Cancer cell immune evasion signatures and uses thereof

Abstract

The present disclosure relates in some aspects to methods for identifying immune evasion signatures in cancers.

Description

Docket No.: 220232001840 CANCER CELL IMMUNE EVASION SIGNATURES AND USES THEREOF CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the priority to U.S. Provisional Application Nos. 63 / 583,185 filed on September 15, 2023 and 63 / 664,687 filed on June 26, 2024, the contents of which are incorporated by reference herein in their entirety. FIELD

[0002] The present disclosure relates in some aspects to gene signatures for cancer cell immune evasion. BACKGROUND

[0003] Immunotherapy for cancer has been transformative for a subset of patients, but these patients are a minority. The potential of immunotherapy is hindered by the ability of cancer cells, along with self-selection for mutations that increase proliferation, to actively use genomic alterations to evade immune detection. In addition, tumor heterogeneity exacerbates the unpredictability of patient responses to immunotherapies, which are often administered to broad patient populations and ultimately do not benefit the majority of patients. Phenotypic and pharmacodynamic biomarkers are used to gauge therapy effectiveness post-treatment but are not designed to predict responses. Methods are therefore needed to prospectively select patients who will respond to immunotherapy based approaches in order to improve the standard-of-care of immunotherapies as well as to develop novel immunotherapies. Provided herein are methods that address such and other needs. SUMMARY

[0004] In some aspects, provided herein is a method of identifying a gene signature for determining if a cancer is immune evasive, comprising the steps of: a) detecting expression levels of a first set of genes in cancer samples containing cancer cells, wherein the cancer samples are of the same cancer type; b) determining an immune evasion score based on the expression levels of the first set of genes for each sample in step a; c) selecting samples comprising immune evasion scores in about the top 33rdpercentile (‘immune evasive high’) and about the bottom 33rdpercentile (‘immune evasive low’) among the cancer samples; d) detecting genes that are differentially expressed between the immune evasive high and the immune evasive 1 sf-5975534Docket No.: 220232001840 low samples; and e) identifying a second set of genes from the differentially expressed genes detected in step d, wherein the expression levels of the second set of genes can classify if the cancer is immune evasive.

[0005] In other aspects, provided herein, is a method of identifying targets for cancer therapy, comprising the steps of: a) detecting expression levels of a first set of genes in cancer samples containing cancer cells, wherein the cancer samples are of the same cancer type; b) determining an immune evasion score based on the expression levels of the first set of genes for each sample in step a; c) selecting samples comprising immune evasion scores in about the top 33rdpercentile (‘immune evasive high’) and about the bottom 33rdpercentile (‘immune evasive low’) among the cancer samples; d) detecting genes that are differentially expressed between the immune evasive high and the immune evasive low samples; e) identifying a second set of genes from the differentially expressed genes detected in step d, wherein the expression levels of the second set of genes can classify if the cancer is immune evasive; and f) identifying one or more cancer treatment targets from the second set of genes.

[0006] In some embodiments, the first set of genes comprises ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1.

[0007] In some embodiments, the cancer cells are colorectal cancer, lung cancer, non-small cell lung cancer, lung adenocarcinoma, lung squamous cell carcinoma, skin cancer, or melanoma.

[0008] In some embodiments, the second set of genes comprises one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6. sf-5975534Docket No.: 220232001840

[0009] In some embodiments, the second set of genes comprises one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1.

[0010] In some embodiments, the cancer cells are colorectal cancer cells and the second set of genes comprise CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6.

[0011] In some embodiments, the cancer cells are colorectal cancer cells and the second set of genes comprise CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6.

[0012] In some embodiments, the cancer cells are non-small cell lung cancer (NSCLC) cells and the second set of genes comprise APP, DPYSL3, EID1, EIF1B, EMILIN1, FAM168B, FBLN1, FSCN1, FSTL1, GJA1, GLG1, GPC1, HLA-F, IGFBP4, IRF7, LAMC1, LAP3, MARVELD1, P4HA1, PALLD, PDPN, PLOD3, PLP2, PSMB8, RTN4, SLC38A2, SLC3A2, TNC, TRAC, YKT6, YWHAG, CCL5, CD74, COL1A2, COL5A1, CTSS, DTX3L, FKBP9, HLA-B, HLA-DMA, HLA-DPA1, HLA-E, IFI27, IFI35, IFI44, IFI6, IFIT3, ISG15, LGALS3BP, MMP14, OAS3, PARP14, PSME2, SLC2A1, STAT1, STAT2, TRBC1, and UBE2L6.

[0013] In some embodiments, the cancer cells are non-small cell lung cancer (NSCLC) cells not having genetic mutations in ALK, EGFR, or KRAS and the second set of genes comprise APP, C20orf27, CCT5, CPE, CYB5R3, DDX50, DPYSL3, EID1, EMILIN1, EPHB3, FBLN1, FSCN1, FSTL1, GJA1, GPC1, HLA-F, IGFBP4, LAMC1, LAP3, MARVELD1, PDPN, PFN2, PLOD3, PLP2, RTN4, SLC39A6, SLC3A2, TLCD3A, TNC, CCL5, CD74, COL1A2, CTSS, DTX3L, FKBP9, GBP1, GLG1, HLA-B, HLA-DMA, IFI27, IFI35, IFI44, IFI6, IFIT3, ISG15, LGALS3BP, PSME2, SLC2A1, STAT1, STAT2, TRAC, TRBC1, UBE2L6, and YWHAQ.

[0014] In some embodiments, the cancer cells are lung adenocarcinoma (LUAD) cells and the second set of genes comprise ARL4C, C4orf3, CLIC4, DPYSL3, DUSP5, FAM168B, FKBP9, FSCN1, FSTL1, GJA1, HLA-DMB, HLA-F, HTRA1, IGFBP4, IGFBP5, ITGB1, ITGB5, LRRC8A, MARVELD1, MRC2, PDGFRB, PSMB9, RTN4, SPIN1, TOB1, TRBC1, VIM, sf-5975534Docket No.: 220232001840 BST2, CALD1, CAVIN1, CCN2, CTSS, DTX3L, HLA-B, HLA-DMA, HLA-DOA, IFI27, IFI35, IFI44, IFI6, IFIT3, ISG15, LGALS3BP, OAS1, OAS3, PARP14, PSMB8, PSME2, SERPINE1, SLC50A1, STAT1, STAT2, TRAC, TRAFD1, and UBE2L6.

[0015] In some embodiments, the cancer cells are lung squamous cell carcinoma (LUSC) cells and the second set of genes comprise COL1A2, CPE, CTNNB1, DSTN, EIF1B, EMILIN1, FBLN1, GJA1, GLG1, GPC1, IGFBP4, KDELR2, LAMC1, LAP3, P4HA1, PLOD3, PSMB9, PSME2, RHBDD2, SNAI2, ST13, TAP1, TGFBI, TMED4, TRAC, VEGFB, YKT6, CCL5, CD53, CD74, CHPF, CTSS, CYB5R3, DTX3L, GBP1, GNA12, HLA-DMA, HLA-F, IFI27, IFI35, IFI44, IFIT3, ISG15, LGALS3BP, NT5DC2, PCOLCE, STAT1, STAT2, TRBC1, and UBE2L6.

[0016] In some embodiments, the second set of genes are identified using elastic net regression analysis.

[0017] In some embodiments, the immune evasion score is determined using normalized RNAexpression levels in the following equation: ^^^^^^ ^^^^^^^ ^^^^^ =number of up-regulated genes, j=gene, M=total number of down-regulated genes.

[0018] In other aspects, provided herein is a method of identifying an individual having immune evasive high colorectal cancer, comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises i) one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; ii) one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii) ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, sf-5975534Docket No.: 220232001840 STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; and c) identifying the colorectal cancer is likely to be immune evasive high if the immune evasion score in step b is higher than a reference value. In some embodiments, the immune evasion score is determinedusing normalized RNA expression levels in the following equation: ^^^^^^ ^^^^^^^ ^^^^^ =number ofup-regulated genes, j=gene, and M=total number of down-regulated genes. In some embodiments, the immune evasion score is determined using normalized RNA expression levelsin the following equation: ^^^^^^ ^^^^^^^ ^^^^^ = ^^^^∑^^ ^^^ ^^^^^^^^^^^^^ −∑^^ ^^^ ^^^^^^^^^^^ ^^^, where i=gene, N=total number of up-regulated genes, j=gene,M=total number of down-regulated genes; and wherein the reference value is about 1.0. Examples of the up-regulated genes and down-regulated genes are shown in Table 1, Table 2, and Table 6 herein.

