Methods for determining cancer treatment efficacy

Abstract

Disclosed herein are methods for predicting whether a cancer subject is likely to benefit from treatment with one or more antibody-drug conjugates. Further disclosed herein are methods for treating a subject determined to be likely to benefit from one or more antibody-drug conjugates with one or more antibody-drug conjugates. In some aspects, the present invention provides methods for identifying a subject as likely to respond to one or more antibody-drug conjugate (ADC) treatments.

Description

[Technical Field]

[0001] Related Applications This application claims the benefit of U.S. Provisional Application No. 63 / 426,344, filed November 17, 2022, U.S. Provisional Application No. 63 / 446,014, filed February 15, 2023, and U.S. Provisional Application No. 63 / 464,406, filed May 5, 2023, the entire teachings of which are incorporated herein by reference. [Background technology]

[0002] Background of the Invention Antibody-drug conjugates (ADCs) represent an important new class of targeted cancer therapy for solid tumors, with several drugs approved in recent years and promising late-phase candidates. 1 Approximately 80 ADCs are currently in various stages of development, with over 600 ongoing clinical trials, demonstrating a wide spectrum of objective response rates. 16 Despite varying response rates, most ADC development strategies do not utilize predictive biomarkers. In some instances, target protein expression is used to select patients (e.g., Her2-targeted ADCs). 2,3 ), or enrich clinical trial results (e.g., folate receptor-targeting ADCs 4 ) but in patients with metastatic triple-negative breast cancer treated with ADCs. 19 and patients with metastatic urothelial cancer 20 Correlation analyses performed in recent studies have revealed that ADC target expression alone, whether measured via RNA or protein levels, correlates poorly with objective response rates in these ADC-treated patients. Therefore, to date, development strategies have pursued unselected patients in tumor types with high unmet need (e.g., metastatic bladder, triple-negative breast, ovarian) known to express the target. 1 Furthermore, recent clinical trials have demonstrated toxicities associated with ADCs that are comparable to those of standard-of-care chemotherapy. 16 . Thus, there is a need for a universal biomarker that can predict the effectiveness of available ADC treatments for individual patients, thereby increasing the chances of a successful treatment outcome while simultaneously reducing unnecessary patient exposure to toxicities associated with other ADCs identified as less likely to provide the desired treatment outcome. As described herein, the inventors utilized next-generation sequencing molecular data from over 20,000 subjects with advanced cancer and published objective response rates from 22 clinical trials and cohorts across nine antibody-drug conjugate treatments to develop a multivariate biomarker algorithm that can identify cancer patients, regardless of tumor type, who are likely to benefit from one or more ADC treatments. [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Fu, Z., Li, S., Han, S., Shi, C. & Zhang, Y. Antibody drug conjugate: the “biological missile” for targeted cancer therapy. Signal Transduction and Targeted Therapy 7, 93 (2022). https: / / doi.org:10.1038 / s41392-022-00947-7 [Non-patent document 2] Coats, Steven, et al. “Antibody-Drug Conjugates: Future Directions in Clinical and Translational Strategies to Improve the Therapeutic IndexAdvances in Antibody-Drug Conjugate Clinical Development.” Clinical Cancer Research 25.18 (2019): 5441-5448. https: / / aacrjournals.org / clincancerres / article / 25 / 18 / 5441 / 81676 / Antibody-Drug-Conjugates-Future-Directions-in [Non-patent document 3] Verma, S. et al. Trastuzumab Emtansine for HER2-Positive Advanced Breast Cancer. New England Journal of Medicine 367, 1783-1791 (2012). https: / / doi.org:10.1056 / NEJMoa1209124 [Non-patent document 4] Modi, S. et al. Trastuzumab Deruxtecan in Previously Treated HER2-Positive Breast Cancer. New England Journal of Medicine 382, ​​610-621 (2019). https: / / doi.org:10.1056 / NEJMoa1914510 [Non-Patent Document 5] Loriot, Yohann, et al. “Efficacy of sacituzumab govitecan (SG) in locally advanced (LA) or metastatic urothelial cancer (mUC) by trophoblast cell surface antigen 2 (Trop-2) expression.” (2023): 4579-4579. [Non-patent document 6] Bardia, Aditya, et al. “Trop-2 mRNA expression and association with clinical outcomes with sacituzumab govitecan (SG) in patients with HR+ / HER2-metastatic breast cancer (mBC): Biomarker results from the phase 3 TROPiCS-02 study.” (2023): 1082-1082. Summary of the Invention [Means for solving the problem]

[0004] Summary of the Invention In some aspects, the present invention provides a method for identifying a subject as likely to respond to one or more antibody-drug conjugate (ADC) therapies, the method comprising: (A) measuring, from a biological tissue sample obtained from the subject, the expression level of at least one gene product associated with each of the one or more ADC therapies; (B) measuring, in the same biological tissue sample of step (A), the expression level of one or both of (i) at least one gene product associated with cell adhesion, and (ii) one or more gene products associated with proliferation, wherein, if the expression level of the one or more gene products associated with proliferation is measured, calculating therefrom the average of all of the measured expression levels of the gene products associated with proliferation, thereby obtaining a proliferation gene expression level; (C) optionally, determining tumor cell content in the same tumor tissue sample of steps (A) and (B); (D) calculating the ADC treatment response score (ADC and (iv) the determined tumor cell content.

[0005] In some embodiments, the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with the corresponding ADC therapy, the expression level of at least one gene product associated with cell adhesion, and the determined proliferation gene expression level. In some embodiments, the determined proliferation gene expression level and the expression level of the at least one gene product associated with the corresponding ADC therapy are both positively associated with the likelihood that a patient will respond to the corresponding ADC therapy, and the expression level of the at least one gene product associated with cell adhesion is negatively associated with the likelihood that a patient will respond to the same corresponding ADC therapy.

[0006] In some embodiments, the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with a corresponding ADC therapy, the measured expression level of at least one gene product associated with cell adhesion, and tumor cell content. In some embodiments, the tumor cell content and the expression level of the at least one gene product associated with a corresponding ADC therapy are both positively associated with the likelihood that a patient will respond to the corresponding ADC therapy, and the expression level of the at least one gene product associated with cell adhesion is negatively associated with the likelihood that a patient will respond to the same corresponding ADC therapy.

[0007] In some embodiments, the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with the corresponding ADC therapy, the expression level of at least one gene product associated with cell adhesion, the determined proliferation gene expression level, and tumor cell content. In some embodiments, all of the determined proliferation gene expression level, the tumor cell content, and the expression level of the at least one gene product associated with the corresponding ADC therapy are positively associated with the likelihood that the patient will respond to the corresponding ADC therapy, and the expression level of at least one gene product associated with cell adhesion is negatively associated with the likelihood that the patient will respond to the same corresponding ADC therapy.

[0008] In some embodiments, the at least one gene product associated with each of the one or more ADC therapies comprises an RNA transcript individually selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2 MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, P79, EGFR, integrin beta-6, ROR1, Globo H, IL2RA, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, NCAM1, LY75, ALCAM, and CEACAM5.

[0009] In some embodiments, measuring the expression level of one or more gene products associated with proliferation includes measuring the expression level of one or more gene products associated with proliferation, including BIRC5, BRCA1, BRCA2, BUB1, BUB1B, CCNA2, CCNB1, CCNB2, CCNE1, CCNE2, CCNF, CCNG2, CDC20, CDC25A, CDC25B, CDC25C, CDC45L, CDC6, CDC7, CDCKN1A p21, CDCKN3, CDK4, CDKN1C, CDKN2A, CDKN2C, CDKN2D p19, CDKN3, CENPA, CENPE, CENPF, CHAF1A, CHK1, CKS1, CKS2, CKS2, DHFR, DHFR, E2-EPF, E2F1, E2F3, E2F5, FEN1, FOXM1, KNSL2, KNSL5, KPNA2, LMNB2, MAD2, MAD2L1, MAPK13 , MCM2, MCM3, MCM4, MCM5, MCM6, MK167, MNAT1, MSH2, MYBL2, NASP, NEK2, NPAT, ORC1, PA2G4, PCNA, PES1, PKMYT1, PLK, PRIM1, PTTG1, RAB6KIFL, RACGAP1, RAD51, RAD54L, RAN In some embodiments, measuring the expression level of a gene product of one or more genes selected from the group consisting of RAN, RFC4, RPA3, RRM1, RRM2, SLBP, STK12, STK15, STK18, STK6, TOP2A, TROAP, TUBB, TYMS, UBE2C, and UNG. In some embodiments, measuring the expression level of one or more gene products associated with proliferation comprises measuring the expression level of one, two, or all three of the MYBL2, TOP2A, and / or UBE2C gene products.

[0010] In some embodiments, each of the gene products associated with cell adhesion affects at least two of cellular adherens junctions, anchoring junctions, cell-substrate adherens junctions, cell-substrate junctions, or focal adhesions. In some embodiments, measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression level of a gene product of at least one gene selected from the group consisting of ATP2A2, BAIAP2, CD151, CHP1, CYFIP1, CYTH3, DAG1, DSC2, GIT1, HSP90B1, HSPA5, LIMK1, MAPK1, PACSIN2, PDIA3, PVR, REXO2, RPL22, RPLP1, RPLP2, RPS11, RPS16, RPS5, SDCBP SNAP23, SNTB1, and SRP68.

[0011] In one embodiment, the step of measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression of a PVR gene product. In some embodiments, the step of measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression level of a single gene product associated with cell adhesion. In some embodiments, the single gene product is a PVR gene product. In some embodiments, the single gene product is not a PVR gene product.

[0012] In some embodiments, measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression levels of two or more gene products associated with cell adhesion, and then determining the cell adhesion gene product expression level by averaging the expression of the two or more gene products associated with cell adhesion.

[0013] In some embodiments, the expression level of the at least one gene product associated with the corresponding ADC treatment is weighted by a factor of approximately 0.3 to 0.65, the expression level of the at least one gene product associated with cell adhesion is weighted by a factor of approximately −0.8 to −1, and the determined proliferation gene expression level is weighted by a factor of approximately 0.2 to 0.4.

[0014] In some embodiments, the expression level of the at least one gene product associated with the corresponding ADC treatment is weighted by a factor of approximately 0.3, the expression level of the at least one gene product associated with cell adhesion is weighted by a factor of approximately −0.9, and the tumor cell content is weighted by a factor of approximately 0.8.

[0015] In some embodiments, the expression level of the at least one gene product associated with the corresponding ADC treatment is weighted by a factor of approximately 0.45, the expression level of the at least one gene product associated with cell adhesion is weighted by a factor of approximately −1, the determined proliferation gene expression level is weighted by a factor of approximately 0.55, and the tumor cell content is weighted by a factor of approximately 0.07.

[0016] In some embodiments, each of the one or more ADC TRSs is determined by further taking into account a bias variable, which is a static offset adjusted to produce biomarker frequencies that match published objective response rates in clinical trials. In some embodiments, the bias variable is weighted by a factor of approximately -0.25.

[0017] In some embodiments, the predetermined threshold is set at percentiles of ranked ADC treatment response scores determined from tumor tissue samples of a first cohort of subjects utilizing at least the expression level of the at least one gene product associated with a first corresponding ADC therapy, where each percentile corresponds to a greater percentage of subjects in the first cohort who do not respond to the same first corresponding ADC therapy.

[0018] In some embodiments, the predetermined threshold is set at a percentile of ranked ADC treatment response scores determined from tumor tissue samples of a first and second cohort of subjects using expression levels of at least a first and a second gene product associated with the first and second corresponding ADC therapies, where each percentile corresponds to a greater percentage of subjects in the first and second cohorts who do not respond to the at least first and second corresponding ADC therapies. In some embodiments, the predetermined threshold is zero, and an ADC TRS indicating that the subject is likely to respond to ADC treatment is an ADC TRS having a value greater than zero.

[0019] In some embodiments, the first and / or second cohort of subjects is a pan-cancer cohort or a matched tumor type cohort. In some embodiments, the first cohort of subjects and the second cohort of subjects are the same cohort of subjects.

[0020] In some embodiments, the expression level of the at least one gene product associated with each of one or more ADC treatments, the expression level of at least one gene product associated with cell adhesion, and the tumor cell content are log2 transformed and / or Z-score normalized prior to step (D)(1), and the expression level of the one or more gene products associated with proliferation is log2 transformed and / or Z-score normalized prior to averaging the expression levels of the gene products associated with proliferation to obtain the proliferation gene expression level in step (B).

[0021] In some embodiments, the tumor cell content is a molecularly determined tumor cell content calculated based on somatic and germline variant allele frequencies and / or copy number profiles.

[0022] In some embodiments, the method further comprises measuring the expression level of at least one housekeeping gene selected from CIAO1, EIF2B1, and HMBS in the tumor tissue sample, and normalizing the expression levels of the at least one gene product associated with the one or more ADC therapies, the at least one gene product associated with cell adhesion, and the one or more gene products associated with proliferation to the at least one housekeeping gene expression level to obtain normalized expression levels of the at least one gene product associated with each of the one or more ADC therapies, the at least one gene product associated with cell adhesion, and the one or more gene products associated with proliferation.

[0023] In some embodiments, the expression products of the at least one gene associated with each of the one or more ADC therapies, cell adhesion, and one or more genes associated with proliferation are individually selected from ribonucleic acid (RNA) and protein.

[0024] In some embodiments, the gene expression products of the at least one gene associated with each of the one or more ADC therapies, the one or more genes associated with cell adhesion, and proliferation are proteins, and measuring the expression levels requires the use of immunohistochemical techniques.

[0025] In some embodiments, the gene expression products of the at least one gene associated with each of the one or more ADC therapies, the one or more genes associated with cell adhesion, and proliferation are RNA, and measuring the expression levels requires utilizing RNA sequencing techniques.

[0026] In some embodiments, the one or more ADC therapies each comprise a monoclonal antibody, at least one functional fragment thereof, or a bispecific antibody targeting at least one epitope of at least one antigen selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2 MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, P79, EGFR, integrin beta-6, ROR1, Globo H, IL2RA, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, NCAM1, LY75, ALCAM, and CEACAM5.

[0027] In some embodiments, the antibody comprises a bispecific antibody capable of targeting two epitopes of the same antigen or epitopes of two separate antigens, wherein the same antigen or the two antigens are selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2 MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, P79, EGFR, integrin beta-6, ROR1, Globo H, IL2RA, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, NCAM1, LY75, ALCAM, and CEACAM5.

[0028] In some embodiments, the antibody or fragment thereof is directly or indirectly conjugated to a cytotoxic drug.

[0029] In some embodiments, the cytotoxic drug is a DNA replication inhibitor selected from the group consisting of alkylating agents, DNA polymerase inhibitors, nitrogen mustards, and topoisomerase inhibitors.

[0030] In some embodiments, the antibody, at least one functional fragment thereof, or the bispecific antibody is fused to a protein that is toxic to cancer cells. In some embodiments, the cytotoxic drug is a topoisomerase inhibitor.

[0031] In some embodiments, the subject has or is suspected of having a cancer that is not approved for the indicated use of the one or more ADC therapies. In some embodiments, the tumor tissue sample is or is suspected of containing bladder cancer, salivary gland cancer, endometrial cancer, ovarian cancer, cervical cancer, head and neck cancer, non-melanoma skin cancer, thyroid cancer, cancer of unknown primary origin, cancer of the central or peripheral nervous system, neuroendocrine tumor, melanoma, esophagogastric cancer, small intestine cancer, sarcoma, hepatobiliary cancer, pancreatic cancer, gastrointestinal stromal tumor, renal cell carcinoma, glioma, appendix cancer, breast cancer, small cell lung cancer, prostate cancer, esophagogastric cancer, non-small cell lung cancer, pancreatic cancer, lymphoma, or colorectal cancer.

[0032] In some embodiments, the tumor tissue sample is a formalin-fixed, paraffin-embedded (FFPE) tumor tissue sample. In some embodiments, the tumor tissue sample comprises at least 20% tumor content.

[0033] In some embodiments, the method further comprises step (E) administering said at least one of said one or more ADC therapies to a subject identified in step (D)(1) as likely to respond to said one or more ADC therapies.

[0034] In some embodiments, each of the one or more ADC TRSs is determined without taking into account tumor cell content.

[0035] In another aspect, the present invention provides a method for selecting from one or more antibody-drug conjugate (ADC) therapies among two or more ADC therapies identified as most beneficial for treating cancer in a subject, the method comprising: (A) measuring the expression level of at least one gene product associated with each of the or more ADC therapies from a biological tissue sample obtained from the subject; (B) measuring, in the same biological tissue sample of step (A), the expression level of one or both of (i) at least one gene product associated with cell adhesion, and (ii) one or more gene products associated with proliferation, wherein if expression levels of more than one gene product associated with proliferation are measured, calculating therefrom an average of all of the expression levels of the measured gene products associated with proliferation, thereby obtaining a proliferation gene expression level; (C) optionally, determining tumor cell content in the same tumor tissue sample of steps (A) and (B); (D)(1) calculating an ADC treatment response score (ADC TRS) for each of the two or more ADC therapies, and calculating an ADC treatment response score (ADC TRS) for each of the two or more ADC therapies; (D)(2) if ADC TRSs associated with two or more ADC therapies are above a predetermined threshold associated with a beneficial patient treatment outcome, ranking the at least two ADC TRSs by the value by which each ADC treatment response score exceeds the predetermined threshold, and selecting the highest ranked ADC therapy for administration to the subject.

[0036] In some embodiments, the method further comprises step (E) of administering the selected highest ranked ADC therapy to the subject. In some embodiments, step (E) further comprises administering at least one other, lower-ranked ADC above the predetermined threshold to the subject in combination with the highest ranked ADC therapy. In some embodiments, step (E) does not comprise administering another ADC therapy in combination with the highest ranked ADC therapy.

[0037] In another aspect, the present invention provides a method for treating cancer in a subject who is likely to respond to one or more antibody-drug conjugate (ADC) therapies, comprising: (a) measuring, in a tumor tissue sample obtained from the subject, i) the expression level of at least one gene product associated with each of the corresponding one or more ADC therapies, and at least one of the following: ii) at least one gene product associated with cell adhesion, and iii) one or more gene products associated with proliferation; (b) measuring the expression levels of one or more housekeeping genes in the same tumor tissue sample of step (a), and further measuring the expression levels of the at least one gene product associated with each of the one or more ADC therapies of step (a), the at least one gene product associated with cell adhesion, and the at least one gene product associated with proliferation; (c) normalizing the expression levels of the gene product, and the one or more gene products associated with proliferation to the expression levels of the one or more housekeeping genes to obtain normalized expression levels of the at least one gene product associated with the one or more ADC treatments, the gene product associated with cell adhesion, and the one or more genes associated with proliferation, respectively; (c) if the gene products of the one or more genes associated with proliferation are measured and normalized, determining a proliferation gene expression level by averaging the normalized expression levels of the one or more gene products associated with proliferation; (d) optionally, determining tumor cell content in the same tumor tissue sample of steps (a) and (b); (e)(1) calculating the calculated ADC treatment response score (ADC identifying a subject who is likely to benefit from said one or more ADC therapies if one or more of a plurality of ADC TRSs (a TRS or a TRS) are above one or more corresponding predetermined thresholds, wherein each of said one or more ADC TRSs is determined from (i) the measured expression level of said at least one gene product associated with a corresponding ADC therapy and at least two of the following: (ii) the measured level of said at least one gene product associated with cell adhesion, (iii) the determined proliferation gene expression level, and (iv) the determined tumor cell content;and (f) administering an effective amount of said one or more ADC therapies to a subject identified as likely to benefit from said one or more ADC therapies;

[0038] In some embodiments, the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with the corresponding ADC therapy, the expression level of at least one gene product associated with cell adhesion, and the determined proliferation gene expression level. In some embodiments, the determined proliferation gene expression level and the expression level of the at least one gene product associated with the corresponding ADC therapy are both positively associated with the likelihood that a patient will respond to the corresponding ADC therapy, and the expression level of the at least one gene product associated with cell adhesion is negatively associated with the likelihood that a patient will respond to the same corresponding ADC therapy.

[0039] In some embodiments, the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with a corresponding ADC therapy, the measured expression level of at least one gene product associated with cell adhesion, and tumor cell content. In some embodiments, the tumor cell content and the expression level of the at least one gene product associated with a corresponding ADC therapy are both positively associated with the likelihood that a patient will respond to the corresponding ADC therapy, and the expression level of the at least one gene product associated with cell adhesion is negatively associated with the likelihood that a patient will respond to the same corresponding ADC therapy.

[0040] In some embodiments, the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with the corresponding ADC therapy, the expression level of at least one gene product associated with cell adhesion, the determined proliferation gene expression level, and tumor cell content. In some embodiments, the determined proliferation gene expression level, the tumor cell content, and the expression level of the at least one gene product associated with the corresponding ADC therapy are all positively associated with the likelihood that a patient will respond to the corresponding ADC therapy, and the expression level of at least one gene product associated with cell adhesion is negatively associated with the likelihood that a patient will respond to the same corresponding ADC therapy.

[0041] In some embodiments, the at least one gene product associated with each of the one or more ADC therapies comprises an RNA transcript individually selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2 MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, P79, EGFR, integrin beta-6, ROR1, Globo H, IL2RA, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, NCAM1, LY75, ALCAM, and CEACAM5.

[0042] In some embodiments, measuring the expression level of one or more gene products associated with proliferation includes measuring the expression level of one or more gene products associated with proliferation, including BIRC5, BRCA1, BRCA2, BUB1, BUB1B, CCNA2, CCNB1, CCNB2, CCNE1, CCNE2, CCNF, CCNG2, CDC20, CDC25A, CDC25B, CDC25C, CDC45L, CDC6, CDC7, CDCKN1A p21, CDCKN3, CDK4, CDKN1C, CDKN2A, CDKN2C, CDKN2D p19, CDKN3, CENPA, CENPE, CENPF, CHAF1A, CHK1, CKS1, CKS2, CKS2, DHFR, DHFR, E2-EPF, E2F1, E2F3, E2F5, FEN1, FOXM1, KNSL2, KNSL5, KPNA2, LMNB2, MAD2, MAD2L1, MAPK13 , MCM2, MCM3, MCM4, MCM5, MCM6, MK167, MNAT1, MSH2, MYBL2, NASP, NEK2, NPAT, ORC1, PA2G4, PCNA, PES1, PKMYT1, PLK, PRIM1, PTTG1, RAB6KIFL, RACGAP1, RAD51, RAD54L, RAN In some embodiments, measuring the expression level of a gene product of one or more genes selected from the group consisting of RAN, RFC4, RPA3, RRM1, RRM2, SLBP, STK12, STK15, STK18, STK6, TOP2A, TROAP, TUBB, TYMS, UBE2C, and UNG. In some embodiments, measuring the expression level of one or more gene products associated with proliferation comprises measuring the expression level of one, two, or all three of the MYBL2, TOP2A, and / or UBE2C gene products.

[0043] In some embodiments, each of the gene products associated with cell adhesion affects at least two of cellular adherens junctions, anchoring junctions, cell-substrate adherens junctions, cell-substrate junctions, or focal adhesions. In some embodiments, measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression level of a gene product of at least one gene selected from the group consisting of ATP2A2, BAIAP2, CD151, CHP1, CYFIP1, CYTH3, DAG1, DSC2, GIT1, HSP90B1, HSPA5, LIMK1, MAPK1, PACSIN2, PDIA3, PVR, REXO2, RPL22, RPLP1, RPLP2, RPS11, RPS16, RPS5, SDCBP SNAP23, SNTB1, and SRP68.

[0044] In one embodiment, the step of measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression of a PVR gene product. In some embodiments, the step of measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression level of a single gene product associated with cell adhesion. In some embodiments, the single gene product is a PVR gene product. In some embodiments, the single gene product is not a PVR gene product.

[0045] In some embodiments, measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression levels of two or more gene products associated with cell adhesion, and then determining the cell adhesion gene product expression level by averaging the expression of the two or more gene products associated with cell adhesion.

[0046] In some embodiments, the expression level of the at least one gene product associated with the corresponding ADC treatment is weighted by a factor of approximately 0.3 to 0.65, the expression level of the at least one gene product associated with cell adhesion is weighted by a factor of approximately −0.8 to −1, and the determined proliferation gene expression level is weighted by a factor of approximately 0.2 to 0.4.

[0047] In some embodiments, the expression level of the at least one gene product associated with the corresponding ADC treatment is weighted by a factor of approximately 0.3, the expression level of the at least one gene product associated with cell adhesion is weighted by a factor of approximately −0.9, and the tumor cell content is weighted by a factor of approximately 0.8.

[0048] In some embodiments, the expression level of the at least one gene product associated with the corresponding ADC treatment is weighted by a factor of approximately 0.45, the expression level of the at least one gene product associated with cell adhesion is weighted by a factor of approximately −1, the determined proliferation gene expression level is weighted by a factor of approximately 0.55, and the tumor cell content is weighted by a factor of approximately 0.07.

[0049] In some embodiments, each of the one or more ADC TRSs is determined by further taking into account a bias variable, which is a static offset adjusted to produce biomarker frequencies that match published objective response rates in clinical trials. In some embodiments, the bias variable is weighted by a factor of approximately -0.25.

[0050] In some embodiments, the predetermined threshold is set at percentiles of ranked ADC treatment response scores determined from tumor tissue samples of a first cohort of subjects utilizing at least the expression level of the at least one gene product associated with a first corresponding ADC therapy, where each percentile corresponds to a greater percentage of subjects in the first cohort who do not respond to the same first corresponding ADC therapy.

[0051] In some embodiments, the predetermined threshold is set at a percentile of ranked ADC treatment response scores determined from tumor tissue samples of the first and second cohorts of subjects utilizing expression levels of at least first and second gene products associated with at least the first and second corresponding ADC therapies, where each percentile corresponds to a greater percentage of subjects in the first and second cohorts who do not respond to the at least first and second corresponding ADC therapies.

[0052] In some embodiments, the predetermined threshold is set to zero, and an ADC TRS indicating that the subject is likely to benefit from ADC treatment is an ADC TRS having a value greater than zero.

