Methods of identifying and treating an immune poor cancer

Abstract

The disclosure provides diagnostic methods, therapeutic methods, compositions and kits for the treatment of cancer. The disclosure is based, at least in part, on the discovery that poorly immunogenic "cold" tumors, which respond poorly to immune checkpoint blockade (ICB) therapy, are strongly associated with impaired TGF0 signaling and error-prone DNA repair, alternative end-joining (alt-EJ). The disclosed methods include approaches for enhancing tumor immunogenicity and overcoming resistance to checkpoint blockade in such cancers with a combination of a genotoxic therapy and inhibition of immunosuppressive cytokines (e.g., TGF0). Selection of suitable cancer patients who are likely to benefit from such therapy are also disclosed, as well as methods of predicting and monitoring therapeutic response.

Description

METHODS OF IDENTIFYING AND TREATING AN IMMUNE POOR CANCERSTATEMENT OF GOVERNMENT SUPPORT

[0001] This invention was made with Government support under grant number R01 CA239235 awarded by the National Institutes of Health. The Government has certain rights in the invention.FIELD

[0002] The present disclosure is directed to diagnostic and therapeutic methods for the treatment of cancer. More particularly, it concerns methods for identifying immune checkpoint blockade-resistant cancers or immune poor cancers and methods for treating such cancers by sensitizing them to immune checkpoint therapy. Also provided are related kits and assays.BACKGROUND

[0003] Immune checkpoint blockade (ICB) therapy has revolutionized the treatment landscape for a number of cancers over the last few decades demonstrating the promise of harnessing the immune system to control and eradicate tumor growth. However, only a minority of patients, ranging from 10 to 58%, benefit from ICB therapy with the majority of patients being primarily resistant to such treatment or acquiring resistance after an initial response. As such, identification of biomarkers of response is an intense area of research. Among these, the tumor mutational burden (TMB), which represents tumor DNA non- synonymous somatic mutations potentially giving raise to immunogenic neoantigens (Peggs KS, Segal NH, Allison JP. 2007. Cancer Cell. 12(3):192-199; Segal et al. 2008. Cancer Res. 68(3):889-892) is correlated to response to ICB in different tumor types. DNA repair deficits are a hallmark of cancer and genetic instability due to alterations in DNA repair and replication genes can increase immunogenicity through high TMB and subsequent neoantigen production, release, and presentation.

[0004] To date, the TMB is used as a predictive biomarker for non-small cell lung cancer (NSCLC) in clinical practice (Willis et al., 2019. Oncotarget. 10(61):6604-6622). In contrast, poorly immunogenic tumors with low TMB, such as pancreatic and prostate cancers, are inherently more resistant to treatment with ICB. Patients with melanoma were found to have better response to anti-PD-1 treatment if tumor cells were enriched for mutations in BRCA2, an important homologous recombination DNA repair gene (Hugo et al., 2016. Cell.165(1 ):35-44). Similar findings were demonstrated in ovarian cancer, in which BRCA1 / 2- mutated tumors demonstrated high neoantigen loads (Strickland et al., 2016. Oncotarget. 7(12):13587-13598). Alterations in additional DNA damage response genes, including ATM,POLE, FANCA, ERCC2, and MSH6, have recently shown correlation with high TMB and improved clinical outcomes to ICB in urothelial cancer (Teo et al., 2018. J Clin Oncol. 36(17):1685-1694). Furthermore, tumors with deficiencies in DNA mismatch repair genes leading to microsatellite instability demonstrated high mutational burden with enhanced response to ICB across a wide range of histologies (Le et al., 2015. N Engl J Med. 372(26) :2509-2520; Le et al., 2017. Science. 357(6349) :409-413).

[0005] ICB using inhibitors to programmed cell death protein 1 or programmed deathligand 1 (PD-1 / PD-L1 ) has shown significant and durable responses in cancer, and it is now the standard of care for many adult cancers (Sharma, P. & Allison, J.P. 2015. Science (New York, N.Y.) 348: 56-61). Increased PD-L1 expression has been correlated with immune response and is currently used as a biomarker for ICB therapy in NSCLC and urothelial carcinoma (Topalian et al., 2012. N Engl J Med. 366:2443-2454; Martin et al., 2015. Prostate Cancer Prostatic Dis. 18(4):325-332).

[0006] Additionally, elevated numbers of tumor-infiltrating lymphocytes (TILs) have been noted in responsive cancers. Based on the T-cell infiltrate, Chen and Mellman defined three tumor phenotypes that strongly correlate to response to ICB (Chen DS, Mellman I. 2017. Nature. 541 :321 — 30; Chen et al., 2016. Cancer Discov. 6(8):827-837). Among them, the immune-desert and the immune-excluded phenotypes, also called cold tumors or immune- poor tumors, are non-inflamed tumors that are usually nonresponsive to ICB. Conversely, the immune-inflamed phenotype, or hot tumor, is strongly infiltrated with T cells and is more often responsive to ICB. Patients with cold tumors are considered unlikely to respond to ICB and have poor outcomes in response to standard therapy (Fridman et al., 2012. Nat Rev Cancer. 12(4):298-306).

[0007] Immune checkpoint inhibitors (ICIs) exert their antitumor effects by activating T cells in the tumor microenvironment. Thus, the limited efficacy of ICB may be attributed to a range of immune related mechanisms including, but not limited to, insufficient antigen recognition by T cells, MHC dysfunction, impaired T-cell migration and / or infiltration, reduced T-cell cytotoxicity, irreversible T cell exhaustion, increased presence of immunosuppressive regulatory T cells or myeloid suppressor cells, immunosuppressive microenvironment, enhanced expression of ICIs, or increased Th2 and Th 17 mediators. However, the molecular pathways by which most cancers resist ICB remain unknown. This is compounded by incomplete understanding of the biology of the immune tumor microenvironment (TME) and the diversity of the systemic immune environment (Hugo et al., 2016. Cell. 165(1 ):35-44; Spranger et al., Proc Natl Acad Sci U SA. 113(48):E7759-E7768).

[0008] Accordingly, there is a need for methods for selecting patients who are less likely to respond to immunotherapies and to develop alternative strategies for the treatment of these patients. Additionally, ICB is not without side effects. Although severe side effects are only occasionally observed, immune-mediated side effects including myocarditis, hypophysitis, and colitis are potentially life threatening (Geukes et al., 2018. ESMO Open. 3(1):e000278; Varricchi et al., 2017. ESMO Open. 2(4):e000247; Doherty et al., 2017. ESMO Open. 2(4):e000268; Kastrisiou et al., 2017. ESMO Open. 2(4):e000217; Aya et al., 2016. ESMO Open. 2016;1 (1):e000032). Further, the pre-selection of patients who are likely to respond well to a medicine, drug, or combination therapy may reduce the number of patients needed in a clinical study or accelerate the time needed to complete a clinical development program (M. Cockett et al., 2000, Current Opinion in Biotechnology, 11 :602- 609). Additionally, ICB bears a potential financial burden to the health system, considering the currently still very high treatment costs. Hence, stratifying patients to optimize benefit is essential. ICB should be applied with an optimal cost-effectiveness profile based on reliable, robust biomarkers. There remains a need in the art for methods of treating patients with immune poor cancers, and the disclosure provides such methods.

[0009] Treating immune poor cancer remains challenging due to the complex dynamic balance of immunity and the high heterogeneity of tumors. Treatment responses are patient-dependent highlighting the urgent need to identify predictive biomarkers of short- and long-term clinical efficacy. Additionally, clinical distinction between the immune desert and the immune excluded phenotypes, which can be considered immune poor tumors (or in some instances non-inflammatory, lacking immune cells, or having quiescent immune cells) and the responsive immune-inflamed tumors is highly important for the design of nextgeneration immunotherapy agents and the possibility of a tailored medicine. Today, diagnostic testing for cancer patients is not fully embedded into clinical practice; testing rates range from 7% to 50% depending on the type of cancer (Chawla et al., 2018. J Med Econ. 21 (6):543-552). Diagnostic-based treatment can help identify optimal combinations of immune-based therapies for any given patient. Moreover, therapies that promote the conversion of these former two immunophenotypes into inflamed tumors could help reduce resistance to ICBs and provide a new treatment option. Further, primary and acquired resistance to ICB therapy hampers its clinical benefit. Yet, resistance is complicated and multifactorial encompassing immune-based mechanisms like loss of neoantigens or PD-L1 , defects in antigen presentation signaling, dysfunction of the local immune, and T cell exclusion and other biological processes e.g., deregulated tumour immunometabolism and epigenetic dysfunction. To expand the effectiveness of immunotherapy approaches in refractory patients, it is imperative to elucidate which resistance mechanisms are the mostdominant in limiting the efficacy of immunotherapy and address them with new combination therapies.SUMMARY

[0010] The disclosure provides methods and compositions for identifying and treating a subject afflicted with an immune poor cancer. More specifically, the disclosure provides a method of identifying and treating a human subject suffering from an immune poor cancer, the method comprising:(i) identifying the subject suffering from the immune poor cancer, wherein the subject comprises cancer cells comprising a DNA damage repair (DDR) deficit phenotype; and(ii) treating the subject identified in step (i) using a treatment regimen comprising sequentially administering to the subject(a) a genotoxic therapy to kill the cancer cells;(b) a TGF-beta inhibitor (TGFpi i) or interleukin 6 (IL-6) to release immune suppression of the cancer cells; and(c) an immune checkpoint inhibitor or an immunotherapy.

[0011] In some aspects, the DDR deficit phenotype is determined by assessing TGF-p signaling competency and assessing alternative end-joining (alt-EJ) activation in a sample comprising cancer cells from the subject, wherein when impaired TGF-p signaling and impaired alt-EJ activation are observed, the cancer cells are determined to comprise the DDR deficit phenotype. In some aspects, impaired TGF-p signaling is determined by measuring an expression level of at least one TGFp-associated gene selected from the group ABCG1 , AMIGO2, CA12, CCDC99, CCL20, CHRNA9, COL4A2, CTGF, DLC1 , DNAJB9, DSC2, ENC1 , F3, FAP, FGF2, FN1 , HEY1 , HMGA2, ID1 , IGF2BP3, IGFBP3, JAG1 , KLF4, LAMB3, LAMC2, LARP6, LIPG, MAFF, MMD, PDGFC, PLEK2, LEXNA2, PSTN, PSCD1 , RICS, RNF24, RUNX1 , SAMSN1 , LAMC2, SERPINE1 , SERPINE2, SH2D2A, SH2D4A, SLC20A1 , SLC22A4, TGIF1 , THBS1 , TMEPAI, TNC, TNFRAF12A, and VACN, wherein a low level of expression of the at least one TGFp-associated gene indicates impaired TGF-p pathway activity in the cancer cells. In some aspects, impaired alt-EJ pathway activity is determined by measuring an expression level of at least one alt-EJ- associated gene selected from the group APE2, APEX1 , ASF1 A, CDKN2D, CIB1 , DNA2, FAAP24, FANCM, GEN1 , HRAS1 , LIG1 , LIG3, MEN1 , MRE11A, MSH3, MSH6, MTH1 , MTOR, NABP2, NTHL1 , PALB2, PARP1 , PARP3, POLA1 , POLM, POLQ, PRP19, RAD51 D, RBBP8, RRM2, RUVBL2, SOD1 , TIP60, UNG, WRN, and XRCC1 , wherein a high level ofexpression of the at least one alt-EJ-associated gene indicates impaired alt-EJ pathway activity in the cancer cells.

[0012] In some aspects, the immune poor cancer is identified by detecting in the sample the expression level of at one or more genes selected from the group consisting of: CD40LG, TBX21 , SH2D1 , PYHIN1 , ZNF831 , CD6, THEMIS, UBASH3A, TRAT1 , EOMES, GRAP2, ZAP70, SIRPG, ICOS, FASLG, CD8A, CD8B, ITK, GZMA, KLRK1 , GZMH, GZMK, CD3D, CD3E, CD3G, CLEC10A, CD1 E, CD1 C, VSIG4, CD33, CD300LB, MS4A7, LY86, CLEC5A, LILRB4, FCER1A, LIL4B3, CSF1 R, LILRA1 , ADORA3, MPEG1 , FCN1 , RNASE6, FPR3, CYBB, MS4A4A, MS4A4E, OLR1 , CD163, WDFY4, SIGLEC1 , CD300E, CDH11 , DCN, PDGFRA, COL1A2, ISLR, COL1 A1 , FNDC1 , BGN, COL5A2, POSTN, PCDH18, ADAMTS1 , MXRA5, SULF1 , EDNRA, PRRX1 , COL3A1 , THY1 , LUM, COL12A1 , BACH2, TRABD2A, IL7R, HDAC4, NR3C2, ADD3, PABPC1 , PABPC3, MFHAS1 , DSC1 , SELL, SESN1 , FASLG, CD8A, SETBP1 , CTSW, APOBEC3, APOBECC3, BTNL8, HLA-DPB1 , ULBP3, RAD51 , WNT9A, HLA-DMA, CERS5, CD8B, NKG7, FAM156A, ZNF696, MCM5, DPF3, TTC24, YARS1 , EBP, TRPS1 , GZMH, VCAM1 , LAG3, CRIM1 , NAA40, GRIK4, PSMB9, HLA-DMB, SERP2, HOXB4, CASP7, TMCC2, ARPC5L, MCTP2, LYST, FOXP3, IL2RA, CD80, CD177, LAIR2, CCR8, CCL22, TNFRSF13B, TNFRSF18, APOE, TREM2, C1 QB, C1 QC, VSIG4, S100A8, S100A9, VCAN, FCN1 , LYZ, IDO1 , 01 OORF54, XCR1 , CLEC9A, BATF3, FCER1A, CD1 C, CD1 E, CD1 D, and CLEC10A, or as provided in Table 2.

[0013] In some aspects, a method of the disclosure further comprises detecting the presence of tumor infiltrating lymphocytes (TIL) in the sample.

[0014] In some aspects, TGF-p signaling competency and alt-EJ activation are assessed by measuring the expression levels of selected TGFp-associated genes and alt-EJ- associated genes and obtaining an integrated score. In some aspects, the integrated score is a pAlt score. In some aspects, the pAlt score is calculated according to the following equation:PAlt scores = (TGF / 3max- TGFpi')2+ (AltEjmin- AltEjt)2- V (AltEjmax- AltEJi)2+ (TGF / 3min- TGFftt)2wherein:TGF / 3minis Lowest value among all TGFp scores;TGF / 3maxis Highest value among all TGFp scores;TGFfii is the sample / TGFp score;AltEjminis Lowest value among all AltEj scores;AltEjmaxis Highest value among all AltEj scores; and AltEjt is the sample / AltEj score.

[0015] In some aspects, a p-alt score above a selected threshold indicates the cancer cells comprise the DDR deficit phenotype.

[0016] In some aspects, determining the expression levels of the genes is carried out by RT-PCR, a hybridization assay, a microarray assay, RNA sequencing, a Northern blot, a Western blot, an immunohistochemistry assay, or an ELISA.

[0017] In some aspects, genotoxic therapy is radiation therapy, cisplatin, chlorambucil, busulfan, carboplatin, carmustine, chlorambucil, cyclophosphamide, dacarbazine, daunorubicin, doxorubicin, epirubicin, etoposide, idarubicin, ifosfamide, irinotecan, lomustine, mechlorethamine, melphalan, mitomycin C, mitoxantrone, oxaliplatin, temozolomide, or topotecan.

[0018] In some aspects, TGFpii is a polypeptide, a small molecule, or a nucleic acid. In some aspects, such polypeptide is an anti-TGF-p antibody, a soluble TGF-p receptor, or a peptide. In some aspects, such small molecule is galunisertib (LY2157299), LY2382770, LY3022859, SB-431542, SD208, SM16, tranilast, pirfenidone, TEW-7197, PF-03446962, or pyrrole-imidazole polyamide. In some aspects, such nucleic acid is trabedersen (AP12009) or belagenpumatucel-L.

[0019] In some aspects, the anti-TGF-p antibody is fresolimumab, metelimumab, lerdelimumab, 1 D11 , or 2G7, or a derivative of fresolimumab, metelimumab, lerdelimumab, 1 D11 , or 2G7. In some aspects, the peptide is disitertide (P144).

[0020] In some aspects, the IL-6 inhibitor is an anti-IL-6R antibody.

[0021] In some aspects, the anti-IL-6R antibody is tocilizumab or satralizumab.

[0022] In some aspects, treatment with the genotoxic therapy and the immune checkpoint inhibitor or immunotherapy increases the presence of immune infiltrates in cancer cells or in a cancerous tumor of the subject, wherein the immune infiltrates comprise one or more of natural killer (NK) cells, T cells, B cells, neutrophils, macrophages, or dendritic cells in the tumor.

[0023] In some aspects, the T cells are CD3+ T cells, CD4+ T cells, CD8+ T cells, and / or Teff cells. In some aspects, the T cells are detected by immunofluorescence.

[0024] In some aspects, the immune checkpoint inhibitor inhibits expression and / or activity of a checkpoint protein in the subject, wherein the checkpoint protein is CTLA-4, PDL1 , PDL2, PD1 , B7-H3, B7-H4, BTLA, HVEM, TIM3, GAL9, LAG3, VISTA, KIR, 2B4, CD160, CGEN-15049, CHK 1 , CHK2, TIGIT, BTLA, IDO3, A2aR, or B-7 family ligands, or acombination of any thereof. In some aspects, the immune checkpoint inhibitor is ipilimumab, tremelimumab, pembrolizumab, atezolizumab, avelumab, durvalumab, or nivolumab.

[0025] The disclosure also provides a method of sensitizing an immune-poor cancer with a high |3Alt score in a subject to immune checkpoint blockade (ICB)-based therapy comprising administering to the subject an effective amount of a genotoxic therapy and an immunosuppressive antagonist.

[0026] In some aspects, the cancer is bladder cancer, brain cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, gastric cancer, glioblastoma, glioma, head and neck cancer, lung cancer, melanoma, mesothelioma, nasopharyngeal cancer, ovarian cancer, pancreatic cancer, prostate cancer, renal cancer, testicular cancer, thyroid cancer, skin cancer, and uterine cancer.

[0027] In some aspects, the treatment produces at least one therapeutic effect selected from the group consisting of reduction in size of a tumor, reduction in number of metastatic lesions over time, a complete response, partial response, stable disease, increase in overall response rate, increase in overall survival, and an increase in progression-free survival.

[0028] The disclosure also provides a kit for determining the expression levels of(i) at least one TGFp-associated gene selected from the group consisting of ABCG1 , AMIGO2, CA12, CCDC99, CCL20, CHRNA9, COL4A2, CTGF, DLC1 , DNAJB9, DSC2, ENC1 , F3, FAP, FGF2, FN1 , HEY1 , HMGA2, ID1 , IGF2BP3, IGFBP3, JAG1 , KLF4, LAMB3, LAMC2, LARP6, LIPG, MAFF, MMD, PDGFC, PLEK2, PLXNA2, POSTN, PSCD1 , RICS, RNF24, RUNX1 , SAMSN1 , SERPINE1 , SERPINE2, SH2D2A, SH2D4A, SLC20A1 , SLC22A4, TGIF1 , THBS1 , TMEPAI, TNC, TNFRAF12A, and VCAN, and(ii) at least one Alt-EJ-associated gene selected from the group consisting of APE2, APEX1 , ASF1A, CDKN2D, CIB1 , DNA2, FAAP24, FANCM, GEN1 , HRAS1 , LIG1 , LIG3, MEN1 , MRE11A, MSH3, MSH6, MTH1 , MTOR, NABP2, NTHL1 , PALB2, PARP1 , PARP3, POLA1 , POLM, POLQ, PRP19, RAD51 D, RBBP8, RRM2, RUVBL2, SOD1 , TIP60, UNG, WRN, and XRCC1 in a sample, wherein the kit comprises:(a) primers for recognizing the at least one of TGFp-associated gene and the at least one Alt-EJ-associated gene, or an array comprising said primers; and(b) instructions for performing a method for determining the expression levels of the at least one TGFp-associated gene and the at least one Alt-EJ-associated gene.

[0029] In some aspects, the primers of the kit recognize at least two TGFp-associated genes and at least two Alt-EJ-associated genes.

[0030] Further aspects and advantages of the disclosure will be apparent to those of ordinary skill in the art from a review of the following detailed description, taken in conjunction with the drawings. It should be understood, however, that the detailed description (including the drawings and the specific examples), while indicating embodiments of the disclosed subject matter, are given by way of illustration only, because various changes and modifications within the spirit and scope of the disclosure will become apparent to those skilled in the art from this detailed description.BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Fig. 1 A-E: mTDT exhibit a range of |3Alt that is correlated with radiation sensitivity and tumor immune phenotype. Fig. 1A is a heatmap of unsupervised hierarchical clustering of TGFp (pink) and alt-EJ (green) gene signatures in RNAseq data with |3Alt scores annotated above (red=positive, teal=negative) for 12 mTDT families (n=70). Fig. 1 B is a similar heatmap for primary Trp53 null mammary cancers (n=62). Fig. 1C is a similar heatmap for 4T1 (green, n=12) and LLC (purple, n=12) subcutaneous tumors. Fig. 1 D is a similar heatmap for pre-clinical models from the TISMO database (n=714). Fig. 1 E is a UMAP (uniform manifold approximation and projection) visualization of the H (n=3) and K (n=3) mTDT immune landscapes.

[0032] Fig. 2A-E: mTDT exhibit a range of |3Alt that is correlated with radiation sensitivity and tumor immune phenotype. Fig. 2A scatter plot showing negative correlation of TGFp and alt-EJ signatures scores among mTDT (n= 70, R=-0.56, P<0.0001). Fig. 2B is a box and whisker plot depicting distribution of pAlt scores across A-L mTDT families (n = 4-6 per group). Fig. 2C is a Kaplan-Meier plot depicting survival of F (green), H (gold) and K (purple) mTDT following sham treatment (dotted line, F n=14, H n=8, K n=14) or irradiation with 10 Gy (solid line, F n=14, H n=10, K n=14). P<0.0001 , log-rank test. Fig. 2D is a box plot comparing the percent live intra-tumoral CD45+ cells in F (n=10), H (n=5) and K (n=9) mTDT. *** denotes P<0.0001 of mean ± SEM from one-way ANOVA. Fig. 2E is a plot of frequencies of inflammatory (CD11b+CD86+), activated (CD11b+SiglecF+CD115+), G- MDSC (CD11b+Ly6G+), M-MDSC (CD11b+Ly6C+), and tumor associated macrophages (TAM, CD11 b+CD206+) myeloid subsets in H and K mTDT (n=4) from cytometry by time of flight (CyTOF analysis). Data represent mean ± SEM, *** denotes P<0.001 from unpaired t- tests.