[0019] In other aspects, provided herein is a method of identifying an individual having immune evasive low colorectal cancer, comprising the steps of: a) detecting expression levels of a set of genes in cells of a colorectal cancer sample from the individual, wherein the set of genes comprises i) one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; ii) one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii) ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, 5 sf-5975534Docket No.: 220232001840 DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; and c) identifying the colorectal cancer is likely to be immune evasive low if the immune evasion score in step b is lower than a reference value. In some embodiments, the immune evasion score is determinedusing normalized RNA expression levels in the following equation: ^^^^^^ ^^^^^^^ ^^^^^ =number ofup-regulated genes, j=gene, and M=total number of down-regulated genes. In some embodiments, the immune evasion score is determined using normalized RNA expression levelsin the following equation: ^^^^^^ ^^^^^^^ ^^^^^ = ^^^^∑^^ ^^^ ^^^^^^^^^^^^^ −∑^^ ^^^ ^^^^^^^^^^^ ^^^, where i=gene, N=total number of up-regulated genes, j=gene,M=total number of down-regulated genes; and wherein the reference value is about -1.0. Examples of the up-regulated genes and down-regulated genes are shown in Table 1, Table 2, and Table 6 herein.

[0020] In other aspects, provided herein is a method of identifying an individual having immune evasive high colorectal cancer, comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; and c) identifying the colorectal cancer is likely to be immune evasive high if the immune evasion score in step b is higher than a reference value. In some embodiments, the immune evasion score is determined using normalized RNA expression levels sf-5975534Docket No.: 220232001840 in the following equation: ^^^^^^ ^^^^^^^ ^^^^^ =^^^^ ∗ ^^^^^^^^^^^^^ where i=gene, N=total number of genes, a = coefficient associated with gene, and expression = normalized RNA expression level. In some embodiments, the immune evasion score is determined usingnormalized RNA expression levels in the following equation: ^^^^^^ ^^^^^^^ ^^^^^ =∑^^ ^^^^ ∗ ^^^^^^^^^^^^^ where i=gene, N=28, a = coefficient associated with gene, wherein ahas a value that is about the coefficient value shown in Table 5, and expression = normalized RNA expression level; and wherein the reference value is about 0.195.

[0021] In other aspects, provided herein is a method of identifying an individual having immune evasive low colorectal cancer, comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; and c) identifying the colorectal cancer is likely to be immune evasive low if the immune evasion score in step b is lower than a reference value. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in the following equation:N=total number of genes, a = coefficient associated with gene, and expression = normalized RNA expression level. In some embodiments, the immune evasion score is determined usingnormalized RNA expression levels in the following equation: ^^^^^^ ^^^^^^^ ^^^^^ =∑^^ ^^^^ ∗ ^^^^^^^^^^^^^ where i=gene, N=28, a = coefficient associated with gene, wherein ahas a value that is about the coefficient value shown in Table 5, and expression = normalized RNA expression level; and wherein the reference value is about -0.256.

[0022] In other aspects, provided herein is a method of identifying an individual having immune evasive high colorectal cancer, comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; and c) identifying 7 sf-5975534Docket No.: 220232001840 the colorectal cancer is likely to be immune evasive high if the immune evasion score in step b is higher than a reference value. In some embodiments, the immune evasion score is determinedusing normalized RNA expression levels in the following equation: ^^^^^^ ^^^^^^^ ^^^^^ =∑^^ ^^^^ ∗ ^^^^^^^^^^^^^ where i=gene, N=total number of genes, a = coefficient associatedwith gene, and expression = normalized RNA expression level. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in the following equation:coefficient associated with gene, wherein a has a value that is about the coefficient value shown in Table 7, and expression = normalized RNA expression level; and wherein the reference value is about 0.209.

[0023] In other aspects, provided herein is a method of identifying an individual having immune evasive low colorectal cancer, comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; and c) identifying the colorectal cancer is likely to be immune evasive low if the immune evasion score in step b is lower than a reference value. In some embodiments, the immune evasion score is determinedusing normalized RNA expression levels in the following equation: ^^^^^^ ^^^^^^^ ^^^^^ =∑^^ ^^^^ ∗ ^^^^^^^^^^^^^ where i=gene, N=total number of genes, a = coefficient associatedwith gene, and expression = normalized RNA expression level. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in the following equation:coefficient associated with gene, wherein a has a value that is about the coefficient value shown in Table 7, and expression = normalized RNA expression level; and wherein the reference value is about -0.237.

[0024] In other aspects, provided herein is a method of treating an individual with colorectal cancer (CRC), comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises i) one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; ii) one or 8 sf-5975534Docket No.: 220232001840 more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii) ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; c) identifying the colorectal cancer is likely to be immune suppressive (or immune evasive high) if the immune evasion score in step b is higher than a reference value; and d) administering a cancer therapy to the individual that comprises a therapeutic targeting one or more targets selected from the group consisting of ADORA2A, ADRA2C, ADSSL1, AGT, AHCY, AKR1B10, ALOX15, AMZ1, ANO1, ANXA3, AQP1, ARHGAP6, ASGR1, BDNF, BMP2, C12orf49, CA1, CA4, CACNA2D1, CADM1, CALU, CAVIN1, CCBE1, CCL5, CCL21, CCN2, CCNE1, CD177, CHST11, CLBA1, CLIC4, COL6A2, COL27A1, CPE, CTSB, DDX58, DTX3L, DUSP9, EMB, EML5, ENPP4, ESD, FLNA, FN1, GDF15, GDPD1, GEM, GOT1, GPC1, GRIN2D, GPRC5B, GREB1, GREM1, GRIK2, HAP1, HAVCR1, HAVCR2, HGF, HLA-DQB2, HMGA2, HTRA1, IFI27, IFI27L2, IFI35, IFIT2, IGFBP4, IGFBP5, IL11, IRF9, ISG15, ISM1, ITGB3, ITPR1, JAM2, KAT2A, KDM5A, KHK, KIF17, LAP3, LAMC1, LBH, LGALS1, LGALS3BP, LGALS7, MFAP4, MMP2, MPP6, MTCL1, MYADM, NPFFR1, OAS1, OAS2, OAS3, PADI2, PARP14, PCDH19, PHF11, PIN1, PIP5K2A, PIP5K2B, PIP5K2C, PKN3, PLK1, PLXNA4, PMAIP1, POSTN, sf-5975534Docket No.: 220232001840 PPM1L, PRSS12, PSMB9, PSME2, PTGES, PTGS1, PVR, RAB15, RTL8C, SAMHD1, SCARB1, SCD, SERPINE1, SFRP2, SH3BP5, SHC2, SIGLEC15, SLC2A1, SLC14A1, SLC6A8, SLC7A5, SLFN13, SLITRK6, SPARCL1, SPP1, SRPX2, STAT1, STAT2, ST3GAL5, SULF1, SYN1, TAP1, TCF7L2, THBS1, TIMP2, TIMD4, TMEM97, TMEM176B, TMEM179, TNC, TOR3A, TRAP1, TRIM5, TRIM21, TRIM34, TUBA1A, TWIST2, UBA7, UBE2L6, USP11, VAT1, VIM, WNT10A, and ZNFX1. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in the following equation:where i=gene, N=total number of up-regulated genes, j=gene, and M=total number of down- regulated genes. In some embodiments, the immune evasion score is determined usingnormalized RNA expression levels in the following equation: ^^^^^^ ^^^^^^^ ^^^^^ =number of up-regulated genes, j=gene, M=total number of down-regulated genes; and wherein the reference value is about 1.0. Examples of the up-regulated genes and down-regulated genes are shown in Table 1, Table 2, and Table 6 herein.

