016). https://doi.org/10.1038/nbt.3437). For each tumor suppressor gene, a unique sgRNAs predicted to be the most likely to produce null alleles was selected; preference was given to sgRNAs that were previously validated in vivo (Rogers et al., Nat Methods. 2017 Jul;14(7):737-742. doi: 10.1038/nmeth.4297; Rogers et al., Nat Genet. 2018 Apr;50(4):483-486. doi: 10.1038/s41588- 018-0083-2; Winters et al. Nat Commun. 2017 Dec 12;8(l):2053. doi: 10.1038/s41467-017- 01519-y. PMID: 29233960; PMCID: PMC5727199, sgRNAs with the highest predicted cutting efficiencies, as well as those targeting exons conserved in all known splice isoforms (ENSEMBL), closest to splice acceptor/splice donor sites, positioned earliest in the gene coding region, occurring upstream of annotated functional domains (InterPro; UniProt), and occurring upstream of known human lung adenocarcinoma mutation sites. QnA-j6-sgRNA Cre vectors containing each sgRNA were generated as previously described (Rogers et al.. Nat Methods.

2017 Jul;14(7):737-742. doi: 10.1038/nmeth.4297). Briefly, Q5 site-directed mutagenesis (NEB E0554S) was used to insert sgRNAs into the parental lentiviral vector containing the U6 promoter as well as PGK-Cre.

[00163] 2. Barcode Diversification of Lenti-sgRNA/Cre

[00164] To enable quantification of the number of cancer cells in individual tumors in parallel using high-throughput sequencing, the Lenii-sgRHA Cre vectors with a 46bp multi-component barcode cassette that would be unique to each tumor were diversified by virtue of stable integration of the lentiviral vector into the initial transduced cell. This 46 bp DNA barcode cassette was comprised of a known 6-nucleotide ID specific to the vector backbone (vectorlD), a 10-nucleotide ID specific to each individual sgRNA (sgID), and a 30-nucleotide random barcode containing 20 degenerate bases (random BC).

[00165] The 46 bp barcode cassette for each sgRNA was flanked by universal Illumina® TruSeq adapter sequences and synthesized as single stranded DNA oligos. Forward and reverse primers complimentary to the universal TruSeq sequences and containing 5’ tails with restriction enzyme sites (Asci and Notl) were used in a PCR reaction to generate and amplify double stranded barcode cassettes for cloning. Each Lenti-sgRNA-Cre vector and its matching insert barcode PCR product was digested with Asci and Noth

[00166] To generate a large number of uniquely barcoded vectors, 1 pg of linear vector and 50ng of insert were ligated with T4 DNA ligase in a 100 pl ligation reaction. After 5 hours of incubation at room temperature, ligated DNA was precipitated by centrifugation at 14k for 12 min after adding 5 pl Glycogen (5mg/ml) and 280 pl 100% Ethanol into the ligation reaction. The DNA pellet was washed with 80% Ethanol and air dried before being resuspended with 10 pl water. This 10 pl well-dissolved DNA was transformed into 100 pl of Sure Electrical Competent Cells using BioRad electroporation system following their manual. Electroporatio- transformed cells were immediately recovered by adding into 5 ml pre-warmed SOC media. From this 5 ml cells in SOC medium, 10 pl were further diluted with LB ampicillin broth and a final dilution of 1 :200K was plated on LB ampicillin plate for incubation at 37°C. The remaining cells in SOC medium were mixed gently and thoroughly before being inoculated into 100 ml LB/Ampicillin broth, shaking at 220 rpm at 37°C overnight. The next day, colony number on LB/Ampicillin plate were counted to estimate the complexity of each library while 100 ml bacteria culture were pelleted for plasmid purification.

[00167] Eight colonies from each library were picked and PCR screened for verification of the specific sgRNA sequence and corresponding barcode sequence among these 8 colonies. The final purified library plasmid for each library is again sequence verified.