[0033] Fig. 3A-K: Radiation-promoted ICB response of mTDT is augmented by TGFp blockade. Fig. 3A is a schematic of the treatment schedule, showing primary endpoints and tissue collection. Fig. 3B is a Kaplan-Meier survival plot of F, H and K mTDT treated with IgG isotype (grey, n=32), anti-PD-L1 (pink, n=25), TGFpi (yellow green, n=23), anti-PDL1 +TGFpi (purple, n=30) RT (black, n=36), RT+anti-PDL1 (red, n=24), RT+TGFpi (green, n=35) and RT+TGFpi+anti-PDL1 (blue, n=29). Fig. 3C is a Kaplan-Meier survival plot of F, H and K mTDT treated with IgG isotype (grey, n=32), RT (black, n=36), RT+anti-PD-L1 (red, n=24), RT+TGFpi (green, n=35) and RT+TGFpi+anti-PD-L1 (blue, n=29). Log-rank test, P<0.0001 . Fig. 3D shows tumor growth curves following treatment of tumors classified as responders (R, orange, n=121 ) and non-responders (NR, black, n=132). Data points are mean ± SEM, growth curves were fitted to the exponential model. P<0.001 from two-way ANOVA. Fig. 3E shows Kaplan-Meier survival curves for responders (orange, n=86), non- responders (black, n=120) and sham (grey, n=52). *** denotes P<0.001 from log-rank test. Fig. 3F is a box plot showing the frequency of responders was significantly different a function of treatment. P<0.005 determined by two-way ANOVA. Fig. 3G is an experimental schematic of tissue collection and analysis for multispectral flow cytometry. Fig. 3H is a plot of the relative fold change of NK, CD4, CD8 and CD3 T cell number in responders (n=25) vs non-responders (n=22). *** denotes P<0.001 from one-way ANOVA. Fig. 31 is a plot of the relative fold change of CD4, CD8, and CD3 T cell number in blood of responders (n=25) vs non-responders (n=22). *P<0.05, ** P<0.01 , *** denotes P<0.001 from one-way ANOVA. Fig. 3J is a plot of response rates of F (green), H (gold) and K (purple) mTDT as a function of treatment. Fig. 3K is a Kaplan-Meier plot of F mTDT treated with IgG isotype (grey, n=10), RT (black, n=10), RT+anti-PD-L1 (red, n=12), RT+TGFpi (green, n=5) and RT+TGFpi+anti-PD-L1 (blue, n=7). Log-rank test, P<0.0001.

[0034] Fig. 4A-K: mTDT response to ICB is enhanced by radiation and TGFp inhibition. Fig. 4A shows tumor growth curves of mTDT designated as non-responders (black, n=52) and responders (orange, n=52). Fig. 4B is a plot of tumor weight at early sacrifice for IgG isotype (gray, n=17), responders (orange, n=32) and non-responders (black, n=36). Data are represented as mean ± SEM, significance designated as * P<0.05, ** P<0.01 , ***P<0.001 determined one-way ANOVA. Fig. 4C is a plot of comparing pAlt scores in responders (orange, n=29) and non-responders (black, n=22). Fig. 4D is a plot of percent CD45-normalized intra-tumoral neutrophils. Fig. 4E is a plot of percent CD45-normalized intra-tumoral macrophages. Fig. 4F is a plot of immune cell activation in NK cells, CD8 T cells and CD4 T cells between responders (orange, n=25) and non-responders (black, n=22). Statistical significance denoted as * P<0.05 and ** P<0.01 determined by unpaired t- tests. Fig. 4G-4J are plots showing no significant difference in (Fig. G) CD11 b+Ly6C+ cells, (Fig. H) B cells, (Fig. I) neutrophils, and (Fig. J) dendritic cells abundance in blood between responders (orange, n=28) and non-responders (black, n=22). Data are depicted as mean ± SEM, and significance determined by unpaired t-tests. Fig. 4K shows Kaplan-Meier survivalcurves for responders (orange, n=24), non-responders (black, n=35) and sham (grey, n=10). *** denotes P<0.001 from log-rank test.

[0035] Fig. 5A-D: |3Alt is correlated with tumor immune archetypes in pre-clinical models and cancer patients in a tissue-agnostic manner. Fig. 5A is a heat map unsupervised hierarchical clustering of TelS expression among mTDT (n=70) with |3Alt scores annotated above. Fig. 5B is a plot showing |3Alt scores distribution between immune archetypes, immune-rich to immune desert from left to right, as assigned in Combes et al., 2022. Cell 185: 184-203. e119. Fig. 5C is a heat map of unsupervised hierarchical clustering of TelS scores in the Combes et al. subset of TCGA (n=4341) with FGA and TMB depicted below. Fig. 5D is a heat map of TelS scores of TCGA BRCA cohort (n=1082) supervised by |3Alt annotated as in Fig. 5C.

[0036] Fig. 6A-C: |3Alt is correlated with tumor immune archetypes in a tissue-agnostic manner in pre-clinical models and cancer patients. Fig. 6A is a heat map unsupervised hierarchical clustering of TelS expression in primary Trp53 null mammary cancers (n=62) annotated with pAlt scores. Fig. 6B is a heat map unsupervised hierarchical clustering of TelS expression in 4T1 (green, n=12) and LLC (purple, n=12) subcutaneous tumors annotated with pAlt scores. Fig. 6C is a heat map of supervised hierarchical clustering depicting TelS expression for the TCGA subset (n=4341) with pAlt scores annotated above and fraction of genome altered and TMB below. Fig. 6D is a heat map of unsupervised hierarchical clustering heatmap of IFN I gene signature in the TCGA subset (n=4341) with PAlt scores annotated above. Fig. 6E is a heat map of unsupervised hierarchical clustering heatmap of IFN I gene signature in the TCGA BLCA (n=405) with pAlt scores annotated above. Fig. 6F is a heat map of unsupervised hierarchical clustering heatmap of IFN I gene signature in the TCGA LUSC (n=496) with pAlt scores annotated above. Fig. 6G shows scatterplots showing the correlation of pAlt and the indicated scores for TCGA BLCA (n=405) and BRCA (n=1082). *** denotes P<0.001 , N.S. not significant.

[0037] Fig. 7A-H: ICB responders are characterized by high pAlt are immunologically poor tumors. Fig. 7A is a scatter plot showing that TGFp and alt-EJ signatures scores are negatively correlated in the IMvigor210 patient cohort (n=299, R=-0.43, P<0.0001 ). Fig. 7B is a scattered boxplot of pAlt scores (mean ± SEM) of patients defined as partial (PR) and complete response (CR) (orange, n=69) versus stable disease (SD) and progressive disease (PD) (purple, n=230). P<0.0005 determined by Wilcoxon test. Fig. 7C is a scattered boxplot of pAlt scores (mean ± SEM) of metastatic melanoma patients from Hugo et al., 2016. Cell 165, 35-44, treated with anti-PD-1 (n=27) defined as CD / PR (orange, n=15) and PD (purple, n=12). Wilcoxon test. P=0.06. Fig. 7D is a heatmap of TelS scores heatmap of IMvigor210 patients (n=329) supervised by |3Alt annotated for response, TMB and quartilesdesignated from Q1 (red) to Q4 (blue). Fig. 7E is a plot of the distribution of patient response (PD=partial disease, SD=stable disease, PR= partial response, CR= complete response) for |3Alt Q1 (n=79) and Q4 (n=75) designated in D. Chi-square test, P<0.0001. Fig. 7F is a Kaplan-Meier survival plot for Q1 (red, n=79) versus Q4 (blue, n=75) in IMvigor210. HR:0.62, P=0.011 via log-rank test. Fig. 7G is a heatmap displaying TelS scores expression in IMvigor210 cohort with same annotations as D. Fig. 7H is a scattered boxplot of pAlt scores (mean ± SEM) in immune-poor (teal, n=32) and immune-rich (pink, n=34) tertile. P<0.0001 determined by Wilcoxon test.

[0038] Fig. 8A-D: High |3Alt ICB responders are characteristically immunologically poor tumors. Fig. 8A is a heat map of unsupervised hierarchical clustering of TGFp (orange) and alt-EJ (blue) gene signatures across IMvigor210 metastatic bladder cancer patients treated with anti-PD-L1 (n=348). pAlt scores are annotated above. Fig. 8B is a heat map of unsupervised hierarchical clustering of type I IFN gene signatures in the IMvigor210 melanoma patients (n=348) with pAlt scores annotated above. Fig. 8C is a plot of the distribution of response types (PD=partial disease, SD=stable disease, PR= partial response, CR= complete response) in Q1 (cohort 1 n=16, cohort 2 n=58) and Q4 (cohort 1 n=16, cohort 2 n=57) pAlt quartiles of IMvigor210 patients. Fig. 8D is a plot of TelS summary scores for Q1 (red, cohort 1 , n=16; cohort 2, n=58) and Q4 (blue, cohort 1 , n=16; cohort 2, n=57) pAlt quartiles of IMvigor210 patients. Wilcoxon test.

[0039] Fig. 9A-F: High pAlt tumors respond to ICB by converting from immune-poor to immune-rich. Fig. 9A is a heatmap of TelS scores for Riaz et al., 2017. Cell 171 , 934-949 e916 melanoma patients (n=110) annotated with pAlt scores above and response below. Responders (R, brown, n=18) and non-responders (NR, black, n=92). Time of biopsy (black pretreatment n=51 , white on-treatment n=50). Fig. 9B is a heatmap of ICB responsive patients from Riaz et al., 2017. Cell 171 , 934-949 e916 with paired biopsies, annotated with PAlt scores. Fig. 9C is a violin plot displaying percent intra-tumoral CD45+ cells for responders (R, orange), non-responders (NR, black) and sham (grey) of F (R n=12, NR n=10, sham n=10), H (R n =11 , NR n=11 , sham n =5) and K (R n =12, NR n =28, sham n=9) mTDT. Each point represents a mouse. P<0.05 denoted as *, P<0.01 denoted as ** from two-way ANOVA. Fig. 9D is a plot of the fraction of myeloid (red) and lymphoid (blue) immune cells in F mTDT responders (R, n=12), non-responders (NR, n=10) and sham (n=7). P<0.05 denoted as * from two-way ANOVA. Fig. 9E is a plot of immune cell composition of responders versus non-responders as a function of treatment. Fig. 9F is a plot of the percent NK cells normalized to CD45+ F mTDT responders versus non-responders as a function of indicated treatment (IgG - grey, RT - orange, anti-PD-L1 - green, TGF i - purple, n=). P<0.001 denoted as *** from one-way ANOVA. Fig. 9G is a t-SNE ((t-distributed Stochastic Neighbor Embedding) plot (n=3-5) depicting tumor NK cells (yellow) and CD45+ (purple) cells in F mTDT treated as defined in (F) normalized for CD45+ infiltration. Fig. 9H is a plot of representative histograms of Ki67 in intra-tumoral NK cells from F mTDT color coded for indicated treatment.

[0040] Fig. 10A-F: High |3Alt tumors converting from immune-poor to immune-rich. Fig. 10A is a heatmap of unsupervised hierarchical clustering of type I IFN gene signatures for Riaz et al., 2017. Cell 171 , 934-949 e916 pre- and on-treatment biopsies from melanoma patients (n=110) annotated with |3Alt scores above and response type (brown, responders, n=18; black nonresponders, n=92) and time of biopsy (on-treatment white, pre-treatment black). Fig. 10B is a heatmap similar to Fig. 10A for those patients classified as responders with paired biopsies (n=9). Pre-treatment and on-treatment sample of the same patient labeled by color. The relative fold change of responders (n=28) compared to nonresponders (n=22) for the number of (Fig. 10C) intra-tumoral NK cells, CD4 T cells. CD8 T cells and CD3 T cells (Fig. 10D) Ki67+ NK cells, CD8 T cells and CD4 T cells and (Fig. 10E) circulating CD4 T cells, CD8 T cells and CD3 T cells. Data are mean ± SEM, significance determined by unpaired t-tests are denoted as * P<0.05, ** P<0.01 , *** P<0.001 . Fig. 10F shows t-SNE plots of tumor immune cell composition color-coded for cell types between responders and non-responders F mTDT treated with IgG isotype, anti-PD-L1 , TGFpi, anti- PDL1+TGFpi, RT, RT+anti-PD-L1 , RT+TGFpi and RT+TGFpi+anti-PD-L1 .

[0041] Fig. 11 A-J: NK cells are required for conversion from immune-poor to immune-rich TME and response to ICB. Fig. 11 A is a bar graph showing percent CD45+ intra-tumoral NK cells between F mTDT responders (R, orange, n=13), non-responders (NR, black, n=10) and sham (grey, n=8) left and representative density plot to right. Fig. 11 B left panel is a plot of percent NK cell Ki67+ cells between F mTDT responders (orange, n=13), non-responders (black, n=10) and sham (grey, n=8) in tumor; right panel is a representative fluorescence histogram is shown. Fig. 11C is a schematic of NK cell depletion experiment, primary endpoint and tissue collection. The percent (mean ± SEM) intra-tumoral (Fig. 11 D) CD45+ cells, (Fig. 11 E) CD45-normalized NK cells, (Fig. 11 F) CD8 T cells IL-2+ left and representative histogram right, and (Fig. 11G) cDC-1 CXCL9+ cells left and representative histogram right from F mTDT treated with IgG isotype without (n=2) or with NK cell depletion (n=5), RT without (n=5) or with (n=5) NK cell depletion and RT+TGFpi+anti-PD-L1 without (n=5) or with (n=5) NK cell depletion, representative fluorescence histograms are shown (right). * denotes P<0.05, ** denotes P<0.01 from one-way ANOVA. Fig. 11H is a violin plot of percent of IL-2+, TNFa, and CXCL9 (left panel), in PBMC, cDC-1 and MDSC (right panel) in F mTDT following triple treatment without (blue) and with (teal) NK cell depletion. * denotes P<0.05, ** denotes P<0.01 from unpaired t-tests. Fig. 111 is a heatmap of TelS ofB16 melanoma tumors at baseline (black, n=24) compared to those designated responders to ICB regimens (orange, n=12). The TelS score of responders is significantly enriched, P<0.0001 Chi square test, (Fig. 11 J) NK cell activation signature enrichment scores of untreated B16 melanoma (black, n=18) or those designated responders to ICB regimens (orange, n=12). *** denotes P<0.0001 from unpaired t-test.

[0042] Fig. 12A-F: NK cells are required for high |3Alt TME conversion from immune-poor to immune-rich and response to ICI. Fig. 12A is a column plot of percent circulating NK cells, Ki67+ NK cells (Fig. 12B) and CD49b-CD49a+ innate lymphoid cells (ILC) (Fig. 12C) in blood of mice bearing F mTDT treated with IgG isotype without (n=2) or with NK cell depletion (n=5), RT without (n=5) or with (n=5) NK cell depletion, and RT+TGFpi+anti-PDL1 without (n=5) or with (n=5) NK cell depletion. Data are mean ± SEM, * P<0.05, ** P<0.01 , *** P<0.001 determined by one-way ANOVA corrected for multiple comparisons. Fig. 12D is a heatmap of unsupervised hierarchical clustering of TelS scores expression of untreated tumors (n=384) from TISMO database annotated above with |3Alt scores. Fig. 12E shows violin plots of enrichment scores for gene ontology signatures of NK cell chemotaxis, cytokine production, degranulation and NK mediated immunity for untreated (black, n=18) or responders to ICI regimens (orange, n=12) B16 melanoma tumors from TISMO. * denotes P<0.05 and *** denotes P<0.001 determined by unpaired t-test. Fig. 12F is a heatmap depicting gene expression of NK cell ligands (left) and receptors (right) of the high |3Alt ICI responders (n=2) pre and on treatment from Riaz et al., 2017. Cell 171 , 934-949 e916 melanoma patients.DETAILED DESCRIPTION

[0043] Transforming growth factor beta (TGFp) is a secretory cytokine known for its pleiotropic roles in cancer progression through regulating cell proliferation, differentiation, apoptosis, and migration. However, TGFp’s critical role in the DNA damage response (DDR) and maintenance of genome integrity is less appreciated.

[0044] In cells, DNA damage is a regular occurrence that may result from normal cellular processes such as errors that occur during DNA replication, or due to environmental exposure to DNA damaging agents such as radiation. Damaged DNA may contain nucleotide modifications and / or single-strand breaks (SSBs) and double-strand break (DSBs), which if not properly repaired accumulate leading to genomic instability, apoptosis, or senescence. Thus, to maintain genomic integrity, mammalian cells have developed a DNA damage response (DDR) and multiple repair mechanisms. Compared to excision mechanisms which remove damaged or misincorporated bases from DNA by base excision repair (BER), nucleotide excision repair (NER), and mismatch repair (MMR), DNA double-strand break repair (DSBR) corrects double-stranded breaks (DSBs), which are considered the most genotoxic. DSBR occurs via four major pathways: non-homologous end joining (NHEJ), homologous recombination (HR), single strand annealing (SSA) and alternative end joining (alt-EJ), which is also known as microhomology end joining (MMEJ). HR and NHEJ are believed to be efficient processes that repair DSBs with little error and they are the dominant pathways used for DSB repair. In contrast, both Alt-EJ and SSA are error-prone repair mechanisms that are inherently mutagenic. These mechanisms are used mostly as back up mechanisms when HR and NHEJ fail.

[0045] TGFp regulates the expression or function of key DNA repair proteins including, but not limited to, proteins encoded by ATM (ataxia telangiectasia mutated), BRCA1 (breast cancer 1 gene), FANCD2 (Fanconi anemia complementation group D2) and LIG4 (DNA ligase 4), which are necessary for maintenance of genomic integrity (Guix et al., 2022. Clin Cancer Res. 28(7) : 1372-1382). Inhibition or loss of TGFp signaling decreases error free DNA repair by HR and NHEJ and increases the use of error-prone alt-EJ repair. The switch to alt-EJ leads to genome instability i.e., a higher tumor mutational burden (TMB) resulting from DNA translocations or deletions. This would be expected to lead to higher tumor immune infiltration due to the increased formation of immunogenic neoantigens, similar to MMR-deficient (dMMR) cancers discussed above. Surprisingly, the inventors of the disclosure have discovered that the majority of cancers (75% in IMvigor210) using error- prone alt-EJ due to compromised TGFp signaling are immune poor and are expected to be refractory to immune checkpoint blockade (ICB) therapy. IMvigor210 was a multicenter, single-arm, phase 2 trial that investigated efficacy and safety of atezolizumab (antiprogrammed death-ligand 1 [PD-L1]) in metastatic urothelial bladder cancer registered as clinical trial number NCT02108652. Additionally, it was found in experimental models that a combination of a genotoxic and an immunosuppression inhibitor or immunosuppression antagonist, e.g., a TGFp inhibitor, could sensitize such tumors to ICB.

[0046] Thus, the disclosure provides methods and compositions for identifying and treating a human subject afflicted with an immune poor cancer. Histologically, tumors can be categorized as having an inflamed, immune desert, or immune-excluded phenotype based on the spatial localization of immune cells with respect to the tumor and stromal compartments (Hegde et al., 2016. Clin Cancer Res. 22(8) : 1865-1874.). Inflamed tumors are those in which lymphocytes have infiltrated the tumor bed, excluded tumors are those in which lymphocytes are restricted to the interface between tumor and tissue, and immune deserts are those with a paucity of tumor infiltrating lymphocytes (TILs). In immune excluded tumors, the majority of T-cells migrate along aligned collagen and fibronectin fibers that run circumferentially around the tumor. See, e.g., Salmon et al., 2012. J. Clin. Invest. 122:899-910. In some aspects, a tumor is categorized as an immune excluded tumor if CD8+ cells are observed substantially or exclusively in stroma immediately adjacent to or within a main tumor mass. In some aspects, a tumor is categorized as an immune desert tumor if the prevalence of CD8+ lymphocytes is low (e.g., less than about ten CD8+ lymphocytes (e.g., about 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 , or 0 CD8+ lymphocytes) in an area of tumor and tumor- associated stroma at a magnification of about 200x, for example, as calculated as the average of 10 representative fields of view).

[0047] Both the immune desert and the immune excluded phenotypes can be considered non-inflammatory immune tumors. Accordingly, in some aspects, an immune poor cancer refers to a cancer with an immune-excluded or an immune desert phenotype. In one embodiment, an immune poor cancer comprises cancer cells exhibiting a DNA damage repair (DDR) deficit phenotype. In one embodiment, the DDR deficit phenotype is determined by assessing the levels of TGF-p and alt-EJ pathway activities with low TGF-p pathway activity and high alt-EJ pathway activity indicating a DDR deficit phenotype. Further provided is a method of treating the identified subject using a treatment regimen comprising a genotoxic therapy, an immunosuppressive antagonist and an immune checkpoint inhibitor or an immunotherapy. An “immunosuppressive antagonist”, as used herein, means an antagonist that blocks or inhibits immunosuppression. Examples of an immunosuppressive inhibitor include, but are not limited to, a TGFp inhibitor (TGFpi), which inhibits TGFp- mediated immunosuppression, thereby enhancing anti-tumor immunity in the tumor microenvironment (TME). ICB therapies have shown promise in immune-inflamed tumors, however, such success is not evident in immune-excluded or desert tumors. In one embodiment, the combination treatment disclosed herein sensitizes an immune poor cancer to ICB therapy. In one embodiment, the disclosed treatment converts immune poor cancers to an immune inflamed phenotype.

[0048] The various elements and implementations of this general method are described next.

[0049] List of abbreviations

[0050] Throughout the detailed description and examples of the disclosure the following abbreviations will be used:Abbreviation MeaningDSB Double strand breaksDSBR Double strand break repairHR Homologous recombinationICB immune checkpoint blockadeICI Immune checkpoint inhibitorNHEJ Non-homologous end-joiningMMEJ Microhomology-mediated end-joiningAlt-EJ Alternative end-joining — which is the same as MMEJTGFpi i TGFpi inhibitorOS Overall survivalPFS Progression free survivalPR Partial responseOR Overall responseORR Overall response rateCR Complete responsePR Partial responseDoR Duration of responseDFS Disease Free SurvivalSD Stable DiseaseRECIST Response Evaluation Criteria in Solid Tumors, published in Eisenhauer et al., 2009. Eur J Cancer. 45(2):228-247TCGA The Cancer Genome AtlasTIL Tumor Infiltrating LymphocytesTELS Tumor-educated immune signatures

[0051] The terms Alt-EJ and MMEJ may be used interchangeably herein.

[0052] So that the disclosure may be more readily understood, certain technical and scientific terms are specifically defined below. Unless specifically defined elsewhere in this document, all other technical and scientific terms used herein have the meaning commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0053] The terms "diagnosis" and "monitoring" are commonplace and well-understood in medical practice. By means of further explanation and without limitation the term "diagnosis" generally refers to the process or act of recognizing, deciding on or concluding on a diseaseor condition in a subject on the basis of symptoms and signs and / or from results of various diagnostic procedures (such as, for example, from knowing the presence, absence and / or quantity of one or more biomarkers characteristic of the diagnosed disease or condition).

[0054] The term "monitoring" generally refers to the follow-up of a disease or a condition in a subject for any changes which may occur over time.

[0055] The terms "prognosing" or "prognosis" generally refer to an anticipation on the progression of a disease or condition and the prospect (e.g., the probability, duration, and / or extent) of recovery. A good prognosis of the diseases or conditions taught herein may generally encompass anticipation of a satisfactory partial or complete recovery from the diseases or conditions, preferably within an acceptable time period. A good prognosis of such may more commonly encompass anticipation of not further worsening or aggravating of such, preferably within a given time period. A poor prognosis of the diseases or conditions as taught herein may generally encompass anticipation of a substandard recovery and / or unsatisfactorily slow recovery, or to substantially no recovery or even further worsening of such.

