Synergistic targeting of immune checkpoint and growth factor in cancer treatment

Abstract

Described herein are methods and compositions that can be used to treat subjects with cancer by administering combinations of antibodies that target TIGIT and antibodies that target Jag1.

Description

[0001] Attorney Docket No.29618-0465WO1 / BWH 2023-131 Synergistic Targeting of Immune Checkpoint and Growth Factor in Cancer Treatment CLAIM OF PRIORITY This application claims the benefit of U.S. Provisional Application Serial No. 63 / 561,488, filed on March 5, 2024. The entire contents of the foregoing are incorporated herein by reference. SEQUENCE LISTING This application contains a Sequence Listing that has been submitted electronically as an XML file named “29618-0465WO1.xml.” The XML file, created on 2025-03-05, is 20400 bytes in size. The material in the XML file is hereby incorporated by reference in its entirety. FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT This invention was made with Government support under Grant Nos. AI073748, AI039671, and AI144166 awarded by the National Institutes of Health. The Government has certain rights in the invention. TECHNICAL FIELD Described herein are methods and compositions that can be used to treat subjects with cancer by administering combinations of antibodies or antigen binding portions thereof that target T cell immunoreceptor with Ig and ITIM domains (TIGIT) and antibodies that target to jagged canonical Notch ligand 1 (Jag1). BACKGROUND Beyond their canonical role in immune tolerance, regulatory T cells (Tregs) have emerged as pivotal orchestrators in tissue homeostasis and repair. In cancer, most of the tumor-promoting functions of Tregs are attributed to immune suppression and induction of T cell exhaustion. SUMMARY The direct role of Tregs in malignant neoplasia and growth has not been studied. Here, by delving into the functional interaction between the checkpoint Attorney Docket No.29618-0465WO1 / BWH 2023-131 receptor TIGIT, expressed on immune cells, with its ligand, CD155 (encoded by the gene PVR) on tumor cells, we uncovered a novel Tregs-tumor cell crosstalk that promotes tumor growth. Using single-cell RNA- and TCR- sequencing together with conditional genetic deletion of TIGIT on various immune cell subsets, we demonstrate that of all the immune cells, expression of TIGIT on Tregs is most critical in regulating anti-tumor immunity and tumor cell growth. Loss of TIGIT on Tregs impaired Treg stability and fostered the expansion of cytotoxic tumor-specific effector CD8+T cells. Importantly, through direct interaction with its ligand CD155 expressed on melanoma cells, TIGIT expressed on Tregs induced an oncogenic program in tumor cells, including induction of the oncogene Hes1, which promotes tumor self-renewal and growth. Moreover, we identified a similar interaction between TIGIT on Tregs and CD155 on human tumor cells, by developing and using a computational tool, HiLo and by in situ multiplexed immunofluorescence characterization of Tregs and tumor cells. Mechanistically, TIGIT-CD155 interaction induced an oncogenic program including Hes1 expression in malignant cells via CD155 signaling domain. Reciprocally, CD155 induced the expression of the Notch ligand Jagged 1 (Jag1) in Tregs, but not in CD8+T cells, thereby amplifying the tumor-promoting function of TIGIT+Tregs. Notably, co-blockade of TIGIT and Jagged1 in vivo blunted tumor progression, highlighting a novel potential combination approach for cancer immunotherapy. While Tregs typically promote tumor growth through immune suppression, our findings shed light on an unexpected function of the bi-directional signaling between TIGIT+Tregs cells and CD155+tumor cells in promoting an oncogenic program and tumor growth. Therefore, described herein is a novel mechanism by which Tregs, via the TIGIT:CD155 pathway, inhibits anti-tumor immunity and concurrently promotes an oncogenic program in tumor cells, thereby enabling enhanced tumor growth. Provided herein are methods of treating a cancer in a subject. The methods comprise administering to the subject a therapeutically effective amount of a combination of antibodies that target T cell immunoreceptor with Ig and ITIM domains (TIGIT) and antibodies that target to jagged canonical Notch ligand 1 (Jag1). Also provided are antibodies that target TIGIT and antibodies that target Jag1, for use in a method of treating cancer in a subject. A number of exemplary antibodies are described herein. Antigen binding portions of the antibodies can also be used. Attorney Docket No.29618-0465WO1 / BWH 2023-131 In some embodiments, the subject has a carcinoma. In some embodiments, the carcinoma is colon cancer, melanoma, breast cancer, or brain cancer. In some embodiments, the brain cancer is glioblastoma. In some embodiments, the subject does not have a lung cancer, e.g., does not have a lung adenocarcinoma. In some embodiments, the combination of antibodies that target TIGIT and antibodies that target Jag1 is administered as a single composition. In some embodiments, the subject is human (and the antibodies target human TIGIT and Jag1). In some embodiments, the cancer has a level of surface expression of CD155 and / or NOTCH1 above a reference level. In some embodiments, the methods further comprise providing a sample comprising cells from the cancer in the subject; determining levels of surface expression of CD155 and / or NOTCH1; and comparing those levels to a reference level. In some embodiments, the reference levels are levels in a cohort of cells or cancers that are sensitive to treatment with a combination of antibodies that target TIGIT and antibodies that target Jag1. In some embodiments, the methods further comprise identifying a cancer as having levels of CD155 and / or NOTCH1 above a reference level, and administering the treatment to the subject. The methods can include the administration of the antibody proteins themselves, or mRNA encoding the antibodies. Additionally, provided herein are compositions comprising antibodies (or antigen binding portions thereof) that target TIGIT and antibodies that target Jag1. In some embodiments, the compositions further comprise a pharmaceutically acceptable carrier. Also provided are the compositions for use in a method of treating cancer in a subject. The compositions can include the antibody proteins themselves, or mRNA encoding the antibodies. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Methods and materials are described herein for use in the present invention; other, suitable methods and materials known in the art can also be used. The materials, methods, and examples are illustrative only and not intended Attorney Docket No.29618-0465WO1 / BWH 2023-131 to be limiting. All publications, patent applications, patents, sequences, database entries, and other references mentioned herein are incorporated by reference in their entirety. In case of conflict, the present specification, including definitions, will control. Other features and advantages of the invention will be apparent from the following detailed description and figures, and from the claims. DESCRIPTION OF DRAWINGS FIGs.1A-M: TIGIT expression on Tregs is required to promote melanoma growth. (A) Uniform manifold approximation and projection (UMAP) embedding of all cells sequenced from published scRNAseq data depicting tumor- infiltrating cells isolated from murine (22) (left) or human melanoma (23) (right) colored by cell type. TIGIT expressing-cell frequency (barplots) and relative expression (Violin plots) in various cells populations is shown (bottom). (B-J) Subcutaneous B16F10 melanoma growth in control (TIGITfl / fl), global knockout mice (CMVCreX TIGITfl / fl) (B) or conditional deletion of TIGIT in various antigen presenting cells (C) including myeloid cells (LysMCreX TIGITfl / fl) (D), dendritic cells (CD11cCreX TIGITfl / fl) (E), B cells (CD19CreX TIGITfl / fl) (F) or in T cells (G), all T cells (CD4CreX TIGITfl / flor TIGITΔTcells) (H), in CD8 T cells (E8iCreX TIGITfl / flor TIGITΔCD8) (I) or in Tregs (Foxp3ert2CreX TIGITfl / flor TIGITΔTreg) (J). (K and L) Flow cytometry analysis of TILs 16 days post tumor cell implantation, derived from Foxp3ert2Creand TIGITΔTregmice implanted with B16F10 s.c (n=8 mice per group). Representative FACS plot and percentage of IL-10+and IFNγ+cells within tumor- infiltrating Tregs (K) and Granzyme B (GzmB) expressing tumor-infiltrating CD8+T cells. Data are mean ± SEM and pooled from at least two to three independent experiments. Repeated measures two-way ANOVA test in B, D, E, F, H, I and I; two- tailed Student’s t-test in K and L. *P < 0.05; **P < 0.01; ****P < 0.0001; ns, not significant. FIGs.2A-H: Dual expression of TIGIT on Tregs and PVR on tumor cells is critical to confer a tumor growth advantage and reinforce Treg suppressive function. (A) Flow cytometry analysis of TILs 16 days post tumor cell implantation, derived from C57Bl6 / J mice implanted with B16F10 tumors. Histogram (left) and heatmap (right) depicting CD155 expression in T cells (CD3e+), B cell (CD19+), Myeloid cells (CD3e- CD19-), and Tumor cells (defined as Live CD45- FSChiSSCHi) Attorney Docket No.29618-0465WO1 / BWH 2023-131 (n=9 mice). (B) Left: Illustration representing B16F10 cells with steady CD155 expression (B16F10Control) and the construction of CD155 knockdown cells (B16F10CD155KD). Right: Barplot depicting CD155 surface expression (MFI) in CD45- FSCHiSSCHicells derived from B16F10 TILs ex vivo (n=6 mice per group). (C-E) Dual subcutaneous B16F10Controland B16F10CD155KDtumor growth in WT TIGITfl / fland TIGITKOmice (C) or TIGITΔCD8and Cre- control (D) or FOXP3ert2Creand TIGITΔTreg(E). (F) Barplot depicting flow cytometry analysis of IL-10+and IFNγ+infiltrated Tregs frequencies derived from B16F10Controland B16F10CD155KDtumors from FOXP3ert2Creand TIGITΔTregmice. (G) B16F10Controland B16F10CD155KDcells were cultured alone or cocultured with FOXP3GFP Tregs (WT), FOXP3GFP x TIGIT KO Tregs (TIGIT KO) and WT Tregs in combination with anti-TIGIT (clone 1B4) antibodies for three days. Barplot depicting Fold change of tumor cell number between day 3 and day 0. (H) After the coculture, Tregs were FACS-sorted and assessed for suppression assay. Barplot depicting Frequency of suppression of CD8+T cells by WT Tregs not cocultured (circle), cocultured previously for 3 days in the presence of B16F10Controlor B16F10CD155KD, or TIGITKOTregs cultured alone (triangle pointing down, red) (n=3 mice per group). Data are mean ± SEM and pooled from at least two independent experiments. Repeated measures two-way ANOVA test in C, D and E; One way ANOVA with Tukey's multiple comparisons test in F, G and H. **P < 0.01; ***P < 0.001; ****P < 0.0001; ns, not significant. FIGs.3A-C: TIGIT-expressing Tregs regulate tumor cell expansion by maintaining an oncogenic program within CD155-expressing tumor cells. (A) Workflow for single-cell transcriptome profiling of 14,782 viable leukocytes from the TME and dLN samples derived from B16F10Controland B16F10CD155KDtumors from FOXP3ert2Creand TIGITΔTregmice 16 days post tumor cell implantation. (B) MA plot, M (log ratio) and A (mean average), of gene expression comparing B16 cells derived from “CD155WTTIGITWT” vs “CD155KDTIGITKO” conditions. (C) Combined scores of Hallmark pathways for the genes upregulated (top) or downregulated (bottom) in B16 tumor cells derived from “CD155WTTIGITWT” condition. FIGs.4A-M: TIGIT-expressing Tregs preferentially interact with PVR- expressing tumor cells and are associated with a cancer stemness program in human tumors. (A) Schematic depicting the HiLo algorithm allowing the classification of samples based on the level of expression of selected genes (TIGIT Attorney Docket No.29618-0465WO1 / BWH 2023-131 and PVR) within selected cell types (Tregs and tumor cells). (B) UMAP of human melanoma TILs(23) colored by HiLo categories, HiHi (High TIGIT on Tregs and High PVR on tumor cells), HiLo (High TIGIT on Tregs and Low PVR on tumor cells), LoHi (Low TIGIT on Tregs and High PVR on tumor cells), LoLo (Low TIGIT on Tregs and Low PVR on tumor cells). (C) Selected top Hallmark pathways enriched for the genes upregulated in tumor cells derived from “HiHi” patient samples from Jerby- Arnon, et al(23). P* denotes Benjamini-Hochberg adjusted p-values. (D) Violin plots depicting the expression of cancer stemness signature (39) in tumor cells from different HiLo categories. (E) Venn diagram of the genes upregulated in HiHi tumors across multiple datasets. The red partition refers to the intersecting upregulated 53 genes among these tumors. (F) Correlation analysis between the expression of the “HiHi” and cancer stemness (39) signatures in TCGA Pan-cancer tumor samples. Two-tailed approximate z-test showed the Pearson correlation coefficient is significantly different from 0. (G) Survival analyses comparing patients expressing high vs. low “HiHi” signature in TCGA-Pan-cancer cohort (all 33 tumor types; left panel) and TCGA-restricted cohort (BRCA+SCKM+LUAD+KIRC; right panel). Kaplan-Meier curves were compared using Mantel-Cox log-rank test. Hazard ratios were estimated based on the Cox proportional hazards model. (H-K) Multiplexed IF imaging with the Orion method followed by H&E staining of the same section from four human melanoma samples, using an automated slide stainer and scanning of the H&E-stained slide in transillumination (brightfield) mode. (H) H&E and Schematic of the experimental plan. (I-M) Combined neighborhood enrichment (NE) analysis between annotated cell types in spatial coordinates among the four tumor samples. Heatmap data are Z-scores depicting tumor cells expressing or not CD155, Tregs, Tconv, CD8 T cells (I), tumor cells expressing or not CD155 and / or KI67, and Tregs expressing or not TIGIT (J). (K) Violin plot depicting the NE score of TIGIT+ Tregs relative to tumor cells expressing or not CD155 and / or KI67 across samples (dots). (L) Combined neighborhood enrichment analysis between annotated cell types in spatial coordinates among four human melanoma FFPE tumor samples. Heatmap values are Z-scores. (M) Frequency of KI67+CD155+and CD155- tumor cells across samples. Two-tailed Student’s t-test in (D). *P < 0.05.One way ANOVA with multiple comparison in L. *P < 0.05; ***P < 0.001; ns, not significant. Attorney Docket No.29618-0465WO1 / BWH 2023-131 FIGs.5A-M: CD155 downstream signaling is required to increase oncogenesis and cancer stemness program. (A) Schematic representing the protein domain in CD155 structure. CD155 WT represent intact CD155 with extracellular domain, transmembrane domain (TM), ITIM motif and cytoplasmic domain (CP), CD155 ΔCP represent CD155 without cytoplasmic domain (ΔCP). Immunoblot showing CD155 protein expression in WT and ΔCP B16F10 cells. Actin was used as a loading control. (B) B16F10Controland B16F10CD155ΔCPcells were cultured alone or cocultured with FOXP3GFP Tregs(WT Tregs), and WT Tregs in combination with anti- TIGIT (clone 