[0053] In some embodiments, the first and / or second cohort of subjects is a pan-cancer cohort or a matched tumor type cohort. In some embodiments, the first cohort of subjects and the second cohort of subjects are the same cohort of subjects.

[0054] In some embodiments, the gene expression level of the at least one gene product associated with each of one or more ADC therapies, the measured expression level of the at least one gene product associated with cell adhesion, and the tumor cell content are log2 transformed and / or Z-score normalized prior to step (E)(1), and the expression level of the one or more gene products associated with proliferation is log2 transformed and / or Z-score normalized prior to averaging the expression levels of the gene products associated with proliferation to obtain the proliferation gene expression level in step (C).

[0055] In some embodiments, the tumor cell content is a molecularly determined tumor cell content calculated based on somatic and germline variant allele frequencies and / or copy number profiles.

[0056] In some embodiments, the expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene associated with cell adhesion, and the one or more genes associated with proliferation are individually selected from ribonucleic acid (RNA) and protein.

[0057] In some embodiments, the gene expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene associated with cell adhesion, and the one or more genes associated with proliferation are proteins, and measuring the expression levels requires the use of immunohistochemical techniques.

[0058] In some embodiments, the gene expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene associated with cell adhesion, and the one or more genes associated with proliferation are RNA, and measuring the expression levels requires utilizing RNA sequencing techniques.

[0059] In some embodiments, the one or more ADC therapies each comprise a monoclonal antibody, at least one functional fragment thereof, or a bispecific antibody targeting at least one epitope of at least one antigen selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2 MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, P79, EGFR, integrin beta-6, ROR1, Globo H, IL2RA, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, NCAM1, LY75, ALCAM, and CEACAM5.

[0060] In some embodiments, the antibody comprises a bispecific antibody capable of targeting two epitopes of the same antigen or epitopes of two separate antigens, wherein the same antigen or the two antigens are selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2 MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, P79, EGFR, integrin beta-6, ROR1, Globo H, IL2RA, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, NCAM1, LY75, ALCAM, and CEACAM5.

[0061] In some embodiments, the antibody or fragment thereof is directly or indirectly conjugated to a cytotoxic drug. In some embodiments, the cytotoxic drug is a DNA replication inhibitor selected from the group consisting of alkylating agents, DNA polymerase inhibitors, nitrogen mustards, and topoisomerase inhibitors. In some embodiments, the antibody, at least one functional fragment thereof, or the bispecific antibody is fused to a protein that is toxic to cancer cells. In some embodiments, the cytotoxic drug is a topoisomerase inhibitor.

[0062] In some embodiments, the subject has or is suspected of having a cancer for which the one or more ADC therapies are not approved for their indicated use. In some embodiments, the tumor tissue sample is suspected of comprising bladder cancer, salivary gland cancer, endometrial cancer, ovarian cancer, cervical cancer, head and neck cancer, non-melanoma skin cancer, thyroid cancer, cancer of unknown primary origin, cancer of the central or peripheral nervous system, neuroendocrine tumor, melanoma, esophagogastric cancer, small intestine cancer, sarcoma, hepatobiliary cancer, pancreatic cancer, gastrointestinal stromal tumor, renal cell carcinoma, glioma, appendix cancer, breast cancer, small cell lung cancer, prostate cancer, esophagogastric cancer, non-small cell lung cancer, pancreatic cancer, lymphoma, or colorectal cancer.

[0063] In some embodiments, the tumor tissue sample is a formalin-fixed, paraffin-embedded (FFPE) tumor tissue sample. In some embodiments, the tumor tissue sample comprises at least 20% tumor content.

[0064] In some embodiments, the one or more ADC therapies include at least two therapies, and step (E)(1) comprises calculating at least two ADC treatment response scores, wherein if the subject is identified as likely to respond to at least two ADC therapies, the method further comprises step (E)(2) ranking the at least two ADC treatment response scores by the value by which each ADC treatment response score exceeds the predetermined threshold, and identifying the highest ranked ADC treatment as most likely to benefit the subject.

[0065] In another aspect, the invention provides a computer-implemented method for selecting a patient presenting with a solid cancerous tumor for treatment with one or more antibody-drug conjugate (ADC) therapies, the method comprising the steps of: (A) receiving, from a biological tissue sample obtained from tumor tissue of the patient, measured expression levels of at least one gene product associated with each of the one or more ADC therapies; (B)(1) receiving expression levels of one or both of (i) at least one gene product associated with cell adhesion, and (ii) one or more gene products associated with proliferation, wherein said expression levels of (i) and (ii) are (B)(2) if said measured expression levels of said one or more gene products associated with proliferation are received, calculating therefrom, by a computer, an average of all of said received and measured expression levels of said gene products associated with proliferation, thereby obtaining a proliferation gene expression level; (C) optionally, receiving an indication of tumor cell content of the same tumor tissue sample of steps (A) and (B); (D)(1) identifying a subject who is likely to respond to said one or more ADC therapies if either: (i) a calculated ADC treatment response score (ADC (D)(i)(a)-(d)(i)(b) of the present invention relates to a method for determining whether one or more of the ADC TRSs (i.e., one or more of the ADC TRSs) are above one or more corresponding predetermined thresholds, wherein each of the one or more ADC TRSs is determined from the received and measured expression level of the at least one gene product associated with a corresponding ADC therapy and at least two of the following: (2) the received and measured expression level of the at least one gene product associated with cell adhesion, (3) the calculated proliferation gene expression level from the received expression levels of one or more gene products associated with proliferation, and (4) the received indication of tumor cell content; and (E) selecting the patients identified as likely to respond to the one or more ADC therapies for treatment with the therapies, wherein at least steps (A)-(D)(i)(i) are performed by a suitably programmed computer.

[0066] In some embodiments, step (E) comprises selecting the patient to receive treatment with the one or more ADC therapies as part of a clinical trial. In some embodiments, the clinical trial is a basket trial. In some embodiments, the method further comprises step (F) treating the selected patient with the one or more ADC therapies determined to be likely to induce a response in the patient.

[0067] In some embodiments, the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with the corresponding ADC therapy, the expression level of the at least one gene product associated with cell adhesion, and the determined proliferation gene expression level. In some embodiments, the determined proliferation gene expression level and the expression level of the at least one gene product associated with the corresponding ADC therapy are both positively associated with the likelihood that a patient will benefit from the corresponding ADC therapy, and the expression level of the at least one gene product associated with cell adhesion is negatively associated with the likelihood that a patient will benefit from the same corresponding ADC therapy.

[0068] In some embodiments, the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with a corresponding ADC therapy, the expression level of the at least one gene product associated with cell adhesion, and tumor cell content. In some embodiments, the tumor cell content and the expression level of the at least one gene product associated with a corresponding ADC therapy are both positively associated with the likelihood that a patient will benefit from the corresponding ADC therapy, and the expression level of the at least one gene product associated with cell adhesion is negatively associated with the likelihood that a patient will benefit from the same corresponding ADC therapy.

[0069] In some embodiments, the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with the corresponding ADC therapy, the expression level of the at least one gene product associated with cell adhesion, the determined proliferation gene expression level, and tumor cell content. In some embodiments, each of the determined proliferation gene expression level, the tumor cell content, and the expression level of the at least one gene product associated with the corresponding ADC therapy is positively associated with the likelihood that the patient will benefit from the corresponding ADC therapy, and the expression level of the at least one gene product associated with cell adhesion is negatively associated with the likelihood that the patient will benefit from the same corresponding ADC therapy.

[0070] In some embodiments, each of the one or more ADC TRSs is determined by further taking into account a bias variable, where the bias variable is a static offset adjusted to produce biomarker frequencies that match published objective response rates in clinical trials.

[0071] In some embodiments, the predetermined threshold is set at percentiles of ranked ADC treatment response scores determined from tumor tissue samples of a first cohort of subjects utilizing at least the expression level of the at least one gene product associated with a first corresponding ADC therapy, where each percentile corresponds to a greater percentage of subjects in the first cohort who do not respond to the same first corresponding ADC therapy. In some embodiments, the first cohort of subjects is a pan-cancer cohort or a matched tumor type cohort.

[0072] In some embodiments, the predetermined threshold is set to zero, and an ADC TRS indicating that the subject is likely to benefit from ADC treatment is an ADC TRS having a value greater than zero.

[0073] In some embodiments, the gene expression level of the at least one gene product associated with each of one or more ADC therapies, the expression level of the at least one gene product associated with cell adhesion, and, optionally, the tumor cell content, are log2 transformed and / or Z-score normalized prior to step (D)(1), and the expression level of the one or more gene products associated with proliferation is log2 transformed and / or Z-score normalized prior to averaging the expression levels of the gene products associated with proliferation to obtain the proliferation gene expression level in step (B)(2).

[0074] In some embodiments, the tumor cell content is a molecularly determined tumor cell content calculated based on somatic and germline variant allele frequencies and / or copy number profiles.

[0075] In some embodiments, the expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene associated with cell adhesion, and the one or more genes associated with proliferation are individually selected from ribonucleic acid (RNA) and protein.

[0076] In some embodiments, the gene expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene associated with cell adhesion, and the one or more genes associated with proliferation are proteins, and measuring the expression levels requires the use of immunohistochemical techniques.

[0077] In some embodiments, the gene expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene associated with cell adhesion, and the one or more genes associated with proliferation are RNA, and measuring the expression levels requires utilizing RNA sequencing techniques.

[0078] In some embodiments, the one or more ADC therapies each comprise a monoclonal antibody, at least one functional fragment thereof, or a bispecific antibody that targets at least one epitope of at least one antigen selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2 MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, P79, EGFR, integrin beta-6, ROR1, Globo H, IL2RA, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, NCAM1, LY75, ALCAM, and CEACAM5. In some embodiments, the antibody or fragment thereof is directly or indirectly conjugated to a cytotoxic drug or fused to a cytotoxic protein.

[0079] In some embodiments, the subject has or is suspected of having a cancer for which the one or more ADC therapies are not approved for their indicated use. In some embodiments, the tumor tissue sample is suspected of comprising bladder cancer, salivary gland cancer, endometrial cancer, ovarian cancer, cervical cancer, head and neck cancer, non-melanoma skin cancer, thyroid cancer, cancer of unknown primary origin, cancer of the central or peripheral nervous system, neuroendocrine tumor, melanoma, esophagogastric cancer, small intestine cancer, sarcoma, hepatobiliary cancer, pancreatic cancer, gastrointestinal stromal tumor, renal cell carcinoma, glioma, appendix cancer, breast cancer, small cell lung cancer, prostate cancer, esophagogastric cancer, non-small cell lung cancer, pancreatic cancer, lymphoma, or colorectal cancer.

[0080] In some embodiments, the tumor tissue sample is a formalin-fixed, paraffin-embedded (FFPE) tumor tissue sample. In some embodiments, the tumor tissue sample comprises at least 20% tumor content.

[0081] In another aspect, the present invention provides a method for identifying a subject as likely to benefit from an anti-TROP2-based therapy, the method comprising: (a) measuring, in a tumor tissue sample obtained from the subject, expression levels of a TROP2 gene product and one or more gene products associated with proliferation; (b) measuring expression levels of one or more housekeeping genes in the same tumor tissue sample of step (a), wherein the one or more housekeeping genes comprise three genes selected from CIAO1, EIF2B1, HMBS, CTCF, GGNBP2, ITGB7, MYC, and SLC4A1AP, and further normalizing the expression levels of the TROP2 gene product and one or more gene products associated with proliferation of step (a) to the three housekeeping genes to obtain normalized expression levels of the TROP2 and one or more gene products associated with proliferation; and (c) averaging the normalized expression levels of the one or more gene products associated with proliferation, thereby obtaining a normalized expression level of the TROP2 and one or more gene products associated with proliferation. (d) determining tumor cell content in the same tumor tissue sample of step (a); and (e) identifying the subject as likely to benefit from the anti-TROP2-based therapy if either: i) an aggregate biomarker score is above a predetermined threshold, wherein the aggregate biomarker score is calculated from the measured expression level of the TROP2 gene product in combination with at least one of the determined proliferation gene expression level and / or the determined tumor cell content, or ii) the measured expression level of the TROP2 gene product is higher than a median TROP2 expression level obtained from tumor tissue samples of a first cohort of subjects, and at least one of the determined proliferation gene expression level and / or the determined tumor cell content is higher than a median proliferation gene expression level and / or median tumor cell content obtained from tumor tissue samples of the same first cohort of subjects.

[0082] In some embodiments, the subject is identified as likely to benefit from the anti-TROP2-based therapy if the measured expression level of the TROP2 gene product, the determined proliferation gene expression level, and the determined tumor cell content are all higher than the corresponding median level of TROP2 expression, proliferation gene expression, and median tumor cell content obtained from tumor tissue samples of the same first cohort of subjects.

[0083] In some embodiments, the subject is identified as likely to benefit from the anti-TROP2-based therapy if one or more of the measured expression level of the TROP2 gene product, the determined proliferation gene expression level, and / or the determined tumor cell content falls within the highest quartile of TROP2 expression level, proliferation gene expression level, and / or tumor cell content values ​​obtained from tumor tissue samples from the same first cohort of the subject.

[0084] In some embodiments, the first cohort of subjects is a pan-cancer cohort or a matched tumor type cohort.

[0085] In some embodiments, measuring the expression level of one or more gene products associated with proliferation is selected from the group consisting of BIRC5, BRCA1, BRCA2, BUB1, BUB1B, CCNA2, CCNB1, CCNB2, CCNE1, CCNE2, CCNF, CCNG2, CDC2, CDC20, CDC25A, CDC25B, CDC25C, CDC45L, CDC6, CDC7, CDCKN1A p21, CDCKN3, CDK4, CDKN1C, CDKN2A, CDKN2C, CDKN2D p19, CDKN3, CENPA, CENPE, CENPF, CHAF1A, CHK1, CKS1, CKS2, CKS2, DHFR, DHFR, E2-EPF, E2F1, E2F3, E2F5, FEN1, FOXM1, KNSL2, KNSL5, KPNA2, LMNB2, MAD2, MAD2L1, MAPK13 , MCM2, MCM3, MCM4, MCM5, MCM6, MK167, MNAT1, MSH2, MYBL2, NASP, NEK2, NPAT, ORC1, PA2G4, PCNA, PES1, PKMYT1, PLK, PRIM1, PTTG1, RAB6KIFL, RACGAP1, RAD51, RAD54L, RAN In some embodiments, measuring the expression level of a gene product of one or more genes selected from the group consisting of RAN, RFC4, RPA3, RRM1, RRM2, SLBP, STK12, STK15, STK18, STK6, TOP2A, TROAP, TUBB, TYMS, UBE2C, and UNG. In some embodiments, measuring the expression level of one or more gene products associated with proliferation comprises measuring the expression level of one, two, or all three of the MYBL2, TOP2A, and / or UBE2C gene products.

[0086] In some embodiments, the collective biomarker score is determined by a combination of the expression level of the TROP2 gene product, the determined proliferation gene expression level, and tumor cell content.

[0087] In some embodiments, the predetermined threshold is set to a percentile of the ranked collective biomarker scores determined from tumor tissue samples of a second cohort of subjects, the percentile corresponding to the percentage of subjects in the second cohort that do not respond to anti-TROP2-based therapy.

[0088] In some embodiments, the second cohort of subjects is a pan-cancer cohort or a matched tumor type cohort. In some embodiments, the first cohort of subjects and the second cohort of subjects are the same cohort of subjects.

[0089] In some embodiments, the gene expression levels of TROP2, one or more genes associated with proliferation, and tumor cell content are log2 transformed and / or median centered to 10 prior to step (d).

[0090] In some embodiments, the tumor cell content is a molecularly determined tumor cell content calculated based on somatic and germline variant allele frequencies and / or copy number profiles.

[0091] In some embodiments, one, two, or three of the housekeeping genes are selected from CIAO1, EIF2B1, and HMBS.

[0092] In some embodiments, the collective biomarker score is determined by adding the measured expression level of the TROP2 gene product to approximately 1 / 3 to 2 / 3 of the determined proliferation gene expression level and approximately 4 to 8 times the determined tumor cell content.

[0093] In some embodiments, the expression products of TROP2 and one or more genes associated with proliferation are individually selected from ribonucleic acid (RNA) and protein.

[0094] In some embodiments, the gene expression product of TROP2 and at least one of the one or more genes associated with proliferation is a protein, and measuring the expression level requires the use of immunohistochemical techniques.

[0095] In some embodiments, the gene expression product of at least one of TROP2 and one or more genes associated with proliferation is RNA, and measuring its expression level requires the use of RNA sequencing techniques.

[0096] In some embodiments, the anti-TROP2-based therapy comprises an anti-TROP2 antibody or fragment thereof.

[0097] In some embodiments, the anti-TROP2 antibody or fragment thereof is directly or indirectly conjugated to a cytotoxic drug, hi some embodiments, the cytotoxic drug is a DNA replication inhibitor selected from the group consisting of alkylating agents, DNA polymerase inhibitors, nitrogen mustards, and topoisomerase inhibitors.

[0098] In some embodiments, the anti-TROP2 antibody or fragment thereof is fused to a protein that is toxic to cancer cells. In some embodiments, the cytotoxic drug is a topoisomerase inhibitor. In some embodiments, the anti-TROP2-based therapy is sacituzumab govitecan.

[0099] In some embodiments, the subject has or is suspected of having a cancer for which an anti-TROP2-based therapy has not been approved for its indicated use. In some embodiments, the tumor tissue sample is or is suspected of containing bladder cancer, endometrial cancer, breast cancer, small cell lung cancer, prostate cancer, esophagogastric cancer, non-small cell lung cancer, pancreatic cancer, or colorectal cancer.

[0100] In some embodiments, the tumor tissue sample is a formalin-fixed, paraffin-embedded (FFPE) tumor tissue sample. In some embodiments, the tumor tissue sample comprises at least 20% tumor content.

[0101] In some embodiments, the method further comprises administering an anti-TROP2-based therapy to the subject identified as likely to benefit from the therapy. [Brief explanation of the drawings]

[0102] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0103] [Figure 1] Figure 1 shows the correlation analysis of SG biomarker positivity rates in the validation cohort with the objective response rates observed in the IMMU-12-01 basket trial.

[0104] [Figure 2] Figure 2 shows biomarker distribution across the complete molecular cohort for TROP2 expression.

[0105] [Figure 3] FIG. 3 shows biomarker distribution across the complete molecular cohort for proliferation gene expression.

[0106] [Figure 4] Figure 4 shows biomarker distribution across the complete molecular cohort with respect to tumor cell content.

[0107] [Figure 5] Figure 5 shows a comparison of TROP2 gene expression by RNA sequencing (% above the median for all solid tumors) and TROP2 protein expression by immunohistochemistry (% with moderate or strong staining) across 45 tumor types.

[0108] [Figure 6] Figure 6 shows biomarker relationships in the complete molecular cohort for (A) TOP2A vs. UBE2C expression, (B) TROP2 expression vs. proliferation gene expression, (C) TROP2 expression vs. tumor cell content, and (D) proliferation gene expression vs. tumor cell content.

[0109] [Figure 7] FIG. 7 shows individual biomarker rate correlations with objective response rate for (A) TROP2, (B) proliferation gene expression, and (C) tumor cell content.

[0110] [Figure 8] Figure 8 shows the correlation analysis between the SG biomarker positivity rate in the discovery cohort and the objective response rate observed in the IMMU-12-01 basket trial 8.

[0111] [Figure 9] Figure 9 shows SG biomarker status as related to biomarker factors: TROP2 expression (y-axis), proliferation gene expression (x-axis) and tumor cell content (binned by panel) in the full molecular cohort. Biomarker-positive samples are colored red and biomarker-negative samples are colored blue.

[0112] [Figure 10] FIG. 10 shows a correlation analysis of objective response rates observed across 22 clinical trials and cohorts across nine antibody-drug conjugate treatments.

[0113] [Figure 11] FIG. 11 shows a heat map depicting biomarker positivity rates across 10 antibody-drug conjugates and 28 tumor types.

[0114] [Figure 12]FIG. 12 shows the best antibody-drug conjugate response scores summarized per patient and by tumor type.

[0115] [Figure 13] FIG. 13 shows the overall best antibody-drug conjugate response scores.

[0116] [Figure 14] FIG. 14 lists the most common antibody-drug conjugate response score biomarker profiles ranked by number of target counts called.

[0117] [Figure 15] Figure 15 lists the absolute correlation between the top 20 gene expression levels or copy number genes and ADC ORR.

[0118] [Figure 16] FIG. 16 shows the model architecture for ADC treatment response score including ADC target expression levels, proliferation gene expression levels, PVR gene expression, as well as bias inputs, with a positive biomarker call threshold set to >0.

[0119] [Figure 17] FIG. 17 shows biomarker rate correlations with objective response rates for biomarkers including ADC target gene product expression, proliferation gene expression, and PVR gene expression.

[0120] [Figure 18] Figure 18 shows a heatmap depicting the estimated biomarker positivity rates for different cancers and for nine different ADCs. Outlined boxes indicate the availability of corresponding published ORR.

[0121] [Figure 19]Figure 19 shows biomarker rate correlations with objective response rates for biomarkers including ADC target gene product expression, provided for comparison purposes. As can be seen by the low concordance correlation coefficient of 0.46, ADC target expression is a poor predictor of objective response rate to ADC treatment.

[0122] [Figure 20] FIG. 20 shows biomarker rate correlations with objective response rates for biomarkers including ADC target gene product expression and tumor cell content (tumor content), provided for comparison purposes.

[0123] [Figure 21] FIG. 21 shows biomarker rate correlations with objective response rates for biomarkers including ADC target gene product expression and proliferation gene expression, provided for comparison purposes. DETAILED DESCRIPTION OF THE INVENTION

[0124] Detailed Description of the Invention Some definitions For convenience, certain terms employed in the specification, examples, and appended claims are collected here. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0125] The term "antibody" is used herein in its broadest sense and encompasses a variety of antibody structures, including, but not limited to, monoclonal antibodies, polyclonal antibodies, and multispecific antibodies (e.g., bispecific antibodies).

[0126] The term "monoclonal antibody," as used herein, refers to an antibody obtained from a population of substantially homogeneous antibodies (i.e., the individual antibodies comprising the population are identical and / or bind to the same epitope), excluding possible variant antibodies (e.g., containing naturally occurring mutations or that arise during production of a monoclonal antibody preparation, such variants being generally present in minor amounts). In contrast to polyclonal antibody preparations (which typically include different antibodies directed against different determinants (epitopes)), each monoclonal antibody of a monoclonal antibody preparation is directed against a single determinant on an antigen. Thus, the modifier "monoclonal" indicates the character of the antibody as being obtained from a substantially homogeneous population of antibodies, and is not to be construed as requiring production of the antibody by any method. For example, monoclonal antibodies to be used in accordance with the present invention may be made by a variety of techniques, including, but not limited to, hybridoma methods, recombinant DNA methods, phage display methods, and methods utilizing transgenic animals containing all or part of the human immunoglobulin loci; such methods and other exemplary methods for making monoclonal antibodies are described herein.

[0127] A "human antibody" is an antibody having an amino acid sequence corresponding to that of an antibody generated by a human and / or generated using any of the techniques for generating human antibodies known to those skilled in the art. This definition of a human antibody specifically excludes humanized antibodies that contain non-human antigen-binding residues. Human antibodies can be generated using a variety of techniques known in the art, including those described in Cole et al., Monoclonal Antibodies and Cancer Therapy, Alan R. Liss, p. 77 (1985); Boerner et al., J. Immunol, 147(I):86-95 (1991). See also van Dijk and van de Winkel, Curr. Opin. Pharmacol, 5: 368-74 (2001). Human antibodies can be produced by transgenic animals that have been engineered to produce such antibodies in response to antigen challenge but that have been rendered incompetent at their endogenous loci (e.g., immunized HuMab mice (see, e.g., Nils Lonberg et al., 1994, Nature 368:856-859, WO 98 / 24884, WO 94 / 25585, WO 93 / 1227, WO 92 / 22645, WO 92 / 03918, and WO 01 / 09187 for HuMab mice), xenomice (XENOMOUSE TM For techniques, see, e.g., U.S. Patent Nos. 6,075,181 and 6,150,584) or Trianni mice (for Trianni mice, see, e.g., WO 2013 / 063391, WO 2017 / 035252 and WO 2017 / 136734)).

[0128] The term "humanized antibody" refers to an antibody that has been engineered to contain one or more human framework regions in the variable region together with non-human (e.g., mouse, rat, or hamster) complementarity-determining regions (CDRs) of the heavy and / or light chain. In certain embodiments, a humanized antibody contains sequences that are entirely human except for the CDR regions. Humanized antibodies are typically less immunogenic to humans relative to non-humanized antibodies and therefore provide therapeutic benefits in certain situations. Those skilled in the art are aware of humanized antibodies and also know techniques suitable for their production. See, for example, Hwang, WYK, et al., Methods 36:35, 2005; Queen et al., Proc. Natl. Acad. Sci. USA, 86:10029-10033, 1989; Jones et al., Nature, 321:522-25, 1986; Riechmann et al., Nature, 332:323-27, 1988; Verhoeyen et al., Science, 239:1534-36, 1988; Orlandi et al., Proc. Natl. Acad. Sci. USA, 86:3833-37, 1989; U.S. Patent Nos. 5,225,539; 5,530,101; 5,585,089; 5,693,761; 5,693,762; No. 6,180,370; and Selick et al., WO 90 / 07861, each of which is incorporated herein by reference in its entirety.

[0129] As used herein, the term "bispecific antibody" refers to a monoclonal (often human or humanized) antibody having binding specificities for at least two different antigens. In the present invention, one of the binding specificities may be directed against CLDN18.2, and the other may be against any other antigen (e.g., a cell surface protein, a receptor, a receptor subunit, a tissue-specific antigen, a virus-derived protein, a virus-encoded envelope protein, a bacterial-derived protein, or a bacterial surface protein, etc.).