[0025] In other aspects, provided herein is a method of treating an individual with colorectal cancer (CRC), comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises i) one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; ii) one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii) ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, sf-5975534Docket No.: 220232001840 RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; c) identifying the colorectal cancer is likely to be immune evasive low if the immune evasion score in step b is lower than a reference value; and d) administering a cancer therapy to the individual that comprises a therapeutic targeting one or more targets selected from the group consisting of AXL, C10orf54, CD112, CD137, CD152, CD155, CD172a, CD223, CD27, CD272, CD274, CD38, CD40, CD47, CD96, cGAMP, cGAS, CSF1, CSF1R, CXCR4, ENPP1, ENTPD1, ENTPD2, GITR, HPK1, ICOS, ICOSLG, IDO, IFNB1, IKZF1, IKZF2, IL-1B, IL-6, IRF3, IRF7, LSGAL3, LSGAL9, MAVS,MERTK, NKG2A, NKG2D, NT5E, PDCD1, PDCD2, RIG-I, SAMHD1, SHP2, STAT3, STING, TANK, TBK1, TGFB1,TIGIT, TLR7, TLR9, TNFA, TNFRSF4, TREX1, TYRO3, USP1, USP18, and VEGF. In some embodiments, the immune evasion score is determined using normalized RNAexpression levels in the following equation: ^^^^^^ ^^^^^^^ ^^^^^ =number of up-regulated genes, j=gene, and M=total number of down-regulated genes. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in thefollowing equation:∑^^ ^^^ ^^^^^^^^^^^ ^^^ where i=gene, N=total number of up-regulated genes, j=gene, M=totalnumber of down-regulated genes; and wherein the reference value is about -1.0. Examples of the up-regulated genes and down-regulated genes are shown in Table 1, Table 2, and Table 6 herein.

[0026] In other aspects, provided herein is a method of treating an individual with colorectal cancer (CRC), comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises i) one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; ii) one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii) ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, 11 sf-5975534Docket No.: 220232001840 GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; c) identifying the colorectal cancer is likely to be immune suppressive (or immune evasive high) if the immune evasion score in step b is higher than a reference value; and d) administering a cancer therapy to the individual that comprises a therapeutic targeting one or more targets selected from the group consisting of ADORA2A, ADRA2C, ADSSL1, AGT, AHCY, AKR1B10, ALOX15, AMZ1, ANO1, ANXA3, AQP1, ARHGAP6, ASGR1, BDNF, BMP2, C12orf49, CA1, CA4, CACNA2D1, CADM1, CALU, CAVIN1, CCBE1, CCL5, CCL21, CCN2, CCNE1, CD177, CHST11, CLBA1, CLIC4, COL6A2, COL27A1, CPE, CTSB, DDX58, DTX3L, DUSP9, EMB, EML5, ENPP4, ESD, FLNA, FN1, GDF15, GDPD1, GEM, GOT1, GPC1, GRIN2D, GPRC5B, GREB1, GREM1, GRIK2, HAP1, HAVCR1, HAVCR2, HGF, HLA-DQB2, HMGA2, HTRA1, IFI27, IFI27L2, IFI35, IFIT2, IGFBP4, IGFBP5, IL11, IRF9, ISG15, ISM1, ITGB3, ITPR1, JAM2, KAT2A, KDM5A, KHK, KIF17, LAP3, LAMC1, LBH, LGALS1, LGALS3BP, LGALS7, MFAP4, MMP2, MPP6, MTCL1, MYADM, NPFFR1, OAS1, OAS2, OAS3, PADI2, PARP14, PCDH19, PHF11, PIN1, PIP5K2A, PIP5K2B, PIP5K2C, PKN3, PLK1, PLXNA4, PMAIP1, POSTN, PPM1L, PRSS12, PSMB9, PSME2, PTGES, PTGS1, PVR, RAB15, RTL8C, SAMHD1, SCARB1, SCD, SERPINE1, SFRP2, SH3BP5, SHC2, SIGLEC15, SLC2A1, SLC14A1, SLC6A8, SLC7A5, SLFN13, SLITRK6, SPARCL1, SPP1, SRPX2, STAT1, STAT2, ST3GAL5, SULF1, SYN1, TAP1, TCF7L2, THBS1, TIMP2, TIMD4, TMEM97, TMEM176B, TMEM179, sf-5975534Docket No.: 220232001840 TNC, TOR3A, TRAP1, TRIM5, TRIM21, TRIM34, TUBA1A, TWIST2, UBA7, UBE2L6, USP11, VAT1, VIM, WNT10A, and ZNFX1. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in the following equation:number of genes, a = coefficient associated with gene, and expression = normalized RNA expression level. In some embodiments, the immune evasion score is determined using normalized RNAexpression levels of the set of genes in step a (iv) in the following equation:coefficientassociated with gene, wherein a has a value that is about the coefficient value shown in Table 5, and expression = normalized RNA expression level; and wherein the reference value is about 0.195. In some embodiments, the immune evasion score is determined using normalized RNAexpression levels of the set of genes in step a (v) in the following equation:coefficientassociated with gene, wherein a has a value that is about the coefficient value shown in Table 7, and expression = normalized RNA expression level; and wherein the reference value is about 0.209.

[0027] In other aspects, provided herein is a method of treating an individual with colorectal cancer (CRC), comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises i) one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; ii) one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii) ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv) sf-5975534Docket No.: 220232001840 CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; c) identifying the colorectal cancer is likely to be immune evasive low if the immune evasion score in step b is lower than a reference value; and d) administering a cancer therapy to the individual that comprises a therapeutic targeting one or more targets selected from the group consisting of AXL, C10orf54, CD112, CD137, CD152, CD155, CD172a, CD223, CD27, CD272, CD274, CD38, CD40, CD47, CD96, cGAMP, cGAS, CSF1, CSF1R, CXCR4, ENPP1, ENTPD1, ENTPD2, GITR, HPK1, ICOS, ICOSLG, IDO, IFNB1, IKZF1, IKZF2, IL-1B, IL-6, IRF3, IRF7, LSGAL3, LSGAL9, MAVS,MERTK, NKG2A, NKG2D, NT5E, PDCD1, PDCD2, RIG-I, SAMHD1, SHP2, STAT3, STING, TANK, TBK1, TGFB1,TIGIT, TLR7, TLR9, TNFA, TNFRSF4, TREX1, TYRO3, USP1, USP18, and VEGF. In some embodiments, the immune evasion score is determined using normalized RNAexpression levels in the following equation: ^^^^^^ ^^^^^^^ ^^^^^ =^^^^ ∗^^^^^^^^^^^^^ where i=gene, N=total number of genes, a = coefficient associated with gene, andexpression = normalized RNA expression level. In some embodiments, the immune evasion score is determined using normalized RNA expression levels of the set of genes in step a (iv) in the following equation: ^^^^^^ ^^^^^^^ ^^^^^ =^^^^ ∗ ^^^^^^^^^^^^^ where i=gene, N=28, a = coefficient associated with gene, wherein a has a value that is about the coefficient value shown in Table 5, and expression = normalized RNA expression level; and wherein the reference value is about -0.256. In some embodiments, the immune evasion score is determined using normalized RNA expression levels of the set of genes in step a (v) in the following equation:coefficient associated with gene, wherein a has a value that is about the coefficient value shown in Table 7, and expression = normalized RNA expression level; and wherein the reference value is about -0.237.

[0028] In some embodiments, the cancer sample is selected from the group consisting of a cancer biopsy sample, a liquid biopsy, a cancer cell, and a cancer organoid. In some sf-5975534Docket No.: 220232001840 embodiments, the cancer cell or the cancer organoid is patient derived. In some embodiments, the cancer cell or the cancer organoid is not patient derived.

[0029] In some embodiments, the individual is a human.

[0030] In some embodiments, the expression levels are mRNA expression levels. In some embodiments, the mRNA expression levels are detected by quantitative PCR or nanostring. In some embodiments, the mRNA expression levels are normalized based on the expression level of house-keeping genes. In some embodiments, the RNA expression levels are normalized to log2(TPM+1) values. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The drawings illustrate certain features and advantages of this disclosure. These embodiments are not intended to limit the scope of the appended claims in any manner.

[0032] FIG. 1 displays a summary of the method to develop an immune evasion signature described herein. Step 1: cancer samples of a given type are divided into tertiles based on Immune Evasion Signature 1 (IES 1), Step 2: genes with a significant change between the two patient populations are used to generate an immune evasion predictive model, Step 3: the model with the lowest number of genes that maintains strong correlation with IES 1 is selected as an immune evasion signature for the given cancer type.

[0033] FIGs. 2A-2B depict the enrichment of a 97 gene immune evasion signature (Immune Evasion Signature 1) in a particular subtype (Consensus Molecular Subtype, CMS) of colorectal cancer (CRC). FIG. 2A shows the waterfall plot of the immune evasion signature 1 score for CMS1 and CMS4 CRC samples. FIG. 2B shows the relationship between the four different CMS subtypes and enrichment of the three Immune Evasion Signature 1 classifications (immune evasion signature 1 -low, -medium, and -high).