1B4) antibodies for three days. Barplot depicting fold change of tumor cells number between day 3 and day 0. (C) Tumor growth of B16F10Controland B16F10CD155ΔCPcells implanted in FoxP3ert2Cremice and TIGITΔTreg. (D) At day 15 post tumor growth, tumor cells (viable CD45- SSChighFSChighcells) were isolated and FACS-sorted. mRNA expression of Psmb8 and Hes1 genes was measured by qPCR and actin was used for normalization. (E and F) Psmb8 and Hes1 were overexpressed in B16F10CD155ΔCPcells. Tumor growth of control cells B16F10CD155ΔCP OE control, B16F10CD155ΔCP OE Psmb8(E) or B16F10CD155ΔCP OE Hes1(F) in TIGITΔTregmice. (G) Percentages of IL-10+and IFNγ+cells within tumor-infiltrating Tregs (left) and Granzyme B (GzmB) expressing tumor-infiltrating CD8+T cells (right) determined by flow cytometry. Data are mean ± SEM and pooled from at least two to three independent experiments. Repeated measures two-way ANOVA test in D, E and F; two-tailed Student’s t-test in D and G. (H) Immunoblots representing the protein expression of Notch1, Cleaved Notch1, MAML, RBPj, Hes1 and CD155 in B16F10Controlcells and B16F10CD155ΔCPcells cultured in vitro in presence or absence of TIGIT-Fc for 15minutes and 6 hour time points. Actin was used as a loading control. (I) Cell number count of B16F10Control, B16F10CD155ΔCP(left) and B16F10 CD155KD(left) cells cultured in presence of mIgG or TIGIT-Fc for up to 72 hours. (J) FACS plot and percentage of JAG1+cells among Tregs, Tconv and CD8+T cells derived from B16F10Controland B16F10CD155KDTILs in FoxP3ert2Creor TIGITΔTregmice 16 days post tumor cell implantation. (K) Representative FACS plot and percentage of JAG1+cells among Tregs and CD8+T cells derived from WT or TIGIT KO mice (FoxP3+Tregs: upper panel, CD8+: lower panel) cultured in presence of either mIgG or CD155-Fc; bottom left, percentage of Ki67 in CD8+T cells, Tconv and Tregs; bottom right, percentage of IFNγ+, TNFα+, IL-2+, IL-10+CD8+T cells and Attorney Docket No.29618-0465WO1 / BWH 2023-131 Tconv cells in tumors of FOXP3ert2creand TIGITΔTregmice. (L) C57Bl / 6J implanted with B16-OVA melanoma and treated with either anti-TIGIT, anti-JAG1, anti-TIGIT + anti-JAG1 (combo), or isotype controls (n=5 mice per group). (M, Left) TIGIT+cells fractions and their mean expression in various T cell population from the reanalyzed data from Zheng et al. (M, Right): Log2 Fold change (FC) of top differentially expressed and violin plot depicting Effector Treg signature score between TIGIT+and TIGIT- Tregs. Repeated measures two-way ANOVA test in C, E, F, I and L. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; ns, not significant. FIGs.6A-C: Co-regulation of both PVR / TIGIT and Notch1 / Jag1 pathways in cancer cells and Tregs. (A) Immunoblot representing Hes1 expression in B16F10Controlcells and B16F10CD155ΔCPcells cultured in the presence of mouse IgG or TIGIT-Fc for 2, 4, 6 and 24 hours. (B) Immunoblot representing Hes1 expression in B16F10Controlcells and B16F10CD155ΔCPcells treated with TIGIT-Fc, Notch Inhibitor DAPT (10uM) and MAML Inhibitor (10uM) for 6 hours. Actin was used as a loading control. (C) B16F10Controlcells and B16F10CD155ΔCPlysates treated with mIgG or TIGIT-Fc were immunoprecipitated with anti-CD155 antibodies and immunoblotted with anti-cleaved Notch1 and anti-CD155 antibodies.5% input is depicted (C, right). FIGs.7A-C: Jag1 synergizes with TIGIT to promote tumor cell growth. (A) Histogram depicting the relative mRNA expression of Notch ligands jag1, jag2, Delta1, Delta3 and Delta4 in iTregs derived from WT and TIGIT KO mice and cultured in the presence of CD155-Fc (10ug / ml) for 3 days. (B) B16F10Controlcells and B16F10CD155ΔCPwere cultured for 3 days in the presence of mIgG, TIGIT-Fc and combined or not with Amphiregulin (AREG) (Left), or Jag1 (Right). Cell growth was measured by Alamar blue assay. (C) Fold change of B16F10 cell count (day 3 vs day 0 counts), co-cultured or not with iTregs in presence of isotypes controls (Rabbit and Armenian Hamster IgG), anti-TIGIT, anti-Jag1 or anti-Jag1+anti-TIGIT antibodies for 3 days. One way ANOVA with Tukey's multiple comparisons A and C. Two-tailed Student’s t-test in B. *P < 0.05;**P < 0.01; ***P < 0.001; ****P < 0.0001; ns, not significant. FIGs.8A-B: TIGIT / JAG1 dual blockade is more effective in mice with intact TIGITexpressing Tregs. (A) Subcutaneous B16F10Controltumor growth in FOXP3ert2Creand TIGITΔTregmice treated with isotype control, anti-TIGIT, anti-JAG1 or both (combo). (B) Log2 fold change of tumor weights relative to the isotype Attorney Docket No.29618-0465WO1 / BWH 2023-131 control group. Data are mean ± SEM. Repeated measures two-way ANOVA in A and one-way ANOVA with Tukey correction for multiple comparisons in B. ****P < 0.0001; ns, not significant. FIGs.9A-B. Tigit and Jag1 synergize to promote growth of various tumor cell types. (A) Depicted cell lines were cultured for 3 days in the presence of mIgG (left bars), TIGIT-Fc (second bars), Jag1 (third bars), and combined TIGIT-Fc and Jag1 (right most bars). Cell growth was measured by Alamar blue assay. (B) Flow cytometry histograms of CD155 and NOTCH1 surface expression on the depicted tumor cell lines. Two way ANOVA with Tukey's multiple comparisons A. **P < 0.01;****P < 0.0001; ns, not significant. DETAILED DESCRIPTION Targeting co-inhibitory receptors has revolutionized cancer treatment. Receptors like CTLA-4, PD-1, and LAG3, found on effector T cells, contribute to T cell exhaustion, impede T cell activity, and foster tumor growth. While therapies blocking these receptors are approved for various cancers, they do not benefit all patients due to inherent or acquired resistance. This limitation has sparked interest in other co-inhibitory receptors, such as TIM-3 and TIGIT, which are present on exhausted T cells and are potential targets for cancer immunotherapy (1, 2). Despite their known inhibitory impact on effector T cells, the broader roles of these receptors on other immune cells and their direct influence on malignant cell growth have not been fully elucidated (3-7). While these receptors are primarily expressed on immune cells, some of their ligands, such as PD-L1 (for PD1) and CD155 (for TIGIT), are highly expressed on malignant cells. The direct interaction between these receptors and their tumor-expressed ligands to aid tumor evasion or even enhance tumor growth, has mainly be reported in the context of PD-1-PD-L1 interaction but largely remains poorly explored (8, 9). CD226 and TIGIT are a co-stimulatory and co-inhibitory receptor, respectively, that share ligands, thus participating in an immune-regulatory pathway analogous to the CD28 / CTLA-4 pathway. Therapeutic modulation of the CD226 / TIGIT pathway is now being evaluated for its effects on anti-tumor immunity, with multiple clinical trials currently underway for the treatment of solid tumors (1, 10). In the tumor microenvironment (TME), TIGIT is highly expressed on both human and mouse NK cells, CD8+effector T cells and Tregs (11-14). Polio Virus Attorney Docket No.29618-0465WO1 / BWH 2023-131 cellular Receptor (PVR) or CD155, the primary ligand for TIGIT, is expressed on dendritic cells and other antigen-presenting cells (15-17). Engagement of TIGIT by CD155 on dendritic cells (DCs) induces tolerogenic DCs, by reverse-signaling through CD155 into the DCs, resulting in inhibition of the pro-inflammatory cytokine IL-12 and induction of the suppressive cytokine IL-10 (18). Although expression of CD155 is not limited to immune cells, as it is widely expressed on malignant cells, in both mouse and human tumors (19, 20), whether an interaction between TIGIT on immune cells and CD155 on malignant cells has a cell intrinsic function in the malignant cells has not been explored. Our work has previously indicated an important role for TIGIT in Treg in shaping anti-tumor CD8+T cell responses (21), but the mechanisms were unclear and whether TIGIT-CD155 interactions had any direct influence on tumor cells has not been investigated. Such mechanistic understanding of TIGIT interactions with tumor cells remains critical for developing novel immunotherapies that inhibit this TIGIT-CD155 interaction and thereby limit tumor growth and tumor escape from immune control. Here, by combining scRNA-seq analysis of mouse and human melanoma with conditional deletion of TIGIT on multiple immune cell subsets in mouse models of melanoma, we identified TIGIT expression in Foxp3+ Tregs as the most critical in inducing tumor growth inhibition. While TIGIT expression on Foxp3+ regulatory T cells maintained the stability and suppressive function of Tregs, the interaction between TIGIT+ Tregs and CD155+ malignant cells additionally induced and sustained an oncogenic program in the malignant cells, including the induction of the oncogene Hes1, and promotion of tumor cell growth. We demonstrate a critical role of TIGIT-CD155 signaling in inducing Hes1 expression by Notch signaling into the malignant cells. Reciprocally, we show that TIGIT interaction with CD155 induces the surface expression of the Notch ligand Jagged-1 (Jag1), specifically in Tregs, but not in CD8+ T cells. The role of Tregs in cancer has often been perceived through their immunosuppressive functions. However, our findings paint a more intricate picture, revealing Tregs as pivotal actors in the direct promotion of tumor growth and oncogenic pathways, by utilizing the TIGIT-CD155 signaling pathway. Indeed, to evade immune surveillance, tumor cells can upregulate the expression of co-inhibitory ligands to disarm anti-tumor T cell responses by engagement of co-inhibitory Attorney Docket No.29618-0465WO1 / BWH 2023-131 receptors such as TIGIT on T cells. Here, through selective genetic deletion of TIGIT, we demonstrate that of all the immune cells, TIGIT plays its most significant role on Tregs in regulating anti-tumor immunity. This is consistent with a previous report showing that the majority of tumor-infiltrating Tregs express TIGIT, and that transfer of TIGIT-deficient Tregs, but not of TIGIT-deficient CD8+T cells, was able to reduce tumor growth in vivo (21). Although, prior studies have primarily focused on the role of TIGIT in NK cell and CD8+T cell dysfunction (11, 12, 42-45), TIGIT is abundantly expressed on Tregs in tumor tissues and regulates Treg function and stability, as we and others have previously reported (21, 46, 47). Importantly, our studies, using conditional deletion of TIGIT in immune cell subsets, demonstrate TIGIT expression in Tregs plays a critical and non-redundant role in promoting tumor growth and uncover a novel mechanism by which CD155-TIGIT signaling induces / sustains a pro-tumorigenic program in tumor cells thereby promoting their growth. Our analysis reveals a critical role for TIGIT driven CD155 signaling in directly promoting tumor growth and an oncogenic program in a CD155-dependent manner in tumor cells. Although CD155 has been described to play a role in cell adhesion (48), its functions upon ligation by TIGIT have been poorly understood as cultures of CD155+tumor cells with recombinant TIGIT had no impact on tumor cell growth in vitro. Our studies demonstrate that TIGIT directly signals into tumor cells via CD155 to activate oncogenic signaling pathway supporting tumorigenesis. This TIGIT and CD155 interaction induces Notch1 signaling and subsequent Hes1 expression, which is a key driver of oncogenesis (31, 41). Our results further demonstrate a direct interaction between the CD155 cytoplasmic domain and the active cleaved form of Notch1, indicating the formation of a potentially novel signaling complex upstream of induction of Hes1. The precise signaling events downstream of the CD155-Notch1 interaction that result in induction of the oncogenic program and tumor cell stemness are not clear and necessitate further investigation, as Notch1 signaling is complex and works in a context-dependent manner (49-51). However, this effect of TIGIT+Tregs on oncogenesis may be independent of inhibitory functions of Tregs in the immune system, as Tregs have been shown to regulate various aspects of tissue growth and homeostasis (52). Indeed, tissue Tregs have been shown to regulate hair follicle growth, metabolism, and wound healing, Attorney Docket No.29618-0465WO1 / BWH 2023-131 independently of their suppressive effects in the immune system (53). In fact, previous studies have shown that Tregs can maintain hair follicle stem-cells via Notch signaling, and our studies show that tumor cells have similarly co-opted that Notch- Jagged pathway (54), but TIGIT-CD155 interaction on Tregs and tumor cells appears to be critical first step in promoting oncogenic reprogramming in tumor cells. Therefore, TIGIT-CD155 and Jagged-Notch pathways, cooperatively synergize to promote tumor growth. Consistent with this observation is our finding that co- blockade of both TIGIT and Notch pathways gave the maximal tumor growth inhibition in vivo, further supporting synergy between the two pathways. Our observations raise an important question of why TIGIT expression in Tregs, but not in CD8+T cells and Tconv cells, promotes tumor growth and activation of oncogenic pathways in tumor cells. We discovered that CD155 plays a vital role in amplifying the tumor-promoting function of TIGIT+Tregs by inducing the expression of the Notch ligand, Jagged 1 in Tregs, but the CD155-TIGIT interaction does not induce Jagged 1 in CD8+T cells. This further emphasizes the distinct roles that Tregs and CD8+T cells play in tumor progression, and the importance of targeting Tregs specifically in cancer immunotherapies. Indeed, it will be important to study whether clinical efficacy of anti-TIGIT antibodies is linked to regulation of Treg function in human clinical trials. The unique mechanisms that we have uncovered for TIGIT- CD155 signaling suggest that blockade of TIGIT-CD155 will provide a distinct signaling mechanism for tumor growth inhibition from the PD-1 / PD-L1pathway blockade. Indeed, preclinical tumor studies have shown that blockade of both TIGIT and the PD-1 / PD-L1 pathway resulted in greater efficacy (11, 55) in inhibiting tumor growth. By analysis of data and samples from multiple human cancers, we showed a significant association of high TIGIT expression on Tregs with high CD155 expression in tumor cells, which was further associated with major oncogenic and cancer stemness programs, suggesting the potential impact of this pathway in human tumors as well. The HiLo algorithm we developed can be applied to explore the role of other receptor-ligand pairs and may enable further characterization of other pathways involved in oncogenesis and other biological functions. Finally, spatial analysis of human melanoma revealed a direct interaction between TIGIT on Tregs Attorney Docket No.29618-0465WO1 / BWH 2023-131 and CD155 on human tumor cells, indicating that this pathway is likely to be active and consequential in human melanoma. In summary, our studies shed light on the interactions between TIGIT on Tregs