[0056] The terms also encompass prediction of a disease. The terms "predicting,” or "prediction" generally refer to an advance declaration, indication or foretelling of a disease or condition in a subject not (yet) having said disease or condition. For example, a prediction of a disease or condition in a subject may indicate a probability, chance or risk that the subject will develop said disease or condition, for example within a certain time period or by a certain age. Said probability, chance or risk may be indicated inter alia as an absolute value, range or statistics, or may be indicated relative to a suitable control subject or subject population (such as, e.g., relative to a general, normal or healthy subject or subject population). Hence, the probability, chance or risk that a subject will develop a disease or condition may be advantageously indicated as increased or decreased, or as fold-increased or fold-decreased relative to a suitable control subject or subject population. As used herein, the term "prediction" of the conditions or diseases as taught herein in a subject may also particularly mean that the subject has a 'positive' prediction of such, i.e., that the subject is at risk of having such (e.g., the risk is significantly increased vis-a-vis a control subject or subject population). The term "prediction of no" diseases or conditions as taught herein as described herein in a subject may particularly mean that the subject has a 'negative' prediction of such, i.e., that the subject's risk of having such is not significantly increased vis- a-vis a control subject or subject population.

[0057] "Treatment" or "therapy" of a subject refers to any type of intervention or process performed on, or the administration of an active agent to, the subject with the objective ofreversing, alleviating, ameliorating, inhibiting, slowing down or preventing the onset, progression, development, severity or recurrence of a symptom, complication or condition, or biochemical indicia associated with a disease.

[0058] The term "responder,” for example with regard to immune blockade therapy, refers to a subject whose cancer upon administration of an immune checkpoint inhibitor shows slower progression, significant amelioration, or cure of the cancer.

[0059] The term "non-responder," for example with regard to immune blockade therapy, refers to a subject whose cancer upon administration of an immune checkpoint inhibitor shows progression or lacks significant amelioration or cure of the cancer or after a period of response to treatment develops acquired resistance to therapy. These patients are also termed to be refractory to immune checkpoint blockade (ICB) therapy.

[0060] The term, “gene signature” or “gene expression signature” refers to a combined group of genes or proteins in a cell whose coordinated mRNA or protein expression pattern is representative of a biological pathway, process, or phenotype, or clinical outcome. Gene signatures can be used to differentiate subpopulations based on risk regardless of treatment (prognostic gene signatures) or to select a group of patients for whom a particular treatment will be effective (predictive gene signatures), thus maximizing individual benefit and minimizing toxicity see e.g., van't Veer, L. J., & Bernards, R. 2008. Nature. 452(7187):564- 70; Cantini et al., 2017. NPJ Syst Biol Appl. 4:2.

[0061] As used herein, a “gene signature” may also encompass any gene or genes, or protein or proteins, whose expression profile or whose occurrence is associated with a specific cell type, subtype, or cell state of a specific cell type or subtype within a population of cells. Increased or decreased expression or activity or prevalence may be compared between different cells in order to characterize or identify for instance specific cell (sub)populations. A gene signature, as used herein, may thus refer to any set of up- and down-regulated genes between different cells or cell (sub)populations derived from geneexpression analysis. For example, a gene signature may comprise a list of genes differentially expressed in a distinction of interest. It is to be understood that also when referring to proteins (e.g. differentially expressed proteins), such may fall within the definition of “gene” signature.

[0062] The signature may also be used to suggest for instance particular therapies, or to follow up treatment, or to suggest ways to modulate immune systems. Not being bound by any theory, a combination of cell subtypes having a particular signature may indicate an outcome. Not being bound by a theory, the presence of specific cells and cell subtypes are indicative of a particular response to treatment, such as including increased or decreasedsusceptibility to treatment. The signature may indicate the presence of one particular cell type. In one embodiment, the signatures are used to detect multiple cell states or hierarchies that occur in subpopulations of immune cells that are linked to particular pathological condition (e.g. cancer), or linked to a particular outcome or progression of the disease, or linked to a particular response to treatment of the disease. Signatures may be functionally validated as being uniquely associated with a particular immune phenotype. Induction or suppression of a particular signature may consequentially be associated with or causally drive a particular immune phenotype.

[0063] The signature according to certain aspects of the disclosure comprises or consists of one or more genes and / or proteins, such as for instance 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10 or more. In certain aspects, the signature may comprise or consist of two or more genes and / or proteins, such as for instance 2, 3, 4, 5, 6, 7, 8, 9, 10 or more. In certain aspects, the signature may comprise or consist of three or more genes and / or proteins, such as for instance 3, 4, 5, 6, 7, 8, 9, 10 or more. In certain aspects, the signature comprises or consists of four or more genes and / or proteins, such as for instance 4, 5, 6, 7, 8, 9, 10 or more. In certain aspects, the signature comprises or consists of five or more genes and / or proteins, such as for instance 5, 6, 7, 8, 9, 10 or more. In certain aspects, the signature comprises or consists of six or more genes and / or proteins, such as for instance 6, 7, 8, 9, 10 or more. In certain aspects, the signature comprises or consists of seven or more genes and / or proteins, such as for instance 7, 8, 9, 10 or more. In certain aspects, the signature comprises or consists of eight or more genes and / or proteins, such as for instance 8, 9, 10 or more. In certain aspects, the signature comprises or consists of nine or more genes and / or proteins, such as for instance 9, 10 or more. In certain aspects, the signature comprises or consists of ten or more genes and / or proteins, such as for instance 10, 11 , 12, 13, 14, 15, or more. It is to be understood that a signature according to the disclosure may, in various aspect, also include a combination of genes or proteins.

[0064] As used herein, a “gene panel,” refers to a collection of genes that have been grouped for testing, enabling simultaneous analysis of multiple genes known to be associated with a particular disease, syndrome or phenotype. A gene panel may include anything from two to over 1 ,000 genes.

[0065] The term “detecting” is used herein in the broadest sense to include both qualitative and quantitative measurements of a target molecule. Detecting includes identifying the mere presence of the target molecule in a sample as well as determining whether the target molecule is present in the sample at detectable levels. Detecting may be direct or indirect.

[0066] A gene expression signature may be determined by any method known in the art. Methods for detecting, characterizing, and / or quantitating, nucleic acid sequences; and for detecting, characterizing, and / or quantitating, mRNA expression, are known to persons skilled in the art, and include, but are not limited to, for example, PCR procedures, RT-PCR, quantitative PCR or RT-PCR, Northern blot analysis, RNA protection assay, microarray analysis, hybridization methods, serial analysis of gene expression (SAGE), hybridization based on digital barcode quantification assays, multiplex RT-PCR, digital drop PCR (ddPCR), qRT-PCR, qPCR, UV spectroscopy, DNA sequencing, RNA sequencing, nextgeneration sequencing, including RNAseq, lysate-based hybridization assays utilizing branched DNA signal amplification, such as the QuantiGene 2.0 Single Plex, and branched DNA analysis methods.

[0067] Any method known to those of skill in the art to detect proteins can be used, including immunological assay methods, wherein the ability of an assay to separate, detect and / or quantify the protein is conferred by specific binding between a separable, detectable and / or quantifiable immunological binding agent (antibody) and the marker. Immunological assay methods include without limitation immunohistochemistry (IHC), immunocytochemistry (ICC), flow cytometry, mass cytometry, fluorescence activated cell sorting (FACS), fluorescence microscopy, fluorescence based cell sorting using microfluidic systems, immunoaffinity adsorption based techniques such as affinity chromatography, magnetic particle separation, magnetic activated cell sorting or bead based cell sorting using microfluidic systems, enzyme-linked immunosorbent assay (ELISA) and ELISPOT based techniques, radioimmunoassay (RIA), Western blot, etc.

[0068] The terms “level of expression” or “expression level” in general are used interchangeably and generally refer to the amount of a gene transcript or gene product (protein) in a biological sample. “Expression” generally refers to the process by which genes are transcribed into a polynucleotide and then translated into a polypeptide. Fragments of the transcribed polynucleotide, the translated polypeptide, or polynucleotide and / or polypeptide modifications (e.g., posttranslational modification of a polypeptide) shall also be regarded as expressed whether they originate from a transcript generated by alternative splicing or a degraded transcript, or from a post-translational processing of the polypeptide, e.g., by proteolysis. An expression level for more than one gene of interest may be determined by aggregation methods known to one skilled in the art and also disclosed herein, including, for example, by calculating the median or mean of all the expression levels of the genes of interest. Before aggregation, the expression level of each gene of interest may be normalized by using statistical methods known to one skilled in the art and also disclosed herein, including, for example, normalized to the expression level of one or morehousekeeping genes, or normalized to a total library size, or normalized to the median or mean expression level value across all genes measured. In some instances, before aggregation across multiple genes of interest, the normalized expression level of each gene of interest may be standardized by using statistical methods known to one skilled in the art including, for example, by calculating the Z-score of the normalized expression level of each gene of interest.

[0069] "Increased expression," "elevated expression levels," or "elevated levels" refers to an increased expression or increased levels of a biomarker, genes, or gene panel in an individual or a sample relative to a control, such as an individual or individuals who are not suffering from the disease or disorder (e.g., cancer) or an internal control (e.g., housekeeping biomarker).

[0070] "Reduced expression," "reduced expression levels," or "reduced levels" refers to a decrease expression or decreased levels of a biomarker, genes, proteins, or gene panel in an individual relative to a control, such as an individual or individuals who are not suffering from the disease or disorder (e.g., cancer) or an internal control (e.g., housekeeping biomarker).

[0071] The term "housekeeping biomarker" refers to a biomarker or group of biomarkers (e.g., polynucleotides and / or polypeptides) which are typically similarly present in all cell types. In some aspects, the housekeeping biomarker is a "housekeeping gene." A "housekeeping gene" refers herein to a gene or group of genes which encode proteins whose activities are essential for the maintenance of cell function and which are typically similarly present in all cell types.

[0072] The term “low” or “lower”, as used herein, generally means lower by a statically significant amount; for the avoidance of doubt, "low" means a statistically significant value at least 10% lower than a reference level, for example a value at least 20% lower than a reference level, at least 30% lower than a reference level, at least 40% lower than a reference level, at least 50% lower than a reference level, at least 60% lower than a reference level, at least 70% lower than a reference level, at least 80% lower than a reference level, at least 90% lower than a reference level, up to and including 100% lower than a reference level (i.e. absent level as compared to a reference sample).

[0073] The term “high” or “higher”, as used herein, generally means higher by a statically significant amount relative to a reference level; for the avoidance of doubt, "high" means a statistically significant value at least 10% higher than a reference level, for example at least 20% higher, at least 30% higher, at least 40% higher, at least 50% higher, at least 60% higher, at least 70% higher, at least 80% higher, at least 90% higher, at least 100% higher,at least 2-fold higher, at least 3-fold higher, at least 4-fold higher, at least 5-fold higher, at least 10-fold higher or more, as compared to a reference level.

[0074] The term “statistically significant” or “significantly” refers to statistical significance and generally means a two standard deviation below normal, or lower, concentration of the marker. The term refers to statistical evidence that there is a difference. It is defined as the probability of making a decision to reject the null hypothesis when the null hypothesis is actually true. The decision is often made using the p-value.

[0075] It is to be understood that “differentially expressed” genes / proteins include genes / proteins which are up- or down-regulated as well as genes / proteins which are turned on or off. When referring to up- or down-regulation, in certain aspects, such up- or downregulation is preferably at least two-fold, such as two-fold, three-fold, four-fold, five-fold, or more, such as for instance at least ten-fold, at least 20-fold, at least 30-fold, at least 40-fold, at least 50-fold, or more. Alternatively, or in addition, differential expression may be determined based on common statistical tests, as is known in the art.

[0076] When referring to induction or activation, or alternatively suppression of a particular signature, preferable is meant induction or alternatively suppression (or upregulation or downregulation) of at least one gene / protein of the signature, such as for instance at least two, at least three, at least four, at least five, at least six, or all genes / proteins of the signature.

[0077] The term “sample”, as used herein, refers to a composition that is obtained or derived from a subject of interest that contains a cellular and / or other molecular entity that is to be characterized and / or identified, for example, based on physical, biochemical, chemical, and / or physiological characteristics. Samples include, but are not limited to, tissue samples, biopsy samples, circulating tumor cells, primary or cultured cells or cell lines, cell supernatants, cell lysates, platelets, serum, plasma, vitreous fluid, lymph fluid, synovial fluid, follicular fluid, seminal fluid, amniotic fluid, milk, whole blood, blood-derived cells, urine, cerebro-spinal fluid, saliva, sputum, tears, perspiration, mucus, tumor lysates, Formalin Fixed Paraffin Embedded (FFPE) tissue samples, frozen tissue samples, tissue extracts such as homogenized tissue, tumor tissue, cellular extracts, and combinations thereof.

[0078] A “tumor-infiltrating immune cell”, as used herein, refers to any immune cell present in a tumor or a sample thereof. Tumor-infiltrating immune cells include, but are not limited to, intratumoral immune cells, peritumoral immune cells, other tumor stroma cells (e.g., fibroblasts), or any combination thereof. Such tumor-infiltrating immune cells can be, for example, T lymphocytes (such as CD8+T lymphocytes and / or CD4+T lymphocytes), B lymphocytes, or other bone marrow-lineage cells, including granulocytes (e.g., neutrophils,eosinophils, and basophils), monocytes, macrophages (e.g., CD68+ / CD163+ macrophages), dendritic cells (e.g., interdigitating dendritic cells), histiocytes, and natural killer (NK) cells.

[0079] Tumor infiltrating lymphocytes (TILs) include, but are not limited to, CD8+ cytotoxic T cells, Th1 and Th17 CD4+ T cells and natural killer cells. TILs can generally be defined either biochemically, using cell surface markers, or functionally, by their ability to infiltrate tumors and effect treatment. TILs can be generally categorized by expressing one or more of the following biomarkers: CD4, PD-1 , CCR7, CD27, CD28, CCR7, CD25, CD95, CD8, CD45RA, CD3, CD57, CD127, CD62L, and NKp46. TILS may further be characterized by potency — for example, TILS may be considered potent if, for example, interferon (IFN) release is greater than about 50 pg / mL, greater than about 100 pg / mL, greater than about 150 pg / mL, or greater than about 200 pg / mL. Interferon can include interferon gamma (IFNy).

[0080] The term “immune inflamed tumor,” as used herein, refers to a tumor (e.g., a solid tumor) characterized by CD8+ T-cell infiltration and PD-L1 expression. See, e.g., Herbst et al. Nature 515:563-567, 2014 and Hegde et al. Clin. Cane. Res. 22: 1865-1874, 2016. In some aspects, a tumor is categorized as an immune inflamed tumor if CD8+ cells are observed in direct contact with malignant epithelial cells, either in the form of spillover of stromal infiltrates into tumor cell aggregates or of diffuse infiltration of CD8+ cells in aggregates or sheets of tumor cells.

[0081] The term “immune excluded tumor,” as used herein, refers to a tumor (e.g., a solid tumor) characterized by accumulation of T-cells in the extracellular matrix-rich stroma. See, e.g., Herbst et al. Nature 515:563-567, 2014 and Hegde et al. Clin. Cane. Res. 22: 1865- 1874, 2016. In immune excluded tumors, the majority of T-cells migrate along aligned collagen and fibronectin fibers that run circumferentially around the tumor. See, e.g., Salmon et al. J. Clin. Invest. 122:899-910, 2012. In some aspects, a tumor is categorized as an immune excluded tumor if CD8+ cells are observed substantially or exclusively in stroma immediately adjacent to or within a main tumor mass.

[0082] The term “immune desert tumor,” as used herein, refers to a tumor (e.g., a solid tumor) with a paucity of infiltrating lymphocytes within the tumor or surrounding stroma. See, e.g., Herbst et al. Nature 515:563-567, 2014 and Hegde et al. Clin. Cane. Res. 22: 1865- 1874, 2016. In some aspects, a tumor is categorized as an immune desert tumor if the prevalence of CD8+ cells is low (e.g., less than about 10 CD8+ cells (e.g., about 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 , or 0 CD8+ cells) in an area of tumor and tumor-associated stroma at a magnification of about 200x, for example, as calculated as the average of 10 representative fields of view).

[0083] The term "anti-cancer therapy" refers to a therapy useful in treating cancer. Examples of anti-cancer therapeutic agents include, but are limited to, e.g., chemotherapeutic agents, growth inhibitory agents, immunotherapies, immune checkpoint inhibitors, cytotoxic agents, agents used in radiation therapy, anti-angiogenesis agents, apoptotic agents, anti-tubulin agents, and other agents to treat cancer. Combinations thereof are also included in the disclosure.

[0084] The term “anti-cancer agent” a composition (e.g., compound, drug, antagonist, inhibitor, modulator) having antineoplastic properties or the ability to inhibit the growth or proliferation of cells. In some aspects, an anti-cancer agent is a genotoxic agent (e.g., a DNA-damaging agent or drug). In some aspects, the anti-cancer agent is a TGFB inhibitor (TGFpi). In some aspects, an anti-cancer agent is an agent identified herein having utility in methods of treating cancer.

[0085] As used herein “genotoxic therapy” refers to a treat of a tumor or cancer which utilizes the destructive properties of the treatment to induce DNA damage into tumor or cancer cells. Genotoxic agents efficiently clear tumor cells because the treatment-induced DNA damage is commonly translated into programmed cell death, such as apoptosis, senescence, autophagy and ferroptosis. Accordingly, genotoxic therapy is standard-of- care (SOC) for many cancers. In non-limiting examples, a genotoxic therapy may include g- irradiation, alkylating agents such as nitrogen mustards (chlorambucil, cyclophosphamide, ifosfamide, melphalan), nitrosoureas (streptozocin, carmustine, lomustine), alkyl sulfonates (busulfan), triazines (dacarbazine, temozolomide) and ethylenimines (thiotepa, altretamine), platinum drugs such as cisplatin, carboplatin, oxalaplatin, antimetabolites such as 5- fluorouracil, 6- mercaptopurine, capecitabine, cladribine. clofarabine, cytarabine, floxuridine, fludarabine, gemcitabine, hydroxyurea, methotrexate, pemetrexed, pentostatin, thioguanine, anthracyclines such as daunorubicin, doxorubicin, epirubicin, idarubicin , anti-tumor antibiotics such as actinomycin-D, bleomycin, mitomycin-C, mitoxantrone, topoisomerase inhibitors such as topoisomerase I inhibitors (topotecan, irinotecan) and topoisomerase II inhibitors (etoposide, teniposide, mitoxantrone), mitotic inhibitors such as taxanes (paclitaxel, docetaxel), epothilones (ixabepilone), vinca alkaloids (vinblastine, vincristine, vinorelbine), and estramustine. In some aspects, the additional active agent is a WEE1 inhibitor, such as AZD1775.

[0086] ‘Standard of care” or “SOC” is used herein to refer to treatment that is accepted by medical experts as a proper treatment for a certain type of disease and that is widely used by health care professionals. SOC is also called best practice, standard medical care, and standard therapy.

[0087] The term, “TGFp inhibitor” or “TGFpi” refers to any agent capable of antagonizing biological activities or function of the TGFp growth factor (e.g., TGFpi , TGFP2 and / or TGFP3). The term encompasses pan-TGFp inhibitors, which refers to any agent that is capable of inhibiting or antagonizing all three isoforms of TGFp. The term is not intended to limit its mechanism of action and includes, for example, small molecules, neutralizing inhibitors, receptor antagonists, soluble ligand traps, and activation inhibitors of TGFp.

[0088] The term "immunotherapy" refers to the treatment of a subject afflicted with, or at risk of contracting or suffering a recurrence of, a disease by a method comprising inducing, enhancing, suppressing or otherwise modifying an immune response. Examples of immunotherapy include, but are not limited to, immune checkpoint inhibitors, adoptive cell transfer (ACT) for example using T cells, bispecific antibodies (bsAbs), antibody-drug conjugates (ADCs, or monoclonal antibodies.

[0089] The term "immune checkpoint" refers to a molecule such as a protein in the immune system which provides signals to its components in order to balance an immune response. Immune checkpoint proteins may include, but are not limited to, PD1 (also known as PD-1 ; Programmed Death 1 receptor), 0X40, 4-1 BB, CTLA4 (Cytotoxic T-Lymphocyte- Associated protein 4, CD152), PD-L1 , PD-L2, LAG-3 (Lymphocyte Activation Gene-3), A2AR (Adenosine A2A receptor), B7-H3 (CD276), B7-H4 (VTCN1 ), BTLA (B and T Lymphocyte Attenuator, CD272), IDO (Indoleamine 2,3-dioxygenase), KIR (Killer-cell Immunoglobulin-like Receptor), TIM 3 (T-cell Immunoglobulin domain and Mucin domain 3), VISTA (V-domain Ig suppressor of T cell activation), and IL-2R (interleukin-2 receptor).

[0090] An "immune response" refers to the action of a cell of the immune system (for example, T lymphocytes, B lymphocytes, natural killer ( K) cells, macrophages, eosinophils, mast cells, dendritic cells and neutrophils) and soluble macromolecules produced by any of these cells or the liver (including Abs, cytokines, and complement) that results in selective targeting, binding to, damage to, destruction of, and / or elimination from a vertebrate's body of invading pathogens, cells or tissues infected with pathogens, cancerous or other abnormal cells, or, in cases of autoimmunity or pathological inflammation, normal human cells or tissues.

[0091] As used herein, the term “immune checkpoint inhibitor” or “immune checkpoint modulator” refers to a molecule, compound, or composition blocks or inhibits the function of an immune checkpoint protein. Immune checkpoint inhibitors are well known in the art and are commercially or clinically available. These include, but are not limited to, agonists, antagonists, or antibodies that modulate immune checkpoint proteins. Illustrative examplesof checkpoint inhibitors, referenced by their target immune checkpoint protein, are provided as follows:

[0092] Immune checkpoint inhibitors comprising anti-PD-1 antibody include, but are not limited to, sasanlimab (PF-6801591), nivolumab (MDX 1106), pembrolizumab (MK- 3475), pidilizumab (CT-011 ), cemiplimab (REGN2810), tislelizumab (BGB-A317), spartalizumab (PDR001 ), mAb15, MEDI-0680 (AMP-514), BGB-108, GLS-010 (WBP- 3055), AK-103 (HX- 008), CS-1003, HLX-10, MGA-012, BI-754091 , JS-001 (toripalimab), JNJ-63723283, genolimzumab (CBT-501 ), LZM-009, BCD-100, camrelizumab (SHR- 1210), Sym-021 , ABBV-181 , AK-105, BAT-1306, and AGEN-2034, or combinations thereof.

[0093] Immune checkpoint inhibitors comprising an anti-OX40 antibody include, but are not limited to, PF-04518600, MEDI6469, MEDI0562 (tavolixizumab), MEDI6383, MOXR0916, RG-7888, GSK-3174998, BMS-986178, GBR-8383, and ABBV-368.