[0034] FIG. 3 depicts differential gene expression volcano plots comparing CRC samples that have high levels of the Immune Evasion Signature 1 vs CRC samples that have low levels of Immune Evasion Signature 1. The 1019 genes with statistically significant differences in gene expression between the two populations were used to identify a customized immune evasion signature, Immune Evasion Signature 2 (Table 2). sf-5975534Docket No.: 220232001840

[0035] FIGs. 4A-4B depict the correlation between Immune Evasion Signature 1 score and Immune Evasion Signature 2 score on The Cancer Genome Atlas Colon Adenocarcinoma (TCGA COAD) dataset. FIG. 4A shows the correlation between Immune Evasion Signature 1 score and Immune Evasion Signature 2 prior to optimization of Immune Evasion Signature 2, with an R2value of 0.84 observed between the two different gene signature scores. FIG. 4B shows the improved correlation between Immune Evasion Signature 1 and Immune Evasion Signature 2 after optimization of Immune Evasion Signature 2, with an R2value of 0.87.

[0036] FIGs. 5A-5C depict the correlation between Immune Evasion Signature 1 score and Immune Evasion Signature 2 score in three independent CRC data sets available from NCBI SRA and GEO sites: PRJNA387172 (FIG. 5A), GSE152430 (FIG. 5B), and GSE20067 (FIG. 5C).

[0037] FIGs. 6A-6B depict the waterfall plot of the Immune Evasion Signature 2 (FIG. 6A) and Immune Evasion Signature 1 (FIG. 6B) scores for CMS1 and CMS4 CRC samples. CMS4 subtype CRC is more enriched for Immune Evasion Signature 2 than Immune Evasion Signature 1.

[0038] FIGs. 7A-7C depict the distribution of the Immune Evasion Signature 2 in TCGA CRC tumors (FIG. 7A), melanoma (FIG. 7B) and lung adenocarcinoma (FIG. 7C).

[0039] FIG. 8A depicts the correlation between Immune Evasion Signature 1 score and Immune Evasion Signature 3 score on the TCGA COAD dataset. An R2value of 0.86 was observed between the two different gene signature scores.

[0040] FIGs. 8B-8D depict the correlation between Immune Evasion Signature 1 score and Immune Evasion Signature 3 score of colorectal tumors of different stages and / or after different treatments on the GSE152430 dataset (FIG. 8B), the Sidra-LUMC AC-ICAM dataset (FIG. 8C) and the GSE179351 dataset (FIG. 8D). The GSE152430 dataset (FIG. 8B) assesses RNA expression of primary stage 2 colorectal tumors (n=49). The Sidra-LUMC AC-ICAM dataset (FIG. 8C) assesses RNA expression of primary, treatment naïve tumor stages I-IV (n=348). The GSE1179351 dataset (FIG. 8D) assesses RNA expression of metastatic colorectal tumors, wherein patients received a median of four therapies including chemotherapy and chemoRT14 (n=25). sf-5975534Docket No.: 220232001840

[0041] FIG. 9 depicts the progression-free survival over time for individuals with advanced non-small cell lung cancer (NSCLC) with high or low Immune Evasion Signature 1 scores who were treated with anti-PD-1 or anti-PD-L1 therapies.

[0042] FIG. 10A depicts differential gene expression volcano plots comparing NSCLC samples that have high levels of the Immune Evasion Signature 1 vs NSCLC samples that have low levels of Immune Evasion Signature 1. The 2006 genes with statistically significant differences in gene expression between the two populations were used to identify a customized immune evasion signature, Immune Evasion Signature 4 (Table 8).

[0043] FIG. 10B depicts the correlation between Immune Evasion Signature 1 score and Immune Evasion Signature 4 score. An R2value of 0.784 was observed between the two different gene signature scores.

[0044] FIG. 11A depicts differential gene expression volcano plots comparing NSCLC samples not having genetic mutations in ALK, EGFR, or KRAS that have high levels of the Immune Evasion Signature 1 vs NSCLC samples not having genetic mutations in ALK, EGFR, or KRAS that have low levels of Immune Evasion Signature 1. The 1869 genes with statistically significant differences in gene expression between the two populations were used to identify a customized immune evasion signature, Immune Evasion Signature 5 (Table 12).

[0045] FIG. 11B depicts the correlation between Immune Evasion Signature 1 score and Immune Evasion Signature 5 score on the TCGA LUAD & LUSC datasets (excluding tumors containing mutations in ALK, EGFR, or KRAS). An R2value of 0.85 was observed between the two different gene signature scores.

[0046] FIGs. 11C-11D depict the correlation between Immune Evasion Signature 1 score and Immune Evasion Signature 5 score of lung cancer (NSCLC) tumors of different stages and after treatment with PD-1 blockers on the GSE190265 dataset (FIG. 11C) and the GSE181820 dataset (FIG. 11D). The GSE190265 dataset (FIG. 11C) assesses RNA expression of human NSCLC tumors of mixed treatment lines (n=43). The GSE181820 dataset (FIG. 11D) assesses RNA expression of human NSCLC tumors at multiple stages and driver mutations (n=22).

[0047] FIG. 12A depicts differential gene expression volcano plots comparing lung adenocarcinoma (LUAD) samples that have high levels of the Immune Evasion Signature 1 vs LUAD samples that have low levels of Immune Evasion Signature 1. The 714 genes with sf-5975534Docket No.: 220232001840 statistically significant differences in gene expression between the two populations were used to identify a customized immune evasion signature, Immune Evasion Signature 6 (Table 16).

[0048] FIG. 12B depicts the correlation between Immune Evasion Signature 1 score and Immune Evasion Signature 6 score. An R2value of 0.842 was observed between the two different gene signature scores.

[0049] FIG. 13A depicts differential gene expression volcano plots comparing lung squamous cell carcinoma (LUSC) samples that have high levels of the Immune Evasion Signature 1 vs LUSC samples that have low levels of Immune Evasion Signature 1. The 1103 genes with statistically significant differences in gene expression between the two populations were used to identify a customized immune evasion signature, Immune Evasion Signature 7 (Table 20).

[0050] FIG. 13B depicts the correlation between Immune Evasion Signature 1 score and Immune Evasion Signature 7 score. An R2value of 0.664 was observed between the two different gene signature scores. DETAILED DESCRIPTION

[0051] All publications, comprising patent documents, scientific articles and databases, referred to in this application are incorporated by reference in their entirety for all purposes to the same extent as if each individual publication were individually incorporated by reference. If a definition set forth herein is contrary to or otherwise inconsistent with a definition set forth in the patents, applications, published applications and other publications that are herein incorporated by reference, the definition set forth herein prevails over the definition that is incorporated herein by reference.

[0052] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described. I. OVERVIEW

[0053] The invention uses cancer cell driven immune suppression and relationships between cancer cell biology and immune characteristics of tumors to prospectively identify patients who will respond to both standard-of-care and novel immunotherapies. The patient selection is based on cancer cell features, via measurement of a cancer cell intrinsic gene expression signatures. The necessary patient sample is easily obtained via a tumor core biopsy or liquid biopsy and the sf-5975534Docket No.: 220232001840 gene signature analysis can occur alongside initial molecular characterization of the cancer (analysis of mutational status e.g.), which is often used to determine treatment plan in the case of targeted therapies. Thus, selecting patients who will respond to cancer immunotherapies is envisioned to occur much like that for molecularly targeted cancer therapies. This will increase the number of patients who respond to cancer immunotherapies beyond a minority.

[0054] Provided herein are methods of identifying a gene signature for determining if a cancer is immune evasive comprising the steps of: a) detecting expression levels of a first set of genes in cancer samples containing cancer cells, wherein the cancer samples are of the same cancer type; b) determining an immune evasion score based on the expression levels of the first set of genes for each sample in step a; c) selecting samples comprising immune evasion scores in about the top 33rd percentile (‘immune evasive high’) and about the bottom 33rd percentile (‘immune evasive low’) among the cancer samples; d) detecting genes that are differentially expressed between the immune evasive high and the immune evasive low samples; and e) identifying a second set of genes from the differentially expressed genes detected in step d, wherein the expression levels of the second set of genes can classify if the cancer is immune evasive. Thus, beneficially, the present invention provides a reliable method to identify individuals having immune evasive cancers using the gene signatures described herein. The gene signatures deriving from the immune evasion models described herein are associated and / or retrospectively predict lack of response to checkpoint inhibitor immunotherapy in both colorectal cancer and non-small cell lung cancer. Surprisingly, the signature genes in the immune evasion models described herein represent gain of function immune evasion capabilities and are distinct from previously identified immune evasion parameters such as deficiencies in antigen presentation, interferon-gamma signaling pathways, and others.