[0130] The term "antibody fragment" refers to a molecule other than an intact antibody that contains a portion of the intact antibody and that binds the antigen to which the intact antibody binds. Examples of antibody fragments include, but are not limited to, Fv, Fab, Fab', Fab'-SH, F(ab)2; diabodies; linear antibodies; and single-chain antibody molecules (e.g., scFv). Papain digestion of antibodies produces two identical antigen-binding fragments, so-called "Fab" fragments and a residual "Fc" fragment (the name reflects the ability to readily crystallize). The Fab fragment consists of the variable region domain of a heavy (H) chain (VH) along with an entire light (L) chain (VL), and the first constant domain (CH1) of one heavy chain. Pepsin treatment of an antibody produces a single large F(ab)2 fragment that roughly corresponds to two disulfide-linked Fab fragments with bivalent antigen-binding activity and is still capable of cross-linking antigen. Fab fragments differ from F(ab)2 fragments by having several additional residues at the carboxy terminus of the CH1 domain including one or more cysteines from the antibody hinge region. Fab'-SH is the designation herein for Fab' in which the cysteine ​​residues of the constant domains bear a free thiol group. F(ab')2 antibody fragments were originally produced as pairs of Fab' fragments, which have hinge cysteines between them. Other chemical linkages of antibody fragments are also known.

[0131] The term "expression" refers to the cellular processes (including, but not limited to, transcription, translation, folding, modification, and processing, as applicable) involved in producing RNA and proteins, and, if appropriate, secreting the proteins. "Expression product" includes RNA transcribed from a gene and polypeptides obtained by translation of mRNA transcribed from a gene.

[0132] The term "RNA" is defined as ribonucleic acid.

[0133] The term "polynucleotide" is used interchangeably with "nucleic acid" herein to refer to a polymer of nucleotides. Typically, polynucleotides of the invention are composed of nucleosides naturally found in DNA or RNA (e.g., adenosine, thymidine, guanosine, cytidine, uridine, deoxyadenosine, deoxythymidine, deoxyguanosine, and deoxycytidine) linked by phosphodiester bonds. However, the term also encompasses molecules containing nucleosides or nucleoside analogs, whether found in naturally occurring nucleic acids or not, including chemically or biologically modified bases, modified backbones, and the like, and such molecules may be preferred for certain applications. When the application refers to a polynucleotide, it is understood that DNA, RNA, and in each case both single-stranded and double-stranded forms (as well as the complement of each single-stranded molecule) are provided. "Polynucleotide sequence," as used herein, can refer to the polynucleotide material itself and / or sequence information (i.e., the sequence of letters used as abbreviations for bases) that biochemically characterize a specific nucleic acid. Polynucleotide sequences presented herein are presented in the 5' to 3' direction unless otherwise indicated.

[0134] The terms "subject" and "individual" are used interchangeably herein and refer to animals, e.g., humans, from whom cells may be obtained and / or who are provided treatment (including prophylactic treatment) with cells as described herein. With respect to treatment of those infections, conditions, or disease states specific for a particular animal (e.g., a human subject), the term subject refers to that particular animal. The terms "non-human animal" and "non-human mammal," when used interchangeably herein, include mammals such as rats, mice, rabbits, sheep, cats, dogs, cows, pigs, and non-human primates. The term "subject" also encompasses any vertebrate, including, but not limited to, mammals, reptiles, amphibians, and fish. Advantageously, however, the subject is a mammal such as a human or other mammal such as a domesticated mammal (e.g., dog, cat, horse, etc.), or a production mammal (e.g., cow, sheep, pig, etc.).

[0135] The terms "treating" and "treatment" refer to administering to a subject an effective amount of a composition such that the subject experiences a reduction in at least one symptom of the disease or an improvement in the disease (e.g., a beneficial or desired clinical result). For purposes of this invention, a beneficial or desired clinical result includes, but is not limited to, alleviation of one or more symptoms, whether detectable or undetectable, a decrease in the extent of the disease, a stable (i.e., not worsening) state of the disease, a delay or slowing of disease progression, an improvement or palliation of the disease state, and remission (whether partial or total). Treating may also refer to prolonging survival compared to the expected survival in the absence of treatment. Thus, those skilled in the art will understand that treatment may improve the disease state but may not be completely curative for the disease. As used herein, the term "treatment" includes prophylaxis. Alternatively, treatment is "effective" if the progression of the disease is reduced or halted. "Treatment" can also mean prolonging survival as compared to expected survival if not receiving treatment.

[0136] The terms "decrease," "reduced," "reduction," "decrease," and "inhibit" are all used herein to generally mean a decrease by a statistically significant amount. However, for the avoidance of doubt, "reduced," "reduction," or "decrease" or "inhibit" means a decrease of at least 10% compared to a reference level, for example, at least about 20%, or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90%, or a decrease up to and including 100% (i.e., no level when compared to a reference level), or any decrease between 10-100% compared to a reference level.

[0137] The terms "increased," "increase," "enhance," or "activate" are all used herein to generally mean an increase by a statistically significant amount; for the avoidance of any doubt, the terms "increased," "increase," "enhance," or "activate" mean an increase of at least 10% compared to a reference level, for example, at least about 20%, or at least about 30%, or at least about 40%, or at least about 50%, or at least about 60%, or at least about 70%, or at least about 80%, or at least about 90%, or an increase up to and including 100%, or any increase between 10-100% compared to a reference level, or at least about 2-fold, or at least about 3-fold, or at least about 4-fold, or at least about 5-fold, or at least about 10-fold increase compared to a reference level, or any increase between 2-fold and 10-fold or more than 10-fold.

[0138] The terms "statistically significant" or "significantly" refer to statistical significance and generally mean that the concentration of the marker is less than or equal to two standard deviations (2SD) of normal (please note). The term refers to statistical evidence that a difference exists. It is defined as the probability of making a decision to reject the null hypothesis when the null hypothesis is in fact true. The decision is often made using a p-value.

[0139] Methods for identifying subjects likely to benefit from ADC therapy and methods for selecting subjects for treatment with ADC therapy

[0140] In certain aspects, the present invention relates to methods for identifying a cancer subject as likely to respond to one or more antibody-drug conjugate (ADC) therapies based on a gene product expression profile measured in a tissue sample obtained from the subject. In some embodiments, the method comprises the steps of: (A) measuring the expression level of at least one gene product associated with each of the one or more ADC therapies from a biological tissue sample obtained from the subject; (B) measuring, in the same biological tissue sample of step (A), the expression level of one or both of i) at least one gene product associated with cell adhesion, and ii) one or more gene products associated with proliferation, wherein, if the expression level of a gene product associated with proliferation is measured, calculating therefrom an average of all the expression levels of the measured gene products associated with proliferation, thereby obtaining a proliferation gene expression level. and (D)(1) identifying the cancer subject as likely to respond to the one or more ADC therapies if (i) the measured expression level of the at least one gene product associated with a corresponding ADC therapy, and at least one of (ii) the measured expression level of at least one gene product associated with cell adhesion and (iii) the measured expression levels of one or more gene products associated with proliferation exceed a predetermined threshold, or alternatively, if an ADC treatment response score calculated therefrom exceeds a corresponding predetermined threshold associated with a positive response to the one or more ADC therapies.

[0141] In some embodiments, measuring at least one gene product associated with each of the one or more ADC therapies comprises quantifying one or more gene products of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2 MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, P79, EGFR, integrin beta-6, ROR1, Globo H, IL2RA, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, NCAM1, LY75, ALCAM, and CEACAM5. In some embodiments, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, or 31 distinct gene products are measured. In some embodiments, at least 2, at least 5, at least 10, at least 15, or at least 20 gene products are measured.

[0142] In some embodiments, measuring the expression level of one or more gene products associated with proliferation comprises measuring the expression level of gene products of one or more genes that are cell cycle regulated and related to DNA replication, mitotic processes / stages, spindle assembly, tubulin, mitotic surveillance, cell adhesion, chromosome metabolism, and histone formation. In some embodiments, genes having gene products associated with proliferation include BIRC5, BRCA1, BRCA2, BUB1, BUB1B, CCNA2, CCNB1, CCNB2, CCNE1, CCNE2, CCNF, CCNG2, CDC2, CDC20, CDC25A, CDC25B, CDC25C, CDC45L, CDC6, CDC7, CDCKN1A p21, CDCKN3, CDK4, CDKN1C, CDKN2A, CDKN2C, CDKN2D p19, CDKN3, CENPA, CENPE, CENPF, CHAF1A, CHK1, CKS1, CKS2, CKS2, DHFR, DHFR, E2-EPF, E2F1, E2F3, E2F5, FEN1, FOXM1, KNSL2, KNSL5, KPNA2, LMNB2, MAD2, MAD2L1, MAPK13 , MCM2, MCM3, MCM4, MCM5, MCM6, MK167, MNAT1, MSH2, MYBL2, NASP, NEK2, NPAT, ORC1, PA2G4, PCNA, PES1, PKMYT1, PLK, PRIM1, PTTG1, RAB6KIFL, RACGAP1, RAD51, RAD54L, RAN The one or more genes selected from the group consisting of RAN, RFC4, RPA3, RRM1, RRM2, SLBP, STK12, STK15, STK18, STK6, TOP2A, TROAP, TUBB, TYMS, UBE2C, and UNG. In some embodiments, measuring the expression level of one or more gene products associated with proliferation comprises measuring the expression level of one, two, or all three of the MYBL2, TOP2A, and / or UBE2C gene products.

[0143] The inventors have recently discovered that adding additional variables enhances the predictive power of the method in predicting a given patient's response to an ADC therapy. Thus, in some embodiments, the ADC TRS is determined by a combination of at least the expression level of at least one gene product associated with the corresponding ADC therapy, one or both of tumor cellularity and proliferation gene expression levels, and the expression level of one additional gene product. In some embodiments, the predictive power of the ADC TRS can be enhanced by adding a variable including the expression level of at least one gene product associated with cell adhesion. Consequently, in certain embodiments, the ADC TRS is determined by the expression level of one gene product associated with the corresponding ADC therapy, the proliferation gene expression level, and at least one gene product associated with cell adhesion.

[0144] In some embodiments, the determined proliferation gene expression level and the expression level of the at least one gene product associated with the corresponding ADC therapy are both positively associated with the likelihood that the patient will respond to the corresponding ADC therapy, and the expression level of the at least one gene product associated with cell adhesion is negatively associated with the likelihood that the patient will respond to the same corresponding ADC therapy. In some embodiments, the expression level of the at least one gene product associated with the corresponding ADC therapy is weighted by a factor of about 0.3 to 0.7, about 0.35 to 0.65, about 0.4 to about 0.5, or about 0.4, 0.45, 0.48, 0.50, 0.55, 0.60, 0.65, 0.68, or 0.7. In some embodiments, the expression level of the at least one gene product associated with cell adhesion is weighted by a factor of about -1.5 to -0.5, -1.25 to -0.75, -1.1 to -0.8, -1 to -0.8, or about -1.05, -1.0, -0.95, -0.90, or -0.85. In some embodiments, the determined proliferation gene expression level is weighted by a factor of about 0.15 to 0.60, about 0.20 to 0.40, or about 0.45, 0.40, 0.38, 0.35, 0.30, or 0.25.

[0145] According to one aspect of the present invention, the gene product associated with cell adhesion is a gene product whose expression level in a cohort of cancer patients is provided, and this expression level is negatively correlated with the objective response rate to at least one ADC treatment in the same cohort of cancer patients. The statistical method used to determine the negative correlation between the objective response rate and the expression level of the gene product associated with cell adhesion is not particularly limited and is within the ordinary skill of those skilled in the art, including those methods well known in the art (e.g., Spearman correlation or Pearson correlation). In some embodiments, the correlation coefficients associated with each cell adhesion-related gene product obtained by such analysis can be ranked and selected based on their proximity to -1. Thus, in some embodiments, a cancer patient is considered to be likely to respond to at least one ADC treatment only if the expression of at least one gene product associated with cell adhesion is reduced compared to a reference level. In some embodiments, the reference level is the average expression level of the same gene product associated with cell adhesion in the cancer subjects of the ADC treatment cohort. In some embodiments, a cancer patient is considered likely to respond to at least one ADC therapy only if the expression level of at least one gene product associated with cell adhesion is at least two standard deviations below the mean expression level of the same gene product in cancer subjects in the ADC therapy treatment cohort.

[0146] In some embodiments, the gene product associated with cell adhesion is a gene product that affects cellular components of cell adhesion, including adherens junctions, anchoring junctions, cell-matrix adherens junctions, cell-matrix junctions, and focal adhesions. In certain embodiments, the gene product associated with cell adhesion affects at least two cellular components selected from the group consisting of adherens junctions, anchoring junctions, cell-matrix adherens junctions, cell-matrix junctions, and focal adhesions. In some embodiments, the gene product associated with cell adhesion affects at least three, at least four, or all five of the cellular components. In some embodiments, the gene product associated with cell adhesion is selected both because of its negative correlation with objective response rate and because it affects all five cellular components selected from the group consisting of adherens junctions, anchoring junctions, cell-matrix adherens junctions, cell-matrix junctions, and focal adhesions. By way of non-limiting example, Table 3 outlines genes whose expression levels of gene products associated with cell adhesion negatively correlate with objective response rates in the cohort of cancer subjects, and the specific cellular components of cell adhesion that are affected by their respective expression levels as discussed above.

[0147] In some embodiments, measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression level of a gene product of at least one gene selected from the group consisting of ATP2A2, BAIAP2, CD151, CHP1, CYFIP1, CYTH3, DAG1, DSC2, GIT1, HSP90B1, HSPA5, LIMK1, MAPK1, PACSIN2, PDIA3, PVR, REXO2, RPL22, RPLP1, RPLP2, RPS11, RPS16, RPS5, SDCBP SNAP23, SNTB1, and SRP68. In certain embodiments, the expression levels of two or more gene products of two or more genes selected from the group consisting of ATP2A2, BAIAP2, CD151, CHP1, CYFIP1, CYTH3, DAG1, DSC2, GIT1, HSP90B1, HSPA5, LIMK1, MAPK1, PACSIN2, PDIA3, PVR, REXO2, RPL22, RPLP1, RPLP2, RPS11, RPS16, RPS5, SDCBP SNAP23, SNTB1, and SRP68 are measured. When the expression levels of two or more gene products associated with cell adhesion are measured, an average expression level can be calculated to provide the cell adhesion gene product expression level. Thus, in some embodiments, a cancer patient is considered likely to respond to ADC therapy only if the cell adhesion gene product expression level is reduced compared to the reference level. Before averaging the expression levels of multiple gene products associated with cell adhesion, each gene product expression level can be normalized and / or log 2 transformed. To facilitate the normalization of each gene product expression level, the mean and standard deviation of the corresponding gene product expression level in a cancer patient cohort can be used. Thus, in some embodiments, a patient is identified as likely to respond to ADC treatment only if the normalized cell adhesion gene product expression level is less than zero.

[0148] In one embodiment, measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression of the PVR gene product. Furthermore, at least 1, 2, 3, 4, 5, 10, 15, 20, 25, or 30 additional gene products associated with cell adhesion may be measured along with the PVR gene product. In some embodiments, measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression level of a single gene product associated with cell adhesion. In some embodiments, only the expression of the PVR gene product is measured. In some embodiments, only the expression of the SRP68 gene product is measured. In some embodiments, only the expression of the SNTB1 gene product is measured. In some embodiments, only the expression of the SNAP23 gene product is measured. In some embodiments, only the expression of the SDCBP gene product is measured. In some embodiments, only the expression of the RPS5 gene product is measured. In some embodiments, only the expression of the RPS16 gene product is measured. In yet another embodiment, only the expression of the RPS11 gene product is measured. In some embodiments, only the expression of the RPLP2 gene product is measured. In some embodiments, only the expression of the RPLP1 gene product is measured. In some embodiments, only the expression of the REXO2 gene product is measured. In some embodiments, only the expression of the PDIA3 gene product is measured. In some embodiments, only the expression of the PACSIN2 gene product is measured. In some embodiments, only the expression of the MAPK1 gene product is measured. In some embodiments, only the expression of the LIMK1 gene product is measured. In some embodiments, only the expression of the HSPA5 gene product is measured. In some embodiments, only the expression of the HSP90B1 gene product is measured. In some embodiments, only the expression of the GIT1 gene product is measured. In some embodiments, only the expression of the DSC2 gene product is measured. In some embodiments, only the expression of the DAG1 gene product is measured.In some embodiments, only the expression of the CYTH3 gene product is measured. In some embodiments, only the expression of the CYFIP1 gene product is measured. In some embodiments, only the expression of the CHP1 gene product is measured. In some embodiments, only the expression of the CD151 gene product is measured. In some embodiments, only the expression of the BAIAP2 gene product is measured. In some embodiments, only the expression of the ATP2A2 gene product is measured.

[0149] In some embodiments, the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with the corresponding ADC therapy, the expression level of at least one gene product associated with cell adhesion, the determined proliferation gene expression level, and tumor cell content. In some embodiments, the determined proliferation gene expression level, tumor cell content, and the expression level of at least one gene product associated with the corresponding ADC therapy are all positively associated with the likelihood that the patient will respond to the corresponding ADC therapy, and the expression level of at least one gene product associated with cell adhesion is negatively associated with the likelihood that the patient will respond to the same corresponding ADC therapy.

[0150] In some embodiments, the expression level of at least one gene product associated with the corresponding ADC therapy is weighted by a factor of approximately 0.30 to 0.70, approximately 0.35 to 0.65, approximately 0.4 to approximately 0.5, or approximately 0.40, 0.45, 0.48, or 0.50. In some embodiments, the expression level of at least one gene product associated with cell adhesion is weighted by a factor of approximately -1.5 to -0.5, -1.25 to -0.75, -1.1 to 0.8, or approximately -1.05, -1.0, -0.95, -0.90, or -0.85. In some embodiments, the determined proliferation gene expression level is weighted by a factor of approximately 0.75 to 0.35, 0.65 to 0.40, 0.60 to 0.50, or approximately 0.65, 0.60, 0.55, 0.50, or 0.45. In some embodiments, the tumor cell content is weighted by a factor of approximately 0.25 to 0.01, 0.20 to 0.02, 0.17 to 0.04, or approximately 0.09, 0.08, 0.07, 0.06, or 0.05. In some embodiments, the expression level of at least one gene product associated with the corresponding ADC therapy is weighted by a factor of approximately 0.45, the expression level of at least one gene product associated with cell adhesion is weighted by a factor of approximately −0.98, the determined proliferation gene expression level is weighted by a factor of approximately 0.55, and the tumor cell content is weighted by a factor of approximately 0.07.

[0151] In some embodiments, each of the one or more ADC TRSs is determined by further taking into account a bias variable, where the bias variable is a static offset adjusted to produce biomarker frequencies that match published objective response rates in clinical trials. In some embodiments, the bias variable is weighted by a factor of approximately -0.65 to -0.1, -0.55 to -0.15, -0.35 to 0.20, or approximately -0.22, -0.23, -0.24, -0.25, -0.25, -0.26, -0.27, or -0.28. In some embodiments, the bias variable is weighted by a factor of approximately -0.26.

[0152] As discussed above, ADC TRS is determined by a number of variables that are individually weighted based on their influence on the probability that a subject is likely to respond to ADC treatment.When these variables are combined, a score indicating that a subject is likely to respond to ADC treatment is obtained when it exceeds a certain predetermined threshold.In some embodiments, the predetermined threshold is zero, and the ADC TRS indicating that the subject is likely to respond to ADC treatment is the ADC TRS with a value greater than zero.

[0153] In some embodiments, the predetermined threshold is set at percentiles of ranked ADC treatment response scores determined from tumor tissue samples of a first cohort of subjects utilizing at least the expression level of the at least one gene product associated with a first corresponding ADC therapy, where each percentile corresponds to a greater percentage of subjects in the first cohort who do not respond to the same first corresponding ADC therapy.

[0154] In some embodiments, the predetermined threshold is set at a percentile of ranked ADC treatment response scores determined from tumor tissue samples of the first and second cohorts of subjects utilizing expression levels of at least first and second gene products associated with at least the first and second corresponding ADC therapies, where each percentile corresponds to a greater percentage of subjects in the first and second cohorts who do not respond to the at least first and second corresponding ADC therapies.

[0155] In some embodiments, the first cohort of subjects is a pan-cancer cohort or a matched tumor type cohort. In some embodiments, the first cohort is a matched tumor type cohort. The matched tumor type cohort is not limited to, and includes those tumors / cancers known to those skilled in the art and disclosed herein. In some embodiments, the matched tumor type is appendix cancer, bladder cancer, breast cancer, cervical cancer, CNS and PNS cancer, colorectal cancer, endometrial cancer, esophagogastric cancer, gastrointestinal stromal tumor, glioma, head and neck cancer, hepatobiliary cancer, lymphoma, melanoma, neuroendocrine tumor, non-small cell lung cancer, ovarian cancer, prostate cancer, renal cell carcinoma, salivary gland cancer, sarcoma, non-melanoma skin cancer, small intestine cancer, small cell lung cancer, or thyroid cancer.

[0156] In yet another embodiment, the first cohort is a pan-cancer cohort. Such a pan-cancer cohort can include 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more different cancers represented in the cohort. In certain embodiments, the cohort comprises three or more cancers represented by the group consisting of appendix cancer, bladder cancer, breast cancer, cervical cancer, CNS and PNS cancer, colorectal cancer, endometrial cancer, esophagogastric cancer, gastrointestinal stromal tumors, glioma, head and neck cancer, hepatobiliary cancer, lymphoma, melanoma, neuroendocrine tumors, non-small cell lung cancer, ovarian cancer, prostate cancer, renal cell carcinoma, salivary gland cancer, sarcoma, non-melanoma skin cancer, small intestine cancer, small cell lung cancer, and thyroid cancer.

[0157] In some embodiments, the first cohort is a clinical trial cohort including cancer patients receiving at least one antibody-drug conjugate therapy. In some embodiments, the patients have advanced cancer and are selected for inclusion in the clinical trial cohort because they exhibit expression of one target of a candidate antibody-drug conjugate therapy. In some embodiments, the advanced cancer is radiologically confirmed to have metastasized or to have relapsed or been determined to be refractory to prior chemotherapy treatment. In some embodiments, the patients have advanced solid tumors. In preferred embodiments, the cohort includes at least 50 patients, at least 75 patients, at least 100 patients, at least 125 patients, at least 150 patients, at least 175 patients, at least 200 patients, at least 250 patients, at least 300 patients, at least 400 patients, at least 500 patients, or more. In some embodiments, the cohort has at least 200 patients. In some embodiments, the clinical trial cohort is a basket trial / pan-cancer cohort. In some embodiments, the first cohort is a tumor-specific cohort.

[0158] In some embodiments, the ADC treatment response score is determined by a combination of the expression level of at least one gene product associated with each of the one or more corresponding ADC therapies, the determined proliferation gene expression level, and the tumor cell content. In some embodiments, the predetermined threshold is set to a percentile of ranked ADC treatment response scores determined from tumor tissue samples of a second cohort of subjects, the percentile corresponding to the percentage of subjects in the second cohort who do not respond to the one or more ADC therapies.

[0159] In some embodiments, the second cohort of subjects is a pan-cancer cohort or a matched tumor type cohort. In some embodiments, the second cohort is a matched tumor type cohort. The matched tumor type cohort is not limited to, and includes those tumors / cancers known to those skilled in the art and disclosed herein. In some embodiments, the matched tumor type is appendix cancer, bladder cancer, breast cancer, cervical cancer, CNS and PNS cancer, colorectal cancer, endometrial cancer, esophagogastric cancer, gastrointestinal stromal tumor, glioma, head and neck cancer, hepatobiliary cancer, lymphoma, melanoma, neuroendocrine tumor, non-small cell lung cancer, ovarian cancer, prostate cancer, renal cell carcinoma, salivary gland cancer, sarcoma, non-melanoma skin cancer, small intestine cancer, small cell lung cancer, or thyroid cancer.

[0160] In yet another embodiment, the second cohort is a pan-cancer cohort. Such a pan-cancer cohort can include 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more different cancers represented in the cohort. In certain embodiments, the cohort comprises three or more cancers selected from the group consisting of appendix cancer, bladder cancer, breast cancer, cervical cancer, CNS and PNS cancer, colorectal cancer, endometrial cancer, esophagogastric cancer, gastrointestinal stromal tumors, glioma, head and neck cancer, hepatobiliary cancer, lymphoma, melanoma, neuroendocrine tumors, non-small cell lung cancer, ovarian cancer, prostate cancer, renal cell carcinoma, salivary gland cancer, sarcoma, non-melanoma skin cancer, small intestine cancer, small cell lung cancer, and thyroid cancer.

[0161] In some embodiments, the first cohort of subjects and the second cohort of subjects are the same cohort of subjects.

[0162] In some embodiments, the gene expression level of the at least one gene product associated with each of one or more ADC therapies, the expression level of the at least one gene product associated with cell adhesion, and the optional tumor cell content are log2 transformed and / or Z-score normalized prior to step (D)(1), and the expression level of the one or more gene products associated with proliferation is log2 transformed and / or Z-score normalized prior to averaging the expression levels of the gene products associated with proliferation to obtain the proliferation gene expression level in step (B).

[0163] As discussed above, the gene expression levels of the one or more gene products associated with ADC treatment, proliferation, cell adhesion, and, optionally, the tumor cell content value are preferably subjected to normalization to minimize the influence of outliers and facilitate enhanced predictive power of the method. In some embodiments, the normalization is Z-score normalization. In some embodiments, the normalization is min-max normalization. In some embodiments, the normalization is Z-score normalization based on data from a large number of solid tumors (e.g., data obtained from at least 5,000, at least 10,000, at least 15,000, or at least 20,000 individual solid tumor samples). In some embodiments, the Z-score normalization is based on data from more than 15,000 solid tumors in the Strata Trial (NCT03061305).

