Methods and materials for assessing and treating cancers

Abstract

This document relates to methods and materials involved in assessing and / or treating a mammal (e.g., a human) having cancer (e.g., uveal melanoma or metastatic cancer). For example, methods and materials provided herein can be used to identify a cancer (e.g., uveal melanoma or metastatic cancer) as being likely to respond to an adoptive cell therapy (ACT; e.g., a tumor infiltrating lymphocyte (TIL) therapy). In another example, methods and materials provided herein can be used to treat a mammal (e.g., a human) having cancer (e.g., uveal melanoma or metastatic cancer) where the cancer treatment is selected based on whether or not the cancer is likely to be responsive to an ACT (e.g., a TIL therapy).

Description

[0001] METHODS AND MATERIALS FOR ASSESSING AND TREATING CANCERS

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] This application claims the benefit of U.S. Patent Application Serial No. 63 / 572,023, filed on March 29, 2024. The disclosure of the prior application is considered part of (and is incorporated by reference in) the disclosure of this application.

[0004] TECHNICAL FIELD

[0005] This document relates to methods and materials involved in assessing and / or treating a mammal (e.g., a human) having cancer (e.g., uveal melanoma or metastatic cancer). For example, methods and materials provided herein can be used to identify a cancer (e.g., uveal melanoma or metastatic cancer) as being likely to respond to an adoptive cell therapy (ACT; e.g., a tumor infiltrating lymphocyte (TIL) therapy). In another example, methods and materials provided herein can be used to treat a mammal (e.g., a human) having cancer (e.g., uveal melanoma or metastatic cancer) where the cancer treatment is selected based on whether or not the cancer is likely to be responsive to an ACT (e.g., a TIL therapy).

[0006] BACKGROUND INFORMATION

[0007] Significant advances have been made in the treatment of metastatic cutaneous melanoma (CM) using immune checkpoint inhibition (ICI) targeting the cytotoxic T- lymphocyte-associated protein 4 (CTLA-4), the programmed cell death protein 1 (PD-1), and the lymphocyte-activation gene 3 protein (LAG-3) (Hodi et al., N. Engl. J. Med., 363:711- 723 (2010); Larkin et al., N. Engl. J. Med., 381 :1535-1546 (2019); Samstein et al., Nat. Genet., 51 :202-206 (2019); Yarchoan et l, N. Engl. J. Med, 377:2500-2501 (2017); and Tawbi et al., N. Engl. J. Med., 386:24-34 (2022)). Unfortunately, ICI therapy has not shown comparable activity against most other solid tumors, especially those with low tumor mutational burden (TMB) (Samstein et al., Nat. Genet., 51:202-206 (2019); and Kalbasi & Ribas, Nat. Rev. Immunol, 20:25-39 (2020)). To improve immunotherapeutic strategies for this large group of unresponsive cancers, the studies were focused on uveal melanoma (UM), a prototypic ICI resistant cancer with low TMB (Yarchoan et al, N. Engl. J. Med., 377:2500- 2501 (2017); Algazi et al., Cancer, 122:3344-3353 (2016); Piulats et al, J. Clin. Oncol., 39:586-598 (2021); Jager et al., Nat. Rev. Dis. Primers, 6:24 (2020); and Singh et al., Ophthalmology, 118:1881-1885 (2011)). With an annual incidence of about 6 per million in Europe and the United States, UM is a rare cancer accounting for 3% of all melanomas (Jager et al., Nat. Rev. Dis. Primers, 6:24 (2020); and Singh et al., Ophthalmology, 118: 1881 : 1885 (2021)). Although both CM and UM develop from transformed melanocytes, UM uniquely arises from the pigmented epithelium of the uveal tract, an immune privileged site (Niederkom, Front Immunol., 3: 148 (2012), and has an unusual predilection to aggressively metastasize to the liver which results in a dismal prognosis (Jager et al., Nat. Rev. Dis. Primers, 6:24 (2020)). In further distinction, immunotherapies demonstrating efficacy against metastatic CM have shown disappointing results against UM (Algazi et al., Cancer, 122:3344-3353 (2016); and Piulats et al., J. Clin. Oncol., 39:586-598 (2021)), leading to speculation that UM is an immunologically ‘cold’ variant of melanoma. However, there has been recent therapeutic progress with the clinical introduction of tebentafusp, a bispecific glycoprotein 100 peptide-human leukocyte antigen (HLA)-directed CD3 T cell engager, which has intriguingly improved overall survival in patients with metastatic UM yet has demonstrated only limited ability to mediate tumor regression (Carvajal et al., Nat. Med., 28:2364-2373 (2022); and Nathan et al., N Engl. J. Med, 385: 1196-1206 (2021)). To reconcile these paradoxical findings and develop more effective immunotherapeutics for metastatic UM, the previous discovery that a subset of UM metastases naturally harbor tumor infiltrating lymphocytes (TIL) with potent autologous anti -turn or reactivity (Rothermel et al., Clin. Cancer Res., 22:2237-2249 (2016)) and that adoptive cell therapy (ACT) administering such TIL could mediate cancer regression in 35% of patients with metastatic UM, including individuals who were refractory to ICI (Chandran et al., Lancet Oncol., 18:792-802 (2017)) was built upon. These observations suggested that occult immune responses exist and can be exploited to treat metastatic UM.

[0008] SUMMARY

[0009] This document provides methods and materials for assessing and / or treating a mammal (e.g., a human) having cancer (e.g., uveal melanoma or metastatic cancer). In some cases, this document provides methods and materials for identifying whether or not a cancer (e g., a uveal melanoma or a metastatic cancer) is likely to respond to an ACT such as a TIL therapy (e.g., by detecting an Uveal Melanoma Immunogenic Score (UMIS) of the cancer). For example, this document provides methods and materials for detecting an UMIS of a cancer (e.g., a uveal melanoma or a metastatic cancer). In some cases, an UMIS can be detected in a sample (e.g., a tissue sample containing cancer cells such as one or more uveal melanoma cancer cells) from a mammal having cancer (e.g., uveal melanoma or metastatic cancer). For example, a sample obtained from a mammal having cancer (e.g., uveal melanoma or metastatic cancer) can be assessed to determine if the mammal is likely to respond to an ACT (e.g., a TIL therapy) based, at least in part, on an UMIS of the sample.

[0010] Comprehensive immunogenomic profiling was performed on a large and diverse group of human UM metastases compiled to uncover the tumor microenvironmental properties that underlie its occult immunogenicity and promote its resistance and susceptibility to different classes of immunotherapy. It was found that over half of these metastases harbor tumor infiltrating lymphocytes with potent autologous tumor specificity, despite having low tumor mutational burden and resistance to prior immunotherapies, including immune checkpoint inhibition and the bispecific T cell engager tebentafusp. These T cell infiltrated metastases display activated antigen presenting cells, chronic interferon signaling, and diverse T cell receptor repertoires. However, strikingly low intratumoral T cell receptor clonality and transcriptionally non-proliferative tumor infiltrating lymphocytes were observed within the tumor microenvironment even after immune checkpoint inhibition and tebentafusp therapy, demonstrating that these immunotherapies were insufficient to induce proliferation of the tumor reactive tumor infiltrating lymphocytes. To harness the therapeutic potential of these quiescent tumor infiltrating lymphocytes, rapid tumor transcriptomic profiling was developed to enable their selective in vivo identification and ex vivo liberation to counter their growth suppression. It was demonstrated that adoptive transfer of these transcriptomic selected tumor infiltrating lymphocytes can promote tumor immunity in patients with metastatic uveal melanoma when other immunotherapies are incapable.

[0011] In general, one aspect of this document features a method for identifying a mammal having cancer likely to respond to an adoptive cell therapy (ACT). The method comprises, or consists essentially of, (a) determining that a sample obtained from a mammal and including cancer cells has an UMIS of at least 0.2 or a truncated UMIS of at least 0.2, where the UMIS is calculated based on the 2394 nucleic acids of Table 1, where the truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table 1, where at least one nucleic acid of Table 3 is included for the calculation of said truncated UMIS; and (b) classifying the cancer as being likely to respond to the ACT. The mammal can be a human. The ACT can include a TIL therapy. The cancer can include a solid tumor. The method can include determining that the sample has the UMIS of at least 0.2. The method can include determining that the sample has the truncated UMIS of at least 0.2. At least one nucleic acid of Table 2 can be excluded for the calculation of the truncated UMIS. The truncated UMIS can be calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1. The truncated UMIS can be calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1.

[0012] In another aspect, this document features a method for identifying a mammal having cancer unlikely to respond to an ACT. The method comprises, or consists essentially of, (a) determining that a sample obtained from a mammal and including cancer cells has an UMIS of less than 0.2 or a truncated UMIS of less than 0.2, where the UMIS is calculated based on the 2394 nucleic acids of Table 1, where the truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table 1, where at least one nucleic acid of Table 3 is included for the calculation of the truncated UMIS; and (b) classifying the cancer as being unlikely to respond to the ACT. The mammal can be a human. The ACT can include a TIL therapy. The cancer can include a solid tumor. The method can include determining that the sample has the UMIS of less than 0.2. The method can include determining that the sample has the truncated UMIS of less than 0.2. At least one nucleic acid of Table 2 can be excluded for the calculation of the truncated UMIS. The truncated UMIS can be calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1. The truncated UMIS can be calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1. In another aspect, this document features a method for selecting a treatment for a mammal having cancer. The method comprises, or consists essentially of, (a) determining that a sample obtained from a mammal and including cancer cells has an UMIS of at least 0.2 or a truncated UMIS of at least 0.2, where the UMIS is calculated based on the 2394 nucleic acids of Table 1, where the truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table 1, where at least one nucleic acid of Table 3 is included for the calculation of the truncated UMIS; and (b) selecting an ACT as a treatment of cancer for the mammal. The mammal can be a human. The ACT can include a TIT therapy. The cancer can include a solid tumor. The method can include determining that the sample has the UMIS of at least 0.2. The method can include determining that the sample has the truncated UMIS of at least 0.2. At least one nucleic acid of Table 2 can be excluded for the calculation of the truncated UMIS. The truncated UMIS can be calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1. The truncated UMIS can be calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1.

[0013] In another aspect, this document features a method for a treatment for a mammal having cancer. The method comprises, or consists essentially of, (a) determining that a sample obtained from a mammal and including cancer cells has an UMIS of less than 0.2 or a truncated UMIS of less than 0.2, where the UMIS is calculated based on the 2394 nucleic acids of Table 1, where the truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table 1, where at least one nucleic acid of Table 3 is included for the calculation of the truncated UMIS; and (b) selecting a cancer treatment other than an ACT for the mammal. The mammal can be a human. The ACT can include a TIU therapy. The cancer can include a solid tumor. The method can include determining that the sample has the UMIS of less than 0.2. The method can include determining that the sample has the truncated UMIS of less than

[0014] 0.2. At least one nucleic acid of Table 2 can excluded for the calculation of the truncated UMIS. The truncated UMIS can be calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1. The truncated UMIS can be calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1. The cancer treatment can include radiation therapy. The cancer treatment can include administering an anti-cancer agent selected from the group consisting of a chemotherapy, a targeted therapy, and an angiogenesis inhibitor.

[0015] In another aspect, this document features a method for preparing a treatment for a mammal having cancer. The method comprises, or consists essentially of, (a) determining that a sample obtained from a mammal and including cancer cells has an UMIS of at least 0.2 or a truncated UMIS of at least 0.2, where the UMIS is calculated based on the 2394 nucleic acids of Table 1, where the truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table 1, where at least one nucleic acid of Table 3 is included for the calculation of said truncated UMIS; and (b) expanding TILs obtained from the mammal ex vivo to obtain expanded TILs for administration to the mammal. The mammal can be a human. The cancer can include a solid tumor. The method can include determining that the sample has the UMIS of at least 0.2. The method can include determining that the sample has the truncated UMIS of at least 0.2. At least one nucleic acid of Table 2 can be excluded for the calculation of the truncated UMIS. The truncated UMIS can be calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1. The truncated UMIS can be calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1.

[0016] In another aspect, this document features a method for preparing a cancer treatment. The method comprises, or consists essentially of, expanding TILs obtained from a mammal identified as having an UMIS of at least 0.2 or a truncated UMIS of at least 0.2 to form a cell population for administration to the mammal to treat cancer, where the UMIS is calculated based on the 2394 nucleic acids of Table 1, where the truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table 1, and where at least one nucleic acid of Table 3 is included for the calculation of the truncated UMIS. The mammal can be a human. The cancer can include a solid tumor. The method can include determining that the sample has the UMIS of at least 0.2. The method can include determining that the sample has the truncated UMIS of at least 0.2. At least one nucleic acid of Table 2 can excluded for the calculation of the truncated UMIS. The truncated UMIS can be calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1. The truncated UMIS can be calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1. The method can further include administering at least a portion of the cell population to the mammal.

[0017] In another aspect, this document features a method for treating a mammal having cancer. The method comprises, or consists essentially of, (a) determining that a sample obtained from a mammal and including cancer cells has an UMIS of at least 0.2 or a truncated UMIS of at least 0.2, where the UMIS is calculated based on the 2394 nucleic acids of Table 1, where the truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table 1, where at least one nucleic acid of Table 3 is included for the calculation of the truncated UMIS; and (b) administering an ACT to the mammal. The mammal can be a human. The ACT can include a TIL therapy. The cancer can include a solid tumor. The method can include determining that the sample has the UMIS of at least 0.2. The method can include determining that the sample has the truncated UMIS of at least 0.2. At least one nucleic acid of Table 2 can be excluded for the calculation of the truncated UMIS. The truncated UMIS can be calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1. The truncated UMIS can be calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to

[0018] 2000 of the nucleic acids of Table 1.

[0019] In another aspect, this document features a method for treating cancer. The method comprises, or consists essentially of, administering an ACT to a mammal identified as having an UMIS of at least 0.2 or a truncated UMIS of at least 0.2, where the UMIS is determined from a sample obtained from the mammal and including cancer cells, where the UMIS is calculated based on the 2394 nucleic acids of Table 1, where the truncated UMIS can be calculated based on 250 to 2393 of the nucleic acids of Table 1, and where at least one nucleic acid of Table 3 is included for the calculation of the truncated UMIS. The mammal can be a human. The ACT can include a TIL therapy. The cancer can include a solid tumor. The method can include determining that the sample has the UMIS of at least 0.2. The method can include determining that the sample has the truncated UMIS of at least 0.2. At least one nucleic acid of Table 2 can be excluded for the calculation of the truncated UMIS. The truncated UMIS can be calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1. The truncated UMIS can be calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1 .

[0020] In another aspect, this document features a method for treating a mammal having cancer. The method comprises, or consists essentially of, (a) determining that a sample obtained from a mammal and including cancer cells has an UMIS of less than 0.2 or a truncated UMIS of less than 0.2, where the UMIS is calculated based on the 2394 nucleic acids of Table 1, where the truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table 1, where at least one nucleic acid of Table 3 is included for the calculation of the truncated UMIS; and (b) administering a cancer treatment other than an ACT to the mammal. The mammal can be a human. The ACT can include a TIL therapy. The cancer can include a solid tumor. The method can include determining that the sample has the UMIS of less than 0.2. The method can include determining that the sample has the truncated UMIS of less than 0.2. At least one nucleic acid of Table 2 can be excluded for the calculation of the truncated UMIS. The truncated UMIS can be calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1. The truncated UMIS can be calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1. The cancer treatment can include performing surgery. The cancer treatment can include radiation therapy. The cancer treatment can include administering an anti-cancer agent selected from the group consisting of a chemotherapy, a targeted therapy, and an angiogenesis inhibitor.

[0021] In another aspect, this document features a method for treating cancer. The method comprises, or consists essentially of, administering a cancer treatment other than an ACT to a mammal identified as having cancer cells having an UMIS of less than 0.2 or a truncated UMIS of less than 0.2, where the UMIS is calculated based on the 2394 nucleic acids of Table 1, where the truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table 1, and where at least one nucleic acid of Table 3 is included for the calculation of the truncated UMIS. The mammal can be a human. The ACT can include a TIL therapy.

[0022] The cancer can include a solid tumor. The method can include determining that the sample has the UMIS of less than 0.2. The method can include determining that the sample has the truncated UMIS of less than 0.2. At least one nucleic acid of Table 2 can be excluded for the calculation of said truncated UMIS. The truncated UMIS can be calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1. The truncated UMIS can be calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1. The cancer treatment can include performing surgery. The cancer treatment can include radiation therapy. The cancer treatment can include administering an anti-cancer agent selected from the group consisting of a chemotherapy, a targeted therapy, and an angiogenesis inhibitor.

[0023] In another aspect, this document features a method for identifying a mammal having uveal melanoma likely to respond to an ACT. The method comprises, or consists essentially of, (a) determining that a sample obtained from a mammal having uveal melanoma and including uveal melanoma cells has an UMIS of at least 0.2, and (b) classifying the uveal melanoma as being likely to respond to the ACT. The mammal can be a human. The ACT can include a TIL therapy.

[0024] In another aspect, this document features a method for identifying a mammal having uveal melanoma unlikely to respond to an ACT. The method comprises, or consists essentially of, (a) determining that a sample obtained from a mammal having uveal melanoma and including uveal melanoma cells has an UMIS of less than 0.2, and (b) classifying the uveal melanoma as being unlikely to respond to the ACT. The mammal can be a human. The ACT can include a TIL therapy.

[0025] In another aspect, this document features a method for selecting a treatment for a mammal having uveal melanoma. The method comprises, or consist essentially of, (a) determining that a sample obtained from a mammal having uveal melanoma and including uveal melanoma cells has an UMIS of at least 0.2, and (b) selecting an ACT as a treatment of uveal melanoma for the mammal. The mammal can be a human. The ACT can include a TIL therapy.

[0026] In another aspect, this document features a method for selecting a treatment for a mammal having uveal melanoma. The method comprises, or consists essentially of, (a) determining that a sample obtained from a mammal having uveal melanoma and including uveal melanoma cells has an UMIS of less than 0.2, and (b) selecting a uveal melanoma treatment other than an ACT for the mammal. The mammal can be a human. The ACT can include a TIL therapy. The uveal melanoma treatment can include administering an anti- uveal melanoma agent selected from the group consisting of a chemotherapy, a targeted therapy, and an angiogenesis inhibitor.

[0027] In another aspect, this document features a method for treating a mammal having uveal melanoma. The method comprises, or consists essentially of, (a) determining that a sample obtained from a mammal having uveal melanoma and including uveal melanoma cells has an UMIS of at least 0.2, and (b) expanding TILs obtained from the mammal ex vivo to obtain expanded TILs for administration to the mammal. The mammal can be a human. The uveal melanoma can include a solid tumor.

[0028] In another aspect, this document features a method for preparing a uveal melanoma treatment. The method comprises, or consists essentially of, expanding TILs obtained from a mammal identified as having an UMIS of at least 0.2 to form a cell population for administration to the mammal to treat uveal melanoma. The mammal can be a human. The method can include administering at least a portion of the cell population to the mammal.

[0029] In another aspect, this document features a method for treating a mammal having uveal melanoma. The method comprises, or consists essentially of, (a) determining that a sample obtained from a mammal having uveal melanoma and including uveal melanoma cells has an UMIS of at least 0.2, and (b) administering an ACT to the mammal. The mammal can be a human. The ACT can include a TIL therapy.

[0030] In another aspect, this document features a method for treating uveal melanoma. The method comprises, or consists essentially of, administering an ACT to a mammal identified as having an UMIS of at least 0.2, where the UMIS is determined from a sample obtained from the mammal and including uveal melanoma cells. The mammal can be a human. The ACT can include a TIL therapy.

[0031] In another aspect, this document features a method for treating a mammal having uveal melanoma. The method comprises, or consists essentially of, (a) determining that a sample obtained from the mammal and including uveal melanoma cells has an UMIS of less than 0.2, and (b) administering a uveal melanoma treatment other than an ACT to the mammal. The mammal can be a human. The ACT can include a TIL therapy. The uveal melanoma treatment can include performing surgery. The uveal melanoma treatment can include radiation therapy. The uveal melanoma treatment can include administering an anti- uveal melanoma agent selected from the group consisting of a chemotherapy, a targeted therapy, and an angiogenesis inhibitor.

[0032] In another aspect, this document features a method for treating uveal melanoma. The method comprises, or consists essentially of, administering a uveal melanoma treatment other than an ACT to a mammal identified as having uveal melanoma cells having an UMIS of less than 0.2. The mammal can be a human. The ACT can include a TIL therapy. The uveal melanoma treatment can include performing surgery. The uveal melanoma treatment can include radiation therapy. The uveal melanoma treatment can include administering an anti- uveal melanoma agent selected from the group consisting of a chemotherapy, a targeted therapy, and an angiogenesis inhibitor.

[0033] 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 pertains. Although methods and materials similar or equivalent to those described herein can be used to practice the invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

[0034] The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims. DESCRIPTION OF THE DRAWINGS

[0035] Figures 1A-1C. Clinicogenomic landscape of metastatic uveal melanoma. (Figure

[0036] 1A) Diversity of source tissues of resected metastases. (Figure IB) Distribution of source tissues of resected metastases. (Figure 1C) Clinicogenomic annotation of individual metastases. Each column represents a single metastasis. BAP1 mRNA z-scores were calculated using log2 (normalized counts).

[0037] Figures 2A-2D. Unbiased tumor transcriptomics reveals T cell-inflamed uveal melanoma metastases. (Figure 2A) Unsupervised clustering of Spearman’s rank correlation coefficients derived from correlating PC coordinates (columns) with enrichment of hallmark signatures (row) for each individual metastatic sample (n=100). Natural clusters identified by the row dendrogram are split, labeled (A, B, C, D), and annotated for visualization. (Figure 2B) Heatmaps illustrating heterogeneity of hallmark signature enrichment across UM metastases (n=100). Rows correspond to hallmark signatures listed in Figure 2A. Columns within each heatmap represent individual metastases. Each heatmap was clustered by metastases separately to display tumor heterogeneity within each hallmark cluster. Z-scores were calculated per row. (Figure 2C) Matrix of mean Spearman’s rank correlation coefficients for each cluster-PC combination. (Figure 2D) Three-dimensional principal component analysis (PCA) plots displaying enrichment scores for selective hallmark immune related pathways identified in Cluster B. Euclidean distance was used for hierarchical clustering (Figures 2A-2B). Statistical comparisons were performed using Spearman’s rank correlation (Figures 2A-2D).