[0055] Also provided herein are method of identifying an individual having immune evasive high colorectal cancer, comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises i) one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; ii) one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii) ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, sf-5975534Docket No.: 220232001840 GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b) determining an immune evasion score for the cancer based on the expression levels of the set of genes in step a; and c) identifying the colorectal cancer is likely to be immune evasive high if the immune evasion score in step b is higher than a reference value.

[0056] Further provided are methods of identifying an individual having immune evasive low colorectal cancer, comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises i) one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; ii) one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii) ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, sf-5975534Docket No.: 220232001840 RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; and c) identifying the colorectal cancer is likely to be immune evasive low if the immune evasion score in step b is lower than a reference value.

[0057] Also provided herein is a method of treating an individual with colorectal cancer (CRC), comprising the steps of: a) detecting expression levels of a set of genes a colorectal cancer sample from the individual, wherein the set of genes comprises i) one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; ii) one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii) ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; c) identifying the colorectal cancer is likely to be immune suppressive (or immune evasive high) if the immune evasion score in step b is higher than a reference value; and d) administering a cancer therapy to the individual that comprises a sf-5975534Docket No.: 220232001840 therapeutic targeting one or more targets selected from the group consisting of ADORA2A, ADRA2C, ADSSL1, AGT, AHCY, AKR1B10, ALOX15, AMZ1, ANO1, ANXA3, AQP1, ARHGAP6, ASGR1, BDNF, BMP2, C12orf49, CA1, CA4, CACNA2D1, CADM1, CALU, CAVIN1, CCBE1, CCL5, CCL21, CCN2, CCNE1, CD177, CHST11, CLBA1, CLIC4, COL6A2, COL27A1, CPE, CTSB, DDX58, DTX3L, DUSP9, EMB, EML5, ENPP4, ESD, FLNA, FN1, GDF15, GDPD1, GEM, GOT1, GPC1, GRIN2D, GPRC5B, GREB1, GREM1, GRIK2, HAP1, HAVCR1, HAVCR2, HGF, HLA-DQB2, HMGA2, HTRA1, IFI27, IFI27L2, IFI35, IFIT2, IGFBP4, IGFBP5, IL11, IRF9, ISG15, ISM1, ITGB3, ITPR1, JAM2, KAT2A, KDM5A, KHK, KIF17, LAP3, LAMC1, LBH, LGALS1, LGALS3BP, LGALS7, MFAP4, MMP2, MPP6, MTCL1, MYADM, NPFFR1, OAS1, OAS2, OAS3, PADI2, PARP14, PCDH19, PHF11, PIN1, PIP5K2A, PIP5K2B, PIP5K2C, PKN3, PLK1, PLXNA4, PMAIP1, POSTN, PPM1L, PRSS12, PSMB9, PSME2, PTGES, PTGS1, PVR, RAB15, RTL8C, SAMHD1, SCARB1, SCD, SERPINE1, SFRP2, SH3BP5, SHC2, SIGLEC15, SLC2A1, SLC14A1, SLC6A8, SLC7A5, SLFN13, SLITRK6, SPARCL1, SPP1, SRPX2, STAT1, STAT2, ST3GAL5, SULF1, SYN1, TAP1, TCF7L2, THBS1, TIMP2, TIMD4, TMEM97, TMEM176B, TMEM179, TNC, TOR3A, TRAP1, TRIM5, TRIM21, TRIM34, TUBA1A, TWIST2, UBA7, UBE2L6, USP11, VAT1, VIM, WNT10A, and ZNFX1.

[0058] Also provided herein is a method of treating an individual with colorectal cancer (CRC), comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises i) one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; ii) one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii) ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, sf-5975534Docket No.: 220232001840 TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; c) identifying the colorectal cancer is likely to be immune evasive low if the immune evasion score in step b is lower than a reference value; and d) administering a cancer therapy to the individual that comprises a therapeutic targeting one or more targets selected from the group consisting of AXL, C10orf54, CD112, CD137, CD152, CD155, CD172a, CD223, CD27, CD272, CD274, CD38, CD40, CD47, CD96, cGAMP, cGAS, CSF1, CSF1R, CXCR4, ENPP1, ENTPD1, ENTPD2, GITR, HPK1, ICOS, ICOSLG, IDO, IFNB1, IKZF1, IKZF2, IL-1B, IL-6, IRF3, IRF7, LSGAL3, LSGAL9, MAVS,MERTK, NKG2A, NKG2D, NT5E, PDCD1, PDCD2, RIG-I, SAMHD1, SHP2, STAT3, STING, TANK, TBK1, TGFB1,TIGIT, TLR7, TLR9, TNFA, TNFRSF4, TREX1, TYRO3, USP1, USP18, and VEGF.

[0059] Thus, beneficially, the present invention provides a reliable method to identify gene signatures capable of characterizing cancers with immune suppressive or immune permissive qualities in cancer types including colorectal cancer and lung cancer. Moreover, the present invention provides methods to identify individuals having immune evasive cancers using the gene signatures described herein. The gene signatures derived from the immune evasion models described herein are associated and / or retrospectively predict lack of response to checkpoint inhibitor immunotherapy in colorectal cancer. Surprisingly, the signature genes in the immune evasion models described herein represent gain of function immune evasion capabilities and are distinct from previously identified immune evasion parameters such as deficiencies in antigen presentation, interferon-gamma signaling pathways, and others. II. DEFINITIONS

[0060] As used in this specification and the appended claims, the singular forms “a”, “an” and “the” include plural referents unless the content clearly dictates otherwise. Thus, for example, sf-5975534Docket No.: 220232001840 reference to “a molecule” optionally includes a combination of two or more such molecules, and the like.

[0061] The term “about” as used herein refers to the usual error range for the respective value readily known to the skilled person in this technical field. Reference to “about” a value or parameter herein includes (and describes) embodiments that are directed to that value or parameter per se. In certain embodiments, the term “about” includes the designated value ± 15%, such as ± 15%, ± 14%, ± 13%, ± 12%, ± 11%, ± 10%, ± 9%, ± 8%, ± 7%, ± 6%, ± 5%, ± 4%, ± 3%, ± 2%, or ± 1% the designated value.

[0062] The term, “about X-Y” used herein has the same meaning as “about X to about Y.”

[0063] As used herein, "administering" is meant as a method of giving a dosage of a compound (e.g., an antagonist) or a pharmaceutical composition (e.g., a pharmaceutical composition including an antagonist) to a subject (e.g., a patient). Administering can be by any suitable means, including parenteral, intrapulmonary, and intranasal, and, if desired for local treatment, intralesional administration. Parenteral infusions include, for example, intramuscular, intravenous, intraarterial, intraperitoneal, or subcutaneous administration. Dosing can be by any suitable route, e.g., by injections, such as intravenous or subcutaneous injections, depending in part on whether the administration is brief or chronic. Various dosing schedules including but not limited to single or multiple administrations over various time-points, bolus administration, and pulse infusion are contemplated herein.

[0064] It is understood that aspects and embodiments of the invention described herein include “comprising,” “consisting,” and “consisting essentially of” aspects and embodiments.