and CD155 on tumor cells and identify a novel mechanism by which TIGIT expression on Tregs promotes tumor growth and oncogenesis. These mechanistic insights uncovered novel pathways that can synergize with TIGIT, and co-blockade of TIGIT and Jagged 1 can be used to increase efficacy of current anti-TIGIT strategies (56) and result in the development of effective cancer immunotherapies. Thus, described herein are compositions and methods for the treatment of cancer using combinations of antibodies that target TIGIT and antibodies that target Jagged 1 (Jag1). Methods of Treatment The methods described herein can be used to treat subjects with cancer by administering combinations of antibodies that target (bind to) TIGIT and antibodies that target Jag1. Generally, the methods include administering a therapeutically effective amount of a treatment as described herein, to a subject who is in need of, or who has been determined to be in need of, such treatment. In some embodiments, the methods include administering a therapeutically effective amount of a treatment comprising a combinations of antibodies that target TIGIT and antibodies that target Jag1; the methods can optionally include a standard treatment comprising chemotherapy, radiotherapy, and / or resection (e.g., wherein the present methods are used as an adjuvant therapy). The antibodies that target TIGIT and antibodies that target Jag1 can be administered in a single composition; in separate compositions but at substantially the same time (e.g., on the same day or within 1 to 2 hours of each other); or in separate compositions at different times (e.g., on different days). As used in this context, to “treat” means to ameliorate at least one symptom of the disorder associated with abnormal apoptotic or differentiative processes. For example, a treatment can result in a reduction in tumor size or growth rate. Administration of a therapeutically effective amount of a combination as described herein for the treatment of cancer will result in a reduction in tumor size or decreased growth rate, a reduction in risk or frequency of reoccurrence, a delay in reoccurrence, Attorney Docket No.29618-0465WO1 / BWH 2023-131 a reduction in metastasis, increased survival, and / or decreased morbidity and mortality, inter alia. As used herein, the terms “cancer”, “hyperproliferative” and “neoplastic” refer to cells having the capacity for autonomous growth, i.e., an abnormal state or condition characterized by rapidly proliferating cell growth. Hyperproliferative and neoplastic disease states may be categorized as pathologic, i.e., characterizing or constituting a disease state, or may be categorized as non-pathologic, i.e., a deviation from normal but not associated with a disease state. The term is meant to include all types of cancerous growths or oncogenic processes, metastatic tissues or malignantly transformed cells, tissues, or organs, irrespective of histopathologic type or stage of invasiveness. “Pathologic hyperproliferative” cells occur in disease states characterized by malignant tumor growth. Examples of non-pathologic hyperproliferative cells include proliferation of cells associated with wound repair. The terms “cancer” or “neoplasms” include malignancies of the various organ systems, such as affecting lung, breast, thyroid, lymphoid, gastrointestinal, and genito-urinary tract, as well as adenocarcinomas which include malignancies such as most colon cancers, renal-cell carcinoma, prostate cancer and / or testicular tumors, non-small cell carcinoma of the lung, cancer of the small intestine and cancer of the esophagus. The term “carcinoma” is art recognized and refers to malignancies of epithelial or endocrine tissues including respiratory system carcinomas, gastrointestinal system carcinomas, genitourinary system carcinomas, testicular carcinomas, breast carcinomas, prostatic carcinomas, endocrine system carcinomas, and melanomas. In some embodiments, the disease is renal carcinoma or melanoma. Exemplary carcinomas include those forming from tissue of the cervix, lung, prostate, breast, head and neck, colon and ovary. The term also includes carcinosarcomas, e.g., which include malignant tumors composed of carcinomatous and sarcomatous tissues. An “adenocarcinoma” refers to a carcinoma derived from an organ or glandular tissue or in which the tumor cells form recognizable glandular structures. Also included is squamous cell carcinoma, which originates in the squamous epithelium. The term “sarcoma” is art recognized and refers to malignant tumors of mesenchymal derivation (arising from supportive and connective tissues such as Attorney Docket No.29618-0465WO1 / BWH 2023-131 bones, tendons, cartilage, muscle, and fat), and can include bone and soft tissue sarcomas. Methods of diagnosing a subject as having cancer, e.g., one of the above cancers are known in the art, and can include clinical observation, imaging studies, histology, biopsies, pathology, and so on. The cancers can preferably have surface expression of CD155 and / or NOTCH1 above a reference level. Thus, the methods can include obtaining a sample comprising cells from a cancer in a subject and determining levels of surface expression of CD155 and / or NOTCH1, and comparing those levels to a reference level. Exemplary reference levels can include levels in a cohort of cells or cancers that are sensitive to treatment with a combination treatment as described herein; methods for determining suitable reference levels are known in the art. If a cancer is determined to have levels of CD155 and / or NOTCH1 above a reference level, the subject can be treated or selected for a treatment described herein. The presence and / or level of a protein can be evaluated using methods known in the art, e.g., using standard electrophoretic and quantitative immunoassay methods for proteins, including but not limited to, Western blot; enzyme linked immunosorbent assay (ELISA); biotin / avidin type assays; protein array detection; radio-immunoassay; immunohistochemistry (IHC); immune-precipitation assay; FACS (fluorescent activated cell sorting); mass spectrometry (Kim (2010) Am J Clin Pathol 134:157-162; Yasun (2012) Anal Chem 84(14):6008-6015; Brody (2010) Expert Rev Mol Diagn 10(8):1013-1022; Philips (2014) PLOS One 9(3):e90226; Pfaffe (2011) Clin Chem 57(5): 675-687). The methods typically include revealing labels such as fluorescent, chemiluminescent, radioactive, and enzymatic or dye molecules that provide a signal either directly or indirectly. As used herein, the term “label” refers to the coupling (i.e. physically linkage) of a detectable substance, such as a radioactive agent or fluorophore (e.g. phycoerythrin (PE) or indocyanine (Cy5), to an antibody or probe, as well as indirect labeling of the probe or antibody (e.g. horseradish peroxidase, HRP) by reactivity with a detectable substance. In some embodiments, an ELISA method may be used, wherein the wells of a microtiter plate are coated with an antibody against which the protein is to be tested. The sample containing or suspected of containing the biological marker is then applied to the wells. After a sufficient amount of time, during which antibody-antigen Attorney Docket No.29618-0465WO1 / BWH 2023-131 complexes would have formed, the plate is washed to remove any unbound moieties, and a detectably labelled molecule is added. Again, after a sufficient period of incubation, the plate is washed to remove any excess, unbound molecules, and the presence of the labeled molecule is determined using methods known in the art. Variations of the ELISA method, such as the competitive ELISA or competition assay, and sandwich ELISA, may also be used, as these are well-known to those skilled in the art. In some embodiments, an IHC method may be used. IHC provides a method of detecting a biological marker in situ. The presence and exact cellular location of the biological marker can be detected. Typically a sample is fixed with formalin or paraformaldehyde, embedded in paraffin, and cut into sections for staining and subsequent inspection by confocal microscopy. Current methods of IHC use either direct or indirect labelling. The sample may also be inspected by fluorescent microscopy when immunofluorescence (IF) is performed, as a variation to IHC. Mass spectrometry, and particularly matrix-assisted laser desorption / ionization mass spectrometry (MALDI-MS) and surface-enhanced laser desorption / ionization mass spectrometry (SELDI-MS), is useful for the detection of biomarkers of this invention. (See U.S. Patent No.5,118,937; 5,045,694; 5,719,060; 6,225,047) In some embodiments, the presence and / or level of CD155 and / or NOTCH1 is comparable to or above the level of CD155 and / or NOTCH1 in the reference, then the subject can be treated with or selected for treatment with a combination treatment as described herein. Suitable reference values can be determined using methods known in the art, e.g., using standard clinical trial methodology and statistical analysis. The reference values can have any relevant form. In some cases, the reference comprises a predetermined value for a meaningful level of CD155 and / or NOTCH1, e.g., a control reference level that represents a level in a subject who is sensitive to treatment with a combination treatment as described herein. The predetermined level can be a single cut-off (threshold) value, such as a median or mean, or a level that defines the boundaries of an upper or lower quartile, tertile, or other segment of a clinical trial population that is determined to be statistically different from the other segments. It can be a range of cut-off (or threshold) values, such as a confidence interval. It can be established based upon Attorney Docket No.29618-0465WO1 / BWH 2023-131 comparative groups, such as where association with sensitivity to treatment in one defined group is a fold higher, or lower, (e.g., approximately 2-fold, 4-fold, 8-fold, 16-fold or more) than the sensitivity to treatment in another defined group. It can be a range, for example, where a population of subjects (e.g., control subjects) is divided equally (or unequally) into groups, such as a low-likelihood of response group, a medium-likelihood of response group and a high-likelihood of response group, or into quartiles, the lowest quartile being subjects with the lowest likelihood and the highest quartile being subjects with the highest likelihood, or into n-quantiles (i.e., n regularly spaced intervals) the lowest of the n-quantiles being subjects with the lowest likelihood and the highest of the n-quantiles being subjects with the highest likelihood. Antibodies The term “antibody” as used herein refers to an immunoglobulin molecule; the present methods and compositions can also use an antigen-binding portion of an immunoglobulin molecule. Examples of antigen-binding portions of immunoglobulin molecules include F(ab) and F(ab')2 fragments, which retain the ability to bind antigen. The antibody can be polyclonal, monoclonal, recombinant, chimeric, de- immunized or humanized, fully human, non-human, (e.g., murine), or single chain antibody. In some embodiments the antibody has effector function and can fix complement. In some embodiments, the antibody has reduced or no ability to bind an Fc receptor. For example, the antibody can be an isotype or subtype, fragment or other mutant, which does not support binding to an Fc receptor, e.g., it has a mutagenized or deleted Fc receptor binding region. Methods for making antibodies and fragments thereof are known in the art, see, e.g., Harlow et. al., editors, Antibodies: A Laboratory Manual (1988); Goding, Monoclonal Antibodies: Principles and Practice, (N.Y. Academic Press 1983); Howard and Kaser, Making and Using Antibodies: A Practical Handbook (CRC Press; 1st edition, Dec 13, 2006); Kontermann and Dübel, Antibody Engineering Volume 1 (Springer Protocols) (Springer; 2nd ed., May 21, 2010); Lo, Antibody Engineering: Methods and Protocols (Methods in Molecular Biology) (Humana Press; Nov 10, 2010); and Dübel, Handbook of Therapeutic Antibodies: Technologies, Emerging Developments and Approved Therapeutics, (Wiley-VCH; 1 edition September 7, 2010). Attorney Docket No.29618-0465WO1 / BWH 2023-131 Anti-TIGIT Antibodies The methods include the administration of antibodies (or antigen binding portions thereof) that bind to T cell immunoreceptor with Ig and ITIM domains (TIGIT). The antibodies can be made using methods known in the art, or can be obtained, e.g., from a commercial source. An exemplary sequence of human TIGIT protein is available in GenBank at RefSeq Acc. No. NP_776160.2. Exemplary anti- TIGIT antibodies that can be used in the methods described herein include AB154; MK‐7684; BMS‐986207; ASP8374; Tiragolumab (MTIG7192A; RG6058); (Etigilimab (OMP‐313M32)); ADI-30293 and ADI-30278 as described in WO2020020281; 10A7, as described in U.S. Pub. No.2009 / 0258013; 1.6B2, 1.10A5, 1.7E7, 1.15C8, 4.1D3 or a variant thereof, e.g., 4.1D3.Q1E, or a humanized version thereof, e.g., as described in WO2017053748A2; COM902 as described in US12152084; Clone 31282 or a variant thereof described in US10329349B2; 7D4 or the humanized 7D4 variant, 1A11, as described in 20230416362; or 313R12. See, e.g., Harjunpää and Guillerey, Clin Exp Immunol 2019 Dec 11[Online ahead of print], DOI: 10.1111 / cei.13407;20200062859; and 20200040082. Humanized versions of any of the above can also be used. The methods can include the administration of the antibody proteins themselves, or mRNA encoding the antibodies. Anti-Jagged1 Antibodies The methods include the administration of antibodies (or antigen binding portions thereof) that bind to jagged canonical Notch ligand 1 (jagged-1 or Jag1). The antibodies can be made using methods known in the art, or can be obtained, e.g., from a commercial source. An exemplary sequence of human Jag1 is available in GenBank at RefSeq Acc. No. NP_000205.1. Exemplary anti-Jag1 antibodies include J1-142B, J1-65D, J1-156A, J1-183D and J1-187B, as described in Masiero et al., Mol Cancer Ther.2019 Nov;18(11):2030-2042; A-2, B-3, C-1, A-1, and A-1-S101T as described in US10858440B2; or an anti-Jag1 antibody described in WO2011063237A2, WO2012106529A1, WO2013052155A1 (e.g., 4D11, 4B2, 4E7, 4E11, 6B7, and / or 6F8 described therein), WO2013192550A2, WO2014028446A1 (e.g., A, A-1, A-2, B, B-l, B- 2, B-3, C, C-l, D, D-l, D-2, D-3, D-4 or D-5 described therein), WO2014111704A1, WO2014151866A1, US20150252117A1, WO2018222770 (e.g., Attorney Docket No.29618-0465WO1 / BWH 2023-131 15D11.1 described therein), US9914774B2, US9944700B2, or US20190023802A1. Humanized versions of any of the above can also be used. The methods can include the administration of the antibody proteins themselves, or mRNA encoding the antibodies. Compositions Additionally, provided herein are compositions that comprise one or both of the antibodies (or antigen binding portions thereof) described herein, e.g., compositions comprising combinations of