[0038] Figures 3A-3H. Development of an Uveal Melanoma Immunogenomic Score (UMIS) according to some embodiments. (Figure 3A) Workflow for the development of an UMIS. (Figure 3B) Correlation of UMIS scores calculated by the cohort-independent method (singscore) with UMIS scores calculated by the cohort-dependent method (gene set variation analysis (GSVA)). (Figure 3C) Annotation of UMIS genes using Human Genome Organization (HUGO) Gene Nomenclature Committee (HGNC). (Figure 3D) Functional annotation of protein-coding genes within UMIS using Database for Annotation, Visualization and Integrated Discovery (DAVID), and Human Molecular Signatures

[0039] Database Gene Ontology Biological Process gene set collection. (Figure 3E) Distribution of UMIS scores across the cohort of 100 metastases. (Figure 3F) Gene set enrichment analysis of differentially expressed genes between high UMIS and low UMIS UM metastases. The ten pathways with the lowest false discovery rate (FDR) are displayed. (Figure 3G) Comparison of UMIS by source tissue of resected metastases (n=100 biologically independent samples; liver=56, subcutaneous=20, lung=6, other=18). (Figure 3H) Correlation of UMIS with TMB. Statistical comparisons were performed using Spearman’s rank correlation with overlaid simple linear regression to illustrate linearity (Figures 3B and 3H), DAVID modified Fisher’s exact test (Figure 3D), fast preranked gene set enrichment analysis (Figure 3F), or Kruskal- Wallis test by ranks (Figure 3G).

[0040] Figures 4A-4M. UMIS uncovers in vivo drivers of T cell recruitment and exclusion. (Figure 4A) Uniform manifold approximation and projection (UMAP) plot of all cells analyzed from six UM metastases. Magnified panel shows immune subset of cells after reclustering. Cell labeling is with a broad classification. (Figure 4B) Proportion of overall cell types within UMIS groups. Fold enrichment refers to proportion ratio (high UMIS / low UMIS). (Figure 4C) Proportion of lymphoid broad cell types within UMIS groups. Fold enrichment refers to proportion ratio (high UMIS / low UMIS). (Figure 4D) Volcano plot of lymphoid granular cell types within UMIS groups. Fold enrichment refers to proportion ratio (high UMIS / low UMIS). (Figure 4E) Selected genes from differential gene expression analysis of high UMIS versus low UMIS lymphoid cells. Bars indicate medians and log2fc refers to log2 (fold change). (Figure 4F) Proportion of myeloid broad cell types within UMIS groups. Fold enrichment refers to proportion ratio (high UMIS / low UMIS). (Figure 4G) Selected genes from differential gene expression analysis of high UMIS versus low UMIS myeloid cells. Bars indicate medians and log2fc refers to log2 (fold change). (Figure 4H) Heatmap of differentially expressed genes between high UMIS and low UMIS tumor cells. Columns are individual cells; rows are genes. Cells are grouped by the UMIS level of their metastasis. The genes included had log2 (fold change) > |0.5| and FDR < 0.05. Z-scores were calculated per row. (Figure 41) Selected genes from differential gene expression analysis of high UMIS versus low UMIS tumor cells. Bars indicate medians and log2fc refers to log2 (fold change). UMAP plots display all cells within each UMIS subset. (Figure 4J) Correlation of UMIS with immune resistance program scores in UM metastases (n=100). (Figure 4K) Comparison of immune resistance program scores by UMIS level in UM metastases (high UMIS n=50, low UMIS n=50; total n=100 biologically independent samples). (Figure 4L) Correlation of SNHG7 with CTNNB1 transcript expression in UM metastases (n=100). Units are log2 (normalized counts) from bulk RNAseq. (Figure 4M) Correlation of SNHG7 with canonical melanoma marker transcripts (S100A1, SOXIO, MITF) in UM metastases (n=100). Units are log2 (normalized counts) from bulk RNAseq. Statistical comparisons were performed using propeller (arcsin square root transformation of proportions) (Figures 4B, 4C, 4D, and 4F), Wilcoxon rank-sum test (two-tailed) (Figures 4E, 4G, 41, and 4K), and Spearman’s rank correlation with overlaid simple linear regression to illustrate linearity (Figures 4J, 4L, and 4M).

[0041] Figures 5A-5J. UMIS predicts anti-tumor potency of ex vivo expanded TIL. (Figure 5 A) Workflow for parallel analysis of source tumor transcriptomics and expanded TIL antitumor reactivity. (Figure 5B) Example of TIL culture anti-tumor reactivity screening from source tumor UM #100. From left, tumor fragments (n=24) are cultured individually for about two weeks before overnight coculture with autologous tumor cells and measurement of 4-1BB (CD137) expression by flow cytometry and IFN-y release by ELISA. Final reactivity measurement subtracts background reactivity of TIL (TIL alone) and non-specific reactivity (TIL + autologous APCs). (Figure 5C) Correlation between % 4- IBB + CD3 + T cells and IFN-y release among the 24 fragment cultures from UM #100 after overnight tumor coculture. (Figure 5D) Individual TIL fragment culture anti-tumor reactivity as assessed by 4-1BB upregulation and IFN-y release from source tumor UM #100. TIL cultures were classified as tumor reactive if their 4-1BB expression was >1% (dotted line) and twice background or IFN-y release was >100 pg / mL (dotted line) and twice background. Percentage tumor reactive TIL cultures was defined as 100*(tumor reactive TIL cultures) / (total TIL cultures). (Figure 5E) Distribution of percent tumor reactive TIL cultures among the cohort of 100 metastases. (Figure 5F) Correlation of UMIS with percent tumor reactive TIL cultures. Color of each point denotes tumor digest viability percentage. (Figure 5G) Correlative benchmarking of UMIS against published gene expression profiles and tumor biomarkers (n=100 metastases). All correlations are with percent tumor reactive TIL cultures. (Figure 5H) Predictive benchmarking of UMIS against published gene expression profiles and tumor biomarkers (n=100 metastases). Receiver operating characteristic (ROC) curves and accompanying statistics are for variables’ prediction of > 33% tumor reactive TIL cultures. (Figure 51) Disparate UMIS and TIL culture reactivity from synchronous hepatic metastases in UM patient #1. (Figure 5J) Validation of UMIS’ ability to predict ex vivo TIL reactivity in an independent metastatic biopsy cohort (n=20 metastases). ROC curve and area under curve (AUC) value is for UMIS’ prediction of > 33% tumor reactive TIL cultures. Statistical comparisons were performed using Spearman’s rank correlation with overlaid simple linear regression to illustrate linearity (Figures 5C, 5F, and 5G) or univariate logistic regression (Figures 5H and 5J). Gene expression profiles were calculated using singscore to best assess their cohort-independent predictive ability.

[0042] Figures 6A-6J. UMIS identifies quiescent TIL resistant to ICI and tebentafusp but sensitive to ex vivo expansion and adoptive transfer. (Figure 6A) T cell receptor beta (TRB) repertoire analysis of bulk RNAseq of UM metastases (n=88). ICI refers to treatment history prior to metastatic biopsy. (Figure 6B) Proportion of proliferative T cells in UMIS groups by single cell atlas. (Figure 6C) Comparisons of TRB diversity and clonality in ICI or tebentafusp untreated versus treated metastases (ICI: 48 treated, 40 untreated; tebentafusp: 12 treated, 76 untreated). (Figure 6D) Ex vivo TIL expansion from treatment naive and refractory patients (n=19). Listed therapies were received prior to metastatic biopsy. Changes in TIL cell counts (left), TRB diversity (middle), and TRB clonality (right) are shown for source metastases and corresponding TIL cultures post rapid expansion protocol (post-REP TIL). Metastases’ TIL counts were conservatively estimated to be <106. TRB repertoires were characterized with targeted single cell T cell receptor (TCR) repertoire analysis. (Figure 6E) Examples of TRB dynamics with ex vivo TIL expansion. Bubble plots represent unique TRB clonotypes with bubble size indicating percentage of total clonotypes. Shown are representative examples for each pre-harvest treatment group (neither=UM #73, ICI=UM #50, tebentafusp=UM #59, both=UM #49). (Figure 6F) Schematic for evaluation of UMIS. (Figure 6G) Correlation of source metastasis UMIS with TIL infusion product reactivity (n=17). (Figure 6H) Correlation of source metastasis UMIS with maximum percent change in tumor size from baseline (RECIST vl.l) after TIL ACT. RECIST response line is drawn at - 30% (n=19). (Figure 61) Comparison of UMIS between responders (R; n=6) and nonresponders (NR; n=13) to TIL ACT. The median UMIS of the NR group (0.246) was used as a clinical response threshold for outcome analyses. (Figure 6J) Time-to-event curves of post- ACT survivals by UMIS response thresholds (n=19). Progression-free survival used progressive disease as the event (median follow-up (months): high=5.09, low=2.07). Overall survival used death as the event (median follow-up (months): high=20.97, low=4.90). Hazard ratios (HR) are for above versus below threshold groups. Statistical comparisons were performed using Spearman’s rank correlation with overlaid simple linear regression to illustrate linearity (Figures 6A, 6G, and 6H), Fishers exact test (Figure 6B), Wilcoxon ranksum test (two-tailed) (Figures 6C and 61), Kruskal-Wallis test by ranks (Figure 6C), Wilcoxon signed-rank test (two-tailed) (Figure 6D), or logrank test (Figure 6J).

[0043] Figures 7A-7F: Detailed genomic profiling of metastatic uveal melanoma. (Figure 7 A) Variant types detected in metastases (n=92 with whole genome sequencing data). The bar graph (top) displays frequency of individual variant types. The stacked bar graph (bottom) displays variant types in individual samples. (Figure 7B) Specific variants detected in metastases (n=92 with whole genome sequencing data). The bar graph (top) displays frequency of individual specific variants. The stacked bar graph (bottom) displays specific variants in individual samples. (Figure 7C) The ten most frequently mutated genes within the cohort (n=92 with whole genome sequencing data). Specific variants displayed within stacked bars. (Figure 7D) Somatic interaction analysis of the ten most frequently mutated genes (n=92 with whole genome sequencing data). (Figure 7E) Evaluation of genomic mutational contributions to oncogenic signaling pathways (n=92 with whole genome sequencing data). (Figure 7F) Comparison of TMB by source tissue of resected metastases (n=100). Statistical comparisons were performed using Fisher’s exact test (Figure 7D) or Kruskal-Wallis test by ranks (Figure 7F).

[0044] Figures 8A-8D: PC analysis allows for unbiased identification of pertinent transcriptomic stratifiers. (Figure 8A) Scree plot of percent variance contributed by PCs. PCs that sum to 80% of variance contributed are shown (PC 1 -24). Remaining 76 PCs contribute remaining variance in decreasing levels and are not shown. PCs 1, 2, and 3 were selected for downstream analysis based upon their significant percent variance contributed. (Figure 8B) Three-dimensional PCA plot displaying UM metastases on PCs 1, 2, and 3. (Figure 8C) Two-dimensional PCA plots displaying UM metastases in combinations of PCs 1, 2, and 3. (Figure 8D) Two-dimensional PCA plots displaying UM metastases on combinations of PCs 1, 2, and 3 with overlaid enrichment scores for hallmark immune related pathways identified in cluster B. Statistical comparisons were performed using Spearman’s rank correlation (Figure 8D).

[0045] Figures 9A-9C: High UMIS and low UMIS metastases transcriptomics differ but genomics do not. (Figure 9 A) Gene set enrichment analysis of differentially expressed genes between high UMIS and low UMIS UM metastases using the Human Molecular Signatures Database Gene Ontology Biological Process gene set collection. The ten pathways with the lowest FDR and a positive normalized enrichment score (NES) are displayed, along with the ten pathways with the lowest FDR and a negative NES. (Figure 9B) Forest plot comparing mutational odds between high UMIS and low UMIS metastases (n=92). Genes listed are the ten most frequently mutated within the cohort. (Figure 9C) HLA allele expression derived from RNAseq in high UMIS and low UMIS metastases (n=100).

[0046] Figures 10A-10F: Quality control and atlas mapping of single cells from uveal melanoma metastases. (Figure 10A) UMAP plot of all cells analyzed from six metastases (UM #). (Figure 10B) UMAP plot of all cells analyzed from six metastases. (Figure IOC) Total cells from and proportion of overall cell types within individual metastases. (Figure 10D) UMAP plot of all cells analyzed from six metastases. Magnified panel is immune subset of cells after reclustering and mapping onto new UMAP coordinates. Cell labeling employed granular classification. (Figure 10E) UMAP of the immune cellular fraction. (Figure 10F) UMAP of the immune cellular fraction.

[0047] Figures 11A-11D: Single cell transcriptomics of lymphoid and myeloid cells in high versus low UMIS metastases. (Figure 11 A) Proportion of CD8+exhausted and CD8+cytotoxic T cells that express TCF7. (Figure 1 IB) Selected genes from differential gene expression analysis of high UMIS versus low UMIS lymphoid cells. Bars indicate medians and log2fc refers to log2 (fold change). (Figure 11C) Volcano plot of myeloid granular cell types within UMIS groups. Fold enrichment refers to proportion ratio (high UMIS / low UMIS). (Figure HD) Selected genes from differential gene expression analysis of high

[0048] UMIS versus low UMIS myeloid cells. Bars indicate medians and log2fc refers to log2 (fold change). Statistical comparisons were performed using Wilcoxon rank-sum test (two-tailed) (Figures 1 IB and 1 ID) or propeller (arcsin square root transformation of proportions) (Figure 11C).

[0049] Figures 12A-12C: Single cell transcriptomics of tumor cells in high versus low UMIS metastases. (Figure 12A) UMAP plot of all cells analyzed from six metastases. Magnified panel shows the tumor cellular fraction after reclustering. (Figure 12B) Selected genes from differential gene expression analysis of high UMIS versus low UMIS tumor cells. Bars indicate medians and log2fc refers to Iog2 (fold change). UMAP plots display all cells within each UMIS subset. (Figure 12C) Correlation of SNHG7 with canonical transcriptional activity markers (ACTB, GAPDH). Units are log2 (normalized counts) from bulk RNAseq. Statistical comparisons were performed using Wilcoxon rank-sum test (two-tailed) (Figure 12B) or Spearman’s rank correlation with overlaid simple linear regression to illustrate linearity (Figure 12C).

[0050] Figures 13A-13F: Ex vivo TIL expansion and tumor reactivity is not predicted by clinicogenomics but is predicted by UMIS. (Figure 13A) Comparison of percent tumor reactive TIL cultures by source tissue of resected metastases (n=100 biologically independent samples; liver=56, subcutaneous=20, lung=6, other=18). (Figure 13B) Correlation of percent tumor reactive TIL cultures with TMB. (Figure 13C) Forest plot comparing mutational odds between metastases with > 33% or < 33% tumor reactive TIL cultures (n=92). Genes listed are the ten most frequently mutated within the cohort. (Figure 13D) HLA allele expression derived from RNAseq in metastases with > 33% or < 33% tumor reactive TIL cultures (n=100). (Figure 13E) Comparison of tumor digest viability percentage among high UMIS metastases (n=50) by detection of tumor reactive TIL (no reactive TIL n=16, reactive TIL n=34). (Figure 13F) Correlation of UMIS with tumor digest viability percentage. Statistical comparisons were performed using Kruskal -Wallis test by ranks (Figure 13 A), Spearman’s rank correlation with overlaid simple linear regression to illustrate linearity (Figures 13B and 13F), Fisher’s exact test (Figures 13C and 13D), or Wilcoxon rank-sum test (two-tailed) (Figure 13E).

[0051] Figures 14A-14B: UMIS is spatially consistent and clinically feasible. (Figure 14A) UMIS spatial consistency across regions of a single tumor (UM #101). Tumor fragments from labeled areas A-E were used to determine UMIS in different regions of a single tumor. (Figure 14B) UMIS core biopsy clinical feasibility pilot study (UM #102, UM #103). For each of these cases, four 18-gauge core biopsies were obtained with radiologic guidance. One core underwent pathologic confirmation of tumor, while the other three were placed in RNA preservative solution at room temperature (UM #102 shown in picture). These were then shipped overnight and processed upon arrival. Both biopsied tumors were eventually surgically resected and random fragments used to calculate UMIS values.

[0052] Figure 15: UMIS levels are not associated with survival differences. Time-to-event curves of survival and metastasis by UMIS level (n=84 patients) are shown. For patients with multiple analyzed metastases, an algorithm was used to select representative metastases to determine the patients’ UMIS levels. Overall survival used death as the event (median follow-up (years): high=6.65, low=4.55). Time to metastatic disease was measured from time of primary diagnosis to first metastatic diagnosis and used development of metastatic disease as the event (median follow-up (years): high=2.40, low=2.66). Survival with metastatic disease was measured from time of first metastatic diagnosis and used death as the event (median follow-up (years): high=2.63, low=2.29). HR are for high UMIS versus low UMIS groups. Statistical comparisons were performed using logrank test (Figure 15 A).

[0053] Figures 16A-16E: TCR repertoire analyses reveal variable diversity but suppressed clonality. (Figure 16A) T cell receptor alpha (TRA) repertoire analysis of bulk RNAseq of UM metastases (n=82). ICI and tebentafusp untreated and treated refers to patient therapy prior to metastatic biopsy. (Figure 16B) Single cell TCR repertoire analysis of UM metastases (n=6). Bubble plots represent unique TCR clonotypes with bubble size indicating percentage of total clonotypes. Clonotypes analyzed are paired alpha-beta chains. The Shannon index was used to represent diversity, while the 1 - Pielou’s index was used to represent clonality. (Figure 16C) TRB repertoire analysis from bulk RNAseq of UM metastases (n=88). Tebentafusp untreated and treated refers to patient therapy prior to metastatic biopsy. (Figure 16D) Comparisons of TRA diversity and clonality in ICI or tebentafusp untreated versus treated metastases (ICI: 43 treated, 39 untreated; tebentafusp: 12 treated, 70 untreated). (Figure 16E) Public versus private analysis of TRB and TRA repertoires from bulk RNAseq of UM metastases (n=100). Statistical comparisons were performed using Spearman’s rank correlation with overlaid simple linear regression to illustrate linearity (Figures 16A and 16C), Wilcoxon rank-sum test (two-tailed) (Figure 16D), or Kruskal-Wallis test by ranks (Figure 16D).

[0054] Figures 17A-17C: Ex vivo expansion rescues clonally suppressed TIL. (Figure 17A) Ex vivo TIL expansion from treatment naive and refractory patients (n=19). Listed therapies were received prior to metastatic biopsy. Changes in TIL cell counts (left), TRA diversity (middle), and TRA clonality (right) are shown for source metastases and corresponding TIL cultures post-REP TIL. Metastases’ TIL cell counts were conservatively estimated to be < 106. TRA repertoires were characterized with targeted TCR repertoire analysis. (Figure 17B) TRB repertoire dynamics in individual pairs of parental metastases and post-REP TIL. Repertoires are derived from targeted TCR repertoire analysis of metastases (n=19). Packed circle plots depict percentages of individual clonotypes within the repertoire. (Figure 17C) TRA repertoire dynamics in individual pairs of parental metastases and post-REP TIL. Repertoires are derived from targeted TCR repertoire analysis of metastases (n=19). Packed circle plots depict percentages of individual clonotypes within the repertoire. Statistical comparisons were performed using Wilcoxon signed-rank test (two-tailed) (Figure 17a).

[0055] Figures 18A-18C: Flow cytometry gating strategies. (Figure 18A) General gating strategy to characterize source tumor phenotype by flow cytometry. Single viable lymphocytes were first selected using morphology gates followed by propidium iodide negative gate. These cells were subsequently gated on CD3+T cells and finally assessed for CD4 and CD8 expression. (Figure 18B) General gating strategy to characterize expanded TIL phenotype by flow cytometry. Single viable lymphocytes were first selected using morphology gates followed by propidium iodide negative gate. These cells were subsequently gated on CD3+T cells and finally assessed for CD4 and CD8 expression. (Figure 18C) General gating strategy to characterize TIL tumor reactivity by flow cytometry. Single viable lymphocytes were first selected using morphology gates followed by propidium iodide negative gate. These cells were subsequently gated on CD3+T cells and finally assessed for CD8 plus CD137 (4-1BB) expression.

[0056] Figure 19: A schematic showing steps and times involved to perform current methods in practice for screening a tumor tissue for TIL reactivity (left side) as compared to steps and times involved to screen a tumor tissue using an exemplary method of one embodiment provided herein (right side).

[0057] Figure 20: A schematic showing UMIS development according to some embodiments.

[0058] Figure 21 : UMIS predicted TIL reactivity in the combined pan-cancer cohort.

[0059] Figure 22: Determination of clinically relevant success:futility cutoffs for selected gene sets. The success: futility cutoff was defined as the ability to generate > 10% tumor reactive TIL cultures from a single metastasis per the gold standard laboratory assay. The graphs display candidate gene set enrichment scores using singscores (x-axis) versus % tumor reactive TIL cultures (y-axis) generated from 194 individual metastases. Success:futility cutoffs were derived from training plus validation cohorts of metastases (n = 133) and denote the singscore enrichment values equating to 25% probability of yielding significant tumor reactive TIL cultures (>10). Dashed lines and values represent success:futility cutoffs overlaid onto the total cohort of 194 individual metastases.

[0060] Figure 23: UMIS predicted maximum percent change in tumor size from baseline Response Evaluation Criteria in Solid Tumors (RECIST vl. l) response in samples from patients undergoing TIL therapy (n = 49).

[0061] Figure 24: Example of UMIS predicting TIL reactivity and response to TIL therapy in a patient with refractory metastatic peritoneal mesothelioma.

[0062] Figure 25: UMIS level of tumors across histologies.