[0094] Immune checkpoint inhibitors comprising a 4-1 BB agonist 4-1 BB agonist include, but are not limited to, utomilumab (PF-05082566), 1 D8, 3Elor, 4B4, H4-1 BB-M127, BBK2, 145501 , antibody produced by cell line deposited as ATCC No. HB-11248, 5F4, C65-485, urelumab (BMS-663513), 20H4.9-lgG-1 (BMS-663031 ), 4E9, BMS-554271 , BMS-469492, 3H3, BMS- 469497, MOR-6032, MOR-7361 , MOR-7480, MOR-7480.1 , MOR-7480.2, MOR-7483, MOR-7483.1 , MOR-7483.2, 3EI, 53A2, 1 D8, and 3B8.

[0095] Immune checkpoint inhibitors comprising anti-CTLA4 antibody include, but are not limited to, ipilimumab (10DI), tremelimumab, and AGEN-1884.

[0096] Immune checkpoint inhibitors comprising a B7-H3 inhibitor include, but are not limited to, MGA271 .

[0097] Immune checkpoint inhibitors comprising an LAG3 inhibitor include, but are not limited to, IMP321 , BMS-986016.

[0098] Immune checkpoint inhibitors comprising a KIR inhibitor include, but are not limited to, IPH2101 (lirilumab). An immune checkpoint inhibitor targeting I L-2R, for preferentially depleting Treg cells (e.g., FoxP-3+ CD4+ cells), comprises IL-2-toxin fusion proteins, which include, but are not limited to, denileukin diftitox (Ontak).

[0099] The term, “"interleukin 6" or "IL-6", as used herein refers to the gene, mRNA, or protein encoded by the IL6 gene. The IL6 gene has a gene map locus of 7p21 , 7pl5.3, and 7p21 -pl5 as described by, respectively, Entrez Gene cytogenetic band, band, Ensembl cytogenetic band, and the HGNC cytogenetic band. The term IL6 refers to a native protein in human of 212 amino acids with a molecular weight of about 23,718 Da as described in UniProtKB / Swiss-Prot database, and is a member of the IL6 family, that currently isdescribed as including IL-6, IL-11 , leukemia inhibitory factor (LIF), oncostatin M (OSM), cardiotrophin- 1 (CT-1), ciliary neurotrophic factor (CNTF), and cardiotrophin-like cytokine (CLC). The term IL6 is inclusive of the protein, gene product and / or gene referred to by such alternative designations as: interleukin 6 (interferon, beta 2), IFNB2, IL-6, BSF2, HGF, HSF, Hybridoma growth factor, Interferon beta-2, BSF-2, CDF, IFN-beta-2, B-cell stimulatory factor 2, CTL differentiation factor, B-cell differentiation factor, and interleukin BSF-2. IL-6 is a multi-functional cytokine that influences several immune and physiological processes. IL-6 was initially identified as a differentiation factor involved in the activation of B lymphoid cells (Hirano et al., 1986. Nature. 324(6092) :73-6).

[0100] The term "complete response" or "CR" means the disappearance of all signs of cancer (e.g., disappearance of all target lesions) in response to treatment. This does not always mean the cancer has been cured.

[0101] The term “disease-free survival” (DFS) means the length of time after primary treatment for a cancer ends that the patient survives without any signs or symptoms of that cancer. As used herein, the term “duration of response” (DoR) means the length of time that a tumor continues to respond to treatment without the cancer growing or spreading. Treatments that demonstrate improved DoR can produce a durable, meaningful delay in disease progression.

[0102] The terms "objective response" and “overall response” refer to a measurable response, including complete response (CR) or partial response (PR). The term "overall response rate" (ORR) refers to the sum of the complete response (CR) rate and the partial response (PR) rate.

[0103] The term “overall survival” (OS) means the length of time from either the date of diagnosis or the start of treatment for a disease, such as cancer, that patients diagnosed with the disease are still alive. OS is typically measured as the prolongation in life expectancy in patients who receive a certain treatment as compared to patients in a control group (i.e., taking either another drug or a placebo).

[0104] The term "partial response" or "PR" refers to a decrease in the size of one or more tumors or lesions, or in the extent of cancer in the body, in response to treatment. For example, in a preferred embodiment, PR refers to at least a 30% decrease in the sum of the longest diameters (SLD) of target lesions, taking as reference the baseline SLD.

[0105] The term "progression free survival" or “PFS” refers to the length of time during and after treatment during which the disease being treated (e.g., cancer) does not get worse. PFS, also referred to as “Time to Tumor Progression,” may include the amount of timepatients have experienced a CR or PR, as well as the amount of time patients have experienced SD.

[0106] The term "progressive disease" or "PD" refers to a cancer that is growing, spreading or getting worse. In a preferred embodiment, PR refers to at least a 20% increase in the SLD of target lesions, taking as reference the smallest SLD recorded since the treatment started, or to the presence of one or more new lesions.

[0107] The term “stable disease” or “SD” refers to a cancer that is neither decreasing nor increasing in extent or severity.

[0108] The term "sustained response" refers to the sustained effect on reducing tumor growth after cessation of a treatment. For example, the tumor size may be the same size or smaller as compared to the size at the beginning of the medicament administration phase. In some aspects, the sustained response has a duration of at least the same as the treatment duration, at least 1 .5x, 2x, 2.5x, or 3x length of the treatment duration, or longer.

[0109] The anti-cancer effect of the method of treating cancer, including “objective response,” “complete response,” “partial response,” “progressive disease,” “stable disease,” “progression free survival,” and “duration of response,” as used herein, may be defined and assessed by the investigators using RECIST v1 .1 (Eisenhauer et al., New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1 ), Eur J of Cancer 2009, 45(2):228-47).

[0110] Where a numeric term is preceded by “about” or “approximately” the term includes the stated number and values ±10% of the stated number. For example, a gene signature consisting of about 10 genes may have between 9 and 11 genes.

[0111] The disclosure provides, in some aspects, gene signatures prognostic of immune poor cancers with a DDR deficit phenotype which are predictive of sensitivity to a combination of anti-cancer agents, including a genotoxic therapy, an immunosuppressive inhibitor e.g., a TGFpi inhibitor (TGFpi i), and an immune checkpoint inhibitor or an immunotherapy. Thus, some aspects of the disclosure include the detection and quantification of certain gene panels in a sample. Panels of TGFp-associated and alt-EJ- associated genes, which are indicative of a DDR deficit phenotype were previously disclosed (see WO 2021 / 252945, which is incorporated by reference herein in its entirety).

[0112] A “panel” or grouping of one or more TGFp-associated genes may be selected from the group: ATP binding cassette subfamily G member 1 , gene name “ABCG1”;Adhesion molecule with Ig like domain 2, gene name “AMIG02”; Carbonic anhydrase 12, gene name “CA12”; Coiled-Coil Domain-Containing Protein 99 “CCDC99”; C-C MotifChemokine Ligand 20 “CCL20”; Cholinergic Receptor Nicotinic Alpha 9 Subunit “CHRNA9”; Collagen Type IV Alpha 2 Chain “COL4A2”; Connective Tissue Growth Factor “CTGF”; DLC1 Rho GTPase Activating Protein “DLC1 ” ; DnaJ Heat Shock Protein Family (Hsp40) Member B9 “DNAJB9”; Desmocollin 2 “DSC2”; Ectodermal-Neural Cortex 1 “ENC1 ”; Coagulation Factor III, Tissue Factor “F3”; Fibroblast Activation Protein Alpha “FAP”; Fibroblast Growth Factor 2 “FGF2”; Fibronectin 1 “FN1”; Hes Related Family BHLH Transcription Factor With YRPW Motif 1 “HEY1”; High Mobility Group AT-Hook 2 “HMGA2”; Inhibitor Of DNA Binding 1 “ID1 Insulin Like Growth Factor 2 MRNA Binding Protein 3 “IGF2BP3”; Insulin Like Growth Factor Binding Protein 3 “IGFBP3”; Jagged Canonical Notch Ligand 1 “JAG1 ”; KLF Transcription Factor 4 “KLF4”; Laminin Subunit Beta 3 “LAMB3”; Laminin Subunit Gamma 2 “LAMC2”; La Ribonucleoprotein 6, Translational Regulator “LARP6”; Lipase G, Endothelial Type “LIPG”; MAF BZIP Transcription Factor F “MAFF”;Monocyte To Macrophage Differentiation Associated “MMD”; Platelet Derived Growth Factor C “PDGFC”; Pleckstrin 2 “PLEK2”; Plexin A2 “PLXNA2”; Periostin “POSTN”; Pseudogene Similar To Part Of Pleckstrin Homology, Sec7 And Coiled-Coil Domains 1 (Cytohesin 1) “PSCD1 ”; Rho GTPase Activating Protein 32 (ARHGAP32) “RICS”; Ring Finger Protein 24 “RNF24”; RUNX Family Transcription Factor 1 “RUNX1”; SAM Domain, SH3 Domain And Nuclear Localization Signals 1 “SAMSN1”; Serpin Family E Member 1 “SERPINE1 ”; Serpin Family E Member 2 “SERPINE2”; SH2 Domain Containing 2A “SH2D2A”; SH2 Domain Containing 4A “SH2D4A”; Solute Carrier Family 20 Member 1 “SLC20A1”; Solute Carrier Family 22 Member 4 “SLC22A4”; TGFB Induced Factor Homeobox 1 “TGIF1 Thrombospondin 1 “THBS1 ”; Prostate Transmembrane Protein, Androgen Induced 1 (PMEPA1) “TMEPAI”; Tenascin C “TNC”; Tumor Necrosis Factor Receptor superfamily member 12A “TNFRAF12A”; and Versican “VCAN.”

[0113] A “panel” or grouping of one or more altEJ-associated genes may be selected from the group: Apurinic / Apyrimidinic Endodeoxyribonuclease 2 “APE2”; Apurinic / Apyrimidinic Endodeoxyribonuclease 1 “APEX1”; Anti-Silencing Function 1A Histone Chaperone “ASF1 A”; Cyclin Dependent Kinase Inhibitor 2D “CDKN2D”; Calcium And Integrin Binding 1 “CIB1”; DNA Replication Helicase / Nuclease 2 “DNA2”; FA Core Complex Associated Protein 24 “FAAP24”; FA Complementation Group M “FANCM”; GEN1 Holliday Junction 5' Flap Endonuclease “GEN1”; HRas Proto-Oncogene, GTPase “HRAS1 ”; DNA Ligase 1 “LIG1 DNA Ligase 3 “LIG3”; Menin 1 “MEN1”; MRE11 Homolog, Double Strand Break Repair Nuclease “MRE11 A”; MutS Homolog 3 “MSH3”; MutS Homolog 6 “MSH6”; Nudix Hydrolase 1 (NUDT1) “MTH1”; Mechanistic Target Of Rapamycin Kinase “MTOR”; Nucleic Acid Binding Protein 2 “NABP2”; Nth Like DNA Glycosylase 1 “NTHL1”; Partner And Localizer Of BRCA2 “PALB2”; Poly(ADP-Ribose) Polymerase 1 “PARP1 ”; Poly(ADP-Ribose) Polymerase FamilyMember 3 “PARP3”; DNA Polymerase Alpha 1 , Catalytic Subunit “POLA1 DNA Polymerase Mu “POLM”; DNA Polymerase Theta “POLQ”; Pre-MRNA Processing Factor 19 “PRP19”; RAD51 Paralog D “RAD51 D”; RB Binding Protein 8, Endonuclease “RBBP8”; Ribonucleotide Reductase Regulatory Subunit M2 “RRM2”; RuvB Like AAA ATPase 2 “RLIVBL2”; Superoxide Dismutase 1 “SOD1 ”; Lysine Acetyltransferase 5 “TIP60”; Uracil DNA Glycosylase “UNG”; WRN RecQ Like Helicase “WRN”; and X-Ray Repair Cross Complementing 1 “XRCC1.”

[0114] A summary of the gene names for the TGFp-associated and alt-EJ-associated genes is provided above are summarized in Table 1 below.

[0115] Table 1 : TGFp-associated and alt-EJ-associated gene signatures

[0116] In some aspects, a panel of one or more of the TGFp-associated genes in Table 1 are used to determine the DDR deficit phenotype.

[0117] In various aspects, the panel may comprise one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 44, 45, 46, 47, 48, 49, or all 50 of the TGFp-associated genes TGFp-associated genes in Table 1 . The panels may comprise additional TGFp-associated genes not listed herein.

[0118] In some aspects, a panel of one or more of the Alt-EJ-associated genes in Table 1 are used to determine the DDR deficit phenotype.

[0119] In some aspects, the panel comprises one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, or all 36 of all the Alt-EJ- associated genes in Table 1 . The panel may comprise additional altEJ- associated genes not listed.

[0120] In some aspects of the disclosure, TGFp-associated and altEJ-associated genes are particularly highly indicative of the DDR deficit phenotype. In some aspects, the TGFp- associated genes having high prognostic relevance are FAP, FN1 , POSTN, SERPINE1 , and THBS1 . In some aspects, the altEJ-associated genes having high prognostic relevance are GEN1 , RRM2, DNA2, POLQ, and LIG1 . Thus, in some aspects, the panels of TGFp and alt- EJ associated genes will comprise one or more of these highly prognostic genes. In some aspects, selected TGFp-associated genes of a panel will comprise one or more of FAP, FN1 , POSTN, SERPINEI, and THBS1 . In some aspects, selected TGFp-associated genes of the panel will comprise FAP, FN1 , POSTN, SERPINEI, and THBS1 . In some aspects, the selected altEJ- associated genes of the panel will comprise one or more of GEN1 , RRM2, DNA2, POLQ, and LIG1 . In some aspects, the selected altEJ-associated genes of the panel will comprise GEN1 , RRM2, DNA2, POLQ, and LIG1 .

[0121] The aforementioned TGFp-associated genes and altEJ-associated genes listed above are the human gene forms. It will be understood that the scope of the disclosure extends to non-human orthologs and homologs of the aforementioned genes, including for use in assaying the cancer cells of other species such as test animals. For example, the scope of the disclosure encompasses the murine, rat, zebrafish, drosophila, and other non- human versions of the enumerated genes.

[0122] In the methods of the disclosure, the determination of low TGF-p pathway activity in cells is made by the ascertainment of “low” expression of the selected genes. Low, in this context, may comprise a value that is below a selected threshold baseline value. The threshold value may comprise any suitable baseline, for example: the expression level of all genes measured in the assay; the expression level of all genes; the expression level of selected benchmark genes; the expression level of an internal control (e.g., selected housekeeping genes); the expression level observed in a control (e.g., an individual or individuals who are not suffering from the disease or disorder (e.g., cancer), like non- cancerous or other selected cells); the expression level of the gene panel observed in a sample obtained prior to administration of a therapy (e.g., an anti-cancer therapy that includes a genotoxic therapy (e.g., cisplatin), an immunosuppressive inhibitor (e.g., a TGF-p antagonist, e.g., an anti-TGF-p antibody) and an immunotherapy (e.g., a PD-L1 axis binding antagonist, e.g., an anti-PD-L1 antibody (e.g., atezolizumab)); or the level in a sample previously obtained from the individual at a prior time or any other measure known in the art for setting a gene expression comparative baseline. In various implementations, low expression is expression that is at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, or at least 90% below the selected threshold. In the case of multiple genepanels, the expression level of the various genes may be averaged or normalized by means known in the art to determine whether the measured values represents “low” gene expression by the panel as a whole.

[0123] Likewise, with regards to altEJ-associated genes, a determination of alt-EJ activation or high alt-EJ pathway activity in the assayed cancer cells is made when the expression of the selected altEJ-associated genes is “high.” High expression, in this context, may comprise a value that is above a selected threshold baseline value. The threshold value may comprise any suitable baseline, for example: the expression level of all genes measured in the assay; the expression level of all genes; the expression level of selected benchmark genes; the expression level of an internal control (e.g., selected housekeeping genes); the expression level observed in a control (e.g., an individual or individuals who are not suffering from the disease or disorder (e.g., cancer), like non-cancerous or other selected cells); the expression level of the gene panel observed in a sample obtained prior to administration of a therapy (e.g., an anti-cancer therapy that includes a genotoxic therapy (e.g., cisplatin), an immunosuppressive inhibitor (e.g., a TGF-p antagonist, e.g., an anti-TGF- P antibody) and an immunotherapy (e.g., a PD-L1 axis binding antagonist, e.g., an anti-PD- L1 antibody (e.g., atezolizumab)); or the level in a sample previously obtained from the individual at a prior time or any other measure known in the art for setting a gene expression comparative baseline. In various implementations, high expression is expression that is at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 90%; at least 100%, at least 200%, at least 300%, at least 400%, or at least 500%, at least ten times, at least 20 times, at least 50 times, or at least 100 times above the selected threshold. In the case of multiple gene panels, the expression level of the various genes may be averaged or normalized by means known in the art to determine whether the measured values represents “high” expression of the panel as a whole.

[0124] If both low TGFp-associated gene expression and high alt-EJ activation associated gene expression is observed in cancer cells of the sample, the cancer cells are deemed to have the DDR deficit phenotype.

[0125] The expression of the disclosed TGFp and alt-EJ gene signatures can be integrated into a single integrated score that can be used to identify and classify patients with DDR deficient phenotype cancers. The use of integrated scores is known in the art and any number of such scoring systems may be utilized by practitioners to provide a facile method of assessing DDR deficit phenotype. In a general implementation, the integrated score of the disclosure encompasses the development of equations that utilize measured TGFp-associated genes and alt-EJ-associated genes expression data to generate a numericvalue indicative of both the level of TGFp signaling impairment and the level alt-EJ activation. This number may then be compared to a threshold value, standard curve, or other numeric guide to categorize sample cancer cells as having the DDR deficit phenotype, or other categories, such as “likely has DDR deficit phenotype” vs. “likely does not have DDR deficit phenotype”; “does not have DDR deficit phenotype” vs. “has mild DDR deficit phenotype” vs. “has severe DDR deficit phenotype,” etc. Integrated scores may use weighting coefficients for the various TGFp- associated genes and alt-EJ-associated genes to improve resolution. Alternatively, each gene in the signature may be afforded equal weight in the calculation of the integrated score.

[0126] In one embodiment, the disclosure provides a classifier model to determine DDR deficit status. For example, a classifier or predictive model generated using statistical methods such as: machine learning classifiers such as random forest, support vector machines, and newer deep learning and neural network approach and other statistical model generating methods known in the art. The output of the model may be a classification, score, or other output indicative of the assayed cancer cells risk or probability of having the DDR deficit phenotype or not.

[0127] In one embodiment, determination of DDR deficit status is assessed using an integrated score, or a transcriptomic score, called the “p-alt score.” The p-alt score, which is derived from two gene signatures consisting of genes induced by TGFp and DNA repair genes suppressed by TGFp, has been validated for predicting response to chemoradiation in human cancers (Guix et al., Clinical Cancer Research 28, 1372-1382 (2022); Liu et al., Science Translational Medicine 13, eabc4465 (2021 )), and is disclosed in U.S. Patent Application Publication No. US 2023 / 0348988, and is incorporated herein by reference in its entirety. The p-alt score reflects the functional relationship between TGFp and DNA repair (Guix et al., supra; Liu et al., supra) and recently reported tumor educated immune signatures (TelS) that define immune archetypes (Combes et al., Nature 591 , 124-130 (2021)). In a given cohort, the p-alt score is calculated as:0Alt scores = (TGF / 3max- TGFfit)2+ (AltEjmin- AltEjt)2- V (AltEjmax- AltEJi)2+ (TGF0min- TGFfit)2wherein:TGF0minis Lowest value among all TGFp scores; TGF0maxis Highest value among all TGFp scores; TGFPi is The sample / TGFp score;AltEjminis Lowest value among all AltEj scores;AltEjmaxis Highest value among all AltEj scores; and AltEj t is The sample / AltEj score; wherein an |3Alt score above a selected threshold indicative of DDR deficit phenotype, is indicative of amenability to the selected treatment.

[0128] The |3Alt score will range from -1 .0 to 1 .0, and, in one aspect, a score above a defined threshold in the cohort or relative to a defined standard is indicative of the DDR deficit phenotype. Exemplary thresholds above which cancer cells of a sample are determined to have the DDR deficit phenotype include, scores above zero, for example, 0.1 , 0.2, 0.3, 0.4, 0.5, 0.6, 0.7. 0.8, and 0.9.

[0129] The use of transcriptional signatures provides a facile and rapid means of assessing the DDR deficit phenotype that may be employed with clinically relevant platforms. However, it will be understood that the scope of the disclosure encompasses any method of assessing DDR deficit phenotype by measuring TGFp signaling impairment and alt-EJ activation in cancer cells, including the use of methodologies other than gene expression biomarkers. Functional assays may be utilized instead of transcriptional data, or in combination with transcriptional data. For example, TGFp signaling competency may be assessed by quantification of TGFp in samples, for example by immunochemical detection of TGFp or its effectors. Alternatively, functional assays for TGFp activity may be used, for example, measuring the phosphorylation of Ataxia telangiectasia mutated (ATM), a TGFp- regulated effector of DDR processes, for example, as described in Nyati et al., 2017. Methods Mol Biol. 1596: 131-145 and Williams et al., 2013. Int J Radiat Oncol Biol Phys. 86(5):969-77. In another embodiment, TGFp activity is assayed by measuring the phosphorylation state of SMAD2, for example by methods such as those described in Farrington et al., 2007. Biomarkers 12(3):313-330 and Nyati et al., 2011. Clin Cancer Res. 17(23):7424-7439. Alternative assays for alt-EJ include measurement of unrepaired DNA damage as exemplified by the frequency of 53BP1 foci in cancer cells of the sample several hours following irradiation, for example, as described in Clin Cancer Research 24:6001 - 6014. Another method of measuring alt-EJ activity utilizes CRISPR-induced breaks followed by sequencing of the break sites to assess repair efficacy, for example, as described in Hussain et al., 2021. Nucleic Acids Res. 49(13):e74.

[0130] In some aspects, the present disclosure involves analyzing gene signatures associated with particular immune cells or immune cell (sub)populations as defined herein. The signature gene may indicate the presence of one particular cell type. In one embodiment, the signature genes may indicate that dysfunctional or activated tumor infiltrating T-cells are present. In one embodiment, immune cell states of tumor infiltrating cell subpopulations are detected. In some aspects, the presence of certain immune celltypes or immune cell states within a tumor may indicate that the tumor will be resistant to a treatment. In one embodiment, an exclusion gene signature may be used to detect multiple cell states that occur in a subpopulation of tumor cells that are linked to resistance to targeted therapies and progressive tumor growth. As used herein, “exclusion signatures” refer to signatures in malignant cells that correlate to immune cell exclusion and an immune poor phenotype.

