Gene signatures predictive of cancer cell response to immunomodulatory therapy

Abstract

The present disclosure relates to combination therapies for cancer treatment that include immune modulators and agents that inhibit phospholipid signaling or expression. The present disclosure also provides methods for identifying subjects suitable for immune modulator cancer therapies based on cancer cell expression profiles. The present disclosure provides a method of treating cancer in a subject in need thereof including administering an agent that modulates PTDSS1 expression or signaling and an immune modulator to the subject, thereby treating the cancer in the subject.

Description

GENE SIGNATURES PREDICTIVE OF CANCER CELL RESPONSE TO IMMUNOMODULATORY THERAPYCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of priority under 35 U.S.C. § 119(e) to U.S. Provisional Application No. 63 / 550,954, filed February 7, 2024. The content of the prior application is considered part of and is hereby incorporated by reference in its entirety.INCORPORATION BY REFERENCE OF SEQUENCE LISTING

[0002] The material in the accompanying sequence listing is hereby incorporated by reference into this application. The accompanying sequence listing xml file, name MDA1280- 1 WO, Sequence Listing ST26.xml, was created on January 29, 2025 and is 15,413 bytes.BACKGROUND OF THE INVENTIONFIELD OF THE INVENTION

[0003] The present disclosure generally relates to biomarkers for cancer and more specifically to modulating phospholipid expression and signaling in cancer cells during treatment with immune modulators.BACKGROUND INFORMATION

[0004] Immune checkpoint therapy (ICT) that targets immune checkpoints CTLA-4, PD- 1 or PD-L1 has been transformative in many cancer types, providing durable clinical responses that significantly improve patient survival. In particular, ICT has provided a critical form of treatment for bladder cancer, the 6thmost common cancer type in the United States, since the first FDA approval of ICT for bladder cancer in 2016. However, many cancer patients do not respond to ICT, while others respond during early stages of treatment but develop resistance that later renders ICT ineffective. In particular, 75%-85% patients do not respond to ICT, highlighting the urgent need for new therapeutic strategies to improve ICT efficacy.

[0005] Many tumor intrinsic factors have been identified that contribute to primary and acquired resistance to ICT. These factors include: antigen presentation deficiency caused by inactivating mutations in genes of the antigen presentation machinery B2M, TAP1 / 2 and MHC- I; defective interferon gamma (IFNγ) response caused by mutations in STAT1, JAK1 / 2; and oncogenic pathways which impair intratumoral immune cell function, including PTEN, WNT / β-catenin, EGFR and KRAS. Various factors in the tumor microenvironment, namelytumor extrinsic factors, also play important roles in resistance to ICT, such as lack of T cell infiltration16; upregulation of other immune checkpoints such as TIM-3, LAG-3 and VISTA17-20; and immunosuppressive stromal cell, immune cells, and molecules.

[0006] Many efforts have been devoted to improving ICT response in bladder cancer using preclinical models, including combination of immune checkpoint inhibitors targeting different steps of T cell mediated anti-tumor immunity, combination with radiation therapy, and inhibition of epigenetic modulators. However, systematic exploration of potential targets that improve ICT response for cancer is still lacking.SUMMARY OF THE INVENTION

[0007] The present invention is based on the seminal discovery that inhibiting phospholipid expression and signaling enhances anticancer immunomodulatory therapies. In particular, it was determined that genetic and pharmacological inhibition of PTDSS 1 in tumor cells increases their immunogenicity and sensitivity to ICT by promoting the IFNγ pathway response and subsequent development of a pro-inflammatory tumor microenvironment conducive to immune mediated tumor clearance.

[0008] Leveraging these discoveries, embodiments of the present disclosure provide a method of treating cancer in a subject in need thereof that includes administering an agent that modulates PTDSS 1 expression and an immune modulator to the subject, thereby treating the cancer in the subject.

[0009] In one aspect, the agent that modulates PTDSS 1 expression includes an RNA-guided nuclease, miRNA, siRNA, or shRNA. In another aspect, the agent that modulates PTDSS 1 expression, the immune modulator, or a combination thereof is targeted to a cancer cell. In a further aspect, the agent that modulates PTDSS 1 expression, the immune modulator, or a combination thereof is configured for uptake by the cancer cell.

[0010] In an additional aspect, the agent that modulates PTDSS 1 expression is coupled to or coencapsulated with the immune modulator. In certain aspects, the agent that modulates PTDSS 1 expression and the immune modulator are connected by a chemical linker. In particular aspects, the immune modulator is an immune checkpoint inhibitor.

[0011] In further aspects, the immune modulator is a type I interferon receptor agonist or a type II interferon receptor agonist. In a particular aspect, the immune checkpoint inhibitor is a CTLA-4 inhibitor, a PD-1 inhibitor, a PD-L1 inhibitor, a PD-L2 inhibitor, a LAG-3 inhibitor, a KIR inhibitor, a TIM-3 inhibitor, a TIGIT inhibitor, a 4- IBB inhibitor, a 4-1 BBL inhibitor, a GITR inhibitor, a GITRL inhibitor or a galectin inhibitor.

[0012] In one aspect, the CTLA-4 inhibitor is ipilimumab, tremelimumab, BMS-986218, AGEN1181, AGEN1884, BMS-986249, MK-1308, REGN-4659, ADU-1604, CS-1002 , BCD-145, APL-509, JS-007, BA-3071, ONC-392, AGEN-2041, JHL-1155, KN-044, CG- 0161, ATOR-1144, PBI-5D3H5, BPI-002, FPT-155, PF-06936308, MGD-019, KN-046, MEDI-5752, XmAb-20717, or AK-104.

[0013] In another aspect, the PD-1 inhibitor is nivolumab, pembrolizumab, pilizumab, BGB- 108, SHR-1210, PDR-001, PF-06801591, IBI-308, GB-226, STI-1110, or mDX-400.

[0014] In a further aspect, the PD-L1 inhibitor is atezolizumab, avelumab, AMP-224, MEDI- 0680, RG-7446, GX-P2, durvalumab, KY-1003, KD-033, M8B-0010718C, TSR-042, ALN- PDL, STI-A1014, GS-4224, CX-072, or BMS-936559.

[0015] In an additional aspect, the LAG-3 inhibitor is relatlimab.

[0016] In a certain aspect, the TIM-3 inhibitor is TSR-022, LY-3321367, MBG-453, or INCAGN-2390.

[0017] In a particular aspect, the TIGIT inhibitor is BMS-986207, RG-6058, or AGEN- 1307.

[0018] In another aspect, the 4- IBB inhibitor is urelumab, utomilumab, emfizatamab, BMS- 663513, PF-05082566, PRS-343, RG7827, ADG106, 1NBRX-105, CTX-471, BNT311, BNT311, RG6706, MP0310, BNT312, AGE.N2373, LVGN6051 , ATOR-1017, STA551, orND-021.

[0019] In a further aspect, the GITR inhibitor is MEDI1873, FPA-154, INCAGN-1876, TRX-518, BMS-986156, MK-1248, or GWN-323.

[0020] In an additional aspect, the galectin inhibitor is thiodigalactoside, β-D-lactosyl- steroid, GB1 I07, lactulose-L-leucine, modified citrus pectin, PectaSol-C, GCS-100, GM-CT- 01, belapectin, DB16, DB21, OTX008, PTX013, LLS30, or LLS2.

[0021] In some aspects, the immune checkpoint inhibitor is a PD-1 inhibitor or a PD-L1 inhibitor. In other aspects, the immune checkpoint inhibitor is a PD-1 inhibitor.

[0022] In further aspects, the immune checkpoint inhibitor is an antibody or an antibody fragment. In additional aspects, the immune modulator is IFNγ or IFNα.

[0023] In certain aspects, the cancer is selected from bile duct cancer, bladder cancer, brain cancer, breast cancer, carcinoma, cervical cancer, colorectal cancer, endometrial cancer, epitheloid carcinoma of the bone, esophageal cancer, gallbladder cancer, gastric cancer, glioblastoma, hepatocellular cancer, large cell lung carcinoma, leukemia, lung cancer, medulloblastoma, melanoma, ovarian cancer, non-small cell lung cancer, pancreatic cancer, prostate cancer, renal cell cancer or thyroid cancer. In a particular aspect, the cancer is bladder cancer.

[0024] In another embodiment, the present disclosure provides a method of identifying a subject with cancer as a candidate for treatment with an immune modulator that includes: a) determining that: i) cancer cells from the subject have diminished PTDSS1 expression; ii) the cancer cells from the subject include at least one of increased B2M, CXCL9, CXCL10, STAT1 or TAPI expression; iii) the cancer cells from the subject include diminished RAS signaling; iv) immune cells in a tumor microenvironment of the cancer include increased CCL5 expression, increased APOE expression, increased NOS2 expression, increased Cl QB expression, increased UBB expression, oorr aa combination thereof; v) the tumor microenvironment of the cancer includes an elevated ratio of Th1 to Th2 CD4+cells; vi) CD8+effector T cells in the tumor microenvironment of the cancer include elevated expression levels of Prfl, Nkg7, Gzmb, Gzmk, or a combination thereof; vii) the tumor microenvironment of the cancer includes an elevated ratio of M1 to M2 macrophages; or viii) a combination thereof; and classifying the subject as a likely responder to the immune modulator if any one of i)-viii) are determined, thereby identifying the subject as suitable for treatment with the immune modulator.

[0025] In one aspect, the method further includes administering the immune modulator to the subject, thereby treating the cancer in the subject. In another aspect, the determining includes comparison to a control sample. In a particular aspect, the control sample is: i) a biological sample from a subject that does not have cancer; ii) a non-cancerous biological sample from the subject; or a sample from a subject with a cancer that does not have diminished PTDSS1 expression.

[0026] In some aspects, the cancer is selected from bile duct cancer, bladder cancer, brain cancer, breast cancer, carcinoma, cervical cancer, colorectal cancer, endometrial cancer, epitheloid carcinoma of the bone, esophageal cancer, gallbladder cancer, gastric cancer, glioblastoma, hepatocellular cancer, large cell lung carcinoma, leukemia, lung cancer, medulloblastoma, melanoma, ovarian cancer, non-small cell lung cancer, pancreatic cancer, prostate cancer, renal cell cancer or thyroid cancer. In certain aspects, the cancer is bladder cancer.

[0027] In particular aspects, the determining includes mass spectrometry, liquid chromatography, gas chromatography, nuclear magnetic resonance, fluorometric analysis, a protein binding assay, mRNA profiling, genomic profiling, PCR, RNA sequencing, Western blot analysis, Northern blot analysis, microarray analysis, SAGE, FISH, flow cytometry, or a combination thereof.

[0028] In one aspect, the method includes determining that the cancer cells have diminished PTDSS 1 expression. In a particular aspect, the diminished expression is about 95%, about 90%, about 80%, about 70%, about 60%, about 50%, about 40%, about 30%, about 25%, about 20%, about 15%, about 10%, or about 5% of the PTDSS 1 expression in a non-cancerous cell of a cell type from which the cancer is derived.

[0029] In some aspects, the method includes determining that the cancer cells comprise increased B2M, CXCL9, CXCL10, STAT1 or TAPI expression or a combination thereof. In particular aspects, the increased expression is about 105%, about 110%, about 120%, about 130%, about 140%, about 150%, about 175%, about 200%, about 250%, about 300%, about 400%, or about 500% of the B2M, CXCL9, CXCL10, STAT1 or TAPI expression in a non- cancerous cell of a cell type from which the cancer is derived.

[0030] In further aspects, the method includes determining that the cancer cells have diminished RAS signaling. In additional aspects, the determining includes measuring phosphorylated MEK, phosphorylated ERK, or a combination thereof. In particular aspects, a level of phosphorylated MEK or phosphorylated ERK in the cancer cells is about 95%, about 90%, about 80%, about 70%, about 60%, about 50%, about 40%, about 30%, or about 25% of a level of phosphorylated MEK or phosphorylated ERK in a non-cancerous cell of a cell type from which the cancer is derived.

[0031] In additional aspects, the method includes determining that immune cells in the tumor microenvironment of the cancer include increased CCL5 expression, increased APOE expression, increased NOS2 expression, increased C1QB expression, increased UBB expression or a combination thereof. In particular aspects, the increased expression is about 105%, about 110%, about 120%, about 130%, about 140%, about 150%, about 175%, about 200%, about 250%, about 300%, about 400%, or about 500% of the CCL5 expression, APOE expression, NOS2 expression, Cl QB expression, UBB expression, or the combination thereof in immune cells from a tumor microenvironment of a cancer that does not have diminishedPTDSS 1 expression.

[0032] In one aspect, the immune cells are myeloid cells.

[0033] In another aspect, the method includes determining that the tumor microenvironment of the cancer has an elevated ratio of Th1 to Th2 CD4+cells. In a particular aspect, the elevated ratio of Th1 to Th2 CD4+cells in the tumor microenvironment of the cancer is about 1 .1 -, about 1.2-, about 1.3-, about 1.4-, about 1.5-, about 1.75-, about 2-, about 2.25-, about 2.5-, about 3- , about 4-, or about 5-fold higher than a ratio of Th1 to Th2 CD4+cells in a tumor microenvironment of a cancer that does not have diminished PTDSS 1 expression. In a furtheraspect, the elevated ratio of Th1 to Th2 CD4+cells is about 2:3, about 5:6, about 1 :1, about 7:6, about 4:3, about 3:2, about 5:3, about 11:6, about 2:1, about 5:2, about 3:1, or about 7:2.

[0034] In an additional aspect, the method includes determining that CD8+effector T cells in the tumor microenvironment of the cancer comprise elevated expression levels of Prfl, Nkg7, Gzmb, Gzmk or a combination thereof. In another aspect, the method includes detecting Prfl, Gzmb, or Gzmk in CD8+effector T cells in the tumor microenvironment of the cancer. In certain aspects, the elevated expression is about 105%, about 110%, about 120%, about 130%, about 140%, about 150%, about 175%, about 200%, about 250%, about 300%, about 400%, or about 500% of the Prfl expression, the Nkg7 expression, the Gzmb expression, the Gzmk expression, or the combination thereof in immune cells from a tumor microenvironment of a cancer that does not have diminished PTDSS1 expression.

[0035] In a further aspect, the method includes determining whether the tumor microenvironment of the cancer comprises an elevated ratio of M1 to M2 macrophages. In a particular aspect, the elevated ratio of M1 to M2 macrophages in the tumor microenvironment of the cancer is about 1.1-, about 1.2-, about 1.3-, about 1.4-, about 1.5-, about 1.75-, about 2- , about 2.25-, about 2.5-, about 3-, about 4-, or about 5-fold higher than a ratio of M1 to M2 macrophages in a tumor microenvironment of a cancer that does not have diminished PTDSS1 expression. In certain aspects, the determining includes measuring CD206 expression in macrophages cells from the tumor microenvironment.

[0036] In some aspects, the immune modulator is an immune checkpoint inhibitor, a cytokine, a chemokine, an interleukin, an immunomodulatory imide drug, a toll-like receptor agonist, an oligodeoxynucleotide, a glucan, a type I interferon receptor agonist, or a type II interferon receptor agonist. In further aspects, the immune modulator is an immune checkpoint inhibitor, a type I interferon receptor agonist, or a type II interferon receptor agonist. In a certain aspect, the immune modulator is an immune checkpoint inhibitor. In one aspect, the immune checkpoint inhibitor is selected from a CTLA-4 inhibitor, a PD-1 inhibitor, a PD-L1 inhibitor, a PD-L2 inhibitor, a LAG -3 inhibitor, a KIR inhibitor, a TIM-3 inhibitor, a TIGIT inhibitor, a 4- 1BB inhibitor, a 4-1 BBL inhibitor, a GITR inhibitor, a GITRL inhibitor, a galectin inhibitor or a combination thereof. In a particular aspect, the immune checkpoint inhibitor is a PD-1 inhibitor.

[0037] In another aspect, the immune modulator is a type I interferon receptor agonist or a type II interferon receptor agonist. In some such aspects, the immune modulator is IFNγ or IFNα.

[0038] In certain aspects, the cancer is a solid tumor.

[0039] In certain embodiments, the present disclosure provides a method of treating cancer in a subject in need thereof that includes administering an agent that inhibits PTDSS 1 and an immune modulator to the subject, thereby treating the cancer in the subject.

[0040] In one aspect, the agent that inhibits PTDSS 1 includes DS55980254. In another aspect, the agent that inhibits PTDSS 1, the immune modulator, or a combination thereof is targeted to a cancer cell. In a further aspect, the agent that inhibits PTDSS 1, the immune modulator, or a combination thereof is configured for uptake by the cancer cell.