[0164] The process for Z-score normalization of the gene expression level of the at least one gene product associated with each one or more ADC treatments, the at least one gene product associated with proliferation, the at least one gene product associated with cell adhesion, and optionally tumor cell content is well understood by those skilled in the art.Generally, Z-score normalization is carried out by first determining the mean and standard deviation for a given data set, and then normalizing data points by subtracting the median value therefrom and dividing by the calculated standard deviation.For example, the mean and standard deviation are determined for the expression level of the gene product associated with ADC treatment based on the data from more than 10,000 solid tumor samples.Then, the patient's expression level of this same gene product is subtracted from the calculated mean and divided by the calculated standard deviation to provide Z-score normalized expression value.This is then repeated for the one or more gene products associated with proliferation, the tumor cell content, and any additional gene products associated with each one or more ADC treatments, thus providing Z-score normalized data for use in the method.

[0165] In some embodiments, the method further comprises measuring the expression level of at least one housekeeping gene selected from CIAO1, EIF2B1, and HMBS in the tumor tissue sample, and normalizing the expression levels of at least one gene product associated with the one or more ADC therapies, the at least one gene product associated with cell adhesion, and the one or more gene products associated with proliferation to the at least one housekeeping gene expression to obtain normalized expression levels of at least one gene product associated with each of the one or more ADC therapies, the at least one gene product associated with cell adhesion, and the one or more gene products associated with proliferation.Thus, in some embodiments, the method includes the steps of: (a) measuring, in a tumor tissue sample obtained from the subject, the expression level of i) at least one gene product associated with each corresponding one or more ADC treatments, ii) at least one gene product associated with cell adhesion, and iii) one or more gene products associated with proliferation; (b) measuring, in the same tumor tissue sample of step (a), the expression level of one or more housekeeping genes, wherein the one or more housekeeping genes comprise three genes selected from CIAO1, EIF2B1, HMBS, CTCF, GGNBP2, ITGB7, MYC, and SLC4A1AP, and at least one gene product associated with each of the one or more ADC treatments of step (a), (c) further normalizing the expression levels of the at least one gene product associated with cell adhesion and the one or more gene products associated with proliferation to the three housekeeping genes to obtain corresponding normalized expression levels of at least one gene product associated with one or more ADC treatments, at least one gene product associated with cell adhesion, and one or more genes associated with proliferation; (d) optionally, determining tumor cell content in the same tumor tissue sample of steps (a) and (b); (e)(1) calculating the calculated ADC treatment response score (ADC identifying a subject who is likely to benefit from the one or more ADC therapies if one or more of the ADC TRSs (gene expression levels) are above one or more corresponding predetermined thresholds, wherein each of the one or more ADC TRSs is determined from at least two of: (i) the measured expression level of the at least one gene product associated with the corresponding ADC therapy; (ii) the measured expression level of the at least one gene product associated with cell adhesion; (iii) the determined proliferation gene expression level; and (iv) the determined tumor cell content.

[0166] In some embodiments, the ADC TRS is determined by adding the expression level of a first gene product associated with a first ADC treatment to approximately 1.5 times the determined tumor cell content and approximately 1 / 4 of the determined proliferation gene expression level.

[0167] In some embodiments, the ADC treatment response score is determined as follows: ADC treatment response score = 1 * [target expression] + 1.5 * [tumor cell content] + 0.25 * [proliferative gene expression] Here, the values ​​of [target expression], [tumor cell content], and [proliferation gene expression] were z-score normalized and / or log2 transformed in advance.

[0168] In some embodiments, the expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene product associated with cell adhesion, and the one or more genes associated with proliferation are individually selected from ribonucleic acid (RNA) and protein. In some embodiments, the gene expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene product associated with cell adhesion, and the one or more genes associated with proliferation are proteins, and measuring their expression levels requires the use of immunohistochemistry. In some embodiments, the gene expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene product associated with cell adhesion, and the one or more genes associated with proliferation are RNA, and measuring their expression levels requires the use of RNA sequencing techniques, quantitative real-time polymerase chain reaction (qPCR), Northern hybridization, microarrays, or serial analysis of gene expression (SAGE). In some embodiments, the RNA sequencing techniques include shotgun RNA sequencing or full-length RNA sequencing.

[0169] In some embodiments, the one or more ADC therapies each comprise a monoclonal antibody, at least one functional fragment thereof, or a bispecific antibody that targets at least one epitope of at least one antigen selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2 MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, P79, EGFR, integrin beta-6, ROR1, Globo H, CD142, CD25, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, CD56, LY75, CD166, and CEACAM5. In some embodiments, the antibody or fragment thereof is conjugated directly or indirectly to a cytotoxic drug. In some embodiments, the cytotoxic drug is a DNA replication inhibitor selected from the group consisting of alkylating agents, DNA polymerase inhibitors, nitrogen mustards, and topoisomerase inhibitors. In some embodiments, the antibody, at least one functional fragment thereof, or the bispecific antibody is fused to a protein that is toxic to cancer cells. In some embodiments, the cytotoxic drug is a topoisomerase inhibitor.

[0170] In some embodiments, the antibody comprises a bispecific antibody capable of targeting two epitopes of the same antigen or epitopes of two separate antigens, wherein the same antigen or the two antigens are selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2, MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, P79, EGFR, integrin beta-6, ROR1, Globo H, CD142, CD25, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, CD56, LY75, CD166, and CEACAM5.

[0171] In some embodiments, the one or more ADC therapies are selected from the group consisting of: Mirvetuximab soravtansine, Tisotumab vedotin-tiftv, Trastuzumab deruxtecan, Enfortumab vedotin, Trastuzumab emtansine, STRO-002, PF-06804103, Cofetuzumab pelidotin, W0101, ZW49, ASN-004, XMT-1592, XMT-1536, BAT8001, ABGn-107, Lorbotuzumab mertansine, AVID100, B003, MEN1309, CX-2009, SAR408701, Anetumab ravtansine, trastuzumab duocarmazine, MGC018, SYD1875, DS-7300a, U3-1402, DS-6157a, DS-1062a, MORAB-202, enapotamab vedotin, BA3011, CX-2029, SGN-CD228A, telisotuzumab vedotin, disitimab vedotin, ALT-P7, MRG002, MRG003, OBI-999, SGN-B6A, VLS-101, ladiratuzumab vedotin, tisotumab vedotin, ARX788, FS-1502, A166, TR 1801-ADC, camidanlumab tesirine, sercultamab talirine, TAK-164, SHR-A1403, NBE-002, SKB-264, BDC-1001, SBT6050, BA 3021, FOR-46, ABBV-011, ABBV-155, DP303c, GQ1001, BB-1701, and SHR-A1811. In some embodiments, the subject has or is suspected of having a cancer for which the one or more ADC therapies are not approved for their indicated use.

[0172] In some embodiments, the tumor tissue sample is a formalin-fixed, paraffin-embedded (FFPE) tumor tissue sample. In some embodiments, the tumor tissue sample comprises at least 20% tumor content.

[0173] In some embodiments, the method further comprises step (E) administering said at least one of said one or more ADC therapies to a subject identified in step (D)(1) as likely to respond to said one or more ADC therapies.

[0174] In some embodiments, each of the one or more ADC TRSs is determined without taking into account tumor cell content.

[0175] In embodiments of the methods disclosed herein, the biological sample (i.e., sample) is any suitable sample type. In some embodiments, the sample is derived from plasma, blood, serum, saliva, sputum, feces, tumor, cell-free DNA, circulating tumor cells, or other biological sample. In some embodiments, the sample is a blood sample. In some embodiments, the biological sample is a tumor specimen. In some embodiments, the sample is derived from a subject having or at risk of having cancer. The type of cancer is not limited and can be any suitable cancer. Exemplary cancers include, but are not limited to: acoustic neuroma; adenocarcinoma; adrenal cancer; angiosarcoma (e.g., lymphangiosarcoma, lymphangioendotheliosarcoma, angiosarcoma); appendix cancer; benign monoclonal gammopathy; bile duct cancer cancer) (e.g., cholangiocarcinoma); bladder cancer; breast cancer (e.g., adenocarcinoma of the breast, papillary carcinoma of the breast, adenocarcinoma of the breast, medullary carcinoma of the breast); brain cancer (e.g., meningioma, glioblastoma, glioma (e.g., astrocytoma, oligodendroglioma), medulloblastoma); bronchial cancer; carcinoid tumor; cervical cancer (e.g., cervical adenocarcinoma); choriocarcinoma; chordoma; craniopharyngioma; colorectal cancer (e.g., colon cancer, rectal cancer, colorectal adenocarcinoma); connective tissue cancer; epithelial carcinoma; ependymoma; endotheliosarcoma (e.g., Kaposi's sarcoma, multiple idiopathic hemorrhagic sarcoma) sarcoma); endometrial cancer (e.g., uterine carcinoma, uterine sarcoma); esophageal cancer (e.g., esophageal adenocarcinoma, Barrett's adenocarcinoma); Ewing's sarcoma; eye cancer (e.g., intraocular melanoma, retinoblastoma); familial polycythemia; gallbladder cancer; stomach cancer (e.g., gastric adenocarcinoma); gastrointestinal stromal tumor (GIST); germ cell tumor; head and neck cancer (e.g., head and neck squamous cell carcinoma, oral cancer (e.g., oral squamous cell carcinoma), throat cancer (e.g., laryngeal cancer, pharyngeal cancer, nasopharyngeal cancer, oropharyngeal cancer));Hematopoietic cancers (e.g., leukemias such as acute lymphocytic leukemia (ALL) (e.g., B-cell ALL, T-cell ALL), acute myeloid leukemia (AML) (e.g., B-cell AML, T-cell AML), chronic myeloid leukemia (CML) (e.g., B-cell CML, T-cell CML), and chronic lymphocytic leukemia (CLL) (e.g., B-cell CLL, T-cell CLL)); lymphomas (e.g., Hodgkin's lymphoma (HL) (e.g., B-cell HL, T-cell HL) and non-Hodgkin's lymphoma (NHL) (e.g., B-cell NHL such as Diffuse large cell lymphoma (DLCL) (e.g., diffuse large B-cell lymphoma), follicular lymphoma, chronic lymphocytic leukemia / small lymphocytic lymphoma (CLL / SLL), mantle cell lymphoma (MCL), marginal zone B-cell lymphoma (e.g., mucosa-associated lymphoid tissue (MALT) lymphoma, nodal marginal zone B-cell lymphoma, splenic marginal zone B-cell lymphoma), primary mediastinal B-cell lymphoma, Burkitt lymphoma, lymphoplasmacytic lymphoma (i.e., Waldenstrom's macroglobulinemia), hairy cell leukemia (HCL), immunoblastic large cell lymphoma, precursor B-lymphoblastic lymphoma lymphoma) and primary central nervous system (CNS) lymphoma; and T-cell NHL (e.g., precursor T-cell lymphoblastic lymphoma / leukemia, peripheral T-cell lymphoma (PTCL) (e.g., cutaneous T-cell lymphoma (CTCL) (e.g., mycosis fungoides, Sézary syndrome), angioimmunoblastic T-cell lymphoma, extranodal natural killer T-cell lymphoma, enteropathy-associated T-cell lymphoma, subcutaneous panniculitis-like T-cell lymphoma, and anaplastic large cell lymphoma); mixed one or more leukemias / lymphomas as above; and Multiple myeloma (MM), heavy chain diseases (e.g., alpha chain disease, gamma chain disease, mu chain disease); hemangioblastoma; hypopharyngeal carcinoma; inflammatory myofibroblastic tumor; immune cell amyloidosis; kidney cancer (e.g., nephroblastoma (also known as Wilms' tumor, renal cell carcinoma); liver cancer (e.g., hepatocellular carcinoma (HCC), malignant liver cancer); lung cancer (e.g., bronchogenic carcinoma, small cell lung cancer (SCLC), non-small cell lung cancer (NSCLC), lung adenocarcinoma); leiomyosarcoma (LMS); mastocytosis (e.g., systemic mastocytosis); muscle cancer; myelodysplastic syndrome (MDS); mesothelioma;Myeloproliferative disorders (MPDs) (e.g., polycythemia vera (PV), essential thrombocythemia (ET), idiopathic myeloid metaplasia (AMM) (also known as myelofibrosis (MF)), chronic idiopathic myelofibrosis, chronic myelogenous leukemia (CML), chronic neutrophilic leukemia (CNL), hypereosinophilic syndrome (HES)); neuroblastoma; neurofibromas (e.g., neurofibromatosis (NF) type 1 or 2, schwannomatosis); neuroendocrine cancers (e.g., gastroenteropancreatic neuroendocrine tumors) tumor) (GEP-NET), carcinoid tumor); osteosarcoma (e.g., bone cancer); ovarian cancer (e.g., cystadenocarcinoma, ovarian embryonal carcinoma, ovarian adenocarcinoma); papillary adenocarcinoma; pancreatic cancer (e.g., pancreatic adenocarcinoma, intraductal papillary mucinous neoplasm (IPMN), pancreatic islet cell tumor); penile cancer (e.g., Paget's disease of the penis and scrotum); pinealoma; primitive neuroectodermal tumor (PNT); plasma cell neoplasm; paraneoplastic syndromes; intraepithelial neoplasia; prostate cancer (e.g., prostate adenocarcinoma); rectal cancer; rhabdomyosarcoma; salivary gland cancer; skin cancer (e.g., squamous cell carcinoma (SCC), keratoacanthoma (KA), melanoma, basal cell carcinoma (BCC)); small intestine cancer (e.g., appendix cancer); soft tissue sarcoma (e.g., malignant fibrous histiocytoma (MFH), liposarcoma, malignant peripheral nerve sheath tumor (MPNST), chondrosarcoma, fibrosarcoma, myxosarcoma); sebaceous gland carcinoma; small intestine cancer; sweat gland carcinoma; synovial sarcoma; testicular cancer (e.g., seminoma, testicular embryonal carcinoma); thyroid cancer (e.g., papillary thyroid carcinoma, papillary thyroid carcinoma (PTC), medullary thyroid carcinoma); urethral cancer; vaginal cancer; and vulvar cancer (e.g., Paget's disease of the vulva). In some embodiments, the cancer is lung cancer or prostate cancer.

[0176] In some embodiments, the tumor tissue sample is or is suspected to contain bladder cancer, salivary gland cancer, endometrial cancer, ovarian cancer, cervical cancer, head and neck cancer, non-melanoma skin cancer, thyroid cancer, cancer of unknown primary origin, cancer of the central or peripheral nervous system, neuroendocrine tumor, melanoma, esophagogastric cancer, small intestine cancer, sarcoma, hepatobiliary cancer, pancreatic cancer, gastrointestinal stromal tumor, renal cell carcinoma, glioma, appendix cancer, breast cancer, small cell lung cancer, prostate cancer, esophagogastric cancer, non-small cell lung cancer, pancreatic cancer, lymphoma, or colorectal cancer.

[0177] In another aspect, the present invention provides a method for selecting from one or more antibody-drug conjugate (ADC) therapies among two or more ADC therapies identified as most beneficial for treating cancer in a subject, the method comprising: (A) measuring the expression level of at least one gene product associated with each of the one or more ADC therapies from a biological tissue sample obtained from the subject; (B) measuring, in the same biological tissue sample of step (A), the expression level of one or both of (i) at least one gene product associated with cell adhesion, and (ii) one or more gene products associated with proliferation, wherein if expression levels of more than one gene product associated with proliferation are measured, calculating therefrom an average of all of the expression levels of the measured gene products associated with proliferation, thereby obtaining a proliferation gene expression level; (C) optionally, determining tumor cell content in the same tumor tissue sample of steps (A) and (B); (D)(1) calculating an ADC treatment response score (ADC TRS) for each of the two or more ADC therapies, and determining a proliferation gene expression level for the two or more ADC therapies; (D)(2) if ADC TRSs associated with two or more ADC therapies are above a predetermined threshold associated with a beneficial patient treatment outcome, ranking the at least two ADC TRSs by the value by which each ADC treatment response score exceeds the predetermined threshold, and selecting the highest ranked ADC therapy for administration to the subject.

[0178] In some embodiments, the method further comprises step (E) of administering the selected highest ranked ADC therapy to the subject. In some embodiments, step (E) further comprises administering at least one other, lower-ranked ADC above the predetermined threshold to the subject in combination with the highest ranked ADC therapy. In some embodiments, step (E) does not comprise administering another ADC therapy in combination with the highest ranked ADC therapy.

[0179] In some embodiments, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, or fifteen or more ADC therapies are identified as therapies to which the cancer patient is likely to respond. In some embodiments, two or more, or three or more ADC therapies are administered sequentially or simultaneously to the patient. In some embodiments, the patient has already been treated with a first-line therapy, a second-line therapy, a third-line therapy, or a fourth-line therapy before receiving the one or more ADC therapies identified as the therapy to which the cancer patient is likely to respond.

[0180] In some embodiments, a subject is identified as likely to respond to the one or more ADC therapies if the measured expression level of at least one gene product associated with each of the one or more corresponding ADC therapies, the determined proliferation gene expression level, and the determined tumor cell content are all higher than the corresponding median level of expression of the at least one gene product, proliferation gene expression, and median tumor cell content obtained from tumor tissue samples of the same first cohort of the subject, but the measured expression level of the at least one gene product associated with cell adhesion (e.g., a PVR gene product) is below the median expression level of the at least one corresponding gene product associated with cell adhesion (e.g., a PVR gene product) in the same first cohort of subjects. In other embodiments, the measured expression level of at least one gene product, the determined proliferation gene expression level, and the determined tumor cell content associated with each of the one or more ADC therapies may be 10%, 15%, 20%, 25%, 30%, 35%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, 125%, 150%, 175%, 200%, 250%, 300% or more higher than the corresponding median level of at least one gene product expression, proliferation gene expression, and median tumor cell content obtained from tumor tissue samples of the same first cohort of subjects. In some other embodiments, the measured expression level of at least one gene product, the determined proliferation gene expression level, and the determined tumor cell content associated with each of the one or more ADC therapies may be 1.1-fold, 1.2-fold, 1.25-fold, 1.5-fold, 1.75-fold, 1.9-fold, 2-fold, 3-fold, 4-fold, 5-fold, or more higher than the corresponding median level of at least one gene product expression, proliferation gene expression, and median tumor cell content obtained from tumor tissue samples of the same first cohort of subjects.

[0181] In some embodiments, the subject is identified as likely to respond to one or more ADC therapies if the measured expression level of at least one gene product associated with each of the one or more ADC therapies, the determined proliferation gene expression level, and / or the determined tumor cell content falls within the highest quartile of values ​​for the one or more gene product expression levels, proliferation gene expression levels, and / or tumor cell content obtained from a tumor tissue sample from the same first cohort of the subject, but the expression level of the at least one gene product associated with cell adhesion (e.g., a PVR gene product) falls within the lowest quartile of expression level of the corresponding at least one gene product associated with cell adhesion (e.g., a PVR gene product) from a tumor sample obtained from the same first cohort of subjects. In some embodiments, the subject is identified as likely to respond to the one or more ADC therapies if one or more of the measured expression level of the at least one gene product, the determined proliferation gene expression level, and / or the determined tumor cell content is in the top 30%, top 25%, top 20%, top 15%, top 10%, top 5%, top 3%, or top 1% of the at least one gene product expression level, proliferation gene expression level, and / or tumor cell content values ​​obtained from tumor tissue samples of the same first cohort of the subject. In some embodiments, the subject is identified as likely to respond to the one or more ADC therapies if the expression level of the at least one gene product associated with cell adhesion (e.g., a PVR gene product) is within the bottom 30%, bottom 25%, bottom 20%, bottom 15%, bottom 10%, bottom 5%, bottom 3%, or bottom 1% of the expression level of the corresponding at least one gene product associated with cell adhesion (e.g., a PVR gene product) from tumor tissue samples from the same first cohort of the subject.

[0182] In another aspect, the invention provides a computer-implemented method for selecting a patient presenting with a solid cancerous tumor for treatment with one or more antibody-drug conjugate (ADC) therapies, the method comprising: (A) receiving, from a biological tissue sample obtained from the patient's tumor tissue, measured expression levels of at least one gene product associated with each of the one or more ADC therapies; (B)(1) receiving expression levels of one or both of: (i) at least one gene product associated with cell adhesion, e.g., a PVR gene product, and (ii) one or more gene products associated with proliferation; (B)(2) if the measured expression levels of the one or more gene products associated with proliferation are received, calculating therefrom, by a computer, an average of all of the received and measured expression levels of the gene products associated with proliferation, thereby obtaining a proliferation gene expression level; (C) optionally, receiving an indication of tumor cell content of the same tumor tissue sample of steps (A) and (B); (D)(1) identifying a subject who is likely to respond to the one or more ADC therapies if any of the following occurs: (i) a calculated ADC treatment response score (ADC one or more of the ADC TRSs) exceed one or more corresponding predetermined thresholds, wherein each of the one or more ADC TRSs is determined from (1) the received and measured expression level of the at least one gene product associated with a corresponding ADC therapy and at least two of the following: (2) the received and measured expression level of the at least one gene product associated with cell adhesion, e.g., a PVR gene product; (3) the calculated proliferation gene expression level from the received expression levels of one or more gene products associated with proliferation; and (4) the received indication of tumor cell content;and (E) selecting the patients identified as likely to respond to the one or more ADC therapies for treatment with the therapies, wherein at least steps (A) through (D)(1)(i) are carried out by a suitably programmed computer;

[0183] In some embodiments, step (E) comprises selecting the patient to receive treatment with the one or more ADC therapies as part of a clinical trial. In some embodiments, the clinical trial is a basket trial. In some embodiments, the method further comprises step (F) treating the selected patient with the one or more ADC therapies determined to be likely to induce a response in the patient.

[0184]

[0185] Methods of Targeted Treatment for Subjects Likely to Respond to Targeted Treatment

[0186] In some aspects, the present invention provides a method of treating cancer in a subject determined to be likely to respond to one or more antibody-drug conjugate (ADC) therapies, the method comprising: (A) measuring an expression level of at least one gene product associated with each of the one or more ADC therapies from a biological tissue sample obtained from the subject; (B) measuring, in the same biological tissue sample of step (A), the expression levels of one or both of (i) at least one gene product associated with cell adhesion, and (ii) one or more gene products associated with proliferation, wherein, if the expression levels of the one or more gene products associated with proliferation are measured, calculating therefrom an average of all expression levels of the measured gene products associated with proliferation, thereby obtaining a proliferation gene expression level; (C) optionally, determining tumor cell content in the same tumor tissue sample of steps (A) and (B); (D) calculating a (1) calculated ADC treatment response score (ADC and (E) administering at least one of the one or more ADC therapies to the subject identified as likely to respond to the one or more ADC therapies if one or more of the ADC TRSs (gene expression levels) associated with the corresponding ADC therapy are above one or more corresponding predetermined thresholds, wherein each of the one or more ADC TRSs is determined from at least two of: (i) the measured expression level of the at least one gene product associated with the corresponding ADC therapy, (ii) the measured expression level of the at least one gene product associated with cell adhesion, (iii) the determined proliferation gene expression level, and (iv) the determined tumor cell content.

[0187] In some embodiments, measuring the expression level of one or more gene products associated with proliferation comprises measuring the expression level of gene products of one or more genes that are cell cycle regulated and related to DNA replication, mitotic processes / stages, spindle assembly, tubulin, mitotic surveillance, cell adhesion, chromosome metabolism, and histone formation. In some embodiments, genes having gene products associated with proliferation include BIRC5, BRCA1, BRCA2, BUB1, BUB1B, CCNA2, CCNB1, CCNB2, CCNE1, CCNE2, CCNF, CCNG2, CDC2, CDC20, CDC25A, CDC25B, CDC25C, CDC45L, CDC6, CDC7, CDCKN1A p21, CDCKN3, CDK4, CDKN1C, CDKN2A, CDKN2C, CDKN2D p19, CDKN3, CENPA, CENPE, CENPF, CHAF1A, CHK1, CKS1, CKS2, CKS2, DHFR, DHFR, E2-EPF, E2F1, E2F3, E2F5, FEN1, FOXM1, KNSL2, KNSL5, KPNA2, LMNB2, MAD2, MAD2L1, MAPK13 , MCM2, MCM3, MCM4, MCM5, MCM6, MK167, MNAT1, MSH2, MYBL2, NASP, NEK2, NPAT, ORC1, PA2G4, PCNA, PES1, PKMYT1, PLK, PRIM1, PTTG1, RAB6KIFL, RACGAP1, RAD51, RAD54L, RAN The gene or genes are one or more selected from the group consisting of RAN, RFC4, RPA3, RRM1, RRM2, SLBP, STK12, STK15, STK18, STK6, TOP2A, TROAP, TUBB, TYMS, UBE2C, and UNG. In some embodiments, measuring the expression level of one or more gene products associated with proliferation comprises measuring the expression level of one, two, or all three of the MYBL2, TOP2A, and / or UBE2C gene products. In certain embodiments, measuring the expression level of one or more gene products associated with proliferation comprises measuring the expression level of the TOP2A and UBE2C gene products.

[0188] The inventors have recently discovered that adding additional variables enhances the predictive power of the method in predicting a given patient's response to an ADC therapy. Thus, in some embodiments, the ADC TRS is determined by a combination of at least the expression level of at least one gene product associated with the corresponding ADC therapy, one or both of tumor cell content and proliferation gene expression levels, and the expression level of one additional gene product. In some embodiments, the predictive power of the ADC TRS can be enhanced by adding a variable including the expression level of at least one gene product associated with cell adhesion. Consequently, in certain embodiments, the ADC TRS is determined by the expression level of one gene product associated with the corresponding ADC therapy, the proliferation gene expression level, and the expression level of at least one gene product associated with cell adhesion.