antibodies that target TIGIT and antibodies that target Jag1. The methods described herein include the use of pharmaceutical compositions comprising or consisting of antibodies that target TIGIT and / or antibodies that target Jag1 as an active ingredient (e.g., optionally wherein no other active ingredient is included or administered). The compositions can include the antibody proteins themselves, or mRNA encoding the antibodies. Pharmaceutical compositions typically include a pharmaceutically acceptable carrier. As used herein the language “pharmaceutically acceptable carrier” includes saline, solvents, dispersion media, coatings, antibacterial and antifungal agents, isotonic and absorption delaying agents, and the like, compatible with pharmaceutical administration. Pharmaceutical compositions are typically formulated to be compatible with its intended route of administration. Examples of routes of administration include parenteral, e.g., intravenous, intradermal, subcutaneous, oral (e.g., inhalation), transmucosal, and rectal administration. Methods of formulating suitable pharmaceutical compositions are known in the art, see, e.g., Remington: The Science and Practice of Pharmacy, 21st ed., 2005; and the books in the series Drugs and the Pharmaceutical Sciences: a Series of Textbooks and Monographs (Dekker, NY). For example, solutions or suspensions used for parenteral, intradermal, or subcutaneous application can include the following components: a sterile diluent such as water for injection, saline solution, fixed oils, polyethylene glycols, glycerine, propylene glycol or other synthetic solvents; antibacterial agents such as benzyl alcohol or methyl parabens; antioxidants such as ascorbic acid or sodium bisulfite; chelating agents such as ethylenediaminetetraacetic acid; buffers such as acetates, citrates or phosphates and agents for the adjustment of tonicity such as sodium chloride or dextrose. pH can be Attorney Docket No.29618-0465WO1 / BWH 2023-131 adjusted with acids or bases, such as hydrochloric acid or sodium hydroxide. The parenteral preparation can be enclosed in ampoules, disposable syringes or multiple dose vials made of glass or plastic. Pharmaceutical compositions suitable for injectable use can include sterile aqueous solutions (where water soluble) or dispersions and sterile powders for the extemporaneous preparation of sterile injectable solutions or dispersion. For intravenous administration, suitable carriers include physiological saline, bacteriostatic water, Cremophor EL™ (BASF, Parsippany, NJ) or phosphate buffered saline (PBS). In all cases, the composition must be sterile and should be fluid to the extent that easy syringability exists. It should be stable under the conditions of manufacture and storage and must be preserved against the contaminating action of microorganisms such as bacteria and fungi. The carrier can be a solvent or dispersion medium containing, for example, water, ethanol, polyol (for example, glycerol, propylene glycol, and liquid polyetheylene glycol, and the like), and suitable mixtures thereof. The proper fluidity can be maintained, for example, by the use of a coating such as lecithin, by the maintenance of the required particle size in the case of dispersion and by the use of surfactants. Prevention of the action of microorganisms can be achieved by various antibacterial and antifungal agents, for example, parabens, chlorobutanol, phenol, ascorbic acid, thimerosal, and the like. In many cases, it will be preferable to include isotonic agents, for example, sugars, polyalcohols such as mannitol, sorbitol, sodium chloride in the composition. Prolonged absorption of the injectable compositions can be brought about by including in the composition an agent that delays absorption, for example, aluminum monostearate and gelatin. Sterile injectable solutions can be prepared by incorporating the active compound in the required amount in an appropriate solvent with one or a combination of ingredients enumerated above, as required, followed by filtered sterilization. Generally, dispersions are prepared by incorporating the active compound into a sterile vehicle, which contains a basic dispersion medium and the required other ingredients from those enumerated above. In the case of sterile powders for the preparation of sterile injectable solutions, the preferred methods of preparation are vacuum drying and freeze-drying, which yield a powder of the active ingredient plus any additional desired ingredient from a previously sterile-filtered solution thereof. Attorney Docket No.29618-0465WO1 / BWH 2023-131 Oral compositions generally include an inert diluent or an edible carrier. For the purpose of oral therapeutic administration, the active compound can be incorporated with excipients and used in the form of tablets, troches, or capsules, e.g., gelatin capsules. Oral compositions can also be prepared using a fluid carrier for use as a mouthwash. Pharmaceutically compatible binding agents, and / or adjuvant materials can be included as part of the composition. The tablets, pills, capsules, troches and the like can contain any of the following ingredients, or compounds of a similar nature: a binder such as microcrystalline cellulose, gum tragacanth or gelatin; an excipient such as starch or lactose, a disintegrating agent such as alginic acid, Primogel, or corn starch; a lubricant such as magnesium stearate or Sterotes; a glidant such as colloidal silicon dioxide; a sweetening agent such as sucrose or saccharin; or a flavoring agent such as peppermint, methyl salicylate, or orange flavoring. For administration by inhalation, the compounds can be delivered in the form of an aerosol spray from a pressured container or dispenser that contains a suitable propellant, e.g., a gas such as carbon dioxide, or a nebulizer. Such methods include those described in U.S. Patent No.6,468,798. Systemic administration of a therapeutic compound as described herein can also be by transmucosal or transdermal means. For transmucosal or transdermal administration, penetrants appropriate to the barrier to be permeated are used in the formulation. Such penetrants are generally known in the art, and include, for example, for transmucosal administration, detergents, bile salts, and fusidic acid derivatives. Transmucosal administration can be accomplished through the use of nasal sprays or suppositories. For transdermal administration, the active compounds are formulated into ointments, salves, gels, or creams as generally known in the art. The compounds can be prepared with carriers that will protect the therapeutic compounds against rapid elimination from the body, such as a controlled release formulation, including implants and microencapsulated delivery systems. Biodegradable, biocompatible polymers can be used, such as hydrogels, ethylene vinyl acetate, polyanhydrides, polyglycolic acid, collagen, polyorthoesters, and polylactic acid. Such formulations can be prepared using standard techniques, or obtained commercially, e.g., from Alza Corporation and Nova Pharmaceuticals, Inc. Nanoparticles and liposomal suspensions (including liposomes or nanoparticles targeted to selected cells with peptides or monoclonal antibodies to cellular antigens) Attorney Docket No.29618-0465WO1 / BWH 2023-131 can also be used as pharmaceutically acceptable carriers. These can be prepared according to methods known to those skilled in the art, for example, as described in U.S. Patent No.4,522,811. Suitable formulations can include those described, e.g., in US20200147213; US6267958B1; US20070059302; US6171586; and others. The methods can also include delivery using microneedles, e.g., as described in Wang et al., Nano Lett.2016 Apr 13;16(4):2334-40. The pharmaceutical compositions can be included in a container, pack, or dispenser together with instructions for administration. EXAMPLES The invention is further described in the following examples, which do not limit the scope of the invention described in the claims. Materials and Methods The following materials and methods were used in the Examples below. Human samples cohort and tissue preparation The research described in this manuscript complies with all relevant ethical regulations and was reviewed and approved by the Institutional Review Boards (IRBs) at BWH. Blocks of FFPE melanomas (Mel_001: AMSBIO ABL_02 and Mel_002: AMSBIO E031_09 and Mel_003 and Mel_004 from the BWH Pathology Department archives were cut at 5-µm thickness using a rotary microtome, and the sections were mounted onto Superfrost Plus microscope glass slides (Thermo Fisher, 12-550-15). The slides were dried at 37 °C overnight and baked at 59 °C for 1 h. Slides were stored at 4 °C until use. Mice C57BL / 6J, CD11cCre, LysmCre, Cd19Cre, E8iCre, CD4Creand FOXP3EGFP-Cre-ERT2(named FOXP3ert2Cre) mice were purchased from Jackson Laboratory and bred in our facility or used for experiments after at least one week of housing in our facility. Tigitfl / fl, FoxP3-IRES-GFP, Tigit- / -and Tigit- / -x FoxP3-IRES-GFP mice were generated on C57BL / 6J background and described previously (46, 57, 58). Tigitfl / flmice were crossed to the above cre lines to generate the TIGIT conditional knockout mice. Mice at the age of 8–10 weeks were used for experiments. All experiments were conducted in accordance with animal protocols approved by the Harvard Medical Area Standing Committee on Animals or BWH IACUC. Attorney Docket No.29618-0465WO1 / BWH 2023-131 Cell lines B16F10 mouse melanoma, Yummer 1.7 mouse melanoma, 4T1 breast cancer, KP lung cancer, LLC / 2 lung cancer, CT26 mouse colon carcinoma, and MC38 mouse colon adenocarcinoma cell lines were obtained from ATCC. B16-OVA cells (B16F10 cells engineered to express OVA) were kindly provided by Kai Wucherpfennig (Dana-Farber Cancer Institute, Boston, MA). B16F10CD155KD(Knockdown for CD155) was generated using pLKO.1 clone ID TRCN0000112545 (obtained from the RNAi Consortium at the Broad Institute). HEK293T cells were transfected, and the resulting Lentivirus was then used to infect B16F10 cell line to generate a CD155 knockdown cell line. B16F10CD155ΔCPwas generated using a CRISPR-Cas9 system. We designed guide RNAs (GTACTCACCTCCCTCTCTGA (SEQ ID NO:1) and AGCCAAACAGCACAAGGTGA (SEQ ID NO:2)) to create a 658 bp deletion in exons 7 and 8 of murine CD155 without frameshift. Two guide RNAs were separately cloned into backbone pSpCas9(BB)-2A-GFP (Addgene 48138#) for transfection. B16F10 cells were co-transfected with plasmids containing two guide RNAs together using Polyjet (SignaGen Laboratories, SL100688). At 72hrs after transfection, transfection efficiency was approximately 60%. Single transfected cells (GFP positive) were sorted into individual U-bottom culture wells for single cell cloning. After 3 weeks, single clones were successfully amplified for further genetic validation. At the genomic level, the targeted region on Pvr gene locus was amplified and Sanger sequenced to confirm the correctly edited clones which have two alleles with the correct cutting site on both guide RNA regions without frameshift. At the RNA level, RT-PCR was performed followed by Sanger sequencing to confirm the expression of a truncated Pvr transcript. At the protein level, flow cytometry analysis was performed using CD155 antibody (Biolegend 131508, clone Tx56) to confirm the expression of the truncated Pvr transcript at the protein level. B16F10CD155ΔCP OE Psmb8and B16F10CD155ΔCP OE Hes1were generated using a lentiviral activation system. Virus particles from Santa Cruz Biotechnology (Psmb8 sc-421450-LAC; Hes1 sc-420832- LAC) were used to transduce B16F10 cells followed by drug selection (Puromycin dihydrochloride sc-108071), Hygromycin B sc-29067 and Blasticidin S HCl sc- 495389. Overexpression efficiency was detected using RT-qPCR after drug selection for 7 days. B16F10CD155KDand B16F10CD155ΔCPwere compared to their respective control counterparts carrying an empty vector which was used as a scrambled- Attorney Docket No.29618-0465WO1 / BWH 2023-131 sequence control (named B16F10Control). All cells were cultured in a humidified, 5% CO2incubator at 37 °C, and grown in RPMI or DMEM with 10% fetal bovine serum (FBS) and 100 U ml−1 penicillin / streptomycin (Life Technologies). All cell lines were tested and were negative for mycoplasma contamination. Tumor models For primary tumor growth experiments, MC38 (1 × 106), B16F10 (2.5 × 105), B16-OVA (5 × 105) cells were subcutaneously (s.c) injected into the right flank in a final volume of 100 µL. Tumor growth was measured using digital calipers, and tumor sizes were recorded. For primary tumor cell dissemination experiments, 2 × 105B16F10 cells were injected intravenously (i.v) in the tail vein, lungs were harvested on day 14, and B16F10 colonies were counted using a dissecting microscope. In vivo antibody treatments In some experiments, mice were treated with 250µg of anti-TIGIT (clone 1B4) (Cell Essentials, Inc) and / or 250µg of anti-Jagged-1 (named anti-JAG1, clone HMJ1- 29)(BioLegend) or 250µg of isotype controls (mouse IgG1 for 1B4 and Armenian Hamster IgG for HMJ1-29) intraperitoneally (i.p) on days 7, 9 and 11 post tumor implant. In vitro tumor cell cultures B16F10 cells cultured in R10 medium consisting of RPMI, Penicillin / Streptomycin, 10% inactivated Fetal Calf Serum, sodium pyruvate (Gibco), NEAA (Gibco). Prior to experiments, 1 x 105– 5 X 105cells were seeded in either 12 well or 6 well cell culture dishes (Nunc). On reaching 80% confluency these were treated with either recombinant mouse TIGIT-Fc (R&D Systems, #7267-TG-050, 1ug / ml)) or mouse IgG (Invitrogen, 10400C, 1ug / ml)) for different time durations. To inhibit Notch pathways, cells were treated with 5uM DAPT (MedChem Express, HY- 13027) or 5uM IMR-1 (MedChem Express, HY-10043) with or without TIGIT-Fc. For viability and proliferation assay, 5 X 104B16F10 cells with either intact CD155 or deleted cytoplasmic domain CD155 (dCP) were seeded in 96 well tissue culture flat bottom plates. Prior to culturing cells, 96 well plates were coated with Mouse TIGIT Fc (R&D Systems, 7267-TG-050, 1ug / ml), recombinant mouse Jagged1 protein (Abcam, ab109346, 5ug / ml) or mouse IgG (Invitrogen, 10400C) either individually or in combination. Cells were then cultured for either 48 hours or 72 hours and stained with alamarBlue™ (Invitrogen) as per manufacturer protocol. Viability and Attorney Docket No.29618-0465WO1 / BWH 2023-131 proliferation ability of cells were then assessed in a Promega plate reader by recording the fluorescence excitation wavelength of 540–570 nm (peak excitation is 570 nm) and fluorescence emission at 580–610 nm (peak emission is 585 nm). Absorbance of alamarBlue™ was recorded at 570nm wavelength. Proliferation was also quantified by counting the cells at the end of experiment and comparing it with initial seeding density. Preparation of cell suspensions Single-cell suspensions were prepared from mouse lymph nodes, spleens or tumors as previously described