[0041] In an additional aspect, the agent that inhibits PTDSS 1 is coupled to or coencapsulated with the immune modulator. In certain aspects, the agent that inhibits PTDSS 1 and the immune modulator are connected by a chemical linker. In particular aspects, the immune modulator is an immune checkpoint inhibitor.BRIEF DESCRIPTION OF THE DRAWINGS

[0042] FIGS, 1a-1 e illustrate a schematic of an in vivo CRISPR screening method and a set of plots of expression and survival data. FIG. la illustrates a schematic of an in vivo CRISPR knockout screening method for identifying regulators of immune checkpoint inhibitor responses. FIG. 1b illustrates a plot of sgRNA composition changes in PBS and anti-PDl treated tumors with axes representing castle scores. FIG. 1c illustrates a heat map of functional enrichment scores for sgRNAs in anti-PD-1 and PBS-treated mice. FIG. Id illustrates a plot comparing survival in subjects with high and low PTDSS 1 expression generated using data from the TCGA-HCC dataset. FIG. le illustrates a plot comparing survival in subjects with high and low PTDSS1 expression generated using data from the TCGA-BLCA dataset.

[0043] FIGS. 2a-2h illustrate a series of plots that display gene enrichment analysis results in PTDSS 1 knockdown and control cancer cells. FIG. 2a illustrates a plot of normalized enrichment scores for pathways enriched or diminished in PTDSS 1 knockdown and control cancer cells. FIG. 2b illustrates a plot of gene set enrichment analyses showing that interferon gamma responses are enhanced in PTDSS1 knockdown and control cancer cells. FIG. 2c illustrates a heatmap showing differential gene expression in PTDSS 1 knockdown and control cancer cells treated with buffer or PD-l-targeted antibodies. FIG. 2d illustrates a series of Western blot results of PTDSS 1 knockdown and control cancer cells treated with IFNγ for various periods of time. FIG. 2e illustrates a set of plots of FACS results of surface MHC-I levels on PTDSS 1 knockdown and control cancer cells treated with or without IFNγ for 48 hours. FIG. 2f illustrates a set of plots of FACS results of surface B2M levels on PTDSS 1knockdown and control cancer cells treated with or without IFNγ for 48 hours. FIG. 2g illustrates a set of plots of FACS results of surface H2kb-S11NFEKL (SEQ ID NO: 16) levels on PTDSS1 knockdown and control cancer cells treated with or without SIINFEKL (SEQ ID NO: 16) for 6 hours. FIG. 2h illustrates a plot of FACS results of cell death of control or PTDSS1 knockdown cells co-cultured with OT-I CD8+ T cells at different ratios.

[0044] FIGS. 3a-3f illustrate a series of plots of immune cell frequencies and expression in tumor microenvironments of PTDSS1 knockdown and control cancers. FIG. 3a illustrates a pair of plots of cell clusters in total CD45 cells and total T cell identified by scRNAseq. FIG. 3b illustrates a pair of plots of frequencies of CD8 and CD4 T effector cell clusters in total CD45 cells. FIG. 3c illustrates a set of plots of Th1 and Th2 CD4 T cell clusters from among total T cells. FIG. 3d illustrates a set of plots of Tex, Ifng+CD8+Teffand Gzmk+CD8+Tetr clusters in total T cells. FIG. 3e illustrates a dotplot that shows expression levels of T cell related genes in different T cell clusters. FIG. 3f illustrates a set of violin plots showing expression levels of cytotoxic function related genes in cells in the Gzmk+CD8+Teffcluster.

[0045] FIGS. 4a-4i illustrate a set of plots that summarize myeloid cell frequencies and expression in tumor microenvironments of PTDSS1 knockdown and control cancers. FIG. 4a illustrates a plot that summarizes functional enrichment analyses of differentially expressed genes in macrophage cluster identified by scRNAseq in PBS treated tumors. FIG. 4b illustrates a dotplot that shows expression levels of macrophage related genes in different macrophage clusters identified by scRNAseq. FIG. 4c illustrates a series of violin plots of expression of Cd86 and Cxc19 in cells in the 0- and 1 -macrophage clusters. FIG. 4d illustrates a set of violin plots that summarize expression of Mrc1 and Fn1 in cells in the 3,4,7,13,16-macrophage clusters. FIG. 4e illustrates a box plot of frequencies of Nos2 expressing cells in total CD45 cells in different conditions, FIG. 4f illustrates a set of tSNE plots showing the expression level of Nos2 in all immune cells in different conditions. FIG. 4g illustrates plot showing the top differentially expressed genes in Nos2 expressing myeloid cluster in PBS cancer cells. FIG. 4h illustrates a plot showing the association of Nos2+ myeloid cluster gene signature Z -scores with patient clinical responses in the IMvigor210 trial. FIG. 4i illustrates a plot showing the association of Nos2+ myeloid cluster gene signature Z-scores with patient clinical responses in the Gide2019 PD1 melanoma cohort.

[0046] FIGS. 5a-5f illustrate a set of plots of tumor volume and survival of mice transplanted with wild-type or PTDSS1 knockdown cancer cells and treated with PBS or anti-PD-1. FIG. 5a illustrates a series of representative tumor growth curves in mice transplanted with wildtype of PTDSS1 knockdown MB49 cells treated with PBS or anti-PD-1 . FIG. 5b illustrates aseries of representative Kaplan-Meier survival curves of mice transplanted with PTDSS1 knockdown or wild-type MB49 tumors. FIG. 5c illustrates a series of representative tumor growth curves in mice transplanted with wild-type of PTDSS1 knockdown B16F10 cells treated with PBS or anti-PD-1. FIG. 5d illustrates a series of representative Kaplan-Meier survival curves of mice transplanted with PTDSS1 knockdown or wild-type B16F10 tumors. FIG. 5e illustrates a plot of correlations of PTDSS1 RNA or protein levels with anti-PD-1 responses in melanoma patients treated with anti-PD-1 as a function of time. FIG. 5f illustrates a plot of PTDSS1 protein levels in complete response, partial response, and progressive disease cohorts in melanoma patients treated with anti-PD1.

[0047] FIGS. 6a-6e illustrate an image of Western blot analyses and plots of in vivo CRISPR screen performance. FIG. 6a illustrates a Western blot analysis of Cas9 levels in MB49 Cas9 expressing cells. FIG. 6b illustrates a graph showing sgRNA library representation in transfection plasmid libraries (p) and lentivirus transfected cell libraries (c). FIG. 6c illustrates a plot of tumor growth curves of mice in the CRISPR screen. FIG. 6d illustrates a pair of plots showing screening quality as represented by distributions of sgRNAs targeting essential genes (ESS) and non-essential genes (NES). FIG. 6e illustrates a plot of PTDSS1 copy number alteration status in selected cancer studies.

[0048] FIGS. 7a-7d illustrate a series of plots showing IFNy response pathway enrichment in PTDSS1 deficient cells. FIG. 7a illustrates a plot of RNA expression levels of Ptdssl in vector control MB49 cells (WT) or Ptdssl knockdown (KD) clones. FIG. 7b illustrates a plot showing GSEA analysis of transcriptomes of KD#1 and WT tumor cells treated with PBS. FIG. 7c illustrates a set of Western blot results and plots that quantify phosphorylated ERK (T202 / Y204) and phosphorylated MEK (S217 / 221) level in WT or KD cells. FIG. 7d illustrates a plot showing relative phosphatidylserine lipid (PS) level in WT or KD cells as quantified by mass spectrometry.

[0049] FIGS. 8a-8e illustrate a heatmap and set of plots showing that PTDSS 1 in tumor cells induced infiltration of Th1 cells. FIG. 8a illustrates a heatmap showing mean antigen staining levels in different immune clusters identified by CyTOF. FIG. 8b illustrates a tSNE plot of the immune cell clusters identified by CyTOF. FIG. 8c illustrates a plot of frequencies total T cell, CD8 and CD4 T cell clusters in total CD45 cells. FIG. 8d illustrates a plot of frequencies of Th1 and Th2 CD4 T cell clusters in total T cells. FIG. 8e illustrates a dotplot showing expression levels of cell-type specific genes in different immune clusters identified by scRNAseq.

[0050] FIGS. 9a-9d illustrate a series of plots showing loss of PTDSS1 in tumor cells polarized macrophages towards M1 state. FIG. 9a illustrates a set of plots that show the frequency of CD206lowand CD206highmacrophage clusters identified by CyTOF. FIG. 9b illustrates a plot of CD206lowto CD206highratios. Fig. 9c illustrates a set of plots of RNA expression levels of Nos2 and Mrcl in bone marrow derived macrophage cultured with different tumor cell conditioned media during in vitro macrophage polarization. FIG. 9d illustrates a plot that shows the frequency of iNOS+myeloid cell cluster in wild-type and PTDSS1 knockdown tumors exposed to PBS or anti-PD-1.

[0051] FIGS. 10a-10e illustrate a series of plots of cell proliferation and tumor volume data, an image of tumors, and an illustration that depicts interactions between tumor and immune cells. FIG. 10a illustrates a plot of Incucyte cell proliferation assay results of wild-type and PTDSS 1 knockdown clones. FIG. 10b illustrates a plot of individual tumor growth curves of NSG mice implanted with control or PTDSS 1 knockdown cells. FIG. 10c illustrates a plot of representative sizes of wild-type and PTDSS 1 knockdown tumors treated with PBS or anti- PD-1. FIG. 10d illustrates an image of wild-type and PTDSS 1 knockdown tumors treated with PBS or anti-PD-1. FIG. 10c illustrates a graphic depiction of the effect of PTDSS1 inhibition in tumor cells.

[0052] FIGS. 11a-11i illustrate a set of plots and images of western blot analyses that show that PTDSS1KDresults in increased MHC-I expression and antigen presentation resulting in enhanced T cell killing. FIG. 11a illustrates a plot of GSEA analysis between wild-type and PTDSS1KDcells. FIG. 11b illustrates a plot of surface expression of MHC-I with and without exogenous IFNγ stimulation. FIG. 11c illustrates a bar graph of surface expression of MHC-I with and without exogenous IFNy stimulation. FIG. 11d illustrates a plot of surface expression of B2m with and without exogenous IFNγ stimulation. FIG. lie illustrates a bar graph of surface expression of B2m with and without exogenous IFNγ stimulation. FIG. 11f illustrates a western blot that shows pStat1, Stat1, and β-actin levels in wild-type and PTDSS1KDcells in response to IFNγ. FIG. 11g illustrates a plot of H2kb-SIINFEKL (SEQ ID NO: 16) in wildtype and PTDSS1KDcells. FIG. 11h illustrates a bar graph of H2kb-SIINFEKL (SEQ ID NO: 16) in wild-type and PTDSS1KDcells. FIG. 11i illustrates a bar graph of antigen-specific T cell-mediated killing of wild-type and PTDSS1KDcells.

[0053] FIGS. 12a-12b illustrate a set of gene-set enrichment analysis (GSEA) plots that show that tumor cells exhibit a transcriptional shift between reliance on OxPHOS to glycolysis upon PTDSS1KD. FIG. 12a illustrates a GSEA plot of wild-type tumor cells. FIG. 12b illustrates a GSEA plot of PTDSS1KDcells.

[0054] FIGS. 13a-13b illustrate a set of bar graphs that show relative RNA expression in supernatant from wild-type and PTDSS1KDcells. FIG. 13a illustrates a plot of Nos2 RNA levels. FIG. 13b illustrates a plot of Mrcl levels.

[0055] FIGS. 14a-14b illustrate a plot and heatmap that show that PTDS SI -deficient cells are also deficient in genes involved in DNA repair machinery. FIG. 14a illustrates a GSEA plot of RNAseq data. FIG. 14b illustrates a heatmap of DNA repair pathway genes in wildtype and PTDSS1KDcells.

[0056] FIG. 15 illustrates a schematic diagram showing the experiment plan to evaluate the therapeutic potential of pharmacological inhibition of PTDSS1 plus anti-PD-1 therapy.

[0057] FIG. 16 illustrates a graph that shows tumor volume over time with the treatment of DS55980254 in combination with anti-PDl.DETAILED DESCRIPTION OF THE INVENTION

[0058] Before the present compositions and methods are described, it is to be understood that this invention is not limited to particular compositions, methods, and experimental conditions described, as such compositions, methods, and conditions may vary. It is also to be understood that the terminology used herein is for purposes of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only in the appended claims.

[0059] As used in this specification and the appended claims, the singular forms “a,” “an”, and “the" include plural references unless the context clearly dictates otherwise. Thus, for example, references to “the method” includes one or more methods, and / or steps of the type described herein which will become apparent to those persons skilled in the art upon reading this disclosure and so forth.

[0060] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.

[0061] Unless defined otherwise, 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. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the invention, it will be understood that modifications and variations are encompassed within the spirit and scope of the instant disclosure. The preferred methods and materials are now described.

[0062] The present disclosure is based on the discovery of targets that improve immune checkpoint therapy (ICT) response during disease treatment. As further detailed in the examples disclosed herein, multiple biomarkers that correlate with ICT susceptibility were discovered with in vivo CRISPR knockout screening in a mouse bladder cancer model treated with ICT. This screen systematically explored the genes involved in regulating tumor response to immune checkpoint therapy. Parallel analyses were then performed using expression profiles from human cancer patients. The present invention is also based on the seminal discovery that inhibiting phospholipid signaling enhances anticancer immunomodulatory therapies. In particular, it was determined that chemical inhibition of PTDSS1 in tumor cells increases their immunogenicity and sensitivity to ICT.

[0063] In particular, it was surprisingly found that diminished PTDSS1 expression in cancer cells significantly increases responsiveness to immunomodulatory therapies. For example, it was shown that disrupted phosphatidylserine expression increases tumor cell immunogenicity, enhances sensitivity to type I and type II interferons, and promotes development of a pro- inflammatory tumor microenvironment that includes increased frequencies of CD4 T cells and 1NOS+ myeloid cells and increased ratios of M1-to-M2 macrophages.

[0064] Leveraging these discoveries, aspects of the present disclosure provide a method of treating cancer in a subject in need thereof that includes administering an agent that modulates PTDSS 1 expression and an immune modulator to the subject, thereby treating the cancer in the subject.

[0065] PTDSS 1 (“phosphatidylserine synthase 1”) is an enzyme that catalyzes the conversion of phosphatidylcholine and phosphatidylethanolamine to phosphatidylserine. Phosphatidylserine, when localized in the cytoplasmic leaflet, serves as docking sites important for activation of multiple signaling pathways including RAS, AKT and PKC.20-23 Externalized phosphatidylserine can be recognized by PS receptors (PSR) on phagocytes, leading to clearance of stressed or apoptotic cells. However, the role of PTDSS 1, and specifically its role beyond production of phosphatidylserine, has not previously been explored in the context of immune modulatory therapies.

[0066] It was surprisingly discovered herein that modulating PTDSS 1 expression in tumor cells not only increases their response to IFNγ and overall immunogenicity, but also induces changes within tumor microenvironments (e.g., as detailed in FIG. 10e), favoring development of an inflammatory anti-tumor TME and synergizing with immune modulators such as anti- PD-1 to promote tumor clearance. It was further shown herein that PTDSS 1 modulation can induce ER stress and immunogenic cell death in PTDSS2-deficient tumors. Of particularimportance, it was shown that modulating PTDSS1 expression enhances the efficacy of immune modulator therapies for treating cancer.

[0067] The term “subject” as used herein refers to any individual or patient to which the subject methods are performed. Generally, the subject is human, although as will be appreciated by those in the art, the subject may be an animal. Thus, other animals, including vertebrate such as rodents (including mice, rats, hamsters and guinea pigs), cats, dogs, rabbits, farm animals including cows, horses, goats, sheep, pigs, chickens, etc., and primates (including monkeys, chimpanzees, orangutans and gorillas) are included within the definition of subject.

[0068] The terms “administration of and “administering” should be understood to mean providing a pharmaceutically acceptable composition in a therapeutically effective amount to a subject in need of thereof to achieve a desired outcome. For example, the immune modulator and the agent that inhibits phospholipid expression in a cancer cell can be administered in a single pharmaceutically acceptable composition to the subject. Alternatively, the immune modulator and the agent that inhibits phospholipid expression in a cancer cell can be administered in separate pharmaceutically acceptable compositions to the subject.

[0069] By “pharmaceutically acceptable” it is meant the carrier, diluent or excipient must be compatible with the other ingredients of the formulation and not deleterious to the recipient thereof. For example, the carrier, diluent, or excipient or composition thereof may not cause any undesirable biological effects or interact in an undesirable manner with any of the other components of the pharmaceutical composition in which it is contained.

[0070] The term “treatment” is used interchangeably herein with the term “therapeutic method” or “therapy” and refers to 1) therapeutic treatments or measures that cure, slow down, lessen symptoms of, and / or halt progression of a diagnosed pathologic conditions or disorder, and / or 2) prophylactic / preventative measures. Those in need of treatment may include individuals already having a particular medical disorder as well as those who may ultimately acquire the disorder (i.e., those needing preventive measures).

[0071] The terms “therapeutically effective amount”, “effective dose,” “therapeutically effective dose”, “effective amount,” or the like refer to that amount of the subject compound that will elicit the biological or medical response of a tissue, system, animal or human that is being sought by the researcher, veterinarian, medical doctor or other clinician. Generally, the response is either amelioration of symptoms in a patient or a desired biological outcome (e.g. tumor suppression). Such amount should be sufficient to treat cancer. The effective amount can be determined as described herein.