[0189] In some embodiments, the determined proliferation gene expression level and the expression level of the at least one gene product associated with the corresponding ADC therapy are both positively associated with the likelihood that the patient will respond to the corresponding ADC therapy, and the expression level of the at least one gene product associated with cell adhesion is negatively associated with the likelihood that the patient will respond to the same corresponding ADC therapy. In some embodiments, the expression level of the at least one gene product associated with the corresponding ADC therapy is weighted by a factor of about 0.3 to 0.7, about 0.35 to 0.65, about 0.4 to about 0.5, or about 0.4, 0.45, 0.48, 0.50, 0.55, 0.60, 0.65, 0.68, or 0.7. In some embodiments, the expression level of the at least one gene product associated with cell adhesion is weighted by a factor of about -1.5 to -0.5, -1.25 to -0.75, -1.1 to -0.8, -1 to -0.8, or about -1.05, -1.0, -0.95, -0.90, or -0.85. In some embodiments, the determined proliferation gene expression level is weighted by a factor of about 0.15 to 0.60, about 0.20 to 0.40, or about 0.45, 0.40, 0.38, 0.35, 0.30, or 0.25.

[0190] According to one aspect of the present invention, the gene product associated with cell adhesion is a gene product whose expression level in a cohort of cancer patients is provided, and this expression level is negatively correlated with the objective response rate to at least one ADC treatment in the same cohort of cancer patients. The statistical method used to determine the negative correlation between the objective response rate and the expression level of the gene product associated with cell adhesion is not particularly limited and is within the ordinary skill of those skilled in the art, including those methods well known in the art (e.g., Spearman correlation or Pearson correlation). In some embodiments, the correlation coefficients associated with each cell adhesion-related gene product obtained by such analysis can be ranked and selected based on their proximity to -1. Thus, in some embodiments, a cancer patient is considered to be likely to respond to at least one ADC treatment only if the expression of at least one gene product associated with cell adhesion is reduced compared to a reference level. In some embodiments, the reference level is the average expression level of the same gene product associated with cell adhesion in the cancer subjects of the ADC treatment cohort. In some embodiments, a cancer patient is considered likely to respond to at least one ADC therapy only if the expression level of at least one gene product associated with cell adhesion is at least two standard deviations below the mean expression level of the same gene product in cancer subjects in the ADC therapy treatment cohort.

[0191] In some embodiments, the gene product associated with cell adhesion is a gene product that affects cellular components of cell adhesion, including adherens junctions, anchoring junctions, cell-matrix adherens junctions, cell-matrix junctions, and focal adhesions. In certain embodiments, the gene product associated with cell adhesion affects at least two cellular components selected from the group consisting of adherens junctions, anchoring junctions, cell-matrix adherens junctions, cell-matrix junctions, and focal adhesions. In some embodiments, the gene product associated with cell adhesion affects at least three, at least four, or all five of the cellular components. In some embodiments, the gene product associated with cell adhesion is selected both because of its negative correlation with objective response rate and because it affects all five cellular components selected from the group consisting of adherens junctions, anchoring junctions, cell-matrix adherens junctions, cell-matrix junctions, and focal adhesions. By way of non-limiting example, Table 1 above outlines genes whose expression levels of gene products associated with cell adhesion negatively correlate with objective response rates in the cohort of cancer subjects, and the specific cellular components of cell adhesion that are affected by their respective expression levels as discussed above.

[0192] In some embodiments, measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression level of a gene product of at least one gene selected from the group consisting of ATP2A2, BAIAP2, CD151, CHP1, CYFIP1, CYTH3, DAG1, DSC2, GIT1, HSP90B1, HSPA5, LIMK1, MAPK1, PACSIN2, PDIA3, PVR, REXO2, RPL22, RPLP1, RPLP2, RPS11, RPS16, RPS5, SDCBP SNAP23, SNTB1, and SRP68. In certain embodiments, the expression levels of two or more gene products of two or more genes selected from the group consisting of ATP2A2, BAIAP2, CD151, CHP1, CYFIP1, CYTH3, DAG1, DSC2, GIT1, HSP90B1, HSPA5, LIMK1, MAPK1, PACSIN2, PDIA3, PVR, REXO2, RPL22, RPLP1, RPLP2, RPS11, RPS16, RPS5, SDCBP SNAP23, SNTB1, and SRP68 are measured. When the expression levels of two or more gene products associated with cell adhesion are measured, an average expression level can be calculated to provide the cell adhesion gene product expression level. Thus, in some embodiments, a cancer patient is considered likely to respond to ADC therapy only if the cell adhesion gene product expression level is reduced compared to the reference level. Before averaging the expression levels of multiple gene products associated with cell adhesion, each gene product expression level can be normalized and / or log 2 transformed. To facilitate the normalization of each gene product expression level, the mean and standard deviation of the corresponding gene product expression level in a cancer patient cohort can be used. Thus, in some embodiments, a patient is identified as likely to respond to ADC treatment only if the normalized cell adhesion gene product expression level is less than zero.

[0193] In one embodiment, measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression of the PVR gene product. Furthermore, at least 1, 2, 3, 4, 5, 10, 15, 20, 25, or 30 additional gene products associated with cell adhesion may be measured along with the PVR gene product. In some embodiments, measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression level of a single gene product associated with cell adhesion. In some embodiments, only the expression of the PVR gene product is measured. In some embodiments, only the expression of the SRP68 gene product is measured. In some embodiments, only the expression of the SNTB1 gene product is measured. In some embodiments, only the expression of the SNAP23 gene product is measured. In some embodiments, only the expression of the SDCBP gene product is measured. In some embodiments, only the expression of the RPS5 gene product is measured. In some embodiments, only the expression of the RPS16 gene product is measured. In yet another embodiment, only the expression of the RPS11 gene product is measured. In some embodiments, only the expression of the RPLP2 gene product is measured. In some embodiments, only the expression of the RPLP1 gene product is measured. In some embodiments, only the expression of the REXO2 gene product is measured. In some embodiments, only the expression of the PDIA3 gene product is measured. In some embodiments, only the expression of the PACSIN2 gene product is measured. In some embodiments, only the expression of the MAPK1 gene product is measured. In some embodiments, only the expression of the LIMK1 gene product is measured. In some embodiments, only the expression of the HSPA5 gene product is measured. In some embodiments, only the expression of the HSP90B1 gene product is measured. In some embodiments, only the expression of the GIT1 gene product is measured. In some embodiments, only the expression of the DSC2 gene product is measured. In some embodiments, only the expression of the DAG1 gene product is measured.In some embodiments, only the expression of the CYTH3 gene product is measured. In some embodiments, only the expression of the CYFIP1 gene product is measured. In some embodiments, only the expression of the CHP1 gene product is measured. In some embodiments, only the expression of the CD151 gene product is measured. In some embodiments, only the expression of the BAIAP2 gene product is measured. In some embodiments, only the expression of the ATP2A2 gene product is measured.

[0194] In some embodiments, the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with the corresponding ADC therapy, the expression level of at least one gene product associated with cell adhesion, the determined proliferation gene expression level, and tumor cell content. In some embodiments, the determined proliferation gene expression level, tumor cell content, and the expression level of at least one gene product associated with the corresponding ADC therapy are all positively associated with the likelihood that the patient will respond to the corresponding ADC therapy, and the expression level of at least one gene product associated with cell adhesion is negatively associated with the likelihood that the patient will respond to the same corresponding ADC therapy.

[0195] In some embodiments, the expression level of at least one gene product associated with the corresponding ADC therapy is weighted by a factor of approximately 0.30 to 0.70, approximately 0.35 to 0.65, approximately 0.4 to approximately 0.5, or approximately 0.40, 0.45, 0.48, or 0.50. In some embodiments, the expression level of at least one gene product associated with cell adhesion is weighted by a factor of approximately -1.5 to -0.5, -1.25 to -0.75, -1.1 to 0.8, or approximately -1.05, -1.0, -0.95, -0.90, or -0.85. In some embodiments, the determined proliferation gene expression level is weighted by a factor of approximately 0.75 to 0.35, 0.65 to 0.40, 0.60 to 0.50, or approximately 0.65, 0.60, 0.55, 0.50, or 0.45. In some embodiments, the tumor cell content is weighted by a factor of approximately 0.25 to 0.01, 0.20 to 0.02, 0.17 to 0.04, or approximately 0.09, 0.08, 0.07, 0.06, or 0.05. In some embodiments, the expression level of at least one gene product associated with the corresponding ADC therapy is weighted by a factor of approximately 0.45, the expression level of at least one gene product associated with cell adhesion is weighted by a factor of approximately −0.98, the determined proliferation gene expression level is weighted by a factor of approximately 0.55, and the tumor cell content is weighted by a factor of approximately 0.07.

[0196] In some embodiments, each of the one or more ADC TRSs is determined by further taking into account a bias variable, where the bias variable is a static offset adjusted to produce biomarker frequencies that match published objective response rates in clinical trials. In some embodiments, the bias variable is weighted by a factor of approximately -0.65 to -0.1, -0.55 to -0.15, -0.35 to 0.20, or approximately -0.22, -0.23, -0.24, -0.25, -0.25, -0.26, -0.27, or -0.28. In some embodiments, the bias variable is weighted by a factor of approximately -0.26.

[0197] As discussed above, ADC TRS is determined by a number of variables that are individually weighted based on their influence on the probability that a subject is likely to respond to ADC treatment.When these variables are combined, a score indicating that a subject is likely to respond to ADC treatment is obtained when it exceeds a certain predetermined threshold.In some embodiments, the predetermined threshold is zero, and the ADC TRS indicating that the subject is likely to respond to ADC treatment is the ADC TRS with a value greater than zero.

[0198] In some embodiments, the predetermined threshold is set at percentiles of ranked ADC treatment response scores determined from tumor tissue samples of a first cohort of subjects utilizing at least the expression level of the at least one gene product associated with a first corresponding ADC therapy, where each percentile corresponds to a greater percentage of subjects in the first cohort who do not respond to the same first corresponding ADC therapy.

[0199] In some embodiments, the predetermined threshold is set at a percentile of ranked ADC treatment response scores determined from tumor tissue samples of the first and second cohorts of subjects utilizing expression levels of at least first and second gene products associated with at least the first and second corresponding ADC therapies, where each percentile corresponds to a greater percentage of subjects in the first and second cohorts who do not respond to the at least first and second corresponding ADC therapies.

[0200] In some embodiments, the first cohort of subjects is a pan-cancer cohort or a matched tumor type cohort. In some embodiments, the first cohort is a matched tumor type cohort. The matched tumor type cohort is not limited to, and includes those tumors / cancers known to those skilled in the art and disclosed herein. In some embodiments, the matched tumor type is appendix cancer, bladder cancer, breast cancer, cervical cancer, CNS and PNS cancer, colorectal cancer, endometrial cancer, esophagogastric cancer, gastrointestinal stromal tumor, glioma, head and neck cancer, hepatobiliary cancer, lymphoma, melanoma, neuroendocrine tumor, non-small cell lung cancer, ovarian cancer, prostate cancer, renal cell carcinoma, salivary gland cancer, sarcoma, non-melanoma skin cancer, small intestine cancer, small cell lung cancer, or thyroid cancer.

[0201] In yet another embodiment, the first cohort is a pan-cancer cohort. Such a pan-cancer cohort can include 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more different cancers represented in the cohort. In certain embodiments, the cohort comprises three or more cancers represented by the group consisting of appendix cancer, bladder cancer, breast cancer, cervical cancer, CNS and PNS cancer, colorectal cancer, endometrial cancer, esophagogastric cancer, gastrointestinal stromal tumors, glioma, head and neck cancer, hepatobiliary cancer, lymphoma, melanoma, neuroendocrine tumors, non-small cell lung cancer, ovarian cancer, prostate cancer, renal cell carcinoma, salivary gland cancer, sarcoma, non-melanoma skin cancer, small intestine cancer, small cell lung cancer, and thyroid cancer.

[0202] In some embodiments, the first cohort is a clinical trial cohort including cancer patients receiving at least one antibody-drug conjugate therapy. In some embodiments, the patients have advanced cancer and are selected for inclusion in the clinical trial cohort because they exhibit expression of one target of a candidate antibody-drug conjugate therapy. In some embodiments, the advanced cancer is radiologically confirmed to have metastasized or to have relapsed or been determined to be refractory to prior chemotherapy treatment. In some embodiments, the patients have advanced solid tumors. In preferred embodiments, the cohort includes at least 50 patients, at least 75 patients, at least 100 patients, at least 125 patients, at least 150 patients, at least 175 patients, at least 200 patients, at least 250 patients, at least 300 patients, at least 400 patients, at least 500 patients, or more. In some embodiments, the cohort has at least 200 patients. In some embodiments, the clinical trial cohort is a basket trial / pan-cancer cohort. In some embodiments, the first cohort is a tumor-specific cohort.

[0203] In some embodiments, the ADC treatment response score is determined by a combination of the expression level of at least one gene product associated with each of the one or more corresponding ADC therapies, the determined proliferation gene expression level, and the tumor cell content. In some embodiments, the predetermined threshold is set to a percentile of ranked ADC treatment response scores determined from tumor tissue samples of a second cohort of subjects, the percentile corresponding to the percentage of subjects in the second cohort who do not respond to the one or more ADC therapies.

[0204] In some embodiments, the second cohort of subjects is a pan-cancer cohort or a matched tumor type cohort. In some embodiments, the second cohort is a matched tumor type cohort. The matched tumor type cohort is not limited to, and includes those tumors / cancers known to those skilled in the art and disclosed herein. In some embodiments, the matched tumor type is appendix cancer, bladder cancer, breast cancer, cervical cancer, CNS and PNS cancer, colorectal cancer, endometrial cancer, esophagogastric cancer, gastrointestinal stromal tumor, glioma, head and neck cancer, hepatobiliary cancer, lymphoma, melanoma, neuroendocrine tumor, non-small cell lung cancer, ovarian cancer, prostate cancer, renal cell carcinoma, salivary gland cancer, sarcoma, non-melanoma skin cancer, small intestine cancer, small cell lung cancer, or thyroid cancer.

[0205] In yet another embodiment, the second cohort is a pan-cancer cohort. Such a pan-cancer cohort can include 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more different cancers represented in the cohort. In certain embodiments, the cohort comprises three or more cancers selected from the group consisting of appendix cancer, bladder cancer, breast cancer, cervical cancer, CNS and PNS cancer, colorectal cancer, endometrial cancer, esophagogastric cancer, gastrointestinal stromal tumors, glioma, head and neck cancer, hepatobiliary cancer, lymphoma, melanoma, neuroendocrine tumors, non-small cell lung cancer, ovarian cancer, prostate cancer, renal cell carcinoma, salivary gland cancer, sarcoma, non-melanoma skin cancer, small intestine cancer, small cell lung cancer, and thyroid cancer.

[0206] In some embodiments, the first cohort of subjects and the second cohort of subjects are the same cohort of subjects.

[0207] In some embodiments, the gene expression level of the at least one gene product associated with each of one or more ADC therapies, the expression level of at least one gene product associated with cell adhesion, and the tumor cell content are log2 transformed and / or Z-score normalized prior to step (D)(1), and the expression level of the one or more gene products associated with proliferation is log2 transformed and / or Z-score normalized prior to averaging the expression levels of the gene products associated with proliferation to obtain the proliferation gene expression level in step (B).

[0208] As discussed above, the gene expression levels of the one or more gene products associated with ADC therapy, cell adhesion, proliferation, and tumor cell content values ​​are preferably subjected to normalization to minimize the influence of outliers and facilitate enhanced predictive power of the method. In some embodiments, the normalization is Z-score normalization. In some embodiments, the normalization is min-max normalization. In some embodiments, the normalization is Z-score normalization based on data from a large number of solid tumors (e.g., data obtained from at least 5,000, at least 10,000, at least 15,000, or at least 20,000 individual solid tumor samples). In some embodiments, the Z-score normalization is based on data from more than 15,000 solid tumors in the Strata Trial (NCT03061305).

[0209] In some embodiments, the method further comprises measuring the expression level of at least one housekeeping gene selected from CIAO1, EIF2B1, and HMBS in the tumor tissue sample, and normalizing at least one gene product associated with the one or more ADC therapies, the at least one gene product associated with cell adhesion, and the one or more gene products associated with proliferation to the at least one housekeeping gene expression to obtain normalized expression levels of at least one gene product associated with each of the one or more ADC therapies, the at least one gene product associated with cell adhesion, and the one or more gene products associated with proliferation. Accordingly, in some embodiments, the present invention provides a method for treating cancer in a subject who is likely to respond to one or more antibody-drug conjugate (ADC) therapies, the method comprising: (a) measuring, in a tumor tissue sample obtained from the subject, the expression level of i) at least one gene product associated with each corresponding one or more ADC therapies, ii) at least one gene product associated with cell adhesion, and iii) one or more gene products associated with proliferation; (b) measuring, in the same tumor tissue sample of step (a), the expression level of one or more housekeeping genes, and measuring the expression level of each of the one or more ADC therapies of step (a). (c) further normalizing the expression levels of the at least one gene product associated with ADC therapy, the at least one gene product associated with cell adhesion, and the one or more gene products associated with proliferation to the expression levels of the one or more housekeeping genes to obtain normalized expression levels of the at least one gene product associated with ADC therapy, the at least one gene product associated with cell adhesion, and the one or more genes associated with proliferation, respectively; (c) when the gene products of the one or more genes associated with proliferation are measured and normalized, determining the proliferation gene expression level by averaging the normalized expression levels of the one or more gene products associated with proliferation;(d) optionally, determining tumor cell content in the same tumor tissue sample of steps (a) and (b); (e)(1) identifying subjects likely to benefit from the one or more ADC therapies if one or more of the calculated ADC treatment response scores (ADC TRS) are above one or more corresponding predetermined thresholds, wherein each of the one or more ADC TRSs is determined from at least two of: (i) the measured expression level of the at least one gene product associated with the corresponding ADC therapy, (ii) the measured level of the at least one gene product associated with cell adhesion, (iii) the determined proliferation gene expression level, and (iv) the determined tumor cell content; and (f) administering an effective amount of the one or more ADC therapies to the subjects identified as likely to benefit from the one or more ADC therapies;

[0210] In some embodiments, the ADC TRS is determined by adding the expression level of a first gene product associated with a first ADC treatment to approximately 1.5 times the determined tumor cell content and approximately 1 / 4 of the determined proliferation gene expression level.

[0211] In some embodiments, the ADC treatment response score is determined as follows: ADC treatment response score = 1 * [target expression] + 1.5 * [tumor cell content] + 0.25 * [proliferative gene expression] Here, the values ​​of [target expression], [tumor cell content], and [proliferation gene expression] were z-score normalized and / or log2 transformed in advance.

[0212] In some embodiments, the expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene product associated with cell adhesion, and the one or more genes associated with proliferation are individually selected from ribonucleic acid (RNA) and protein. In some embodiments, the gene expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene product associated with cell adhesion, and the one or more genes associated with proliferation are proteins, and measuring their expression levels requires the use of immunohistochemistry. In some embodiments, the gene expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene product associated with cell adhesion, and the one or more genes associated with proliferation are RNA, and measuring their expression levels requires the use of RNA sequencing technology or quantitative real-time polymerase chain reaction (qPCR).

[0213] In some embodiments, the one or more ADC therapies each comprise a monoclonal antibody, at least one functional fragment thereof, or a bispecific antibody that targets at least one epitope of at least one antigen selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2 MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, P79, EGFR, integrin beta-6, ROR1, Globo H, CD142, CD25, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, CD56, LY75, CD166, and CEACAM5. In some embodiments, the antibody or fragment thereof is conjugated directly or indirectly to a cytotoxic drug. In some embodiments, the cytotoxic drug is a DNA replication inhibitor selected from the group consisting of alkylating agents, DNA polymerase inhibitors, nitrogen mustards, and topoisomerase inhibitors. In some embodiments, the antibody, at least one functional fragment thereof, or the bispecific antibody is fused to a protein that is toxic to cancer cells. In some embodiments, the cytotoxic drug is a topoisomerase inhibitor.

[0214] In some embodiments, the antibody comprises a bispecific antibody capable of targeting two epitopes of the same antigen or epitopes of two separate antigens, wherein the same antigen or the two antigens are selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2, MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, P79, EGFR, integrin beta-6, ROR1, Globo H, CD142, CD25, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, CD56, LY75, CD166, and CEACAM5.

[0215] In some embodiments, the one or more ADC therapies are selected from the group consisting of: Mirvetuximab soravtansine, Tisotumab vedotin-tiftv, Trastuzumab deruxtecan, Enfortumab vedotin, Trastuzumab emtansine, STRO-002, PF-06804103, Cofetuzumab pelidotin, W0101, ZW49, ASN-004, XMT-1592, XMT-1536, BAT8001, ABGn-107, Lorbotuzumab mertansine, AVID100, B003, MEN1309, CX-2009, SAR408701, Anetumab ravtansine, trastuzumab duocarmazine, MGC018, SYD1875, DS-7300a, U3-1402, DS-6157a, DS-1062a, MORAB-202, enapotamab vedotin, BA3011, CX-2029, SGN-CD228A, telisotuzumab vedotin, disitimab vedotin, ALT-P7, MRG002, MRG003, OBI-999, SGN-B6A, VLS-101, ladiratuzumab vedotin, tisotumab vedotin, ARX788, FS-1502, A166, TR 1801-ADC, camidanlumab tesirine, sercultamab talirine, TAK-164, SHR-A1403, NBE-002, SKB-264, BDC-1001, SBT6050, BA 3021, FOR-46, ABBV-011, ABBV-155, DP303c, GQ1001, BB-1701, and SHR-A1811. In some embodiments, the subject has or is suspected of having a cancer for which the one or more ADC therapies are not approved for their indicated use.

[0216] In some embodiments, the tumor tissue sample is a formalin-fixed, paraffin-embedded (FFPE) tumor tissue sample. In some embodiments, the tumor tissue sample comprises at least 20% tumor content.

[0217] In some embodiments, each of the one or more ADC TRSs is determined without taking into account tumor cell content.

[0218] In embodiments of the methods disclosed herein, the biological sample (i.e., sample) is any suitable sample type. In some embodiments, the sample is derived from plasma, blood, serum, saliva, sputum, feces, tumor, cell-free DNA, circulating tumor cells, or other biological sample. In some embodiments, the sample is a blood sample. In some embodiments, the biological sample is a tumor specimen. In some embodiments, the sample is derived from a subject having or at risk of having cancer. The type of cancer is not limited and can be any suitable cancer. Exemplary cancers include, but are not limited to: acoustic neuroma; adenocarcinoma; adrenal cancer; angiosarcoma (e.g., lymphangiosarcoma, lymphangioendotheliosarcoma, angiosarcoma); appendix cancer; benign monoclonal gammopathy; bile duct cancer cancer) (e.g., cholangiocarcinoma); bladder cancer; breast cancer (e.g., adenocarcinoma of the breast, papillary carcinoma of the breast, adenocarcinoma of the breast, medullary carcinoma of the breast); brain cancer (e.g., meningioma, glioblastoma, glioma (e.g., astrocytoma, oligodendroglioma), medulloblastoma); bronchial cancer; carcinoid tumor; cervical cancer (e.g., cervical adenocarcinoma); choriocarcinoma; chordoma; craniopharyngioma; colorectal cancer (e.g., colon cancer, rectal cancer, colorectal adenocarcinoma); connective tissue cancer; epithelial carcinoma; ependymoma; endotheliosarcoma (e.g., Kaposi's sarcoma, multiple idiopathic hemorrhagic sarcoma) sarcoma); endometrial cancer (e.g., uterine carcinoma, uterine sarcoma); esophageal cancer (e.g., esophageal adenocarcinoma, Barrett's adenocarcinoma); Ewing's sarcoma; eye cancer (e.g., intraocular melanoma, retinoblastoma); familial polycythemia; gallbladder cancer; stomach cancer (e.g., gastric adenocarcinoma); gastrointestinal stromal tumor (GIST); germ cell tumor; head and neck cancer (e.g., head and neck squamous cell carcinoma, oral cancer (e.g., oral squamous cell carcinoma), throat cancer (e.g., laryngeal cancer, pharyngeal cancer, nasopharyngeal cancer, oropharyngeal cancer));Hematopoietic cancers (e.g., leukemias such as acute lymphocytic leukemia (ALL) (e.g., B-cell ALL, T-cell ALL), acute myeloid leukemia (AML) (e.g., B-cell AML, T-cell AML), chronic myeloid leukemia (CML) (e.g., B-cell CML, T-cell CML), and chronic lymphocytic leukemia (CLL) (e.g., B-cell CLL, T-cell CLL)); lymphomas (e.g., Hodgkin's lymphoma (HL) (e.g., B-cell HL, T-cell HL) and non-Hodgkin's lymphoma (NHL) (e.g., B-cell NHL such as Diffuse large cell lymphoma (DLCL) (e.g., diffuse large B-cell lymphoma), follicular lymphoma, chronic lymphocytic leukemia / small lymphocytic lymphoma (CLL / SLL), mantle cell lymphoma (MCL), marginal zone B-cell lymphoma (e.g., mucosa-associated lymphoid tissue (MALT) lymphoma, nodal marginal zone B-cell lymphoma, splenic marginal zone B-cell lymphoma), primary mediastinal B-cell lymphoma, Burkitt lymphoma, lymphoplasmacytic lymphoma (i.e., Waldenstrom's macroglobulinemia), hairy cell leukemia (HCL), immunoblastic large cell lymphoma, precursor B-lymphoblastic lymphoma lymphoma) and primary central nervous system (CNS) lymphoma; and T-cell NHL (e.g., precursor T-cell lymphoblastic lymphoma / leukemia, peripheral T-cell lymphoma (PTCL) (e.g., cutaneous T-cell lymphoma (CTCL) (e.g., mycosis fungoides, Sézary syndrome), angioimmunoblastic T-cell lymphoma, extranodal natural killer T-cell lymphoma, enteropathy-associated T-cell lymphoma, subcutaneous panniculitis-like T-cell lymphoma, and anaplastic large cell lymphoma); mixed one or more leukemias / lymphomas as above; and Multiple myeloma (MM), heavy chain diseases (e.g., alpha chain disease, gamma chain disease, mu chain disease); hemangioblastoma; hypopharyngeal carcinoma; inflammatory myofibroblastic tumor; immune cell amyloidosis; kidney cancer (e.g., nephroblastoma (also known as Wilms' tumor, renal cell carcinoma); liver cancer (e.g., hepatocellular carcinoma (HCC), malignant liver cancer); lung cancer (e.g., bronchogenic carcinoma, small cell lung cancer (SCLC), non-small cell lung cancer (NSCLC), lung adenocarcinoma); leiomyosarcoma (LMS); mastocytosis (e.g., systemic mastocytosis); muscle cancer; myelodysplastic syndrome (MDS); mesothelioma;Myeloproliferative disorders (MPDs) (e.g., polycythemia vera (PV), essential thrombocythemia (ET), idiopathic myeloid metaplasia (AMM) (also known as myelofibrosis (MF)), chronic idiopathic myelofibrosis, chronic myelogenous leukemia (CML), chronic neutrophilic leukemia (CNL), hypereosinophilic syndrome (HES)); neuroblastoma; neurofibromas (e.g., neurofibromatosis (NF) type 1 or 2, schwannomatosis); neuroendocrine cancers (e.g., gastroenteropancreatic neuroendocrine tumors) tumor) (GEP-NET), carcinoid tumor); osteosarcoma (e.g., bone cancer); ovarian cancer (e.g., cystadenocarcinoma, ovarian embryonal carcinoma, ovarian adenocarcinoma); papillary adenocarcinoma; pancreatic cancer (e.g., pancreatic adenocarcinoma, intraductal papillary mucinous neoplasm (IPMN), pancreatic islet cell tumor); penile cancer (e.g., Paget's disease of the penis and scrotum); pinealoma; primitive neuroectodermal tumor (PNT); plasma cell neoplasm; paraneoplastic syndromes; intraepithelial neoplasia; prostate cancer (e.g., prostate adenocarcinoma); rectal cancer; rhabdomyosarcoma; salivary gland cancer; skin cancer (e.g., squamous cell carcinoma (SCC), keratoacanthoma (KA), melanoma, basal cell carcinoma (BCC)); small intestine cancer (e.g., appendix cancer); soft tissue sarcoma (e.g., malignant fibrous histiocytoma (MFH), liposarcoma, malignant peripheral nerve sheath tumor (MPNST), chondrosarcoma, fibrosarcoma, myxosarcoma); sebaceous gland carcinoma; small intestine cancer; sweat gland carcinoma; synovial sarcoma; testicular cancer (e.g., seminoma, testicular embryonal carcinoma); thyroid cancer (e.g., papillary thyroid carcinoma, papillary thyroid carcinoma (PTC), medullary thyroid carcinoma); urethral cancer; vaginal cancer; and vulvar cancer (e.g., Paget's disease of the vulva). In some embodiments, the cancer is lung cancer or prostate cancer.