[0131] Tumor cells appear to recruit, “educate” and maintain populations of cancer- associated fibroblasts (CAFs), endothelial cells, and tumor-promoting immune cell types that, collectively, suppress antitumor immune cell types while maintaining sufficient inflammatory and angiogenic potential in the TME to promote tumor growth and progression. Immune cells that tend to promote tumor progression via immunosuppression include type 2 macrophages (M2), Th2 CD4 + cells, regulatory T (Treg) cells, activated B cells, and type 2 neutrophils (N2) (Burkholder et al., 2014. Biochim Biophys Acta. 1845(2):182-201). Importantly, there appear to be synergistic and mutually reinforcing cytokine signal networks between tumor-suppressing immune cells, in which Th2 cells, tumor-associated M2 macrophages and N2 neutrophils (TAMs and TANs, respectively), B cells, granulocytic myeloid-derived suppressor cells (MDSCs), and Tregs all appear to play crucial roles. (Burkholder et al., 2014. Biochim Biophys Acta. 1845(2):182-201).

[0132] In some aspects of the disclosure, low expression of one or more tumor-educated immune signatures (TelS) is used to determine the immune poor phenotype. As used herein, “tumor-educated immune signature,” refers to a gene signature that measures a preexisting but suppressed immune response within tumors. TelS may encompass gene signatures from different subtypes as well as the stroma.

[0133] In some aspects, TelS is a T cell related TelS comprising genes selected from: CD40 Ligand “CD40LG”; T-Box Transcription Factor 21 “TBX21”; SH2 domain containing 1A “SH2D1 A”; pyrin and HIN domain family member 1 “PYHIN1”; Zinc finger protein 831 “ZNF831 ”; CD6 Molecule “CD6”; Thymocyte Selection Associated “THEMIS”; Ubiquitin Associated And SH3 Domain Containing A “UBASH3A”; T Cell Receptor Associated Transmembrane Adaptor 1 “TRAT1 ”; Eomesodermin “EOMES”; GRB2 Related Adaptor Protein 2 “GRAP2”; Zeta Chain Of T Cell Receptor Associated Protein Kinase 70 “ZAP70”; Signal Regulatory Protein Gamma “SIRPG”; Inducible T Cell Costimulator “ICOS”; Fas Ligand “FASLG”; CD8 Subunit Alpha “CD8A”; CD8 Subunit Beta “CD8B”; IL2 Inducible T Cell Kinase “ITK”; Granzyme A “GZMH”; Granzyme K “GZMK”; CD3 Delta Subunit Of T-Cell Receptor Complex “CD3D”; CD3 Epsilon Subunit Of T-Cell Receptor Complex “CD3E”; and CD3 Gamma Subunit Of T-Cell Receptor Complex “CD3G.”

[0134] In some aspects, TelS is a Myeloid related TelS comprising genes selected from: C-Type Lectin Domain Containing 10A “CLEC10A”; CD1 e Molecule “CD1 E”; CD1c Molecule “CD1 C”; V-Set And Immunoglobulin Domain Containing 4 “VSIG4”; CD33 Molecule “CD33”; CD300 Molecule Like Family Member B “CD300LB”; Membrane Spanning 4-Domains A7 “MS4A7”; Lymphocyte Antigen 86 “LY86”; C-Type Lectin Domain Containing 5A “CLEC5A”; Leukocyte Immunoglobulin Like Receptor B4 “LILRB4”; Colony Stimulating Factor 1 Receptor “CSF1 R”; Leukocyte Immunoglobulin Like Receptor A1 “LILRA1 Adenosine A3 Receptor “ADORA3”; Macrophage Expressed 1 “MPEG1”; Ficolin 1 “FCN1 ”; Ribonuclease A Family Member K6 “RNASE6”; Formyl Peptide Receptor 3 “FPR3”; Cytochrome B-245 Beta Chain “CYBB”; Membrane Spanning 4-Domains A4A “MS4A4A”; Membrane Spanning 4- Domains A4E “MS4A4E”; Oxidized Low Density Lipoprotein Receptor 1 “0LR1 ”; CD163 Molecule “CD163”; WDFY Family Member 4 “WDFY4”; Sialic Acid Binding Ig Like Lectin 1 “SIGLEC1 ”; and CD300e Molecule “CD300E.”

[0135] In some aspects the TelS is a stroma related TelS comprising genes selected from: Cadherin 11 “CDH11 ”; Decorin “DCN”; Platelet Derived Growth Factor Receptor Alpha “PDGFRA”; Collagen Type I Alpha 2 Chain “COL1A2”; Immunoglobulin Superfamily Containing Leucine Rich Repeat “ISLR”; Collagen Type I Alpha 1 Chain ‘COL1A1 ”;Fibronectin Type III Domain Containing 1 “FNDC1 ”; Biglycan “BGN”; Collagen Type V Alpha2 Chain “COL5A2”; Periostin “POSTN”; Protocadherin 18 “PCDH18”; ADAM Metallopeptidase With Thrombospondin Type 1 Motif 1 “ADAMTS1”; Matrix Remodeling Associated 5 “MXRA5”; Sulfatase 1 “SULF1”; Endothelin Receptor Type A “EDNRA”; Paired Related Homeobox 1 “PRRX1 ”; Collagen Type III Alpha 1 Chain “COL3A1”; Thy-1 Cell Surface Antigen “THY1 ”; Lumican “LUM”; and Collagen Type XII Alpha 1 Chain “COL12A1.”

[0136] In some aspects the TelS is a CD4 related TelS comprising genes selected from: BTB Domain And CNC Homolog 2 “BACH2”; TraB Domain Containing 2A “TRABD2A”; Interleukin 7 Receptor “I L7R”; Histone Deacetylase 4 “HDAC4”; Nuclear Receptor Subfamily3 Group C Member 2 “NR3C2”; Adducin 3 “ADD3”; Poly(A) Binding Protein Cytoplasmic 1 “PABPC1 ”; Poly(A) Binding Protein Cytoplasmic 3 “PABPC3”; Multifunctional ROCO Family Signaling Regulator 1 “MFHAS1 ”; Desmocollin 1 “DSC1 ”; Selectin L“SELL”; and Sestrin 1 “SESN1.”

[0137] In some aspects of the disclosure, the TelS is a CD8 related TelS comprising genes selected from: Fas Ligand “FASLG”; CD8 Subunit Alpha “CD8A”; SET Binding Protein 1 “SETBP1”; Cathepsin W “CTSW”; Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3C “APOBEC3C”; Apolipoprotein B MRNA Editing Enzyme Catalytic Subunit 3G “APOBEC3G”; Butyrophilin Like 8 “BTNL8”; HLA-DPB1 “Major Histocompatibility Complex,Class II, DP Beta 1 “HLA-DPB1”; LIL16 Binding Protein 3 “LILBP3”; RAD51 Recombinase “RAD51 ”; Wnt Family Member 9A “WNT9A”; Major Histocompatibility Complex, Class II, DM Alpha “HLA-DMA”; Ceramide Synthase 5 “CERS5”; CD8 Subunit Beta “CD8B”; Natural Killer Cell Granule Protein 7 “NKG7”; Family With Sequence Similarity 156 Member A “FAM156A”; Zinc Finger Protein 696 “ZNF696”; Minichromosome Maintenance Complex Component 5 “MCM5”; Double PHD Fingers 3 “DPF3”; Tetratricopeptide Repeat Domain 24 “TTC24”;Tyrosyl-TRNA Synthetase 1 “YARS1”; EBP Cholestenol Delta-lsomerase “EBP”;Transcriptional Repressor GATA Binding 1 “TRPS1 ”; Granzyme H “GZMH”; Vascular Cell Adhesion Molecule 1 “VCAM1”; Lymphocyte Activating 3 “LAG3”; Cysteine Rich Transmembrane BMP Regulator 1 “CRIM1”; N-Alpha-Acetyltransferase 40, NatD Catalytic Subunit “NAA40”; Glutamate Ionotropic Receptor Kainate Type Subunit 4 “GRIK4”;Proteasome 20S Subunit Beta 9 “PSMB9”; Major Histocompatibility Complex, Class II, DM Beta “HLA-DMB”; Stress Associated Endoplasmic Reticulum Protein Family Member 2 “SERP2” Homeobox B4 “HOXB4” Caspase 7 “CASP7”; Transmembrane And Coiled-Coil Domain Family 2 “TMCC2”; Actin Related Protein 2 / 3 Complex Subunit 5 Like “ARPC5L”; Multiple C2 And Transmembrane Domain Containing 2 “MCTP2”; and Lysosomal Trafficking Regulator “LYST.”

[0138] In some aspects the TelS is a Treg related TelS comprising genes selected from: Forkhead Box P3 “FOXP3”; Interleukin 2 Receptor Subunit Alpha “IL2RA”; CD80 Molecule “CD80”; CD177 Molecule “CD177”; Leukocyte Associated Immunoglobulin Like Receptor 2 “LAIR2”; C-C Motif Chemokine Receptor 8 “CCR8” C-C Motif Chemokine Ligand 22 “CCL22”; TNF Receptor Superfamily Member 13B “TNFRSF13B”; and TNF Receptor Superfamily Member 18 “TNFRSF18.”

[0139] In one embodiment the TelS is a macrophage related TelS comprising genes selected from: Apolipoprotein E “APOE”; Triggering Receptor Expressed On Myeloid Cells 2 “TREM2”; Complement C1q B Chain “C1QB”; Complement C1q C Chain “C1QC”; and V-Set And Immunoglobulin Domain Containing 4 “VSIG4.”

[0140] In another embodiment the TelS is a classical monocyte related TelS comprising genes selected from: S100 Calcium Binding Protein A8 (S100A8); S100 Calcium Binding Protein A9 “S100A9”; Versican “VCAN”; Ficolin 1 “FCN1 ”; and Lysozyme “LYZ.”

[0141] In one embodiment the TelS is a cDC1 related TelS comprising genes selected from: Indoleamine 2,3-Dioxygenase 1 “IDO1”; Chromosome 1 Open Reading Frame 54 “C1 orf54”; X-C Motif Chemokine Receptor 1 “XCR1 ”; C-Type Lectin Domain Containing 9A “CLEC9A” and Basic Leucine Zipper ATF-Like Transcription Factor 3 “BATF3.”

[0142] In one embodiment the TelS is a cDC2 related TelS comprising genes selected from: Fc Epsilon Receptor la“FCER1A”; CD1c Molecule “CD1C”; CD1 e Molecule “CD1 E”; CD1d Molecule “CD1 D”; and C-Type Lectin Domain Containing 10A “CLEC10A.”

[0143] A list of gene names for the TelS disclosed above is provided in Table 2 below by cell type.

[0144] Table 2: Tumor-educated immune signatures (TelS)

[0145] In some aspects, a panel of one, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 40, 50, 75, 100, 125, 140 or more of the TelS disclosed in Table 2 are used to determine the immune poor phenotype. The panel may comprise additional tumor-educated immune signatures genes not listed.

[0146] In some aspects, the panel may comprise one, two, three, four, five, six, seven, eight, nine, or all ten of the immune cell subpopulations disclosed in Table 2.

[0147] The disclosed tumor-educated immune signatures are particularly important for monitoring therapeutic response because they can be used to characterize a pre- and posttreatment immune response (e.g., robust vs non-robust immune response).

[0148] In some aspects, a variety of alternative assays known to those skilled in the art can be utilized to detect multiple markers expressed in immune infiltrates in a tumor. Such detection techniques include, but are not limited to, for example, fluorescence-activated cell sorting (FACS), magnetically-activated cell sorting (MACS), immunofluorescence, immunocytochemistry, spatial transcriptomics or spatial proteomics.

[0149] The gene expression signatures derived using the methods described herein may be useful to identify patients whose cancers have a DDR Deficit and immune poor phenotype and who are most likely to achieve a clinical benefit from treatment with the combination of a genotoxic therapy, an immunosuppression inhibitor and an immune checkpoint inhibitor. This utility supports the use of such gene signatures in a variety of research and commercial applications, including but not limited to, clinical trials of immune checkpoint inhibitors in which patients are selected on the basis of their expression levels of the disclosed gene signatures, diagnostic methods and products for determining a patient's gene signature score or for classifying a patient as positive or negative for a gene signature biomarker, personalized treatment methods which involve tailoring a patient's drug therapy based on the patient's gene signature score, as well as pharmaceutical compositions and drug products comprising a genotoxic therapy, an immunosuppression inhibitor and an immune checkpoint inhibitor for use in treating the identified patients.

[0150] The utility of any of the research and commercial applications claimed herein does not require that 100% of the patients who test positive for a gene signature biomarker achieve an anti-tumor response to the combination of a genotoxic therapy, an immunosuppression inhibitor and an immune checkpoint inhibitor; nor does it require a diagnostic method or kit to have a specific degree of specificity or sensitivity in determining the presence or absence of a biomarker in every subject, nor does it require that a diagnostic method claimed herein be 100% accurate in predicting for every subject whether the subject is likely to have a beneficial response to the combination of a genotoxic therapy, an immunosuppression inhibitor and an immune checkpoint inhibitor. Thus, as used herein, the terms "determine", "determining" and "predicting" should not be interpreted as requiring a definite or certain result; instead these terms should be construed as meaning either that a claimed method provides an accurate result for at least the majority of subjects or that the result or prediction for any given subject is more likely to be correct than incorrect.Preferably, the accuracy of the result provided by a method of the disclosure is one that a skilled artisan or regulatory authority would consider suitable for the particular application in which the method is used.

[0151] Similarly, the utility of the claimed drug products and treatment methods does not require that the claimed or desired effect is produced in every subject having cancer; all thatis required is that a clinical practitioner, when applying his or her professional judgment consistent with all applicable norms, decides that the chance of achieving the claimed effect of treating a given subject (i.e., patient) according to the claimed method or with the claimed composition or drug product is a reasonable chance.

[0152] In some aspects, a gene signature score is determined in a sample of tumor tissue removed from a subject. The tumor may be primary or recurrent, and may be of any type (as described herein throughout), any stage (e.g., Stage I, II, III, or IV or an equivalent of other staging system), and / or histology. The subject may be of any age, gender, treatment history and / or extent and duration of remission. In some aspects, the subject is a mammal. In some aspects, the subject is a human or human subject.

[0153] The tumor sample can be obtained by a variety of procedures including, but not limited to, surgical excision, aspiration or biopsy. The tissue sample may be sectioned and assayed as a fresh specimen; alternatively, the tissue sample may be frozen for further sectioning. In various aspects, the tissue sample is preserved by fixing and embedding in paraffin or the like.

[0154] The tumor tissue sample may be fixed by conventional methodology, with the length of fixation depending on the size of the tissue sample and the fixative used. Neutral buffered formalin, glutaraldehyde, Bouin's and paraformaldehyde are nonlimiting examples of fixatives. In some aspects, the tissue sample is fixed with formalin. In some aspects, the fixed tissue sample is also embedded in paraffin to prepare an FFPE tissue sample.

[0155] Typically, the tissue sample is fixed and dehydrated through an ascending series of alcohols, infiltrated and embedded with paraffin or other sectioning media so that the tissue sample may be sectioned. Alternatively, the tumor tissue sample is first sectioned and then the individual sections are fixed. In some aspects, the gene signature score for a tumor is determined using FFPE tissue sections of about 3-4 millimeters, and preferably 4 micrometers, which are mounted and dried on a microscope slide.

[0156] Once a suitable sample of tumor tissue has been obtained, it is analyzed to quantitate the RNA expression level for one or more of the genes in Tables 1 and 2, or for a gene signature derived therefrom. The phrase "determine the RNA expression level of a gene" as used herein refers to detecting and quantifying RNA transcribed from that gene. The term "RNA transcript" includes mRNA transcribed from the gene, and / or specific spliced variants thereof and / or fragments of such mRNA and spliced variants.

[0157] A person skilled in the art will appreciate that a number of methods can be used to isolate RNA from the tissue sample for analysis. For example, RNA may be isolated fromfrozen tissue samples by homogenization in guanidinium isothiocyanate and acid phenolchloroform extraction. Commercial kits are available for isolating RNA from FFPE samples.

[0158] If the tumor sample is an FFPE tissue section on a glass slide, it is possible to perform gene expression analysis on whole cell lysates rather than on isolated total RNA.

[0159] Persons skilled in the art are also aware of several methods useful for detecting and quantifying the level of RNA transcripts within the isolated RNA or whole cell lysates. Quantitative detection methods include, but are not limited to, arrays (i.e., microarrays), quantitative real time PCR (RT-PCR), multiplex assays, nuclease protection assays, and Northern blot analyses. Generally, such methods employ labeled probes that are complimentary to a portion of each transcript to be detected. Probes for use in these methods can be readily designed based on the known sequences of the genes and the transcripts expressed thereby. Suitable labels for the probes are well-known and include, e.g., fluorescent, chemilumnescent and radioactive labels.

[0160] In one embodiment, the quantification of TGFp-associated genes, altEJ- associated genes and tumor-educated immune signature genes is achieved by means of barcoded probes. Barcoded probe systems directly count the number of transcripts in a sample. These platforms utilize a set of nucleic acid probes specific for each transcript of the target genes, wherein the probes are conjugated to molecular barcodes. Barcodes may comprise heteropolymers of fluorescent moieties, wherein the order of the fluorescent moieties creates a unique identifier for each construct. These are hybridized to mRNA in the sample in solution phase, then hybridized probes are immobilized on a solid substrate and imaged by fluorescent microscopy to count the number of transcripts by reading of the barcodes. This methodology enables direct counting of transcripts without amplification or cDNA steps that can introduce bias. Exemplary barcode platforms include the NCOUNTER(TM) system by Nanostring Technologies (Seattle, WA, US).

[0161] The disclosure also provides other transcript quantification methods. Such other transcript quantification methods include, but are not limited to, RNAseq Next Generation Sequencing technologies. In a general method, messenger RNA in the sample is fragmented and reverse transcribed into cDNA fragments. These are subsequently amplified and read by high throughput sequencing devices. Alternatively, mRNA in the sample may be read directly in some platforms. The use of random primers results in amplification of the entire transcriptome while targeted primers can be used to selectively amplify genes of interest. Exemplary RNAseq platforms include TEMP-O- SEQ(TM) (Bio- Spyder Inc., Carlsbad, CA, US) and ION APLISEQ(TM) (ThermoFisher, Waltham, MA, US).

[0162] In some aspects, the expression of the selected TGFp-associated genes, altEJ- associated genes and tumor-educated immune signature genes may be measured by use of a microarray, as known in the art. In this technique, the sample RNA is converted to cDNA, fluorescently labeled, and presented to an array of complementary nucleic acid probes specific for the transcripts of interest, immobilized on a solid support such as a chip or bead. Fluorescent signal is quantified to determine expression level. Exemplary microarrays for expression analysis include DYNABEAD(TM) (ThermoFisher, Waltham, MA, US) and Agilent arrays (Agilent, Santa Clara, CA, US).

[0163] In some aspects, the expression of the selected TGFp-associated genes, altEJ- associated genes and tumor-educated immune signature genes is measured using quantitative PCR (qPCR). mRNA in the sample is transcribed to cDNA, then amplified using primer pairs specific for the transcripts of interest, followed by quantification. Exemplary qPCR platforms include CFX OPUS(TM) (Bio-Rad, Hercules CA, US) and APPLIED BIOSYSTEMS(TM) qPCR platforms.

[0164] Assaying tumor samples for expression of the genes or gene signature described herein can facilitate the facile and convenient assessment of DDR deficit and immune infiltration phenotype in a sample and may be performed using an assay kit that has been specially designed for this purpose.

[0165] As used herein, a “kit” or an “assay kit” refers to an aggregated collection of products that can be used to quantify two or more DDR deficit or immune poor phenotype related genes of the disclosure in a sample. In one implementation, the assay kit will comprise a suite of two or more polynucleotide probes. Each polynucleotide probe will comprise a sequence that is complementary to and which will, under suitable conditions, hybridize to an mRNA or cDNA of a selected TGF - associated gene, altEJ-associated gene, or tumor-educated immune signature genes. Probes may comprise any complementary subsequence of the selected gene; probes may be engineered to increase specificity or stability; probes may be modified to contain sequences that increase detection sensitivity. In some embodiments, probe length is about 10-1 ,000 base pairs in length, for example, comprising about 50, 100, or 200 base pairs. For example, in the case of NANOSTRING(TM) type probes, probes of about 100 base pairs, preferentially complementary to the 3’ end of the target mRNAs are used. The probes will comprise a unique, distinguishable subsequence of the targeted cDNA or mRNA nucleic acid, selected for optimal hybridization depending on the sequencing platform. In some embodiments, the probes are immobilized on a substrate, such as a bead or planar biochip, as in a gene expression microarray.

[0166] The kits may further comprise polynucleotide probes for mRNAs or cDNAs of reference genes, such as housekeeping genes, as known in the art. The probes may comprise labels such as enzymatic, fluorescent, metal, radiolabel or chemiluminescent labels, for example, fluorescent protein polymers acting as barcodes, for the quantification of target species. The probes may further comprise conjugation moieties for the attachment of sequencing adapters, solid phase binding, or other functionalizations. The probes may comprise nucleic acid sequences for binding of primers to amplify the bound target. The probes may comprise DNA, PNA, or other nucleic acid compositions capable of hybridization to mRNAs or cDNAs. The kits may comprise elements such as reference standards, washing solutions, buffering solutions, reagents, printed instructions for use, and containers. The assay kits of the disclosure may comprise assay biochips or microfluidic devices for sample analysis. The assay kits may further encompass software, e.g. non- transitory computer readable storage medium comprising a set of instructions for operating a computer program which aids in carrying out the measurement and analysis of gene expression levels of the target genes.

[0167] Thus, the disclosure provides kits or kits used to run an assay. In some aspects, a kit of the disclosure comprises: two or more polynucleotide probes, wherein each probe comprises a sequence that is complementary to and which will, under suitable conditions, hybridize to, an mRNA or cDNA of a target transcript, wherein, the assay kit comprises probes for one or more TGFp-associated gene selected from the group ABCG1 , AMIGO2, CA12, CCDC99, CCL20, CHRNA9, COL4A2, CTGF, DLC1 , DNAJB9, DSC2, ENC1 , F3, FAP, FGF2, FN1 , HEY1 , HMGA2, ID1 , IGF2BP3, IGFBP3, JAG1 , KLF4, LAMB3, LAMC2, LARP6, LIPG, MAFF, MMD, PDGFC, PLEK2, LEXNA2, PSTN, PSCD1 , RICS, RNF24, RUNX1 , SAMSN1 , LAMC2, SERPINE1 , SERPINE2, SH2D2A, SH2D4A, SLC20A1 , SLC22A4, TGIF1 , THBS1 , TMEPAI, TNC, TNFRAF12A, VACN; probes for one or more altEJ-associated genes selected from the group: APE2, APEX1 , ASF1 A, CDKN2D, CIB1 , DNA2, FAAP24, FANCM, GEN1 , HRAS1 , LIG1 , LIG3, MEN1 , MRE11 A, MSH3, MSH6, MTH1 , MTOR, NABP2, NTHL1 , PALB2, PARP1 , PARP3, POLA1 , POLM, POLQ, PRP19, RAD51 D, RBBP8, RRM2, RUVBL2, SOD1 , TIP60, UNG, WRN, XRCC1 ; and probes for one or more tumor-educated immune signature genes for one or more of the immune cell subtypes disclosed in the tables herein.