[0072] As non-limiting examples, the agent that modulates PTDSS1 expression can include an RNA-guided nuclease (e.g., a CRISPR-associated nuclease such as Cas9, Cas12, Cas13, or Casl4), miRNA, siRNA, or shRNA. The RNA-guided nuclease can be configured to knockout PTDSS1 expression in a target cell, for example by cleaving or excising a portion of a PTDSS 1 gene or a regulatory sequence thereof. The RNA-guided nuclease can also be configured to cleave or degrade PTDSS 1 mRNA.

[0073] As used herein, the phrases “modulated expression,” “altered expression”, and the like refer to any change in the expression level of a gene. The change in expression level may, for example, be a measurable change in expression of a transcription product of the gene, a protein encoded by the gene, or a biomolecule whose expression level is modulated by the gene, such as a change in intracellular glycerol levels resulting from modulated expression of hormonesensitive lipase. Modulated expression may be relative to expression level in identical cells not subjected to a particular treatment or condition.

[0074] In particular aspects, the agent that modulates PTDSS 1 expression diminishes PTDSS 1 expression in the subject. For example, the agent that modulates PTDSS 1 expression can diminish PTDSS 1 expression by silencing a PTDSS 1 gene or antagonizing PTDSS 1. Illustrative examples of agents that diminish PTDSS 1 expression include those disclosed in Yoshihama et al., Cancer Res, 2022; 82(21 ):4031-4043, Guo et al., Set Rep 8, 2822 (2018), and Omi et al, J Cell Biol, 2024; 223(2) :e202212074.

[0075] As used herein, the phrases “inhibits expression”, “reduces expression”, “diminishes the expression level of expression”, and the like refer to decreases in the expression level of a gene, and can encompass protein expression levels, mRNA expression levels, lipid expression levels, metabolite expression levels, and combinations thereof unless otherwise specified.

[0076] In another aspect, the agent that modulates PTDSS 1 expression, the immune modulator, or a combination thereof is targeted to a cancer cell. In such cases, modulated expression may specifically encompass changes in expression in cancer and / or targeted cells. As an example, the agent that modulates PTDSS 1 expression and / or the immune modulator can be delivered in a tumor-targeted liposome (e.g., packaged within a single liposomal particle configured for targeted uptake by a cancer cell), coupled to a tumor-targeted antibody or antibody fragment, or coupled to a tumor-targeting nanoparticle. The agent that modulates PTDSS 1 expression and / or the immune modulator can also be configured for uptake by the cancer cell. For example, the agent that modulates PTDSS 1 expression and the immune modulator can be coupled to an antibody targeted to a surface antigen expressed by the cancercell, wherein binding of the antibody to the target antigen triggers endocytotic uptake of the antibody.

[0077] Additionally, the agent that modulates PTDSS1 expression can be coupled to or coencapsulated with the immune modulator. This can be achieved by connecting the agent that modulates PTDSS 1 expression and the immune modulator by a chemical linker. The chemical linker can optionally be cleavable, such that the agent that modulates PTDSS 1 expression separates from the immune modulator upon contact to an enzyme (e.g., a protease) or condition (e.g., a low pH tumor microenvironment) conditioned for cleavage of the linker. Examples of chemical linkers applicable to the present disclosure include those detailed in Jain et al., Pharmaceutical Research, 2015; 32:3526 and Bargh et al., Chem. Soc. Rev., 2109; 45:4361.

[0078] The immune modulator can be an immune checkpoint inhibitor. As used herein, the terms “immune checkpoint inhibitor” and “checkpoint inhibitor” refer to molecules (e.g., small organic molecules, oligopeptides, proteins, etc.) that inhibit one or more checkpoint proteins (e.g., diminish the signaling activity of an immune checkpoint protein). Checkpoint proteins generally regulate immune activity and self-tolerance mechanisms. Examples of checkpoint proteins include, B7-H4, BTLA, CD27, CD28, CD40, CD80, CD 122, CD 137, CTLA-4, GITR, ICOS, KR, NOX2, 0X40, PD-1, PD-L1, TIM-3, VISTA, and SIGLEC7. In some aspects, an immune checkpoint inhibitor includes an antibody, or a native checkpoint protein ligand modified to antagonize its cognate checkpoint protein. Examples of immune checkpoint inhibitors consistent with the present disclosure include those disclosed in Darvin et al, Experimental & Molecular Medicine, 2018; 50:1.

[0079] In further aspects, the immune modulator is a type I interferon receptor agonist or a type II interferon receptor agonist. As used herein, the terms “type I interferon”, “type I IFN”, and “interferon type I” refer to cytokines that bind to the cell surface receptor complex IFNAR (also known as the type I IFN receptor), and include IFN-α, IFN-β, IFN-ε, IFN-κ, IFN-τ, IFN- δ IFN-ζ, as well as subtypes thereof (e.g., IFNA1). Type I interferon receptor agonists consistent with the present disclosure include natural, engineered, and synthetic species that agonize IFNAR.

[0080] As used herein, the terms “type II interferon,” “type II IFN”, and “interferon type II” refer to IFNγ, a cytokine that binds to the type II interferon cell-surface receptor (also known as the IFN -gamma receptor (IFNGR)). IFNγ regulates innate and adaptive immunity and modulates numerous signaling pathways including the JAK-STAT, mTOR, MARK, and PI3K / AKT pathways. Type II interferon receptor agonists consistent with the present disclosure include natural, engineered, and synthetic species that agonize IFNGR.

[0081] In another aspect, the immune checkpoint inhibitor is a CTLA-4 inhibitor, a PD-1 inhibitor, a PD-L1 inhibitor, a PD-L2 inhibitor, a LAG-3 inhibitor, a KIR inhibitor, a TIM-3 inhibitor, a TIGIT inhibitor, a 4-1BB inhibitor, a 4-1BBL inhibitor, a GITR inhibitor, a GITRL inhibitor or a galectin inhibitor. In a particular, the immune checkpoint inhibitor is a PD-1 inhibitor or a PD-L1 inhibitor. In certain aspects, the immune checkpoint inhibitor is a PD-1 inhibitor.

[0082] Examples of CTLA-4 inhibitors consistent with the present disclosure include ipilimumab, tremelimumab, BMS-986218, AGEN1181, AGEN1884, BMS-986249, MK- 1308, REGN-4659, ADU-1604, CS-1002 , BCD-145, APL-509, JS-007, BA-3071, ONC-392, AGEN-2041, JHL-1155, KN-044, CG-0161, ATOR-1144, PBI-5D3H5, BPI-002, FPT-155, PF-06936308, MGD-019, KN-046, MEDI-5752, XmAb-20717, and AK-104.

[0083] Examples of PD- 1 inhibitors consistent with the present disclosure include nivolumab, pembrolizumab, pilizumab, BGB-108, SHR-1210, PDR-001, PF-06801591, IBI-308, GB-226, STI-1110, and mDX-400.

[0084] Examples of PD-L1 inhibitors consistent with the present disclosure include atezolizumab, avelumab, AMP-224, MED1-0680, RG-7446, GX-P2, durvalumab, KY-1003, KD-033, MSB-0010718C, TSR-042, ALN-PDL, ST1-A1014, GS-4224, CX-072, and BMS- 936559.

[0085] Examples of LAG-3 inhibitors consistent with the present disclosure include relatlimab and those disclosed in International Patent Publication WO2015138920.

[0086] Examples of TIM-3 inhibitors consistent with the present disclosure include TSR-022, LY-3321367, MBG-453, and LNCAGN-2390.

[0087] Examples of T1G1T inhibitors consistent with the present disclosure include BMS- 986207, RG-6058, and AGEN-1307.

[0088] Examples of 4-1BB inhibitors consistent with the present disclosure include urelumab, utomilumab, emfizatamab, BMS-663513, PF-05082566, PRS-343, RG7827,ADG106, INBRX-105, CTX-471, BNT311, BNT311, RG6706, MP0310, BNT312, AGEN2373, LVGN6051, ATOR-1017, STA551, and ND-021.

[0089] Examples of GITR inhibitors consistent with the present disclosure include MEDI1873, FPA-154, INCAGN-1876, TRX-518, BMS-986156, MK-1248, and GWN-323.

[0090] Examples of galectin inhibitors consistent with the present disclosure include thiodigalactoside, β-D-lactosyl-steroid, GB1107, lactulose-L-leucine, modified citrus pectin, PectaSol-C, GCS-100, GM-CT-01, belapectin, DB16, DB21, OTX008, PTX013, LLS30, and LLS2.

[0091] In further aspects, the immune checkpoint inhibitor is an antibody or an antibody fragment.

[0092] In additional aspects, the immune modulator is IFNγ, IFNα. The immune modulator may also be a fragment or mutant of IFNγ that retains interferon gamma receptor binding activity or a fragment or mutant of IFNα that retains IFNAR binding activity.

[0093] The compositions and methods of the present disclosure are useful for treating a variety of cancers. As used herein, the term “cancer” refers to a group diseases characterized by abnormal and uncontrolled cell proliferation starting at a primary site and having the potential to invade and to spread to secondary sites. Virtually all organs can be affected by cancer, reflected by the fact that more than 100 types of cancer have been observed in humans. Cancers can result from many causes including genetic predisposition, viral infection, exposure to ionizing radiation, exposure to environmental pollutants, tobacco and or alcohol use, obesity, poor diet, lack of physical activity or any combination thereof. While cancers often develop multifaceted resistances to chemotherapeutic and immunomodulatory therapies, the compositions and methods of the present disclosure can be particularly useful for sensitizing and treating multi-drug resistant cancers that are unresponsive to conventional therapies.

[0094] Examples of cancers may include but are not limited to Acute Lymphoblastic Leukemia, Adult; Acute Lymphoblastic Leukemia, Childhood; Acute Myeloid Leukemia, Adult; Adrenocortical Carcinoma; Adrenocortical Carcinoma, Childhood; AIDS-Related Lymphoma; AIDS-Related Malignancies; Anal Cancer; Astrocytoma, Childhood Cerebellar; Astrocytoma, Childhood Cerebral; Bile Duct Cancer, Extrahepatic; Bladder Cancer; Bladder Cancer, Childhood; Bone Cancer, Osteosarcoma / Malignant Fibrous Histiocytoma; Brain Stem Glioma, Childhood; Brain Tumor, Adult; Brain Tumor, Brain Stem Glioma, Childhood; Brain Tumor, Cerebellar Astrocytoma, Childhood; Brain Tumor, Cerebral Astrocytoma / Malignant Glioma, Childhood; Brain Tumor, Ependymoma, Childhood; Brain Tumor, Medulloblastoma, Childhood; Brain Tumor, Supratentorial Primitive Neuroectodermal Tumors, Childhood; Brain Tumor, Visual Pathway and Hypothalamic Glioma, Childhood; Brain Tumor, Childhood (Other); Breast Cancer; Breast Cancer and Pregnancy; Breast Cancer, Childhood; Breast Cancer, Male; Bronchial Adenomas / Carcinoids, Childhood: Carcinoid Tumor, Childhood; Carcinoid Tumor, Gastrointestinal; Carcinoma, Adrenocortical; Carcinoma, Islet Cell; Carcinoma of Unknown Primary; Central Nervous System Lymphoma, Primary; Cerebellar Astrocytoma, Childhood; Cerebral Astrocytoma / Malignant Glioma, Childhood; Cervical Cancer; Childhood Cancers; Chronic Lymphocytic Leukemia; Chronic Myelogenous Leukemia; Chronic Myeloproliferative Disorders; Clear Cell Sarcoma of Tendon Sheaths;Colon Cancer; Colorectal Cancer, Childhood; Cutaneous T-Cell Lymphoma; Endometrial Cancer; Ependymoma, Childhood; Epithelial Cancer, Ovarian; Esophageal Cancer; Esophageal Cancer, Childhood; Ewing's Family of Tumors; Extracranial Germ Cell Tumor, Childhood; Extragonadal Germ Cell Tumor; Extrahepatic Bile Duct Cancer; Eye Cancer, Intraocular Melanoma; Eye Cancer, Retinoblastoma; Gallbladder Cancer; Gastric (Stomach) Cancer; Gastric (Stomach) Cancer, Childhood; Gastrointestinal Carcinoid Tumor; Germ Cell Tumor, Extracranial, Childhood; Germ Cell Tumor, Extragonadal; Germ Cell Tumor, Ovarian; Gestational Trophoblastic Tumor; Glioma. Childhood Brain Stem; Glioma. Childhood Visual Pathway and Hypothalamic; Hairy Cell Leukemia; Head and Neck Cancer; Hepatocellular (Liver) Cancer, Adult (Primary); Hepatocellular (Liver) Cancer, Childhood (Primary); Hodgkin's Lymphoma, Adult; Hodgkin's Lymphoma, Childhood; Hodgkin's Lymphoma During Pregnancy; Hypopharyngeal Cancer; Hypothalamic and Visual Pathway Glioma, Childhood; Intraocular Melanoma; Islet Cell Carcinoma (Endocrine Pancreas); Kaposi's Sarcoma; Kidney Cancer; Laryngeal Cancer; Laryngeal Cancer, Childhood; Leukemia, Acute Lymphoblastic, Adult; Leukemia, Acute Lymphoblastic, Childhood; Leukemia, Acute Myeloid, Adult; Leukemia, Acute Myeloid, Childhood; Leukemia, Chronic Lymphocytic; Leukemia, Chronic Myelogenous; Leukemia, Hairy Cell; Lip and Oral Cavity Cancer; Liver Cancer, Adult (Primary); Liver Cancer, Childhood (Primary); Lung Cancer, Non-Small Cell; Lung Cancer, Small Cell; Lymphoblastic Leukemia, Adult Acute; Lymphoblastic Leukemia, Childhood Acute; Lymphocytic Leukemia, Chronic; Lymphoma, AIDS — Related; Lymphoma, Central Nervous System (Primary); Lymphoma, Cutaneous T-Cell; Lymphoma, Hodgkin's, Adult; Lymphoma, Hodgkin's; Childhood; Lymphoma, Hodgkin's During Pregnancy; Lymphoma, Non-Hodgkin's, Adult; Lymphoma, Non-Hodgkin’s, Childhood; Lymphoma, Non-Hodgkin's During Pregnancy; Lymphoma, Primary Central Nervous System; Macroglobulinemia, Waldenstrom's; Male Breast Cancer; Malignant Mesothelioma, Adult; Malignant Mesothelioma, Childhood; Malignant Thymoma; Medulloblastoma, Childhood; Melanoma; Melanoma, Intraocular; Merkel Cell Carcinoma; Mesothelioma, Malignant; Metastatic Squamous Neck Cancer with Occult Primary; Multiple Endocrine Neoplasia Syndrome, Childhood; Multiple Myeloma / Plasma Cell Neoplasm; Mycosis Fungoides; Myelodysplasia Syndromes; Myelogenous Leukemia, Chronic; Myeloid Leukemia, Childhood Acute; Myeloma, Multiple; Myeloproliferative Disorders, Chronic; Nasal Cavity and Paranasal Sinus Cancer; Nasopharyngeal Cancer; Nasopharyngeal Cancer, Childhood; Neuroblastoma; Non-Hodgkin's Lymphoma, Adult; Non-Hodgkin's Lymphoma, Childhood; Non-Hodgkin's Lymphoma During Pregnancy; Non-Small Cell Lung Cancer; Oral Cancer, Childhood; OralCavity and Lip Cancer; Oropharyngeal Cancer; Osteosarcoma / Malignant Fibrous Histiocytoma of Bone; Ovarian Cancer, Childhood; Ovarian Epithelial Cancer; Ovarian Germ Cell Tumor; Ovarian Low Malignant Potential Tumor; Pancreatic Cancer; Pancreatic Cancer, Childhood, Pancreatic Cancer, Islet Cell; Paranasal Sinus and Nasal Cavity Cancer; Parathyroid Cancer; Penile Cancer; Pheochromocytoma; Pineal and Supratentorial Primitive Neuroectodermal Tumors, Childhood; Pituitary Tumor; Plasma Cell Neoplasm / Multiple Myeloma; Pleuropulmonary Blastoma; Pregnancy and Breast Cancer; Pregnancy and Hodgkin's Lymphoma; Pregnancy and Non-Hodgkin's Lymphoma; Primary Central Nervous System Lymphoma; Primary Liver Cancer, Adult; Primary Liver Cancer, Childhood; Prostate Cancer; Rectal Cancer; Renal Cell (Kidney) Cancer; Renal Cell Cancer, Childhood; Renal Pelvis and Ureter, Transitional Cell Cancer; Retinoblastoma; Rhabdomyosarcoma, Childhood; Salivary Gland Cancer; Salivary Gland ‘Cancer, Childhood; Sarcoma, Ewing's Family of Tumors; Sarcoma, Kaposi's; Sarcoma (Osteosarcoma Malignant Fibrous Histiocytoma of Bone; Sarcoma, Rhabdomyosarcoma, Childhood; Sarcoma, Soft Tissue, Adult; Sarcoma, Soft Tissue, Childhood; Sezary Syndrome; Skin Cancer; Skin Cancer, Childhood; Skin Cancer (Melanoma); Skin Carcinoma, Merkel Cell; Small Cell Lung Cancer; Small Intestine Cancer; Soft Tissue Sarcoma, Adult; Soft Tissue Sarcoma, Childhood; Squamous Neck Cancer with Occult Primary, Metastatic; Stomach (Gastric) Cancer; Stomach (Gastric) Cancer, Childhood; Supratentorial Primitive Neuroectodermal Tumors, Childhood; T-Cell Lymphoma, Cutaneous; Testicular Cancer; Thymoma, Childhood; Thymoma, Malignant; Thyroid Cancer; Thyroid Cancer, Childhood; Transitional Cell Cancer of the Renal Pelvis and Ureter; Trophoblastic Tumor, Gestational; Unknown Primary Site, Cancer of, Childhood; Unusual Cancers of Childhood; Ureter and Renal Pelvis, Transitional Cell Cancer; Urethral Cancer; Uterine Sarcoma; Vaginal Cancer; Visual Pathway and Hypothalamic Glioma, Childhood; Vulvar Cancer; Waldenstrom's Macro globulinemia; and Wilms’ Tumor.