[0219] In some embodiments, the tumor tissue sample is or is suspected to contain bladder cancer, salivary gland cancer, endometrial cancer, ovarian cancer, cervical cancer, head and neck cancer, non-melanoma skin cancer, thyroid cancer, cancer of unknown primary origin, cancer of the central or peripheral nervous system, neuroendocrine tumor, melanoma, esophagogastric cancer, small intestine cancer, sarcoma, hepatobiliary cancer, pancreatic cancer, gastrointestinal stromal tumor, renal cell carcinoma, glioma, appendix cancer, breast cancer, small cell lung cancer, prostate cancer, esophagogastric cancer, non-small cell lung cancer, pancreatic cancer, lymphoma, or colorectal cancer.

[0220] In another aspect, the present invention provides a method for selecting from one or more antibody-drug conjugate (ADC) therapies among two or more ADC therapies identified as most beneficial for treating cancer in a subject, the method comprising: (A) measuring the expression level of at least one gene product associated with each of the one or more ADC therapies from a biological tissue sample obtained from the subject; (B) measuring, in the same biological tissue sample of step (A), the expression level of one or both of (i) at least one gene product associated with cell adhesion, and (ii) one or more gene products associated with proliferation, wherein if expression levels of more than one gene product associated with proliferation are measured, calculating therefrom an average of all of the expression levels of the measured gene products associated with proliferation, thereby obtaining a proliferation gene expression level; (C) optionally, determining tumor cell content in the same tumor tissue sample of steps (A) and (B); (D)(1) calculating an ADC treatment response score (ADC TRS) for each of the two or more ADC therapies, and determining a proliferation gene expression level for the two or more ADC therapies; (D)(2) if ADC TRSs associated with two or more ADC therapies are above a predetermined threshold associated with a beneficial patient treatment outcome, the method comprising: (i) determining that each of the one or more ADC TRSs exceeds a predetermined threshold associated with a beneficial patient treatment outcome, wherein each of the one or more ADC TRSs is determined from the measured expression level of the at least one gene product associated with a corresponding ADC therapy and at least two of the following: (ii) the measured expression level of at least one gene product associated with cell adhesion, (iii) the determined proliferation gene expression level, and (iv) the determined tumor cell content; (D)(2) if ADC TRSs associated with two or more ADC therapies are above a predetermined threshold associated with a beneficial patient treatment outcome, ranking the at least two ADC TRSs by the value by which each ADC treatment response score exceeds the predetermined threshold, and administering the highest ranked ADC therapy to the subject.

[0221] In some embodiments, the one or more ADC therapies include at least two therapies, and step (D)(1) comprises calculating at least two ADC treatment response scores, wherein if the subject is identified as likely to respond to at least two ADC therapies, the method further comprises, between steps (D)(1) and (E), step (D)(2) ranking the at least two ADC treatment response scores by the value at which each ADC treatment response score exceeds the predetermined threshold, and during step (E), administering to the subject at least the highest-ranked ADC therapy. In some embodiments, step (E) further comprises administering to the subject at least one other, lower-ranked ADC therapy that exceeds the predetermined threshold in combination with the highest-ranked ADC therapy. In some embodiments, step (E) does not comprise administering another ADC therapy in combination with the highest-ranked ADC therapy.

[0222] In some embodiments, two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, or fifteen or more ADC therapies are identified as therapies to which the cancer patient is likely to respond. In some embodiments, two or more, or three or more ADC therapies are administered sequentially or simultaneously to the patient. In some embodiments, the patient has already been treated with a first-line therapy, a second-line therapy, a third-line therapy, or a fourth-line therapy before receiving the one or more ADC therapies identified as the therapy to which the cancer patient is likely to respond.

[0223] In some embodiments, a subject is identified as likely to respond to the one or more ADC therapies if the measured expression level of at least one gene product associated with each of the one or more corresponding ADC therapies, the determined proliferation gene expression level, and the determined tumor cell content are all higher than the corresponding median level of expression of the at least one gene product, proliferation gene expression, and median tumor cell content obtained from tumor tissue samples of the same first cohort of the subject, but the measured expression level of the at least one gene product associated with cell adhesion (e.g., a PVR gene product) is below the median expression level of the at least one corresponding gene product associated with cell adhesion (e.g., a PVR gene product) in the same first cohort of subjects. In other embodiments, the measured expression level of at least one gene product, the determined proliferation gene expression level, and the determined tumor cell content associated with each of the one or more ADC therapies may be 10%, 15%, 20%, 25%, 30%, 35%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, 125%, 150%, 175%, 200%, 250%, 300% or more higher than the corresponding median level of at least one gene product expression, proliferation gene expression, and median tumor cell content obtained from tumor tissue samples of the same first cohort of subjects. In some other embodiments, the measured expression level of at least one gene product, the determined proliferation gene expression level, and the determined tumor cell content associated with each of the one or more ADC therapies may be 1.1-fold, 1.2-fold, 1.25-fold, 1.5-fold, 1.75-fold, 1.9-fold, 2-fold, 3-fold, 4-fold, 5-fold, or more higher than the corresponding median level of at least one gene product expression, proliferation gene expression, and median tumor cell content obtained from tumor tissue samples of the same first cohort of subjects.

[0224] In some embodiments, the subject is identified as likely to respond to one or more ADC therapies if the measured expression level of at least one gene product associated with each of the one or more ADC therapies, the determined proliferation gene expression level, and / or the determined tumor cell content falls within the highest quartile of values ​​for the one or more gene product expression levels, proliferation gene expression levels, and / or tumor cell content obtained from a tumor tissue sample from the same first cohort of the subject, but the expression level of the at least one gene product associated with cell adhesion (e.g., a PVR gene product) falls within the lowest quartile of expression level of the corresponding at least one gene product associated with cell adhesion (e.g., a PVR gene product) from a tumor sample obtained from the same first cohort of subjects. In some embodiments, the subject is identified as likely to respond to the one or more ADC therapies if one or more of the measured expression level of the at least one gene product, the determined proliferation gene expression level, and / or the determined tumor cell content is in the top 30%, top 25%, top 20%, top 15%, top 10%, top 5%, top 3%, or top 1% of the at least one gene product expression level, proliferation gene expression level, and / or tumor cell content values ​​obtained from tumor tissue samples of the same first cohort of the subject. In some embodiments, the subject is identified as likely to respond to the one or more ADC therapies if the expression level of the at least one gene product associated with cell adhesion (e.g., a PVR gene product) is within the bottom 30%, bottom 25%, bottom 20%, bottom 15%, bottom 10%, bottom 5%, bottom 3%, or bottom 1% of the expression level of the corresponding at least one gene product associated with cell adhesion (e.g., a PVR gene product) from tumor tissue samples from the same first cohort of the subject.

[0225] Methods for identifying subjects likely to benefit from anti-TROP2 therapy

[0226] Sacituzumab govitecan (SG, TRODELVY®) is a Trop-2 ADC that combines a humanized anti-TROP2 monoclonal antibody with the topoisomerase I inhibitor SN-38 via a cleavable CL2A linker. 5 SG is a treatment for patients with unresectable or metastatic triple-negative breast cancer (TNBC) after two or more prior systemic therapies. 5 and patients with locally advanced or metastatic bladder cancer who have previously received platinum-containing chemotherapy and a PD-1 or PD-L1 inhibitor 6 When TROP2 protein expression was evaluated post hoc in a TNBC study, all objective responses occurred in patients with moderate or strong staining, which represented nearly the entire study population (88%), limiting the opportunity for stratification. 7 In the IMMU-12-01 basket trial, objective responses were observed in 8 of 9 solid tumor types enrolling 10 or more patients, with response rates ranging from 0% (0 / 16) in pancreatic cancer to 33.3% (36 / 108) in TNBC. 8 .

[0227] Given the significant variability in objective response rates observed across tumor types, there is a need to develop predictive biomarkers of Trop-2 ADC response across solid tumors. The provision of such biomarkers would improve our ability to select patients with an increased likelihood of benefiting from selected ADCs, better align their use within currently approved indications, and offer opportunities to extend the benefits of ADCs to additional tumor types.

[0228] By utilizing available next-generation sequencing (NGS)-based molecular profiling data from an advanced solid tumor cohort (n=23,968), we were able to develop a multivariate biomarker algorithm that infers the objective response rates observed across tumor types to selected ADCs.

[0229] In some aspects, the disclosure provides a method of identifying a subject as likely to benefit from an anti-TROP2-based therapy, the method comprising: (a) measuring, in a tumor tissue sample obtained from the subject, expression levels of a TROP2 gene product and one or more gene products associated with proliferation; (b) measuring, in the same tumor tissue sample of step (a), expression levels of one or more housekeeping genes, wherein the one or more housekeeping genes comprise three genes selected from CIAO1, EIF2B1, HMBS, CTCF, GGNBP2, ITGB7, MYC, and SLC4A1AP, and further normalizing the expression levels of the TROP2 gene product and one or more gene products associated with proliferation in step (a) to the three housekeeping genes to obtain normalized expression levels of the one or more genes associated with TROP2 and proliferation; and (c) calculating the proliferation gene expression levels relative to the normalized expression levels of the one or more gene products associated with proliferation. (c) determining tumor cell content in the same tumor tissue sample of step (a); and (d) identifying the subject as likely to benefit from the anti-TROP2-based therapy if either i) an aggregate biomarker score is above a predetermined threshold, wherein the aggregate biomarker score is calculated from a combination of the measured expression level of the TROP2 gene product and at least one of the determined proliferation gene expression level and / or the determined tumor cell content, or ii) the measured expression level of the TROP2 gene product is higher than a median TROP2 expression level obtained from tumor tissue samples of a first cohort of subjects, and at least one of the determined proliferation gene expression level and / or the determined tumor cell content is higher than a median proliferation gene expression level and / or median tumor cell content obtained from tumor tissue samples of the same first cohort of subjects.

[0230] In some embodiments, the subject is identified as likely to benefit from the anti-TROP2-based therapy if the measured expression level of the TROP2 gene product, the determined proliferation gene expression level, and the determined tumor cell content are all higher than the corresponding median levels of TROP2 expression, proliferation gene expression, and median tumor cell content obtained from tumor tissue samples of the same first cohort of subjects.

[0231] In other embodiments, the measured expression level of the TROP2 gene product, the determined proliferation gene expression level, and the determined tumor cell content may be 10%, 15%, 20%, 25%, 30%, 35%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, 125%, 150%, 175%, 200%, 250%, 300% or more higher than the corresponding median level of TROP2 expression, proliferation gene expression, and median tumor cell content obtained from tumor tissue samples of the same first cohort of subjects. In some other embodiments, the measured expression level of the TROP2 gene product, the determined proliferation gene expression level, and the determined tumor cell content may be 1.1-fold, 1.2-fold, 1.25-fold, 1.5-fold, 1.75-fold, 1.9-fold, 2-fold, 3-fold, 4-fold, 5-fold or more higher than the corresponding median level of TROP2 expression, proliferation gene expression, and median tumor cell content obtained from tumor tissue samples of the same first cohort of subjects.

[0232] In some embodiments, the subject is identified as likely to benefit from the anti-TROP2-based therapy if one or more of the measured expression level of the TROP2 gene product, the determined proliferation gene expression level, and / or the determined tumor cell content falls within the highest quartile of TROP2 expression levels, proliferation gene expression levels, and / or tumor cell content obtained from tumor tissue samples from the same first cohort of subjects. In some embodiments, the first cohort of subjects is a pan-cancer cohort or a matched tumor type cohort.

[0233] In some embodiments, measuring the expression level of one or more gene products associated with proliferation comprises measuring the expression level of a gene product of one or more genes that are cell cycle regulated and involved in DNA replication, mitotic processes / stages, spindle assembly, tubulin, mitotic surveillance, cell adhesion, chromosome metabolism, and histone formation. In some embodiments, genes having gene products associated with proliferation include BIRC5, BRCA1, BRCA2, BUB1, BUB1B, CCNA2, CCNB1, CCNB2, CCNE1, CCNE2, CCNF, CCNG2, CDC2, CDC20, CDC25A, CDC25B, CDC25C, CDC45L, CDC6, CDC7, CDCKN1A p21, CDCKN3, CDK4, CDKN1C, CDKN2A, CDKN2C, CDKN2D p19, CDKN3, CENPA, CENPE, CENPF, CHAF1A, CHK1, CKS1, CKS2, CKS2, DHFR, DHFR, E2-EPF, E2F1, E2F3, E2F5, FEN1, FOXM1, KNSL2, KNSL5, KPNA2, LMNB2, MAD2, MAD2L1, MAPK13 , MCM2, MCM3, MCM4, MCM5, MCM6, MK167, MNAT1, MSH2, MYBL2, NASP, NEK2, NPAT, ORC1, PA2G4, PCNA, PES1, PKMYT1, PLK, PRIM1, PTTG1, RAB6KIFL, RACGAP1, RAD51, RAD54L, RAN The one or more genes selected from the group consisting of RAN, RFC4, RPA3, RRM1, RRM2, SLBP, STK12, STK15, STK18, STK6, TOP2A, TROAP, TUBB, TYMS, UBE2C, and UNG. In some embodiments, measuring the expression level of one or more gene products associated with proliferation comprises measuring the expression level of one, two, or all three of the MYBL2, TOP2A, and / or UBE2C gene products.

[0234] In some embodiments, the collective biomarker score is determined by a combination of the expression level of the TROP2 gene product, the determined proliferation gene expression level, and tumor cell content. In some embodiments, the predetermined threshold is set to a percentile of the ranked collective biomarker score determined from tumor tissue samples of a second cohort of subjects, the percentile corresponding to the percentage of subjects in the second cohort who do not respond to anti-TROP2-based therapy.

[0235] In some embodiments, the second cohort of subjects is a pan-cancer cohort or a matched tumor type cohort. In some embodiments, the first cohort of subjects and the second cohort of subjects are the same cohort of subjects.

[0236] In some embodiments, the gene expression levels of TROP2, one or more genes associated with proliferation, and tumor cell content are log2 transformed and / or median-centered to 10 prior to step (d). In some embodiments, the tumor cell content is a molecularly determined tumor cell content calculated based on somatic and germline variant allele frequencies and / or copy number profiles. In some embodiments, one, two, or three of the housekeeping genes are selected from CIAO1, EIF2B1, and HMBS.

[0237] In some embodiments, the collective biomarker score is determined by adding the measured expression level of the TROP2 gene product to approximately 1 / 3 to 2 / 3 of the determined proliferation gene expression level and approximately 4 to 8 times the determined tumor cell content.

[0238] In some embodiments, the collective biomarker score is determined as follows: Collective biomarker score = [TROP2] + 0.6 * [proliferation] + 6 * [tumor cell content].

[0239] In some embodiments, the expression products of one or more genes associated with TROP2 and proliferation are individually selected from ribonucleic acid (RNA) and protein. In some embodiments, the gene expression product of at least one of the one or more genes associated with TROP2 and proliferation is protein, and measuring its expression level requires the use of immunohistochemical techniques. In some embodiments, the gene expression product of at least one of the one or more genes associated with TROP2 and proliferation is RNA, and measuring its expression level requires the use of RNA sequencing techniques.

[0240] In some embodiments, the anti-TROP2-based therapy comprises an anti-TROP2 antibody or fragment thereof. In some embodiments, the anti-TROP2 antibody or fragment thereof is directly or indirectly conjugated to a cytotoxic drug. In some embodiments, the cytotoxic drug is a DNA replication inhibitor selected from the group consisting of alkylating agents, DNA polymerase inhibitors, nitrogen mustards, and topoisomerase inhibitors. In some embodiments, the anti-TROP2 antibody or fragment thereof is fused to a protein that is toxic to cancer cells. In some embodiments, the cytotoxic drug is a topoisomerase inhibitor. In some embodiments, the anti-TROP2-based therapy is sacituzumab govitecan.

[0241] In some embodiments, the subject has or is suspected of having a cancer for which an anti-TROP2-based therapy has not been approved for its indicated use. In some embodiments, the tumor tissue sample is or is suspected of containing bladder cancer, endometrial cancer, breast cancer, small cell lung cancer, prostate cancer, esophagogastric cancer, non-small cell lung cancer, pancreatic cancer, or colorectal cancer.

[0242] In some embodiments, the tumor tissue sample is a formalin-fixed, paraffin-embedded (FFPE) tumor tissue sample. In some embodiments, the tumor tissue sample contains at least 20% tumor content. In some embodiments, the method further includes administering an anti-TROP2-based therapy to the subject identified as likely to benefit from the anti-TROP2-based therapy. [Example]

[0243] Example 1

[0244] We considered three candidate biomarkers: TROP2 gene expression, cell proliferation gene expression, and molecularly defined tumor cell content (i.e., the percentage of tumor cells present in macro-dissected tumor specimens). Across solid tumors, TROP2 showed highest expression in epithelial tumors such as bladder, head and neck, vagina, and cervix, while lowest expression was observed in non-epithelial tumors such as glioma, GIST, lymphoma, melanoma, and sarcoma (Figure 2). Furthermore, TROP2 gene expression correlated with protein expression across 45 tumor types. 10 (r=0.93, Table 1, Figure 5). Proliferative gene expression showed low variability across tumor types, with small cell lung cancer (SCLC) standing out as the most proliferative tumor type (Figure 3). Tumor cell content showed wide variability across tumor types, with pancreatic cancer having the lowest median tumor cell content of 30%, and adrenal, CNS, GIST, and SCLC having median tumor cell content of 80% or higher (Figure 4). The three candidate biomarkers did not correlate with each other (Figure 6).

[0245] To evaluate the ability of the above biomarkers, alone and in combination, to predict SG response, we randomly divided 14,410 tumor profiles from nine tumor types with response data into a discovery cohort (n=7,177) and a validation cohort (n=7,233). Based on a weighted mean objective response rate of 14.7%, we fixed the overall positive biomarker rate at 25% for each candidate biomarker, expecting strong enrichment for response but not complete concordance. We then evaluated the Pearson correlation between tumor-type-specific biomarker rates and objective response rates. Individual biomarkers produced only weak correlations with SG response (Figure 6), failing to reach statistical significance: TROP2 expression (r=0.38, p=0.23), proliferation gene expression (r=0.15, p=0.76), and tumor cell content (r=0.35, p=0.43).

[0246] In contrast, the optimized linear equation combining all three biomarkers correlated strongly with response both when using tumor type-specific biomarker rates derived from the discovery cohort (r = .83, p = .006) (Figure 8) and the independent validation cohort (r = .82, p = .007) (Figure 1).

[0247] SG biomarker score = [TROP2] + 0.6 * [proliferation] + 6 * [tumor cell content]

[0248] Notably, the biomarker algorithm was far superior to TROP2 expression alone, suggesting that biomarker factors act synergistically to confer clinical response. Considering the mechanism of action of SG, a plausible model for response is (1) higher target expression leads to increased ADC binding, internalization, and payload cleavage; and (2) higher tumor cell content leads to a greater proportion of released payload molecules diffusing to neighboring tumor cells (i.e., ADC bystander effect). 11 and (3) the higher the tumor cell proliferation, the greater the likelihood that the payload molecule will block DNA replication and cause tumor cell death.12 The distribution of biomarker factors and positive biomarker calls across the complete cohort is shown in Figure 9, with the level of TROP2 expression required for a positive biomarker call varying dynamically as a function of tumor cell content and proliferation gene expression.

[0249] Next, we applied the biomarker algorithm to all tumor types represented in the complete cohort as outlined in Table 1 below. [Table 1-1] [Table 1-2]

[0250] Among tumor types with observed responses in basket trials, biomarker positivity rates ranged from 9.9% in colorectal cancer to 57.4% in bladder cancer. While objective response rates in unselected patients were sufficient for FDA approval in TNBC and bladder cancer, patient selection based on biomarker algorithms is likely to yield improved response rates in other tumor types. Our analysis identifies additional tumor types with high biomarker positivity rates, representing potential opportunities for further expanding the use of SG. Cancers of the head and neck, cervix, salivary gland, skin (non-melanoma), and ovary all had positive biomarker rates >30%, with rare squamous cell carcinomas of the penis (89%), anus (67%), and vulva (44%) having the highest biomarker rates among them. Given that the majority of tumor types had biomarker positivity rates >5%, the implications for pembrolizumab in patients with solid tumors with high tumor mutation burden are unclear. 13 A similar cross-tumor type biomarker approach could also be considered for SG.

[0251] In summary, we have identified a novel biomarker algorithm that can predict SG response across solid tumors. These biomarkers may improve the ability to select patients with an increased likelihood of benefiting from SG, providing opportunities not only to better tailor its use within currently approved indications but also to expand the benefits of SG to additional tumor types. Further research should further evaluate the biomarker algorithm in patients previously treated with SG and in prospective clinical trials. The biomarker approach of combining target expression with proliferation and tumor cell content to predict response is likely to be generalizable to ADCs as a class, with the potential to further optimize use and maximize benefit.

[0252] method Clinical Datasets

[0253] Tumor-type-specific objective response rates were assessed in nine selected tumor types enrolling 10 or more patients in the IMMU-12-01 basket trial. 8 Given the similar results, the overall objective response rate for breast cancer was set as the average of triple-negative and hormone receptor-positive breast cancer.

[0254] Molecular Datasets

[0255] Molecular data were analyzed using analytically validated comprehensive genomic profiling and targeted quantitative transcriptome profiling next-generation sequencing (NGS) studies as part of the Strata Trial (NCT03061305), a large-scale multicenter observational study (as previously described). 14,15 The molecular dataset was collected using StrataNGS (Strata Oncology, Ann Arbor, MI), a clinical trial program. The data were collected from consecutive patients with tumor surface area >2mm2 received between November 20, 2019 and July 14, 2022. 2The study included 23,968 formalin-fixed, paraffin-embedded tumor biopsies or resection specimens with a tumor surface area <2 mm. Other specimen types (e.g., needle biopsy aspirates, cell blocks, aspirates) and sizes (tumor surface area <2 mm) were excluded. 2 ) were excluded from the analysis. RNA-sequencing-based gene expression values ​​were log2-transformed and median-centered at 10. Proliferative gene expression was calculated as the mean of TOP2A and UBE2C. Molecularly defined tumor cell content was calculated based on somatic and germline variant allele frequencies and copy number profiles, and then analyzed as previously described. 14 , log2 transformed.

[0256] Gene vs. protein expression analysis

[0257] Published protein immunohistochemistry data 10 Tumor types from the study were mapped to a molecular dataset for comparison, and tumor-type-specific proportions of gene expression above the solid tumor median were compared to proportions with moderate or strong staining by Pearson's correlation coefficient (Figure 5, Table 2). [Table 2-1] [Table 2-2]

[0258] Predictive biomarker analysis

[0259] The molecular dataset was randomly divided into a discovery cohort and a validation cohort for the purpose of multivariate biomarker optimization (discovery) and validation (validation). For each quantitative biomarker evaluated (TROP2, proliferation gene expression, tumor cell content, SG biomarker score), the biomarker threshold was set so that the top 25% of samples in the nine tumor types with response data were biomarker positive and the bottom 75% were biomarker negative. The relationship between tumor type-specific positive biomarker rates and objective response rates was assessed using Pearson's correlation coefficient. The SG biomarker score coefficient was optimized in the discovery cohort by fixing the TROP2 coefficient at 1.0 and the biomarker positive rate at 25%, and then varying the proliferation gene expression and tumor cell content coefficients in 0.1 increments to maximize the Pearson coefficient. The SG biomarker score was then evaluated using the validation cohort.

[0260] Example 2

[0261] In an attempt to further enhance the predictive power of the ADC Treatment Response Score as a pan-cancer predictor of response to ADC therapy as a group, additional molecular variables were considered. The data used in this example included 25 published ADC objective response rates (ORRs) along with over 15,000 high-quality v4 RNA libraries obtained from the Strata Trial (NCT03061305).