[0057] . Briefly, tumors were dissociated mechanically and digested with 1 mg / mL collagenase A and 0.1 mg / mL DNase1 for 20 min at 37°C. Lymph nodes and spleens were mechanically dissociated, digested with 0.1 mg / mL collagenase A and 0.01 mg / mL DNase1 for 20 min at 37°C, and passed through a 40-µm cell strainer and lysed of red blood cells (RBCs; using ACK buffer), then washed with cold PBS and spun down at x g. In vitro co-culture assays Single-cell suspensions were prepared from mouse lymph nodes and spleens as described above. Viable TCRβ+CD4+Foxp3+cells (Natural regulatory T cells (nTregs) or naïve CD44−CD62L+CD4+T cells were FACS-sorted using a BD FACS Aria III flow cytometer (BD Biosciences) from FoxP3-IRES-GFP or Tigit- / -x FoxP3- IRES-GFP reporter mice. For naïve CD4+T cells, cells were activated with plate- bound anti-CD3 (1µg / ml, clone 145-2C11, Bio X Cell) and anti-CD28 (1µg / ml, clone PV-1, Bio X Cell) antibodies on flat-bottom 96-well plates for 3 days in the presence of human TGF-β1 (2ng / mL, Miltenyi Biotec) for the differentiation of Induced regulatory T cells (iTregs). One day prior to co-culture, 50 x 104B16F10CD155KD, B16F10CD155ΔCPcells or their respective controls were seeded in round-bottom 96- well plates.50 x 104nTregs or iTregs cells were then cultured alone or cocultured with tumor cells in the presence of 50ug / mL anti-TIGIT (clone 1B4) and / or 90ug / mL of anti-JAG1 (clone HMJ1-29) for the time indicated on the figures or figure legends. In all co-culture conditions including tumor cells alone, anti-CD3 / anti-CD28 beads (Dynabeads, Invitrogen) and 20 U / ml IL-2 (BioLegend) were added to the culture medium. Cells were then harvested, counted on a hemocytometer, and analyzed by flow cytometry. Attorney Docket No.29618-0465WO1 / BWH 2023-131 For in vitro suppression assays, viable TCRβ+CD8+T cells were isolated and FACS-sorted from WT mice, labeled with 5 mM CellTrace Violet and stimulated with anti-CD3 / anti-CD28 beads (Dynabeads, Invitrogen) in presence of two different ratios of FACS sorted GFP+WT or Tigit- / -nTregs previously co-cultured or not with tumor cells. CD8+T cell proliferation was read out after 72h by flow cytometry and the division index of responder cells was analyzed using FlowJo based on the division of Cell Trace Violet. Suppression was then calculated with the formula % Suppression = (1-DivTreg / DivAlone) x100% (DivTreg stands for the division index of responder cells with Tregs, and DivAlone stands for the division index of responder cells activated without Tregs). In vitro T cell cultures Splenic iTregs and total CD8+T cells were FACS-sorted using a BD FACS Aria III flow cytometer (BD Biosciences) from FoxP3-IRES-GFP or Tigit- / -x FoxP3- IRES-GFP reporter mice and activated with plate-bound anti-CD3 (1µg / ml, clone 145-2C11, Bio X Cell) and anti-CD28 (1µg / ml, clone PV-1, Bio X Cell) antibodies on flat-bottom 96-well plates for 3 days in the presence of 10ug / ml of recombinant mouse CD155-Fc (R&D Systems, 9670-CD) or mouse IgG (Invitrogen). Cells were then harvested and analyzed by flow cytometry. Flow cytometry and fluorescence-activated cell sorting (FACS) Single-cell suspensions were prepared from mouse spleens or tumors as previously described (59). Briefly, lymph nodes and spleens were mechanically dissociated, homogenized, and passed through a 40-µm cell strainer and lysed of red blood cells (RBCs; using ACK buffer) then washed with cold PBS and spun down at x g. Tumors were dissociated mechanically and digested with 1 mg / mL collagenase A and 0.1 mg / mL DNase1 for 20 min at 37°C. Live / dead cell discrimination was performed using Live / Dead Fixable viability dye e506 (eBioscience). Surface antibodies used in this study were against: CD45 (30-F11), TCRb (H57-597), CD3e (17A2), TCR^^, CD8a (53-6.7), CD4 (RM4-5), CD19 (6D5), B220, Ly6C (HK1.4), Ly6G (1A8), CD11c (N418), CD11b (M1 / 70), CD64 (X54-5 / 7.1), TIGIT (1G9,GIGD7), NK1.1 (PK136), MHC II (I-A / E, M5 / 114.15.2), CD155 (TX56) The following cell populations were identified based on cell marker expression: CD4 T cells (CD45+TCR^+CD4+), Tregs (CD45+TCR^+CD4+FoxP3+), CD8+T cells (CD45+TCR^+CD8+), B cells (CD45+B220+CD19+), natural killer (NK) cells Attorney Docket No.29618-0465WO1 / BWH 2023-131 (CD45+NK1.1+), PMN (CD45+CD11b+Ly-6CintLy6G+), dendritic cells (DCs) (CD45+CD11c+I-A / Ehigh), macrophages (Macs) (CD45+CD11b+Ly-6C- Ly6G- CD64+), ^^ T cells (CD45+CD3e+TCR^^+). For intra- cytoplasmic cytokine (ICC) staining, cells were stimulated with phorbol myristate acetate (50 ng ml−1) and ionomycin (1 μg ml−1). Permeabilized cells were then stained with antibodies against IL-2 (JES6-5H4), TNF-^ (MP6-XT22) and IFN-^ (XMG1.2) and IL-10 (JES5-16E3). For FOXP3 (FJK-16), tBet (4B10), KI67 (16A8) and Granzyme B (2C5 / F5) staining were performed using the Foxp3 / Transcription Factor Staining Buffer Set (eBioscience). All data were collected on a BD Symphony A5 (BD Biosciences) and analyzed with FlowJo software (Tree Star). Multiplexing and droplet-based single-cell RNA-seq (scRNAseq) and TCR-seq (scTCR-seq) For the examination of FOXP3ert2Creand TIGITΔTregmice implanted with B16F10Controland B16F10CD155KDtumors, viable leukocytes were FACS-sorted from tumors (50% TCRβ+CD4+cells, 30% total CD45- cells and 20% total CD45+cells), dLN and ndLN (100% CD45+cells). Cells were resuspended in PBS containing 2% FCS and stained with oligo tagged TotalSeq antibodies (BioLegend) for 30 minutes on ice. Cells were washed and pooled accordingly, centrifuged at 1,200 rcf for 5 minutes at 4^C and resuspended in PBS + 2% FCS.8 samples were combined into each channel: B16F10Controland B16F10CD155KDTILs and their respective dLN derived from one biological replicate of each genotype. scTCR-seq and 5' feature barcoding were separated into droplet emulsions using the Chromium Single Cell 5' V2 Solution, according to manufacturer’s instructions (10x Genomics). scRNA- sequencing libraries (5’) and 5' feature barcoding libraries were sequenced on an Illumina Nextseq 550 using the 75-cycle kit to a depth of 100 million reads per library. Pre-processing of droplet-based scRNAseq data and VDJ-seq Three sample sets were loaded, each sample set on two separate 10x channels. Sample sets included samples from tumor and draining lymph node 16 days post tumor injection. Cells from a separate location and condition were hashed separately to be distinguishable in the analysis. Hashed scRNA-seq expression profiles were processed in Terra (https: / / app.terra.bio / ) through the ‘demultiplexing’ workflow in scCloud / Cumulus (v 0.8.0(60)), a wrapper for cellranger_mkfastq, cellranger_count Attorney Docket No.29618-0465WO1 / BWH 2023-131 (v 3.0.2), and cumulus_adt. Profiles were mapped to the pre-built mouse reference mm10, cellranger reference v1.2.0 (Ensembl v84 gene annotation), specifying that the profiles were obtained with the 10x 5’ chemistry. Cell profiles were matched with antibody derived tags (ADT) counts to assign their identity, as samples from condition and locations had been associated with unique combinations of 2 hashing antibodies. Cells with incorrect combinations of hashing antibodies were discarded from the analysis. Separately, reads from the VDJ libraries (TCR) were processed with Cumulus, using the pre-built reference GRCm38_vdj_v3.1.0, part of cellranger reference 3.1.0, annotation built from Ensembl Mus_musculus.GRCm38.94.gtf. Filtered_contig annotations and filtered_contig.fasta from the two separate channels of each sample set (technical replicates) were merged before further processing. RNA profiles were then processed with Scanpy (v 1.7.2). Cells were filtered out if their fraction of mitochondrial genes was ≥ 4.5% or if they had < 1,000 counts or < 300 or >6,000 genes. Genes detected in ≤1 cell were also filtered out. Each cell transcriptome was scaled to sum to 10K, and expression values were further normalized with log1p, finally obtaining log (TP10K +1) values for each gene. Highly variable genes were selected using the highly_variable_genes function in scanpy, with min_mean=0.01, max_mean=3, min_disp=0.25. Normalized values were then scaled to unit variance with a max_value for standard deviation equal to 10. Dimensionality reduction with UMAP, using a k-nearest neighbor graph (k=15), was performed after batch correction with Harmony (61) (using the harmony-pytorch wrapper) on biological replicates. Preprocessing described above was repeated after removing these cells from the dataset. Finally, the dataset included 14,782 cells, 17,763 genes with 1,658 genes identified as highly variable genes. Differentially expressed genes in scRNA-seq Differential expression analysis was performed with a two-sided t-test or Wilcoxon rank-sum test as indicated in the figure’s legend, using scanpy’s “rank_genes_groups” function. Subsequently, genes were retained if the fraction of expressing cells within the considered group was ≥0.1, the fraction of expressing cells in the other group was ≤0.90, and the fold-change between groups was at least 1 (Fig. 3G and E). Attorney Docket No.29618-0465WO1 / BWH 2023-131 Analysis of scTCR-seq data TCR sequences for each single T cell were assembled by CellRanger vdj pipeline (v.3.1.0) as described above, leading to the identification of CDR3 sequences and the re-arranged TCR gene. TCR repertoire analysis was performed with Scirpy (62) (v.4.2). TCR diversity and TCR clonal size were estimated using scirpy.tl.alpha_diversity and scirpy.pl.clonal_expansion (performing the normalization), respectively. V(D)J gene usage was estimated with scirpy.pl.vdj_usage. Analysis of published scRNA-seq studies Processed melanoma scRNA-seq data was obtained from our earlier study with human leukocytes profiled from patients pre- or post-ICB (63) without any change to processing, using the same expression values and cluster assignments as previously reported (63). Additionally, we obtained published and processed scRNA- seq data from multiple human TILs datasets(34, 35, 37, 38, 64). In the HiHi signature analysis (Figure 4 and S6), malignant tumor cells from the HiHi samples are compared to those from the LoLo samples using scanpy’s “rank_genes_groups” function (Wilcoxon rank-sum test), and genes were filtered to keep those with Benjamini-Hochberg adjusted p-value < 0.05 and log2 fold change > 0.5. Gene Set Enrichment Analysis Gene set enrichment for Hallmark 2020 gene sets was performed using GSEApy with default parameters(65). HiLo HiLo is a Python package for analyzing dual marker expression profiles across individuals. It categorizes individuals (e.g. patient samples) based on the mean expression of two genes (^^^and ^^ଶ) in their corresponding cell subsets of interest (^^^,^for ^^^and ^^^,ଶfor individual ^^). First, the samples with too few cells in subset (e.g. less than 10 cells) were labeled as undetermined, and were excluded from further steps. Next, for individual ^^, the mean expression of gene ^^ in subset ^^^,^was calculated as: 1 ^^^ Attorney Docket No.29618-0465WO1 / BWH 2023-131 , where ^^^,^is the expression of gene ^^ in cell ^^. Then the median and standard deviation of the mean expression levels are computed across individuals, denoted as ^^^^and ^^^^, respectively. Subsequently, individual ^^ will be labeled as “Hi” (high) for gene ^^ if ^^^,^^^^^, labeled as “Lo” (low) for gene ^^ if ^^^,^ ^ ^^^, or “NA” otherwise. The thresholds,^^^ and ^^^, are defined as ^^^ ൌ ^^^^ ^ ^^^ ⋅ ^^^^ and ^^^ ൌ ^^^^ െ ^^^ ⋅ ^^^^, where ^^^, ^^^ ∈^0,^∞^are tuning for the classification strictness androbustness. ^^ ൌ ^^^^were combined to create dual-marker categories. Survival analysis and correlation analysis Survival analysis and correlation analysis of gene expression signatures were performed using web server GEPIA2 (Tang et al., 2019), based on TCGA and GTEx databases. Survival analysis were performed for the Pan-cancer (33 cancers) and 4- cancer cohorts independently (Figure 4G). In each analysis, HiHi signature expression scores were computed for every sample, and the cohort was divided into high and low expression groups by the median value (50% cut-off). The significance of the differences between the Kaplan-Meier survival curves were determined with the Mantel-Cox log-rank test, and the hazard ratios (HRs) were calculated and tested based on the Cox proportional hazards model. Pearson correlation coefficient (r) was computed for the signature expression levels in all TCGA / GTEx tumor samples and the significance was determined with two-tailed approximate z-test, where the null hypothesis is r = 0. Immunofluorescence antibodies Antibodies were obtained in carrier-free PBS and conjugated directly to either biotin for α-SMA, digoxygenin for pan-cytokeratin or to ArgoFluor™ dyes (RareCyte, Inc.) using amine conjugation chemistry. After determining labeling efficiency using absorbance spectroscopy, the conjugated antibodies were diluted in PBS-Antibody Stabilizer (CANDOR Bioscience GmbH, Catalog No.130050) to a concentration of 200 µg / mL. Antibodies validated in immunofluorescence for this study are S100 (clone EPR5251), CD45 (clone D9M81), CD68 (clone D4B9C), CD20 (clone L26), CD4 (clone N1UG0), CD8a (clone AMC908), CD155 (clone EPR22672), TIGIT (clone BLR047F), CD11c (clone D3V1E), FOXP3 (clone 236 / E7), Pan-CK (clone AE1 / AE3 / C11). Attorney Docket No.29618-0465WO1 / BWH 2023-131 Immunofluorescence staining Slides were de-paraffinized and subjected to antigen retrieval for 5 minutes at 95°C followed by 5 minutes at 107°C, using pH8.5 EZ-AR 2 Elegance buffer (BioGenex, Catalog No. HK547-XAK). To reduce tissue autofluorescence, slides were placed in a transparent reservoir containing 4.5% H2O2 and 24 mM NaOH in PBS and illuminated with white light for 60 minutes followed by 365 nm light for 30 minutes at room temperature as previously described(66, 67). Slides were rinsed with surfactant wash buffer (0.025% Triton X-100 in PBS), placed in a humidified stain tray, and incubated in Image-iT™ FX Signal Enhancer (Thermo Fisher, Catalog No. I36933) for 15 minutes at room temperature. After rinsing with surfactant wash buffer, the slides were placed in a humidity tray and stained with the panel of fluor- and hapten-labeled primary antibodies in PBS-Antibody Stabilizer (CANDOR Bioscience GmbH, Catalog No.130050) containing 5% mouse serum and 5% rabbit serum for 2 hours at room temperature. Slides were then rinsed again with surfactant wash buffer and placed in a humidified stain tray and incubated with Hoechst 33342 (Thermo Fisher Catalog no. H3570), ArgoFluor™ 845 mouse-anti-DIG, and ArgoFluor™ 875-conjugated streptavidin in PBS-Antibody Stabilizer containing 10% goat serum for 30 minutes at room temperature. The slides were then rinsed a final time with surfactant wash buffer and PBS, coverslipped with ArgoFluor™ Mounting Media (RareCyte, Inc.) and dried overnight. One-shot antibody IF imaging with the Orion instrument Whole slides were scanned using the Orion instrument using acquisition settings optimized for the specific antibody panels. Briefly, acquisition channel parameters were defined for each biomarker plus an additional channel dedicated to tissue autofluorescence, and included excitation laser, emission center wavelength (CWL), and exposure times. The nuclear channel was scanned at low resolution to identify tissue boundaries, followed by surface mapping at 20X to find the tissue in the z-axis. Whole tissue was acquired at 20X following the surface map within the specified tissue boundaries by collecting all channels for a single field of view (FOV) before proceeding to the next