[0095] In certain aspects, the cancer is selected from bile duct cancer, bladder cancer, brain cancer, breast cancer, carcinoma, cervical cancer, colorectal cancer, endometrial cancer, epitheloid carcinoma of the bone, esophageal cancer, gallbladder cancer, gastric cancer, glioblastoma, hepatocellular cancer, large cell lung carcinoma, leukemia, lung cancer, medulloblastoma, melanoma, ovarian cancer, non-small cell lung cancer, pancreatic cancer, prostate cancer, renal cell cancer or thyroid cancer. In a particular aspect, the cancer is bladder cancer. Alternatively, the cancer can be bladder cancer, or melanoma, carcinoma (e.g., hepatocellular carcinoma). In particular aspects, the cancer is a solid tumor. In some aspects, the cancer is resistant (e.g., comprises a primary or adaptive resistance) to theimmunomodulatory therapy. In other aspects, the cancer is resistant to an immunomodulatory therapy different than the immunomodulatory therapy administered to the subject.

[0096] The term “immune modulator” as used herein refers to any therapeutic agent that modulates the immune system. Examples of immune modulators include but is not limited to eicosanoids, cytokines, prostaglandins, interleukins, chemokines, checkpoint regulators, TNF superfamily members, TNF receptor superfamily members and interferons. Specific examples of immune modulators include PGI2, PGE2, PGF2, CCL14, CCL19, CCL20, CCL21, CCL25, CCL27, CXCL12, CXCL13, CXCL-8, CCL2, CCL3, CCL4, CCL5, CCL11 , CXCL10, IL1, IL2, IL3, IL4, IL5, IL6, IL7, IL8, IL9, IL10, IL11, IL12, IL13, IL15, IL17, IL17, INF-α, INF- β, INF-ε, INF-γ, G-CSF, TNF-α, CTLA, CD20, PD1, PD1L1, PD1L2, ICOS, CD200, CD52, LTα, LTαβ, LIGHT, CD27L, 41 BBL, FasL, Ox40L, April, TL1A, CD30L, TRAIL, RANKL, BAFF, TWEAK, CD40L, EDAI, EDA2, APP, NGF, TNFR1, TNFR2, LTβR, HVEM, CD27, 4-1BB, Fas, 0x40, AITR, DR3, CD30, TRAIL-R1, TRAIL-R2, TRAIL-R3, TRAIL-R4, RANK, BAFFR, TACI, BCM A, Fnl4, CD40, E D AR XEDAR, DR6, DcR3, NGFR-p75, and Taj. Other examples of immune modulators include tocilizumab (Actemra), CDP870 (Cimzia), enteracept (Enbrel), adalimumab (Humira), Kineret, abatacept (Orencia), infliximab (Remicade), rituzimab (Rituxan), golimumab (Simponi), Avonex, Rebif, ReciGen, Plegridy, Betaseron, Copaxone, Novatrone, natalizumab (Tysabri), fingolimod (Gilenya), teriflunomide (Aubagio), BG12, Tecfidera, and alemtuzumab (Campath, Lemtrada).

[0097] In another embodiment, the present disclosure provides a method of identifying a subject with cancer as a candidate for treatment with an immune modulator that includes: a) determining that: i) cancer cells from the subject have diminished PTDSS1 expression; ii) the cancer cells from the subject include at least one of increased B2M, CXCL9, CXCL10, STAT1 or TAPI expression; iii) the cancer cells from the subject include diminished RAS signaling; iv) immune cells in a tumor microenvironment of the cancer include increased CCL5 expression, increased APOE expression, increased NOS2 expression, increased Cl QB expression, increased UBB expression, or a combination thereof; v) the tumor microenvironment of the cancer includes an elevated ratio of Th1 to Th2 CD4+cells; vi) CD8+effector T cells in the tumor microenvironment of the cancer include elevated expression levels of Prf1, Nkg7, Gzmb, Gzmk, or a combination thereof; vii) the tumor microenvironment of the cancer includes an elevated ratio of M1 to M2 macrophages; or viii) a combination thereof; and classifying the subject as a likely responder to the immune modulator if any one of i)-viii) are determined, thereby identifying the subject as suitable for treatment with the immunemodulator. In particular aspects, the method further includes administering the immune modulator to the subject, thereby treating the cancer in the subject.

[0098] The determining can include comparison to a control sample. As non-limiting examples, the control sample can be: i) a biological sample from a subject that does not have cancer; ii) a non-cancerous biological sample from the subject; or a sample from a subject with a cancer that does not have diminished PTDSS1 expression.

[0099] The method is useful for identifying subjects with a broad range of cancer types as candidates for immune modulator treatment. For example, cancers amenable to the presently disclosed methods include bile duct cancer, bladder cancer, brain cancer, breast cancer, carcinoma, cervical cancer, colorectal cancer, endometrial cancer, epitheloid carcinoma of the bone, esophageal cancer, gallbladder cancer, gastric cancer, glioblastoma, hepatocellular cancer, large cell lung carcinoma, leukemia, lung cancer, medulloblastoma, melanoma, ovarian cancer, non-small cell lung cancer, pancreatic cancer, prostate cancer, renal cell cancer or thyroid cancer. In certain aspects, the cancer is bladder cancer. In a particular embodiment, the cancer is bladder cancer. In another embodiment, the cancer is melanoma. In a further embodiment, the cancer is carcinoma. In a specific embodiment, the cancer is hepatocellular carcinoma. In an additional embodiment, the cancer is a solid tumor.

[0100] The determining can utilize one or more analytical techniques useful for identifying cellular, tissue, or systemic-level expression. The determining can include genetic level analysis (e.g., identification of somatic mutations in a tumor), analysis of transcription or splicing, analysis of protein or metabolite expression, or a combination thereof. For example, the determining can include mass spectrometry, a protein binding assay, mRNA profiling, PCR, RNN-seq, microarray analysis, SAGE, FISH, flow cytometry or a combination thereof. In some aspects, the determining includes mass spectrometry (e.g., to identify expression of a protein or metabolite), liquid chromatography, gas chromatography, nuclear magnetic resonance, fluorometric analysis, a protein binding assay, mRNA profiling, genomic profiling, PCR, RNA sequencing, Western blot analysis, Northern blot analysis, microarray analysis, SAGE, FISH, flow cytometry, or a combination thereof.

[0101] In one aspect, the method includes determining that the cancer cells have diminished PTDSS1 expression. In a particular aspect, the diminished expression is about 95%, about 90%, about 80%, about 70%, about 60%, about 50%, about 40%, about 30%, about 25%, about 20%, about 15%, about 10%, or about 5% of the PTDSS 1 expression in a non-cancerous cell of a cell type from which the cancer is derived.

[0102] In some aspects, the method includes determining that the cancer cells comprise increased B2M, CXCL9, CXCL10, STAT1 or TAPI expression or a combination thereof. In particular aspects, the increased expression is about 105%, about 110%, about 120%, about 130%, about 140%, about 150%, about 175%, about 200%, about 250%, about 300%, about 400%, or about 500% of the B2M, CXCL9, CXCL10, STAT1 or TAPI expression in a non- cancerous cell of a cell type from which the cancer is derived.

[0103] In further aspects, the method includes determining that the cancer cells have diminished RAS signaling. In additional aspects, the determining includes measuring phosphorylated MEK, phosphorylated ERK, or a combination thereof. In particular aspects, a level of phosphorylated MEK or phosphorylated ERK in the cancer cells is about 95%, about 90%, about 80%, about 70%, about 60%, about 50%, about 40%, about 30%, or about 25% of a level of phosphorylated MEK or phosphorylated ERK in a non-cancerous cell of a cell type from which the cancer is derived.

[0104] In additional aspects, the method includes determining that immune cells in the tumor microenvironment of the cancer include increased CCL5 expression, increased APOE expression, increased NOS2 expression, increased C1QB expression, increased UBB expression or a combination thereof. In particular aspects, the increased expression is about 105%, about 110%, about 120%, about 130%, about 140%, about 150%, about 175%, about 200%, about 250%, about 300%, about 400%, or about 500% of the CCL5 expression, APOE expression, NOS2 expression, C1QB expression, UBB expression, or the combination thereof in immune cells from a tumor microenvironment of a cancer that does not have diminishedPTDSS1 expression.

[0105] In one aspect, the immune cells are myeloid cells.

[0106] In another aspect, the method includes determining that the tumor microenvironment of the cancer has an elevated ratio of Th1 to Th2 CD4+cells. In a particular aspect, the elevated ratio of Th1 to Th2 CD4+cells in the tumor microenvironment of the cancer is about 1.1-, about 1.2-, about 1.3-, about 1.4-, about 1.5-, about 1.75-, about 2-, about 2.25-, about 2.5-, about 3- , about 4-, or about 5-fold higher than a ratio of Th1 to Th2 CD4+cells in a tumor microenvironment of a cancer that does not have diminished PTDSS1 expression. In a further aspect, the elevated ratio of Th1 to Th2 CD4+cells is about 2:3, about 5:6, about 1:1, about 7:6, about 4:3, about 3:2, about 5:3, about 11:6, about 2:1, about 5:2, about 3:1, or about 7:2.

[0107] In an additional aspect, the method includes determining that CD8+effector T cells in the tumor microenvironment of the cancer comprise elevated expression levels of Prf1 , Nkg7, Gzmb, Gzmk or a combination thereof. In another aspect, the method includes detecting Prf1,Gzmb, or Gzmk in CD8+effector T cells in the tumor microenvironment of the cancer. In certain aspects, the elevated expression is about 105%, about 110%, about 120%, about 130%, about 140%, about 150%, about 175%, about 200%, about 250%, about 300%, about 400%, or about 500% of the Prfl expression, the Nkg7 expression, the Gzmb expression, the Gzmk expression, or the combination thereof in immune cells from a tumor microenvironment of a cancer that does not have diminished PTDSS1 expression.

[0108] In particular embodiments, the method includes determining whether the tumor microenvironment of the cancer includes an elevated ratio of M1 to M2 macrophages. A surprising observation disclosed herein is that PTDSS1 knockdown cancers included M1 macrophage polarized tumor microenvironments (TMEs). In particular, ‘M1 ’ function related genes Cd86 and Cxcl9 were upregulated in the ‘M1 -like’ macrophage clusters (cluster 0 and 1) from PTDSS1 knockdown tumors and were further enhanced by treatment with anti-PD-1. Concurrently, expression of ‘M2’ function related genes Mrc1 and Fn1 were lower in the ‘M2- like’ macrophage clusters (cluster 3,4,7,13,16,19,21) from PTDSS1 knockdown tumors and were lowest in PTDSS1 knockdown tumors treated with anti-PD-1. As PTDSS1 knockdown cells exhibited enhanced responsiveness to ICT, these results suggest that polarization towards inflammatory tumor microenvironments improves the potency of immunomodulatory therapies. As non-limiting examples, the elevated ratio of M1 to M2 macrophages in the tumor microenvironment of the cancer is about 1.1-, about 1.2-, about 1.3-, about 1.4-, about 1.5-, about 1.75-, about 2-, about 2.25-, about 2.5-, about 3-, about 4-, or about 5-fold higher than a ratio of M1 to M2 macrophages in a tumor microenvironment of a cancer that does not have diminished PTDSS1 expression. In certain aspects, the determining includes measuring CD206 expression in macrophages cells from the tumor microenvironment.

[0109] In some aspects, the immune modulator is an immune checkpoint inhibitor, a cytokine, a chemokine, an interleukin, an immunomodulatory imide drug, a toll-like receptor agonist, an oligodeoxynucleotide, a glucan, a type I interferon receptor agonist, or a type II interferon receptor agonist. In further aspects, the immune modulator is an immune checkpoint inhibitor, a type I interferon receptor agonist, or a type II interferon receptor agonist. In a certain aspect, the immune modulator is an immune checkpoint inhibitor. In one aspect, the immune checkpoint inhibitor is selected from a CTLA-4 inhibitor, a PD-1 inhibitor, a PD-L1 inhibitor, a PD-L2 inhibitor, a LAG-3 inhibitor, a KIR inhibitor, a TIM-3 inhibitor, a TIGIT inhibitor, a 4-1BB inhibitor, a 4-1 BBL inhibitor, a GITR inhibitor, a GITRL inhibitor, a galectin inhibitor or a combination thereof. In a particular aspect, the immune checkpoint inhibitor is a PD-1 inhibitor.

[0110] In another aspect, the immune modulator is a type I interferon receptor agonist or a type II interferon receptor agonist. In some such aspects, the immune modulator is IFNγ or IFNα.

[0111] In certain embodiments, the present disclosure provides a method of treating cancer in a subject in need thereof that includes administering an agent that inhibits PTDSS1 and an immune modulator to the subject, thereby treating the cancer in the subject.

[0112] As used herein, the term “inhibits PTDSS 1” refers to blocking the activity of the phosphatidylserine synthase 1 (PTDSS 1) protein. As non-limiting examples, the agent that inhibits PTDSS 1 can include DS55980254, DS68591889, and DS07551382.

[0113] In one aspect, the agent that inhibits PTDSS 1 includes DS55980254.

[0114] As used herein the term “DS55980254” refers to a drug that inhibits phosphatidylserine synthase 1 (PTDSS 1). DS55980254 is a potent and selective inhibitor of PTDSS 1. DS55980254 suppresses the production of PS by PTDSS1. In some aspects, the agent that inhibits PTDSS 1 isDS55980254 chemical structure.The structure and characteristics of DS55980254 is described in Yoshihama et al., Cancer Res., 2022; 82(21 ):4031 , the content of which is herein incorporated by reference in its entirety.

[0115] In another aspect, the agent that inhibits PTDSS 1, the immune modulator, or a combination thereof is targeted to a cancer cell. In a further aspect, the agent that inhibits PTDSS 1, the immune modulator, or a combination thereof is configured for uptake by the cancer cell.

[0116] In an additional aspect, the agent that inhibits PTDSS1 is coupled to or coencapsulated with the immune modulator. In certain aspects, the agent that inhibits PTDSS 1 and the immune modulator are connected by a chemical linker. In particular aspects, the immune modulator is an immune checkpoint inhibitor.

[0117] In further aspects, the immune modulator is a type I interferon receptor agonist or a type II interferon receptor agonist. In a particular aspect, the immune checkpoint inhibitor is a CTLA-4 inhibitor, a PD-1 inhibitor, a PD-L1 inhibitor, a PD-L2 inhibitor, a LAG-3 inhibitor,a KIR inhibitor, a TIM-3 inhibitor, a TIGIT inhibitor, a 4-1 BB inhibitor, a 4-1 BBL inhibitor, a GITR inhibitor, a GITRL inhibitor or a galectin inhibitor.

[0118] In one aspect, the CTLA-4 inhibitor is ipilimumab, tremelimumab, BMS-986218, AGEN1181, AGEN1884, BMS-986249, MK-1308, REGN-4659, ADU-1604, CS-1002 , BCD-145, APL-509, JS-007, BA-3071, ONC-392, AGEN-2041, JHL-1155, KN-044, CG- 0161, ATOR-1144, PBI-5D3H5, BPI-002, FPT-155, PF-06936308, MGD-019, KN-046, MEDI-5752, XmAb-20717, or AK-104.

[0119] In another aspect, the PD-1 inhibitor is nivolumab, pembrolizumab, pilizumab, BGB- 108, SHR-1210, PDR-001, PF-06801591, IBI-308, GB-226, STI-1110, or mDX-400.

[0120] In a further aspect, the PD-L1 inhibitor is atezolizumab, avelumab, AMP-224, MEDI- 0680, RG-7446, GX-P2, durvalumab, KY-1003, KD-033, MSB-0010718C, TSR-042, ALN- PDL, STI-A1014, GS-4224, CX-072, or BMS-936559.