[0168] In some aspects, the disclosure encompasses two or more PCR primer pairs for the selective amplification of mRNA and / or cDNA sequences of the target transcripts. In one aspect, the kit of the disclosure comprises: two or more PCR primer pairs, wherein each primer pair comprises two single-stranded oligonucleotide PCR primers, the two primers being of sequence selected to hybridize with an mRNA or cDNA of a selected transcript, andto enable, under suitable conditions, PCR amplification of sequences found on the target transcript, the target transcript being a transcript of a target gene; wherein the kit comprises primer pairs for the amplification of one or more TGFp- associated genes selected from the group ABCG1 , AMIG02, CA12, CCDC99, CCL20, CHRNA9, COL4A2, CTGF, DLC1 , DNAJB9, DSC2, ENC1 , F3, FAP, FGF2, FN1 , HEY1 , HMGA2, ID1 , IGF2BP3, IGFBP3, JAG1 , KLF4, LAMB3, LAMC2, LARP6, LIPG, MAFF, MMD, PDGFC, PLEK2, LEXNA2, PSTN, PSCD1 , RICS, RNF24, RUNX1 , SAMSN1 , LAMC2, SERPINE1 , SERPINE2, SH2D2A, SH2D4A, SLC20A1 , SLC22A4, TGIF1 , THBS1 , TMEPAI, TNC, TNFRAF12A, VACN; and wherein the kit comprises primer pairs for the amplification of one or more altEJ- associated genes selected from the group: APE2, APEX1 , ASF1A, CDKN2D, CIB1 , DNA2, FAAP24, FANCM, GEN1 , HRAS1 , LIG1 , LIG3, MEN1 , MRE11A, MSH3, MSH6, MTH1 , MTOR, NABP2, NTHL1 , PALB2, PARP1 , PARP3, POLA1 , POLM, POLQ, PRP19, RAD51 D, RBBP8, RRM2, RUVBL2, SOD1 , TIP60, UNG, WRN, XRCC1 ; and further wherein the kit comprises primer pairs for the amplification of one or more of the tumor-educated immune signature genes for one or more of the immune cell subtypes disclosed in the tables provided herein.

[0169] In some aspects, the primers may comprise primers of any length suitable for PCR amplification of target sequences, for example, being 15-40 base pairs in length. Primers may comprise DNA, PNA, or other nucleic acid compositions capable of hybridization to mRNAs or cDNAs. The primer sequences may be selected using any number of methods known in the art for primer design, such as Primer-BLAST (available at https: / / www.ncbi.nlm.nih.gov / tools / primer-blast / ) or OLIGO (for example, available at https: / / www.oligo.net / downloads.html).

[0170] The disclosure provides tools and methods for assessing a DDR deficit phenotype. As set forth above, immune poor cancers having the DDR deficit phenotype are particularly amenable to certain treatments, including genotoxic treatments, an immunosuppression inhibitor, and immunotherapy. This discovery regarding immune poor cancers enables patient stratification and personalized medical treatment, wherein subjects may be directed to efficacious treatment options while avoiding the expense, side effects, and other risks of ineffective or incompatible treatments.

[0171] Thus, the disclosure includes a method of identifying subjects having a cancer (e.g., comprising cancer cells) that have an immune poor and a DDR deficit phenotype, and if the subject is identified as having a cancer (or cancer cells) with the immune poor tumor and DDR deficit phenotype, a treatment suitable for the cancer is administered. In one aspect, the disclosure provides a method of selecting patients who will respond well to a combination of a genotoxic therapy, an immunosuppression inhibitor, and an immunecheckpoint inhibitor or immunotherapy, by assessing the immune poor and DDR deficit phenotype in cancer cells of the subject, and if the cancer cells are determined to exhibit the immune poor and DDR deficit phenotypes, the subject is deemed a candidate for the combination treatment.

[0172] In another aspect, the disclosure provides a method of identifying and treating a subject afflicted with an immune poor cancer, the method comprising: identifying a subject with an immune poor cancer as comprising cancer cells comprising a DNA damage repair (DDR) deficit phenotype; and treating the subject identified in step (i) using a treatment regimen comprising at least three steps comprising (a) initially administering to the subject a genotoxic therapy to kill the cancer cells; (b) next administering to the subject a TGF-beta inhibitor (TGFpi i) or interleukin 6 (IL-6) to release immune suppression of the cancer cells; and (c) next administering to the subject an immune checkpoint inhibitor or an immunotherapy.

[0173] In various aspects, the cancer cells comprise cells of a carcinoma, sarcoma, or hematopoietic cancer. In some aspects, the cancer is a carcinoma selected from the group consisting of bladder cancer, brain cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancers, gastric cancer, glioblastoma, glioma, head and neck cancer, lung cancer, melanoma, mesothelioma, nasopharyngeal cancer, ovarian cancer, pancreatic cancer, prostate cancer, renal cancer, testicular cancer, thyroid cancer, skin cancer, and uterine cancer. In some aspects, the cancer is a sarcoma selected from the group consisting of undifferentiated pleomorphic sarcoma, epithelioid sarcoma, liposarcoma, and leiomyosarcoma. In some aspects, the cancer is a hematopoietic cancer selected from the group consisting of leukemia, lymphoma, and myeloma.

[0174] In some aspects, the subject is initially administered a genotoxic treatment. Genotoxic treatments that induce DSBs are particularly effective, however treatments that induce single strand breaks or other types of DNA damage may be used as well. In a first implementation, the genotoxic treatment comprise the administration of a therapeutically effective amount of a genotoxic agent. As used herein, a therapeutically effective amount is an amount sufficient to produce a measurable biological or therapeutic effect.

[0175] The genotoxic agent may comprise any genotoxic agent known in the art, for example, any of alkylating agents, intercalating agents, topoisomerase poisons, and others known in the art. Exemplary genotoxic agents include, for example, platinum drugs such as cisplatin, carboplatin, and oxaliplatin; antimetabolites such as 5-Fluorouracil, fludarabine and methotrexate; alkylating agents such as temozolomide, MNNG, and dacarbazine; nitrogen mustards, such as chlorambucil and cyclophosphamide; and topoisomerase poisons, suchas camptothecin based drugs and etoposide. Additional examples of genotoxic agents include 1 ,3-bis(2-chloroethyl)-l-nitrosourea (BCNll), busulfan, carmustine, chlorambucil, cyclophosphamide, dacarbazine, daunorubicin, doxorubicin, epirubicin, idarubicin, ifosfamide, irinotecan, lomustine, mechlorethamine, melphalan, mitomycin C, mitoxantrone, temozolomide, and topotecan.

[0176] In other aspects, the genotoxic treatment comprises the administration of a therapeutically effective amount of ionizing radiation to the cancer cells of the subject. In one embodiment, the ionizing radiation is administered as an external beam therapy, as known in the art. Exemplary external beam therapies include X-rays, gamma rays (for example, as delivered by Cobalt-60 devices), high energy electrons (for example, as delivered by linear accelerators), proton beams, high linear energy transfer particles, and neutron beams.

[0177] In one aspect, the administration of ionizing radiation is achieved by administration of a therapeutically effective amount of a radiopharmaceutical agent. A radiopharmaceutical agent is a composition of matter comprising a radioisotope that may be introduced to the body to deliver ionizing radiation to target tissues or organs. The radioisotope may be any known in the art, for example, a B-emitter such as Sumarium-153, Lutetium-177, Yttrium-90, lodine-131 ; or an alpha-emitter such as Astatine-211 , Actinium-225, Bismuth-213, Bismuth- 212, Radium-223, Thorium-227, and Lead-212. In some implementations, the radiopharmaceutical comprises the radionucleotide delivered by itself. In various implementations, the radionucleotide is integrated within or conjugated to a delivery moiety for targeted or improved delivery. Exemplary delivery moieties include antibodies (for example, anti-CD33 antibodies such as lintuzumab, anti-CD38 antibodies, anti-CD20 antibodies such as rituximab, and anti-HER2 / neu antibodies), peptides (for example, somatostatin analog peptides and octreotide), small molecules (for example, iobenguane 1- 131 , g-glutamyl folic acid derivatives and the neuropeptide N-acetylaspartylglutamate), liposomes, nanoconstructs, and glass or resin microspheres.

[0178] In some aspects, the ionizing radiation is delivered by use of a brachytherapy implant. Brachytherapy implants include radioactive “seed” bodies, pellets, or wires.

[0179] Brachytherapy implants may comprise any suitable radiation source, for example, Cesium-131 , Cesium-137, Cobalt-60, lridium-192, lodine-125, Palladium- 103, Ruthenium- 106, Radium-226. Typical brachytherapy targets include the prostate, breast tissue, esophagus, head and neck, cervix, and uterine tissues.

[0180] In other aspects, the genotoxic treatment comprises the administration of a therapeutically effective amount of ultraviolet radiation to the cancer cells of the subject. Inone embodiment, the ultraviolet radiation is administered as an external exposure, as known in the art. In other embodiment, ultraviolet radiation is delivered by a source to deep-seated tumors sometimes in conjunction with surgery.

[0181] In some aspects, the genotoxic treatment comprises a combination of genotoxic agents. For example, chemotherapy cocktails are known in the art, including additive combinations, potentiating combinations, and synergistic combinations. Additionally, the genotoxic treatment of the disclosure may comprise the administration of a combination of one or more chemotherapy agents with radiotherapy, chemoradiation combinations that are known in the art.

[0182] PARP Inhibition. Poly [ADP-ribose] polymerase 1 (P ARP-1) is a critical regulator of DNA damage repair, facilitating the repair by activating repair pathways and by its actions on chromatin and repair enzymes. PARP1 activity is crucial to alt-EJ activity. Because cells having the DDR deficit are reliant on alt-EJ, inhibition of PARP 1 is especially effective against such cells. If PARP 1 is inhibited and if other repair pathways are unavailable, the cell is less likely to survive.

[0183] Accordingly, in one embodiment, the scope of the disclosure encompasses the treatment of a subject for cancer, the treatment comprising the steps of: assessing the DDR deficit phenotype in cancer cells of the subject, wherein, if the cancer cells of the subject are determined to have the DDR deficit phenotype, the subject is initially administered a pharmaceutically effective amount of a PARP inhibitor. In various aspects, the PARP inhibitor is selected from the group consisting of: olaparib, rucparib, niraparib, talazoparaib, veliparib, pamiparib, AG1436,1CEP 9722, E7016, 3-aminobenzamide, and BGB-290.

[0184] In some aspects, genotoxic treatment induces immunosuppressive signaling in the tumor microenvironment. In some aspects, genotoxic treatment induces immunosuppressive TGFp signaling. Accordingly, in some aspects, the genotoxic therapy is followed by treatment with an immunosuppression inhibitor.

[0185] In some aspects, the immunosuppression inhibitor is a TGFp, an interleukin 6 (IL- 6), or an interleukin 10 (IL-10) inhibitor.

[0186] In some aspects, the immunosuppression inhibitor is a TGF-p inhibitor. In some aspects, the TGF-p inhibitor is a TGF-p binding antagonist. In some aspects, the TGF-p binding antagonist inhibits the binding of TGF-p to its ligand binding partners. In some aspects, the TGF-p binding antagonist inhibits the binding of TGF-p to a cellular receptor for TGF-p. In some aspects, the TGF-p binding antagonist inhibits activation of TGF-p. In some aspects, the TGF-p inhibitor inhibits TGF-pi , TGF-P2, and / or TGF-P3. In some aspects, the TGF-p inhibitor inhibits TGF-pi , TGF-P2, and TGF-P3. In some aspects, theTGF-p inhibitor inhibits TGF-p receptor-1 (TGFBR1 ), TGF-p receptor-2 (TGFBR2), and / or TGF-p receptor-3 (TGFBR3).

[0187] In some aspects, the TGF-p inhibitor is a polypeptide, a small molecule, or a nucleic acid. In some aspects, the TGF-p antagonist is a polypeptide. In some aspects, the polypeptide is an anti-TGF-p antibody, a soluble TGF-p receptor, or a peptide. In some aspects, the polypeptide is an anti-TGF-p antibody. In some aspects, the anti-TGF-p antibody is a pan-specific anti-TGF-p antibody. In some aspects, the anti-TGF-p antibody is fresolimumab, metelimumab, lerdelimumab, 1 D11 , 2G7, or derivatives thereof. In some aspects, the peptide is disitertide (P144). In some aspects, the TGF-p inhibitor is a small molecule. In some aspects, the small molecule is selected from the group galunisertib (LY2157299), LY2382770, LY3022859, SB-431542, SD208, SM16, tranilast, pirfenidone, TEW-7197, PF-03446962, and pyrrole-imidazole polyamide. In some aspects, the TGF-p inhibitor is a nucleic acid. In some aspects, the nucleic acid is trabedersen (AP12009) or belagenpumatucel-L.

[0188] In some aspects, a DDR deficit is induced in the cancer cells of a subject by inhibiting the molecule by which TGFp signaling suppresses altEJ. Inhibition of the molecule may be achieved by administration of a pharmaceutically effective amount of one or more inhibitors that include any composition known in the art which interferes with the expression, translation, activity, and / or regulatory functions. Molecular inhibitors encompass any number of agents, such as neutralizing antibodies, ligand traps, kinase inhibitors, antisense compositions, and others that interfere with the target by various means, such as reducing bioavailability, interrupting molecular interactions, and inhibiting kinase functions. These molecular targets are the mRNA or encoded proteins of genes that include Hypoxia Induced Factor 1 (gene name HIF1 alpha) and Notch Receptor 1 (gene name NOTCH), or genes CDK7, ZNF143, MYC, ETS1 , GABPA, RBPJ, MYCN, YY1 , among others.

[0189] In some aspects, the immunosuppression inhibitor is an IL6 inhibitor. In some aspects, the IL-6 inhibitor is an anti- 1 L-6R antibody. In a further embodiment, the anti-l L-6R antibody is tocilizumab or satralizumab.

[0190] In some aspects, treatment with the genotoxic therapy and the immunosuppression inhibitor allows increased immune cell infiltration in a tumor.

[0191] In further aspects, genotoxic therapy and the immunosuppression inhibition combined with administration of an immune checkpoint blockade ( ICB) therapy or an immunotherapy enhances an immune response.

[0192] Immunotherapy seeks to activate the patient’s immune system to eliminate cancer cells. To do so, the immune system must recognize aberrant cells. Cells having the DDRdeficit phenotype are unable to efficiently repair DNA. The alt-EJ process upon which they are reliant results in numerous deletions and insertions. These mutagenic factors increase the likelihood of proteins having amino acid substitutions, protein truncations, and other irregularities. Such irregularities in a cancer cell’s genome result in an increased probability for the formation of neoantigens, novel protein motifs displayed on the cancer cell surface that are recognized by immune surveillance and activate immune responses. Accordingly, cancer cells with the DDR deficit phenotype are more likely to have detectable neoantigens. In contrast, in cancers in which TGFp is functioning normally, fewer neoantigens will be present. Moreover, DNA damage from the genotoxic therapy can promote immune recognition of cancer e.g., by antigen release and presentation. In one embodiment, the combination of a genotoxic therapy an immusuppression inhibitor e.g., a TGFpi increases neoantigens and immune infiltration especially in cancer cells having a DDR deficit phenotype and the addition of an immunotherapy promotes T cell cytotoxicity.

[0193] A variety of cancer immunotherapy treatments are currently being deployed or in development, acting by diverse effectors and pathways. By these immunotherapy treatments, immune system ability to target and destroy cancer cells is enhanced.

[0194] Accordingly, in one aspect, the scope of the disclosure encompasses the administration of a therapeutically effective amount of an immunotherapy agent to treat cancer in a subject, wherein the cancer cells of the subject have been determined to have the DDR deficit phenotype.

[0195] Immunotherapy can include checkpoint blockers (CBP), chimeric antigen receptors (CARs), and adoptive T-cell therapy. Antibodies that block the activity of checkpoint receptors, including CTLA-4, PD-1 , Tim-3, Lag-3, and TIGIT, either alone or in combination, have been associated with improved effector CD8+ T cell responses in multiple pre-clinical cancer models (Johnston et al., 2014. Cancer cell 26, 923-937; Ngiow et al., 2011. Cancer research 71 , 3540-3551 ; Sakuishi et al., 2010. The Journal of experimental medicine 207, 2187-2194; and Woo et al., 2012. Cancer research 72, 917-927). Similarly, blockade of CTLA-4 and PD-1 in patients has shown increased frequencies of proliferating T cells (Brahmer et al., 2012. The New England journal of medicine 366, 2455-2465; Hodi et al., 2010. The New England journal of medicine 363, 711 -723; Schadendorf et al., 2015.. Journal of clinical oncology. 33, 1889-1894; Topalian et al., 2012.. The New England journal of medicine 366, 2443-2454; and Wolchok et al., 2017. The New England journal of medicine 377, 1345-1356), often with specificity for tumor antigens, as well as increased CD8+ T cell effector function (Ayers et al., 2017. The Journal of clinical investigation 127, 2930-2940; Das et al., 2015. Journal of immunology 194, 950-959; Gubin et al., 2014. Nature 515, 577-581 ; Huang et al., 2017. Nature 545, 60-65; Kamphorst et al., 2017. ProcNatl Acad Sci U S A 14, 4993-4998; Kvistborg et al., 2014. Science translational medicine 6, 254ra128; van Rooij et al., 2013. Journal of clinical oncology. 31 , e439-442; and Yuan et al., 2008. Proc Natl Acad Sci USAA 05, 20410-20415).

[0196] Accordingly, the success of checkpoint receptor blockade has been attributed to the binding of blocking antibodies to checkpoint receptors expressed on dysfunctional CD8+ T cells and restoring effector function in these cells. The check point blockade therapy may be an inhibitor of any check point protein described herein. The checkpoint blockade therapy may comprise anti-TIM3, anti-CTLA4, anti-PD-L1 , anti-PD1 , anti-TIGIT, anti-LAG3, or combinations thereof. Anti-PD1 antibodies are disclosed in U.S. Pat. No. 8,735,553.Antibodies to LAG-3 are disclosed in U.S. Pat. No. 9,132,281. Anti-CTLA4 antibodies are disclosed in U.S. Pat. Nos. 9,327,014; 9,320,811 ; and 9,062,111 . Specific check point inhibitors include, but are not limited to anti-CTLA4 antibodies (e.g., Ipilimumab and tremelimumab), anti-PD-1 antibodies (e.g., Nivolumab, Pembrolizumab), and anti-PD-L1 antibodies (e.g., Atezolizumab)..

[0197] In one embodiment, the immunotherapy is a cellular immunotherapy agent, such as dendritic cells that have been primed ex-vivo (e.g. Sipuleucel-T), chimeric antigen receptor T-cells (e.g., Tsagenlecleucel and axicabtagene ciloleucel), and tumor-infiltrating lymphocytes primed ex-vivo.

[0198] In one embodiment, the immunotherapy agent is an immunotherapy comprising an agent which primes immune cells in vivo, including: viral constructs that target tumor cells to express antigens or cytokines that stimulate the immune system; a tumor cell lysate; or an antigen-bearing antibody targeted to immune cells such as dendritic cells.

[0199] In one embodiment, the immunotherapy agent is a cytokine, such as interferonalpha, interleukin-2, or GM-CSF.

[0200] In one embodiment, the immunotherapy agent is an antibody or antibody-drug conjugate directed to a cancer-associated antigen.

[0201] The timing of administration of the first, TGFp inhibition treatment and the second selected treatment may be contemporaneous, sequential, or alternating. In a primary embodiment, the first and second treatments are applied contemporaneously, i.e. simultaneously or overlapping in time. In one embodiment, the first and second treatments are administered in combination product as a single dosage form.

[0202] In certain aspects, the treatments disclosed herein produce one or more therapeutic effects selected from the group consisting of tumor regression, abscopal effect inhibition of tumor metastasis, reduction in metastatic lesions over time, reduced use ofchemotherapeutic or cytotoxic agents, reduction in tumor burden, increase in progression- free survival, increase in overall response rate, increase in overall survival, increase in progression-free survival , complete response, partial response, and stable disease.

[0203] The term "regression" does not necessarily imply 100% or complete regression. Rather, there are varying degrees of regression of which one of ordinary skill in the art recognizes as having a potential benefit or therapeutic effect. The term also encompasses delaying the onset of the disease, or a symptom or condition thereof.

[0204] A better understanding of the disclosure and of its advantages will be obtained from the following examples, offered for illustrative purposes only. The examples are not intended to limit the scope of the disclosure. It is understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application and scope of the appended claims.EXAMPLES

[0205] Additional aspects and details of the disclosure will be apparent from the following examples, which are intended to be illustrative rather than limiting.Example 1Material and Methods

[0206] Animals. All animal experiments were performed at the University of California, San Francisco (UCSF, San Francisco, CA). The protocols for animal husbandry and experiments were conducted with approval from the institutional review board and adhered to the NIH Guide for the Care and Use of Laboratory Animals. Eight-week-old BABL / cJ Mus musculus (RRID: IMSF_JAX:000651 ) from Jackson Laboratory (Sacramento, CA) were purchased and housed 5 per cage, fed with Lab diet #5001 Rodent Formular (Purina Animal Nutrition LLC), and supplied water ad libitum.

[0207] Mammary tumor-derived transplants (mTDT). For treatment experiments, 8-10- week-old mice were transplanted orthotopically with F2 mTDT from 3 families, F, H and K as described in Moore et al., Cancer Res. 82(3):365-376 (2022). For all experiments, mice were palpated three times a week until the tumor reached 2x2 mm and then daily upon randomization into treatment groups when the tumor reached 65-120 mm3. Tumor burden was calculated based on 0.52 x w x I2and used to randomize mice to treatment groups as follows: Sham IgG, anti-programmed death ligand (PD-L1), TGFp inhibition with small molecule IPW or antibody ID11 , or single dose radiation treatment and dual and triplecombinations. All agents were administered intraperitoneally 24h before irradiation at the indicated schedule for up to 3 weeks. Anti-PD-L1 (Roche, Inc.) was administered once a week (10 mg / kg, PD-L1 9708, 6E11 ), TGFp neutralizing antibody 1 D11 was administered three times per week (25 mg / kg, Bioxcell BP0057), TGFp small molecule inhibitor IPW-5371 (Innovation Pathways, Inc.; designated as IPW) was administered daily (20 mg / kg), or IgG control antibody (Bioxcell BP0083) three times per week (25 mg / kg). Tumors were irradiated with 10 Gy using small animal radiation research platform (SARRP, XStrahl) individualized plans (Muriplan, Xstrahl) based on arc beam computerized tomography (CT). For in vivo depletion of NK cells, mice received 50 pg / uL i.p. of asialo-GM1 polyclonal antibody (eBioscience™, RRID: AB_10718540) two days before and one day after irradiation. Mice were monitored daily and sacrificed for tumor ulcers or weight loss greater than 15% body weight in accordance with LICSF Institutional Animal Care and Use Committee guidelines. Tumor measurement and collection were conducted by technicians who were blind to the treatment groups. Each individual mouse was assigned into one of two categories based on tumor growth during the first 7-days post-treatment: responders (R) and non-responders (NR). Mice whose tumor volume did not double from the start of treatment until seven days post-treatment were classified as responders, whereas mice whose tumors doubled daily and failed to meet these criteria were classified as non-responders. Sham-irradiated, IgG- treated mice were excluded from therapeutic response estimation.