[0065] The terms "cancer" and "cancerous" refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. Included in this definition are benign and malignant cancers. Examples of a cancer include, but are not limited to, a lung cancer (e.g., a non-small cell lung cancer (NSCLC)), a kidney cancer (e.g., a kidney urothelial carcinoma), a bladder cancer (e.g., a bladder urothelial (transitional cell) carcinoma), a breast cancer, a colorectal cancer (e.g., a colon adenocarcinoma), an ovarian cancer, a pancreatic cancer, a gastric carcinoma, an esophageal cancer, a mesothelioma, a melanoma (e.g., a skin melanoma), a head and neck cancer (e.g., a head and neck squamous cell carcinoma (HNSCC)), a thyroid cancer, a sarcoma (e.g., a soft-tissue sarcoma, a fibrosarcoma, a myxosarcoma, a sf-5975534Docket No.: 220232001840 liposarcoma, an osteogenic sarcoma, an osteosarcoma, a chondrosarcoma, an angiosarcoma, an endotheliosarcoma, a lymphangiosarcoma, a lymphangioendotheliosarcoma, a leiomyosarcoma, or a rhabdomyosarcoma), a prostate cancer, a glioblastoma, a cervical cancer, a thymic carcinoma, a leukemia (e.g., an acute lymphocytic leukemia (ALL), an acute myelocytic leukemia (AML), a chronic myelocytic leukemia (CML), a chronic eosinophilic leukemia, or a chronic lymphocytic leukemia (CLL)), a lymphoma (e.g., a Hodgkin lymphoma or a non- Hodgkin lymphoma (NHL)), a myeloma (e.g., a multiple myeloma (MM)), a mycoses fungoides, a merkel cell cancer, a hematologic malignancy, a cancer of hematological tissues, a B cell cancer, a bronchus cancer, a stomach cancer, a brain or central nervous system cancer, a peripheral nervous system cancer, a uterine or endometrial cancer, a cancer of the oral cavity or pharynx, a liver cancer, a testicular cancer, a biliary tract cancer, a small bowel or appendix cancer, a salivary gland cancer, an adrenal gland cancer, an adenocarcinoma, an inflammatory myofibroblastic tumor, a gastrointestinal stromal tumor (GIST), a colon cancer, a myelodysplastic syndrome (MDS), a myeloproliferative disorder (MPD), a polycythemia Vera, a chordoma, a synovioma, an Ewing's tumor, a squamous cell carcinoma, a basal cell carcinoma, an adenocarcinoma, a sweat gland carcinoma, a sebaceous gland carcinoma, a papillary carcinoma, a papillary adenocarcinoma, a medullary carcinoma, a bronchogenic carcinoma, a renal cell carcinoma, a hepatoma, a bile duct carcinoma, a choriocarcinoma, a seminoma, an embryonal carcinoma, a Wilms' tumor, a bladder carcinoma, an epithelial carcinoma, a glioma, an astrocytoma, a medulloblastoma, a craniopharyngioma, an ependymoma, a pinealoma, a hemangioblastoma, an acoustic neuroma, an oligodendroglioma, a meningioma, a neuroblastoma, a retinoblastoma, a follicular lymphoma, a diffuse large B-cell lymphoma, a mantle cell lymphoma, a hepatocellular carcinoma, a thyroid cancer, a small cell cancer, an essential thrombocythemia, an agnogenic myeloid metaplasia, a hypereosinophilic syndrome, a systemic mastocytosis, a familiar hypereosinophilia, a neuroendocrine cancer, or a carcinoid tumor.

[0066] A "cancer cell" as used herein, refers to any cancer cell present in a cancer or a sample thereof. Cancer cells may be distinguished from other cells that may be present in a cancer sample, for example, stromal cells and tumor-infiltrating immune cells, using methods known in the art and / or described herein. sf-5975534Docket No.: 220232001840

[0067] The term "sample," as used herein, refers to a composition that is obtained or derived from a subject and / or individual of interest that contains a cellular and / or other molecular entity that is to be characterized and / or identified, for example, based on physical, biochemical, chemical, and / or physiological characteristics. For example, the phrase "disease sample" and variations thereof refers to any sample obtained from a subject of interest that would be expected or is known to contain the cellular and / or molecular entity that is to be characterized. Samples include, but are not limited to, tissue samples, primary or cultured cells or cell lines, cell supernatants, cell lysates, platelets, serum, plasma, vitreous fluid, lymph fluid, synovial fluid, follicular fluid, seminal fluid, amniotic fluid, milk, whole blood, plasma, serum, blood-derived cells, urine, cerebro-spinal fluid, saliva, sputum, tears, perspiration, mucus, tumor lysates, and tissue culture medium, tissue extracts such as homogenized tissue, tumor tissue, cellular extracts, and combinations thereof. In some instances, the sample is a whole blood sample, a plasma sample, a serum sample, or a combination thereof. In some embodiments, the sample is from a tumor (e.g., a “tumor sample”), such as from a biopsy. In some embodiments, the sample is a formalin-fixed paraffin-embedded (FFPE) sample.

[0068] A “subject” or an “individual” is a mammal, more preferably a human. Mammals include, but are not limited to, humans, primates, farm animals, sport animals, rodents, and pets (e.g., dogs and cats).

[0069] As used herein, a “reference value” can be an absolute value; a relative value; a value that has an upper and / or lower limit; a range of values; an average value; a median value; a mean value; or a value as compared to a particular control or baseline value.

[0070] A "reference sample," "reference cell," "reference tissue," "control sample," "control cell," or "control tissue," as used herein, refers to a sample, cell, tissue, standard, or level that is used for comparison purposes.

[0071] "Detection" includes any means of detecting, including direct and indirect detection.

[0072] The term "prediction" is used herein to refer to the likelihood that a patient will respond either favorably or unfavorably to a drug or set of drugs. In one embodiment, the prediction relates to the extent of those responses. In one embodiment, the prediction relates to whether and / or the probability that a patient will survive or improve following treatment, for example treatment with a particular therapeutic agent, and for a certain period of time without disease sf-5975534Docket No.: 220232001840 recurrence. The predictive methods of the invention can be used clinically to make treatment decisions by choosing the most appropriate treatment modalities for any particular patient. The predictive methods of the present invention are valuable tools in predicting if a patient is likely to respond favorably to a treatment regimen, such as a given therapeutic regimen, including for example, administration of a given therapeutic agent or combination, surgical intervention, steroid treatment, etc., or whether long-term survival of the patient, following a therapeutic regimen is likely.

[0073] The term "housekeeping gene" refers to a group of genes that codes for proteins whose activities are essential for the maintenance of cell function. These genes are typically similarly expressed in all cell types.

[0074] The terms “immune suppressive” or “immune evasive” refer to conditions within a cancer correlated with immune resistance. Immune suppressive or immune evasive cancers typically generate a microenvironment composed of cellular and soluble components promoting tumor progression and favoring the immune escape of cancers.

[0075] The term “immune permissive” refers to conditions within a cancer correlated with immune response. An immune permissive state within a cancer typically allows for the activity of the host immune system. III. Methods of Identifying Immune Evasion Signatures

[0076] In light of the poor response rates observed with currently available immunotherapies, there is a need for alternative approaches for identifying patient populations that harbor cancers susceptible to immunotherapy or evasive to immunotherapy. Identifying immune permissive cancers from immune suppressive cancers is therefore a major goal within the clinical oncology field. Accordingly, the present disclosure is based in part on the development of a method to identify immune evasion gene signatures within distinct cancer types.

[0077] Provided herein, in some embodiments, are methods of identifying a gene signature for determining if a cancer is immune evasive, comprising the steps of: a) detecting expression levels of a first set of genes in cancer samples containing cancer cells, wherein the cancer samples are of the same cancer type; b) determining an immune evasion score based on the expression levels of the first set of genes for each sample in step a; c) selecting samples comprising immune evasion scores in about the top 33rdpercentile (‘immune evasive high’) and 27 sf-5975534Docket No.: 220232001840 about the bottom 33rdpercentile (‘immune evasive low’) cancer samples; d) detecting genes that are differentially expressed between the immune evasive high and the immune evasive low samples; and e) identifying a second set of genes from the differentially expressed genes detected in step d, wherein the expression levels of the second set of genes can classify if the cancer is immune evasive.

[0078] In some embodiments, the present invention comprises the steps of detecting expression levels from cancer samples. In some embodiments, the cancer sample is selected from the group consisting of bladder, breast, colon and rectal, endometrial, kidney, leukemia, liver, lung, melanoma, non-Hodgkin lymphoma, pancreatic, prostate, and thyroid cancer. In some embodiments, the cancer sample is colorectal cancer, lung cancer, non-small cell lung cancer, lung adenocarcinoma, lung squamous cell carcinoma, skin cancer, or melanoma. In some embodiments, the cancer sample is a colorectal cancer (CRC) sample. In some embodiments, the cancer sample is a lung cancer sample. In some embodiments, the lung cancer is selected from the group consisting of non-small cell lung cancer (NSCLC), NSCLC not having genetic mutations in ALK, EGFR, or KRAS, lung adenocarcinoma, and lung squamous cell carcinoma (LUSC). In some embodiments, the expression levels are mRNA expression levels.

[0079] The invention provides methods comprising detecting expression levels from a mammalian tissue or cells sample. In some embodiments, the gene expression data is obtained from cancer cells isolated from individuals with cancer. In some embodiments, the expression levels are mRNA expression levels or protein expression levels. In some embodiments, the mRNA expression levels are detected by quantitative PCR or nanostring. In some embodiments, the mRNA expression levels are detected by bulk RNA sequencing. In some embodiments, the mRNA expression levels are detected by single cell RNA sequencing. In some embodiments, the expression levels are normalized to log2(TPM+1) values. In some embodiments, the expression levels are normalized to a reference level. In some embodiments, the reference level is the expression level of one or more housekeeping genes.