[00168] Production, Purification, and Titering of Lentivirus

[00169] 24 hours prior to transfection, 2.4 x 10 ⁷ 293T cells were plated on 15 cm tissue culture plate. 30 pg of pPack (packaging plasmid mix) and 15 pg of library plasmid DNA were mixed well in 1.5 ml serum free D-MEM medium before equal volume of serum free D-MEM medium containing 90 pl of LipoD293 was added. The resulted mixture was incubated at room temperature for 10-20min before adding into 293T cells in the 15cm plate. At 24 hours posttransfection, replace the medium containing complexes with 30 ml of fresh D-MEM medium supplemented with 10% FBS, DNase I (1 U/ml), MgC12 (5 mM), and 20mM HEPES, pH 7.4. The entire virus-containing medium from each plate was collected and filtered through a Nalgene 0.2 pm PES filter at 48 hours post-transfection. The viruses were further concentrated by centrifugation at 18,500 rpm, 4°C for 2 hours and the pellet was dissolved in 500 pl PBS buffer. 50 pl virus aliquots were stored at -80°C.

[00170] To determine the titer for packaged library constructs, 1 x 10 ⁵ LSL-YFP MEF cells were transduced with 1 pl of viruses in 1 ml culture medium containing 5 pg/ml polybrene. Transduced cells were incubated for 72 hours before being collected for FACS analysis to measure the percentage of GFP cells. Control viruses were used in parallel to normalize the virus titers. [00171] Pooling of Lenti-sgRNA/Cre vectors

[00172] To generate a pool of barcoded en -sgRRA Cre vectors to generate multiple tumor genotypes within individual mice, barcoded en -sgRRA Cre vectors targeting the 22 tumor suppressor genes (Ape, Arid2, Atm, Atrx, Brca2, Cdkn2a, Cmtr2, Keapl, Kmt2d, Lkbl, Mga, Nfl, p53, Pten, Ptprd, Rbl, RbmlO, Rnf43, Setd2, Smad4, Stag2, and Tscl) and those containing the inert, negative control sgRNAs (sgRosa26-l, sgRosa26-2, sgRosa26-3, sgNT-1, sgNT-2, and sgRT-3) were combined such that the viruses would be at equal ratios in relation to their estimated titers. This pool was then diluted with 1 x DPBS to reach a final viral titer of 180,000 FU per 60 pL.

[00173] Mice and Tumor Initiation

[00174] Kras ^LSL ' ^G12D (K) and Hll ^LSL ' ^Cas9 (Cas9) mice have been described (Jackson et al., Genes & Dev. 2001. 15: 3243-3248 (doi: 10.1101/gad.943001); Chiou c/ a/., Genes & Dev. 2015. 29: 1576-1585 (doi: 10.1101/gad.264861.115)). Lung tumors in Kras ^LSL ' ^G12D/+ , Hl 1 ^LSL - ^Cas9 (KC) mice were initiated via intratracheal delivery of 180,000 functional units (FU) of a lentivirus pool containing barcoded Lenti-U6-sgRNA/PGK-Cre vectors targeting either 22 genes (Ape, Arid2, Atm, Atrx, Brca2, Cdkn2a, Cmtr2, Keapl, Kmt2d, Lkbl, Mga, Nfl, p53, Pten, Ptprd, Rbl, RbmlO, Rnf43, Setd2, Smad4, Stag2, and Tscl) plus 6 negative control sgRNAs (three targeting the Rosa26 gene, which are actively cutting but functionally inert, and 3 non-cutting sgRNAs with no expected genomic target [sgNon-Targeting: sgNT]).

[00175] Drug Dosing

[00176] 12 weeks post tumor initiation, mice were treated with the following:

[00177] Mouse IgGl isotype control (clone PI-0001 -AB) delivered IP, at 5 mg/kg and mouse IgG2 isotype control (clone PI-0002-AB-AF) delivered IP, at 10 mg/kg (n = 20) with once daily dosing (QD), every 5 days (q5d) for two weeks until takedown at 14 weeks post induction.