partially overlapping FOV. Raw image files were processed to correct for system aberrations, then signals from individual targets were isolated to separate channels using the Spectral Matrix obtained with control samples, Attorney Docket No.29618-0465WO1 / BWH 2023-131 followed by stitching of FOVs to generate a continuous open microscopy environment (OME) pyramid TIFF image. Same Section H&E staining and imaging After Orion imaging was complete, slides were de-coverslipped by immersion in 1x PBS at 37°C until the coverslips fell away from the slide. Slides were rinsed in distilled water for 2 minutes, then stained by a routine regressive H&E protocol using Harris Hematoxylin (Leica, Catalog No.3801575) and alcoholic eosin Y (Epredia, Catalog No.71211). Coverslipping was performed with toluene-based mounting media (Thermo Scientific, Catalog No.4112). After drying for 24 hours, slides were scanned on an CyteFinder HT instrument (RareCyte) in brightfield mode, using the same scan area used for IF image acquisition. H&E images were also acquired using an Aperio GT450 microscope (Leica Biosystems), and the H&E images were registered to the IF images using ASHLAR48 and PALOM software (github.com / Yu- AnChen / palom). Orion image processing data quantification Image stitching and segmentation. Image data processing was performed using MCMICRO modules(68). Briefly, stitched, registered, illumination and geometric distortion corrected images were generated by the Orion platform. Single-cell segmentation was performed with UNMICST2 and cell masks were generated by 5- pixel dilation of the nucleus masks. Mean intensity of each channel and morphological features were quantified for each cell masks. Data analysis from MCMICRO output files was performed using scanpy. To compute the neighborhood enrichment between each annotation, nhood_enrichment function in Squidpy(69) was used with its default parameter. Western Blot Following the experiments described in above section of in vitro tumor cell culture, B16F10 cells were washed with ice cold PBS twice and lysed in RIPA lysis buffer supplemented with Protease and phosphatase Inhibitor (Thermo) to prepare protein lysates. Protein concentration was quantified using a G-Bioscience protein quantification kit. Proteins in equal amounts were resolved by pre-casted SDS-PAGE gel (Thermo) electrophoresis and then transferred to 0.4-micron nitrocellulose membranes (BioRad). Blots were then incubated at room temperature for 1 hour in 5 % skim milk (Bio-Rad) and probed with respective primary antibodies at 4℃ Attorney Docket No.29618-0465WO1 / BWH 2023-131 overnight. The primary antibodies used in the study were anti-Hes1 (Cell Signaling Technology, 11988S, 1:1000), anti-Notch1 (Cell Signaling Technology, 3608S, 1:1000), anti-MAML (Cell Signaling Technology, 12166S, 1:1000), anti-RBPj(Cell Signaling Technology T, 5313T, 1:1000), anti-CD155 (Invitrogen, MA5-29762, 1:2000), anti-Beta Actin (Cell Signaling Technology, 4970S, 1:1000). Blots were then probed with specific Anti-rabbit or anti-mouse secondary antibodies conjugated to HRP at room temperature for 2 hours. Proteins bands were visualized with SuperSignal West Femto Chemiluminescent Substrate (Thermo, 34095) and imaged in iBright (Thermo). Image intensities were measured by ImageJ software. Quantitative RT-PCR Total RNA was isolated from B16F10 cells following experimental conditions using RNAeasy mini plus kit (QIAgen) according to the manufacturer’s instructions. 1ug of total RNA was used to prepare cDNA using the iscripr cDNA synthesis kit (Bio-Rad). SYBR green master mix (BioRad) was used to perform qPCR for 10ul reaction volumes in 384 well plate in Applied Biosystem. Actin was used as a housekeeping gene to the normalize the reaction. The list of primers is included in Table 1. Table 1: List of qPCR primer  Gene  #  #  Forward Primer (5’‐3’)  Reverse primer (5’‐3’)  name ...0.2.4.6.8.0.2. Attorney Docket No.29618-0465WO1 / BWH 2023-131 Statistical analysis Unless otherwise specified, all statistical analyses were performed using the two-tailed Student’s t test, Mann-Whitney test or one-way ANOVA test followed by Tukey’s multiple comparison test, using GraphPad Prism software (GraphPad Prism version 8.0). P value less than 0.05 is considered significant (p < 0.05 = *; p < 0.01 = **; p < 0.001 = ***, p < 0.0001=****) unless otherwise indicated. Example 1. Loss of TIGIT in Tregs but not in other immune cells inhibits tumor growth. Although TIGIT is expressed by multiple cell types within the TME, the causal contribution of each TIGIT-expressing cell population in regulating anti-tumor immunity has not been fully explored. To investigate the role of TIGIT on different immune cells, we first examined TIGIT expression in tumor-infiltrating leukocytes (TILs) from murine and human melanoma previously profiled by scRNA-seq by us and others (22, 23). In both mice and humans, high TIGIT expression was observed in Tregs, clonally expanded CD8+T cells, NK cells, with low frequency expression observed within the myeloid and B cell compartments (Fig.1A). We deliberately chose the B16F10 melanoma mouse model, known for its poor immunogenicity and resistance to immunotherapy, to specifically examine how TIGIT expression in different immune cells impacts tumor growth, highlighting the unmet need in this context. First, we deleted TIGIT on all cells by crossing TIGITfl / flto CMVcremice and showed that the loss of TIGIT on all cells resulted in B16F10 tumor growth inhibition, confirming that indeed TIGIT regulates tumor growth control (Fig.1B). Consistent with our findings, TIGIT has been described to regulate anti-tumor NK and T cell functions (24, 25). To identify the specific immune cell population(s) responsible for the protective anti-tumor response observed in the globally TIGIT deficient mice, we generated TIGIT conditional “knock-out” mice (26) by crossing TIGITfl / flto mice expressing cell-specific Cre. Loss of TIGIT on myeloid populations (using LysMcre), DCs (CD11ccre) or B cells (CD19cre), had modest or no effects on B16F10 tumor growth (Fig.1C-F). Loss of TIGIT on all T cells (CD4cre) led to a significant reduction of tumor burden (Fig.1G, H). However, there was no protective anti-tumor immunity in mice with genetic deletion of TIGIT in the CD8 compartment alone (TIGIT^CD8); this confirmed our previous data examining tumor growth in mice reconstituted with either WT or TIGIT-deficient CD8+T cells and indicated that at Attorney Docket No.29618-0465WO1 / BWH 2023-131 least in the B16F10 tumor models (subcutaneous or intravenous models), TIGIT- expression on CD8+T cells is less important in regulating anti-tumor immunity (21) (Fig.1G, I). Because CD4credeletes target genes on Foxp3+Tregs in addition to conventional CD4+T cells (Tconv) and CD8+T cells, we next distinguished the specific contribution of TIGIT on Tregs we implanted tumors in mice with tamoxifen- inducible TIGIT-deletion on Foxp3+Tregs (Foxp3ert2cre) (hereafter referred as TIGIT^Tregs). As this is an inducible Cre, we administered tamoxifen orally 3 days prior to implantation and every 3 days thereafter, for the duration of the experiment. Strikingly, tumor burden was significantly reduced in mice with TIGIT deletion specifically within the Treg compartment in both the B16F10 and B16-OVA melanoma tumor models, as well as in a more immunogenic model using MC38 colon carcinoma (Fig.1J). We confirmed that the TIGIT deletion was restricted to regulatory T cells in these mice by performing flow cytometry. There were no substantial differences in tumor immune infiltrates between Foxp3ert2cre(controls) and TIGIT^Tregsmice, except for a significant increase in the frequency of CD8+T cells in TIGIT^Tregstumors. These CD8+T cells also displayed a higher cytotoxic, more proliferative, and more pro-inflammatory cytokine profile as measured by the expression of Granzyme B, KI67, IFN^, and TNF^ (Fig.1K). While the Tconv compartment showed only a modest increase in TNF^ expression (Fig.1K), TIGIT- deficient Tregs exhibited a higher proliferative profile, impaired IL-10 production and increased IFNγ, which has been implicated in Tregs instability (27) (Fig.1K-L). Consistently, in human tumors, TIGIT-expressing Tregs have a higher expression of a Treg-effector signature and of highly functional Tregs with high expression of canonical Treg-associated genes (e.g., CTLA4, CD25, FOXP3) (Fig.1M). These results indicate a critical role for TIGIT-expression in Tregs in maintaining Treg-cell function and stability, and in promoting tumor growth in vivo. Example 2. Coordinated expression of TIGIT on Tregs and CD155 on malignant cells confer a growth advantage to tumors. The ligands for TIGIT are CD155, CD112 and CD113, with CD155 serving as the principal ligand due to the high affinity of TIGIT for CD155 compared to CD112 and CD113 (28, 29). CD155 is expressed by various immune cell types, including T cells, B cells and APCs but also by many malignant cells themselves (Fig.2A). The malignant cells represent the most abundant CD155-expressing cells in the murine Attorney Docket No.29618-0465WO1 / BWH 2023-131 and human melanoma TME (Fig 2A), and similarly to PD-L1, CD155 expression can be induced by Type I and III interferons suggesting a role in tumor escape. Consequently, we sought to understand the contribution of the TIGIT:CD155 axis provided by direct interaction of CD155-expressing tumor cells with TIGIT- expressing T cells. To address this question, we generated a stable B16F10 cell line with knockdown of CD155 (CD155KD) and compared growth of this tumor cell line to that of the parent B16F10 control cells both in vitro and in vivo (Fig.2B). Interestingly, B16F10 cells with lower CD155 expression demonstrated reduced tumor growth in WT mice, consistent with a role for CD155 expressed in tumor cells promotes tumor growth (Fig.2C). Conversely, using the global TIGIT knock-out (TIGITfl.flx CMVcre, TIGIT KO), tumor growth was not further altered by reduced CD155 expression in the tumor, indicating the necessity for expression of both CD155 on tumor cells and TIGIT expression on the host cells to provide a tumor growth advantage in vivo (Fig.2C). Given the most abundant TIGIT expressing cells within the TME are CD8+T cells and Tregs (Fig 1A), we used our conditional knock- out mice (Fig.1B-J) to elucidate the TIGIT-expressing immune cell population responsible for this joint promotion of tumor growth. TIGIT deficiency only on CD8+T cells did not impact CD155-dependent tumor growth promotion, as the reduced growth of CD155KDtumor cells was not altered in the presence or deletion of TIGIT on CD8+T cells (Fig.2D). In contrast, the tumor growth advantage of CD155- expressing tumor cells was abrogated in TIGIT^Tregsmice, phenocopying the global TIGIT deficient mice (Fig.2E) and accompanied by decreased IL-10 and enhanced IFN^ production by Tregs (Fig.2F). To assess whether TIGIT-expressing Tregs were directly able to promote tumor growth via CD155-interaction, we co-cultured CD155WTor CD155KDB16F10 cells with Tregs derived from WT (^ anti-TIGIT antibody) or TIGIT-deficient mice. Notably, WT Tregs induced a greater expansion of tumor cells in vitro and required TIGIT expression in Tregs and CD155 expression on B16F0 cells respectively (Fig. 2G). Consistently with our previous data monocultured TIGIT KO Tregs had a limited suppressive capacity compared to WT Tregs (21) (Fig.2H). However, post- co-culture, WT Tregs that experienced CD155WTbut not CD155KDB16F10 cells showed a greater capacity to inhibit CD8+T cell proliferation (Fig.2H). These results Attorney Docket No.29618-0465WO1 / BWH 2023-131 indicate that the bidirectional dialog between tumor cells and Tregs through TIGIT and CD155 directly promotes tumor growth and reinforces Tregs function. Example 3. TIGIT-expressing Tregs control tumor cell expansion by inducing an intrinsic oncogenic gene program in CD155-expressing tumor cells To further identify the consequences of bidirectional signaling between Tregs and tumor cells, we performed 5’ single-cell RNA-seq (scRNA-seq) combined with TCR-seq (scRNA / TCR-seq) on CD45+and CD45- cells in the TME and tumor- draining lymph nodes (dLN) from controls and TIGIT^Tregsmice implanted with either CD155WTor CD155KDB16F10 cells. The 14,782 high-quality cell profiles were manually annotated by respective lineages, tissue origin and expression of known marker genes (Fig.3A, Methods). We focused on the transcriptional changes within tumor cells and Tregs across the four experimental groups, named hereafter CD155wtTIGITwt, CD155wtTIGITKO, CD155KDTIGITwt, CD155KDTIGITKO. First, as expected, in models with TIGIT^Tregs(CD155wtTIGITKO, CD155KDTIGITKO), Tregs acquired IFNγ expression and had larger clones compared to counterparts from TIGITWTmodels (CD155wtTIGITwt, CD155KDTIGITwt), specifically in the TME but not dLN and similar to CD4+Tconv and CD8+T cells from TIGIT^Tregsmice. More surprisingly, malignant cells derived from the CD155wtTIGITwtmodel had increased expression of genes and programs associated with oncogenesis (e.g., Irgm1, Braf, Nras, Parp10), the cell cycle, myc targets, compared to malignant cells in all other models, and reduction of inflammatory pathways (Fig.3B-C). Furthermore, expression of CD155 on tumor cells induced upregulation of the immunoproteasome subunit Psmb8, which has been suggested to negatively impact the clinical outcome (30). Importantly, Hes1, which has been implicated in the maintenance of cancer stemness in breast and colon cancer, growth, and resistance to therapy (31-33), was markedly increased in the CD155wtTIGITwtgroup, particularly when compared to the CD155KDTIGITKOcondition where Tregs did not express TIGIT and tumor cells did not express CD155 (Fig.3B-C). These data indicated that the interplay between TIGIT+Treg and CD155+tumor cells may critically regulate cancer stemness and growth. Attorney Docket No.29618-0465WO1 / BWH 2023-131 Example 4. Tregs amplify oncogenic pathway in human cancers via TIGIT-CD155 bidirectional signaling To study the relevance of our findings in human tumors, we re-analyzed previously published scRNA-seq profiles of TILs from human patients (23). We designed a computational procedure, HiLo, to analyze the dual marker expression profiles of defined genes and cell types across individuals (Fig.4A-G and Methods) to classify samples into four groups mirroring the four categories experimentally assessed in mice. Specifically, for each individual (patient sample), HiLo computes the mean expression of PVR (CD155) in malignant cells and the mean expression of TIGIT in Tregs. It then computes a threshold, e.g., the median of the mean expressions, for PVR and TIGIT respectively, and labels samples as “Hi” or “Lo” for each gene if their mean expression are higher or lower than the gene-specific threshold. Of note, samples exhibiting a cell count below a specified threshold in either malignant cells or Tregs will be classified as undeterminate and excluded from the HiLo analysis. The dual marker expression label for each sample will be the concatenation of the two single gene labels, as shown in Fig.4B): PVRHiTIGITHi, PVRHiTIGITLo, PVRLoTIGITHiand PVRLoTIGITLo. In human melanoma (SCKM), basal cell carcinoma (BCC), Renal cell carcinoma (RCC / KIRC), breast and lung adenocarcinomas (BRCA, LUAD) (23, 34-38), tumor cells derived from PVRHiTIGITHishowed enriched gene signatures related to cell cycle, Epithelial Mesenchymal Transition (EMT), DNA replication, Myc targets, and had the highest expression of cancer stemness (39) gene signature score in melanoma (Fig.4C,D), mirroring