[0121] In an additional aspect, the LAG-3 inhibitor is relatlimab.

[0122] In a certain aspect, the TIM-3 inhibitor is TSR-022, LY-3321367, MBG-453, or INCAGN-2390.

[0123] In a particular aspect, the TIGIT inhibitor is BMS-986207, RG-6058, or AGEN-1307.

[0124] In another aspect, the 4-1 BB inhibitor is urelumab, utomilumab, emfizatamab, BMS- 663513, PF-05082566, PRS-343, RG7827, ADG106, INBRX-105, CTX-471, BNT311, BNT311, RG6706, MP0310, BNT312, AGEN2373, LVGN6051, ATOR-1017, STA551, or ND-021.

[0125] In a further aspect, the GITR inhibitor is MED.I1873, FPA-154, 1NCAGN-1876, TRX-518, BMS-986156, MK-1248, or GWN-323.

[0126] In an additional aspect, the galectin inhibitor is thiodigalactoside, β-D-lactosyl- steroid, GB1107, lactulose-L-leucine, modified citrus pectin, PectaSol-C, GCS-100, GM-CT- 01, belapectin, DB16, DB21, OTX008, PTX013, L.LS30, or LLS2.

[0127] In some aspects, the immune checkpoint inhibitor is a PD-1 inhibitor or a PD-L1 inhibitor. In other aspects, the immune checkpoint inhibitor is a PD-1 inhibitor.

[0128] In further aspects, the immune checkpoint inhibitor is an antibody or an antibody fragment. In additional aspects, the immune modulator is IFNγ or IFNα.

[0129] In certain aspects, the cancer is selected from bile duct cancer, bladder cancer, brain cancer, breast cancer, carcinoma, cervical cancer, colorectal cancer, endometrial cancer, epitheloid carcinoma of the bone, esophageal cancer, gallbladder cancer, gastric cancer, glioblastoma, hepatocellular cancer, large cell lung carcinoma, leukemia, lung cancer, medulloblastoma, melanoma, ovarian cancer, non-small cell lung cancer, pancreatic cancer,prostate cancer, renal cell cancer or thyroid cancer. In a particular aspect, the cancer is bladder cancer.

[0130] The effect of DS55980254 on tumor and immune cell subsets can be delineated by interrogating the tumor immune microenvironment using multiple techniques including singlecell RNA sequencing and CyTOF studies.

[0131] For scRNAseq: CD45+ and CD45- cell fractions can be FACS sorted and run by 10X Genomics 5’ scRNAseq. Raw sequencing data (fastq file) can be demultiplexed and analyzed using 10X Genomics Cell Ranger software utilizing standard default settings and the cellranger count command to generate html QC metrics and couple files for each sample.

[0132] In some aspects, tumor growth rate in DS55980254 and immune modulator treated subject is significantly slower than tumor growth rate in an untreated subject or tumor growth rate in a subject treated with an immune modulator alone. In some aspects the slowing of tumor growth rate is dose dependent.EXAMPLES

[0133] The following examples are provided to further illustrate the embodiments of the present invention, but are not intended to limit the scope of the invention. While they are typical of those that might be used, other procedures, methodologies, or techniques known to those skilled in the art may alternatively be used.EXAMPLE 1Experimental MethodsAnimal Experiments

[0134] All animal work was reviewed and approved by MD Anderson Cancer Center’s Institutional Animal Care and Use Committee (IACUC). All mice were maintained in pathogen-free conditions. 4 to 6 weeks old male C57BL / 6 mice were purchased from the Charles River Laboratories. For CRISPR screen, 2x106cells from early passages of the cell pools were subcutaneously injected into each mouse to maintain 200x coverage of the library. Mice were then treated intraperitoneally with three doses of anti-PD-1 (BioXCell, BE0146) or PBS on day 3, 6 and 9 post tumor inoculation (200 / 100 / 100 μg each in 100μl PBS). Tumor length and width was measured every 3 days using digital caliper. Tumor volume was calculated as (length x width2) / 2. For single cell RNA sequencing experiment, CyTOF experiment and tumor growth assays, 2x105KD or WT control cells were subcutaneously injected into mice, followed by the same treatment regime as the screen study, mice wereeuthanized on day 9 to collect tumor samples for scRNAseq and CyTOF. An additional group of mice from each condition were maintained to monitor tumor growth and animal survival till day 40. For in vivo proliferation assay, 2x105KD or control cells were subcutaneously injected into immune deficient NSG (NOD. Cg-PrkdcscidIl2rgtmlWjl / SzJ) mice purchased from the Jackson Laboratory, followed by tumor growth monitoring.In Vivo CRISPR screening and data analysis

[0135] Mouse bladder cancer cell line MB49 (Sigma, SCC148) was first transduced with Cas9-expression plasmid followed by puromycin selection to make Cas9-expressing MB49 stable cell line. The library was divided into two sub-pools, each containing ~800 non-targeting control sgRNAs and ~10000 sgRNAs targeting around 1000 genes that encode kinases, phosphatases, and drag targets. MB49-Cas9 cells were transfected with viruses from the two sub-pools individually at an infection rate of 0.3 to create two cell pools. The cell pools were cultured in vitro for 5 days to ensure successfill gene editing, followed by examination of sgRNAs representation by next generation sequencing. Cell pools were transplanted subcutaneously into mice, followed by treatment with anti-PD-1 or PBS. Mice were euthanized on day 12 and tumor samples were collected. Genomic DNA was extracted from the tumor samples using a Quick-DNA Midiprep Plus kit (ZYMO Research). Barcoded sequencing libraries were prepared by PCR as described. Pooled library was then sequenced on Illumina NextSeq500 Mid output platform by the Advanced Technology Genomics Core at The University of Texas MD Anderson Cancer Center. Software castle was used for data analysis. Briefly, sgRNA counts from each sample were first obtained by mapping the sequencing reads to the library reference, after sequencing depth normalization, sgRNA counts from the two subpools were combined and differential analysis was performed between tumor samples and original cell pools. sgRNA composition changes in the anti-PD-1 treated group and PBS treated group were compared against each other to determine anti-PD-1 specific changes. Functional enrichment analysis was performed using Metascape.Cell Culture and Generation of Knockdown Clones

[0136] MB49 cell lines were cultured in high-glucose DMEM supplemented with 10% fetal bovine serum (FBS) and penicillin-streptomycin (100 U / ml). MB49 Ptdss1 KD (KD) and control cell (WT) cells were established by lentiviral infection of parental MB49 cells using pMD2.G and pSPAX2 lentiviral packaging system and LentiCRISPRv2GFP (Add gene, 82416) with or without sgRNAs targeting PTDSS1. sgRNA sequences information is included in Table 1.Table 1In Vivo Proliferation Assay

[0137] lx104MB49 cells were plated in 96 well plate in triplicates for each cell line. Cells were then cultured for 5 days in IncuCyte (Sartorius). Images were taken every 6 hours to measure cell confluency. Cell proliferation data was generated using the IncuCyte software ZOOM.Western Blotting

[0138] Whole cell lysates were prepared on ice in RIP A buffer (Pierce) with protease and phosphatase inhibitors (Biomake). Protein concentration was measured by Pierce BCA Protein Assay kit. 20ug protein from each sample was loaded on 4-20% precast gradient gel, transferred to PVDF membranes and subjected to staining with the following antibodies: Cas9 (Cell Signaling Technology, 65832), β-Actin (Cell Signaling Technology, 4970S), Stat1 (Cell Signaling Technology, 9172S), pStat1(Tyr701) (Cell Signaling Technology, 9167S), HSP90(Cell Signaling Technology, 4874), phospho-Erkl / 2 (Thr 202 / Tyr204) (Cell Signaling Technology, 4370), phosphor-MEK1 / 2 (Ser217 / 221). Images were taken on ChemiDoc MP system.RNA Isolation and Real-Time Quantitative PCR

[0139] Total RNA was extracted from cells using Quick-RNA miniprep kit (ZYMO research). cDNA was synthesized using 5X All-In-One RT mastermix (Applied Biological Materials Inc.). Real-time qPCR was performed with BrightGreen 2X qPCR MasterMix (Applied Biological Materials Inc.) on the 7500 Fast Real-Time PCR System (Applied Biosystems). Samples were run in triplicate and normalized to Gapdh. Relative mRNA expression level was determined using the 2-ΔΔCt method.RNA Sequencing and Data Analysis

[0140] Total RNA samples were isolated as described above. Stranded mRNA-seq was performed on NextSeq500 High output platform by the Advanced Technology Genomics Core at The University of Texas MD Anderson Cancer Center. Sequencing reads were aligned to mouse genome mm10 using HISAT2 software, transcripts were quantitated using StringTie. Data normalization and differential expression analysis were performed with R package DESeq2. Gene-set enrichment analysis (GSEA) was performed using GSEA java package (v4.0) and hallmark gene sets from MSigDB (v7.4).Flow Cytometry

[0141] Cells were trypsinized, washed with FACS buffer (PBS + 5% FBS + 2mM EDTA), and incubated with TruStain FcX anti-mouse CD 16 / 32 antibody (Biolegend, 101319) for 10 minutes, followed by staining with the following fluorochrome-conjugated antibodies on ice for 30 minutes: H-2Kb / H-2Db (Biolegend, 111507) and β2-microglobulin (Biolegend, 154504). Cells were analyzed on LSR II cytometer (Becton Dickinson).Antigen Presentation Assay and In Vitro T Cell Killing Assay

[0142] For antigen presentation assay, KD or WT tumor cells were first pulsed with 100ng / ml OVA257-264peptide SIINFEKL (SEQ ID NO: 16) for 6h, followed by flow cytometry analysis of SIINFEKL (SEQ ID NO: 16) bounded H2Kb level. For T cell killing assay, CD8+ T cells were first isolated from spleen of male OT-1 mouse using CD8+ T cell isolation kit, and subsequently cultured in T cell culture media supplemented with 30U / ml mouse IL-2, 50μM β-mercaptoethanol and CD3 / CD28 magnetic beads for 5 days. On the day of tumor cell-T cell co-culture, tumor cells were pulsed with SIINFEKL (SEQ ID NO: 16) for 2h, then cell culture media was removed, activated T cells were added to tumor cell culture at different ratios andcultured for 24h. Killing of tumor cell by T cell was measured by flow cytometry analysis of 7-AAD staining.Lipid Profiling by Mass Spectrometry

[0143] 4x106WT or KD cells were seeded 24 hours prior to collection. On collection day, cells were briefly washed with ice cold 0.85% ammonium bicarbonate, then collected with cell scraper. Cells pellets were snap-freezed in liquid nitrogen and stored at -80 °C before mass spectrometry analysis of lipid content using the C30-MS platform at The University of Texas MD Anderson Cancer Center Metabolomics Core.Single Cell RNA Sequencing and Data Analysis

[0144] Tumors were first minced with scissors and digested with 0.67 mg / 'ml Liberase TL (Sigma) and 0.4 mg / ml DNase for 30 minutes at 37 °C with continuous rotation. Samples were further homogenized on 70 μm cell strainer placed on ice to get single cell suspension. CD45+cells were isolated with CD45 microbeads. Single cell RNA sequencing library was generated using the Chromium Single Cell 3’ GEM Library & Gel Bead Kit (10X Genomics, PN- 1000075) and the 10X Genomics Chromium Controller instrument. Briefly, 10000 cells were loaded to the controller to generate single cell-gel beads emulsions. Reverse transcription, cDNA amplification and sample indexing were then performed to generate barcoded single cell libraries. Pooled library was then sequenced on Novaseq 6000 High output platform by the Advanced Technology Genomics Core at The University of Texas MD Anderson Cancer Center. Sequencing data were first processed using the Cell Ranger software from 10X Genomics to obtain raw read counts. R package Seurat was used to filter for high quality singlets, integrate samples from different condition, identify clusters, analyze cluster frequencies and gene expression following analysis. Gene signature z-score was calculated as follows: z-score for each gene is calculated, added up, then divided by square root of number of genes.Mass Cytometry (CyTOF) and Data Analysis

[0145] Single cell suspension was collected as described in single cell RNA sequencing experiment. Cells were washed with RPMI culture media supplemented with 10% FBS before staining. Briefly, 3 million cells per sample were transferred into 96 well plate, washed once with FACS buffer and blocked with 50μL homemade blocking buffer (PBS + 2% rabbit / rat / hamster / bovine / murine serum + 25 μg / ml Fc blocker) for 5 minutes. A metal-labeled surface staining antibody cocktail was then added to each sample and stained for 30 minutes at 4 °C. Cells were then washed with PBS and stained with 2.5 μM Cisplatin (Fluidigm) for exactly 1 minute on ice for dead cell discrimination. After being washed with FACS buffertwice, cells were fixed and permeabilized for 1 hour at 4 °C using the Foxp3 / TF staining buffer set and washed with permeabilization buffer. Metal-labeled intracellular antibody cocktail was then added to each sample and incubated for 30 minutes at 4 °C. Next, samples were washed with MaxPar Barcode Perm buffer and barcoded using the 20-Plex Pd Barcoding kit. After barcoding, samples were washed and resuspended with MaxPar Cell Staining buffer and pooled together. Intercalator-Ir doublet discrimination staining was then performed by incubating the cells in Ir staining buffer (PBS + 1.6% paraformaldehyde + 125 nM Iridium) overnight at 4 °C. The next day, cells were washed once with FACS buffer, once with Milli-Q water, resuspended in Milli-Q water containing 0.1% bovine serum albumin (BSA) and EQ4 calibration beads before acquisition using a Helios mass cytometer.

[0146] Mass cytometry data were normalized to calibration beads signal and de-barcoded using Fluidigm debarcoder software. Samples were manually gated by non-bead, Ir, event length, cisplatin, and specific surface markers (e.g. CD45, CD3e) in FlowJo to obtain the live, intact, single immune cells. Fes files were then exported and loaded into R for downstream analysis following the CyTOF workflow pipelineMacrophage Polarization Assay

[0147] Bone marrow cells from male WT C57BL / 6 mice were first collected, followed by treatment of 20ng / ml M-CSF for 7 days to enable macrophage differentiation. Bone marrow derive macrophages were then cultured in MB49 parental, WT or KD tumor cell conditioned medium supplemented with either 20ng / ml IFNγ and 100ng / ml LPS for M1 polarization or 10ng / ml IL-4 for M2 polarization. The next day, RNA was extracted from macrophages and real-time quantitative PCR was performed to quantify Nos2 and Mrcl expression levels.Public Datasets

[0148] TCGA bladder cancer patient amplification data and overall survival data were downloaded from the cBioPortal and the Human Protein Atlas. Imvigor210 data were obtained from the IMvigor210CoreBiologies R package (Mariathasan, S. et al. Nature 554, 544-548 (2018)). Gide_2019 cohort data were downloaded from the TIDE platform85. Association between PTDSS1 expression level and melanoma patient survival in the Riaz_2017 cohort (patients who had previously been on anti-CTLA-4 therapy [ipilimumab, Ipi] and had progressed onto anti-PD-1 treatment) was generated using the Tumor Immune Dysfunction and Exclusion (TIDE) software86. Association between PTDSS1 protein level and melanoma patient response to anti-PD-1 therapy in the Harel_2019 cohort were generated based on the published data.Statistical Analysis

[0149] All experiments, except for sequencing experiments, were repeated at least three times to ensure data reproducibility. Kaplan-Meier survival analysis, two-tail unpaired Student’s t- test and Mann-Whitney test were performed in GraphPad Prism 9. Sample size for each experiments are indicated in figure legends. Data are expressed as mean with SEM. P values lower than 0.05 were considered statistically significant. *P < 0.05; **P < 0.01; ***p < 0.001; ****P < 0.0001; NS (not significant, P > 0.05).EXAMPLE 2In Vivo CRISPR Screen Identifies Known Regulators of Immune Checkpoint Inhibitor Response

[0150] To systematically explore potential targets in tumor cells that may improve the efficacy of anti-PD-1 therapy, an in vivo CRISPR knockout screen was performed using the MB49 tumor cell line which responds poorly to ICT (FIG. la). A Cas9-expressing MB49 cell line was engineered (FIG. 6a) and transduced with sgRNA libraries targeting 2013 genes encoding kinases, phosphatases, and drug targets as well as 1 123 non-targeting guides (Morgens, D. W. et al. Nat Commun 8, 15178 (2017)). After full representation of the sgRNA library in the cells was confirmed (FIG. 6b), the cell pools were subcutaneously transplanted into C57BL / 6 mice followed by treatment of three doses of anti-PD-1 or PBS. Consistent with previous report, anti-PD-1 was able to delay tumor growth, but the effect was limited (FIG. 6c). Next, tumors from both groups were collected on day 12, followed by genomic DNA isolation and next-generation sequencing to quantify changes in library representation. CRISPR screen performance was validated as suggested by the differential distribution of a list of essential genes (Hart et al. Mol Syst Biol 10, 733 (2014)) compared to non-essential genes in all groups (FIG. 6d).