[0208] RNA sequencing. For bulk RNA sequencing, fragments (~50 mg) of liquid nitrogen preserved mTDT (n=70) were shipped on dry ice to Q2 Solutions | EA Genomics for isolation of total RNA using proprietary methods. The amount and quality of specimens was assessed using Agilent Bioanalyzer and NanoDrop analysis. Total RNA samples that met quality control metrics were enriched for mRNA, fragmented, and converted into indexed cDNA libraries for Illumina sequencing. Generated cDNA libraries were quantified by qPCR using primers specific for Illumina sequencing adapters using TruSeq stranded mRNA at 30 M 50 bp PE assay. Raw sequencing data were received in fastq format. Read mapping was performed using Rsubread package (RRID: SCR_016945) against Mas musculus GRCm38 and human GRCh38 genome (28). Gene expression data are archived in gene expression omnibus (GEO, RRID: SCR_005012).

[0209] Multispectral flow cytometry. Tumor, spleen and blood cells treated under the conditions described were counted with 1 :1 Trypan blue / cells and blocked with 0.25 pg TruStain FcX PLUS antibody (Biolegend Cat#156603, clone S17011 E) per 106cells in 0.1 mL cell staining buffer (CSB, 0.5% fetal bovine serum in PBS) for 30 minutes on ice. Cells were then washed with PBS and incubated with LIVE / DEAD Zombie Aqua (1 :2000, Biolegend Cat#423101) in 0.02 mL PBS and subsequently stained in 0.08 mL CSB using apanel of optimized fluorophore-conjugated cell surface antibodies (Table 3) for 1 hour at 4°C in the dark. Cells were then washed 1X with 2mL CSB followed by centrifugation at 350 x g for 5 mins at 4°C. Aspirated supernatant and resuspended cell pellet in 0.5 ml / tube FluorFix buffer (Biolegend Cat#422101 ) in the dark for 1 h at room temperature for fixation. For intracellular staining, the nucleus was permeabilized by resuspending in 0.5mL 1X intracellular staining permeabilization wash buffer (Biolegend Cat#421002) diluted in distilled water and centrifuged 350 x g for 5 mins at 4°C and incubated with a panel of fluorophore- conjugated intracellular antibodies (Table 3) for 30 minutes in dark at room temperature. Cells were then washed 2X with 2 mL intracellular staining perm wash buffer and centrifuged at 350 x g for 5 mins at 4°C before resuspending cells in 0.5mL CSB and analyzed on a 3- laser Cytek® Northern Lights spectral cytometer (Fremont, CA USA). Experimental samples were unmixed using appropriate cell-based single-color controls in SpectroFlo® (Cytek Biosciences). Data analysis was performed using FCS Express 7 Research Edition De Novo software (Pasadena, CA USA), employing suitable gating strategies for each tissue according to Table 4 below.

[0210] Table 3: Flow Cytometry Antibodies

[0211] Table 4. Flow Cytometry Gating

[0212] Expression of each individual surface marker was expressed by the median fluorescence intensity (MFI). In order to identify and visualize populations, an unsupervised analysis was performed using the dimensional reduction algorithm t-Distributed Stochastic Neighbor Embedding (t-SNE) with Barnes-Hut approximation. All fcs files were merged and 100,000 live CD45+ cells were randomly down sampled from each sample to be subjected to the transformation algorithm to form clusters according to the expression levels of cellsurface phenotyping markers. Samples were grouped and overlayed according to treatment, mTDT family and response to visualize immune cell composition differences.

[0213] Bioinformatics analyses. Gene expression heatmaps were constructed with hierarchical clustering using the R package ComplexHeatmap (Gu, Z., Eils, R. & Schlesner, M., Bioinformatics (Oxford, England) 32, 2847-2849 (2016)). For all heatmaps, gene expression values were normalized z-scores and Euclidian distance and ward. D.2 clustering were used. TCGA somatic mutations at the individual level were obtained following approval by the dbGaP Data Access Committee (project #11689). The “fraction of genome altered,” which corresponds to the percent of the genome in which copy number gains or losses were detected, was obtained from the Oncoprint tab of cBioPortal. pAlt scores for pre-clinical tumors or patients was calculated based on the expression pattern using the previously published TGFp and alt-EJ gene signatures (Liu et al., Sci Transl Med. 13(580) :eabc4465 (2021)), weighted as described (Guix et al., Clin Cancer Res. 28(7): 1372- 1382 (2022)). In brief, the weight of each TGFp gene and the weight of each alt-EJ gene was calculated and the TGFp and alt-EJ weighted expression scores were calculated for each tumor as the sum of the expression of the genes from each signature multiplied by their factors. This pAltscore conveys in one value the relative expression of both signatures in each tumor. The composition of each signature is listed in Table 5 below.

[0214] Table 5: pAlt signature representing TGFp and alt-EJ gene lists

[0215] TelS gene signatures were reported previously in Combes et al., Cell 185(1 ):184- 203. e119 (2022). The feature gene signature scores for the 10 cell types included T cell, myeloid, CD90+CD44+ stroma, CD4, CD8, T regulatory cells, macrophages, monocytes, cDC1 and cDC2 (Combes et al., Cell 185(1 ):184-203. e119 (2022)) are listed in Table 6 below.

[0216] Table 6: Tumor educated immune signatures (TelS) Gene Lists

[0217] Datasets. Gene expression data of the TCGA-pancancer cohort was downloaded from the Genomic Data Commons portal from the file EBPIusPlusAdjustPANCAN_llluminaHiSeq_RNASeqV2.geneExp.tsv. The downloaded gene expression values were trimmed mean of M values normalized, Iog2 transformed and mean-centered per gene by converting them into z-scores. Primary solid tumor samples were analyzed. The source code and processed data for IMvigor210 was accessed in IMvigor210CoreBiologies, a fully documented software and package for the R statistical computing environment (Mariathasan et al., Nature 554(7693) :544-548 (2018)). IMvigorO was accessed on EGAS00001004997 (Banchereau et al., Nat Commun. 12(1 ):3969 (2021 )). The GSE78220 melanoma dataset (n=28 specimens) and the GSE91061 melanoma dataset (n=109 specimen with 58 on-treatment and 51 pre-treatment) from 65 patients were downloaded from the Gene Expression Omnibus (GEO, RRID: SCR_005012) in January 2022 using the R package GEOquery. In GSE78220, samples were pair-end sequenced with read length of 2x100 bps (Illumina HiSeq2000) and amped to the UCSC hg 19 reference genome using Tophat2 (Kim et al., Genome Biol. 14(4):R36 (2013)) and normalized expression levels of genes were expressed in FPKM values as generated by cuffquant and cuttnorm (Trapnell, C., Pachter, L. & Salzberg, S.L. Bioinformatics. 25(9):1105-1111 (2009)). In GSE91061 , raw FASTQ files were aligned on the hg 19 genome using STAR aligner (STAR, RRID:SCR_004463) then counted and annotated using Rsamtools v3.2 and the TxDb.Hsapiens.UCSC.hg19.knownGene transcript database. Raw reads were normalized with regularized-logarithm transformation function with robust estimation.

[0218] Statistical Analysis. Descriptive statistics were used to summarize the data. Frequencies and counts were used to describe categorical variables, while means, standard deviations, medians and inter-quartile range was used for numeric data. Chi-square or Fishers exact test, as appropriate, were used to compare categorical variables, and Mann- Whitney test was used for numeric variables. Overall survival was defined as time from start of randomization of treatment assignment to date of sacrifice, as directed by IACUC guidelines. Hazard ratios and associated 95% confidence intervals were computed using multivariable Cox regression analyses. Proportional hazards were evaluated using the interaction between log of time and each variable; no statistical evidence was found to indicate proportional hazard violation given the P-value was > 0.05. Two-sided p-values less than 0.05 were considered statistically significant. The statistical analysis for all experimental data was performed using Prism 7 (GraphPad Prism, RRID: SCR_002798) and SAS v. 9.4.Example 2 p Alt transcriptomic signature correlates with radiation sensitivity and tumor immune phenotype

[0219] The Alt score is derived from two gene signatures consisting of genes induced by TGFp and alt-EJ DNA repair genes suppressed by TGFp. A high pAlt score indicates loss of TGFp competency and correlates with greater radiosensitivity of human cancer cells in vitro and survival in response to radiotherapy (Guix et al., Clin Cancer Res. 28(7): 1372- 1382 (2022); Liu et al., Sci Transl Med. 13(580) :eabc4465 (2021)). The score has been validated for predicting response to chemoradiation in human cancers (Guix et al., Clin Cancer Res. 28(7):1372-1382 (2022); Liu et al., Sci Transl Med. 13(580) :eabc4465 (2021)), but not in murine models. To this end, mammary tumor-derived transplants (mTDT) from 12 independent Trp53 null mammary tumors in syngeneic BALB / cJ mice that represent diversity and heterogeneity of both immune context and DNA damage response were developed (Moore, J., Ma, L., Lazar, A. A., & Barcellos-Hoff, M. H., Cancer Res. 82(3):365-376 (2022)).

[0220] Unsupervised hierarchical clustering of mTDT RNAseq data grouped TGFp and alt-EJ gene signatures, many of which were characterized by a negative correlation (FIG.1 A). The signatures were similarly anti-correlated in primary Trp53 null mammary tumors (n=62), subcutaneous tumors from Lewis lung cancer and 4T1 established cell lines (n=24), and among tumors (n=714) compiled in the TISMO database (FIG. 1 B-D). For mTDT families, single specimen gene set enrichment (ssGSEA) recapitulated the previously described negative correlation (Pearson correlation coefficient (PCC) R = -0.56, P<0.0001 ) between these two signatures (FIG. 2A). This relationship, which is represented by the integrative Alt score, was calculated for each mTDT family. K, H, and F mTDT families were representative of low, intermediate and high pAlt respectively (FIG. 2B).

[0221] To test whether the predictive capacity of the Alt score holds in mouse tumors, the relative radiosensitivity of K, H, and F mTDT irradiated with 10 Gy (RT) was determined. High pAlt F mTDT were the most sensitive to RT (17.5 days median survival) compared to intermediate Alt H mTDT (14.5 days median survival), and low pAlt K mTDT (12.5 days median survival, log-rank P<0.001 ; FIG. 2C). These data validate Alt scores as predictive of radiation sensitivity, as is evident in human cancer (Guix et al., Clin Cancer Res. 28(7):1372-1382 (2022)).

[0222] The immune phenotypes of mTDT had been previously determined using spatial distribution of CD8 T cells (Moore, J., Ma, L., Lazar, A. A., & Barcellos-Hoff, M. H., Cancer Res. 82(3):365-376 (2022)). Three distinct spatial distributions of lymphocytes have been described; infiltrated tumors in which lymphocytes are distributed but are ineffective incontrolling tumor growth, excluded tumors characterized by lymphocytes restricted to the interface of parenchyma and tumor, and deserts, which are tumors devoid of lymphocytes (Chen, D. S., & Mellman, I. Nature. 541 (7637):321 -330 (2017)). Infiltrated, so-called ‘hot’ tumors, are prime targets for checkpoint immunotherapy, but ‘cold’ tumors devoid of lymphocytes are considered mechanistically resistant (Hegde PS, Karanikas V, Evers S. Clin Cancer Res. 22(8):1865-1874 (2016)).

[0223] Surprisingly, H and K mTDT, which are intermediate and low |3Alt respectively, were classified as infiltrated, whereas high pAlt F mTDT were the desert phenotype (Moore et al., Cancer Res. 82(3):365-376 (2022)). Consistent with this assignment, the proportion of CD45+ immune cells measured by flow cytometry was 78% + 7% S.E. for H mTDT, 58 + 9.5% S.E. in K mTDT, and 10% + 2% S.E. in F mTDT as (FIG. 2D). Cytometry by time of flight (CyTOF) analysis of H and K infiltrated mTDT demonstrated they have distinct immune cell composition (FIG. 1 E). CD11 b positive myeloid cells and tumor associated macrophages were significantly (P<0.0001) enriched in K mTDT compared to H mTDT (FIG. 2E).Example 3TGFp inhibition and radiation promotes immune checkpoint blockade (ICB) response

[0224] The differences in immune composition among mTDT with a range of pAlt scores presented an opportunity to interrogate how pAlt and immune infiltrate interact in response to immunotherapy. High pAlt reports error-prone alt-EJ DNA damage repair (Guix et al., Clin Cancer Res. 28(7):1372-1382 (2022); Liu et al., Clin Cancer Res. 24(23) :6001-6014 (2018)), and correlates with greater sensitivity to genotoxic therapy, the fraction of the genome altered, and a microhomology-flanked indel (Liu et al., Sci Transl Med. 13(580) :eabc4465 (2021)). Interestingly, indels are considered more immunogenic than point mutations (Turajlic et al., Lancet Oncol. 18(8):1009-1021 (2017)). DNA damage from radiation or chemotherapy can promote immune recognition of cancer by various mechanisms, including antigen release and presentation (Formenti, S. C., & Demaria, S., J Natl Cancer Inst.105(4):256-265 (2013)). However, radiation induces TGFp activity, which impedes tumor control and ICB (Vanpouille-Box et al., Cancer Res. 75(11 ):2232-2242 (2015)).

[0225] Here, combinations of anti-PD-L1 , radiation and TGFp inhibition (TGFpi) were tested in F, H and K mTDT (FIG. 3A). F, H and K mTDT-bearing mice were randomized upon tumors with an average volume of approximately 75 mm3to monotherapy consisting of RT, anti-PD-L1 or TGFpi, dual combinations, or triple treatment. Monotherapy with anti-PD- L1 or TGFpi, or their combination had little effect on survival (FIG. 3B). Among mice treated with RT, the Kaplan-Meier survival curves showed modest improvement in conjunction witheither TGFp or PD-L1 inhibition dual treatment (FIG. 3C). The median survival for mice was 14 days for mice treated with RT alone versus 18 days (log-rank P<0.0001) when RT, TGFpi and anti-PD-L1 were combined.

[0226] The proportion of responders was significantly different among treatment groups (FIG. 3F; P=0.0023, two-way ANOVA). As was evident from the survival data, neither anti- PD-L1 nor TGFpi alone or in combination significantly increased the frequency of early response. Approximately 15% of mice treated with either anti-PD-L1 or TGFpi were responders. RT tripled the proportion of responders to 47%. In combination with RT, anti- PD-L1 had little effect (52%), whereas RT and TGFpi increased the proportion of responders to 61%. The triple treatment of RT, anti-PD-L1 and TGFpi resulted in 74% of mice classified as responders.

[0227] Although almost half (45%) of F and H mTDT were responders across treatments compared to 31% of K mTDT (not significant, two-way ANOVA), the immune-poor, high pAlt F mTDT were the most responsive to triple treatment. All F mTDT responded to triple treatment (9 / 9), the highest frequency of response among the three mTDT (FIG. 3J). This is particularly interesting since the high pAlt of F mTDT suggests that the cancer cells are TGFp signaling incompetent, implicating non-malignant cells response to TGFp inhibition in the tumor control. Notably, several studies suggest that prominent TGFp activity is a feature of a TME lacking tumor infiltrating lymphocytes and T cell cytotoxicity (Fridman et al., Nat Rev Cancer. 12(4):298-306 (2012); Ma et al., Clin Cancer Res. 27(6):1778-1791 (2021 )). The survival of F mTDT responders was almost double that of non-responders. Monotherapy with TGFp inhibitor, anti-PD-L1 or the combination was ineffective in increasing survival. RT significantly increased survival from 8 to 11 days (FIG. 3K). The median survival for mice treated with RT and anti-PD-L1 further increased to 13 days, and for RT and TGFp it was 17 days. Triple treatment prolonged survival to 24.5 days, more than double that of RT alone (log rank test, P<0.0001 ).

[0228] Individual mice had two distinct early responses, regardless of treatment type, tumors either grew exponentially after treatment or were controlled during the first 7 days post-treatment (FIG. 4A). This difference, i.e., early control versus growth, led to mice classification across treatment groups as responders and non-responders 7-days posttreatment (FIG. 3D). A similar classification was used recently to characterize mouse tumor response to a bifunctional TGFp trap and anti-PD-1 (Strait et al., Commun Biol. 4(1 ):1005 (2021)). The doubling time of tumors classified as responders (n=121 ) was 9 days (95% Cl 6.6-14.29) and that of non-responders (n=132) was 2.6 days (95% Cl 2.2-3.1). Tumor weight was also significantly less for responders versus non-responders at 7 days (FIG. 4B). Importantly, mice classified as responders had significantly better overall survival comparedto non-responders (FIG. 3E). Compared to median survival of 7 days for untreated mice, the median survival of non-responders was 9 days, whereas it was 16 days for responders (log rank, P<0.0001).

[0229] Uncoupling population response from individual response allowed investigation of biological correlates of response to pinpoint critical differences between responders and non-responders at 7-days post-treatment (FIG. 3G). The |3Alt score was not significantly different 7 days post-treatment regardless of treatment (FIG. 4C). However, the frequency of intratumoral neutrophils and macrophages significantly increased in tumors classified as responders compared to non-responders (FIGs. 4D & 4E). The frequency of tumor infiltrating CD3, CD4, and CD8 T cells increased (FIG. 3H) as did CD4 and CD8 proliferation (FIG. 4F) in tumors classified as responders. Natural killer (NK) cells were also significantly increased in tumors classified as responders (Mann-Whitney test, P<0.0001 , FIG. 3H). Consistent with immune activity within the tumor, the relative number of CD3, CD4 and CD8 T cells increased in the blood of responders compared to non-responders (FIG. 31), whereas other blood immune cells did not change (FIGs. 4F-4I). Hence, the high |3Alt, immune-poor context was unexpectedly associated with durable response, prompting further investigation of the relationship between pAlt and immune context in human cancer.Example 4P Alt correlates with the immune context of human cancer

[0230] A recent immunoprofiling initiative of immune cells isolated from 364 surgical tumor specimens across 12 tissues identified tumor educated immune signatures (TelS) (Combes et al., Cell. 185(1 ):184-203. e19 (2022)). Coordinated tissue processing and systematic profiling of immune cells generated 10 independent cell TelS that together could produce up to 4096 binary variables, but unsupervised clustering revealed only 12 distinct tissueagnostic immune archetypes present across cancers. TelS was used to interrogate the immune context of pAlt in the mTDT RNAseq data (FIG. 5A).

[0231] Consistent with the spatial and composition data, unsupervised clustering of mTDT with TelS signatures showed that low pAlt was strongly associated with immune-rich tumors while high pAlt corresponded to immune-poor tumors. Forty-seven percent (9 / 19) of high PAlt tumors were immune-poor. Primary Trp53 null mammary carcinomas were also analyzed (llla-Bochaca et al., Cancer Res. 74(23)7137-48 (2014)), in which 70% (14 / 20) classified as high pAlt were immune-poor by TelS signatures (FIG. 6A). This dichotomy was also exemplified by the Lewis lung carcinoma (LLC) and 4T1 breast cancer; LLC is high pAlt and immune-poor whereas 4T1 was low pAlt and immune-rich; 50% (12 / 24) of high pAlt tumors were immune-poor (FIG. 6B).

[0232] These data indicated a broad association between pAlt and immune context in mouse tumors that we then tested the association in human immune archetypes (Combes et al., Cell. 185(1 ):184-203.e19 (2022)). TCGA specimens (n=4341) that were classified as immune-rich archetypes had the lowest pAlt, and immune-poor archetypes had the highest PAlt (FIG. 5B). Indeed, unsupervised clustering of TelS of this data set confirmed that immune-poor cancers have the highest pAlt scores (FIG. 5C), further illustrated by the striking tissue agnostic pattern in which high pAlt correlates with greater fraction of genome (FGA) altered and increased TMB but decreased TelS (FIG. 6C). Given the lack of immune cells in tumors with high pAlt, we turned our attention to defective DNA repair. dMMR tumors are usually inflamed because cytosolic DNA sensing from cGAS / STING elicits type I IFN signaling that stimulates T cell recruitment (Lu et al., Cancer Cell. 39(1 ):96-108.e6 (2021)).

[0233] Blocking TGFp signaling compromises double strand break repair and increases alt-EJ that gives rise to microhomology flanked indels (Liu et al., Sci Transl Med.13(580) :eabc4465 (2021)), which are considered more immunogenic than single nucleotide variants (Turajlic et al. Lancet Oncol. 18(8):1009-1021 (2017)). As such, it was expected that high pAlt human cancers would show evidence of cytosolic DNA sensing and type I IFN target induction. However, consistent with the designated immune desert archetypes analysis (Combes et al., Cell. 185(1 ):184-203. e19 (2022)), high pAlt correlates with low expression of type I IFN gene signature (FIG. 6D). Thus, in distinct contrast to dMMR tumors, the genomic consequences of error prone DNA repair due to compromised TGFp signaling does not activate type I IFN signaling, and hence an infiltrated immune TME.

[0234] This relationship was also evident within specific cancers. TGFp and alt-EJ signatures of TCGA bladder cancer (n=405) are significantly negatively correlated (PCC R= - 0.34, P<0.001 ) (Liu et al., Sci Transl Med. 13(580) :eabc4465 (2021)). Unsupervised hierarchical clustering by TelS signatures for TCGA bladder cancer showed a similar parallel association of the transition from immune-rich to poor with transition from low to high pAlt (FIG. 6E).

[0235] To ascertain whether this relationship was generalizable, we analyzed specimens from TCGA that were not included in the immune archetypes assessment. Breast cancer showed a similar pattern of transitions low to high pAlt corresponding to immune-rich to poor TelS (FIG. 5D), as does TCGA lung squamous cell carcinoma (FIG. 6F). A correlation analysis of pAlt in bladder cancer (BLCA) and breast cancer (BRCA) TCGA specimens with 5 previously published immune signatures: chronic inflammatory response, immune response, inflammatory response, response to type I IFN and the inflammatory response pathway was also conducted. pAlt was significantly anti-correlated in 9 of 10 instances (FIG.6G). Together, these preclinical cancer models and human cancer analyses identified that, despite error-prone DNA repair, high |3Alt correlates with an immune-poor TME devoid of IFN signaling.Example 5High pAlt, immune-poor cancer patients respond to ICB

[0236] To test whether patients with tumors showing this specific combination of features respond to ICB, publicly available transcriptomic data from the IMvigor210 trial (Mariathasan et al., Nature. 554(7693) :544-548 (2018)), in which metastatic urothelial cancer patients (N=195) were treated with anti-PD-L1 (atezolizumab) and platinum chemotherapy, was analyzed.

[0237] Unsupervised hierarchical clustering using the TGFp and DNA repair signatures of IMvigor210 RNAseq data identified cancers in which TGFp target genes were high and alt- EJ genes were low, and vice versa (FIG. 8A), recapitulating the relationship seen in TCGA and mouse data sets. The negative correlation of these signatures is statistically significant (PCC R= -0.43, P<0.0001 ; FIG. 7A). Moreover, the mean pAlt scores of patients who experienced a complete or partial response were significantly greater (P=0.0003) than those that had stable or progressive disease (FIG. 7B).