[0063] DETAILED DESCRIPTION

[0064] This document provides methods and materials for assessing and / or treating a mammal (e.g., a human) having cancer (e.g., uveal melanoma or metastatic cancer). In some cases, this document provides methods and materials for identifying whether or not a cancer (e.g., a uveal melanoma or a metastatic cancer) is likely to respond to an ACT such as a TIL therapy (e.g., by detecting an UMIS of the cancer). For example, this document provides methods and materials for detecting an UMIS of a cancer (e.g., a uveal melanoma or a metastatic cancer). In some cases, an UMIS can be detected in a sample (e.g., a tissue sample containing cancer cells such as one or more uveal melanoma or metastatic cancer cells) from a mammal having cancer (e.g., uveal melanoma or metastatic cancer). For example, a sample obtained from a mammal having cancer (e.g., uveal melanoma or metastatic cancer) can be assessed to determine if the mammal is likely to respond to an ACT (e.g., a TIL therapy) based, at least in part, on an UMIS of the sample.

[0065] The immunogenomic landscape of metastatic UM was profiled using bulk and single cell transcriptomics, TCR repertoire analysis, TIL reactivity assessment, and clinical adoptive cell therapy. The findings establish that metastatic UM is not an immunologically ‘cold’ cancer, but instead over half of the analyzed UM metastases harbored tumor reactive TIL, despite having one of the lowest mutational burdens of any solid cancer (Yarchoan et al., N. Engl. J. Med., 377:2500-2501 (2017); Jager et al., Nat. Rev. Dis. Primers, 6:24 (2020); Robertson et al., Cancer Cell, 32:204-220. e215 (2017); and Field et al., Nat. Commun., 9: 116 (2018)) and an equally limited responsiveness to approved immunotherapies, including ICI therapy and tebentafusp (Yarchoan et al., N. Engl. J. Med, 377:2500-2501 (2017); Algazi et al., Cancer, 122:3344-3353 (2016); Piulats et al., J. Clin. Oncol., 39:586-598 (2021); Carvajal et al., Nat. Med., 28:2364-2373 (2022); Nathan et al., N. Engl. J. Med, Carvajal et al., Nat. Rev. Clin. Oncol., 20:99-115 (2023); and Pelster et al., J. Clin. Oncol., 39:599-607)). To avoid sampling bias in the study, a large clinically representative group of UM patients (n=84) and their metastases (n=100), which were procured from a diverse array of organ sites (n=l 1), were analyzed. Further, the metastases in the current study were genomically validated to be of uveal origin by expression of canonical UM somatic alterations and low TMB Yarchoan et al., N. Engl. Med., 377:2500-2501 (2017); Jager et al., Nat. Rev. Dis. Primers, 6:24 (2020); Robertson etal., Cancer Cell, 32:204-220. e215 (2017); Karlsson et al., Nat. Commun., 11 : 1894 (2020); and Shain et al., Nat. Genet., 51: 1123-1130 (2019)). It is believed that the study cohort accurately represents the metastatic immunogenomic landscape of this rare cancer and is uniquely suited to answer two critical questions that have significant therapeutic implications for UM: what factors drive T cell inflammation in metastatic UM, and why does metastatic UM respond so poorly to currently approved immunotherapies?

[0066] To define drivers of immune response against metastatic UM, bulk total RNA sequencing of metastatic biopsies was utilized, and it was found that T cell-inflamed metastases naturally segregated from T cell excluded metastases based upon an unsupervised transcriptomic signature composed of coding, non-coding, and unannotated transcripts. Rather than biasing this gene list with supervised filtering, the entire 2394 gene set was integrated into a gene expression score called UMIS. In some cases, less than the entire 2394 gene set can be used to calculate a score referred to herein as a truncated UMIS. In some cases, a truncated UMIS described herein can be used in place of an UMIS. For example, the last sentence of the first paragraph of the Summary section discloses that a sample obtained from a mammal having cancer (e.g., uveal melanoma or metastatic cancer) can be assessed to determine if the mammal is likely to respond to an ACT (e.g., a TIL therapy) based, at least in part, on an UMIS of the sample. Thus, as described herein, a sample obtained from a mammal having cancer (e.g., uveal melanoma or metastatic cancer) can be assessed to determine if the mammal is likely to respond to an ACT (e.g., a TIL therapy) based, at least in part, on a truncated UMIS of the sample.

[0067] Based upon a unique single cell transcriptomic atlas that was developed specifically for metastatic UM, it was found that UMIS could holistically reflect the multiple cellular components of the tumor microenvironment (Durante et al., Nat. Commun., 11 :496 (2020)). Metastases with low UMIS (versus high UMIS) had a paucity of TIL and were composed of tumor cells with higher beta-catenin transcript expression (CTNNBB), which has been described as a transcriptional repressor of BA 7 / '3-lineage dendritic cell recruitment of CD8+T cells (Spranger et al., Nature, 523:231-235 (2015); Spranger et al., Cancer Cell, 31:711- 723.e714 (2017); and Sharma et aL, Cell, 168:707-723 (2017)). In contrast, high UMIS metastases had lower tumor cell expression of CTNNBJ, increased APC expression of T cell chemoattractant ligands (CXCL10 and CXCL9), greater tumor reactive TIL recruitment, and markedly elevated MHC expression on multiple cell populations within the tumor microenvironment, suggesting prominent interferon signaling. Thus, Wnt / beta-catenin signaling is believed to play an important role in promoting immune exclusion in metastatic UM, similar to prior reports in metastatic CM (Spranger et al., Nature, 523:231-235 (2015); Luke et al., Clin. Cancer Res., 25:3074-3083 (2019); Spranger et al., Cancer Cell, 31 :711- 723. e714 (2017); Sharma et al., Cell, 168P:707-723 (2017); and Wang etaL, Nat. Genet., 55: 19-25 (2023)). Surprisingly, only a single metastasis with a possible activating somatic mutation of the Wnt / beta-catenin pathway was identified, suggesting hotspot mutations are not a common driver of beta-catenin overexpression in UM metastases (Bugter et al., Nat. Rev. Cancer, 21 :5-21 (2021)). However, a strong correlation between the expression of the long non-coding RNA, SNHG7, and CTNNB1 was observed. Based upon several reports that SNHG7 is a positive regulator of CTNNB1 and compelling evidence that in vitro knockdown of SNHG7 leads to downregulation of the Wnt / beta-catenin pathway in various other cancers (Yu et al., Mol. Ther. Nucleic Acids, 17:235-244 (2019); Chen et al., Pathol. Res. Pract., 215:302-307 (2019); Bian et al., Mol. Clin. Oncol., 13:45 (2020); Najafi et al., Front. Cell Dev. Biol., 9:809345 (2021); and Ren et al., Biochem. Biophys. Res. Commun., 496:712-718 (2018)), the mechanistic role of this non-coding RNA in driving T cell exclusion in UM metastases and therapeutic strategies to potentially abrogate its effect in low UMIS metastases are being investigated.

[0068] To better understand why UM metastases rarely regress with approved immunotherapies, TIL from ICI and tebentafusp resistant patients was evaluated. From the total cohort of TIL samples (n=100), it was observed that 55% of UM metastases harbored tumor reactive TIL and there was no difference in the percentage of tumor reactive TIL cultures expanded from ICI and tebentafusp treated metastases versus untreated. Yet, despite the presence of potent TIL in these metastases, it was found that they were strikingly quiescent with an absence of in vivo TIL expansion using TCR clonality analysis and single cell transcriptomics. Interestingly, prior tebentafusp therapy was associated with increased in vivo TCR diversity in the samples, demonstrating its ability as a T cell recruiter (Carvajal et al., Nat. Med., 28:2364-2373 (2022); and Nathan et al., N. Engl. J. Med., 385: 1196-1206 (2021)). However, neither tebentafusp nor ICI therapy were associated with an increase in TCR clonality. The quiescence of these T cells within the tumor microenvironment may explain the low rates of objective tumor regression and the intriguing decoupling of overall response rate as a surrogate for overall survival in UM patients treated with tebentafusp (Carvajal et al, Nat. Med., 23-2364-2373 (2022); Nathan et al., N. Engl. J. Med, 385:1 196- 1206 (2021); and Mariani et al., Br. J. Cancer, 129:772-781 (2023)). In contrast, TCR repertoire studies of cutaneous melanoma metastases have reported significant variance in TCR clonality, with higher pre-treatment clonality being associated with improved response to PD-1 blockage (Valpione et al., Nat. Commun., 12:4098 (2021); Yusko et al., Cancer Immunol. Res., 7:458-465 (2019); Riaz et al., Cell, 171:934-949.e916 (2017); and Tumeh et al., Nature, 515:568-571 (2014)). Interestingly, it was found that the quiescent TIL from ICI and tebentafusp treated UM metastases could demonstrate significant ex vivo expansion, indicating that these T cells were not limited by intrinsic factors such as exhaustion, but rather by extrinsic constraints within the UM tumor microenvironment. In support, it was previously observed that adoptive transfer of tumor reactive TIL could mediate objective regression in ICI refractory UM patients Chandran et al., Lancet Oncol., 18:792:802 (2017)). In sum, these findings reveal that occult T cell responses do exist against metastatic UM but require therapeutic strategies such as ACT to overcome their growth-suppressed state within the tumor microenvironment.

[0069] Finally, the study revealed the importance of UMIS as a tumor intrinsic biomarker to predict TIL potency and clinical response after adoptive transfer in UM patients. Whereas recent reports have proposed phenotypic and transcriptomic markers for the purpose of defining neo-antigen specific TCR sequences from TIL (Krishna et al., Science, 370: 1328- 1334 (2020); Hanada et al., Cancer Cell, 40: 479-493. e476 (2022); and Lowery et al, Science, 375:877-884 (2022)), UMIS is believed to represent a unique tumor biomarker for the identification of tumor reactive TIL capable of ex vivo expansion for clinical adoptive transfer. Importantly, an UMIS level of less than 0.2 identified metastases that were unlikely to yield potent TIL, suggesting that preoperative UMIS measurement could prevent futile invasive surgical harvests. UMIS was found to perform significantly better as a tumor intrinsic biomarker of TIL potency when compared to several focused gene expression signatures of T cell inflammation. It is postulated that the superior performance of an UMIS was a result of its unique derivation from a large unbiased mixture of coding and non-coding transcripts. Further, rather than narrowly reflecting the gene expression of only immune cells, UMIS was developed as a whole-tumor metric that reflected the gene expression of the lymphoid, myeloid, and tumor compartments within the tumor microenvironment.

[0070] Potential limitations of the study include selection bias of the patients and metastases analyzed. The rare nature of UM limited the sample size to 100 metastases. A subset of patients contributed multiple metastases to the study (14 patients with multiple metastases included). These metastases were included to maximize sample size in this rare cancer, but it is recognized that this may be a source of selection bias. Given that UM patients presented with the intent of ACT screening, which has strict eligibility requirements, the analyzed cohort may not represent some elderly individuals or some demonstrating rapidly progressive metastatic disease and declining performance. UMIS was found to correlate with TIL reactivity across 24 geographically unique tumor fragments.

[0071] Once a mammal (e.g., a human) is identified as having a cancer that is responsive to an ACT such as a TIL therapy as described herein, TILs can be obtained from that mammal and expanded ex vivo to create a culture of tumor reactive TILs that can be administered to that mammal to treat the mammal’s cancer. For example, a biopsy that includes cancer cells can be obtained from a mammal (e.g., a human) having cancer and can be assessed to determine if the cancer cells have an UMIS that indicates that the mammal’s cancer is likely to respond to an ACT (e.g., a TIL therapy) as described herein. If the UMIS of the cancer cells indicates that the mammal’s cancer is likely to respond to an ACT (e.g., a TIL therapy), then TILs obtained from that mammal can be expanded ex vivo to create a culture of tumor reactive TILs. Once those tumor reactive TILs are obtained, they can be administered to the mammal to treat the mammal’s cancer.

[0072] Any appropriate method can be used to expand TILs obtained from a mammal (e.g., a human such as a human identified as having a cancer that is responsive to an ACT such as a TIL therapy as described herein) ex vivo. In some cases, TILs can be expanded ex vivo by culturing the TILs in the presence of one or more polypeptides that can promote the growth and / or differentiation of immune cells. Examples of polypeptides that can promote the growth and / or differentiation of immune cells and that can be used to expand TILs ex vivo include, without limitation, interleukin (IL)-2, IL-7, IL-15, IL-21, and anti-CD3 polypeptides (e.g., anti-CD3 antibodies). In some cases, TILs can be expanded ex vivo by culturing the TILs in the presence of one or more IL-2 polypeptides and in the presence of one or more anti-CD3 antibodies. In some cases, TILs can be expanded ex vivo as described elsewhere (see, e.g., Chandran etal., Lancet Oncol., 18: 792-802 (2017) at, for example, page 794).

[0073] In some cases, the methods and materials described herein can be used to predict responsiveness to an ACT (e.g., a TIL therapy) in less than about 2 weeks (e.g., less than about 1 week, less than 14 days, less than 13 days, less than 12, days, less than 11 days, less than 10 days, less than 9 days, less than 8 days, less than 7 days, less than 6 days, less than 5 days, less than 4 days, or less than 4 days). For example, a sample (e.g., a tumor tissue sample) from a mammal having cancer (e.g., uveal melanoma or metastatic cancer) can be assessed to determine if the cancer is likely to respond to an ACT (e.g., a TIL therapy) based, at least in part, on an UMIS of the cancer in from about 2 days to about 2 weeks (e.g., from about 2 days to about 1 week, from about 2 days to about 5 days, from about 5 days to about 2 weeks, from about 1 week to about 2 weeks, or from about 5 days to about 1 week).

[0074] A mammal (e.g., a human) having cancer (e.g., uveal melanoma or metastatic cancer) can be assessed to determine whether the cancer is likely to respond to an ACT (e.g., a TIL therapy) by detecting an UMIS (or a truncated UMIS) of the cancer. For example, a sample (e.g., a tumor tissue sample) obtained from a mammal having cancer can be assessed to determine if the mammal is likely to respond to an ACT (e.g., a TIL therapy) based, at least in part, on the UMIS (or the truncated UMIS) of the cancer. For example, an UMIS of a sample (e.g., a tumor tissue sample) obtained from a mammal having cancer can be calculated based on the 2394 nucleic acids of Table 1. In another example, a truncated UMIS of a sample (e.g., a tumor tissue sample) obtained from a mammal having cancer can be calculated based on at least 250 (e.g., at least 275, at least 300, at least 325, at least 350, at least 400, at least 450, at least 500, at least 750, at least 1000, at least 1250, at least 1500, at least 1750, at least 2000, or at least 2250), but less than all 2394, of the nucleic acids listed in Table 1. In some cases, a truncated UMIS of a sample (e.g., a tumor tissue sample) obtained from a mammal having cancer can be calculated based on at least 250 (e.g., at least 275, at least 300, at least 325, at least 350, at least 400, at least 450, at least 500, at least 750, at least 1000, at least 1250, at least 1500, at least 1750, at least 2000, or at least 2250), but less than all 2394, of the nucleic acids listed in Table 1, provided that at least one or more (e.g., one, two, three, four, five, ten, twenty, or more) of the nucleic acids is not listed in Table 2. In some cases, a truncated UMIS of a sample (e.g., a tumor tissue sample) obtained from a mammal having cancer can be calculated based on at least 250 (e.g., at least 275, at least 300, at least 325, at least 350, at least 400, at least 450, at least 500, at least 750, at least 1000, at least 1250, at least 1500, at least 1750, at least 2000, or at least 2250), but less than all 2394, of the nucleic acids listed in Table 1, provided that at least one or more (e.g., one, two, three, four, five, ten, twenty, or more) of the nucleic acids is listed in Table 3.

[0075] An UMIS for a tumor tissue sample is calculated using all 2394 nucleic acids of Table 1 and a truncated UMIS for a tumor tissue sample is calculated using at least 250 (e.g., at least 275, at least 300, at least 325, at least 350, at least 400, at least 450, at least 500, at least 750, at least 1000, at least 1250, at least 1500, at least 1750, at least 2000, or at least 2250), but less than all 2394, of the nucleic acids listed in Table 1 as follows. First, the nucleic acids are ranked based on their transcript abundance in increasing order for the up-set. Second, mean ranks are separately normalized relative to the theoretical minimum and maximum values, centered on zero and then summed to provide the score (e.g., S total, i Sup.i ~ Sdown.i), which ranges between - 1 and 1. In some cases, a sample with a high score can be interpreted as having a transcriptome that is concordant to the specified signature, and scores reflect the relative mean percentile rank of the UMIS or truncated UMIS gene set within each sample. The score (5) and normalized score (S) are defined as: where dir is the gene set direction (e g., expected up- or down- regulated genes), Sdtr, t is the score for sample i against the directed gene set, R8dm is the rank of gene g in the directed gene set (e.g., with increasing transcript abundance for expected up-regulated genes and decreasing abundance for expected down-regulated genes), Ndtr, t is the number of genes in the expected up- or down-regulated gene set that are observed within the data (i.e., signature genes not present within the RNA abundance data are excluded), Sdtr.t is the normalized score for sample i against genes in the signature, andra / «, t and Smax. < are the theoretical minimum and maximum mean ranks which can be derived from an arithmetic sum (e.g., assuming unique ranks). For a series of n numbers starting at e / ; and with a constant difference d, the sum is calculated as (z? / 2)(2«i + («-l) d). In some cases, setting ai = 1, d= 1, n=Ndir, i and dividing through by Ndir. i is used to obtain the mean:

[0076] In some cases, the maximum value is obtained by setting ai = (Ntotai - Nduf. where Ntotai.i is the total number of genes in sample i. Example 1 provides an example of calculating an UMIS. See, also, Foroutan et al., BMC Bioinformatics, 19:404 (2018) and Leonard-Mural i el al., Nat. Commun., 15(1):2863 (2024).

[0077] Any appropriate mammal having cancer (e.g., uveal melanoma or metastatic cancer) can be assessed, prepared for treatment, and / or treated as described herein. Examples of mammals that can have cancer and can be assessed, prepared for treatment, and / or treated as described herein include, without limitation, humans, non-human primates (e.g., monkeys), dogs, cats, horses, cows, pigs, sheep, mice, and rats. In some cases, a human having cancer (e.g., uveal melanoma or metastatic cancer) can be assessed, prepared for treatment, and / or treated as described herein.

[0078] When assessing a mammal (e.g., a human) having cancer as described herein, preparing a mammal (e.g., a human) having cancer for treatment as described herein, and / or treating a mammal (e.g., a human) having cancer as described herein, the cancer can be any type of cancer. For example, a cancer assessed and / or treated as described herein can include one or more solid tumors. In some cases, a cancer assessed and / or treated as described herein can be a blood cancer. In some cases, a cancer assessed and / or treated as described herein can be a primary cancer. In some cases, a cancer assessed and / or treated as described herein can be a metastatic cancer. In some cases, a cancer assessed and / or treated as described herein can be a refractory cancer. In some cases, a cancer assessed and / or treated as described herein can be a relapsed cancer. Examples of cancers that can be assessed and / or treated as described herein include, without limitation, uveal melanomas, cutaneous melanomas, liver cancers, lung cancers, breast cancers, lymph cancers, pancreatic cancers (e.g., pancreatic adenocarcinomas), spleen cancers, biliary tract cancers, mesotheliomas (e.g., peritoneal mesotheliomas), Merkel cell carcinomas, sarcomas, gastric cancers (e.g., gastric adenocarcinomas), mucosal melanomas, squamous cell carcinomas, colorectal adenocarcinomas, ovarian carcinomas, gastrointestinal stromal tumors, merkel cell carcinomas, neuroendocrine tumors, urothelial carcinomas, eccrine porocarcinomas, paragangliomas, schwannomas, small bowel adenocarcinomas, solitary fibrous tumors, renal cell carcinomas, and adrenocortical carcinomas. In some cases, a mammal (e.g., a human) having cancer and being assessed and / or prepared for treatment (and optionally treated) as described herein can have multiple (e.g., two or more) different types of cancer. In some cases, a mammal (e.g., a human) having cancer and being assessed and / or prepared for treatment (and optionally treated) as described herein can have a cancer that has metastasized to one or more different locations.

[0079] In some cases, the methods described herein can include identifying a mammal (e.g., a human) as having cancer (e.g., metastatic cancer). Any appropriate method can be used to identify a mammal as having cancer. For example, imaging techniques and biopsy techniques can be used to identify mammals (e.g., humans) as having cancer.

[0080] In some cases, an UMIS used to determine whether or not a cancer (e.g., a metastatic cancer) is likely to respond to an ACT (e.g., a TIL therapy) can be calculated based on the 2394 nucleic acids listed in Table 1. For example, an UMIS of a sample (e.g., a tumor tissue sample) obtained from a mammal having cancer can be calculated based on the 2394 nucleic acids listed in Table 1 to determine whether or not the cancer is likely to respond to an ACT (e.g., a TIL therapy). In some cases, if an UMIS of cancer cells obtained from a mammal (e g., a human) having cancer calculated based on the 2394 nucleic acids listed in Table 1 is at least 0.2 (e.g., at least 0.2, at least 0.21, at least 0.22, at least 0.23, at least 0.24, at least 0.25, at least 0.26, at least 0.27, at least 0.28, at least 0.29, at least 0.3, at least 0.4, at least 0.5, or at least 0.6), then that mammal can be identified as having cancer that is likely to respond to an ACT (e.g., a TIL therapy). In some cases, if an UMIS of cancer cells obtained from a mammal (e.g., a human) having cancer calculated based on the 2394 nucleic acids listed in Table 1 is less than 0.2, then that mammal can be identified as having cancer that is unlikely to respond to an ACT (e.g., a TIL therapy). In some cases, a truncated UMIS used to determine whether or not a cancer (e.g., a metastatic cancer) is likely to respond to an ACT (e.g., a TIL therapy) can be calculated based on at least 250 (e.g., at least 275, at least 300, at least 325, at least 350, at least 400, at least 450, at least 500, at least 750, at least 1000, at least 1250, at least 1500, at least 1750, at least 2000, or at least 2250), but less than all 2394, of the nucleic acids listed in Table 1. For example, a truncated UMIS of a sample (e.g., a tumor tissue sample) obtained from a mammal having cancer can be calculated based on at least 250 (e.g., at least 275, at least 300, at least 325, at least 350, at least 400, at least 450, at least 500, at least 750, at least 1000, at least 1250, at least 1500, at least 1750, at least 2000, or at least 2250), but less than all 2394, of the nucleic acids listed in Table 1 to determine whether or not the cancer is likely to respond to an ACT (e.g., a TIL therapy). In some cases, if a truncated UMIS of cancer cells obtained from a mammal (e.g., a human) having cancer calculated based on at least 250 (e.g., at least 275, at least 300, at least 325, at least 350, at least 400, at least 450, at least 500, at least 750, at least 1000, at least 1250, at least 1500, at least 1750, at least 2000, or at least 2250), but less than all 2394, of the nucleic acids listed in Table 1 is at least 0.2 (e.g., at least 0.2, at least 0.21, at least 0.22, at least 0.23, at least 0.24, at least 0.25, at least 0.26, at least 0.27, at least 0.28, at least 0.29, at least 0.3, at least 0.4, at least 0.5, or at least 0.6), then that mammal can be identified as having cancer that is likely to respond to an ACT (e.g., a TIL therapy). In some cases, if a truncated UMIS of cancer cells obtained from a mammal (e.g., a human) having cancer calculated based on at least 250, but less than all 2394, of the nucleic acids listed in Table 1 is less than 0.2, then that mammal can be identified as having cancer that is unlikely to respond to an ACT (e.g., a TIL therapy).