[0151] In order to identify genes that regulate tumor cell responses to anti-PD-1 therapy, sgRNA library composition of the tumors was first compared to that of the original cell pools and then plotted against each other (FIG. lb). A list of 48 sgRNAs showing enrichment or depletion specifically in the anti-PD-1 treated condition were identified (Table 2), suggesting perturbation of the corresponding genes may regulate tumor cells fitness under anti-PD-1 selective pressure. Inspection of the gene list revealed well-known genes essential for immunotherapy, including components of the antigen presentation machinery B2m, H2-kl and H2-q738,39. On the contrary, sgRNAs targeting Cdk4, Man2al and Pikfyve were significantly depleted in the anti-PD-1 treated group, consistent with the recent findings that genetic and pharmacological inhibition of these targets synergize with immune checkpoint blockades. Wenext sought to understand how the rest of the gene targets are involved in mediating anti-PD-1 response. To do so, we performed functional enrichment analysis using genes targeted by the48 sgRNAs, and included genes targeted by the differentially enriched sgRNAs in the PBS group as a reference. As expected, antigen processing and presentation pathway was enriched in the anti-PD-1 treated group (FIG. 1c). Interestingly, autophagy pathway was also enriched in the anti-PD-1 treated group, which supports the increasingly recognized idea that autophagy is a conserved process utilized by tumor cell to evade immune surveillance and combination of autophagy inhibition with ICT is a promising strategy to overcome resistance to ICT.Table 2

[0152] In addition to the autophagy and antigen processing / presentation pathways, the phospholipid metabolism pathway was also enriched in the anti-PD-1 treated group. Of the genes in this pathway (Ptdss1, Medl, Chka, Csnk2a1, Pik3c2a, Pik3c2b, Dgke, Mtmr9, Pikfyve), Ptdss1 is frequently amplified in a variety of cancer types in The Cancer Genome Atlas (TCGA) database (FIG. 6e) and its expression is negatively correlated with survival in cancer patients (FIG. 1d,e), which highlight Ptdssl as a target for cancer therapy.EXAMPLE 3PTDSS1 Deficiency Increases Tumor Cell Immunogenicity and Response to Interferon Gamma

[0153] To understand how knocking out PTDSS1 reduced cell fitness under anti-PD-1 selective pressure, three independent PTDSS1 knockdown clones (KD) were established by CRISPR / Cas9 (FIG. 7a) and compared the transcriptomes to that of empty vector control cells (WT) by RNA sequencing. Gene set enrichment analysis (GSEA) of the transcriptomes revealed that IFNγ response pathway was enriched in the KD cells (FIG. 2a, b). This result was validated in vivo by comparing the transcriptomes of KD#1 tumor cell and WT tumor cells, which showed the enrichment of IFNγ response and IFNα response in KD#1 tumor cells (FIG. 7b). Loss of PTDSS1 upregulated expression of multiple IFNγ regulated genes including B2m, Cxcl9, Cxcl10, Stat1 and Tap1 (FIG. 2c). We also observed elevated basal level of STAT1 , the major signal transducer of IFNγ response pathway, and its phosphorylation when stimulated with IFNγ (FIG. 2d). Expression level of two downstream targets of IFNγ response pathway, MHC-1 and B2m, were also elevated in the KD cells (FIG. 2e,f), suggesting an increased immunogenicity of the KD cells. Indeed, when pulsed with OVA peptide SIINFEKL (SEQ ID NO: 16), KD cells exhibited higher level of H2kb bound SIINFEKL (SEQ ID NO: 16), which translated into increased sensitivity to OT-I CD8+T cell mediated killing (FIG. 2g, h).

[0154] Inhibition of RAS / MAPK signaling pathways have been shown to enhance anti-tumor immunity by upregulating tumor cells expression of MHC I. In this study, in addition to the IFNγ response pathway, KRAS SIGNALING DN (genes downregulated by KRAS) was enriched in KD cells (FIG. 2a), suggesting that knocking down PTDSS1 may dampen RAS signaling. In accordance with this notion, reducing the PS level in plasma membrane has been shown to an effective strategy to inhibit KRAS signaling50. Therefore, we set to evaluate whether the increased MHC I and antigen presentation in the KD cells were due to decreased Ras signaling. Indeed, we confirmed that knocking down PTDSS1 reduced the level of phosphorylated Erk (T202 / Y204) and phosphorylated MEK (S217 / 221) (FIG. 7c), and this was associated with reduced total abundance of phosphotidylserine in the KD cell as quantified by mass spectrometry (FIG. 7d). Together, these results indicate that loss of PTDSS1 in tumor cells downregulates PS level, leading to decreased Ras signaling and increased expression of IFN-regulated genes, which resulted in increased immunogenicity of tumor cells.EXAMPLE 4Loss of PTDSS1 Shapes an Inflammatory Tumor Microenvironment

[0155] Successful anti-tumor response depends on an intricate interplay between tumor cells and their microenvironment. To investigate whether tumor extrinsic factors contribute to the reduced tumor cell fitness to anti-PD-1 in cells transduced with PTDSS1 sgRNAs, KD and WT cells were separately transplanted into mice. The mice were then treated with anti-PD-1 or PBS 3 and 6 days post inoculation. Immune subsets were profiled within the tumor microenvironment (TME) on day 9 by CyTOF. In the untreated tumor, we identified high frequency of macrophage, monocyte, dendritic cell and relatively low abundance of CD4+, CD8+T cells and NK cells according to the staining of classical cell-type specific antigen (FIG. 8a, b). As PD-1 is known as a T cell checkpoint receptor, the lymphoid compartment of the TME was specifically interrogated. Anti-PD-1 treatment induced infiltration of both CD8+and CD4+T cells (FIG. 8c) in WT tumors. Interestingly, intratumoral level of CD4+T cells, specifically the Th1 subset, was also increased in KD tumors, regardless of treatment condition. In contrast, level of Th2 CD4+cells was relatively lower in KD tumors compared to WT tumors (FIG. 8d). To gain a higher resolution of immune subsets within the TME, we performed single cell RNA sequencing (scRNAseq) on tumors from another animal cohort.

[0156] Based on expression of cell-type specific marker genes (FIG. 8e), the immune cells were clustered into high abundance of different myeloid clusters including monocyte and macrophage, type 1 and type 2 conventional dendritic cells, neutrophils and relatively lowabundance of CD4+, CD8+T cells and NK cells (FIG. 3a). Consistent with our CyTOF results, anti-PD-1 treatment increased levels of CD8+and CD4+T effector cells (Teff) in WT tumors (FIG. 3b). Changes in the Th1 and Th2 cell frequencies were also recapitulated (FIG. 3c). scRNAseq enabled us to obtain a closer look into the changes in different CD8+T cell subsets. While anti-PD-1 treatment induced infiltration of exhausted CD8+T cell (Tex), Ifhg expressing effector CD8+T cell (Ifng+CD8+Teff) and Gzmk expressing effector CD8+T (Gzmk+CD8+Teff), knocking down PTDSS 1 in tumor cells induced a further increase in frequency of Gzmk+CD8+Teffcells (FIG. 3d). Expression levels of CD8+T cell cytotoxic function related genes, including Prf1, Nkg7, Gzmb and Gzmk, were also highest in KD tumors treated with anti-PD- 1 (FIG. 3e,f).

[0157] Subsequent analysis focused on macrophages, the most abundant immune populations in the TME. CyTOF analysis of the TME revealed an increased frequency of CD206lowmacrophage cluster and decreased frequency of CD206highcluster in total macrophages (FIG. 9a, b). scRNAseq analysis of the macrophage transcriptomes in the KD and WT tumors showed that pathways including response to interferon-beta, innate and humoral immune response, were enriched in macrophages from the KD tumors, suggesting a shift towards inflammatory status (FIG. 4a). In support of this observation, expression of ‘M1 ’ function related genes Cd86 and Cxcl9 were upregulated in the ‘M1 -like’ macrophage clusters (cluster 0 and 1) from KD tumors and were highest in KD tumors treated with anti-PD-1 (FIG. 4b, c). Similarly, expression of ‘M2’ function related genes Mrcl and Fnl were lower in the ‘M2-like’ macrophage clusters (cluster 3,4,7,13,16,19,21) from KD tumors and were lowest in KD tumors treated with anti-PD-1 (FIG. 4d). In vitro macrophage polarization assay using tumor cell conditioned medium provided further support of this notion, as shown by the increased Nos2 expression and decreased Mrcl expression in bone marrow derived macrophages during M1 and M2 polarization process respectively (FIG. 9c).

[0158] Knocking down PTDSS1 also increased the frequency of an iNOS+myeloid cell cluster which were typically observed in mice responding to ICT. Induction of this iNOS+myeloid cell cluster by anti-PD-1 andPTDSS1 deficiency in tumor cells was synergistic (FIG. 9d). The increased frequency of iNOS+myeloid cell observed in CyTOF was validated by scRNAseq analysis, as shown by the increased abundance of Nos2 expressing myeloid cells (FIG. 4e,f). These cells in the KD tumors had lower expression of Ccl24 and Mrc1 (FIG. 4g), which are important for maintaining M2-like function. These cells also had higher expression of Ccl5, Nos2 and AW112010 in KD tumors. While tumor-promoting effect been described for Ccl5, recent reports also suggest Ccl5 may have an anti-tumor role in the ICT setting, and thisanti-tumor role may depend on CD4+T cell infiltration. Together with the increased CD4+Tefffrequency in KD tumors, these data suggest knocking down Ptdss1 may improve anti-PD-1 response via the Ccl5-CD4 T cell axis. AW112010 is a non-coding RNA which has pro- inflammatory function in bone marrow derived macrophages (BMDM) and is essential for mucosal immunity. The increased AW112010 in the Nos2+myeloid population in KD tumors suggest that knocking down Ptdssl may polarize this myeloid population towards a pro- inflammatory state. A gene signature consisting of the top 5 upregulated genes(Ccl5, Apoe, Nos2, Clqb and Ubb) in the Nos2+myeloid population from the KD tumors correlated with clinical benefit for patients treated with anti-PD-1 / PD-L1 (FIG. 4h,i). Together, these results suggest loss of Ptdss1 in tumor cells shapes an inflammatory anti-tumor microenvironment.

[0159] Further data indicate that the supernatant from PTDSS1KDcells can polarize bone- marrow derived murine macrophages towards an ‘M1 ’, or immunostimulatory, phenotype (FIG. 13a, b).EXAMPLE 5Inhibition of PTDSS1 Synergizes with Anti-PD-1 Treatment

[0160] Having identified the tumor intrinsic and tumor extrinsic changes induced by PTDSS1 deficiency, it was next interrogated whether loss of Ptdss1 in tumor cells could improve antitumor responses with anti-PD-1 therapy. First, to determine whether loss of PTDSS1 led to any differences in cell proliferation and / or survival, cell growth rates were measured in vitro by IncuCyte proliferation assay and in vivo by measuring tumor growth in immune deficient NSG mice. Results showed that the cell growth rates of KD clones and control cells were indistinguishable from each other (FIG. 10a), and the tumor growth curves in NSG mice are comparable between KD tumors and control tumors (FIG. 10b). This data is in line with the report that Ptdss1 knockout mice were viable and fertile compared to their littermate control.

[0161] To evaluate responses with anti-PD-1 therapy, KD or control tumors were injected into mice prior to treatment with anti-PD-1. It was found that KD tumors grew more slowly (FIG. 5a) and were smaller in size (FIG. 10c, d) as compared to control tumors treated with PBS. Mice with KD tumors also exhibited improved survival (FIG. 5b). Together with the intact tumor growth rate in NSG mice, these data suggest PTDSS1 deficiency-induced tumor growth depends on an intact tumor immune microenvironment. In contrast to a modest tumor inhibition effect of anti-PD-1 treatment seen in mice inoculated with WT tumors, anti-PD-1 significantly inhibited KD tumor growth and provided significant improvement in animal survival. The improved response to anti-PD-1 was also observed in Bl 6F 10 melanoma model(FIG. 5c, d). These data suggest knocking down PTDSS1 in tumor cells sensitized tumors to anti-PD-1 treatment. Analysis of published dataset showed that patients with melanoma who were treated with anti-PD-1 had better response to therapy and longer overall survival if they had low PTDSS1 RNA and protein levels (FIG. 5e,f).EXAMPLE 6Evaluation of Genetic Depletion of PTDSS1 on Reversing Resistance To Anti-PD-1 Therapy in Orthotopic Tumor Models

[0162] The effect of PTDSS1 genetic knockdown in ICT-resistant, orthotopic models of bladder (MB49), pancreatic (mT4-LS), and breast cancer (E0771) is evaluated. mT4-LS and E0771 Ptdssl knockdown (Ptdss1KD) and control cells transduced with empty shRNA backbone (WT) are established by lentiviral infection of parental cells using pMD2.G and pSPAX2 lentiviral packaging system and LentiCRISPRv2GFP with or without sgRNAs targeting PTDSS1. Knockdown is confirmed using qPCR and western blot. The role of PTDSS1 in regulating tumor growth and animal survival with or without anti-PDl treatment is then evaluated.

[0163] For the orthotopic breast cancer model, 2x105Ptdss1KDor WT E0771 cells in 30%Matrigel are injected into the mammary fat pad of female C57BL / 6 mice. For the orthotopic bladder model, mice are anesthetized using isoflurane and injected with buprenorphine as prophylactic analgesia (3mg / ml; i.p.) followed by survival surgery to expose the bladder wall; 2x105Ptdss1KDor WT MB49 cells in 30% Matrigel are injected into the bladder wall of C57BL / 6 mice.

[0164] For the orthotopic pancreatic model, mice are also anesthetized using isoflurane and injected with buprenorphine as prophylactic analgesia (3mg / ml; i.p.). 3.5x104mT4-LS cells in 30% Matrigel are surgically implanted into head of the pancreas of C57BL / 6 mice. Mice are then randomized to receive either PBS or three doses of anti-PD-1 (BioXCell, BE0146; 200μg / 100μg / 100μg; n=10 mice / group).

[0165] Two dosing strategies are employed; (1) dosing mice on days 3, 6, and 9 post-tumor inoculation to evaluate response during tumor development and (2) dosing mice on days 7, 10, and 13, to evaluate response in established tumors. Tumor growth is tracked by caliper measurements in the E0771 model and by luciferase imaging in the mT4-LS model. Mice are sacrificed when the tumor volume exceeds 1500mm3or the mice are moribund. Genetic deletion of PTDSS1 enhances responses to anti-PD-1 therapy with improved survival in the proposed orthotopic models (MB49, E0771, mT4-LS).EXAMPLE 7Evaluation of PTDSS2 in Tumor Models

[0166] PTDSS1 has been reported to have some functional redundancy with PTDSS2 in phosphatidylserine production. RNAseq data disclosed herein suggest that PTDSS2 is not increased transcriptionally in response to PTDSS 1 knockdown. To ensure that modulation of tumor cell intrinsic immunogenicity is attributable to PTDSS1, and not a compensatory role of PTDSS2, PTDSS2 is knocked down using CRISPR-Cas9 in Ptdss1KDand WT cells. While double knockout of Ptdss1 / Ptdss2 has been shown to be embryonic lethal, partial knockdown of Ptdss1 is shown to be sufficient for viability in PTDSS2KO. WT / PTDSS2KOand PTDSS1KD / PTDSS2KOtumor cells are delivered orthotopically and evaluated as described above.EXAMPLE 8Mechanism of PTDSS1-Mediated IFN Signaling and Antigen Presentation Pathways in Regulating Anti-PD-1 Response

[0167] This example covers the role of tumor cell intrinsic PTDSS 1 in the regulation of antitumor immune responses. RNAseq was performed on WT and PTDSS1KDMB49 tumor cells. A significant enrichment of genes involved in interferon (IFNγ)-response pathway in tumor cells was found following PTDSS 1 knockdown (FIG. 11a). Increased expression of genes involved in antigen processing and presentation machinery including MHC-I and MHC-I light chain, β2-microglobulm (β2m) was also observed. Flow cytometry analyses confirmed surface level increases in MHC-I and β2m (FIG. 11b-e) in PTDSS1KDat baseline. Significantly increased IFNγ signaling (FIG. 11f) and expression of MHC-I and β2m was also observed when exogenous IFNγ was added (FIG. 11 b-e). PTDSS1KDcells also demonstrated significantly enhanced antigen presentation capacity, as measured by H2kb bound to SIINFEKL peptide (SEQ ID NO: 16) (FIG. 11g, h). Increase antigen-specific T cell mediated tumor cell lysis was also observed in vitro (FIG. 11i). These data suggest that loss ofPtdss1 enhances tumor cell immunogenicity by upregulating MHC-I and antigen presentation via an IFNγ-driven mechanism.