[00178] Anti-PD-1 (clone PI-7114) delivered IP, at 5 mg/kg and mouse IgG2 isotype control (clone PI-0002-AB-AF) delivered IP, at 10 mg/kg (n = 20) with once daily dosing (QD), every 5 days (q5d) for two weeks until takedown at 14 weeks post induction. [00179] Mouse IgGlisotype control (clone PI-0001-AB) delivered IP, at 5 mg/kg and anti- CTLA-4 (clone 9D9 (Simpson et al. J Exp Med. 2013; 210(9): 1695-1710. PMID:23897981) delivered IP, at 10 mg/kg (n = 20) with once daily dosing (QD), every 5 days (q5d) for two weeks until takedown at 14 weeks post induction.

[00180] Anti-PD-1 (clone PI-7114) delivered IP, at 5 mg/kg and anti-CTLA-4 (clone 9D9) delivered IP, at 10 mg/kg (n = 20) with once daily dosing (QD), every 5 days (q5d) for two weeks until takedown at 14 weeks post induction.

[00181] Dissection of Mouse Lungs

[00182] Bulk lung tissue was extracted from euthanized mice as previously described (Rogers etal., Nat Methods. 2017 Jul;14(7):737-742. doi: 10.1038/nmeth.4297). Lung mass measurements were recorded as a proxy for overall lung tumor burden. Lungs were stored at - 80°C prior to subsequent processing.

[00183] All mouse experiments were performed in accordance with Animal Care and Use Committee guidelines.

[00184] Generation of Cell Spike-in Controls

[00185] DNA barcode cassettes comprised of known 46 bp sequences were flanked by universal Illumina® TruSeq adapter sequences and synthesized as single stranded DNA oligos. Forward and reverse primers complimentary to the universal TruSeq sequences and containing 5’ tails with restriction enzyme sites (Xbal and BstBl) were used in a PCR reaction to generate and amplify double stranded barcode cassettes for cloning. A lentivector pRCMERP-CMV- MCS-EFl-TagR-Puro and each of the barcode insert PCR products were digested by Xbal and BstBl restriction enzymes.

[00186] Each digested barcode insert was cloned into linearized vector by T4 DNA ligase and transformed into OmniMax chemical competent cells (Invitrogen). Colonies from each transformation plate were screened by PCR and sequencing. One positive clone from each barcode containing construct was cultured for plasmid DNA extraction. [00187] Virus was packaged from each of the barcoded pRCMERP constructs in 6-well plates using pPack packaging mix and LipoD293 reagent. Virus containing medium were collected at 48 hours post transfection and filtered with Nalgene 0.2 pm PES filter before being frozen down in aliquots at -80°C. Small aliquot of frozen viruses were thawed and added into HEK293 cells in 12-well plate for measuring titer by FACS analysis 72 hours after transduction.

[00188] To generate individual cell line containing each barcode construct, virus containing medium was added to HEK293 cells at MOI 0.1 in 10cm plates. After overnight incubation, cells were recovered in fresh EMEM complete medium for 48 hours before splitting into a new plate containing lug/ml puro in complete EMEM medium for puro selection.

[00189] After 3 days of puro selection, barcode-containing HEK293 cells were recovered in fresh EMEM complete medium without puro for another 3 days before being further expanded in 10cm plates. Each established cell line was quality controlled by PCR amplification of the barcode region from genomic DNA to confirm integration of correct barcode sequences.

[00190] After cell expansion, cells from each barcoded HEK293 cell line were collected and diluted in PBS buffer containing 0.1% BSA to the desired concentrations. These cell suspensions were aliquoted and frozen down at -80°C.