[0262] Molecular candidates were first identified by analyzing and ranking the absolute correlation between available distinct gene product expression levels and published ADC objective response rates. The top 20 ranked gene expression products and copy number variant (CNV) genes were selected for further investigation, as shown in Figure 15.

[0263] The top 20 selected gene expression products and CNVs were individually added to previously identified variables, including expression levels of gene products targeted by the corresponding ADC (ADC target), tumor cell content (TC), and / or proliferation gene expression (proliferation), to train against all non-TROP2 ADC objective response rates (n = 16). Leave-one-out cross-validation (LOOCV) was performed on the training set, holding out the TACSTD2 ADC ORR data points. All variables were Z-score normalized before cross-validation. Parameters of p < 0.05 and combined correlation coefficient (CCC) > 0.8 were used to screen candidate biomarkers for validation against the TROP2 dataset. During validation, parameters of q < 0.05 and CCC > 0.8 were used as cutoffs to identify validated biomarkers. Surprisingly, PVR gene expression, when combined with ADC target, proliferation, and, optionally, TC, was the only top-ranked gene expression variable that provided a validated, robust ADC treatment response score despite numerous validation attempts. Thus, we have shown that the claimed ADC treatment response score outperforms similar predictors that include gene expression parameters that appear to be highly correlated with observed response rates in the training set but have failed to be validated.

[0264] Example 3

[0265] To facilitate the identification of additional candidate model features that further improve the sensitivity and specificity of the model, ARCHS 4 We performed an analysis of publicly available whole-transcriptome RNA read count data (n=33,070) through 17 First, the raw expression read count data were normalized using the DESeq2 method, which takes into account RNA alternative splicing. 18Next, the cancer tissue cohort for each tissue type was divided into two groups via the tandem tumor quartile, and the resulting tissue-specific population portions were then compared to the corresponding tumor-specific objective response rates across various ADC targets via Pearson correlation. The resulting list was ranked by anticorrelation to identify candidates that could balance the primary model factors, ADC target expression and proliferation, that both positively correlated with response rate. The candidate list was filtered to remove genes that exhibited tissue-specific variance in normal function, which could confound response correlation analysis. Thus, only genes with between 30% and 70% of function for each tissue type above the tandem tissue median were considered for subsequent analysis. Finally, genes whose expression in the normal tissue dataset was anticorrelated with ADC response rate (Pearson correlation < -0.3) were eliminated from further consideration because their correlation in the cancer dataset is likely not disease-related.

[0266] The remaining 500 most anti-correlated genes were then examined via gene set enrichment analysis to identify any patterns of common molecular function, biological function, or cellular components. These genes were found to be highly enriched for the cellular component cluster involved in cell adhesion, with 27 of the 500 genes involved in two or more related cellular components, including adherens junctions, anchoring junctions, cell-substrate adherens junctions, cell-substrate junctions, and focal adhesions. These 27 genes and the related cellular components they affect are found in Table 3 below. [Table 3]

[0267] *******

[0268] The description of the embodiments of the present disclosure is not intended to be exhaustive or to limit the disclosure to the precise form disclosed. Specific embodiments of, and examples for, the present disclosure are described herein for illustrative purposes; however, as those skilled in the relevant art will recognize, various equivalent modifications are possible within the scope of the present disclosure. For example, while method steps or functions are shown in a given order, alternative embodiments may perform the functions in a different order, or may perform the functions substantially simultaneously. The teachings of the disclosure provided herein may be applied to other procedures or methods, where appropriate. The various embodiments described herein may be combined to provide further embodiments. Aspects of the present disclosure may be modified, where necessary, to employ the compositions, functions, and concepts of the above documents and the present application to provide still further embodiments of the present disclosure. These and other changes may be made to the present disclosure in light of the detailed description.

[0269] Specific elements of any of the foregoing embodiments can be combined with or substituted for elements in other embodiments. Furthermore, while advantages associated with certain embodiments of the present disclosure are described in the context of those embodiments, other embodiments may also exhibit such advantages, and not all embodiments necessarily exhibit such advantages to fall within the scope of the present disclosure.

[0270] All patents and other publications identified are expressly incorporated herein by reference for the purpose of describing and disclosing, for example, methodology described in such publications that may be used in connection with the present invention. These publications are provided solely for their disclosure prior to the filing date of the present application. Nothing in this regard should be construed as an admission that the inventors are not entitled to antedate such disclosure by virtue of prior invention or prior publication, or for any other reason. All statements as to the date of these documents or indications of a date as to the contents thereof are based on the information available to the applicant and do not constitute any admission as to the accuracy of the dates or contents of these documents.

[0271] Those skilled in the art will readily appreciate that the present invention is well adapted to carry out the objects and obtain the objects and advantages mentioned, as well as those inherent therein. The details of the description and examples herein represent certain specific embodiments and are exemplary and are not intended as limitations on the scope of the invention. Modifications therein and other uses will occur to those skilled in the art. These modifications are encompassed within the spirit of the invention. It will be readily apparent to those skilled in the art that various substitutions and modifications can be made to the invention disclosed herein without departing from the scope and spirit of the invention.

[0272] The articles "a" and "an," as used herein in the specification and claims, should be understood to include plural references unless clearly indicated to the contrary. A claim or description including "or" between one or more members of a group is deemed to be satisfied when one, more than one, or all of the members of the group are present in, employed in, or otherwise relevant to a given product or process, unless indicated to the contrary or otherwise clear from the context. The invention includes embodiments in which exactly one member of the group is present in, employed in, or otherwise relevant to a given product or process. The invention also includes embodiments in which more than one, or all of the group are present in, employed in, or otherwise relevant to a given product or process. Furthermore, it should be understood that the invention provides all variations, combinations, and permutations in which one or more limitations, elements, clauses, descriptive terminology, etc. from one or more of the recited claims are introduced into another claim dependent on the same base claim (or any other claim, as relevant) unless otherwise indicated or unless it is obvious to one of ordinary skill in the art that a contradiction or inconsistency would result. It is contemplated that all embodiments described herein are applicable to all different aspects of the invention, where appropriate. It is also contemplated that any of the above embodiments or aspects may be freely combined with one or more other such embodiments or aspects, where appropriate. When elements are presented as lists, e.g., Markush groups or similar, it is to be understood that each subgroup of the elements is also disclosed, and that any element may be removed from the group. In general, when the invention, or an aspect of the invention, is referred to as comprising particular elements, features, etc., it is to be understood that a particular embodiment of the invention or aspect of the invention consists of or consists essentially of such elements, features, etc. For purposes of simplicity, those embodiments have not in every instance been specifically set forth in so many words herein.It should also be understood that any embodiment or aspect of the invention may be explicitly excluded from the claims, regardless of whether the specific exclusion is set forth herein. For example, any one or more active agents, additives, ingredients, optional agents, organism types, disorders, subjects, or combinations thereof may be excluded.

[0273] Where a claim or description relates to a composition of matter, it should be understood that methods of making or using the composition of matter according to any of the methods disclosed herein, and methods of using the composition of matter for any of the purposes disclosed herein, are aspects of the invention unless otherwise indicated or it would be obvious to one of ordinary skill in the art that a contradiction or inconsistency would arise. Where a claim or description relates to a method, it should be understood that, for example, methods of making compositions useful for practicing the method, and products produced according to the method, are aspects of the invention unless otherwise indicated or it would be obvious to one of ordinary skill in the art that a contradiction or inconsistency would arise.

[0274] When ranges are presented herein, the invention includes embodiments in which the endpoints are included, embodiments in which both endpoints are excluded, and embodiments in which one endpoint is included and the other endpoint is excluded. Unless otherwise indicated, both endpoints should be assumed to be inclusive. Furthermore, unless otherwise indicated or apparent from the context and the understanding of one of ordinary skill in the art, values ​​expressed as ranges should be understood to contemplate any specific value or subrange within the stated range, to the tenth of the unit of the lower limit of the range, in different embodiments of the invention, unless the context clearly dictates otherwise. When a series of numerical values ​​is set forth herein, it is also understood that the invention includes embodiments related analogously to any intervening value or range defined by any two values ​​in the series, with the lowest value taken as a minimum and the highest value taken as a maximum. Numerical values, as used herein, include values ​​expressed as percentages. For any embodiment of the invention in which a numerical value is preceded by "about" or "approximately," the invention includes an embodiment in which the exact value is recited. For any embodiment of the invention where a numerical value is not preceded by "about" or "approximately," the invention includes an embodiment where the value is preceded by "about" or "approximately."

[0275] "Approximately" or "about" generally includes numbers that fall within 1% of the number, or in some embodiments, within 5% of the number, or in some embodiments, within 10% of the number, in either direction (greater or less than the number), unless otherwise stated or otherwise clear from the context (except where such number would impermissibly exceed 100% of the possible value). Unless clearly indicated to the contrary, in any method claimed herein that includes more than one act, the order of the acts of the method is not necessarily limited to the order in which the acts of the method are recited, but it should be understood that the invention also includes embodiments in which the order is so limited. It should also be understood that any product or composition described herein can be considered "isolated" unless otherwise indicated or clear from the context. Table 4-1 Table 4-2

Claims

1. 1. A method for identifying a subject as likely to respond to one or more antibody-drug conjugate (ADC) therapies, the method comprising: (A) measuring from a biological tissue sample obtained from said subject the expression level of at least one gene product associated with each of said one or more ADC therapies; (B) in the same biological tissue sample of step (A), (i) at least one gene product associated with cell adhesion, and (ii) one or more gene products associated with proliferation; measuring the expression level of one or both of wherein when the expression levels of more than one gene product associated with proliferation are measured, calculating therefrom the average of all of the measured expression levels of gene products associated with proliferation, thereby obtaining a proliferation gene expression level; (C) optionally determining tumor cell content in the same tumor tissue sample of steps (A) and (B); (D)(1) identifying subjects who are likely to respond to said one or more ADC therapies if one or more of the calculated ADC treatment response scores (ADC TRS) is above one or more corresponding predetermined thresholds associated with a positive response to said one or more ADC therapies, wherein each of said one or more ADC TRS is: (i) the measured expression level of the at least one gene product associated with a corresponding ADC treatment; and below: (ii) the measured expression level of at least one gene product associated with cell adhesion; (iii) the determined proliferation gene expression level, and (iv) the determined tumor cell content; and determining from at least two of A method that encompasses

2. 2. The method of claim 1, wherein the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with the corresponding ADC therapy, the expression level of at least one gene product associated with cell adhesion, and the determined proliferation gene expression level.

3. 3. The method of claim 2, wherein the determined proliferation gene expression level and the expression level of the at least one gene product associated with a corresponding ADC therapy are both positively associated with the likelihood that the patient will respond to the corresponding ADC therapy, and the expression level of the at least one gene product associated with cell adhesion is negatively associated with the likelihood that the patient will respond to the same corresponding ADC therapy.

4. 2. The method of claim 1, wherein the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with a corresponding ADC therapy, the measured expression level of at least one gene product associated with cell adhesion, and tumor cell content.

5. 5. The method of claim 4, wherein the tumor cell content and the expression level of the at least one gene product associated with the corresponding ADC therapy are both positively associated with the likelihood that a patient will respond to the corresponding ADC therapy, and the expression level of the at least one gene product associated with cell adhesion is negatively associated with the likelihood that a patient will respond to the same corresponding ADC therapy.

6. 2. The method of claim 1, wherein the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with corresponding ADC therapy, the expression level of at least one gene product associated with cell adhesion, the determined proliferation gene expression level, and tumor cell content.

7. The method of claim 6, wherein the determined proliferation gene expression level, the tumor cell content, and the expression level of the at least one gene product associated with a corresponding ADC therapy are each positively associated with the likelihood that the patient will respond to the corresponding ADC therapy, and the expression level of at least one gene product associated with cell adhesion is negatively associated with the likelihood that the patient will respond to the same corresponding ADC therapy.

8. 2. The method of claim 1, wherein the at least one gene product associated with each of the one or more ADC therapies comprises an RNA transcript individually selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2 MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, p79, EGFR, integrin beta-6, ROR1, Globo H, IL2RA, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, NCAM1, LY75, ALCAM, and CEACAM5.

9. The step of measuring the expression level of one or more gene products associated with proliferation may include measuring the expression level of one or more gene products associated with proliferation, such as BIRC5, BRCA1, BRCA2, BUBl, BUBlB, CCNA2, CCNB1, CCNB2, CCNE1, CCNE2, CCNF, CCNG2, CDC2, CDC20, CDC25A, CDC25B, CDC25C, CDC45L, CDC6, CDC7, CDCKN1A p21, CDCKN3, CDK4, CDKN1C, CDKN2A, CDKN2C, CDKN2D p19, CDKN3, CENPA, CENPE, CENPF, CHAF1A, CHK1, CKS1, CKS2, CKS2, DHFR, DHFR, E2-EP F, E2F1, E2F3, E2F5, FEN1, FOXM1, KNSL2, KNSL5, KPNA2, LMNB2, MAD2, MAD2L1, MAPK13 , MCM2, MCM3, MCM4, MCM5, MCM6, MK167, MNAT1, MSH2, MYBL2, NASP, NEK2, NPAT, ORC1, PA2G4, PCNA, PES1, PKMYT1, PLK, PRIM1, PTTG1, RAB6KIFL, RACGAP1, RAD51, RAD54L, RAN RAN, RFC4, RPA3, RRM1, RRM2, SLBP, STK12, STK15, STK18, STK6, TOP2A, TROAP, TUBB, TYMS, UBE2C, and UNG.

10. The method of claim 9, wherein the step of measuring the expression level of one or more gene products associated with proliferation comprises measuring the expression level of one, two or all three of the MYBL2, TOP2A, and / or UBE2C gene products.

11. The method of claim 1, wherein each of the gene products associated with cell adhesion affects at least two of cellular adherens junctions, anchoring junctions, cell-substrate adherens junctions, cell-substrate junctions, or focal adhesions.

12. 2. The method of claim 1, wherein the step of measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression level of a gene product of at least one gene selected from the group consisting of ATP2A2, BAIAP2, CD151, CHP1, CYFIP1, CYTH3, DAG1, DSC2, GIT1, HSP90B1, HSPA5, LIMK1, MAPK1, PACSIN2, PDIA3, PVR, REXO2, RPL22, RPLP1, RPLP2, RPS11, RPS16, RPS5, SDCBP SNAP23, SNTB1, and SRP68.

13. 13. The method of claim 12, wherein said step of measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression of a PVR gene product.

14. The method of claim 12, wherein said step of measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression level of a single gene product associated with cell adhesion.

15. 15. The method of claim 14, wherein the single gene product is a PVR gene product.

16. 15. The method of claim 14, wherein the single gene product is not a PVR gene product.

17. The method of claim 1, wherein the step of measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression levels of two or more gene products associated with cell adhesion, and then determining the cell adhesion gene product expression level by averaging the expression of the two or more gene products associated with cell adhesion.

18. 4. The method of claim 3, wherein the expression level of the at least one gene product associated with a corresponding ADC treatment is weighted by a factor of approximately 0.3 to 0.65, the expression level of the at least one gene product associated with cell adhesion is weighted by a factor of approximately -0.8 to -1, and the determined proliferation gene expression levels are weighted by a factor of approximately 0.2 to 0.

4.

19. 6. The method of claim 5, wherein the expression level of the at least one gene product associated with a corresponding ADC therapy is weighted by a factor of approximately 0.3, the expression level of the at least one gene product associated with cell adhesion is weighted by a factor of approximately -0.9, and the tumor cell content is weighted by a factor of approximately 0.

8.

20. 8. The method of claim 7, wherein the expression level of the at least one gene product associated with a corresponding ADC treatment is weighted by a factor of approximately 0.45, the expression level of the at least one gene product associated with cell adhesion is weighted by a factor of approximately -1, the determined proliferation gene expression levels are weighted by a factor of approximately 0.55, and the tumor cell content is weighted by a factor of approximately 0.

07.

21. 10. The method of claim 1, wherein each of the one or more ADC TRSs is determined by further taking into account a bias variable, wherein the bias variable is a static offset adjusted to produce biomarker frequencies that match published objective response rates in clinical trials.

22. 22. The method of claim 21, wherein the bias variable is weighted by a factor of approximately −0.

25.

23. 2. The method of claim 1, wherein the predetermined threshold is set at percentiles of ranked ADC treatment response scores determined from tumor tissue samples of a first cohort of subjects utilizing at least the expression level of the at least one gene product associated with a first corresponding ADC therapy, wherein each percentile corresponds to a greater percentage of subjects in the first cohort who do not respond to the same first corresponding ADC therapy.

24. 2. The method of claim 1, wherein the predetermined threshold is set at a percentile of ranked ADC treatment response scores determined from tumor tissue samples of first and second cohorts of subjects utilizing expression levels of at least first and second gene products associated with first and second corresponding ADC therapies, wherein each percentile corresponds to a greater percentage of subjects in the first and second cohorts who do not respond to the at least first and second corresponding ADC therapies.

25. 2. The method of claim 1, wherein the predetermined threshold is zero and an ADC TRS indicating that the subject is likely to respond to ADC therapy is an ADC TRS having a value greater than zero.

26. 25. The method of claim 23 or 24, wherein the first and / or second cohort of subjects is a pan-cancer cohort or a matched tumor type cohort.

27. 27. The method of claim 26, wherein the first cohort of subjects and the second cohort of subjects are the same cohort of subjects.

28. 2. The method of claim 1, wherein the expression level of each of the at least one gene product associated with one or more ADC therapies, the expression level of the at least one gene product associated with cell adhesion, and the tumor cell content are log2 transformed and / or Z-score normalized prior to step (D)(1), and the expression level of the one or more gene products associated with proliferation is log2 transformed and / or Z-score normalized prior to averaging the expression levels of the gene products associated with proliferation to obtain the proliferation gene expression level in step (B).

29. 2. The method of claim 1, wherein the tumor cell content is a molecularly determined tumor cell content calculated based on somatic and germline variant allele frequencies and / or copy number profiles.

30. 2. The method of claim 1, further comprising measuring the expression level of at least one housekeeping gene selected from CIAO1, EIF2B1, and HMBS in the tumor tissue sample, and normalizing the expression levels of the at least one gene product associated with the one or more ADC therapies, the at least one gene product associated with cell adhesion, and the one or more gene products associated with proliferation to the at least one housekeeping gene expression level to obtain normalized expression levels of the at least one gene product associated with each of the one or more ADC therapies, the at least one gene product associated with cell adhesion, and the one or more gene products associated with proliferation.

31. 10. The method of claim 1, wherein the expression products of the at least one gene associated with each of the one or more ADC therapies, one or more genes associated with cell adhesion, and proliferation are individually selected from ribonucleic acid (RNA) and protein.

32. 32. The method of claim 31 , wherein the gene expression products of the at least one gene associated with each of the one or more ADC therapies, the one or more genes associated with cell adhesion, and proliferation are proteins, and measuring the expression levels thereof requires utilizing immunohistochemical techniques.

33. 32. The method of claim 31 , wherein the gene expression products of the at least one gene associated with each of the one or more ADC therapies, the one or more genes associated with cell adhesion, and proliferation are RNA, and measuring the expression levels thereof requires utilizing RNA sequencing techniques.

34. 2. The method of claim 1, wherein each of the one or more ADC therapies comprises a monoclonal antibody, at least one functional fragment thereof, or a bispecific antibody targeting at least one epitope of at least one antigen selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2 MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, P79, EGFR, integrin beta-6, ROR1, Globo H, IL2RA, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, NCAM1, LY75, ALCAM, and CEACAM5.

35. 35. The method of claim 34, wherein the antibody comprises a bispecific antibody capable of targeting two epitopes of the same antigen or epitopes of two separate antigens, wherein the same antigen or the two antigens are selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2 MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, p79, EGFR, integrin beta-6, ROR1, Globo H, IL2RA, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, NCAM1, LY75, ALCAM, and CEACAM5.

36. 35. The method of claim 34, wherein the antibody or fragment thereof is directly or indirectly conjugated to a cytotoxic drug.

37. 37. The method of claim 36, wherein the cytotoxic drug is a DNA replication inhibitor selected from the group consisting of alkylating agents, DNA polymerase inhibitors, nitrogen mustards, and topoisomerase inhibitors.

38. 35. The method of claim 34, wherein the antibody, the at least one functional fragment thereof, or the bispecific antibody is fused to a protein that is toxic to cancer cells.

39. 37. The method of claim 36, wherein the cytotoxic drug is a topoisomerase inhibitor.

40. 10. The method of claim 1, wherein the subject has or is suspected of having a cancer for which the one or more ADC therapies are not approved for an indicated use.

41. 2. The method of claim 1, wherein the tumor tissue sample is or is suspected to contain bladder cancer, salivary gland cancer, endometrial cancer, ovarian cancer, cervical cancer, head and neck cancer, non-melanoma skin cancer, thyroid cancer, cancer of unknown primary origin, cancer of the central or peripheral nervous system, neuroendocrine tumor, melanoma, esophagogastric cancer, small intestine cancer, sarcoma, hepatobiliary cancer, pancreatic cancer, gastrointestinal stromal tumor, renal cell carcinoma, glioma, appendix cancer, breast cancer, small cell lung cancer, prostate cancer, esophagogastric cancer, non-small cell lung cancer, pancreatic cancer, lymphoma, or colorectal cancer.

42. 2. The method of claim 1, wherein the tumor tissue sample is a formalin-fixed, paraffin-embedded (FFPE) tumor tissue sample.

43. 10. The method of claim 1, wherein the tumor tissue sample contains at least 20% tumor content.

44. 10. The method of claim 1, further comprising step (E) administering said at least one of said one or more ADC therapies to a subject identified in step (D)(1) as likely to respond to said one or more ADC therapies.

45. 3. The method of claim 2, wherein each of the one or more ADC TRSs is determined without taking into account tumor cell content.

46. 1. A method for selecting from one or more antibody-drug conjugate (ADC) therapies among two or more ADC therapies identified as most beneficial for treating cancer in a subject, the method comprising: (A) measuring the expression level of at least one gene product associated with each of the or more ADC treatments from a biological tissue sample obtained from the subject; (B) in the same biological tissue sample of step (A), (i) at least one gene product associated with cell adhesion, and (ii) one or more gene products associated with proliferation measuring the expression level of one or both of wherein if the expression levels of more than one gene product associated with proliferation are measured, calculating therefrom the average of all expression levels of said measured gene products associated with proliferation, thereby obtaining a proliferation gene expression level; (C) optionally, determining tumor cell content in the same tumor tissue sample of steps (A) and (B); (D)(1) calculating an ADC treatment response score (ADC TRS) for each of said two or more ADC therapies and determining that each of said two or more ADC TRS is above a predetermined threshold associated with a beneficial patient treatment outcome, wherein each of said one or more ADC TRS is (i) the measured expression level of the at least one gene product associated with a corresponding ADC treatment; and below: (ii) the measured expression level of at least one gene product associated with cell adhesion; (iii) the determined proliferation gene expression level, and (iv) the determined tumor cell content; and determining from at least two of (D)(2) if ADC TRSs associated with two or more ADC therapies are above a predetermined threshold associated with a beneficial patient treatment outcome, ranking said at least two ADC TRSs by the value by which each ADC treatment response score exceeds said predetermined threshold, and selecting the highest ranked ADC therapy for administration to the subject; A method that encompasses

47. 47. The method of claim 46, further comprising a step (E) of administering the selected highest ranked ADC therapy to the subject.

48. 48. The method of claim 47, further comprising administering to the subject, in combination with the highest ranked ADC therapy, at least one other, lower ranked ADC that exceeds the predetermined threshold.

49. 48. The method of claim 47, wherein step (E) does not comprise administering another ADC therapy in combination with the highest ranked ADC therapy.

50. 1. A method for treating cancer in a subject determined to be likely to respond to one or more antibody-drug conjugate (ADC) therapies, the method comprising: (a) in a tumor tissue sample obtained from the subject: i) at least one gene product associated with each corresponding one or more ADC treatments and the expression level of below: ii) at least one gene product associated with cell adhesion, and iii) one or more gene products associated with proliferation; and measuring the expression level of at least one of: (b) measuring the expression levels of one or more housekeeping genes in the same tumor tissue sample of step (a), and further normalizing the expression levels of the at least one gene product associated with each of the one or more ADC therapies, the at least one gene product associated with cell adhesion, and one or more gene products associated with proliferation of step (a) to the expression levels of the one or more housekeeping genes to obtain normalized expression levels of each corresponding one or more gene products associated with the one or more ADC therapies, the at least one gene product associated with cell adhesion, and one or more genes associated with proliferation; (c) if gene products of one or more genes associated with proliferation are measured and normalized, determining proliferation gene expression levels by averaging the normalized expression levels of said one or more gene products associated with proliferation; (d) optionally, determining tumor cell content in the same tumor tissue sample of steps (a) and (b); (e)(1) identifying subjects who are likely to benefit from the one or more ADC therapies if one or more of the calculated ADC treatment response scores (ADC TRS) are above one or more corresponding predetermined thresholds, wherein each of the one or more ADC TRSs is: (i) the measured expression level of the at least one gene product associated with a corresponding ADC treatment; and below: (ii) the measured level of the at least one gene product associated with cell adhesion; (iii) the determined proliferation gene expression level, and (iv) the determined tumor cell content; and determining from at least two of (f) administering an effective amount of said one or more ADC therapies to a subject identified as likely to benefit from said one or more ADC therapies; A method that encompasses

51. 51. The method of claim 50, wherein the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with a corresponding ADC therapy, the measured expression level of the at least one gene product associated with cell adhesion, and the determined proliferation gene expression level.

52. 51. The method of claim 50, wherein the determined proliferation gene expression level and the expression level of the at least one gene product associated with a corresponding ADC therapy are both positively associated with the likelihood that the patient will respond to the corresponding ADC therapy, and the measured expression level of the at least one gene product associated with cell adhesion is negatively associated with the likelihood that the patient will respond to the same corresponding ADC therapy.