[0080] In some embodiments, the expression levels, represented in log2(TPM+1) (Transcripts Per Million), are normalized using the expression level of all genes. In some embodiments, the expression levels, represented as raw counts, are normalized using the expression level of all genes. In some embodiments, the expression levels, represented as raw counts, are normalized sf-5975534Docket No.: 220232001840 using a reference set. In some embodiments, the reference set is the expression level of one or more housekeeping genes.

[0081] In some embodiments, the cancer sample is selected from the group consisting of a cancer biopsy sample, a liquid biopsy, a cancer cell, and a cancer organoid. In some embodiments, the cancer cell or the cancer organoid is patient derived. In some embodiments, the cancer cell or the cancer organoid is not patient derived. In some embodiments, the cancer sample contains immune evasive cancer cells. In some embodiments, the cancer sample contains immune permissive cancer cells.

[0082] In some embodiments, the methods described herein comprise the step of detecting expression levels of a first set of genes in cancer samples containing cancer cells, wherein the cancer samples are of the same cancer type. In some embodiments, some cancer sample contains immune evasive cancer cells and some cancer samples contain immune permissive cancer cells. In some embodiments, the immune evasive cancer cells and the immune permissive cancer cells are of the same type of cancer (e.g., colorectal cancer cells or lung cancer cells).

[0083] In some embodiments, the immune suppressive cancers are known to respond poorly to immunotherapies such as anti-PD-1 (Pembrolizumab, Nivolumab, Cemiplimab, Dostarlimab, Retifanimab), anti-PD-L1 (Atezolizumab, Avelumab, Durvalumab), and anti-CTLA-4 (Ipilimumab, Tremelimumab). In some embodiments, the immune permissive cancers samples are known to respond well to immunotherapies. In some embodiments, the immunotherapy targets a protein selected from the group consisting of AXL, C10orf54, CD112, CD137, CD152, CD155, CD172a, CD223, CD27, CD272, CD274, CD38, CD40, CD47, CD96, cGAMP, cGAS, CSF1, CSF1R, CXCR4, ENPP1, ENTPD1, ENTPD2, GITR, HPK1, ICOS, ICOSLG, IDO, IFNB1, IKZF1, IKZF2, IL-1B, IL-6, IRF3, IRF7, LSGAL3, LSGAL9, MAVS, MERTK, NKG2A, NKG2D, NT5E, PDCD1, PDCD2, RIG-I, SAMHD1, SHP2, STAT3, STING, TANK, TBK1, TGFB1,TIGIT, TLR7, TLR9, TNFA, TNFRSF4, TREX1, TYRO3, USP1, USP18, and VEGF.

[0084] In some embodiments, the immune evasive and immune permissive cancers include cancer subtypes. In some embodiments, the cancer subtypes are Consensus Molecular Subtypes 1 through 4 (CMS1-4) of CRC. In some embodiments, the immune suppressive cancer is CMS4 CRC. In some embodiments, the immune permissive cancer is CMS1 CRC. sf-5975534Docket No.: 220232001840

[0085] In some embodiments, the first set of genes comprises ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1.

[0086] In some embodiments, elevated expression of a subset of genes from the first set of genes indicates an immune evasive cancer. In some embodiments, the subset of genes in which elevated expression indicates an immune evasive cancer are selected from the group consisting of ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, and WNT10A. In some embodiments, the subset of genes in which elevated expression indicates an immune evasive cancer are selected from one or more genes in Table 1.

[0087] In some embodiments, reduced expression of a subset of genes from the first set of genes indicates an immune evasive cancer. In some embodiments, the subset of genes in which reduced expression indicates an immune evasive cancer are selected from the group consisting of AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1. In some embodiments, the subset of genes in which reduced expression indicates an immune evasive cancer are selected from one or more genes in Table 1. sf-5975534Docket No.: 220232001840

[0088] In some embodiments, the methods described herein comprise a step of comparing a measured expression level of a marker gene and a reference level. The reference level may be a measured expression level of a reference gene different from a gene within the first or second set of genes or a measured expression level of the same gene within the first or second set of genes in a different sample.

[0089] In some embodiments, the methods described herein comprise the step of determining an immune evasion score based on the expression levels of the first set of genes for each cancer sample. In some embodiments, the immune evasion score based on the expression of the first set of genes is determined using normalized RNA expression levels in the following equation (Equation 1):where i=gene, N=total number of up-regulated genes, j=gene, M=total number of down- regulated genes. In some embodiments, the up-regulated genes and down-regulated genes are selected from Table 1.

[0090] In some embodiments, the methods described herein comprise the step of selecting samples comprising immune evasion scores in about the top 33rdpercentile (‘immune evasive high’) and about the bottom 33rdpercentile (‘immune evasive low’) among the cancer samples (e.g., including immune evasive and immune permissive cancer samples). In some embodiments, the immune evasion scores classify the cancer samples into three immune evasive categories, immune evasive high, immune evasive medium, and immune evasive low. In some embodiments, the immune evasive high cancer samples comprise an immune evasion score from the first set of genes in about the top 33rdpercentile of cancer samples. In some embodiments, the immune evasive medium cancer samples comprise an immune evasion score from the first set of genes between about the top 33rdpercentile and about the bottom 33rdpercentile of cancer samples. In some embodiments, the immune evasive low cancer samples comprise an immune evasion score from the first set of genes in the bottom 33rdpercentile of cancer samples.

[0091] In some embodiments, an immune evasion score greater than a threshold indicates that the cancer is immune evasive. In some embodiments, an immune evasion score less than a sf-5975534Docket No.: 220232001840 threshold indicates that the cancer is immune permissive. In some embodiments, the threshold for defining an immune evasion score as immune suppressive (or immune evasive) is defined as the highest 33rdpercentile of immune evasion scores. In some embodiments, the threshold for defining an immune evasion score as immune permissive is defined as the bottom 33rdpercentile of immune evasion scores.

[0092] In some embodiments, the methods described herein comprise the step of detecting genes that are differentially expressed between the immune evasive high and the immune evasive low samples. In some embodiments, the differentially expressed genes (DEGs) comprise genes within the first set of genes. In some embodiments, the DEGs comprise genes not within the first set of genes. In some embodiments, the differential gene expression analysis detects between about 200 and 3000 DEGs. In some embodiments, the differential gene expression analysis detects about 200, about 300, about 400, about 500, about 600, about 700, about 800, about 900, about 1000, about 1100, about 1200, about 1300, about 1400, about 1500, about 2000, about 2500, or about 3000 DEGs. In some embodiments, the differential gene expression analysis detects about 1019 DEGs.

[0093] In some embodiments, the methods described herein comprise the step of identifying a second set of genes from the differentially expressed genes identified from the immune evasive high and immune evasive low cancer samples, wherein the expression levels of the second set of genes can classify if the cancer is immune evasive.

[0094] In some embodiments, a model is developed to identify the second set of genes from the DEGs between the immune evasive high and immune evasive low cancer samples. In some embodiments, the second set of genes are identified using elastic regression analysis. In some embodiments, the elastic net regression model was developed to minimized the number of descriptors and provided the lowest root mean square estimate (RMSE) between training sets. In some embodiments, the input for the elastic net regression model is RNA expression levels from the DEGs between the immune evasive high and immune evasive low cancer samples. In some embodiments, the RNA expression levels used to develop the elastic net regression model are pre-processed to be centered and scaled. In some embodiments, the outcome predicted by the model is the Immune Evasion Signature score from the first set of genes. sf-5975534Docket No.: 220232001840

[0095] In some embodiments, the elastic net regression model is resampled by 10-fold cross- validation to calculate a RMSE. In some embodiments, the elastic net regression model is resampled about 2 times, about 3 times, about 4 times, about 5 times, about 6 times, about 7 times, about 8 times, about 9 times, or about 10 times. In some embodiments, the elastic net regression model is resampled about 5 times.

[0096] In some embodiments, the elastic net regression model comprises a tuning parameter alpha greater than zero. In some embodiments, the elastic net regression model comprises a tuning parameter alpha of about 0.1, 0.15, 0.20, 0.25, 0.30, 0.35, or 0.40. In some embodiments, the elastic net regression model comprises a tuning parameter alpha of about 0.36. In some embodiments, the elastic net regression model comprises a tuning parameter lambda greater than zero. In some embodiments, the elastic net regression model comprises a tuning parameter lambda of about 0.01, 0.015, 0.02, 0.025, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, or 0.10. In some embodiments, the elastic net regression model comprises a tuning parameter lambda of about 0.018.