[0081] In some cases, a truncated UMIS used to determine whether or not a cancer (e.g., a metastatic cancer) is likely to respond to an ACT (e.g., a TIL therapy) can be calculated based on at least 250 (e.g., at least 275, at least 300, at least 325, at least 350, at least 400, at least 450, at least 500, at least 750, at least 1000, at least 1250, at least 1500, at least 1750, at least 2000, or at least 2250), but less than all 2394, of the nucleic acids listed in Table 1 , provided that at least one or more (e.g., one, two, three, four, five, ten, twenty, or more) of the nucleic acids is not listed in Table 2. For example, a truncated UMIS of a sample (e.g., a tumor tissue sample) obtained from a mammal having cancer can be calculated based on at least 250 (e.g., at least 275, at least 300, at least 325, at least 350, at least 400, at least 450, at least 500, at least 750, at least 1000, at least 1250, at least 1500, at least 1750, at least 2000, or at least 2250), but less than all 2394, of the nucleic acids listed in Table 1, provided that at least one or more of the nucleic acids is not listed in Table 2, to determine whether or not the cancer is likely to respond to an ACT (e.g., a TIL therapy). In some cases, if a truncated UMIS of cancer cells obtained from a mammal (e.g., a human) having cancer calculated based on at least 250 (e.g., at least 275, at least 300, at least 325, at least 350, at least 400, at least 450, at least 500, at least 750, at least 1000, at least 1250, at least 1500, at least 1750, at least 2000, or at least 2250), but less than all 2394, of the nucleic acids listed in Table 1 is at least 0.2 (e.g., at least 0.2, at least 0.21, at least 0.22, at least 0.23, at least 0.24, at least 0.25, at least 0.26, at least 0.27, at least 0.28, at least 0.29, at least 0.3, at least 0.4, at least 0.5, or at least 0.6), provided that at least one or more of the nucleic acids is not listed in Table 2, then that mammal can be identified as having cancer that is likely to respond to an ACT (e.g., a TIL therapy). In some cases, if a truncated UMIS of cancer cells obtained from a mammal (e.g., a human) having cancer calculated based on at least 250, but less than all 2394, of the nucleic acids listed in Table 1 is less than 0.2, provided that at least one or more of the nucleic acids is not listed in Table 2, then that mammal can be identified as having cancer that is unlikely to respond to an ACT (e.g., a TIL therapy).

[0082] In some cases, a truncated UMIS used to determine whether or not a cancer (e.g., a metastatic cancer) is likely to respond to an ACT (e.g., a TIL therapy) can be calculated based on at least 250 (e.g., at least 275, at least 300, at least 325, at least 350, at least 400, at least 450, at least 500, at least 750, at least 1000, at least 1250, at least 1500, at least 1750, at least 2000, or at least 2250), but less than all 2394, of the nucleic acids listed in Table 1, provided that at least one or more (e.g., one, two, three, four, five, ten, twenty, or more) of the nucleic acids is listed in Table 3. For example, a truncated UMIS of a sample (e.g., a tumor tissue sample) obtained from a mammal having cancer can be calculated based on at least 250 (e.g., at least 275, at least 300, at least 325, at least 350, at least 400, at least 450, at least 500, at least 750, at least 1000, at least 1250, at least 1500, at least 1750, at least 2000, or at least 2250), but less than all 2394, of the nucleic acids listed in Table 1, provided that at least one or more of the nucleic acids is listed in Table 3, to determine whether or not the cancer is likely to respond to an ACT (e.g., a TIL therapy). In some cases, if a truncated UMIS of cancer cells obtained from a mammal (e.g., a human) having cancer calculated based on at least 250 (e.g., at least 275, at least 300, at least 325, at least 350, at least 400, at least 450, at least 500, at least 750, at least 1000, at least 1250, at least 1500, at least 1750, at least 2000, or at least 2250), but less than all 2394, of the nucleic acids listed in Table 1 is at least 0.2 (e.g., at least 0.2, at least 0.21, at least 0.22, at least 0.23, at least 0.24, at least 0.25, at least 0.26, at least 0.27, at least 0.28, at least 0.29, at least 0.3, at least 0.4, at least 0.5, or at least 0.6), provided that at least one or more of the nucleic acids is listed in Table 3, then that mammal can be identified as having cancer that is likely to respond to an ACT (e.g., a TIL therapy). In some cases, if a truncated UMIS of cancer cells obtained from a mammal (e.g., a human) having cancer calculated based on at least 250, but less than all 2394, of the nucleic acids listed in Table 1 is less than 0.2, provided that at least one or more of the nucleic acids is listed in Table 3, then that mammal can be identified as having cancer that is unlikely to respond to an ACT (e.g., a TIL therapy).

[0083] In some cases, a truncated UMIS used to determine whether or not a cancer (e.g., a metastatic cancer) is likely to respond to an ACT (e.g., a TIL therapy) can be calculated based on at least 10 percent (e.g., at least 20 percent, at least 30 percent, at least 40 percent, at least 50 percent, at least 60 percent, at least 70 percent, at least 80 percent, or at least 90 percent), but less than all 2394, of the nucleic acids listed in Table 1 . For example, a truncated UMIS of a sample (e.g., a tumor tissue sample) obtained from a mammal having cancer can be calculated based on at least 10 percent (e.g., at least 20 percent, at least 30 percent, at least 40 percent, at least 50 percent, at least 60 percent, at least 70 percent, at least 80 percent, or at least 90 percent), but less than all 2394, of the nucleic acids listed in Table 1 to determine whether or not the cancer is likely to respond to an ACT (e.g., a TIL therapy). In some cases, if a truncated UMIS of cancer cells obtained from a mammal (e.g., a human) having cancer calculated based on at least at least 10 percent (e.g., at least 20 percent, at least 30 percent, at least 40 percent, at least 50 percent, at least 60 percent, at least 70 percent, at least 80 percent, or at least 90 percent), but less than all 2394, of the nucleic acids listed in Table 1 is at least 0.2 (e.g., at least 0.2, at least 0.21, at least 0.22, at least 0.23, at least 0.24, at least 0.25, at least 0.26, at least 0.27, at least 0.28, at least 0.29, at least 0.3, at least 0.4, at least 0.5, or at least 0.6), then that mammal can be identified as having cancer that is likely to respond to an ACT (e.g., a TIL therapy). In some cases, if a truncated UMIS of cancer cells obtained from a mammal (e.g., a human) having cancer calculated based on at least 10 percent, but less than all 2394, of the nucleic acids listed in Table 1 is less than 0.2, then that mammal can be identified as having cancer that is unlikely to respond to an ACT (e.g., a TIL therapy).

[0084] In some cases, a truncated UMIS used to determine whether or not a cancer (e g., a metastatic cancer) is likely to respond to an ACT (e.g., a TIL therapy) can be calculated based on at least 10 percent (e.g., at least 20 percent, at least 30 percent, at least 40 percent, at least 50 percent, at least 60 percent, at least 70 percent, at least 80 percent, or at least 90 percent), but less than all 2394, of the nucleic acids listed in Table 1, provided that at least one or more (e.g., one, two, three, four, five, ten, twenty, or more) of the nucleic acids is not listed in Table 2. For example, a truncated UMIS of a sample (e.g., a tumor tissue sample) obtained from a mammal having cancer can be calculated based on at least 10 percent (e.g., at least 20 percent, at least 30 percent, at least 40 percent, at least 50 percent, at least 60 percent, at least 70 percent, at least 80 percent, or at least 90 percent), but less than all 2394, of the nucleic acids listed in Table 1, provided that at least one or more of the nucleic acids is not listed in Table 2, to determine whether or not the cancer is likely to respond to an ACT (e.g., a TIL therapy). In some cases, if a truncated UMIS of cancer cells obtained from a mammal (e.g., a human) having cancer calculated based on at least at least 10 percent (e.g., at least 20 percent, at least 30 percent, at least 40 percent, at least 50 percent, at least 60 percent, at least 70 percent, at least 80 percent, or at least 90 percent), but less than all 2394, of the nucleic acids listed in Table 1 is at least 0.2 (e.g., at least 0.2, at least 0.21, at least 0.22, at least 0.23, at least 0.24, at least 0.25, at least 0.26, at least 0.27, at least 0.28, at least 0.29, at least 0.3, at least 0.4, at least 0.5, or at least 0.6), provided that at least one or more of the nucleic acids is not listed in Table 2, then that mammal can be identified as having cancer that is likely to respond to an ACT (e.g., a TIL therapy). In some cases, if a truncated UMIS of cancer cells obtained from a mammal (e.g., a human) having cancer calculated based on at least 10 percent, but less than all 2394, of the nucleic acids listed in Table 1 is less than 0.2, provided that at least one or more of the nucleic acids is not listed in Table 2, then that mammal can be identified as having cancer that is unlikely to respond to an ACT (e.g., a TIL therapy).

[0085] In some cases, a truncated UMIS used to determine whether or not a cancer (e.g., a metastatic cancer) is likely to respond to an ACT (e.g., a TIL therapy) can be calculated based on at least 10 percent (e.g., at least 20 percent, at least 30 percent, at least 40 percent, at least 50 percent, at least 60 percent, at least 70 percent, at least 80 percent, or at least 90 percent), but less than all 2394, of the nucleic acids listed in Table 1, provided that at least one or more (e.g., one, two, three, four, five, ten, twenty, or more) of the nucleic acids is listed in Table 3. For example, a truncated UMIS of a sample (e.g., a tumor tissue sample) obtained from a mammal having cancer can be calculated based on at least 10 percent (e.g., at least 20 percent, at least 30 percent, at least 40 percent, at least 50 percent, at least 60 percent, at least 70 percent, at least 80 percent, or at least 90 percent), but less than all 2394, of the nucleic acids listed in Table 1, provided that at least one or more of the nucleic acids is listed in Table 3, to determine whether or not the cancer is likely to respond to an ACT (e.g., a TIL therapy). In some cases, if a truncated UMIS of cancer cells obtained from a mammal (e.g., a human) having cancer calculated based on at least at least 10 percent (e.g., at least 20 percent, at least 30 percent, at least 40 percent, at least 50 percent, at least 60 percent, at least 70 percent, at least 80 percent, or at least 90 percent), but less than all 2394, of the nucleic acids listed in Table 1 is at least 0.2 (e.g., at least 0.2, at least 0.21, at least 0.22, at least 0.23, at least 0.24, at least 0.25, at least 0.26, at least 0.27, at least 0.28, at least 0.29, at least 0.3, at least 0.4, at least 0.5, or at least 0.6), provided that at least one or more of the nucleic acids is listed in Table 3, then that mammal can be identified as having cancer that is likely to respond to an ACT (e.g., a TIL therapy). In some cases, if a truncated UMIS of cancer cells obtained from a mammal (e.g., a human) having cancer calculated based on at least 10 percent, but less than all 2394, of the nucleic acids listed in Table 1 is less than 0.2, provided that at least one or more of the nucleic acids is listed in Table 3, then that mammal can be identified as having cancer that is unlikely to respond to an ACT (e.g., a TIL therapy).

[0086] In some cases, an UMIS used to determine whether or not a cancer (e.g., a metastatic cancer) is likely to respond to an ACT (e.g., a TIL therapy) can be calculated based on all 2394 of the nucleic acids listed in Table 1. For example, an UMIS of a sample (e.g., a tumor tissue sample) obtained from a mammal having cancer can be calculated based on all 2394 of the nucleic acids listed in Table 1 to determine whether or not the cancer is likely to respond to an ACT (e.g., a TIL therapy). In some cases, if an UMIS of cancer cells obtained from a mammal (e.g., a human) having cancer calculated based on all 2394 of the nucleic acids listed in Table 1 is at least 0.2 (e.g., at least 0.2, at least 0.21, at least 0.22, at least 0.23, at least 0.24, at least 0.25, at least 0.26, at least 0.27, at least 0.28, at least 0.29, at least 0.3, at least 0.4, at least 0.5, or at least 0.6), then that mammal can be identified as having cancer that is likely to respond to an ACT (e.g., a TIL therapy). In some cases, if an UMIS of cancer cells obtained from a mammal (e.g., a human) having cancer calculated based on all 2394 of the nucleic acids listed in Table 1 is less than 0.2, then that mammal can be identified as having cancer that is unlikely to respond to an ACT (e.g., a TIL therapy).

[0087] Any appropriate sample from a mammal (e.g., a human) having cancer (e.g., uveal melanoma or metastatic cancer) can be assessed as described herein (e.g., for an UMIS or truncated UMIS of the cancer). In some cases, a sample can be a biological sample. In some cases, a sample can contain one or more cancer cells. In some cases, a sample can contain one or more biological molecules (e.g., nucleic acids such as DNA and RNA, polypeptides, carbohydrates, lipids, hormones, and / or metabolites). Examples of samples that can be assessed as described herein include, without limitation, tissue samples such as tumor tissue samples (e.g., tumor fragments) and tumor single cell suspensions. A sample can be a fresh sample (e.g., a fresh frozen sample). In some cases, one or more biological molecules can be isolated from a sample (e.g., from one or more cancer cells within the sample). For example, nucleic acid can be isolated from a sample and can be assessed as described herein.

[0088] Any appropriate method can be used to obtain a sample (e g., a tumor tissue sample) from a mammal (e.g., a human) having cancer (e.g., uveal melanoma or metastatic cancer). In some cases, a sample can be obtained using a core biopsy or a core-like biopsy.

[0089] In some cases, a sample (e.g., a tumor tissue sample) from a mammal (e.g., a human) having cancer (e.g., uveal melanoma or metastatic cancer) can be obtained without carrying out surgery.

[0090] In some cases, an UMIS or a truncated UMIS of a cancer (e.g., a uveal melanoma or a metastatic cancer) can be used to identify the cancer as being likely to respond to an ACT (e g., a TIL therapy). For example, an UMIS or a truncated UMIS of at least 0.2 (e.g., at least 0.21, at least 0.22, at least 0.23, at least 0.24, at least 0.25, at least 0.26, at least 0.27, at least 0.28, at least 0.29, at least 0.3, at least 0.4, at least 0.5, or at least 0.6) in a sample (e.g., a tumor tissue sample) obtained from a mammal (e.g., a human) having cancer (e.g., uveal melanoma or metastatic cancer) can be used to identify the cancer as being likely to respond to an ACT (e.g., a TIL therapy).

[0091] In some cases, an UMIS or a truncated UMIS of a cancer (e.g., a uveal melanoma or a metastatic cancer) can be used to identify the cancer as not being likely to respond to an ACT (e.g., a TIL therapy). For example, an UMIS or a truncated UMIS of less than 0.2 in a sample (e.g., a tumor tissue sample) obtained from a mammal (e.g., a human) having cancer (e.g., uveal melanoma or metastatic cancer) can be used to identify the cancer as being unlikely to respond to an ACT (e.g., a TIL therapy).

[0092] In some cases, a mammal (e.g., a human) having a cancer (e.g., a uveal melanoma or a metastatic cancer) that is identified as being likely to respond to an ACT (e.g., a TIL therapy) as described herein (e.g., based, at least in part, on an UMIS or a truncated UMIS of the cancer) can be selected to receive an ACT (e.g., a TIL therapy) to treat the cancer. For example, a mammal having a cancer (e.g., a uveal melanoma or a metastatic cancer) and identified as having an UMIS or a truncated UMIS of at least 0.2 (e.g., at least 0.21, at least 0.22, at least 0.23, at least 0.24, at least 0.25, at least 0.26, at least 0.27, at least 0.28, at least 0.29, at least 0.3, at least 0.4, at least 0.5, or at least 0.6) can be selected to receive an ACT (e.g., a TIL therapy).

[0093] In some cases, a mammal (e.g., a human) having a cancer (e.g., a uveal melanoma or a metastatic cancer) that is identified as not being likely to respond to an ACT (e.g., a TIL therapy) as described herein (e.g., based, at least in part, on an UMIS or a truncated UMIS of the cancer) can be selected to receive an alternative cancer treatment (e.g., one or more cancer treatments that are not an ACT) to treat the cancer. For example, a mammal having a cancer (e.g., a metastatic cancer) that is identified as having an UMIS or a truncated UMIS of less than 0.2 can be selected to receive an alternative cancer treatment (e.g., one or more cancer treatments that are not an ACT). This document also provides methods and materials for treating a mammal (e.g., a human) having cancer (e.g., uveal melanoma or metastatic cancer). In some cases, a mammal (e.g., a human) having cancer and assessed as described herein (e.g., to determine whether or not the cancer is likely to respond to an ACT such as a TIL therapy based, at least in part, on an UMIS or a truncated UMIS of the cancer) can be administered or instructed to selfadminister one or more (e.g., one, two, three, four, five, or more) cancer treatments, where the one or more cancer treatments are effective to treat the cancer within the mammal. For example, a mammal having cancer can be administered or instructed to self-administer one or more cancer treatments selected based, at least in part, on whether or not the cancer is likely to respond to an ACT such as a TIL therapy (e.g., based, at least in part, on an UMIS or a truncated UMIS of the cancer).

[0094] When treating a mammal (e.g., a human) having a cancer (e.g., a uveal melanoma or a metastatic cancer) that is identified as being likely to respond to an ACT (e.g., a TIL therapy) as described herein (e.g., based, at least in part, on an UMIS or a truncated UMIS of the cancer), the mammal can be administered or instructed to self-administer an ACT. For example, a mammal having a cancer (e.g., a uveal melanoma or a metastatic cancer) identified as having an UMIS or a truncated UMIS of at least 0.2 (e.g., at least 0.21, at least 0.22, at least 0.23, at least 0.24, at least 0.25, at least 0.26, at least 0.27, at least 0.28, at least 0.29, at least 0.3, at least 0.4, at least 0.5, or at least 0.6) can be administered or instructed to self-administer one or more ACTs. Examples of ACTs that can be administered to a mammal (e.g., a human) having a cancer (e.g., a uveal melanoma or a metastatic cancer) identified has being likely to respond to an ACT as described herein include, without limitation, TIL therapies.

[0095] When treating a mammal (e.g., a human) having a cancer (e.g., a uveal melanoma or a metastatic cancer) that is identified as not being likely to respond to an ACT (e.g., a TIL therapy) as described herein (e.g., based, at least in part, on an UMIS or a truncated UMIS of the cancer), the mammal can be administered or instructed to self-administer one or more (e.g., one, two, three, four, five, or more) alternative cancer treatments (e.g., one or more cancer treatments that are not an ACT such as a TIL therapy). For example, a mammal having a cancer (e.g., a uveal melanoma or a metastatic cancer) identified as having an UMIS or a truncated UMIS of less than 0.2 can be administered or instructed to self-administer one or more alternative cancer treatments that are not an ACT (e.g., a TIL therapy). Examples of alternative cancer treatments that are not an ACT (e.g., a TIL therapy) include, without limitation, performing surgery, performing radiation therapies, and administering one or more anti-cancer agents (e.g., chemotherapies, administering targeted therapies (e.g., monoclonal antibody therapies), and administering angiogenesis inhibitors).

[0096] In some cases, when treating a mammal (e.g., a human) having cancer (e.g., uveal melanoma or metastatic cancer) as described herein, the treatment can be effective to treat the cancer. For example, the number of cancer cells present within a mammal can be reduced using the methods and materials described herein. In some cases, the size (e.g., volume) of one or more tumors present within a mammal can be reduced using the methods and materials described herein. For example, the methods and materials described herein can be used to reduce the size of one or more tumors present within a mammal having cancer (e.g., uveal melanoma or metastatic cancer) by, for example, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, or more percent. In some cases, the methods and materials described herein can be used to treat cancer in a manner such that the size (e.g., volume) of one or more tumors present within a mammal does not increase.

[0097] In some cases, when treating a mammal (e.g., a human) having cancer (e.g., uveal melanoma or metastatic cancer) as described herein, the treatment can be effective to improve survival of the mammal. For example, the methods and materials described herein can be used to improve disease-free survival (e.g., relapse-free survival). For example, the methods and materials described herein can be used to improve progression-free survival. For example, the methods and materials described herein can be used to improve the survival of a mammal having cancer (e.g., uveal melanoma or metastatic cancer) by, for example, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, or more percent. For example, the methods and materials described herein can be used to improve the survival of a mammal having cancer (e.g., uveal melanoma or metastatic cancer) by, for example, at least 6 months (e.g., about 6 months, about 8 months, about 10 months, about 1 year, about 1.5 years, about 2 years, about 2.5 years, or about 3 years). The invention will be further described in the following examples, which do not limit the scope of the invention described in the claims.

[0098] EXAMPLES

[0099] Example 1: Transcriptomic-Guided Tumor Infiltrating Lymphocyte (TIL) Therapy

[0100] This Example describes the identification of a single-sample gene expression score that could rank uveal melanoma metastases based upon the expression level of immune and inflammatory genes.

[0101] Immune checkpoint inhibition has shown success in treating metastatic cutaneous melanoma but has limited efficacy against metastatic uveal melanoma, a rare variant arising from the immune privileged eye. To better understand this resistance, 100 human uveal melanoma metastases were comprehensively profiled using clinicogenomics, transcriptomics, and tumor infiltrating lymphocyte potency assessment. It was found that over half of these metastases harbor tumor infiltrating lymphocytes with potent autologous tumor specificity, despite low mutational burden and resistance to prior immunotherapies. However, strikingly low intratumoral T cell receptor clonality was observed within the tumor microenvironment even after prior immunotherapies. To harness these quiescent tumor infiltrating lymphocytes, a transcriptomic biomarker was developed to enable in vivo identification and ex vivo liberation to counter their growth suppression. Finally, it was demonstrated that adoptive transfer of these transcriptomically selected tumor infiltrating lymphocytes can promote tumor immunity in patients with metastatic uveal melanoma when other immunotherapies are incapable. These data can also be seen in, for example, Leonard- Murali etal., Nat. Commun.. 15(1):2863 (2024).