the patterns observed in mouse. We next determined a multi-cancer HiHi gene signature shared by malignant cells from the PVRHiTIGITHitumors (compared to PVRLoTIGITLomalignant cells for at least 5 out of the 6 abovementioned datasets) (Fig.4E, Methods). There was a significant positive correlation (Pearson’s r = 0.62, P<0.001) between the expression of the PVRHiTIGITHisignature and that of the cancer stemness signature (39) in TCGA tumor samples (Fig.4F). Moreover, patients with tumors with a higher PVRHiTIGITHisignature in tumor cells had a worse prognosis both in a pan-cancer analysis of all 33 tumor types in the TCGA / GTEx(40) database and in a restricted analysis of only the tumor types used to derive the HiHi signature (p<0.0001, HR=1.4 and 1.9, Logrank test, Fig.4G). Therefore, in accordance with our previous data, the results suggest that an interaction between Attorney Docket No.29618-0465WO1 / BWH 2023-131 CD155 on tumor malignant cells and TIGIT on Tregs provides a bidirectional signal promoting oncogenesis in human tumors as well. To further test for direct physical contact between CD155 on tumor cells and TIGIT on Tregs in the TME, we performed multiplex immunofluorescence (mIF) in situ in four human melanoma biopsies (Fig.4H). The neighborhood enrichment analysis to determine the spatial patterns of T cell subsets and tumor cells indicated a preferential neighborhood between CD155+ tumor cells with Tregs compared to CD4 Tconv and CD8 T cells (Fig.4I). Remarkably, TIGIT-expressing Tregs were preferentially found in close proximity and interacting with CD155-expressing tumor cells undergoing proliferation per Ki67 staining in human melanoma biopsies (Fig.4J, K, L, M). Together with our mouse data, these data indicate that TIGIT-CD155 direct interaction between tumor cells and Tregs provides pro-tumoral bidirectional signals in both murine and human melanoma regulating oncogenic gene programs and tumor cells proliferation. Example 5. TIGIT-expressing Tregs and PVR downstream signaling into tumor cells are required to increase the oncogenesis. As the cytoplasmic (CP) tail of CD155 contains an Immunoreceptor Tyrosine- based Inhibitory Motif (ITIM), we examined whether downstream signaling through CD155 regulates the pro-oncogenic program we observed in the presence of TIGIT- expressing Tregs. To this end we constructed B16F10 melanoma cells expressing CD155 that is deleted for its cytoplasmic tail (B16F10CD155^CP) (Fig.5A). The B16F10CD155^CPtumor cells behaved similarly to the CD155 knock-down tumor cells by exhibiting a growth disadvantage both in vitro and in vivo, in that deletion of the cytoplasmic tail of CD155 eliminated the boost in tumor cell growth in vitro in the presence of Tregs (Fig.5B). Similarly, implanted B16F10CD155^CPtumor cells showed reduced tumor growth in vivo, even in mice with intact TIGIT expression on Tregs (Fig.5C). We further characterized the oncogenes Psmb8 and Hes1, as the expression of these two oncogenes in tumor cells ex vivo was dependent upon interaction of CD155 in the tumor cells and of TIGIT in Tregs. By using qPCR, we found that deletion of the cytoplasmic tail of CD155 resulted in decreased mRNA levels of Hes1, but not of Psmb8, in tumor cells analyzed ex vivo in B16F10CD155^CP, as well as in tumors from mice lacking TIGIT expression in Tregs, indicating that unlike Hes1, Psmb8 Attorney Docket No.29618-0465WO1 / BWH 2023-131 expression does not require the signaling downstream CD155 cytoplasmic domain (Fig.5D). Furthermore, genetic overexpression of Hes1, but not Psmb8, in B16F10CD155^CPcells (B16F10CD155^CP OE Hes1) rescued tumor growth defect in TIGIT^Tregsmice (Fig.5E, F). The immunophenotyping of the TME (Fig.5G and S8G-I) revealed that, although the dysfunctional Treg phenotype was not changed in the context of these tumors (Fig.5G, left), the fraction of cytotoxic granzyme B+and IFNγ+, TNFα+double positive CD8+T cell infiltrating the tumors was significantly reduced in line with the increased tumor growth we observed (Fig.5G, right). These results suggest CD155 signaling into tumor cells, mediated by interaction of TIGIT expressed on Tregs with CD155+tumor cells, elicits an oncogenic program, including the expression of the oncogenic transcription factor Hes1. Example 6. CD155 induces a reciprocal signal within TIGIT-expressing Tregs, but not in CD8+T cells to upregulate Jagged 1 (Jag1) expression. We sought to determine whether Hes1 expression in CD155+malignant cells could be induced by recombinant TIGIT protein alone (absent Tregs), and whether this suffices for increased tumor cell growth in vitro, or whether T cells are required to be present. Hes1 protein levels increased after six hours of treatment with TIGIT-Fc of WT B16F10 (B16F10Control) cells grown in vitro (Fig.5H) and was sustained for 24 hours (Fig.6A) but was not observed in the mutant CD155 lacking a cytoplasmic tail (B16F10CD155^CP) cells in the same conditions (Fig.5H, Fig.6A). However, culturing of various B16F10 malignant cells (B16F10CD155 WT, B16F10CD155 KDand B16F10CD155^CPcells) in the presence of recombinant TIGIT-Fc protein alone did not impact malignant cell growth in vitro (Fig.5I). This indicates that while Hes1 is induced in malignant cells by TIGIT-CD155 interaction, this interaction is not sufficient to promote malignant cell growth, suggesting that additional signals are provided by Tregsto promote malignant cell growth. To identify those mechanisms, we examined the impact of recombinant TIGIT protein treatment on other signaling pathways in the malignant cells that may be important for CD155 signaling, Hes1 expression, and malignant cell growth. Hes1 expression is known to be downstream of multiple key oncogenic pathways including the EGFR, Wnt, Hedgehog signaling pathways (41) but the key pathway that induces Hes1 expression, is through Notch receptor (31). Indeed, Western blot analysis showed that the increased expression of Hes1 after six hours of TIGIT-Fc treatment Attorney Docket No.29618-0465WO1 / BWH 2023-131 was accompanied by increased levels of MAML and RBP proteins, critical components of the Notch signaling pathway in WT B16F10 (B16F10Control) cells, but not in the tumor cells that expressed mutant CD155 lacking a cytoplasmic tail (B16F10CD155^CP) (Fig.5H, 6A). Furthermore, Hes1 induction was almost completely inhibited to background levels by the addition of Notch signaling inhibitors (using DAPT or IMR) in vitro (Fig.6B). Notably, upon TIGIT ligation of CD155, the cytoplasmic domain of CD155 binds to the active form of Notch1 (cleaved Notch1), which is required for Notch complex formation and signaling (Fig.6C). This suggests that there is an intimate relationship between the CD155 and Notch1 signaling pathways, which are co-regulated upon TIGIT ligation of CD155. Despite induction of Notch1-Hes1 signaling, tumor cell growth was unchanged by recombinant TIGIT- Fc (Fig.5I). We thus speculated that TIGIT-expressing Tregs interacting with CD155+tumor cells provide additional signals that promote tumor growth, which are not provided by TIGIT+CD8+T cells. Indeed, in tumors with both TIGIT expressing Tregs and CD155 expressing malignant cells there is increased expression of the Notch ligand Jagged 1 (Jag1 hereafter) on the surface of the Tregs, but no such increase in the expression of Jag-1 on CD8+ T cells in the TME (Fig.5J and Fig.7A). In vitro, TIGIT-expressing Tregs cultured treated with recombinant CD155 protein upregulate Jag1 expression on their surface, which was not observed in cultures of TIGIT+ CD8+ T cells treated with recombinant CD155, indicating the importance of CD155-TIGIT signaling in Tregs specifically to drive Jag1 expression (Fig.5K). In vitro, combined treatment of TIGIT-Fc with recombinant Jag1, but not with AREG (ligand of EGFR) nor with either alone, significantly increased the growth of B16F10 malignant cells with intact CD155 cytoplasmic domain (Fig.7B). Additionally, combinatorial antibody blockade of both Jag1 and TIGIT in Treg-malignant cell co- cultures abrogated the tumor growth promoting effect of TIGIT-expressing Tregs, indicating a synergistic pro-tumorigenic role for both pathways (Fig.7C). Finally, we determined whether the TIGIT an Notch pathway can synergize to promote tumor growth in vivo control, by testing the effect of their dual blockade. Whereas monotherapies with anti-TIGIT or anti-Jag1had modest effects on tumor growth, combined blockade with anti-TIGIT and anti-Jag1 resulted in almost complete tumor growth inhibition (Fig.5L). Attorney Docket No.29618-0465WO1 / BWH 2023-131 Using control and Treg-specific TIGIT-deficient (TIGITΔTregs) mice described above, we tested the effects of anti-TIGIT, anti-JAG1, and their combination. While the combination therapy still showed a moderate therapeutic in the TIGITΔTregs mice, the effect was not significant and strongly reduced compared to control mice, highlighting the important role of Tregs in this TIGIT / JAG1 dual blockade strategy (Fig.8A, B). Further, we tested the tumor-promoting role of Tigit-Fc and Jag1-Fc across 8 different cell lines and confirmed their synergistic growth-promoting effect in 6 out of 8 cell lines. The cell lines included MC38 and CT26 for colon cancer, LLC and KP for lung adenocarcinoma, B16 and Yummer for melanoma, and 4T1 for Breast and GL261 for glioblastoma. While we observed a consistent trend, the growth increase was not statistically significant in KrasLA1 / p53R172HΔg (KP, lung adenocarcinoma) and Lewis lung carcinoma (LLC) cell lines (Fig.9). These cell lines, particularly KP cells, exhibit lower surface expression of CD155 and NOTCH1, which may indicate reduced sensitivity to the TIGIT-CD155-mediated growth pathway (Fig.9B). Together these data indicate that engagement of CD155 by TIGIT induces expression of oncogenes in tumor cells promoting their growth and that CD155 in turn induces a reciprocal signal through TIGIT specifically in Tregs to drive Jagged 1 expression, which then amplifies the CD155-Notch-Hes1 signaling cascade and a pro- tumorigenic reprogramming in tumor cells. References 1. A. C. Anderson, N. Joller, V. K. Kuchroo, Lag-3, Tim-3, and TIGIT: Co-inhibitory Receptors with Specialized Functions in Immune Regulation. Immunity 44, 989-1004 (2016). 2. A. Schnell, L. Bod, A. Madi, V. K. Kuchroo, The yin and yang of co- inhibitory receptors: toward anti-tumor immunity without autoimmunity. Cell Res 30, 285-299 (2020). 3. L. B. Alexandrov et al., Signatures of mutational processes in human cancer. Nature 500, 415-421 (2013). 4. C. M. Koebel et al., Adaptive immunity maintains occult cancer in an equilibrium state. Nature 450, 903-907 (2007). Attorney Docket No.29618-0465WO1 / BWH 2023-131 5. V. 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Wu et al., A single-cell and spatially resolved atlas of human breast cancers. Nat Genet 53, 1334-1347 (2021). 38. K. E. Yost et al., Clonal replacement of tumor-specific T cells following PD-1 blockade. Nature medicine 25, 1251-1259 (2019). 39. A. Miranda et al., Cancer stemness, intratumoral heterogeneity, and immune response across cancers. Proceedings of the National Academy of Sciences of the United States of America 116, 9020-9029 (2019). 40. Z. Tang et al., GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 45, W98-W102 (2017). 41. A. Rani, R. Greenlaw, R. A. Smith, C. Galustian, HES1 in immunity and cancer. Cytokine Growth Factor Rev 30, 113-117 (2016). 42. Coblockade of TIGIT and PD-1 Optimizes Antitumor CD8+ T-cell Response. Cancer Discov 12, 1182 (2022). 43. Q. Zhang et al., Blockade of the checkpoint receptor TIGIT prevents NK cell exhaustion and elicits potent anti-tumor immunity. Nat Immunol 19, 723-732 (2018). 44. K. L. Banta et al., Mechanistic convergence of the TIGIT and PD-1 inhibitory pathways necessitates co-blockade to optimize anti-tumor CD8(+) T cell responses. Immunity 55, 512-526 e519 (2022). Attorney Docket No.29618-0465WO1 / BWH 2023-131 45. L. Liu et al., Blocking TIGIT / CD155 signalling reverses CD8(+) T cell exhaustion and enhances the antitumor activity in cervical cancer. J Transl Med 20, 280 (2022). 46. N. Joller et al., Treg cells expressing the coinhibitory molecule TIGIT selectively inhibit proinflammatory Th1 and Th17 cell responses. Immunity 40, 569- 581 (2014). 47. P. Riquelme et al., TIGIT(+) iTregs elicited by human regulatory macrophages control T cell immunity. Nat Commun 9, 2858 (2018). 48. T. Sakisaka, W. Ikeda, H. Ogita, N. Fujita, Y. Takai, The roles of nectins in cell adhesions: cooperation with other cell adhesion molecules and growth factor receptors. Curr Opin Cell Biol 19, 593-602 (2007). 49. I. Maillard, W. S. Pear, Notch and cancer: best to avoid the ups and downs. Cancer Cell 3, 203-205 (2003). 50. J. L. Avila, J. L. Kissil, Notch signaling in pancreatic cancer: oncogene or tumor suppressor? Trends Mol Med 19, 320-327 (2013). 51. P. Ntziachristos, J. S. Lim, J. Sage, I. Aifantis, From fly wings to targeted cancer therapies: a centennial for notch signaling. Cancer Cell 25, 318-334 (2014). 52. S. Dikiy, A. Y. Rudensky, Principles of regulatory T cell function. Immunity 56, 240-255 (2023). 53. A. R. Munoz-Rojas, D. Mathis, Tissue regulatory T cells: regulatory chameleons. Nat Rev Immunol 21, 597-611 (2021). 54. N. Ali et al., Regulatory T Cells in Skin Facilitate Epithelial Stem Cell Differentiation. Cell 169, 1119-1129 e1111 (2017). 55. K. O. Dixon et al., Functional Anti-TIGIT Antibodies Regulate Development of Autoimmunity and Antitumor Immunity. J Immunol 200, 3000-3007 (2018). 56. T. W. Kim et al., Anti-TIGIT Antibody Tiragolumab Alone or With Atezolizumab in Patients With Advanced Solid Tumors: A Phase 1a / 1b Nonrandomized Controlled Trial. JAMA Oncol 9, 1574-1582 (2023). 57. E. Bettelli et al., Reciprocal developmental pathways for the generation of pathogenic effector TH17 and regulatory T cells. Nature 441, 235-238 (2006). Attorney Docket No.29618-0465WO1 / BWH 2023-131 58. N. Joller et al., Cutting edge: TIGIT has T cell-intrinsic inhibitory functions. J Immunol 186, 1338-1342 (2011). 59. N. Chihara et al., Induction and transcriptional regulation of the co- inhibitory gene module in T cells. Nature 558, 454-459 (2018). 60. B. Li et al., Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq. Nat Methods 17, 793-798 (2020). 61. I. Korsunsky et al., Fast, sensitive and accurate integration of single- cell data with Harmony. Nat Methods 16, 1289-1296 (2019). 62. J. S. Lee et al., Harnessing synthetic lethality to predict the response to cancer treatment. Nat Commun 9, 2546 (2018). 63. A. D. Sahu et al., Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy. Mol Syst Biol 15, e8323 (2019). 64. L. Zheng et al., Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 374, abe6474 (2021). 65. Z. Fang, X. Liu, G. Peltz, GSEApy: a comprehensive package for performing gene set enrichment analysis in Python. Bioinformatics 39, (2023). 66. J. R. Lin, M. Fallahi-Sichani, J. Y. Chen, P. K. Sorger, Cyclic Immunofluorescence (CycIF), A Highly Multiplexed Method for Single-cell Imaging. Curr Protoc Chem Biol 8, 251-264 (2016). 67. J. R. Lin et al., Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. Elife 7, (2018). 68. D. Schapiro et al., MCMICRO: a scalable, modular image-processing pipeline for multiplexed tissue imaging. Nat Methods 19, 311-315 (2022). 69. G. Palla et al., Squidpy: a scalable framework for spatial omics analysis. Nat Methods 19, 171-178 (2022). OTHER EMBODIMENTS 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 Attorney Docket No.29618-0465WO1 / BWH 2023-131 the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.