53. 51. The method of claim 50, wherein the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with a corresponding ADC therapy, the measured expression level of the at least one gene product associated with cell adhesion, and tumor cell content.

54. 51. The method of claim 50, wherein the tumor cell content and the expression level of the at least one gene product associated with a corresponding ADC therapy are both positively associated with the likelihood that a patient will respond to the corresponding ADC therapy, and the measured expression level of the at least one gene product associated with cell adhesion is negatively associated with the likelihood that a patient will respond to the same corresponding ADC therapy.

55. 51. The method of claim 50, wherein the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with a corresponding ADC therapy, the measured expression level of at least one gene product associated with cell adhesion, the determined proliferation gene expression level, and tumor cell content.

56. 51. The method of claim 50, wherein the determined proliferation gene expression level, the tumor cell content, and the expression level of the at least one gene product associated with a corresponding ADC therapy are each positively associated with the likelihood that the patient will respond to the corresponding ADC therapy, and the measured expression level of at least one gene product associated with cell adhesion is negatively associated with the likelihood that the patient will respond to the same corresponding ADC therapy.

57. 51. The method of claim 50, wherein the one or more housekeeping genes comprise three genes selected from CIAO1, EIF2B1, HMBS, CTCF, GGNBP2, ITGB7, MYC, and SLC4A1AP.

58. 51. The method of claim 50, wherein the at least one gene product associated with each of the one or more ADC therapies comprises an RNA transcript individually selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2 MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, p79, EGFR, integrin beta-6, ROR1, Globo H, IL2RA, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, NCAM1, LY75, ALCAM, and CEACAM5.

59. The step of measuring the expression level of one or more gene products associated with proliferation may include measuring the expression level of one or more gene products associated with proliferation, such as BIRC5, BRCA1, BRCA2, BUBl, BUBlB, CCNA2, CCNB1, CCNB2, CCNE1, CCNE2, CCNF, CCNG2, CDC2, CDC20, CDC25A, CDC25B, CDC25C, CDC45L, CDC6, CDC7, CDCKN1A p21, CDCKN3, CDK4, CDKN1C, CDKN2A, CDKN2C, CDKN2D p19, CDKN3, CENPA, CENPE, CENPF, CHAF1A, CHK1, CKS1, CKS2, CKS2, DHFR, DHFR, E2-EP F, E2F1, E2F3, E2F5, FEN1, FOXM1, KNSL2, KNSL5, KPNA2, LMNB2, MAD2, MAD2L1, MAPK13 , MCM2, MCM3, MCM4, MCM5, MCM6, MK167, MNAT1, MSH2, MYBL2, NASP, NEK2, NPAT, ORC1, PA2G4, PCNA, PES1, PKMYT1, PLK, PRIM1, PTTG1, RAB6KIFL, RACGAP1, RAD51, RAD54L, RAN RAN, RFC4, RPA3, RRM1, RRM2, SLBP, STK12, STK15, STK18, STK6, TOP2A, TROAP, TUBB, TYMS, UBE2C, and UNG.

60. 51. The method of claim 50, wherein the step of measuring the expression level of one or more gene products associated with proliferation comprises measuring the expression level of one, two or all three of the MYBL2, TOP2A, and / or UBE2C gene products.

61. 51. The method of claim 50, wherein each of the gene products associated with cell adhesion affects at least two of cellular adherens junctions, anchoring junctions, cell-substrate adherens junctions, cell-substrate junctions, or focal adhesions.

62. 51. The method of claim 50, wherein the step of measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression level of a gene product of at least one gene selected from the group consisting of ATP2A2, BAIAP2, CD151, CHP1, CYFIP1, CYTH3, DAG1, DSC2, GIT1, HSP90B1, HSPA5, LIMK1, MAPK1, PACSIN2, PDIA3, PVR, REXO2, RPL22, RPLP1, RPLP2, RPS11, RPS16, RPS5, SDCBP SNAP23, SNTB1, and SRP68.

63. 63. The method of claim 62, wherein said step of measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression of a PVR gene product.

64. 63. The method of claim 62, wherein said step of measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression level of a single gene product associated with cell adhesion.

65. 65. The method of claim 64, wherein the single gene product is a PVR gene product.

66. 65. The method of claim 64, wherein the single gene product is not a PVR gene product.

67. The method of claim 50, wherein measuring the expression level of at least one gene product associated with cell adhesion comprises measuring the expression levels of two or more gene products associated with cell adhesion, and then determining the cell adhesion gene product expression level by averaging the expression of the two or more gene products associated with cell adhesion.

68. 53. The method of claim 52, wherein the expression level of the at least one gene product associated with a corresponding ADC therapy is weighted by a factor of approximately 0.3 to 0.65, the expression level of the at least one gene product associated with cell adhesion is weighted by a factor of approximately -0.8 to -1, and the determined proliferation gene expression levels are weighted by a factor of approximately 0.2 to 0.

4.

69. 55. The method of claim 54, wherein the expression level of the at least one gene product associated with a corresponding ADC therapy is weighted by a factor of approximately 0.3, the expression level of the at least one gene product associated with cell adhesion is weighted by a factor of approximately -0.9, and the tumor cell content is weighted by a factor of approximately 0.

8.

70. 57. The method of claim 56, wherein the expression level of the at least one gene product associated with corresponding ADC therapy is weighted by a factor of approximately 0.45, the expression level of the at least one gene product associated with cell adhesion is weighted by a factor of approximately -1, the determined proliferation gene expression levels are weighted by a factor of approximately 0.55, and the tumor cell content is weighted by a factor of approximately 0.

07.

71. 51. The method of claim 50, wherein each of the one or more ADC TRSs is determined by further taking into account a bias variable, wherein the bias variable is a static offset adjusted to produce biomarker frequencies that match published objective response rates in clinical trials.

72. 72. The method of claim 71, wherein the bias variable is weighted by a factor of approximately −0.

25.

73. 51. The method of claim 50, wherein the predetermined threshold is set at a percentile of ranked ADC treatment response scores determined from tumor tissue samples of a first cohort of subjects utilizing at least the expression level of the at least one gene product associated with a first corresponding ADC therapy, wherein each percentile corresponds to a greater percentage of subjects in the first cohort who do not respond to the same first corresponding ADC therapy.

74. 51. The method of claim 50, wherein the predetermined threshold is set at a percentile of ranked ADC treatment response scores determined from tumor tissue samples of a first and second cohort of subjects utilizing expression levels of at least a first and a second gene product associated with a first and a second corresponding ADC therapies, wherein each percentile corresponds to a greater percentage of subjects in the first and second cohorts who do not respond to the at least a first and a second corresponding ADC therapies.

75. 51. The method of claim 50, wherein the predetermined threshold is zero and an ADC TRS indicating that the subject is likely to respond to ADC therapy is an ADC TRS having a value greater than zero.

76. 75. The method of claim 73 or 74, wherein the first and / or second cohort of subjects is a pan-cancer cohort or matched tumor type.

77. 77. The method of claim 76, wherein the first cohort of subjects and the second cohort of subjects are the same cohort of subjects.

78. 51. The method of claim 50, wherein the gene expression level of the at least one gene product associated with each of one or more ADC therapies, the measured expression of the at least one gene product associated with cell adhesion, and the tumor cell content are log2 transformed and / or Z-score normalized prior to step (e)(1), and the expression level of the one or more gene products associated with proliferation is log2 transformed and / or Z-score normalized prior to averaging the expression levels of gene products associated with proliferation to obtain the proliferation gene expression level in step (c).

79. 51. The method of claim 50, wherein said tumor cell content is a molecularly determined tumor cell content calculated based on somatic and germline variant allele frequencies and / or copy number profiles.

80. 51. The method of claim 50, wherein the expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene associated with cell adhesion, and the one or more genes associated with proliferation are individually selected from ribonucleic acid (RNA) and protein.

81. 81. The method of claim 80, wherein the gene expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene associated with cell adhesion, and the one or more genes associated with proliferation are proteins, and measuring the expression levels thereof requires utilizing immunohistochemical techniques.

82. 81. The method of claim 80, wherein the gene expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene associated with cell adhesion, and the one or more genes associated with proliferation are RNA, and measuring the expression levels thereof requires utilizing RNA sequencing techniques.

83. 51. The method of claim 50, wherein each of the one or more ADC therapies comprises a monoclonal antibody, at least one functional fragment thereof, or a bispecific antibody targeting at least one epitope of at least one antigen selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2 MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, P79, EGFR, integrin beta-6, ROR1, Globo H, IL2RA, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, NCAM1, LY75, ALCAM, and CEACAM5.

84. 84. The method of claim 83, wherein the antibody comprises a bispecific antibody capable of targeting two epitopes of the same antigen or epitopes of two separate antigens, wherein the same antigen or the two antigens are selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2 MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, p79, EGFR, integrin beta-6, ROR1, Globo H, IL2RA, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, NCAM1, LY75, ALCAM, and CEACAM5.

85. 84. The method of claim 83, wherein the antibody or fragment thereof is directly or indirectly conjugated to a cytotoxic drug.

86. 86. The method of claim 85, wherein the cytotoxic drug is a DNA replication inhibitor selected from the group consisting of alkylating agents, DNA polymerase inhibitors, nitrogen mustards, and topoisomerase inhibitors.

87. 84. The method of claim 83, wherein the antibody, the at least one functional fragment thereof, or the bispecific antibody is fused to a protein that is toxic to cancer cells.

88. 86. The method of claim 85, wherein the cytotoxic drug is a topoisomerase inhibitor.

89. 51. The method of claim 50, wherein the subject has or is suspected of having a cancer for which the one or more ADC therapies are not approved for an indicated use.

90. 51. The method of claim 50, wherein the tumor tissue sample is or is suspected to contain bladder cancer, salivary gland cancer, endometrial cancer, ovarian cancer, cervical cancer, head and neck cancer, non-melanoma skin cancer, thyroid cancer, cancer of unknown primary, cancer of the central or peripheral nervous system, neuroendocrine tumor, melanoma, esophagogastric cancer, small intestine cancer, sarcoma, hepatobiliary cancer, pancreatic cancer, gastrointestinal stromal tumor, renal cell carcinoma, glioma, appendix cancer, breast cancer, small cell lung cancer, prostate cancer, esophagogastric cancer, non-small cell lung cancer, pancreatic cancer, lymphoma, or colorectal cancer.

91. 51. The method of claim 50, wherein the tumor tissue sample is a formalin-fixed, paraffin-embedded (FFPE) tumor tissue sample.

92. 51. The method of claim 50, wherein the tumor tissue sample comprises at least 20% tumor content.

93. 51. The method of claim 50, wherein the one or more ADC therapies comprise at least two therapies, and step (e)(1) comprises calculating at least two ADC treatment response scores, and wherein if the subject is identified as likely to respond to at least two ADC therapies, the method further comprises step (e)(2) ranking the at least two ADC treatment response scores by the value by which each ADC treatment response score exceeds the predetermined threshold and identifying the highest ranked ADC therapy as most likely to benefit the subject, and in step (f), administering to the subject an effective amount of the highest ranked ADC therapy.

94. 1. A computer-implemented method for selecting patients presenting with solid cancerous tumors for treatment with one or more antibody-drug conjugate (ADC) therapies, said method comprising: (A) receiving from a biological tissue sample obtained from tumor tissue of said patient a measured expression level of at least one gene product associated with each of said one or more ADC therapies; (B) (1) (i) at least one gene product associated with cell adhesion, and (ii) one or more gene products associated with proliferation; receiving an expression level of one or both of wherein said expression levels of (i) and (ii) are measured in the same biological tumor tissue sample of step (A); (B)(2) when the measured expression levels of the one or more gene products associated with proliferation are received, calculating therefrom, by a computer, an average of all of the received and measured expression levels of the gene products associated with proliferation, thereby obtaining a proliferation gene expression level; (C) optionally receiving an indication of tumor cell content of the same tumor tissue sample of steps (A) and (B); (D)(1) identifying a subject who is likely to respond to said one or more ADC therapies if any of the following occurs: (i) one or more of the calculated ADC treatment response scores (ADC TRS) exceed one or more corresponding predetermined thresholds, wherein each of said one or more ADC TRSs is: (1) the received and measured expression level of the at least one gene product associated with a corresponding ADC treatment; and below: (2) the received and measured expression level of the at least one gene product associated with cell adhesion; (3) the calculated proliferation gene expression level from the received expression levels of one or more gene products associated with proliferation; and (4) the received indication of tumor cell content; determining the value from at least two of the following: (E) selecting the patients identified as likely to respond to the one or more ADC therapies for treatment with the therapies. It encompasses wherein at least steps (A) to (D)(1)(i) are carried out by a suitably programmed computer.

95. 95. The method of claim 94, wherein step (E) comprises selecting said patient to receive treatment with said one or more ADC therapies as part of a clinical trial.

96. 96. The method of claim 95, wherein the clinical trial is a basket trial.

97. 95. The method of claim 94, wherein the method further comprises step (F) treating the selected patient with the one or more ADC therapies determined to be likely to induce a response in the patient.

98. 95. The method of claim 94, wherein the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with the corresponding ADC therapy, the expression level of the at least one gene product associated with cell adhesion, and the determined proliferation gene expression level.

99. The method of claim 98, wherein the determined proliferation gene expression level and the expression level of the at least one gene product associated with a corresponding ADC therapy are both positively associated with the likelihood that a patient will benefit from the corresponding ADC therapy, and the measured expression level of the at least one gene product associated with cell adhesion is negatively associated with the likelihood that a patient will benefit from the same corresponding ADC therapy.

100. 95. The method of claim 94, wherein the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with a corresponding ADC therapy, the measured expression level of the at least one gene product associated with cell adhesion, and tumor cell content.

101. The method of claim 100, wherein the tumor cell content and the expression level of the at least one gene product associated with the corresponding ADC treatment are both positively associated with the likelihood that a patient will benefit from the corresponding ADC treatment, and the measured expression level of the at least one gene product associated with cell adhesion is negatively associated with the likelihood that a patient will benefit from the same corresponding ADC treatment.

102. 95. The method of claim 94, wherein the ADC TRS is determined by a combination of at least the expression level of the at least one gene product associated with a corresponding ADC therapy, the measured expression level of the at least one gene product associated with cell adhesion, the determined proliferation gene expression level, and tumor cell content.

103. The method of claim 102, wherein the determined proliferation gene expression level, the tumor cell content, and the expression level of the at least one gene product associated with a corresponding ADC therapy are each positively associated with the likelihood that a patient will benefit from the corresponding ADC therapy, and the measured expression level of the at least one gene product associated with cell adhesion is negatively associated with the likelihood that a patient will benefit from the same corresponding ADC therapy.

104. 95. The method of claim 94, wherein each of the one or more ADC TRSs is determined by further taking into account a bias variable, wherein the bias variable is a static offset adjusted to produce biomarker frequencies that match published objective response rates in clinical trials.

105. 95. The method of claim 94, wherein the predetermined threshold is set at percentiles of ranked ADC treatment response scores determined from tumor tissue samples of a first cohort of subjects utilizing at least the expression level of the at least one gene product associated with a first corresponding ADC therapy, wherein each percentile corresponds to a greater percentage of subjects in the first cohort who do not respond to the same first corresponding ADC therapy.

106. 106. The method of claim 105, wherein the first cohort of subjects is a pan-cancer cohort or a matched tumor type cohort.

107. 95. The method of claim 94, wherein the predetermined threshold is set to zero and an ADC TRS indicating that the subject is likely to benefit from ADC therapy is an ADC TRS having a value greater than zero.

108. 95. The method of claim 94, wherein the gene expression level of the at least one gene product associated with each of one or more ADC therapies, the measured expression level of the at least one gene product associated with cell adhesion, and optionally the tumor cell content are log2 transformed and / or Z-score normalized prior to step (D)(1), and the expression level of the one or more gene products associated with proliferation is log2 transformed and / or Z-score normalized prior to averaging the expression levels of gene products associated with proliferation to obtain the proliferation gene expression level in step (B)(2).

109. 95. The method of claim 94, wherein said tumor cell content is a molecularly determined tumor cell content calculated based on somatic and germline variant allele frequencies and / or copy number profiles.

110. 95. The method of claim 94, wherein the expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene associated with cell adhesion, and the one or more genes associated with proliferation are individually selected from ribonucleic acid (RNA) and protein.

111. 111. The method of claim 110, wherein the gene expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene associated with cell adhesion, and the one or more genes associated with proliferation are proteins, and measuring the expression levels thereof requires the use of immunohistochemical techniques.

112. 111. The method of claim 110, wherein the gene expression products of the at least one gene associated with each of the one or more ADC therapies, the at least one gene associated with cell adhesion, and the one or more genes associated with proliferation are RNA, and measuring their expression levels requires utilizing RNA sequencing techniques.

113. 95. The method of claim 94, wherein each of the one or more ADC therapies comprises a monoclonal antibody, at least one functional fragment thereof, or a bispecific antibody targeting at least one epitope of at least one antigen selected from the group consisting of SLC39A6, VTCN1, ERBB3, MET, ERBB2, TACSTD2, FOLR1, PVRL4, F3, SLC34A2 MSLN, B7-H3, B7-H4, 5T4, GPR20, AXL, TFR1, P79, EGFR, integrin beta-6, ROR1, Globo H, IL2RA, GCC, MCP, FLT3, PTK7, IGF-1R, AG7, NCAM1, LY75, ALCAM, and CEACAM5.

114. 114. The method of claim 113, wherein the antibody or fragment thereof is directly or indirectly conjugated to a cytotoxic drug or fused to a cytotoxic protein.

115. 95. The method of claim 94, wherein the subject has or is suspected of having a cancer for which the one or more ADC therapies are not approved for an indicated use.

116. 95. The method of claim 94, wherein the tumor tissue sample is or is suspected to contain bladder cancer, salivary gland cancer, endometrial cancer, ovarian cancer, cervical cancer, head and neck cancer, non-melanoma skin cancer, thyroid cancer, cancer of unknown primary, cancer of the central or peripheral nervous system, neuroendocrine tumor, melanoma, esophagogastric cancer, small intestine cancer, sarcoma, hepatobiliary cancer, pancreatic cancer, gastrointestinal stromal tumor, renal cell carcinoma, glioma, appendix cancer, breast cancer, small cell lung cancer, prostate cancer, esophagogastric cancer, non-small cell lung cancer, pancreatic cancer, lymphoma, or colorectal cancer.

117. 95. The method of claim 94, wherein the tumor tissue sample is a formalin-fixed, paraffin-embedded (FFPE) tumor tissue sample.

118. 95. The method of claim 94, wherein the tumor tissue sample comprises at least 20% tumor content.

119. 1. A method for identifying a subject as likely to benefit from an anti-TROP2 based therapy, the method comprising: (a) measuring the expression levels of a TROP2 gene product and one or more gene products associated with proliferation in a tumor tissue sample obtained from the subject; (b) measuring the expression levels of one or more housekeeping genes in the same tumor tissue sample of step (a), wherein the one or more housekeeping genes comprise three genes selected from CIAO1, EIF2B1, HMBS, CTCF, GGNBP2, ITGB7, MYC and SLC4A1AP, and further normalizing the expression levels of the TROP2 gene product and the one or more gene products associated with proliferation of step (a) to the three housekeeping genes to obtain normalized expression levels of the TROP2 and the one or more genes associated with proliferation; (c) determining a proliferation gene expression level by averaging the normalized expression levels of the one or more gene products associated with proliferation; (d) determining the tumor cell content in the same tumor tissue sample of step (a); and (e) In any of the following cases: i) a collective biomarker score exceeds a predetermined threshold, wherein said collective biomarker score is calculated from a combination of said measured expression levels of said TROP2 gene products and at least one of said determined proliferation gene expression levels and / or said determined tumor cell content; or ii) the measured expression level of the TROP2 gene product is higher than the median TROP2 expression level obtained from tumor tissue samples of a first cohort of subjects, and at least one of the determined proliferation gene expression level and / or the determined tumor cell content is higher than the median proliferation gene expression level and / or median tumor cell content obtained from tumor tissue samples of the same first cohort of subjects. identifying the subject as likely to benefit from the anti-TROP2-based therapy; A method that encompasses

120. 120. The method of claim 119, wherein the subject is identified as likely to benefit from the anti-TROP2-based therapy if the measured expression level of the TROP2 gene product, the determined proliferation gene expression level, and the determined tumor cell content are all higher than the corresponding median level of TROP2 expression, proliferation gene expression, and median tumor cell content obtained from tumor tissue samples of the same first cohort of the subject.

121. 120. The method of claim 119, wherein the subject is identified as likely to benefit from the anti-TROP2-based therapy if one or more of the measured expression level of the TROP2 gene product, the determined proliferation gene expression level, and / or the determined tumor cell content falls within the highest quartile of TROP2 expression level, proliferation gene expression level, and / or tumor cell content values ​​obtained from tumor tissue samples of the same first cohort of the subject.

122. 120. The method of claim 119, wherein the first cohort of subjects is a pan-cancer cohort or a matched tumor type cohort.

123. Measuring the expression level of one or more gene products associated with proliferation includes measuring the expression level of one or more gene products associated with proliferation, such as BIRC5, BRCA1, BRCA2, BUBl, BUBlB, CCNA2, CCNB1, CCNB2, CCNE1, CCNE2, CCNF, CCNG2, CDC2, CDC20, CDC25A, CDC25B, CDC25C, CDC45L, CDC6, CDC7, CDCKN1A p21, CDCKN3, CDK4, CDKN1C, CDKN2A, CDKN2C, CDKN2D p19, CDKN3, CENPA, CENPE, CENPF, CHAF1A, CHK1, CKS1, CKS2, CKS2, DHFR, DHFR, E2-EP F, E2F1, E2F3, E2F5, FEN1, FOXM1, KNSL2, KNSL5, KPNA2, LMNB2, MAD2, MAD2L1, MAPK13 , MCM2, MCM3, MCM4, MCM5, MCM6, MK167, MNAT1, MSH2, MYBL2, NASP, NEK2, NPAT, ORC1, PA2G4, PCNA, PES1, PKMYT1, PLK, PRIM1, PTTG1, RAB6KIFL, RACGAP1, RAD51, RAD54L, RAN RAN, RFC4, RPA3, RRM1, RRM2, SLBP, STK12, STK15, STK18, STK6, TOP2A, TROAP, TUBB, TYMS, UBE2C, and UNG.

124. The method of claim 123, wherein measuring the expression level of one or more gene products associated with proliferation comprises measuring the expression level of one, two or all three of the MYBL2, TOP2A, and / or UBE2C gene products.

125. 120. The method of claim 119, wherein the collective biomarker score is determined by a combination of the expression level of the TROP2 gene product, the determined proliferation gene expression level, and tumor cell content.

126. 120. The method of claim 119, wherein the predetermined threshold is set to a percentile of ranked collective biomarker scores determined from tumor tissue samples of a second cohort of subjects, the percentile corresponding to a percentage of subjects in the second cohort who do not respond to anti-TROP2-based therapy.

127. 127. The method of claim 126, wherein the second cohort of subjects is a pan-cancer cohort or a matched tumor type cohort.

128. 127. The method of claim 126, wherein the first cohort of subjects and the second cohort of subjects are the same cohort of subjects.

129. 120. The method of claim 119, wherein the gene expression levels of TROP2, one or more genes associated with proliferation, and tumor cell content are log2 transformed and / or median centered to 10 prior to step (d).

130. 120. The method of claim 119, wherein said tumor cell content is a molecularly determined tumor cell content calculated based on somatic and germline variant allele frequencies and / or copy number profiles.

131. 120. The method of claim 119, wherein one, two or three of the housekeeping genes are selected from CIAO1, EIF2B1, and HMBS.

132. 120. The method of claim 119, wherein the collective biomarker score is determined by adding the measured expression level of TROP2 gene products to approximately ⅓ to ⅔ of the determined proliferation gene expression level and approximately 4 to 8 times the determined tumor cell content.

133. 120. The method of claim 119, wherein the expression products of TROP2 and one or more genes associated with proliferation are individually selected from ribonucleic acid (RNA) and protein.

134. The method of claim 133, wherein the gene expression product of at least one of TROP2 and one or more genes associated with proliferation is a protein, and measuring its expression level requires the use of immunohistochemical techniques.

135. 134. The method of claim 133, wherein the gene expression product of at least one of TROP2 and one or more genes associated with proliferation is RNA, and measuring its expression level requires utilizing RNA sequencing technology.

136. 120. The method of claim 119, wherein the anti-TROP2-based therapy comprises an anti-TROP2 antibody or a fragment thereof.

137. The method of claim 136, wherein the anti-TROP2 antibody or fragment thereof is directly or indirectly conjugated to a cytotoxic drug.

138. 138. The method of claim 137, wherein the cytotoxic drug is a DNA replication inhibitor selected from the group consisting of alkylating agents, DNA polymerase inhibitors, nitrogen mustards, and topoisomerase inhibitors.

139. 138. The method of claim 137, wherein the anti-TROP2 antibody or fragment thereof is fused to a protein that is toxic to cancer cells.

140. 138. The method of claim 137, wherein the cytotoxic drug is a topoisomerase inhibitor.

141. 141. The method of claim 140, wherein the anti-TROP2-based therapy is sacituzumab govitecan.

142. 120. The method of claim 119, wherein the subject has or is suspected of having a cancer for which an anti-TROP2-based therapy has not been approved for its indicated use.

143. 120. The method of claim 119, wherein the tumor tissue sample is suspected of containing or containing bladder cancer, endometrial cancer, breast cancer, small cell lung cancer, prostate cancer, esophagogastric cancer, non-small cell lung cancer, pancreatic cancer, or colon cancer.

144. 120. The method of claim 119, wherein the tumor tissue sample is a formalin-fixed, paraffin-embedded (FFPE) tumor tissue sample.

145. 120. The method of claim 119, wherein the tumor tissue sample contains at least 20% tumor content.

146. 120. The method of claim 119, further comprising administering an anti-TROP2-based therapy to the subject identified as likely to benefit from the therapy.

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