[0102] Results

[0103] Clinicogenomic landscape of metastatic uveal melanoma

[0104] One hundred metastases were surgically procured from 84 UM patients as part of eligibility screening for TIL ACT clinical trials at the National Cancer Institute and the University of Pittsburgh Medical Center between 2013 and 2022 (NCT01814046 and NCT03467516) (Chandran et al., Lancet Oncol., 18:792-802 (2017); and Crompton etal., Ann. Surg. Oncol., 25:565-272 (2018)). Resected metastases originated from 11 unique anatomic locations (Figure 1A), with liver as the predominant procurement site (56%) (Figure IB). Patient demographics revealed a median age of 56 years (range=17-78) and an even gender distribution (52% female, 48% male). Patients had extensive metastatic disease burdens, with 95% having liver involvement, 75% having elevated LDH levels, and 71% with M1B or MIC stage (Amin et al. CA Cancer J. Clin., 67(2):93-99 (2017)) (Figure 1C). Metastases were harvested from both treatment naive patients (24%) and treatment refractory patients (76%). Notably, 46 patients received prior ICI therapy (anti-CTLA4 only = 3, anti- PD-1 only = 11, sequential therapy = 8, and combination therapy = 24) and 9 patients received tebentafusp, of whom none showed objective response (Figure 1C). Somatic mutational analysis of the metastases confirmed a low TMB (median = 0.64 mutations per megabase) with ubiquitous and mutually exclusive presence of established UM driver mutations (GNAQ, GNA11, CYSLTR2 or PLCB4) and frequent secondary alterations of BAP 1 (62%) and SF3B1 (42%) (Figures 1C and 7A-7E). Somatic copy number alterations included chromosome 3 loss (46%) and 8q gain (85%) (Figure 1C). No associations were found between TMB and cohort demographics (Figure 7F). In sum, clinicogenomic profiling established this patient cohort to be broadly representative of advanced UM and the procured metastases to have canonical UM driver alterations and low TMB.

[0105] Unbiased tumor transcriptomics reveals T cell-inflamed uveal melanoma metastases Current tumor biomarkers for immunotherapy susceptibility, such as TMB and PD- Ll, are rarely used in metastatic UM due to the uniformly low expression of these markers in this melanoma variant (Samstein et al., Nat. Genet., 54:202-206 (2019); Yarchoan et al., N. Engl. J. Med, 377:2500-2501 (2017); and Javed et al., Immunotherapy, 9: 1323-1330 (2017)). Thus, alternative immune prognostic metrics were sought by interrogating the transcriptome of UM metastases using total RNA sequencing and unbiased computational profiling. Further, to facilitate a clinically relevant and minimally invasive biopsy approach for in situ tumor characterization, the analysis was restricted to a single random biopsy from each resected metastasis (about 2 mm central core fragment from 93 metastases and about 500,000 cells post tumor dissociation from 7 metastases). PCA revealed the majority of transcriptional variance among the metastases was restricted to PCs 1, 2, and 3 (variance contributed: 19%, 13%, 12% respectively) while the remaining PCs (4-100) each contributed about 5% or less variance (Figure 8A). Metastases were then mapped according to the three main PC coordinates (PCs 1, 2, and 3) (Figures 8B-8C). To determine whether specific cellular pathways and processes were associated with specific PCs, PC coordinates (1, 2, and 3) were correlated with enrichment scores for each of the canonical hallmark gene sets from the Human Molecular Signatures Database (Foroutan et al., BMC Bioinformatics, 19:404 (2018)). Unsupervised clustering (Euclidean distance) of the PC-gene set correlations (Spearman’s rho) identified four discrete clusters (A, B, C, and D) with unique biologic motifs (Figure 2A). Cluster A included cellular metabolism pathways (MYC TARGETS VI, MT0RC1 SIGNALING, OXIDATIVE PHOSPHORYLA TION), Cluster B included immune and inflammatory signaling pathways (INTERFERON ALPHA RESPONSE, INTERFERON GAMMA RESPONSE, ALLOGRAFT REJECTION, IL2 STAT5 SIGNALING), Cluster C included liver dominant physiologic pathways (BILE ACID METABOLISM, COAGULATION, CHOLESTEROL HOMEOSTASIS), and Cluster D included cellular signaling and division (WNT BETA CATENIN SIGNALING, MYC TARGETS V2, G2M CHECKPOINT). The individual metastatic samples were further clustered by their relative expression of each of the hallmark gene set clusters to reveal striking variability across the tumor cohort (Figure 2B). Having identified transcriptomic differences among the metastases, whether any of the three PCs independently correlated with the expression of the gene set clusters was determined. Average Spearman’s rank correlation coefficients (rho) for each of the gene set cluster enrichment scores (A, B, C, and D) were mapped against the individual PCs (1, 2, and 3) (Figure 2C). It was observed that cluster A (cellular metabolism) was strongly correlated with PC3 (mean rho = +0.76) but also weakly correlated with the negative aspect of PCI (mean rho = -0.27). Cluster B (immune and inflammatory signaling) was exclusively correlated with the negative aspect of PC2 (rho = -0.32). Clusters C and D were not found to independently correlate with any of the three PCs. Given the independent association of PC2 with Cluster B immune pathways, it was postulated that PC2 coordinate position was predominantly driven by intrinsic immune and inflammatory gene expression in these metastases. As support, when the enrichment scores for T cell activation gene sets were mapped onto three-dimensional PCA plots of the metastases, it was observed that each gene set had a significant inverse relationship with the PC2 axis (INTERFERON GAMMA RESPONSE versus PC2: rho = -0.56, P = 2.52xl0’9; INTERFERON ALPHA RESPONSE versus PC2: rho = -0.56, P = 2.89xl0'9; ALLOGRAFT REJECTION versus PC2: rho = -0.49, P = 2.27xl0'7) (Figures 2D and 8D). Collectively, unbiased computational profiling revealed PC2 coordinate mapping as an effective initial approach to segregate UM metastases with T cell-inflamed transcriptomic attributes.

[0106] Development of Uveal Melanoma Immunogenomic Score (UMIS)

[0107] Refinement of the rudimentary PC2 variable into a more specific and clinically applicable immune metric for UM metastases was sought. First, the 2394 genes that positively correlated with immune and inflammatory hallmark gene set enrichment (those with negative PC2 loadings) were defined. Rather than biasing this gene list with supervised filtering, the entire list of 2394 genes set forth in Table 1 was utilized to facilitate discovery of novel biologic processes. Further, to enable single-sample prospective analysis, a cohortindependent rank-based gene set scoring method (singscore) (Foroutan et al., BMC Bioinformatics, 19:404 (2018)) was employed to calculate enrichment scores for individual biopsies based upon transcript abundance (transcripts per million; TPM). Using this approach, a single continuous variable was generated for each metastasis called an Uveal Melanoma Immunogenomic Score (UMIS), which reflected the concordance and mean percentile rank of the list of 2394 genes (Table 1) within the sample transcriptome (Figure 3 A). Cohort-independent UMIS (singscore) correlated strongly with the corresponding cohort-dependent score calculated using established pipelines (GSFA), supporting that the single-sample rank-based tool could be used for prospective evaluation of tumor biopsies without batch artifact (Figure 3B) (Foroutan et al., BMC Bioinformatics, 19:404 (2018)). Of the 2394 genes that constitute UMIS, 1527 were protein-coding and the remaining 867 were a mixture of non-coding, unclassified, and pseudo genes (Figure 3C). Functional annotation of UMIS coding genes confirmed pathways related to immune and inflammatory response (Figure 3D). UMIS values ranged from 0.114 to 0.347 across the 100 metastases with a median score of 0.237, which was used as a cutoff to define high and low UMIS groups for categorical comparisons (Figure 3E). Gene set enrichment analysis of high versus low UMIS metastases demonstrated that the most significantly enriched pathways were in the high UMIS group and involved T cell activation (Figures 3F and 9A). UMIS level was observed to be independent of metastatic site (Figure 3G), TMB (Figure 3H), somatic mutations and copy number alterations (Figure 9B), and class I human leukocyte antigen (HLA) alleles (Figure 9C). Thus, UMIS represented a unique single-sample gene expression score derived from an unbiased mixture of coding, non-coding, and unannotated transcripts that could rank UM metastases based upon the expression level of immune and inflammatory genes.

[0108] UMIS uncovers in vivo drivers of T cell recruitment and exclusion

[0109] To characterize the tumor microenvironmental cellular attributes contributing to UMIS, whole-tumor single cell transcriptomics of six UM metastases with disparate UMIS values was performed; three high UMIS (0.300, 0.268, 0.264) versus three low UMIS (0.199, 0.178, 0.162) (Figures 10A-10B). The 93670 analyzed cells were catalogued by building a unique metastatic UM single cell atlas using a two-step process that first categorized cells into large buckets (tumor, immune, and stroma) then assigned specific cellular and lineage labels (myeloid versus lymphoid) to the immune cellular fraction (Figures 4A-4B and 10C- 10E) (Nieto etal., Genome Res., 31 : 1913-1926 (2021); Andreatta & Carmona, Comput. Struct. Biotechnol. J, 19:3796-3798 (2021); and Kang et al., Nat. Commun., 12:5890 (2021)). The single cell analysis of low UMIS tumors had expectedly low numbers of immune cells. However, to maintain the true proportional landscape of specific cell types and avoid manipulation induced transcriptomic changes, the tumor digests were profiled without an additional enrichment step. More lymphoid cells (proportion ratio = 10.50, P = 0.047) and fewer tumor cells (proportion ratio = 0.88, P = 0.047) were observed in high UMIS versus low UMIS metastases (Figures 4B and 10F). Further, the composition of these lymphoid fractions differed, with the high UMIS metastases being enriched with CD8+T cells (proportion ratio = 6.31, P = 6.15xl0-7) and the low UMIS metastases being enriched with CD4+T cells (proportion ratio = 0.63, P = 3.32xl0’4) and T helper / Th!7 T cells (proportion ratio = 0.25, P = 0.024) (Figure 4C). A granular analysis of the lymphoid cells revealed that the high UMIS metastases were enriched for CD8+exhausted T cells (proportion ratio = 40.73, P = 4.04xl0'7) and CD8+cytotoxic T cells (proportion ratio = 4.70, P = 1.13xl0'3) (Figure 4D). It was observed that 9% of the CD8+exhausted and 20% of the CD8+cytotoxic TIL retained transcriptomic expression of TCF7, suggesting possible progenitor capability (Figure 11 A). Differential gene expression of the lymphoid cells revealed the high UMIS metastases had upregulation of genes involving T cell activation (INFRSF9, TNFRSF4), T cell exhaustion (PDCDI, CTLA4, LAG3, HAVCR2, VSIR), lymphocyte activation (STAT1\ interferon response (HLA-A, HLA-B, HLA-C, B2M, IFNGR1, IRF1, IFI27, IFI6, IFITM1, IFITM2, IFITM3), T cell memory (IL7R), lymphocyte trafficking (CXCL13, CCR7, SELL, CXCR3), and T cell progenitor capability (TCF7) (Figures 4E and 1 IB) (Smith-Garvin etal, Annu. Rev. Immuno., 27:591-619 (2009); Ivashkiv, Nat. Rev. Immunol., 18:545-558 (2018); Dersh et al., Nat. Rev. Immuno., 24: 116-128 (2021); Diamond & Farzan, Nat. Rev. Immunol., 13:46-57 (2013); Chandran et al., Cancer Res., 75:3216-3226 (2015); Krishna et al., Science, 370: 1328-1334 (2020); and Chow el al., Immunity, 50: 1498-1512.el495 (2019)). Taken together, these data demonstrate that the TIL found in high UMIS metastases had undergone activation and effector differentiation consistent with an in vivo adaptive anti-tumor response and indicative of a T cell-inflamed microenvironment.

[0110] The myeloid cells found in high UMIS versus low UMIS metastases were investigated (Figures 4F and 11C). Although there was no enrichment of specific myeloid cell types (macrophages, dendritic cells, mast cells) in either group, differential gene expression revealed the high UMIS myeloid cells had upregulated genes involving CD8+T cell recruitment (CXCL10, CXCL9), tumor phagocytosis (SLAMF7), antigen processing TAPI, TAP2), antigen presentation (HLA-A, HLA-B, HLA-C, B2M, HLA-DPB1, HLA- DQB1, HLA-DRB I), and interferon response (IRF1, IRF8, IFI27, IFI6) (Figures 4G and HD) (Dersh et al., Nat. Rev. Immuno., 21 : 116-128 (2021); Diamond & Farzan, Nat. Rev. Immuno., 13:46-57 (2013); Chow et al. , Immunity, 50: 1498-1512. e!495 (2019); Chen et al., Nature, 544:493-197 (2017); Spranger et al., Nature, 523:231-235 (2015); and Duraiswamy et al., Cancer Cell, 39: 1623-1642. e!620 (2021)). These findings support that the T cell- inflamed microenvironment found in high UMIS metastases also included more active myeloid lineage antigen presenting cells (APCs) capable of recruiting CD8+T cells.

[0111] Finally, since UMIS was derived using unbiased whole-tumor transcriptomics it was postulated that this score may also reflect intrinsic differences among the tumor cells within high versus low UMIS metastases. Upon reclustering of tumor cells, distinct separation of cells derived from high versus low UMIS metastases was confirmed (Figure 12A). Differential gene expression revealed high UMIS tumor cells had significantly increased expression of several interferon-inducible transcription factors and elements (IRF1, IFI27, IFI6, IFITM1, IFITM2, IFIIMS) and each of the major histocompatibility complex (MHC) class I molecule heterodimer components (HLA-A, HLA-B, HLA-C, B2M) (Figures 4H-4I and 12B) (Ivashkiv, Nat. Rev. Immuno., 18:545-558 (2018); Dersh etal., Nat. Rev. Immunol., 21 : 116-128 (2021); and Diamond & Farzan, Nat. Rev. Immunol., 13:46-57 (2013)). These findings suggested that high UMIS metastases were composed of IFN-y primed tumor cells that had upregulated MHC class I expression in response to chronic IFN-y secretion from tumor specific CD8+T cells (Ivashkiv, Nat. Rev. Immuno., 18:545-558 (2018); and Dersh el al., Nat. Rev. Immuno., 21 : 116-128 (2021)). In contrast, low UMIS tumor cells had 1.44-fold higher expression of CTNNBI (log2 (fold change) = -0.53, FDR about 0) which encodes the beta-catenin protein (Figures 4H and 4U). Activation of the Wnt / beta-catenin pathway has been implicated in T cell exclusion and may explain the paucity of CD8+T cell infiltrate in low UMIS metastases (Spranger et al., Nature, 523:493-497 (2017); and Uuke et al., Cancer Res., 25:3074-3083 (2019)). In support, a significant inverse relationship across the total metastatic cohort (n=100) between UMIS and the expression of a previously reported immune resistance program was found (Figures 4J-4K) (Jerby-Arnon et al., Cell, 175:984- 997.e924 (2018)). The most upregulated gene in low UMIS tumor cells was the long noncoding RNA, SNHG7, which was 3.48-fold upregulated in low UMIS tumor cells (log2 (fold change) = -1.80, FDR about 0) and has been previously reported as a positive regulator of CTNNBI expression in several cancers (Figures 4H-4I) (Yu et al., Mol. Ther. Nucleic Acids, 17:235-244 (2019); Chen et al., Pathol. Res. Pract., 215:302-307 (2019); Bian et al., Mol. Clin. Oncol., 13:45 (2020); Najafi et al., Front. Cell Dev. Biol., 9:809345 (2021); and Ren et al., Biochem. Biophys. Res. Commun., 406:712-718 (2018)). The findings confirmed a strong association between SNHG7 and CTNNB1 expression level in UM metastases (n=100) (Figure 4L) that was independent of tumor cell abundance as measured by melanomaspecific gene expression (S100A1, SOXIO, MITF) and total RNA quantity (ACTB, GAPDH) (Figures 4M and 12C).

[0112] In sum, single cell transcriptomics demonstrated that UMIS was a holistic metric that reflected the gene expression of the lymphoid, myeloid, and tumor compartments within the tumor microenvironment. Further, UMIS classification of metastases revealed increased CTNNB1 expression by low UMIS tumors cells as a putative driver of immune exclusion. In contrast, high UMIS metastases displayed lower tumor cell CTNNBI expression, more activated APCs, greater CD8 T cell recruitment, and robust interferon signaling.

[0113] UMIS predicts anti-tumor potency of ex vivo expanded TIL

[0114] To validate the transcriptomics demonstrating T-cell inflamed gene expression the specific anti-tumor potency of the endogenous TIL from each of the UM metastases was interrogated. Currently, the assessment of TIL tumor reactivity requires patients to undergo surgical resection of metastases followed by several weeks of ex vivo TIL expansion and finally resource intensive coculture with autologous tumor cells. Thus, it was also investigated whether UMIS could serve as a rapid and minimally invasive clinical tool to predict the tumor specific potency of endogenous TIL. UMIS values, derived from a single random biopsy from each source metastasis (n=100), were compared with the level of TIL anti-tumor reactivity found after conventional ex vivo expansion (Figure 5A). TIL cultures (n about 24) were initiated from each freshly resected metastasis using a standardized ex vivo tumor fragmentation approach to address tumor heterogeneity, as previously described (Chandran el al., Lancet Oncol., 18:792-802 (2017)). The individual TIL fragment cultures were tested for tumor specificity by coculture with autologous tumor digest (versus normal tissue controls) followed by measurement of 4-1BB upregulation on CD3+cells (flow cytometry) and IFN-y release (ELISA), which were found to be strongly correlated (Figures 5B-5C). The percentage of TIL cultures having tumor-specific reactivity from each metastasis was used as a standardized reactivity metric for comparing the level of anti-tumor TIL responses across tumors (Figure 5D). It was found that the frequency of tumor reactive TIL cultures varied significantly among the total cohort (median = 6%; range = 0-100%) with 55 metastases having measurable anti -turn or reactivity and the remaining 45 metastases with no detected reactivity (Figure 5E). Further, the metastases that had undergone prior ICI (n=53) and tebentafusp therapy (n=12) showed no difference in the mean percentage of tumor reactive TIL cultures when compared to samples that had not undergone these treatments (ICI 22% vs no ICI 23%, P = ns; tebentafusp 32% vs no tebentafusp 21%, P = ns) (Figure 5E). The percentage of tumor reactive TIL cultures was also independent of metastatic site, TMB, specific mutation expression, copy number alterations, and class I HLA alleles (Figures 13A-13D). When UMIS of each source metastasis was compared to the percentage of tumor reactive TIL cultures that were generated several weeks later, a strong positive correlation was found (rho = +0.47, P = 7.06xl0'7) (Figure 5F). Notably, reactive TIL cultures were rarely expanded from metastases with an UMIS less than 0.2, suggesting the use of this cutoff as a preoperative threshold to avoid futile surgical resection of noninflamed UM metastases. Interestingly, a small subset of discordant metastases (n=16) with high UMIS values that yielded TIL with no detectable anti-tumor reactivity based upon coculture with autologous tumor digest was observed (Figure 5F). However, upon assessing the quality of these specific tumor digest samples, it was found that they had significantly lower viability when compared to digests (n=34) that yielded concordance between high UMIS and co-culture reactivity (median digest viability: 78% vs 93%, P = 0.046), (Figures 5F and 13E). Since UMIS quantitation was neither associated with nor dependent upon tumor digest viability (Figure 13F), the discordance with anti -tumor reactivity observed with this outlier subset likely stemmed from insufficient stimulatory capacity of these low viability tumor digests. To further characterize the performance of UMIS in the discovery cohort of 100 UM metastases, its ability to predict co-culture anti -tumor reactivity against several other tumor biopsy metrics including TMB, percentage of infiltrating CD8+T cells, and several published gene expression profiles for T cell inflammation was benchmarked (Figures 5G- 5H) (Spranger, Nature, 523:231-235 (2015); Rooney et al., Cell, 160:48-61 (2015); Fehrenbacher et al., Lancet, 387:1837-1846 (2016); and Ayers et al., J. Clin. Invest., 127:2930-2940 (2017)). It was found that UMIS was the strongest performer as both a correlative metric (rho = +0.47, P = 7.06x10'7) and classification metric (AUC = 0.85) for predicting ex vivo TIL reactivity (Figures 5G-5H). Not surprisingly, TMB had no predictive value (rho = +0.01, P = ns; AUC = 0.51) (Figures 5G-5H). In further support of UMIS as a preoperative biomarker, it was found that UMIS level could identify metastases with the greatest yield of tumor reactive TIL among synchronous metastases in individual UM patients (Figure 51). Finally, in a prospective and independent validation cohort of metastatic UM biopsies, the predictive ability of UMIS for ex vivo TIL reactivity was corroborated (n=20, AUC = 0.76) (Figure 5J). Additionally, it was validated that UMIS remained consistent across spatially distinct areas of individual tumors (Figure 14A) and could also be obtained from minimally invasive core biopsies (Figure 14B). Taken together, these findings establish that UMIS, obtained from a metastatic biopsy, could serve as a minimally invasive preoperative biomarker to both identify UM metastases harboring tumor reactive TIL and predict the percentage of tumor reactive TIL cultures that could be expanded without the limitations associated with conventional coculture assays.