[00191] Generation of dsDNA Spike-in Controls

[00192] DNA barcode cassettes comprised of 46 bp barcode cassettes and flanked by universal Illumina® TruSeq adapter sequences as well as additional buffer sequences to extend their total length to >400bp were generated either by direct synthesis of the double-stranded DNA fragments (GeneWiz, IDT) or synthesis of single-stranded DNA oligos (GeneWiz, IDT) with overlapping complementary regions that were extended and amplified via PCR to create double-stranded DNA products that were then purified. Aliquots of these stock double-stranded DNA fragments were diluted to the desired copy numbers using DNase-free ultra-pure H2O and stored at -20°C. [00193] Isolation o f Genomic DNA from Mouse Lungs

[00194] Whole lungs were removed from freezer and allowed to thaw at room temperature. Spikeins were added to each whole lung samples. Added Qiagen Cell Lysis Buffer and proteinase K from Qiagen Gentra PureGene Tissue kit (Cat # 158689) as described in manufacturer protocol. Whole lungs plus spikeins from each mouse were homogenized in the Cell Lysis buffer and Proteinase K solution using a tissue homogenizer (FastPrep-24 5G, MP Biomedicals Cat # 116005500). Homogenized tissue was incubated at 55°C overnight. To remove RNA from tissues samples, RNase A were added with additional spikeins to whole homogenized tissue. To maintain accurate representation of all tumors, DNA was extracted and alcohol precipitated from the entire lung lysate using Qiagen Gentra PureGene kit as described in manufacturer protocol. More spikeins were added to the resuspended DNA.

[00195] Preparation o f sgID-BC Libraries for Sequencing

[00196] Libraries were prepared by amplifying the barcode region from 32 pg of genomic DNA per mouse. The barcode region of the integrated Lenti-sgTWH- C/Cre vectors was PCR amplified using primer pairs that bound the universal Illumina® TruSeq adapters and contained dual unique multiplexing tags. A single-step PCR amplification of barcode regions was used, which was found to be a highly reproducible and quantitative method to determine the number of cancer cells in each tumor. Eight 100 pl PCR reactions per mouse (4 pg DNA per reaction) were performed using Q5 HF HS 2* mastermix ((NEB #M0515) with the following PCR program:

[00197] PCR products were purified using SPRI beads. The concentration of purified PCR products from individual mice was determined by TapeStation (Agilent Technologies). Sets of 20-60 samples were pooled at equal ratios. Samples were sequenced on an Illumina® NextSeq and (Cellecta).

[00198] Analysis of Sequencing Data

[00199] Paired-end sequencing reads were demultiplexed via dual indexes and adapters sequences were trimmed. Paired-end alignments were constructed between mate-paired reads and library-specific databases of the expected oligonucleotide and tumor barcode insert sequences. These alignments were stringently filtered from downstream analysis if they failed to meet any of a number of quality criteria, including:

• Mismatches between the two mate-pairs, which fully overlap one another, at any location.

• Mismatches between the mate-paired reads and expected constant regions of the barcode or spikein to which they best align,

• Any indels in alignments between mate-paired reads and the barcode or spikein to which they best align.

[00200] Following alignment, errors in paired-end reads were corrected via a simple greedy clustering algorithm:

• Reads were dereplicated into read sequence/count tuples, (si, n)

• These tuples were re-ordered from highest to lowest on the basis of their read abundances, {n}.

• This list of tuples were traversed from i = 1 . . .N, taking one of the following actions for each tuple (si, n): o If Si is not within a Hamming distance of 1 from any Sj with j < i, then (si, n) initiates a new cluster. o If Sj is within a Hamming distance of 1 from some Sj with j < i, then it joins the cluster of Sj.

• The resulting clusters are each considered to represent an error-corrected sequence equal to that of the sequence that founded the cluster and read count equal to the sum of the read counts of the dereplicated reads that are members of the cluster.

[00201] Following error correction, the read counts of each unique barcode were converted to tumor cell sizes by dividing the number of error-corrected reads of an oligonucleotide that had been spiked into the sample prior to tissue homogenization and lysis at a fixed, known concentration.