Claims

Attorney Docket No.29618-0465WO1 / BWH 2023-131 WHAT IS CLAIMED IS:

1. A method of treating a cancer in a subject, the method comprising administering to the subject a therapeutically effective amount of a combination of antibodies that target T cell immunoreceptor with Ig and ITIM domains (TIGIT) and antibodies that target to jagged canonical Notch ligand 1 (Jag1).

2. The method of claim 1, wherein the subject has a carcinoma.

3. The method of claim 2, wherein the carcinoma is colon cancer, melanoma, breast cancer, or brain cancer.

4. The method of claim 3, wherein the brain cancer is glioblastoma.

5. The method of claim 1, wherein the subject does not have a lung adenocarcinoma.

6. The method of claim 1, wherein the combination of antibodies that target TIGIT and antibodies that target Jag1 is administered as a single composition.

7. The method of any of claims 1-6, wherein the subject is human.

8. The method of claim 1, wherein the cancer has a level of surface expression of CD155 and / or NOTCH1 above a reference level.

9. The method of claim 1, further comprising providing a sample comprising cells from the cancer in the subject; determining levels of surface expression of CD155 and / or NOTCH1; and comparing those levels to a reference level.

10. The method of claim 9, wherein the reference levels are levels in a cohort of cells or cancers that are sensitive to treatment with a combination of antibodies that target TIGIT and antibodies that target Jag1.

11. The method of claim 9, further comprising identifying a cancer as having levels of CD155 and / or NOTCH1 above a reference level, and administering the treatment to the subject.

12. Antibodies that target TIGIT and antibodies that target Jag1, for use in a method of treating cancer in a subject.Attorney Docket No.29618-0465WO1 / BWH 2023-131 13. The antibodies for the use of claim 12, wherein the subject has a carcinoma.

14. The antibodies for the use of claim 13, wherein the carcinoma is colon cancer, melanoma, breast cancer, or brain cancer.

15. The antibodies for the use of claim 14, wherein the brain cancer is glioblastoma.

16. The antibodies for the use of claim 12, wherein the subject does not have a lung adenocarcinoma.

17. The antibodies for the use of claim 12, wherein the combination of antibodies that target TIGIT and antibodies that target Jag1 is administered as a single composition.

18. The antibodies for the use of any of claims 12-17, wherein the subject is human.

19. The antibodies for the use of claim 12, wherein the cancer has a level of surface expression of CD155 and / or NOTCH1 above a reference level.

20. The antibodies for the use of claim 12, further comprising providing a sample comprising cells from the cancer in the subject; determining levels of surface expression of CD155 and / or NOTCH1; and comparing those levels to a reference level.

21. The antibodies for the use of claim 20, wherein the reference levels are levels in a cohort of cells or cancers that are sensitive to treatment with a combination of antibodies that target TIGIT and antibodies that target Jag1.

22. The antibodies for the use of claim 20, further comprising identifying a cancer as having levels of CD155 and / or NOTCH1 above a reference level, and administering the treatment to the subject.

23. A composition comprising antibodies that target TIGIT and antibodies that target Jag1.

24. The composition of claim 23, further comprising a pharmaceutically acceptable carrier.Attorney Docket No.29618-0465WO1 / BWH 2023-131 25. The composition of claims 23 or 24, for use in a method of treating cancer in a subject.

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