[0115] UMIS identifies quiescent TIL resistant to ICI and lebenlafusp but sensitive to ex vivo expansion and adoptive transfer

[0116] Having found that UMIS strongly correlated with the level of TIL anti-tumor reactivity in metastases, it was surprising to find that high UMIS status was not significantly associated with improved survival in the UM cohort (Figure 15 A). Furthermore, despite discovering tumor reactive TIL within the metastases of 23 UM patients (50%) who received prior ICI and 7 patients (78%) who received tebentafusp, it was noted that none of these patients showed objective tumor regression with these therapies. To investigate these paradoxical findings, the intratumoral TCR repertoire within the source metastases was analyzed (Chiffelle et al., Curr. Opin. BiotechnoL, 65:284-295 (2020)). It was found that the in situ diversity of the TCR beta (TRB; n=88) and TCR alpha (TRA; n=82) chains varied significantly across the total cohort of metastases (Shannon index ranges; TRB = 0.08-4.84, TRA = 0.69-4.72) (Figures 6A and 16A). UMIS was found to strongly correlate with both TRB diversity (rho = +0.54, P = 5.40xl0'8) (Figure 6A) and TRA diversity (rho = +0.45, P = 2.14xl0-5) (Figure 16A) suggesting that high UMIS metastases had more polyclonal T cell infiltrates. In contrast, TRB and TRA clonality, an in vivo surrogate for relative TIL clonal expansion, was low and minimally variant across the samples (1 - Pielou’s index ranges; TRB = 0-0.89, TRA = 0-0.23) (Figures 6A and 16A). No correlations were found between UMIS and the clonality of the TRB (rho = +0.02, P = ns) (Figure 6 A) and TRA (rho = -0.07, P - ns) chains (Figure 16A). The in vivo quiescence of these TIL was further corroborated by single cell TCR repertoire analysis demonstrating low clonality (Figure 16B) and single cell transcriptomics which found that the percentage of proliferative T cells was equivalently low in high and low UMIS metastases (Figures 4C and 6B). Interestingly, prior ICI therapies (n=53) had no influence on TCR diversity compared with untreated samples (Figures 6A, 6C, 16A, and 16D). In contrast, prior tebentafusp treatment (n=12) was associated with greater TCR diversity, consistent with the ability of this bispecific T cell engager to recruit T cells to these metastases (Figures 6A, 6C, 16A, and 16C-16D). However, neither prior ICI nor tebentafusp therapy were associated with an increase in TCR clonality (Figures 6A, 6C, 16A, and 16C-16D), indicating that these immunotherapies were incapable of inducing in vivo proliferation of the endogenous TIL. When specific TRB and TRA sequences were compared across the metastases (n=100), it was found that most of the sequences were private, with rare and limited public expression suggesting unique, rather than shared, antigen targeting (Figure 16E). Cumulatively, these TCR repertoire studies demonstrated that although high UMIS metastases were infiltrated with a unique polyclonal population of TIL, these T cells remained quiescent, even after receiving ICI and tebentafusp therapy.

[0117] To determine whether the deficient proliferation of the intratumoral T cells was due to T cell exhaustion or other intrinsic proliferative defects, clinical scale ex vivo rapid expansion (REP) of TIL from UM metastases that were either naive to ICI and tebentafusp (n=3), or refractory to ICI (n=10), tebentafusp (n=4), or both therapies (n=2) was performed (Figures 6D-6E and 17A-17C). It was observed that TIL from each of the metastases demonstrated approximately 5-log expansion, reaching massive cell counts (median = 7.31xl010, range = LOOxlO^-l.nxlO11) (Figures 6D-6E and 17A). Further, these expanded TIL demonstrated a significant decrease in TCR diversity (P = 4x1 O'6) and a significant increase in TCR clonality (P = 4x1 O'6) as compared to their source metastases using highly specific targeted TCR sequencing (Figures 6D-6E and 17A-17C). These findings suggested that the endogenous TIL were not limited by intrinsic proliferative deficiencies, but instead their growth was likely suppressed by the tumor microenvironment. Taken together, it was observed that the quiescence of endogenous TIL in UM metastases was not reversed with ICI or tebentafusp but could be revived with ex vivo liberation and expansion.

[0118] Based upon the observation that UMIS from a metastatic biopsy could predict the ex vivo potency of quiescent endogenous TIL, it was postulated that UMIS might also predict the clinical efficacy of adoptive transfer of these TIL after ex vivo liberation and expansion (Figure 6F). Of the 100 UM metastases profiled, 19 had been used to manufacture TIL for a previously reported ACT trial in patients with metastatic UM (NCT01814046) (Chandran et al., Lancet Oncol. , 18:792-802 (2017)). Among this treatment cohort, which included six responders and 13 nonresponders, a strong correlation between source tumor UMIS and the ex vivo anti-tumor reactivity of the post-REP TIL infusion product was observed (n=17, rho = +0.61, P = 0.011; two infusion products were not tested due to insufficient tumor) (Figure 6G). Additionally, it was found that UMIS as a continuous variable strongly correlated with magnitude of clinical tumor regression after adoptive transfer in patients with metastatic UM, including ICI refractory individuals (n=19, rho = -0.68, P = 0.001) (Figure 6H). To help define an UMIS threshold value that might have clinical utility in predicting RECIST objective responses (>30% reduction), the median UMIS value of the non-responder group (UMIS = 0.246) was utilized as a response threshold (Figure 61). It was observed that patients having source metastases above this threshold had significantly improved progression-free and overall survival after TIL ACT versus those below the threshold (Figure 6 J). In sum, these findings demonstrate that UMIS, performed on a pre-treatment metastatic biopsy, correlated with the clinical outcome after adoptive transfer of TIL and may serve as a predictive biomarker for the treatment of metastatic UM with ACT.

[0119] Methods

[0120] Patient samples and clinical annotation

[0121] Patients were screened and tumor samples were obtained after informed consent in conjunction with tumor procurement banking protocols associated with two adoptive TIL transfer clinical trials: NCT03467516 (Hillman Cancer Center, UPMC, Pittsburgh, PA, USA) and NCT01814046 (Surgery Branch, NCI, Bethesda, MD, USA). There was no requirement for previous systemic therapy, given the lack of known effective systemic treatments for metastatic UM at the time of study. If patients did receive previous systemic treatment, more than four weeks must have elapsed before initiation of the current trial therapy, and patients’ toxicities must have recovered to a grade 1 or less (except for toxicities such as alopecia or vitiligo). All patients were required to have progressive and measurable metastatic disease with an Eastern Cooperative Oncology Group performance status of 0 or 1 and life expectancy greater than 3 months at the time of enrollment. Patients were required to have adequate hematological, renal, and hepatic function. Patients were excluded if they had active systemic infections, coagulation disorders, or other active major medical illnesses of the immune system (Chandran et al., Lancet Oncol., 18:792-802 (2017)).

[0122] Clinical information, including demographics and treatments, were collected relative to the date of metastatic tumor harvest. Sex was self-reported by patients and consistent with biological sex determined by genomic analysis. Time-to-event data was collected relative to multiple dates: date of primary diagnosis, date of metastatic diagnosis, and date of ACT (if applicable). In cases of patients with multiple metastatic biopsies, an algorithm was adopted for selection of a representative biopsy for the purposes of patient-centered time-to-event analysis (in descending order of priority: metastasis harvested prior to any ACT, metastasis whose TIL was utilized for subsequent ACT, more recently harvested metastasis). Response to ACT was evaluated using RECIST vl.l criteria (Kendig et al., Front. Genet., 10:736 (2019)).

[0123] Tumor procurement, ex vivo TIL culture and tumor reactivity testing

[0124] All patients had surgical metastatectomies as screening for clinical trials NCT03467516 (Hillman Cancer Center, UPMC, Pittsburgh, PA, USA) and NCT01814046 (Surgery Branch, NCI, Bethesda, MD, USA) to procure tumor tissue to generate autologous TIL for therapy (Crompton et al., Ann. Surg. Oncol., 25:565-572 (2018)). After surgical procurement of a metastatic lesion, the fresh tumor underwent sterile dissection. Representative samples of tumor were sent for formal pathological confirmation of UM. To develop a clinically relevant core biopsy approach for in situ tumor characterization, a single random biopsy was obtained from each resected metastasis (about 2 mm central core fragment from 93 metastases and about 500,000 cells post tumor dissociation from seven metastases) and were snap-frozen in liquid nitrogen and stored long term at -80°C for future DNA and RNA extraction.

[0125] TIL cultures were initiated from geographically discrete 1-2 mm3tumor fragments (n about 24) that were placed individually in wells of a 24-well culture plate containing complete media with human AB serum and recombinant interleukin-2 (6000 lU / mL). Remaining fresh tumor was processed by mechanical and enzymatic digestion with the human Tumor Dissociation Kit (Miltenyi Biotec) and GENTLEMACS™ Dissociator (Miltenyi Biotec) to provide a single cell suspension of autologous tumor targets for TIL reactivity testing. Tumor digests underwent flow cytometric phenotyping with propidium iodide followed by the following anti-human monoclonal antibodies: CD3-APC-Cy7, CD8- PE-Cy7, CD4-PE (BD Biosciences). Tumor digest viability was determined by percent propidium iodide negative cells by flow cytometry or percent trypan blue negative cells by manual cell counting (UM #47). After about two weeks of growth, individual TIL fragment cultures were tested for tumor specificity by coculture with autologous tumor cells (versus normal tissue controls) followed by measurement of 4-1BB upregulation on CD3+cells by flow cytometry using anti-human CD137 (4-lBB)-APC (BD Biosciences) and IFN-y release by ELISA (Rothermel et al., Clin. Cancer Res., 22:2237-2249 (2016); and Chandran et al., Lancet Oncol., 18:792-802 (2017)). Tumor single cell suspensions (digests) and peripheral blood mononuclear cells were cryopreserved in freezing media and stored long term in liquid nitrogen. Monocytes were isolated from peripheral blood mononuclear cells using the CD14+MicroBead isolation kit (Miltenyi Biotec). All flow cytometry data was analyzed with FlowJo vl0.8 Software (BD Life Sciences).

[0126] Clinical scale ex vivo rapid expansion protocol (REP) involved selection of individual fragment T cell cultures for further expansion based on proliferative capacity and evidence of autologous tumor reactivity. Final large-scale expansion of selected TIL cultures was done with anti-CD3 antibody (30 ng / mL, Ortho Biotech or 50 ng / mL, Miltenyi Biotec) and recombinant interleukin-2 (3000 lU / mL; Clinigen) in the presence of irradiated peripheral blood mononuclear feeder cells (Chandran et al., Lancet Oncol., 18:792-802 (2017)). The specific anti -tumor reactivity of the infused TIL from NCT01814046 (Surgery Branch, NCI, Bethesda, MD, USA) was assessed by ELISA-based assays. Following overnight co-culture of the TIL with their autologous source tumor, the supernatant from these respective cocultures was assessed by ELISA to determine the tumor-induced IFN-y production as described elsewhere (Chandran et al., Lancet Oncol., 18:792-802 (2017)).

[0127] DNA extraction, library preparation, sequencing, and somatic analysis

[0128] Whole genome sequencing of the majority of samples was performed at the UPMC Genome Center (n=93). Genomic DNA was isolated from tumor samples or peripheral blood mononuclear cells on the automated CHEMAGIC™ 360 (PerkinElmer) instrument according to the manufacturer’s instructions. Extracted DNA was quantitated using Qubit dsDNA BR Assay Kit (Thermo Fisher Scientific). DNA libraries were prepared using the KAPA Hyper Plus Kit (KAPA Biosystems). Genomic DNA was processed through fragmentation, enzymatic end-repair and A-tailing, ligation, and quality check a Standard Sensitivity NGS Fragment Analyzer Kit (Agilent). Libraries with an average size of 450 base pairs (range = 300-600 base pairs) were quantified by qPCR on the LIGHTCYCLER® 480 (Roche) using the KAPA qPCR quantification kit (KAPA Biosystems). The libraries were normalized and pooled as per manufacturer protocol (Illumina). Sequencing was performed using the NovaSeq 6000 platform (Illumina) with 151 base pair paired end reads to an average target depth of 70X coverage. The sequencing data was demultiplexed with bcl2fastq2 v2.20 (Illumina) to produce the fastq files.

[0129] The samples were mapped with Sentieon vl.3.4. Somatic variants were called by TNhaplotyper2 on tumor-normal mode with the best-practice recommended whole genome sequencing setting. Variants were annotated with Funcotator from GATK v4.0.5 (Van der Auwera etal., Curr. Protoc. Bioinformatics, 43: 11.10.11-11.10.33 (2013)). Copy number alterations were called with an in-house developed ensemble method (CNVsenate) with mapped BAM and somatic SNV VCF files. CNVsenate gathers calling results from GATK v4.0.5 (Van der Auwera et al, Curr. Protoc. Bioinformatics, 43: 11.10.11-11.10.33 (2013)), CNVkit vO.9.5 (Talevich et al., PLoS Comput. Biol, 12:el004873 (2016)), CNVnator vO.2.7 (Abyzoy et al., Genome Res., 21 :974-984 (2011)), Manta vl.3.2 (Chen et al, Bioinformatics, 32: 1220-1222 (2016)), Sentieon CNV (201911) (Kendig et al., Front. Genet., 10:736 (2019)) and combines calling with SURVIVOR2 vl.0.3 (Jeffares et al., Nat. Commun., 8:14061 (2017)), then uses machine learning and event size for filtering. The filtered results were annotated with AnnotSV vl.l. 1 (Geoffrey et al., Bioinformatics, 34:3572-3574 (2018)) for affected genes. The denoised copy ratio for chromosomal segments from GATK was primarily used. A customized script was used to calculate the denoised copy ratio for chromosomal arms. Copy number gain was defined as chromosomal arm denoised copy ratio >1.25, while copy number loss was defined as chromosomal arm denoised copy ratio <0.80.

[0130] Somatic SNV VCF files were converted to MAF format with vcf2maf vl .6.19 (mskcc / vcf2maf: vcf2maf vl.6.19 (2020)) and annotated with VEP vl02 (McLaren et al., Genome BioL, 17: 122 (2016)). The MAF cohort was filtered with a genomic data commonslike strategy, including for population allele frequency <2%, coding regions, and presence in dbSNP (Smigielski et al. , Nucleic Acids Res. , 28:352-355 (2000) and COSMIC (Tate et al., Nucleic Acids Res., 34:D941-D947 (2019)). Further filtering was done for only somatic mutations with variant allele frequency >5%. Mutations were manually tabulated for one sample that was unable to be processed into the MAF format (UM #20). All computational processes above were performed on a linux-based amazon web services ec2 instance on the DNAnexus platform (DNAnexus). The MAF file was then processed and summarized using maftools v2.10.05 (Mayakonda et al, Genome Res., 28: 1747-1756 (2018)).

[0131] For six samples (UM #4, #13, #22, #23, #26 and #30) without sufficient tumor tissue for whole genome sequencing, whole exome sequencing was performed as described elsewhere (Rothermel et al., Clin. Cancer Res., 22:2237-2249 (2016)) to assess for canonical UM somatic mutations. For one sample (UM #53) without sufficient tumor tissue for whole genome sequencing DNA and RNA were extracted from paraffin embedded tumor tissue and processed with the Oncomine Comprehensive Assay v3 DNA and RNA primer sets (Thermo Fisher Scientific) according to the manufacturer’s protocol. Alterations assessed were per the UPMC Oncomine panel which has been described elsewhere (Paniccia et al., Gastroenterology, 164 : 117- 133. e 1 17 (2023 )) . RNA extraction, library preparation, sequencing, and read alignment

[0132] Total RNA was isolated from tumor samples on the automated CHEMAGIC™ 360 (PerkinElmer) instrument according to the manufacturer’s instructions. Extracted RNA was quantitated with the Qubit RNA BR Assay Kit (Thermo Fisher Scientific) followed by an RNA quality check using Fragment Analyzer (Agilent). For each sample, RNA libraries were prepared from 100 ng of RNA using the KAPA RNA HyperPrep Kit with RiboErase (Kapa Biosystems) according to the manufacturer’s protocol, followed by a quality check using Fragment Analyzer (Agilent) and quantification by qPCR with the Kapa qPCR quantification kit (Kapa Biosystems). The libraries were normalized, pooled, and sequenced using the NovaSeq 6000 platform (Illumina) to an average of about 50 million 101 base pair paired end reads. The sequencing data was demultiplexed with bcl2fastq2 v2.20 (Illumina) to produce the fastq files.

[0133] Bulk transcriptomic computational analyses

[0134] Sequencing data was quality controlled with FastQC vO.11.7 before and after adapter trimming with cutadapt vl.18 along with assessment of estimated ribosomal content with sortmerna v4.3.4 (Kopylova et al., Bioinformatics, 28:3211-3217 (2012)). Trimmed reads were then aligned with STAR v2.7.5a (Dobin et al., Bioinformatics, 26: 15-21 (2013)) using the Gencode v38 GTF and GRCh38 fasta references (Frankish et al., Nucleic Acids Res., 46:D916-D923 (2021)). Uniquely mapped percentage of reads and total uniquely mapped reads metrics after STAR mapping were used as further quality control metrics. The BAM file was indexed with samtools vl. 10 (Li et al., Bioinformatics, 25:2078-2079 (2009)). Gene counts from the STAR BAM files were calculated with htseq-count v0.13.5 (Anders et al., Bioinformatics, 31 : 166- 169 (2015)).

[0135] Gene names were converted from Ensembl vl03 (Cunningham et al., Nucleic Acids Res., 50:D988-D995 (2022)) to HUGO gene symbols with biomaRt v2.50.3 (Smedley et al., Nucleic Acids Res., 43:W589-598 (2015)). Redundant gene counts after name conversion were summed. Transcripts per million (TPM) were calculated in standard fashion using gene lengths calculated with FeatureCounts vl.6.2 (Liao et al., Bioinformatics, 30:923-930 (2014)). Raw counts were normalized with DESeq2 vl.34.0 (Love et al., Genome BioL, 15:550 (2014)) using default and recommended parameters. Variance stabilizing transformation was performed on the normalized counts and used for principal component analysis (PCA) with PCAtools v2.10.0. PCA was performed using the 10% most variant genes (n=5942) in the dataset. Differential gene expression by UMIS level was performed without any adjustment parameters with default and recommended settings.

[0136] Enrichment scores of gene sets were calculated with singscore vl.14.0 (Foroutan et al., BMC Bioinformatics, 19:404 (2018)) using TPM input. Calculations utilized the unidirectional expected-upregulated mode, with the exception of the immune resistance program score (Jerby-Arnon et al., Cell, 175:984-997. e924 (2018)) which was calculated using the bidirectional mode using separate expected-upregulated and expected- downregulated gene sets. UMIS was calculated with singscore using the unidirectional expected-upregulated mode using with the 2394 genes listed in Table 1 that positively correlated with immune and inflammatory hallmark gene set enrichment (negative PC2 gene loading). A cohort-dependent version of UMIS was also calculated using gene set variation analysis (GSVA) with GSVA vl.42.0 (Hanzelmann el al., BMC Bioinformatics, 14:7 (2013)) using default settings and the same list of genes (Table 1). This was only done for the purposes of comparison to the cohort-independent implementation with singscore and was not used elsewhere. Functional annotation of genes within UMIS (n=2394) was performed with the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (Sherman et al., Nucleic Acids Res., 50:W216-221 (2022)) after filtering for protein coding genes using Human Genome Organization (HUGO) Gene Nomenclature Committee (HGNC) complete set annotation. Similarly, functional annotation of differentially expressed genes between UMIS levels was performed with clusterProfiler v4.2.2 (Wu et al, Innovation, 2: 100141 (2021)) using the fgsea v3.16 method on only protein-coding genes using HGNC complete set annotation. Correlation and clustering analysis of PCs used the Human Molecular Signatures Database Hallmark gene set collection (Liberzon et al, Cell Syst., 1 :417-425 (2015)) while functional annotation used the Human Molecular Signatures Database Gene Ontology Biological Process gene set collection (The Gene Ontology Consortium, Nucleic Acids Res., 47:D330-D338 (2019)). Human leukocyte antigen (HLA) typing of patients was performed using tumor bulk total RNAseq data. The arcasHLA v0.5.0 (Orenbuch et al., Bioinformatics, 36:33-40 (2020)) package was run with default settings to produce an output of genotypes for samples. Representative data for patients with multiple tumor samples was selected using the same algorithm as described elsewhere in the survival analysis. TCR repertoires of tumors were analyzed from bulk total RNAseq data using MiXCR v3.0.12 (Bolotin etal., Nat. Methods, 12:380-381 (2015)) with allowPartialAlignments=true as recommended for bulk RNAseq data. Counts were tabulated per amino acid CDR3 clonotype and used to calculate diversity (Shannon index) and clonality (1 - Pielou’s index) for TRB and TRA chains (Chiffelle et al., Cancer Cell, 32:204-220. e215 (2017)). Samples with one or zero detected unique clonotypes were excluded from diversity and clonality analysis due to mathematically undefinable clonality; this resulted in exclusion of 12 metastases from TRB analysis and 18 metastases from TRA analysis. Public versus private repertoire analysis was performed using immunarch vO.6.9.

[0137] Targeted TCR repertoire library preparation, sequencing, and analysis

[0138] Targeted TCR repertoires of paired tumors and post-REP TIL were derived from respective total RNA. Libraries were prepared using the QIAseq Immune Repertoire RNA Library Kit (Qiagen) per manufacturer’s instructions. Libraries underwent quality check using a Standard Sensitivity NGS Fragment Analyzer Kit (Agilent) and quantification by qPCR with the Kapa qPCR quantification kit (Kapa Biosystems). The libraries were normalized, pooled, and sequenced using the MiSeq platform (Illumina) to an average of about 2.5 million 251 base pair paired end reads. The sequencing data was demultiplexed with bcl2fastq2 v2.20 (Illumina) to produce the fastq files. Sequencing data was processed using the Qiagen Biomedical Genomics Analysis 23.0 (Qiagen) per default recommended settings. Counts were tabulated from output files per amino acid CDR3 clonotype and used to calculate diversity (Shannon index) and clonality (1 - Pielou’s index) for TRB and TRA chains. Single cell RNA sequencing library preparation and sequencing

[0139] Selected tumors and previous treatments were UM #72 (tebentafusp), 83 (ICI and tebentafusp), 100 (ICI), 46 (liver directed therapy), 79 (cytotoxic chemotherapy and ICI), and 80 (liver directed therapy, kinase inhibition, antiangiogenic therapy and ICI). Cryopreserved single cell suspensions of selected tumors were prepared for input into the Chromium Next GEM Single Cell 5’ Reagent Kit v2 (10X Genomics) by thawing in complete media with human AB serum, sequential filtration through 70 mm and 30 mm MACS SmartStrainers (Miltenyi Biotec), and removal of dead cells using the Dead Cell Removal Kit (Miltenyi Biotec) per manufacturer’s protocol. Cell suspensions were inspected to confirm adequate viability (>70%). Each tumor sample was processed in a separate lane of the Chip K, with about 35,000 cells loaded per sample. The 5’ gene expression and TCR V(D)J libraries were then prepared per manufacturer’s instructions. Prior to sequencing, libraries underwent quality check using a Standard Sensitivity NGS Fragment Analyzer Kit (Agilent) and quantification by qPCR with the Kapa qPCR quantification kit (Kapa Biosystems). The libraries were normalized, pooled, and sequenced using the NovaSeq6000 platform (Illumina) to an average of about 30,000 paired-end reads per 5’ gene expression library per cell and about 5,000 paired-end reads per TCR V(D)J library per cell with parameters per manufacturer’s protocol. The sequencing data was demultiplexed with bcl2fastq2 v2.20 (Illumina) to produce the fastq files.