[00202] From these collections of tumor sizes across paired groups of checkpoint inhibitor- treated and vehicle-treated mice, the relative tumor number (RTN) metric was computed as previously described (Li, C., Lin, W.-Y et al. bioRxiv 2020.01.28.923912; doi: https://doi.org/10.1101/2020.01.28.923912).

[00203] Namely, shrinkage of inert tumors was estimated by finding the S that matches the median number of tumors in larger than a cutoff L in such paired groups after the vehicle- treated tumor sizes are multiplied by S (S < 1 when the inhibitor works to shrink tumors). Subsequently, for each non-inert tumor genotype, the ratio of the number of tumors with this genotype larger than L in the control mice to the number of tumors larger than L*S in the treated mice was computed. The resulting ratio was divided by the same ratio computed for the inert tumors, and the log2(») of this ratio of ratios was determined. This metric, RTNscore is expected to be > 0 for resistant genotypes and < 0 for sensitive genotypes.

[00204] In order to generate confidence intervals for RTNscore, bootstrap re-samplings by (1) sampling mice with replacement from the control and therapy arms to match the original group sizes, and the (2) sampling tumors (of all sizes) with replacement from each mouse was generated. For each mouse/tumor bootstrap, the RTNscore was re-computed. A genotype was then considered sensitive if the 95th %ile of these bootstrap RTNscore values fell below 0, or resistant if the 5th %ile exceeded 0 at a cutoff of L = 500, chosen to be large enough that spurious or biologically irrelevant tumors were excluded, but small enough to include substantial numbers of tumors of every genotype, or a large cutoff of L = 5000. The results are shown in FIG. 1, which shows the RTNscore values for each twenty -two biomarker genes.

[00205] FIG. 2 shows the multiplexed analysis of immunotherapy responses in the autothchonous mouse model of lung adenocarcinoma performed in Example 1. FIG. 2A shows an experimental schematic depicting the composition of the pool of barcoded Lenti-sgRNA/Cre vectors (Lenti-D2G^‘P° ⁰ Cre) used in Example 1. FIG. 2B shows mouse genotype, analysis time points, and readouts. FIG. 2C shows effects of each tumor genotype on growth. Tumors at the indicated percentiles of the tumor size distribution for each barcoded Lenti-sgRNA/Cre vector are shown. 95% confidence intervals are indicated. Panel d. shows the treatment groups in this study. Antibodies, dosing, and number of mice are indicated.

[00206] FIG. 3 shows that Keapl -deficiency drives selective sensitivity to a combination therapy comprising a PD-1 inhibitor and a CTLA-4 inhibitor in vivo. FIG. 3 A and FIG. 3B show genotype specific responses to PD-1 inhibitor-treated relative to control -treated mice (FIG. 3 A) and PD-1 inhibitor- and CTLA-4 inhibitor-treated mice relative to control treated mice (FIG. 3B). Significant sensitive effects are shown for Ape in FIG. 3 A, and Ape, Cmtr2, Keapl, Kmt2d, and Nfl in FIG. 3B. Significant resistant effects are shown for Atm in FIG. 3B. FIG. 3C shows genotype specific responses to PD-1 inhibitor-treated and CTLA-4 inhibitor- treated mice relative to PD-1 inhibitor-treated mice. Significant sensitive effects are shown for Keapl and significant resistant effects are shown for Atm in FIG. 3C. FIG. 3D. shows a volcano plot of the magnitude of differential sensitivity to a PD-1 inhibitor treatment and a CTLA-4 inhibitor treatment relative to PD-1 inhibitor treatment alone. FIG. 3 further shows Atm- deficiency drives selective resistance to a combination therapy comprising a PD-1 inhibitor and a CTLA-4 inhibitor in vivo.

[00207] It will be appreciated by those skilled in the art that changes could be made to the embodiments described above without departing from the broad inventive concept thereof. It is understood, therefore, that the present disclosure is not limited to the particular embodiments disclosed, but it is intended to cover modifications within the spirit and scope of the present disclosure as defined by the present description.