[0168] These data provide strong evidence that genetic deletion of PTDSS 1 in tumor cells results in transcriptional enrichment of genes involved in IFNγ-response, increased IFNγ- pathway signaling, and a functional increase in MHC-I surface expression, along with enhanced antigen presentation (FIG. 11a-i). Upregulation of IFNγ-response pathway occurs in PTDSS1KDtumor cells even in the absence of exogenous IFNγ or exposure to immune cells(FIG. 11b-e). Finally, FIG. 12a, b show a metabolic shift following the loss of Ptdssl in MB49 tumor cells.EXAMPLE 9Modulation of PTDSS1-Mediated IFN Signaling

[0169] This example is directed to whether increased IFN response in PTDSS1 knockout cancer cells is due to increased IFNγ production by the cancer cells following deletion of PTDSS1. IFNγ is measured in 100 μL of cell culture supernatant from WT and PTDSS1KDtumor cells using ELISA (Invitrogen). Essentiality of the IFNγ receptor signaling is tested using two approaches. First, IFNγ-blocking experiments are performed using plate bound anti- IFNγ antibody to inhibit binding to the cognate IFNγ-receptor. High-binding 6-well plates will be coated with a neutralizing IFNγ antibody (Invitrogen; XMG1.2) at 5 μg / ml for 3 h (37°C), after which 3x106WT or PTDSS1KDcells are cultured for 1 hour prior to evaluation. Second, Ifngr1 or Stall are knocked down in both WT and PTDSS1KDcells. Lentiviral particles of mouse shRNA specific to Ifngr1 or Stat1 genes and control scramble shRNA (Santa Cruz Biotechnology) are transduced in cells according to the manufacturer’s instruction. Briefly, WT and PTDSS1KDMB49 cells are cultured in 12-well plate to reach 50% confluence, transduced with lentiviral particles and then selected with puromycin-containing culture medium. Single cell colonies are selected and expanded in puromycin-containing culture medium. RT-PCR and western blot are used to confirm the extent of Ifngr1 or Stat1 gene knockdown.

[0170] The IFNγ-receptor signaling pathway is evaluated by western blot, wherein 3x106control and knockdown cells are used to evaluate the expression of downstream proteins associated with IFNγ signaling including JAK1 (CST, 3332), pJAKl(Tyr1034 / 1035)(CST, 3331), STAT1 (CST, 9172), pSTATl(Ty701) (CST, 9167), STAT3 (CST, 9139), pSTAT3 (Tyr705)(CST, 9145). β-Actin (CST, 4970) is used as the loading control. Following the baseline evaluation, these experiments are repeated by treating the WT and knockdown cells with 10ng / ml IFNγ for 0, 2, 5, 10, 30, 60 minutes. Next, to measure cell surface expression of MHC-I and β2m, 0.5x106control and knockdown cells are treated with 0 or 10ng / ml IFNγ for 24 hours then probed with MHC-1 (H-2Kb / H-2Db; Biolegend, 11 1507) and β2m (Biolegend, 154504) and analyzed on LSR II cytometer (Becton Dickinson). For antigen presentation assay, 0.5x106control and various knockdown cells are first pulsed with 100ng / ml OVA257-264 peptide SIINFEKL (SEQ ID NO: 16) for 6h, followed by flow cytometry analysis of SIINFEKL (SEQ ID NO: 16) bounded H2Kb level . To assess changes in T cell killing, naiveCD8 T cells are isolated from spleen and thymus of OT-1 mouse using CD8 T cell isolation kit , and subsequently cultured in T cell culture media supplemented with 30U / ml mouse IL-2 , 50μM β-mercaptoethanol and CD3 / CD28 magnetic beads for 5 days. On the day of tumor cell- T cell co-culture, tumor cells are pulsed with OT-1 specific peptide, SIINFEKL (SEQ ID NO: 16), for 2h, then cell culture media is removed and activated T cells are added to tumor cell culture at different ratios and co-cultured for 24h. Killing of tumor cells by T cells is measured by 7-AAD staining followed by flow cytometry analysis.

[0171] These experiments address whether blocking IFNγ binding to its cognate receptor with a neutralizing antibody or knocking down Ifngr1 / Stat1 mitigates the IFNγ response pathway in PTDSS1KDcells resulting in decreased MHC-I expression, decreased antigen presentation, and reduced antigen-specific T cell-mediated killing of PTDSS1KDtumor cells.EXAMPLE 10Role of PTDSS1-Mediated Regulation of Antigen Presentation on the Modulation ofResponses to Anti-PD1 Therapy In Vivo

[0172] This example addresses whether PTDSS1 -mediated tumor immunogenicity occurs strictly though downstream regulation of MHC-I / APP. β2-microglobulin (MHC-I light chain, β2m) is knocked out in WT and PTDSS1KDtumor cells and injected into mice to determine the impact on tumor growth and survival, both in the presence and absence of anti-PD-1 treatment. RT-PCR and western blot are used to confirm the extent of β2m knockdown. Tumor cells are then administered orthotopically to mice. WT, WT+ β2mKD, PTDSS1KD, and PTDSS1KD+ β2mKDare treated with PBS or anti-PD-1 (day 3, 200μg; day 6, 100μg; day 9, 100μg). Mice are monitored for tumor progression and survival as previously described. Ablation of β2m is shown to abrogate tumor sensitization to anti-PD-1 seen in the PTDSS1KDmodel, indicating that PTDS SI ^-mediated upregulation of MHC-I and increased antigen presentation contributes to the anti-tumor effect seen in vivo.

[0173] To account for the complexity of the tumor immune microenvironment and that IFNγ signaling is only one pathway that may be responsible for the PTDSS1KDsensitivity to anti- PD-1 therapy, WT and PTDSS1KDtumors for cytokines / chemokines that could affect immune responses will also be investigated. The secretome of WT and Ptdss1KDtumors are comprehensively characterized using a 50-plex Mouse Luminex Discovery Assay. Identified cytokines / chemokines are tested for their potential role in anti-tumor responses by blocking the specific cytokine / chemokine with neutralizing antibodies in vivo.EXAMPLE 11Changes in DNA Repair Pathways Associated with the Loss of PTDSS1

[0174] In addition to functional enrichment of MHC-1 expression and antigen processing, GSEA of RNAseq data indicates a significant decrease in the DNA repair pathway in PTDSS1KDtumor cells (FIG. 14a, b). Because defects in the DNA repair machinery can contribute to increased neoantigen burden which can further boost immunogenicity, PTDSS1 may contribute to tumor cell intrinsic immunogenicity by impairing DNA repair and modulating the expression of tumor antigens.EXAMPLE 12Tumor Cell Immunopeptidome Associated with the Loss of PTDSS1

[0175] This example is directed to the role of PTDSS1 on the tumor immunopeptidome and DNA repair. Phosphorylated histone 2AX (H2AX) is quantified as a surrogate measure of genomic DNA damage in WT and PTDSS1KDcells by western blot. Briefly, 1x106WT and Ptdss1KDcells are harvested and lysed in RIP A buffer containing protease inhibitor cocktail. Cell extracts are separated on 10% SDS-PAGE transferred to nitrocellulose membrane, followed by blocking in 5% skim milk in phosphate buffered saline / Tween 20 (PBST) and then probed with phopho-H2AX (Ser139) and H2AX antibody at 4°C overnight, using manufacturer’s protocol.

[0176] To characterize the immunopeptidome, MHC-peptide complexes are isolated from WT and PTDSS1KDcells by immunoprecipitation (anti-H-2Kb B; clone AF6-88.5 ; conjugated to 20 μl FastFlow protein A sepharose bead), eluted with 10% acetic acid, filtered by size exclusion filter (Nanosep 10K, PALL), cleaned up with zip tip, and dried with speed-vac. Samples are resuspended in 3% aceotnitrile / 0.1% formic acid and directly loaded onto an analytical capillary chromatography column (50 μm ID x 15 cm and 1.9 μm C18 beads, ReproSil-Pur).

[0177] The samples are analyzed using an Orbitrap Exploris 480 Mass Spectrometer. Peptides are eluted and analyzed. Resulting mass spectra are analyzed using Proteome Discoverer (v3.0) and searched using Mascot (v2.8). For peptide bound HLA, peptides are searched with no enzyme and variable methionine oxidation. Peptide spectrum matches are filtered by an ion score ≥15, length 8-11, search engine rank of 1, and aggregated across unique peptides. GibbsCluster 2.0 are used for motif analysis. PTDSS1KDcells exhibit increased levels of p-H2AX compared to WT cells, indicating impaired DNA damage repair response. Theincrease in DNA damage in PTDSS1KDcells lead to a significantly increased immunopeptide diversity and neoantigen burden in PTDSS1KDtumors.EXAMPLE 13Contribution of PTDSS1 to Antigen Specific T Cell Expansion and TumorMicroenvironment Remodeling

[0178] CD8 T cells exhibit increased prevalence in PTDSS1KDtumors both at baseline and, more significantly, after treatment with anti-PD-1 therapy. Since expansion of tumor antigenspecific effector CD8 T cells correlates with improved response to ICT, the role of PTDSS1 in the antigen-specific CD8 T cell response is evaluated by MHC tetramer assays and TCR sequencing.

[0179] An MHC tetramer assay is performed to detect and quantify antigen specific CD8 T cells in the tumor microenvironment. The peptide sequence information derived from the immunopeptidome studies is used to identify the peptide sequences for the generation of the peptide-MHC (pMHC) tetramer library. pMHC tetramers are generated. The single cell suspensions from WT and Ptdss1KDorthotopic tumors (n=5 / group) are stained using the pMHC tetramers bearing tumor cell specific antigens. For staining, cells are washed in PBS containing 5% FBS (FACS buffer) and subsequently incubated in the pMHC tetramer-anti-CD45 AF532, anti-CD3-AF488, anti-CD8-BV711 cocktail (1:200 dilution of pMHC: FACS buffer) for 60 minutes on ice. Following incubation, stained cells are washed in FACS buffer before flow cytometry-based detection of tetramer bound tumor antigen-specific CD8 T cells.

[0180] TCR sequencing is performed on intratumoral CD8 T cells in WT and PTDSS1KDtreated with PBS or anti-PDl (n=5 / group) to evaluate the diversity of T cell clones in vivo. Tumor samples are digested into single-cell suspensions and stained using anti-CD3 (Biolegend, 17A2), anti-CD4 (Biolegend, GK1.5) and anti-CD8 (Biolegend, 53-5.8) before flow cytometry assisted sorting of CD8 T cells. Chromium Single Cell 5’ Reagent kit (V 2), Chromium Single Cell Mouse TCR Amplification Kit (10x Genomics) are used to generate the scRNA seq gene expression and TCR seq libraries. Assays are performed according to the manufacturer’s instructions. Briefly, single-cell suspension with 15,000 cells is loaded on the Chromium Controller Instrument (10x Genomics) to generate single-cell gel beads in emulsions followed by reverse transcription reactions. Barcoded full-length cDNA is recovered by using the recovery agent. DynaBeads Myone Silane will be used to clean up cDNA. Subsequently, amplified cDNA is fragmented, end-repaired, A-tailed, index adapter ligated,and library amplified. V(D)J amplication from cDNA is performed, a separate TCR-seq library is generated. Libraries are sequenced on the lllumina-sequencing platform.EXAMPLE 14Differential Immune Cell Abundance and Spatial Interactions Within Tumor Samples from Patients with PTDSS1- High and PTDSS1-Low Expression

[0181] To further interrogate the loss of PTDSS1 in tumor cells induces intratumoral infiltration of effector T cells and M1 -like macrophages, this example is directed to whether and how loss of PTDSS1 in tumor cells orchestrate the TME architecture. Spatial architecture of the TME, which dictates how different components of the TME interact with each other, is a major determinant of ICT efficacy.

[0182] Deidentified, untreated, and anti-PD-1 bladder cancer samples (N=20 each) and melanoma samples (N=20 each) were collected under IRB-approved institutional tissue collection laboratory protocol PA13-0291. Based on PTDSS1 gene expression, the cohort is divided based on a median value into PTDSS1-high and PTDSS1-low samples. Immune infiltration and spatial interactions are profiled by performing CODEX. Briefly, 4 μm FFPE tumor tissue sections are stained with conjugated and barcode-tagged antibodies. The following anti-human and / or anti-mouse antibodies (PTDSS1, CD107A, CD1 1c, CD1 1b, CD103, CD20, CD21, CD23, CD31, CD34, PNAD, Podoplanin, CD44, CD45RO, CD68, CD8, E-cadherin, KI67, and Pan-Cytokeratin, CD45, CD3, CD4, Granzyme B, CD14, CD15, CD57, CD47, EOMES, FOXP3, HLA-DR, MHC-II, ICOS, LAG3, PD-1, T-BET, TOX-1, TCF-1, CCR-7, IDO-1, TMEM119, CD163, CSF-1R, MSR-1, LY6C, LY6G, VISTA and INOS) are purchased (Akoya Biosciences, if available in pre-conjugated form) or are conjugated in-house using commercially available conjugation kits (Akoya). Whole tissue is defined using multiplex immune fluorescence staining. Stained sections are captured using a Phenocycler-Fusion (Akoya). Alignment of images across cycles, stitching of tiles and subtraction of autofluorescence is performed using CODEX® Processor application and a qptiff image is generated once a run is completed. These qptiff generated images are used for downstream image analysis, and enable assessment whether inflammatory TME pre-exist in PTDSS1lowpatients.EXAMPLE 15Evaluation of a PTDSS1 Inhibitor for Improving Response to Immune CheckpointInhibitor Therapy

[0183] This example interrogates of the role of pharmacological inhibition of PTDSS1 with DS55980254 in improving response to anti-PD-1 therapy in subcutaneous tumor models, and furthermore whether PTDSS1 inhibition sensitizes tumors to anti-PD-1 therapy. DS55980254 (molecular formula C29H18F8N4O4; Yoshihama et al., Cancer Res., 2022; 82(21):4031) is a PTDSS1 specific inhibitor that is safe in animal models and can affect tumor regression in murine models of colon cancer and melanoma.

[0184] MB49 cells were injected subcutaneously into male C57BL / 6 mice to establish tumor model. Starting from day 6, the tumor bearing mice were randomized to receive either PBS, anti-PD-1 (BioXCell, BE0146; day 3, 200ug; day 6, 100ug; day 9, 100ug), or anti-PD-1 plus 10mg / kg DS55980254 daily for a total of 14 days. Tumor growth is tracked by caliper measurements. Mice with tumor volume exceeds 1500mm3 are sacrificed.

[0185] This experimental outline is shown in FIG. 15. Mice are sacrificed when the tumor volume exceeds 1500mm3or mice are moribund. Duration of survival and animal body weight are recorded. The tumor growth rate in anti-PD-1 plus DS55980254 treated group is significantly slower than that in anti-PD-1 treated group (FIG. 16).

[0186] Although the invention has been described with reference to the presently preferred embodiment, it should be understood that various modifications can be made without departing from the spirit of the invention. Accordingly, the invention is limited only by the following claims.

Claims

We Claim:1 . A method of treating cancer in a subject in need thereof comprising administering an agent that modulates PTDSS1 expression and an immune modulator to the subject, thereby treating the cancer in the subject.

2. The method of claim 1, wherein the agent that modulates PTDSS1 expression comprises an RNA-guided nuclease, miRNA, siRNA or shRNA.

3. The method of claim 1, wherein the agent that modulates PTDSS1 expression, the immune modulator, or a combination thereof is targeted to a cancer cell.

4. The method of claim 1, wherein the agent that modulates PTDSS1 expression, the immune modulator, or a combination thereof is configured for uptake by the cancer cell.

5. The method of claim 1, wherein the agent that modulates PTDSS1 expression is coupled to or coencapsulated with the immune modulator.

6. The method of claim 5, wherein the agent that modulates PTDSS1 expression and the immune modulator are coupled to or coencapsulated within a nanoparticle, a microparticle, a lipid particle, or a viral particle.

7. The method of claim 5, wherein the agent that modulates PTDSS1 expression and the immune modulator are connected by a chemical linker.

8. The method of claim 1, wherein the immune modulator is an immune checkpoint inhibitor, a cytokine, a chemokine, an interleukin, an immunomodulatory drug, a tolllike receptor agonist, an oligodeoxynucleotide, a glucan, a type I interferon receptor agonist, or a type II interferon receptor agonist.

9. The method of claim 8, wherein the immune modulator is an immune checkpoint inhibitor.

10. The method of claim 8, wherein the immune modulator is a type I interferon receptor agonist or a type II interferon receptor agonist.

11. The method of claim 9, wherein the immune checkpoint inhibitor is a CTLA-4 inhibitor, a PD-1 inhibitor, a PD-L1 inhibitor, a PD-L2 inhibitor, a LAG-3 inhibitor, a KIR inhibitor, a TIM-3 inhibitor, a TIGIT inhibitor, a 4-1 BB inhibitor, a 4-1BBL inhibitor, a GITR inhibitor, a GITRL inhibitor or a galectin inhibitor.