[0140] Single cell RNA sequencing computational processing

[0141] Sequencing data was processed using 10X Genomics Cell Ranger multi v6.1.2 (Zheng et al., Nat. Commun., 8:14049 (2017)) using 10X Genomics Cloud Analysis with introns excluded and an estimated expected cell count of 20,000. Bioinformatic processing of each sample involved adjustment for ambient RNA contamination with SoupX vl.5.2 (Young & Behjati, Gigascience, 9 (2020)) (default settings), normalization with the sctransform v2 method within Seurat 4.1.1 (Choudhary & Satija, Genome Biol, 23:27 (2022); and Hao et al., Cell, 184:3573-3587. e3529 (2021)) (default settings), and estimation and removal of doublets with DoubletFinder v2.0.3 (McGinnis et al., Cell Syst., 8:329- 337.e324 (2019)) (default settings). Cells remaining after quality control and removal of doublets were then input into the cataloging algorithm. This involved first assigning cells to large buckets using UCell v2.1.0 (Andreatta & Carmona, Comput. Struct. Biotechnol. J., 16:3796-3798 (2021)) in the following order: immune (UCell score >0 for gene set of PTPRC), tumor (UCell score >0 for gene set of SOXIO, S100A1, MITF, MLANA, PMEL, TYR stroma (all remaining cells). A TIL atlas was created using a published dataset (Nieto et al., Genome Res., 31 :1913-1923 (2021)) with harmony vO. l.O (Korsunsky et al., Nat. Methods, 16: 1289-1296 (2019)) and symphony vO.l.O (Kang et al., Nat. Commun., 12:5890 (2021)) using settings appropriate for the normalization method of the published dataset. The samples’ immune fractions were then mapped onto the atlas using symphony settings recommended for data normalized with sctransform v2. The higher resolution “level 2” annotation, which included 31 phenotypes, was utilized. The addition of tumor and stroma cells to these immune cells completed the cellular cataloging and various levels (overall, broad, granular) were also assigned to the cells. For pooled analysis, samples were integrated with Seurat 4.1.1 using settings appropriate for sctransform v2 -normalized data.

[0142] Dimensionality reduction and differential gene expression were performed on the integrated Seurat object. Counts of specific cell types were derived from this integrated Seurat object. Comparison of cell type proportions by UMIS level was performed with the propeller function within the speckle v0.0.3 (Phipson et al. , Bioinformatics, 38:4720-4726 (2022)) package using the arcsin square root transformation of proportions method.

[0143] TCR V(D)J repertoires were filtered for most frequent TRA and TRB chains using scRepertoire vl.4.0 (Borcherding et al., FlOOORes, 9:47 (2020)). Counts were tabulated for cells with paired TRA and TRB chains per unique TRA-TRB amino acid CDR3 clonotype and used to calculate diversity (Shannon index) and clonality (1 - Pielou’s index).

[0144] Statistical analysis

[0145] Statistics were calculated using R v4. 1.2 (R Core Team) with RStudio v.2022. 12.0+353 (Rstudio Team) or GraphPad Prism v9.5.0 (GraphPad Software), and specific statistical analyses used are highlighted in the respective figure legends. In general, continuous-continuous associations were assessed with the Spearman’s rank correlation with simple linear regression with 95% confidence intervals only to illustrate linearity. Unpaired categorical-continuous associations were assessed with the Wilcoxon rank-sum test (two- tailed) or Kruskal-Wallis one-way analysis of variance test as appropriate. Paired categorical- continuous associations were assessed with the Wilcoxon signed-rank test (two-tailed). Categorical-categorical associations were assessed with the Fisher’s exact test. Receiver operating characteristic curves were generated using univariate logistic regression and mapping of true positive 1 - specificity versus sensitivity. Areas under the ROC curves were calculated using the trapezoid rule. Time-to-event curves using the Kaplan-Meier method were generated with survminer vO.4.9 and comparisons between categorical groups were assessed with the logrank test. Clustering analysis was performed with ComplexHeatmap v2.10.0 (Gu et al., Bioinformatics, 32:2847-2849 (2016)) and used the default method of Euclidean distance. Where appropriate, multiple comparison adjustment was performed with the false discovery rate (FDR) method using the p.adjust function with method = “fdr” in R. In R the lowest possible numeric value is roughly IxlO’324. Thus, values less than IxlO’324were presented as about 0 rather than listing arbitrary lower limit numbers.

[0146] Utility visualization software

[0147] Aside from software previously mentioned, the following were used for various visualizations throughout the manuscript: tidyverse vl.3.2, ggplot2 v3.4, RColorBrewer vl.1-3, ggprism vl.0.4, patchwork vl.1.2, packcircles vO.3.4, plotly v4.10.0.9001.

[0148] Example 2: 2394 nucleic acids used to generate an UMIS

[0149] An UMIS used to determine whether or not a cancer is likely to respond to an ACT such as a TIL therapy can be calculated based on all 2394 nucleic acids listed in Table 1. The gene name, locus group, and location of the nucleic acids listed in Table 1 are set forth in U.S. Provisional Patent Application 63 / 572,023, filed on March 29, 2024, and publication Uveal melanoma immunogenomics predict immunotherapy resistance and susceptibility (Leonard-Murali etal., Nat. Commun., 15(1):2863 (2024)).

[0150] Table 1. 2394 nucleic acids for generating an UMIS

[0151]

[0152] Example 3: Use of a truncated UMIS that lacks one or more nucleic acids

[0153] A truncated UMIS can be calculated based on at least 250 of the nucleic acids listed in Table 1, provided that at least one (e.g., one, two, three, four, five, ten, 15, 20, 25, 30, 50, 75, 100, or more) of the nucleic acids is not listed in Table 2, and used to determine whether or not a cancer is likely to respond to an ACT such as a TIL therapy.

[0154] Table 2. Exemplary nucleic acids that can be excluded to generate a truncated UMIS

[0155] Example 4: Use of a truncated UMIS that includes nucleic acids listed in Table 3

[0156] A truncated UMIS can be calculated based on at least 250 (e.g., from 250 to 2128, from 250 to 2120, from 250 to 2100, from 250 to 2050, or from 250 to 2000) of the nucleic acids listed in Table 1, provided that at least one or more of the nucleic acids is listed in Table 3, and can be used to determine whether or not a cancer is likely to respond to an ACT such as a TIL therapy. In some cases, a truncated UMIS can be calculated based on at least 250 (e.g., from 250 to 2128, from 250 to 2120, from 250 to 2100, from 250 to 2050, or from 250 to 2000) of the nucleic acids listed in Table 3, and can be used to determine whether or not a cancer is likely to respond to an ACT such as a TIL therapy.

[0157] Table 3. 2129 nucleic acids for generating an UMIS or a truncated UMIS

[0158] Example 5: Assessing Cancer for TIL Reactivity

[0159] This Example describes that an UMIS can be used as a biomarker to predict UM metastases harboring tumor reactive TIL, the percentage of tumor reactive TIL cultures that could be expanded, and clinical outcome for treatment of metastatic solid tumors with ACT.

[0160] The results and methods in this Example re-present and expand on at least some of the results and methods provided in other Examples.

[0161] Results

[0162] UMIS predicts anti-tumor potency of ex vivo expanded TIL

[0163] To validate the transcriptomics demonstrating T-cell inflamed gene expression, the specific anti-tumor potency of the endogenous TIL from each of the metastases was interrogated. Typically, the assessment of TIL tumor reactivity requires patients to undergo surgical resection of metastases followed by several weeks of ex vivo TIL expansion and finally resource intensive co-culture with autologous tumor cells. Thus, it was also investigated whether UMIS could serve as a rapid and minimally invasive clinical tool to predict the tumor specific potency of endogenous TIL. UMIS values, derived from a single random biopsy from each source metastasis (n=194), were compared with the level of TIL anti -tumor reactivity found after conventional ex vivo expansion (Figure 21). TIL cultures (n about 24) were initiated from each freshly resected metastasis using a standardized ex vivo tumor fragmentation approach to address tumor heterogeneity. The individual TIL fragment cultures were tested for tumor specificity by co-culture with autologous tumor digest (versus normal tissue controls) followed by measurement of 4-1BB upregulation on CD3+cells as measured by flow cytometry and lENy release as measured by ELISA, which were found to be strongly correlated. The percentage of TIL cultures having tumor-specific reactivity from each metastasis was used as a standardized reactivity metric for comparing the level of antitumor TIL responses across tumors. When UMIS of each source metastasis was compared to the percentage of tumor reactive TIL cultures that were generated several weeks later, a strong positive correlation (rho=+0.46, =2.12xl0’11) was found (Figure 21). Notably, reactive TIL cultures were rarely expanded from metastases with a UMIS less than 0.2, suggesting the use of this cutoff as a preoperative threshold to avoid futile surgical resection of non-inflamed UM metastases (Figure 22). Taken together, these findings establish that UMIS, obtained from a metastatic biopsy, could serve as a minimally invasive preoperative biomarker to both identify UM metastases harboring tumor reactive TIL and predict the percentage of tumor reactive TIL cultures that could be expanded without the limitations associated with conventional co-culture assays.

[0164] Based upon the observation that UMIS from a metastatic biopsy could predict the ex vivo potency of quiescent endogenous TIL, whether UMIS might also predict the clinical efficacy of adoptive transfer of these TIL after ex vivo liberation and expansion was assessed (Figure 23). Of the metastases profiled, 49 had been used to manufacture TIL for previously reported and ongoing ACT trials. It was found that UMIS as a continuous variable strongly correlated with magnitude of clinical tumor regression after adoptive transfer in patients with metastatic UM, including ICI refractory individuals (n=49, rho=-0.30, P=0.034) (Figure 23). The success of UMIS in predicting response to TIL ACT in a patient with metastatic peritoneal mesothelioma was anecdotally noted (Figure 24). Finally, UMIS across a pancancer cohort of tumors (not all with TIL reactivity data) was measured to assess the landscape of tumor immune reactivity (Figure 25). In sum, these findings demonstrate that UMIS, performed on a pre-treatment metastatic biopsy, correlated with the clinical outcome after adoptive transfer of TIL and may serve as a future predictive biomarker for the treatment of metastatic solid tumors with ACT.

[0165] Methods

[0166] Patient samples and clinical annotation

[0167] Patients were screened and tumor samples were obtained after informed consent in conjunction with tumor procurement banking protocols associated with two adoptive TIL transfer clinical trials: NCT03467516 (Hillman Cancer Center, UPMC, Pittsburgh, PA, USA) and NCT01814046 (Surgery Branch, NCI, Bethesda, MD, USA). There was no requirement for previous systemic therapy, given the lack of known effective systemic treatments for metastatic UM at the time of study. If patients did receive previous systemic treatment, more than four weeks must have elapsed before initiation of the current trial therapy, and patients’ toxi cities must have recovered to a grade 1 or less (except for toxicities such as alopecia or vitiligo). All patients were required to have progressive and measurable metastatic disease with an Eastern Cooperative Oncology Group performance status of 0 or 1 and life expectancy greater than 3 months at the time of enrollment. Patients were required to have adequate hematological, renal, and hepatic function. Patients were excluded if they had active systemic infections, coagulation disorders, or other active major medical illnesses of the immune system.

[0168] Clinical information, including demographics and treatments, were collected relative to the date of metastatic tumor harvest. Sex was self-reported by patients and consistent with biological sex determined by genomic analysis. Time-to-event data was collected relative to multiple dates: date of primary diagnosis, date of metastatic diagnosis, and date of ACT (if applicable). In cases of patients with multiple metastatic biopsies an algorithm was adopted for selection of a representative biopsy for the purposes of patient-centered time-to-event analysis (in descending order of priority: metastasis harvested prior to any ACT, metastasis whose TIL was utilized for subsequent ACT, more recently harvested metastasis. Response to ACT was evaluated using RECIST vl.l criteria (Kendig et al., Front. Genet., 10:736 (2019)).

[0169] Tumor procurement, ex vivo TIL culture and tumor reactivity testing

[0170] All patients had surgical metastatectomies as screening for clinical trials NCT03467516 (Hillman Cancer Center, UPMC, Pittsburgh, PA, USA) and NCT01814046 (Surgery Branch, NCI, Bethesda, MD, USA) to procure tumor tissue to generate autologous TIL for therapy. After surgical procurement of a metastatic lesion, the fresh tumor underwent sterile dissection (Figure 19). Representative samples of tumor were sent for formal pathological confirmation of UM or other histologies. To develop a clinically relevant core biopsy approach for in situ tumor characterization, a single random biopsy was obtained from each resected metastasis (about 2 mm central core fragment from 93 metastases and about 500,000 cells post tumor dissociation from seven metastases) and were snap-frozen in liquid nitrogen and stored long term at -80°C for future DNA and RNA extraction (Figure 19).

[0171] TIL cultures were initiated from geographically discrete 1-2 mm3tumor fragments (n about 24) that were placed individually in wells of a 24-well culture plate containing complete media with human AB serum and recombinant interleukin-2 (6000 lU / mL). Remaining fresh tumor was processed by mechanical and enzymatic digestion with the human Tumor Dissociation Kit (Miltenyi Biotec) and GENTLEMACS™ Dissociator (Miltenyi Biotec) to provide a single cell suspension of autologous tumor targets for TIL reactivity testing. Tumor digests underwent flow cytometric phenotyping with propidium iodide followed by the following anti-human monoclonal antibodies: CD3-APC-Cy7, CD8- PE-Cy7, CD4-PE (BD Biosciences). Tumor digest viability was determined by percent propidium iodide negative cells by flow cytometry or percent trypan blue negative cells by manual cell counting (UM #47). After about two weeks of growth, individual TIL fragment cultures were tested for tumor specificity by coculture with autologous tumor cells (versus normal tissue controls) followed by measurement of 4- IBB upregulation on CD3+cells by flow cytometry using anti-human CD137 (4-lBB)-APC (BD Biosciences) and IFN-y release by ELISA (Rothermel et al., Clin. Cancer Res., 22:2237-2249 (2016); and Chandran et al., Lancet Oncol., 18:792-802 (2017)). Tumor single cell suspensions (digests) and peripheral blood mononuclear cells were cryopreserved in freezing media and stored long term in liquid nitrogen. Monocytes were isolated from peripheral blood mononuclear cells using the CD14+MicroBead isolation kit (Miltenyi Biotec). All flow cytometry data was analyzed with FlowJo vl0.8 Software (BD Life Sciences).

[0172] Clinical scale ex vivo rapid expansion protocol (REP) involved selection of individual fragment T cell cultures for further expansion based on proliferative capacity and evidence of autologous tumor reactivity. Final large-scale expansion of selected TIL cultures was done with anti-CD3 antibody (30 ng / mL, Ortho Biotech or 50ng / mL, Miltenyi Biotec) and recombinant interleukin-2 (3000 lU / mL; Clinigen) in the presence of irradiated peripheral blood mononuclear feeder cells (Chandran et al., Lancet Oncol., 18:792-802 (2017)). The specific anti-tumor reactivity of the infused TIL from NCT01814046 (Surgery Branch, NCI, Bethesda, MD, USA) was assessed by ELISA-based assays. Following overnight co-culture of the TIL with their autologous source tumor, the supernatant from these respective cocultures was assessed by ELISA to determine the tumor-induced IFN-g production as described elsewhere (Chandran et al., Lancet Oncol., 18:792-802 (2017)).

[0173] DNA extraction, library preparation, sequencing, and somatic analysis

[0174] Whole genome sequencing of the majority of samples was performed at the UPMC Genome Center (n=93). Genomic DNA was isolated from tumor samples or peripheral blood mononuclear cells on the automated CHEMAGIC™ 360 (PerkinElmer) instrument according to the manufacturer’s instructions. Extracted DNA was quantitated using Qubit dsDNA BR Assay Kit (Thermo Fisher Scientific). DNA libraries were prepared using the KAPA Hyper Plus Kit (KAPA Biosystems). Genomic DNA was processed through fragmentation, enzymatic end-repair and A-tailing, ligation, and quality check a Standard Sensitivity NGS Fragment Analyzer Kit (Agilent). Libraries with an average size of 450 base pairs (range = 300-600 base pairs) were quantified by qPCR on the LIGHTCYCLER® 480 (Roche) using the KAPA qPCR quantification kit (KAPA Biosystems). The libraries were normalized and pooled as per manufacturer protocol (Illumina). Sequencing was performed using the NovaSeq 6000 platform (Illumina) with 151 base pair paired end reads to an average target depth of 70X coverage. The sequencing data was demultiplexed with bcl2fastq2 v2.20 (Illumina) to produce the fastq files.

[0175] The samples were mapped with Sentieon vl.3.4. Somatic variants were called by TNhaplotyper2 on tumor-normal mode with the best-practice recommended whole genome sequencing setting. Variants were annotated with Funcotator from GATK v4.0.5 (Van der Auwera et al., Curr. Protoc. Bioinformatics, 43: 11.10.11-11.10.33 (2013)). Copy number alterations were called with an in-house developed ensemble method (CNVsenate) with mapped BAM and somatic SNV VCF files. CNVsenate gathers calling results from GATK v4.0.5 (Van der Auwera et al., Curr. Protoc. Bioinformatics, 43: 11.10.11-11.10.33 (2013)), CNVkit vO.9.5 (Talevich et al., PLoS Comput. Biol, 12:el004873 (2016)), CNVnator vO.2.7 (Abyzoy et al., Genome Res., 21 :974-984 (2011)), Manta vl.3.2 (Chen et al. Bioinformatics, 32: 1220-1222 (2016)), Sentieon CNV (201911) (Kendig et al., Front. Genet., 10:736 (2019)) and combines calling with SURVIVOR2 vl.0.3 (leffares et al, Nat. Commun., 8:14061 (2017)), then uses machine learning and event size for filtering. The filtered results were annotated with AnnotSV vl.1.1 (Geoffrey et al., Bioinformatics, 34:3572-3574 (2018)) for affected genes. The denoised copy ratio for chromosomal segments from GATK was primarily used. A customized script was used to calculate the denoised copy ratio for chromosomal arms. Copy number gain was defined as chromosomal arm denoised copy ratio >1.25, while copy number loss was defined as chromosomal arm denoised copy ratio <0.80.

[0176] Somatic SNV VCF files were converted to MAF format with vcf2maf vl .6.19 (mskcc / vcf2maf: vcf2maf vl .6.19 (2020)) and annotated with VEP vl02 (McLaren et al., Genome Biol., 17:122 (2016)). The MAF cohort was filtered with a genomic data commonslike strategy, including for population allele frequency <2%, coding regions, and presence in dbSNP (Smigielski et al., Nucleic Acids Res., 28:352-355 (2000) and COSMIC (Tate etal., Nucleic Acids Res., 34:D941-D947 (2019)). Further filtering was done for only somatic mutations with variant allele frequency >5%. Mutations were manually tabulated for one sample that was unable to be processed into the MAF format (UM #20). All computational processes above were performed on a linux-based amazon web services ec2 instance on the DNAnexus platform (DNAnexus). The MAF file was then processed and summarized using maftools v2.10.05 (Mayakonda et al., Genome Res., 28: 1747-1756 (2018)).

[0177] For six samples (UM #4, #13, #22, #23, #26 and #30) without sufficient tumor tissue for whole genome sequencing, whole exome sequencing was performed as described elsewhere (Rothermel et al., Clin. Cancer Res., 22:2237-2249 (2016)) to assess for canonical UM somatic mutations. For one sample (UM #53) without sufficient tumor tissue for whole genome sequencing DNA and RNA were extracted from paraffin embedded tumor tissue and processed with the Oncomine Comprehensive Assay v3 DNA and RNA primer sets (Thermo Fisher Scientific) according to the manufacturer’s protocol. Alterations assessed were per the UPMC Oncomine panel which has been described elsewhere (Paniccia et al., Gastroenterology, 164: 117-133.el 17 (2023)).

[0178] RNA extraction, library preparation, sequencing, and read alignment

[0179] Total RNA was isolated from tumor samples on the automated CHEMAGIC™ 360

[0180] (PerkinElmer) instrument according to the manufacturer’s instructions. Extracted RNA was quantitated with the Qubit RNA BR Assay Kit (Thermo Fisher Scientific) followed by an RNA quality check using Fragment Analyzer (Agilent). For each sample, RNA libraries were prepared from 100 ng of RNA using the KAPA RNA HyperPrep Kit with RiboErase (Kapa Biosystems) according to the manufacturer’s protocol, followed by a quality check using Fragment Analyzer (Agilent) and quantification by qPCR with the Kapa qPCR quantification kit (Kapa Biosystems). The libraries were normalized, pooled, and sequenced using the NovaSeq 6000 platform (Illumina) to an average of about 50 million 101 base pair paired end reads. The sequencing data was demultiplexed with bcl2fastq2 v2.20 (Illumina) to produce the fastq files.

[0181] Bulk transcriptomic computational analyses

[0182] Sequencing data was quality controlled with FastQC vO.11.7 before and after adapter trimming with cutadapt vl.18 along with assessment of estimated ribosomal content with sortmerna v4.3.4 (Kopylova et al., Bioinformatics, 28:3211-3217 (2012)). Trimmed reads were then aligned with STAR v2.7.5a (Dobin et al., Bioinformatics, 26: 15-21 (2013)) using the Gencode v38 GTF and GRCh38 fasta references (Frankish et al., Nucleic Acids Res., 46:D916-D923 (2021)). Uniquely mapped percentage of reads and total uniquely mapped reads metrics after STAR mapping were used as further quality control metrics. The BAM file was indexed with samtools vl. 10 (Li et al., Bioinformatics, 25:2078-2079 (2009)). Gene counts from the STAR BAM files were calculated with htseq-count v0.13.5 (Anders et al., Bioinformatics, 31 : 166- 169 (2015)).