[0238] Data reported by Hugo and colleagues (Hugo et al., Cell. 165(1 ):35-44 (2016)) from metastatic melanoma patients treated with anti-PD-1 (pembrolizumab), in which 15 of 27 patients were classified as responders was also analyzed. As with IMvigor210, p Alt scores of responders were significantly greater (P<0.05, Mann-Whitney test) than those deemed non-responders (FIG. 7C). Ranking the patients from low pAlt quartile 1 (Q1 ) to high pAlt quartile 4 (Q4) showed a broadly parallel gradation of immune-rich to immune-poor TelS signatures (FIG. 7D).

[0239] Unexpectedly, despite the relatively immune-poor nature of their tumors, patients ranked in pAlt Q4 were significantly enriched for complete or partial response objective response rate (P=0.0031 ; FIG. 7E). Patients in Q4 had a median survival of 10.9 months and experienced better overall survival (hazard ratio 0.62, P=0.011) compared to those ranked in Q1 , whose median survival was 8.1 months (FIG. 7F).

[0240] Given this unexpected relationship, the data were also analyzed by first grouping patients based on their immune infiltrates and then assessing associations with pAlt and response to treatment. Unsupervised hierarchical clustering of TelS signatures showed patients whose tumor was classified as immune-rich or immune-poor (leftmost versusrightmost dendrogram arm; FIG. 7G). Immune-poor tumors had significantly higher |3Alt (P<0.0001 ; FIG. 7H).

[0241] As seen for TCGA data, IMvigor210 clustering with the type I IFN signature was associated with a pattern of low to high Alt (FIG. 8B). IMvigor210 comprised of two cohorts, cohort 1 was treatment naive at the time of immunotherapy while cohort 2 had prior treatment with platinum chemotherapy. Notably, complete and partial response by RECIST criteria for pAlt quartile 4 versus quartile 1 was significantly greater in both (Cohort 1 P<0.0045; Cohort 2 P<0.023; FIG. 8C). Regardless of cohort, pAlt quartile 4 was immune- poor as measured by a sum of TelS scores (FIG. 8D). Overall, 39% of patients who experienced a partial or complete immunotherapy response had tumors that were classified as immune-poor and high pAlt.Example 6High pAlt tumors respond to ICB by converting from immune-poor to immune-rich

[0242] A major question is the mechanism by which ICB treatment can be effective against tumors composed of TGF signaling-incompetent tumor cells with an immune TME lacking lymphocytes.

[0243] It is possible that the immune TME of immune-poor, high pAlt cancers must shift in response to immunotherapy, i.e., following the cold to hot paradigm that is the goal of many combinations such as chemoradiation-immunotherapy.

[0244] Riaz et al. obtained biopsies from advanced melanoma patients (n=43) who progressed on ipilimumab or were ipilimumab-naive, before and after treatment with anti-PD- 1 (nivolumab; study ID CA209-038) (Riaz et al., Cell. 171 (4):934-949.e16 (2017)). Nine patients were classified as responders to anti-PD-1 and 34 as non-responders (Hugo et al., Cell. 165(1 ):35-44 (2016)).

[0245] As with IMvigor210, TGFp and alt-EJ signatures were anti-correlated, but there was no significant difference in mean pAlt of complete and partial responders compared to stable or progressive disease pre-treatment in this small group. As in other patient datasets, high pAlt was associated with low type I IFN signature expression (FIG. 10A). Based on TelS unsupervised clustering, there was a marked association of high pAlt and immune-poor TME (FIG. 9A).

[0246] The pretreatment and on treatment TelS signatures of responders for which transcriptomic data were available (n=9) were compared. Two responders, classified as high pAlt, immune-poor pre-treatment moved to immune-rich in the on-treatment biopsy (FIG. 9B). None classified as immune-rich moved on treatment, regardless of pAlt score.These high |3Alt patients also had low type I IFN signature expression prior to treatment that increased on treatment (FIG. 10B).

[0247] Although the number is limited, these data from humans support the conclusion from the mouse studies that high pAlt, immune-poor tumors can activate cytotoxic immunity.

[0248] To gain insight into the mechanisms responsible for ICB response, we returned to the mTDT model. Tumor infiltrating CD45+ cells significantly increased in immune-poor and high pAlt F mTDT classified as responders versus non-responders (P<0.022, 2-way ANOVA) and compared to sham (P<0.0042, 2-way ANOVA), while the frequency of CD45+ immune cells in H and K mTDT did not change as a function of response (FIG. 9C).

[0249] Multispectral flow cytometry of F tumors indicated that the composition of the immune infiltrate of responders versus non-responders was also altered. Responders had significantly (P<0.05) fewer myeloid cells and more lymphoid cells (FIG. 9D). This was evident by increased frequency of tumor infiltrating CD3 and CD4 T cells and increased proliferation. There was also a marked increase in infiltrating NK cells and NK cell activation (FIGs. 10C & 10D).

[0250] Consistent with immune activity within the tumor, CD3 and CD4 T cells were increased in the circulation of responders (FIG. 10E). Plots from individual monotherapy treatments implicate both RT and TGFpi as drivers of compositional change in responders, whereas anti-PD-L1 had little effect (FIG. 10F), yet NK cells were expanded in tumors classified as responders (FIG. 9E). Indeed, treatment with TGFpi in combination with RT resulted in the most significant expansion and activation of NK cells compared to RT alone or RT combined with anti-PD-L1 (FIGs. 9F-9H).Example 7Response of Alt high, immune-poor tumors to ICB therapy depends on NK cells

[0251] TGFp is recognized as a potent inhibitory cytokine of NK cells that limits their number and function, while recent evidence suggests that NK cells can convert myeloid derived suppressor cells (MDSC) into antigen presenting dendritic cells (Lindau et al., Immunology.138(2): 105-115 (2013)), which is consistent with previous studies in which MDSC were generated in the presence of TGFp (Gonzalez-Junca et al., Cancer Immunol Res. 7(2):306-320 (2019)). Hence a focus on NK cells, which were significantly increased in responders compared to non-responders or sham (ANOVA, P<0.008; FIG. 11 A).

[0252] The proportion of NK cells among responders (n=13) nearly tripled, from 2.8% in non-responders (n=10) to 8% in responders. Moreover, activation as indicated by proliferation measured by Ki67, markedly increased (FIG. 11 B). An antibody to NK cells wasadministered to deplete these cells prior to RT alone or triple treatment with TGFpi and anti- PD-L1 (FIG. 11C). The efficacy of depletion was confirmed by analysis of PBMC, in which both NK cells and proliferating NK cells showed a significant decrease (FIGs. 12A & 12B).

[0253] As in the prior analysis, infiltrating leukocytes increased significantly in tumors treated with triple combination over RT alone, which was reduced to that of untreated tumors by NK depletion (FIG. 11 D). Intratumoral NK cells, which were negative for a marker of innate lymphoid cell 1 (FIG. 12C), were depleted in the triple treatment arm to the negligible levels of untreated tumor (FIG. 11 E). NK depletion prior to triple treatment (n=12) completely abrogated the 75% response rate to this combination (Chi-square, P<0.0001). The response to RT was significantly increased (Chi-square, P=0.0004) from 50% (n=11) to 70% (n=11).

[0254] NK cells are controlled through certain cell-surface receptors that recognize specific ligands on target cells or antigen-presenting cells. These receptors can either stimulate or inhibit the activity of NK cells, and it is their combinatorial signaling, unlike the single receptor activation of T cells, that dictates NK cell response and function (Cerwenka, A., & Lanier, L. L., Nat Rev Immunol. 16(2):112-123 (2016)).

[0255] The expression of NK cell activation receptors and components of the cytotoxic apparatus is inhibited by TGFp, which results in the suppression of NK cell proliferation and activation (Wilson et al., PLoS One. 6(9):e22842 (2011)), whereas NK cells can be activated by interleukin 2 (IL-2), which significantly enhances IL-12 signaling in NK cells (Wang, K. S., Frank, D. A., & Ritz, J., Blood. 95(10):3183-3190 (2000)). Hence, the apparent paradox of NK depletion increasing response to RT but completely blocking response to combination treatment could be attributed to the nuances of signaling in each scenario.

[0256] To test this, intracellular cytokine levels were examined. IL-2 positive CD8 T cells were significantly increased in triple-treated tumors compared to untreated or irradiated tumors, which was abrogated by NK cell depletion (FIG. 11 F). Similarly, CXCL9 positive classical dendritic cells (eDC) were also significantly increased in tumors only following combination treatment (FIG. 11G). Circulating levels of the cytokines CXCL9, IL-2 and TNFa increased by triple treatment were also NK cell dependent (FIG. 11H). Thus, the unique TME of high |3Alt tumor can be primed to checkpoint blockade by treating with RT and TGFp blockade to promote an NK cell-dependent response.

[0257] To determine the generalizability of this mechanism, the TISMO database, which includes syngeneic mouse tumors treated with ICB, was interrogated. As described above, unsupervised hierarchical clustering based on the TelS signatures revealed a gradient of immune-rich to immune-poor that was paralleled by increasing pAlt scores (FIG. 12D). Thewidely used melanoma tumor model, B16, that is high |3Alt and immune-poor as measured by TelS at baseline (FIG. 111) was identified.

[0258] Notably, response to ICB shifted most of the tumors to the immune-rich phenotype (P<0.0001 , Chi-square test). In addition, gene ontology signatures of NK activation were significantly enriched in the responders (FIGs. 11 J & 12E). Genes associated with NK activation were also increased in high |3Alt paired biopsy melanoma patients from Riaz and colleagues, (Riaz et al., Cell. 171 (4):934-949.e16(2017)) in response to anti-PD-1 (FIG.12F).

[0259] Restoration of NK cell function in cancer has been hypothesized as a means to activate tumor immunity (Cerwenka, A., & Lanier, L. L., Nat Rev lmmunol.'\6(2):'\ 12-23 (2016)). The experiments and analyses disclosed herein uncovered an unexpected vulnerability in which an immune-poor TME generated by TGFp signaling incompetent, error- prone tumor cells can be therapeutically targeted to promote ICB response.

[0260] The foregoing description is given for clearness of understanding only, and no unnecessary limitations should be understood therefrom, as modifications within the scope of the disclosure may be apparent to those having ordinary skill in the art.

[0261] Throughout this specification and the claims which follow, unless the context requires otherwise, the word "comprise" and variations such as "comprises" and "comprising" will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.

[0262] Throughout the specification, where compositions are described as including components or materials, it is contemplated that the compositions can also consist essentially of, or consist of, any combination of the recited components or materials, unless described otherwise. Likewise, where methods are described as including particular steps, it is contemplated that the methods can also consist essentially of, or consist of, any combination of the recited steps, unless described otherwise. The disclosure illustratively disclosed herein suitably may be practiced in the absence of any element or step which is not specifically disclosed herein.

[0263] The practice of a method disclosed herein, and individual steps thereof, can be performed manually and / or with the aid of or automation provided by electronic equipment. Although processes have been described with reference to particular aspects, a person of ordinary skill in the art will readily appreciate that other ways of performing the acts associated with the methods may be used. For example, the order of various of the steps may be changed without departing from the scope or spirit of the method, unless describedotherwise. In addition, some of the individual steps can be combined, omitted, or further subdivided into additional steps.

[0264] All patents, patent applications, publications, and references cited herein are hereby fully incorporated by reference in their entireties. In case of conflict between the present disclosure and incorporated patents, publications and references, the disclosure herein should control.

Claims

CLAIMSWhat is claimed is:1 . A method of identifying and treating a human subject suffering from an immune poor cancer, the method comprising:(i) identifying the subject suffering from the immune poor cancer, wherein the subject comprises cancer cells comprising a DNA damage repair (DDR) deficit phenotype; and(ii) treating the subject identified in step (i) using a treatment regimen comprising sequentially administering to the subject(a) a genotoxic therapy to kill the cancer cells;(b) a TGF-beta inhibitor (TGFpi i) or interleukin 6 (IL-6) to release immune suppression of the cancer cells; and(c) an immune checkpoint inhibitor or an immunotherapy.

2. The method of claim 1 , wherein the DDR deficit phenotype is determined by assessing TGF-p signaling competency and assessing alternative end-joining (alt-EJ) activation in a sample comprising cancer cells from the subject, wherein when impaired TGF-p signaling and impaired alt-EJ activation are observed, the cancer cells are determined to comprise the DDR deficit phenotype.

3. The method of claim 2, wherein impaired TGF-p signaling is determined by measuring an expression level of at least one TGFp-associated gene selected from the group ABCG1 , AMIG02, CA12, CCDC99, CCL20, CHRNA9, COL4A2, CTGF, DLC1 , DNAJB9, DSC2, ENC1 , F3, FAP, FGF2, FN1 , HEY1 , HMGA2, ID1 , IGF2BP3, IGFBP3, JAG1 , KLF4, LAMB3, LAMC2, LARP6, LIPG, MAFF, MMD, PDGFC, PLEK2, LEXNA2, PSTN, PSCD1 , RICS, RNF24, RUNX1 , SAMSN1 , LAMC2, SERPINE1 , SERPINE2, SH2D2A, SH2D4A, SLC20A1 , SLC22A4, TGIF1 , THBS1 , TMEPAI, TNC, TNFRAF12A, and VACN, wherein a low level of expression of the at least one TGFp-associated gene indicates impaired TGF-p pathway activity in the cancer cells.

4. The method of claim 2, wherein impaired alt-EJ pathway activity is determined by measuring an expression level of at least one alt-EJ-associated gene selected from the group APE2, APEX1 , ASF1 A, CDKN2D, CIB1 , DNA2, FAAP24, FANCM, GEN1 , HRAS1 , LIG1 , LIG3, MEN1 , MRE11A, MSH3, MSH6, MTH1 , MTOR, NABP2, NTHL1 , PALB2, PARP1 , PARP3, POLA1 , POLM, POLQ, PRP19, RAD51 D, RBBP8, RRM2, RLIVBL2, SOD1 , TIP60, UNG, WRN, and XRCC1 , wherein a high level of expression of the at least one alt-EJ-associated gene indicates impaired alt-EJ pathway activity in the cancer cells.

5. The method of any one of claims 1-4, wherein the immune poor cancer is identified by detecting in the sample the expression level of at least one or more genes selected from the group consisting of: CD40LG, TBX21 , SH2D1 , PYHIN1 , ZNF831 , CD6, THEMIS, UBASH3A, TRAT1 , EOMES, GRAP2, ZAP70, SIRPG, ICOS, FASLG, CD8A, CD8B, ITK, GZMA, KLRK1 , GZMH, GZMK, CD3D, CD3E, CD3G, CLEC10A, CD1 E, CD1C, VSIG4, CD33, CD300LB, MS4A7, LY86, CLEC5A, LILRB4, FCER1A, LIL4B3, CSF1 R, LILRA1 , ADORA3, MPEG1 , FCN1 , RNASE6, FPR3, CYBB, MS4A4A, MS4A4E, OLR1 , CD163, WDFY4, SIGLEC1 , CD300E, CDH11 , DCN, PDGFRA, COL1A2, ISLR, COL1A1 , FNDC1 , BGN, COL5A2, POSTN, PCDH18, ADAMTS1 , MXRA5, SULF1 , EDNRA, PRRX1 , COL3A1 , THY1 , LUM, COL12A1 , BACH2, TRABD2A, IL7R, HDAC4, NR3C2, ADD3, PABPC1 , PABPC3, MFHAS1 , DSC1 , SELL, SESN1 , FASLG, CD8A, SETBP1 , CTSW, APOBEC3, APOBECC3, BTNL8, HLA-DPB1 , ULBP3, RAD51 , WNT9A, HLA-DMA, CERS5, CD8B, NKG7, FAM156A, ZNF696, MCM5, DPF3, TTC24, YARS1 , EBP, TRPS1 , GZMH, VCAM1 , LAG3, CRIM1 , NAA40, GRIK4, PSMB9, HLA-DMB, SERP2, HOXB4, CASP7, TMCC2, ARPC5L, MCTP2, LYST, FOXP3, IL2RA, CD80, CD177, LAIR2, CCR8, CCL22, TNFRSF13B, TNFRSF18, APOE, TREM2, C1QB, C1QC, VSIG4, S100A8, S100A9, VCAN, FCN1 , LYZ, IDO1 , C10ORF54, XCR1 , CLEC9A, BATF3, FCER1A, CD1C, CD1 E, CD1 D, and CLEC10A, or as provided in Table 2.

6. The method of claim 5, further comprising detecting the presence of tumor infiltrating lymphocytes (TIL) in the sample.

7. The method of any one of claims 1-6, wherein TGF-p signaling competency and alt-EJ activation are assessed by measuring the expression levels of selected TGFp- associated genes and alt-EJ-associated genes and obtaining an integrated score.

8. The method of claim 7, wherein the integrated score is a pAlt score.

9. The method of claim 8, wherein the |3Alt score is calculated according to the following equation:wherein:TGF / 3minis Lowest value among all TGFp scores;TGF / 3maxis Highest value among all TGFp scores;TGFfit is The sample / TGFp score;AltEjminis Lowest value among all AltEj scores;AltEjmaxis Highest value among all AltEj scores; and AltEj, is The sample / AltEj score.

10. The method of claim 8 or 9, wherein a p-alt score above a selected threshold indicates the cancer cells comprise the DDR deficit phenotype.11 . The method of any of claims 1-10, wherein determining the expression levels of the genes is carried out by RT-PCR, a hybridization assay, a microarray assay, RNA sequencing, a Northern blot, a Western blot, an immunohistochemistry assay, or an ELISA.

12. The method of any of claims 1-11 , wherein the genotoxic therapy is radiation therapy, cisplatin, chlorambucil, busulfan, carboplatin, carmustine, chlorambucil, cyclophosphamide, dacarbazine, daunorubicin, doxorubicin, epirubicin, etoposide, idarubicin, ifosfamide, irinotecan, lomustine, mechlorethamine, melphalan, mitomycin C, mitoxantrone, oxaliplatin, temozolomide, or topotecan.

13. The method of any of claims 1-12, wherein the TGFpi i is a polypeptide, a small molecule, or a nucleic acid.

14. The method of claim 13, wherein :(i) the polypeptide is an anti-TGF-p antibody, a soluble TGF-p receptor, or a peptide;(ii) the small molecule is galunisertib (LY2157299), LY2382770, LY3022859, SB- 431542, SD208, SM16, tranilast, pirfenidone, TEW-7197, PF-03446962, or pyrroleimidazole polyamide; or(iii) the nucleic acid is trabedersen (AP12009) or belagenpumatucel-L.

15. The method of claim 14, wherein :(i) the anti-TGF-p antibody is fresolimumab, metelimumab, lerdelimumab, 1 D11 , or 2G7, or a derivative of fresolimumab, metelimumab, lerdelimumab, 1 D11 , or 2G7; or(ii) the peptide is disitertide (P144).

16. The method of any one of claims 1-15, wherein the IL-6 inhibitor is an anti-IL- 6R antibody.

17. The method of claim 16, wherein the anti- 1 L-6R antibody is tocilizumab or satralizumab.

18. The method of any one of claims 1-17, wherein treatment with the genotoxic therapy and the immune checkpoint inhibitor or immunotherapy increases the presence of immune infiltrates in cancer cells or in a cancerous tumor of the subject, wherein the immune infiltrates comprise one or more of natural killer (NK) cells, T cells, B cells, neutrophils, macrophages, or dendritic cells in the tumor.

19. The method of claim 18, wherein the T cells are CD3+ T cells, CD4+ T cells, CD8+ T cells, and / or Teff cells.

20. The method of claim 19, wherein the T cells are detected by immunofluorescence.21 . The method of any one of claims 1-20, wherein the immune checkpoint inhibitor inhibits expression and / or activity of a checkpoint protein in the subject, wherein the checkpoint protein is CTLA-4, PDL1 , PDL2, PD1 , B7-H3, B7-H4, BTLA, HVEM, TIM3, GAL9, LAG3, VISTA, KIR, 2B4, CD160, CGEN-15049, CHK 1 , CHK2, TIGIT, BTLA, IDO3, A2aR, or B-7 family ligands, or a combination of any thereof.

22. The method of claim 21 , wherein the immune checkpoint inhibitor is ipilimumab, tremelimumab, pembrolizumab, atezolizumab, avelumab, durvalumab, or nivolumab.

23. A method of sensitizing an immune-poor cancer with a high |3Alt score in a subject to immune checkpoint blockade (ICB)-based therapy comprising administering to the subject an effective amount of a genotoxic therapy and an immunosuppressive antagonist.

24. The method of any one of claims 1-23, wherein the cancer is bladder cancer, brain cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, gastric cancer, glioblastoma, glioma, head and neck cancer, lung cancer, melanoma, mesothelioma, nasopharyngeal cancer, ovarian cancer, pancreatic cancer, prostate cancer, renal cancer, testicular cancer, thyroid cancer, skin cancer, and uterine cancer.

25. The method of any one of claims 1-24, wherein the treatment produces at least one therapeutic effect selected from the group consisting of reduction in size of a tumor, reduction in number of metastatic lesions over time, a complete response, partial response, stable disease, increase in overall response rate, increase in overall survival, and an increase in progression-free survival.

26. A kit for determining the expression levels of(i) at least one TGFp-associated gene selected from the group consisting of ABCG1 , AMIG02, CA12, CCDC99, CCL20, CHRNA9, COL4A2, CTGF, DLC1 , DNAJB9, DSC2, ENC1 , F3, FAP, FGF2, FN1 , HEY1 , HMGA2, ID1 , IGF2BP3, IGFBP3, JAG1 , KLF4, LAMB3, LAMC2, LARP6, LIPG, MAFF, MMD, PDGFC, PLEK2, PLXNA2, POSTN, PSCD1 , RICS, RNF24, RUNX1 , SAMSN1 , SERPINE1 , SERPINE2, SH2D2A, SH2D4A, SLC20A1 , SLC22A4, TGIF1 , THBS1 , TMEPAI, TNC, TNFRAF12A, and VCAN, and(ii) at least one Alt-EJ-associated gene selected from the group consisting of APE2, APEX1 , ASF1A, CDKN2D, CIB1 , DNA2, FAAP24, FANCM, GEN1 , HRAS1 , LIG1 , LIG3, MEN1 , MRE11A, MSH3, MSH6, MTH1 , MTOR, NABP2, NTHL1 , PALB2, PARP1 , PARP3, POLA1 , POLM, POLQ, PRP19, RAD51 D, RBBP8, RRM2, RUVBL2, SOD1 , TIP60, UNG, WRN, and XRCC1 in a sample, wherein the kit comprises:(a) primers for recognizing the at least one of TGFp-associated gene and the at least one Alt-EJ-associated gene, or an array comprising said primers; and(b) instructions for performing a method for determining the expression levels of the at least one TGFp-associated gene and the at least one Alt-EJ-associated gene.

27. The kit of claim 26, wherein the primers recognize at least two TGFp- associated genes and at least two Alt-EJ-associated genes.

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