12. The method of claim 11, wherein the CTLA-4 inhibitor is ipilimumab, tremelimumab, BMS-986218, AGEN1 181, AGEN1884, BMS-986249, MK-1308, REGN-4659, ADU- 1604, CS-1002 , BCD-145, APL-509, JS-007, BA-3071, ONC-392, AGEN-2041 , JHL- 1155, KN-044, CG-0161, ATOR-1144, PBI-5D3H5, BPI-002, FPT-155, PF-06936308, MGD-019, KN-046, MEDI-5752, XmAb-20717, or AK-104.

13. The method of claim 1 1 , wherein the PD-1 inhibitor is nivolumab, pembrolizumab, pilizumab, BGB-108, SHR-1210, PDR-001, PF-06801591 , IBI-308, GB-226, STI- 1110, or mDX-400.

14. The method of claim 11, wherein the PD-Ll inhibitor is atezolizumab, avelumab, AMP- 224, MEDI-0680, RG-7446, GX-P2, durvalumab, KY-1003, KD-033, MSB- 0010718C, TSR-042, ALN-PDL, STI-A1014, GS-4224, CX-072, or BMS-936559.

15. The method of claim 11, wherein the LAG-3 inhibitor is relatlimab.

16. The method of claim 11 , wherein the TIM-3 inhibitor is TSR-022, LY-3321367, MBG-453, or INCAGN-2390.

17. The method of claim 11, wherein the TIGIT inhibitor is BMS-986207, RG-6058, or AGEN-1307.

18. The method of claim 11, wherein the 4-1BB inhibitor is urelumab, utomilumab, emfizatamab, BMS-663513, PF-05082566, PRS-343, RG7827, ADG106, INBRX-105, CTX-471, BNT311, BNT311, RG6706, MP0310, BNT312, AGEN2373, LVGN6051 , ATOR- 1017, STA551 , or ND-021.

19. The method of claim 1 1, wherein the GITR inhibitor is MEDI1873, FPA-154,INCAGN-1876, TRX-518, BMS-986156, MK-1248, or GWN-323.

20. The method of claim 1 1 , wherein the galectin inhibitor is thiodigalactoside, β-D- lactosyl-steroid, GB1 107, lactulose-L-leucine, modified citrus pectin, PectaSol-C, GCS-100, GM-CT-01, belapectin, DB16, DB21, OTX008, PTX013, LLS30, or LLS2.21 . The method of claim 11 , wherein the immune checkpoint inhibitor is a PD-1 inhibitor or a PD-L1 inhibitor.

22. The method of claim 21 , wherein the immune checkpoint inhibitor is a PD-1 inhibitor.

23. The method of claim 9, wherein the immune checkpoint inhibitor is an antibody or an antibody fragment.

24. The method of claim 10, wherein the immune modulator is IFNγ or IFNα.

25. The method of claim 1, wherein the cancer is selected from bile duct cancer, bladder cancer, brain cancer, breast cancer, carcinoma, cervical cancer, colorectal cancer, endometrial cancer, epitheloid carcinoma of the bone, esophageal cancer, gallbladder cancer, gastric cancer, glioblastoma, hepatocellular cancer, large cell lung carcinoma, leukemia, lung cancer, medulloblastoma, melanoma, ovarian cancer, non-small cell lung cancer, pancreatic cancer, prostate cancer, renal cell cancer or thyroid cancer.

26. The method of claim 25, wherein the cancer is bladder cancer.

27. A method of identifying a subject with cancer as a candidate for treatment with an immune modulator comprising: a) determining that: i) cancer cells from the subject have diminished PTDSS1 expression; ii) the cancer cells from the subject comprise at least one of increased B2M, CXCL9, CXCL10, ST ATI or TAPI expression; iii) the cancer cells from the subject comprise diminished RAS signaling; iv) immune cells in a tumor microenvironment of the cancer comprise increased CCL5 expression, increased APOE expression, increased NOS2 expression, increased Cl QB expression, increased UBB expression, or a combination thereof; v) the tumor microenvironment of the cancer comprises an elevated ratio of Th1 to Th2 CD4+cells; vi) CD8+effector T cells in the tumor microenvironment of the cancer comprise elevated expression levels of Prf1, Nkg7, Gzmb, Gzmk, or a combination thereof; vii) the tumor microenvironment of the cancer comprises an elevated ratio of M1 to M2 macrophages; or viii) a combination thereof; and classifying the subject as a likely responder to the immune modulator if any one of i)- viii) are determined, thereby identifying the subject as suitable for treatment with the immune modulator.

28. The method of claim 27, further comprising administering the immune modulator to the subject, thereby treating the cancer in the subject.

29. The method of claim 27, wherein the determining comprises comparison to a control sample.

30. The method of claim 29, wherein the control sample is: i) a biological sample from a subject that does not have cancer, ii) a non-cancerous biological sample from the subject, or iii) a sample from a subject with a cancer that expresses PTDSS1.

31. The method of claim 27, wherein the cancer is selected from bile duct cancer, bladder cancer, brain cancer, breast cancer, carcinoma, cervical cancer, colorectal cancer, endometrial cancer, epitheloid carcinoma of the bone, esophageal cancer, gallbladder cancer, gastric cancer, glioblastoma, hepatocellular cancer, large cell lung carcinoma, leukemia, lung cancer, medulloblastoma, melanoma, ovarian cancer, non-small cell lung cancer, pancreatic cancer, prostate cancer, renal cell cancer or thyroid cancer.

32. The method of claim 31 , wherein the cancer is bladder cancer.

33. The method of claim 27, wherein the determining comprises mass spectrometry, liquid chromatography, gas chromatography, nuclear magnetic resonance, fluorometric analysis, a protein binding assay, mRNA profiling, genomic profiling, PCR, RNA sequencing, Western blot analysis, Northern blot analysis, microarray analysis, SAGE, FISH, flow cytometry, or a combination thereof.

34. The method of claim 27, wherein the method comprises determining that the cancer cells have diminished PTDSS1 expression.

35. The method of claim 34, wherein the diminished expression is about 95%, about 90%, about 80%, about 70%, about 60%, about 50%, about 40%, about 30%, about 25%, about 20%, about 15%, about 10%, or about 5% of the PTDSS1 expression in a non- cancerous cell of a cell type from which the cancer is derived.

36. The method of claim 27, wherein the method comprises determining that the cancer cells comprise increased B2M, CXCL9, CXCL10, STAT1 or TAPI expression or a combination thereof.

37. The method of claim 36, wherein the increased expression is about 105%, about 110%, about 120%, about 130%, about 140%, about 150%, about 175%, about 200%, about 250%, about 300%, about 400%, or about 500% of the B2M, CXCL9, CXCL10, STAT1 or TAPI expression in a non-cancerous cell of a cell type from which the cancer is derived.

38. The method of claim 27, wherein the method comprises determining that the cancer cells comprise diminished RAS signaling.

39. The method of claim 38, wherein the determining comprises measuring phosphorylatedMEK, phosphorylated ERK, or a combination thereof.

40. The method of claim 38, wherein a level of phosphorylated MEK or phosphorylated ERK in the cancer cells is about 95%, about 90%, about 80%, about 70%, about 60%, about 50%, about 40%, about 30%, or about 25% of a level of phosphorylated MEK or phosphorylated ERK in a non-cancerous cell of a cell type from which the cancer is derived.

41. The method of claim 27, wherein the method comprises determining that immune cells in the tumor microenvironment of the cancer comprise increased CCL5 expression, increased APOE expression, increased NOS2 expression, increased C1QB expression, increased UBB expression or a combination thereof.

42. The method of claim 41, wherein the increased expression is about 105%, about 110%, about 120%, about 130%, about 140%, about 150%, about 175%, about 200%, about 250%, about 300%, about 400%, or about 500% of the CCL5 expression, APOE expression, NOS2 expression, C1QB expression, UBB expression, or the combination thereof in immune cells from a tumor microenvironment of a cancer that does not comprise diminished PTDSS1 expression.

43. The method of claim 41, wherein the immune cells are myeloid cells.

44. The method of claim 27, wherein the method comprises determining that the tumor microenvironment of the cancer comprises an elevated ratio of Th1 to Th2 CD4+cells.

45. The method of claim 44, wherein the elevated ratio of Th1 to Th2 CD4+cells in the tumor microenvironment of the cancer is about 1.1-, about 1 .2-, about 1 .3-, about 1 .4-, about 1.5-, about 1.75-, about 2-, about 2.25-, about 2.5-, about 3-, about 4-, or about5-fold higher than a ratio of Th1 to Th2 CD4+cells in a tumor microenvironment of a cancer that expresses PTDSS1.

46. The method of claim 44, wherein the elevated ratio of Th1 to Th2 CD4+cells is about 2:3, about 5:6, about 1 :1, about 7:6, about 4:3, about 3:2, about 5:3, about 1 1 :6, about 2:1, about 5:2, about 3:1, or about 7:2.

47. The method of claim 27, wherein the method comprises determining that CD8+effector T cells in the tumor microenvironment of the cancer comprise elevated expression levels of Prfl, Nkg7, Gzmb, Gzmk or a combination thereof.

48. The method of claim 47, wherein the method comprises detecting Prfl , Gzmb, or Gzmk in CD8+effector T cells in the tumor microenvironment of the cancer.

49. The method of claim 47, wherein the elevated expression is about 105%, about 110%, about 120%, about 130%, about 140%, about 150%, about 175%, about 200%, about 250%, about 300%, about 400%, or about 500% of the Prfl expression, the Nkg7 expression, the Gzmb expression, the Gzmk expression, or the combination thereof in immune cells from a tumor microenvironment of a cancer that does not comprise diminished PTDSS1 expression.

50. The method of claim 27, wherein the method comprises determining whether the tumor microenvironment of the cancer comprises an elevated ratio of M1 to M2 macrophages.

51. The method of claim 50, wherein the elevated ratio of M1 to M2 macrophages in the tumor microenvironment of the cancer is about 1.1 -, about 1.2-, about 1.3-, about 1.4-, about 1.5-, about 1.75-, about 2-, about 2.25-, about 2.5-, about 3-, about 4-, or about 5-fold higher than a ratio of M1 to M2 macrophages in a tumor microenvironment of a cancer that does not comprise diminished PTDSS1 expression.

52. The method of claim 50, wherein the determining comprises measuring CD206 expression in macrophages from the tumor microenvironment.

53. The method of claim 28, wherein the immune modulator is an immune checkpoint inhibitor, a cytokine, a chemokine, an interleukin, an immunomodulatory imide drug, a toll-like receptor agonist, an oligodeoxynucleotide, a glucan, a type I interferon receptor agonist, or a type II interferon receptor agonist.

54. The method of claim 53, wherein the immune modulator is an immune checkpoint inhibitor, a type I interferon receptor agonist, or a type II interferon receptor agonist.

55. The method of claim 54, wherein the immune modulator is an immune checkpoint inhibitor.

56. The method of claim 55, wherein the immune checkpoint inhibitor is selected from a CTLA-4 inhibitor, a PD-1 inhibitor, a PD-L1 inhibitor, a PD-L2 inhibitor, a LAG-3 inhibitor, a KIR inhibitor, a TIM-3 inhibitor, a TIGIT inhibitor, a 4-1BB inhibitor, a 4- 1BBL inhibitor, a GITR inhibitor, a GITRL inhibitor, a galectin inhibitor or a combination thereof.

57. The method of claim 56, wherein the immune checkpoint inhibitor is a PD-1 inhibitor.

58. The method of claim 54, wherein the immune modulator is a type I interferon receptor agonist or a type II interferon receptor agonist.

59. The method of claim 58, wherein the immune modulator is IFNγ or IFNα.

60. The method of claim 27, wherein the cancer is a solid tumor.

61. A method of treating cancer in a subject in need thereof comprising administering an agent that inhibits phosphatidylserine synthase 1 (PTDSS1) and an immune modulator to the subject, thereby treating the cancer in the subject.

62. The method of claim 61, wherein the agent that inhibits PTDSS1 comprises DS55980254.

63. The method of claim 61, wherein the agent that inhibits PTDSS1, the immune modulator, or a combination thereof is targeted to a cancer cell.

64. The method of claim 61, wherein the agent that inhibits PTDSS1, the immune modulator, or a combination thereof is configured for uptake by the cancer cell.

65. The method of claim 61, wherein the agent that inhibits PTDSS1 is coupled to or coencapsulated with the immune modulator.

66. The method of claim 65, wherein the agent that inhibits PTDSS1 and the immune modulator are coupled to or co-encapsulated within a nanoparticle, a microparticle, a lipid particle, or a viral particle.

67. The method of claim 65, wherein the agent that inhibits PTDSS1 and the immune modulator are connected by a chemical linker.

68. The method of claim 61, wherein the immune modulator is an immune checkpoint inhibitor, a cytokine, a chemokine, an interleukin, an immunomodulatory drug, a tolllike receptor agonist, an oligodeoxynucleotide, a glucan, a type I interferon receptor agonist, or a type 11 interferon receptor agonist.

69. The method of claim 68, wherein the immune modulator is an immune checkpoint inhibitor.

70. The method of claim 68, wherein the immune modulator is a type I interferon receptor agonist or a type II interferon receptor agonist.

71. The method of claim 69, wherein the immune checkpoint inhibitor is a CTLA-4 inhibitor, a PD-1 inhibitor, a PD-L1 inhibitor, a PD-L2 inhibitor, a LAG-3 inhibitor, a KIR inhibitor, a TIM-3 inhibitor, a TIGIT inhibitor, a 4- IBB inhibitor, a 4-1 BBL inhibitor, a GITR inhibitor, a GITRL inhibitor, a galectin inhibitor, or a combination thereof.

72. The method of claim 71, wherein the CTLA-4 inhibitor is ipilimumab, tremelimumab, BMS-986218, AGEN1 181, AG.EN1884, BMS-986249, MK-1308, REGN-4659, ADU- 1604, CS-1002 , BCD-145, APL-509, JS-007, BA-3071, ONC-392, AGEN-2041, JHL- 1155, KN-044, CG-0161, ATOR-1144, PBI-5D3H5, BPI-002, FPT-155, PF-06936308, MGD-019, KN-046, MEDI-5752, XmAb-20717, AK-104, or a combination thereof.

73. The method of claim 71 , wherein the PD-1 inhibitor is nivolumab, pembrolizumab, pilizumab, BGB-108, SHR-1210, PDR-001 , PF-06801591, IBI-308, GB-226, STI- 1110, or mDX-400.

74. The method of claim 71 , wherein the PD-L1 inhibitor is atezolizumab, avelumab, AMP- 224, MEDI-0680, RG-7446, GX-P2, durvalumab, KY-1003, KD-033, MSB- 0010718C, TSR-042, ALN-PDL, STI-A1014, GS-4224, CX-072, or BMS-936559.

75. The method of claim 71, wherein the LAG-3 inhibitor is relatlimab.

76. The method of claim 71, wherein the TIM-3 inhibitor is TSR-022, LY-3321367, MBG- 453, or INCAGN-2390.

77. The method of claim 71, wherein the TIGIT inhibitor is BMS-986207, RG-6058, or AGEN-1307.

78. The method of claim 71, wherein the 4-1BB inhibitor is urelumab, utomilumab, emfizatamab, BMS-663513, PF-05082566, PRS-343, RG7827, ADG106, INBRX- 105, CTX-471, BNT311, BNT311, RG6706, MP0310, BNT312, AGEN2373,LVGN6051 , ATOR- 1017, STA551 , or ND-021.

79. The method of claim 71, wherein the GITR inhibitor is MEDI1873, FPA-154,INCAGN-1876, TRX-518, BMS-986156, MK-1248, or GWN-323.

80. The method of claim 71 , wherein the galectin inhibitor is thiodigalactoside, β-D- lactosyl-steroid, GB1 107, lactulose-L-leucine, modified citrus pectin, PectaSol-C, GCS-100, GM-CT-01, belapectin, DB16, DB21, OTX008, PTX013, LLS30, or LLS2.81 . The method of claim 71, wherein the immune checkpoint inhibitor is a PD-1 inhibitor or a PD-L1 inhibitor.

82. The method of claim 81 , wherein the immune checkpoint inhibitor is a PD-1 inhibitor.

83. The method of claim 69, wherein the immune checkpoint inhibitor is an antibody or an antibody fragment.

84. The method of claim 70, wherein the immune modulator is IFNγ or IFNα.

85. The method of claim 61, wherein the cancer is selected from bile duct cancer, bladder cancer, brain cancer, breast cancer, carcinoma, cervical cancer, colorectal cancer, endometrial cancer, epitheloid carcinoma of the bone, esophageal cancer, gallbladder cancer, gastric cancer, glioblastoma, hepatocellular cancer, large cell lung carcinoma, leukemia, lung cancer, medulloblastoma, melanoma, ovarian cancer, non-small cell lung cancer, pancreatic cancer, prostate cancer, renal cell cancer or thyroid cancer.

86. The method of claim 85, wherein the cancer is bladder cancer.

87. The method of claim 61 , wherein the cancer is a solid tumor.

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