[0183] Gene names were converted from Ensembl vl03 (Cunningham et al., Nucleic Acids Res., 50:D988-D995 (2022)) to HUGO gene symbols with biomaRt v2.50.3 (Smedley et al., Nucleic Acids Res., 43:W589-598 (2015)). Redundant gene counts after name conversion were summed. Transcripts per million (TPM) were calculated in standard fashion using gene lengths calculated with FeatureCounts vl.6.2 (Liao et al., Bioinformatics, 30:923-930 (2014)). Raw counts were normalized with DESeq2 vl .34.0 (Love etal., Genome Biol., 15:550 (2014)) using default and recommended parameters. Variance stabilizing transformation was performed on the normalized counts and used for principal component analysis (PCA) with PCAtools v2.10.0. PCA was performed using the 10% most variant genes (n=5942) in the dataset. Differential gene expression by UMIS level was performed without any adjustment parameters with default and recommended settings.

[0184] Enrichment scores of gene sets were calculated with singscore vl.14.0 (Foroutan et al., BMC Bioinformatics, 19:404 (2018)) using TPM input. Calculations utilized the unidirectional expected-upregulated mode, with the exception of the immune resistance program score (Jerby-Arnon et al., Cell, 175:984-997. e924 (2018)) which was calculated using the bidirectional mode using separate expected-upregulated and expected- downregulated gene sets. UMIS was calculated with singscore using the unidirectional expected-upregulated mode using with the 2394 genes listed in Table 1 that positively correlated with immune and inflammatory hallmark gene set enrichment (negative PC2 gene loading). A cohort-dependent version of UMIS was also calculated using gene set variation analysis (GSVA) with GSVA vl.42.0 (Hanzelmann et al., BMC Bioinformatics, 14:7 (2013)) using default settings and the same list of genes (Table 1). This was only done for the purposes of comparison to the cohort-independent implementation with singscore and was not used elsewhere. Functional annotation of genes within UMIS (n=2394) was performed with the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (Sherman et al., Nucleic Acids Res., 50:W216-221 (2022)) after filtering for protein coding genes using Human Genome Organization (HUGO) Gene Nomenclature Committee (HGNC) complete set annotation. Similarly, functional annotation of differentially expressed genes between UMIS levels was performed with clusterProfiler v4.2.2 (Wu et al, Innovation, 2: 100141 (2021)) using the fgsea v3.16 method on only protein-coding genes using HGNC complete set annotation. Correlation and clustering analysis of PCs used the Human Molecular Signatures Database Hallmark gene set collection (Liberzon et al, Cell Syst., 1 :417-425 (2015)) while functional annotation used the Human Molecular Signatures Database Gene Ontology Biological Process gene set collection (The Gene Ontology Consortium, Nucleic Acids Res., 47:D330-D338 (2019)).

[0185] Statistical analysis

[0186] Statistics were calculated using R v4. 1.2 (R Core Team) with RStudio v.2022. 12.0+353 (Rstudio Team) or GraphPad Prism v9.5.0 (GraphPad Software), and specific statistical analyses used are highlighted in the respective figure legends. In general, continuous-continuous associations were assessed with the Spearman’s rank correlation with simple linear regression with 95% confidence intervals only to illustrate linearity. Unpaired categorical-continuous associations were assessed with the Wilcoxon rank-sum test (two- tailed) or Kruskal-Wallis one-way analysis of variance test as appropriate. Paired categorical- continuous associations were assessed with the Wilcoxon signed-rank test (two-tailed). Categorical-categorical associations were assessed with the Fisher’s exact test. Receiver operating characteristic curves were generated using univariate logistic regression and mapping of true positive 1 - specificity versus sensitivity. Areas under the ROC curves were calculated using the trapezoid rule. Time-to-event curves using the Kaplan-Meier method were generated with survminer vO.4.9 and comparisons between categorical groups were assessed with the logrank test. Clustering analysis was performed with ComplexHeatmap v2.10.0 (Gu etal., Bioinformatics, 32:2847-2849 (2016)) and used the default method of Euclidean distance. Where appropriate, multiple comparison adjustment was performed with the false discovery rate (FDR) method using the p.adjust function with method = “fdr” in R. In R the lowest possible numeric value is roughly IxlO’324Thus, values less than IxlO’324were presented as about 0 rather than listing arbitrary lower limit numbers.

[0187] OTHER EMBODIMENTS

[0188] It is to be understood that while the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

Claims

WHAT IS CLAIMED IS:

1. A method for identifying a mammal having cancer likely to respond to an adoptive cell therapy (ACT), wherein said method comprises:(a) determining that a sample obtained from said mammal and comprising cancer cells has an UMIS of at least 0.2 or a truncated UMIS of at least 0.2, wherein said UMIS is calculated based on the 2394 nucleic acids of Table 1, wherein said truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table 1, wherein at least one nucleic acid of Table 3 is included for the calculation of said truncated UMIS; and(b) classifying said cancer as being likely to respond to said ACT.

2. The method of claim 1, wherein said mammal is a human.

3. The method of any one of claims 1-2, wherein said ACT comprises a tumorinfiltrating lymphocyte (TIL) therapy.

4. The method of any one of claims 1-3, wherein said cancer comprises a solid tumor.

5. The method of any one of claims 1-4, wherein said method comprises determining that said sample has said UMIS of at least 0.2.

6. The method of any one of claims 1-4, wherein said method comprises determining that said sample has said truncated UMIS of at least 0.2.

7. The method of claim 6, wherein at least one nucleic acid of Table 2 is excluded for the calculation of said truncated UMIS.

8. The method of claim 6, wherein said truncated UMIS is calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1.

9. The method of claim 6, wherein said truncated UMIS is calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1.

10. A method for identifying a mammal having cancer unlikely to respond to an ACT, wherein said method comprises:(a) determining that a sample obtained from said mammal and comprising cancer cells has an UMIS of less than 0.2 or a truncated UMIS of less than 0.2, wherein said UMIS is calculated based on the 2394 nucleic acids of Table 1, wherein said truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table 1, wherein at least one nucleic acid of Table 3 is included for the calculation of said truncated UMIS; and(b) classifying said cancer as being unlikely to respond to said ACT.

11. The method of claim 10, wherein said mammal is a human.

12. The method of any one of claims 10-11, wherein said ACT comprises a TIL therapy.

13. The method of any one of claims 10-12, wherein said cancer comprises a solid tumor.

14. The method of any one of claims 10-13, wherein said method comprises determining that said sample has said UMIS of less than 0.2.

15. The method of any one of claims 10-13, wherein said method comprises determining that said sample has said truncated UMIS of less than 0.2.

16. The method of claim 15, wherein at least one nucleic acid of Table 2 is excluded for the calculation of said truncated UMIS.

17. The method of claim 15, wherein said truncated UMIS is calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1.

18. The method of claim 15, wherein said truncated UMIS is calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1.

19. A method for selecting a treatment for a mammal having cancer, wherein said method comprises:(a) determining that a sample obtained from said mammal and comprising cancer cells has an UMIS of at least 0.2 or a truncated UMIS of at least 0.2, wherein said UMIS is calculated based on the 2394 nucleic acids of Table 1, wherein said truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table 1, wherein at least one nucleic acid of Table 3 is included for the calculation of said truncated UMIS; and(b) selecting an ACT as a treatment of cancer for said mammal.

20. The method of claims 19, wherein said mammal is a human.

21. The method of any one of claims 19-20, wherein said ACT comprises a TIL therapy.

22. The method of any one of claims 19-21, wherein said cancer comprises a solid tumor.

23. The method of any one of claims 19-22, wherein said method comprises determining that said sample has said UMIS of at least 0.2.

24. The method of any one of claims 19-22, wherein said method comprises determining that said sample has said truncated UMIS of at least 0.2.

25. The method of claim 24, wherein at least one nucleic acid of Table 2 is excluded for the calculation of said truncated UMIS.

26. The method of claim 24, wherein said truncated UMIS is calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1.

27. The method of claim 24, wherein said truncated UMIS is calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1.

28. A method for selecting a treatment for a mammal having cancer, wherein said method comprises:(a) determining that a sample obtained from said mammal and comprising cancer cells has an UMIS of less than 0.2 or a truncated UMIS of less than 0.2, wherein said UMIS is calculated based on the 2394 nucleic acids of Table 1, wherein said truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table 1, wherein at least one nucleic acid of Table 3 is included for the calculation of said truncated UMIS; and(b) selecting a cancer treatment other than an ACT for said mammal.

29. The method of claim 28, wherein said mammal is a human.

30. The method of any one of claims 28-29, wherein said ACT comprises a TIL therapy.

31. The method of any one of claims 28-30, wherein said cancer comprises a solid tumor.

32. The method of any one of claims 28-31, wherein said method comprises determining that said sample has said UMIS of less than 0.2.

33. The method of any one of claims 28-31, wherein said method comprises determining that said sample has said truncated UMIS of less than 0.2.

34. The method of claim 33, wherein at least one nucleic acid of Table 2 is excluded for the calculation of said truncated UMIS.

35. The method of claim 33, wherein said truncated UMIS is calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1.

36. The method of claim 33, wherein said truncated UMIS is calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1.

37. The method of any one of claims 28-36, wherein said cancer treatment comprises radiation therapy.

38. The method of any one of claims 28-37, wherein said cancer treatment comprises administering an anti-cancer agent selected from the group consisting of a chemotherapy, a targeted therapy, and an angiogenesis inhibitor.

39. A method for preparing a treatment for a mammal having cancer, wherein said method comprises:(a) determining that a sample obtained from said mammal and comprising cancer cells has an UMIS of at least 0.2 or a truncated UMIS of at least 0.2, wherein said UMIS is calculated based on the 2394 nucleic acids of Table 1, wherein said truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table 1, wherein at least one nucleic acid of Table 3 is included for the calculation of said truncated UMIS; and(b) expanding TILs obtained from said mammal ex vivo to obtain expanded TILs for administration to said mammal.

40. The method of claim 39, wherein said mammal is a human.

41. The method of any one of claims 39-40, wherein said cancer comprises a solid tumor.

42. The method of any one of claims 39-41, wherein said method comprises determining that said sample has said UMIS of at least 0.2.

43. The method of any one of claims 39-41, wherein said method comprises determining that said sample has said truncated UMIS of at least 0.2.

44. The method of claim 43, wherein at least one nucleic acid of Table 2 is excluded for the calculation of said truncated UMIS.

45. The method of claim 43, wherein said truncated UMIS is calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1.

46. The method of claim 43, wherein said truncated UMIS is calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1.

47. A method for preparing a cancer treatment, wherein said method comprises expanding TILs obtained from a mammal identified as having an UMIS of at least 0.2 or a truncated UMIS of at least 0.2 to form a cell population for administration to said mammal to treat cancer, wherein said UMIS is calculated based on the 2394 nucleic acids of Table 1, wherein said truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table 1, and wherein at least one nucleic acid of Table 3 is included for the calculation of said truncated UMIS.

48. The method of claim 47, wherein said mammal is a human.

49. The method of any one of claims 47-48, wherein said cancer comprises a solid tumor.

50. The method of any one of claims 47-49, wherein said method comprises determining that said sample has said UMIS of at least 0.2.51 . The method of any one of claims 47-49, wherein said method comprises determining that said sample has said truncated UMIS of at least 0.2.

52. The method of claim 51, wherein at least one nucleic acid of Table 2 is excluded for the calculation of said truncated UMIS.

53. The method of claim 51, wherein said truncated UMIS is calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1.

54. The method of claim 51, wherein said truncated UMIS is calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1.

55. The method of any one of claims 47-54, wherein said method further comprises administering at least a portion of said cell population to said mammal.

56. A method for treating a mammal having cancer, wherein said method comprises:(a) determining that a sample obtained from said mammal and comprising cancer cells has an UMIS of at least 0.2 or a truncated UMIS of at least 0.2, wherein said UMIS is calculated based on the 2394 nucleic acids of Table 1, wherein said truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table 1, wherein at least one nucleic acid of Table 3 is included for the calculation of said truncated UMIS; and(b) administering an ACT to said mammal.

57. The method of claim 56, wherein said mammal is a human.

58. The method of any one of claims 56-57, wherein said ACT comprises a TIL therapy.

59. The method of any one of claims 56-58, wherein said cancer comprises a solid tumor.

60. The method of any one of claims 56-59, wherein said method comprises determining that said sample has said UMIS of at least 0.2.

61. The method of any one of claims 56-59, wherein said method comprises determining that said sample has said truncated UMIS of at least 0.2.

62. The method of claim 61, wherein at least one nucleic acid of Table 2 is excluded for the calculation of said truncated UMIS.

63. The method of claim 61, wherein said truncated UMIS is calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1.

64. The method of claim 61, wherein said truncated UMIS is calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1.

65. A method for treating cancer, wherein said method comprises administering an ACT to a mammal identified as having an UMIS of at least 0.2 or a truncated UMIS of at least 0.2, wherein said UMIS is determined from a sample obtained from said mammal and comprising cancer cells, wherein said UMIS is calculated based on the 2394 nucleic acids of Table 1, wherein said truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table1, and wherein at least one nucleic acid of Table 3 is included for the calculation of said truncated UMIS.

66. The method of claim 65, wherein said mammal is a human.

67. The method of any one of claims 65-66, wherein said ACT comprises a TIL therapy.

68. The method of any one of claims 65-67, wherein said cancer comprises a solid tumor.

69. The method of any one of claims 65-68, wherein said method comprises determining that said sample has said UMIS of at least 0.2.

70. The method of any one of claims 65-68, wherein said method comprises determining that said sample has said truncated UMIS of at least 0.2.

71. The method of claim 70, wherein at least one nucleic acid of Table 2 is excluded for the calculation of said truncated UMIS.

72. The method of claim 70, wherein said truncated UMIS is calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1.

73. The method of claim 70, wherein said truncated UMIS is calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1.

74. A method for treating a mammal having cancer, wherein said method comprises:(a) determining that a sample obtained from said mammal and comprising cancer cells has an UMIS of less than 0.2 or a truncated UMIS of less than 0.2, wherein said UMIS is calculated based on the 2394 nucleic acids of Table 1, wherein said truncated UMIS iscalculated based on 250 to 2393 of the nucleic acids of Table 1, wherein at least one nucleic acid of Table 3 is included for the calculation of said truncated UMIS; and(b) administering a cancer treatment other than an ACT to said mammal.

75. The method of claim 74, wherein said mammal is a human.

76. The method of any one of claims 74-75, wherein said ACT comprises a TIL therapy.

77. The method of any one of claims 74-76, wherein said cancer comprises a solid tumor.

78. The method of any one of claims 74-77, wherein said method comprises determining that said sample has said UMIS of less than 0.2.

79. The method of any one of claims 74-77, wherein said method comprises determining that said sample has said truncated UMIS of less than 0.2.

80. The method of claim 79, wherein at least one nucleic acid of Table 2 is excluded for the calculation of said truncated UMIS.

81. The method of claim 79, wherein said truncated UMIS is calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1.

82. The method of claim 79, wherein said truncated UMIS is calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1.

83. The method of any one of claims 74-82, wherein said cancer treatment comprises performing surgery.

84. The method of any one of claims 74-83, wherein said cancer treatment comprises radiation therapy.

85. The method of any one of claims 74-84, wherein said cancer treatment comprises administering an anti-cancer agent selected from the group consisting of a chemotherapy, a targeted therapy, and an angiogenesis inhibitor.

86. A method for treating cancer, wherein said method comprises administering a cancer treatment other than an ACT to a mammal identified as having cancer cells having an UMIS of less than 0.2 or a truncated UMIS of less than 0.2, wherein said UMIS is calculated based on the 2394 nucleic acids of Table 1, wherein said truncated UMIS is calculated based on 250 to 2393 of the nucleic acids of Table 1, and wherein at least one nucleic acid of Table 3 is included for the calculation of said truncated UMIS.

87. The method of claim 86, wherein said mammal is a human.

88. The method of any one of claims 86-87, wherein said ACT comprises a TIL therapy.

89. The method of any one of claims 86-88, wherein said cancer comprises a solid tumor.

90. The method of any one of claims 86-89, wherein said method comprises determining that said sample has said UMIS of less than 0.2.

91. The method of any one of claims 86-89, wherein said method comprises determining that said sample has said truncated UMIS of less than 0.2.

92. The method of claim 91, wherein at least one nucleic acid of Table 2 is excluded for the calculation of said truncated UMIS.

93. The method of claim 91, wherein said truncated UMIS is calculated based on 250 to 2393, 300 to 2393, 400 to 2393, 500 to 2393, 600 to 2393, 700 to 2393, 800 to 2393, 900 to 2393, or 1000 to 2393 of the nucleic acids of Table 1.

94. The method of claim 91, wherein said truncated UMIS is calculated based on 250 to 2390, 250 to 2385, 250 to 2375, 250 to 2350, 250 to 2300, 250 to 2250, 250 to 2200, 250 to 2100, or 250 to 2000 of the nucleic acids of Table 1.

95. The method of any one of claims 86-94, wherein said cancer treatment comprises performing surgery.

96. The method of any one of claims 86-95, wherein said cancer treatment comprises radiation therapy.

97. The method of any one of claims 86-96, wherein said cancer treatment comprises administering an anti-cancer agent selected from the group consisting of a chemotherapy, a targeted therapy, and an angiogenesis inhibitor.

98. A method for identifying a mammal having uveal melanoma likely to respond to an adoptive cell therapy (ACT), wherein said method comprises:(a) determining that a sample obtained from said mammal and comprising uveal melanoma cells has an UMIS of at least 0.2, and(b) classifying said uveal melanoma as being likely to respond to said ACT.

99. The method of claim 98, wherein said mammal is a human.

100. The method of any one of claims 98-99, wherein said ACT comprises a tumorinfiltrating lymphocyte (TIL) therapy.

101. A method for identifying a mammal having uveal melanoma unlikely to respond to an ACT, wherein said method comprises:(a) determining that a sample obtained from said mammal and comprising uveal melanoma cells has an UMIS of less than 0.2, and(b) classifying said uveal melanoma as being unlikely to respond to said ACT.

102. The method of claim 101, wherein said mammal is a human.

103. The method of any one of claims 101-102, wherein said ACT comprises a TIL therapy.

104. A method for selecting a treatment for a mammal having uveal melanoma, wherein said method comprises:(a) determining that a sample obtained from said mammal and comprising uveal melanoma cells has an UMIS of at least 0.2, and(b) selecting an ACT as a treatment of uveal melanoma for said mammal.

105. The method of claim 104, wherein said mammal is a human.

106. The method of any one of claims 104-105, wherein said ACT comprises a TIL therapy.

107. A method for selecting a treatment for a mammal having uveal melanoma, wherein said method comprises:(a) determining that a sample obtained from said mammal and comprising uveal melanoma cells has an UMIS of less than 0.2, and(b) selecting a uveal melanoma treatment other than an ACT for said mammal.

108. The method of claim 107, wherein said mammal is a human.

109. The method of any one of claims 107-108, wherein said ACT comprises a TIL therapy.

110. The method of any one of claims 107-109, wherein said uveal melanoma treatment comprises administering an anti-uveal melanoma agent selected from the group consisting of a chemotherapy, a targeted therapy, and an angiogenesis inhibitor.

111. A method for preparing a treatment for a mammal having uveal melanoma, wherein said method comprises:(a) determining that a sample obtained from said mammal and comprising uveal melanoma cells has an UMIS of at least 0.2, and(b) expanding TILs obtained from said mammal ex vivo to obtain expanded TILs for administration to said mammal.

112. The method of claim 111, wherein said mammal is a human.

113. The method of any one of claims 111-112, wherein said uveal melanoma comprises a solid tumor.

114. A method for preparing a uveal melanoma treatment, wherein said method comprises expanding TILs obtained from a mammal identified as having an UMIS of at least 0.2 to form a cell population for administration to said mammal to treat uveal melanoma.

115. The method of claim 114, wherein said mammal is a human.

116. The method of any one of claims 114-115, wherein said method further comprises administering at least a portion of said cell population to said mammal.

117. A method for treating a mammal having uveal melanoma, wherein said method comprises:(a) determining that a sample obtained from said mammal and comprising uveal melanoma cells has an UMIS of at least 0.2, and(b) administering an ACT to said mammal.

118. The method of claim 117, wherein said mammal is a human.

119. The method of any one of claims 117-118, wherein said ACT comprises a TIL therapy.

120. A method for treating uveal melanoma, wherein said method comprises administering an ACT to a mammal identified as having an UMIS of at least 0.2, wherein said UMIS is determined from a sample obtained from said mammal and comprising uveal melanoma cells.

121. The method of claim 120, wherein said mammal is a human.

122. The method of any one of claims 120-121, wherein said ACT comprises a TIL therapy.

123. A method for treating a mammal having uveal melanoma, wherein said method comprises:(a) determining that a sample obtained from said mammal and comprising uveal melanoma cells has an UMIS of less than 0.2, and(b) administering a uveal melanoma treatment other than an ACT to said mammal.

124. The method of claim 123, wherein said mammal is a human.

125. The method of any one of claims 123-124, wherein said ACT comprises a TIL therapy.

126. The method of any one of claims 123-125, wherein said uveal melanoma treatment comprises performing surgery.

127. The method of any one of claims 123-126, wherein said uveal melanoma treatment comprises radiation therapy.

128. The method of any one of claims 123-127, wherein said uveal melanoma treatment comprises administering an anti-uveal melanoma agent selected from the group consisting of a chemotherapy, a targeted therapy, and an angiogenesis inhibitor.

129. A method for treating uveal melanoma, wherein said method comprises administering a uveal melanoma treatment other than an ACT to a mammal identified as having uveal melanoma cells having an UMIS of less than 0.2.

130. The method of claim 129, wherein said mammal is a human.

131. The method of any one of claims 129-130, wherein said ACT comprises a TIL therapy.

132. The method of any one of claims 129-131, wherein said uveal melanoma treatment comprises performing surgery.

133. The method of any one of claims 129-132, wherein said uveal melanoma treatment comprises radiation therapy.

134. The method of any one of claims 129-133, wherein said uveal melanoma treatment comprises administering an anti-uveal melanoma agent selected from the group consisting of a chemotherapy, a targeted therapy, and an angiogenesis inhibitor.

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