[0097] In some embodiments, the elastic net regression model identifies between about 50 to about 500 genes to include within the model. In some embodiments, the elastic net regression model identifies about 100, about 120, about 140, about 160, about 180, about 200, about 210, about 220, about 230, about 240, about 250, about 300, about 400, or about 500 genes to include within the model. In some embodiments, the elastic net regression model identifies about 209 genes to include within the model.

[0098] In some embodiments, the number of genes included in the second set of genes is optimized by performing net regression analysis with the genes identified from the elastic net regression model as the descriptors. In some embodiments, the alpha parameter is held constant during net regression analysis. In some embodiments, the lambda parameter is adjusted during net regression analysis. In some embodiments, the optimal model is selected by the combination of the lowest number of genes in the model and the coefficient of determination (R2) between the model prediction and the Immune Evasion score using the first set of genes.

[0099] In some embodiments, the second set of genes comprises one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6. In some embodiments, the sf-5975534Docket No.: 220232001840 second set of genes comprises one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1.

[0100] In some embodiments, the second set of genes comprise CALU (Calumenin), CAVIN1 (Caveolae Associated Protein 1), CLIC4 (Chloride intracellular channel 4), COL6A2 (Collagen Type VI Alpha 2 Chain), IGFBP5 (Insulin-like growth factor-binding protein 5), LAMC1 (Laminin Subunit gamma-1), LGALS1 (Galectin-1), MFAP4 (Microfibrillar-associated protein 4), MYADM (Myeloid-associated differentiation marker), PSME2 (Proteasome Activator Subunit 2), RTL8C (Retrotransposon Gag Like 8C), SERPINE1 (Serpin Family E Member 1), TAP1 (Transporter associated with antigen processing 1), TUBA1A (Tubulin Alpha 1a), VAT1 (Vesicle amine transport 1), VIM (Vimentin), DTX3L (Deltex E3 Ubiquitin Ligase 3L), HTRA1 (HtrA Serine Peptidase 1), IFI27 (Interferon Alpha Inducible Protein 27), ISG15 (Interferon- stimulated gene 15), LAP3 (Leucine Aminopeptidase 3), MMP2 (Matrix Metallopeptidase 2), OAS1 (2’-5’-oligoadenylate synthetase 1), PSMB9 (Proteasome 20S Subunit Beta 9), SPARCL1 (SPARC Like 1), STAT1 (Signal transducer and activator of transcription 1), TRIM21 (Tripartite Motif Containing 21), and UBE2L6 (Ubiquitin / ISG15-conjugating enzyme E2 L6). In some embodiments, the second set of genes comprise one or more genes selected from Table 2. In some embodiments, the second set of genes is capable of identifying an immune evasive CRC. In some embodiments, the second set of genes is capable of identifying immune permissive CRC. In some embodiments, the expression levels of the second set of genes can classify if the CRC is immune evasive or immune permissive.

[0101] In some embodiments, the second set of genes comprise CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6. In some embodiments, the second set of genes comprise one or more genes selected from Table 6. In some embodiments, the second set of genes is capable of identifying an immune evasive CRC. In some embodiments, the second set of genes is capable of identifying immune permissive CRC. In some embodiments, the expression levels of the second set of genes can classify if the CRC is immune evasive or immune permissive.

[0102] In some embodiments, the second set of genes comprise APP (Amyloid-Beta Precursor Protein), DPYSL3 (Dihydropyrimidinase Like 3), EID1 (EP300 Interacting Inhibitor of Differentiation 1), EIF1B (Eukaryotic Translation Initiation Factor 1B), EMILIN1 (Elastin 34 sf-5975534Docket No.: 220232001840 Microfibril Interfacer 1), FAM168B (Family With Sequence Similarity 168 Member B), FBLN1 (Fibulin 1), FSCN1 (Fascin Actin-Bundling Protein 1), FSTL1 (Follistatin-Like 1), GJA1 (Gap Junction Protein Alpha 1), GLG1 (Golgi Glycoprotein 1), GPC1 (Glypican-1), HLA-F (Major Histocompatibility Complex, Class I, F), IGFBP4 (Insulin-Like Growth Factor-Binding Protein 4), IRF7 (Interferon Regulatory Factor 7), LAMC1, LAP3, MARVELD1 (MARVEL Domain- Containing Protein 1), P4HA1 (Prolyl 4-Hydroxylase Subunit Alpha 1), PALLD (Palladin, Cytoskeletal Associated Protein), PDPN (Podoplanin), PLOD3 (Procollagen-Lysine,2- Oxoglutarate 5-Dioxygenase 3), PLP2 (Proteolipid Protein 2), PSMB8 (Proteasome 20S Subunit Beta 8), RTN4 (Reticulon 4), SLC38A2 (Solute Carrier Family 38 Member 2), SLC3A2 (Solute Carrier Family 3 Member 2), TNC (Tenascin C), TRAC (T Cell Receptor Alpha Constant), YKT6 (YKT6 V-SNARE Homolog), YWHAG (Tyrosine 3-Monooxygenase / Tryptophan 5- Monooxygenase Activation Protein Gamma), CCL5 (C-C Motif Chemokine Ligand 5), CD74 (Cluster of Differentiation 74), COL1A2 (Collagen Type I Alpha 2), COL5A1 (Collagen Type V Alpha 1 Chain), CTSS (Cathepsin S), DTX3L, FKBP9 (FKBP Prolyl Isomerase 9), HLA-B (Major Histocompatibility Complex, Class I, B), HLA-DMA (Major Histocompatibility Complex, Class II, DM Alpha), HLA-DPA1 (Major Histocompatibility Complex, Class II, DP Alpha 1), HLA-E (Major Histocompatibility Complex, Class I, E), IFI27, IFI35 (Interferon Induced Protein 35), IFI44 (Interferon Induced Protein 44), IFI6 (Interferon Alpha Inducible Protein 6), IFIT3 (Interferon Induced Protein With Tetratricopeptide Repeats 3), ISG15, LGALS3BP (Galectin 3 Binding Protein), MMP14 (Matrix Metallopeptidase 14), OAS3 (2'-5'- Oligoadenylate Synthetase 3), PARP14 (Poly(ADP-Ribose) Polymerase Family Member 14), PSME2, SLC2A1 (Solute Carrier Family 2 Member 1), STAT1, STAT2 (Signal Transducer And Activator Of Transcription 2), TRBC1 (T Cell Receptor Beta Constant 1), and UBE2L6. In some embodiments, the second set of genes comprise one or more genes selected from Table 8. In some embodiments, the second set of genes is capable of identifying an immune evasive lung cancer. In some embodiments, the second set of genes is capable of identifying immune permissive lung cancer. In some embodiments, the expression levels of the second set of genes can classify if the lung cancer is immune evasive or immune permissive.

[0103] In some embodiments, the second set of genes comprise APP, C20orf27 (Chromosome 20 Open Reading Frame 27), CCT5 (Chaperonin Containing TCP1 Subunit 5), CPE (Carboxypeptidase E), CYB5R3 (Cytochrome B5 Reductase 3), DDX50 (DExD-Box Helicase sf-5975534Docket No.: 220232001840 50), DPYSL3, EID1, EMILIN1, EPHB3 (EPH Receptor B3), FBLN1, FSCN1, FSTL1, GJA1, GPC1, HLA-F, IGFBP4, LAMC1, LAP3, MARVELD1, PDPN, PFN2 (Profilin-2), PLOD3, PLP2, RTN4, SLC39A6 (Solute Carrier Family 39 Member 6), SLC3A2, TLCD3A (TLC Domain Containing 3A), TNC, CCL5, CD74, COL1A2, CTSS, DTX3L, FKBP9, GBP1 (Guanylate Binding Protein 1), GLG1, HLA-B, HLA-DMA, IFI27, IFI35, IFI44, IFI6, IFIT3, ISG15, LGALS3BP, PSME2, SLC2A1, STAT1, STAT2, TRAC, TRBC1, UBE2L6, and YWHAQ (Tyrosine 3-Monooxygenase / Tryptophan 5-Monooxygenase Activation Protein Theta). In some embodiments, the second set of genes comprise one or more genes selected from Table 12. In some embodiments, the second set of genes is capable of identifying an immune evasive lung cancer. In some embodiments, the second set of genes is capable of identifying immune permissive lung cancer. In some embodiments, the expression levels of the second set of genes can classify if the lung cancer is immune evasive or immune permissive.

[0104] In some embodiments, the second set of genes comprise ARL4C (ADP Ribosylation Factor Like GTPase 4C), C4orf3 (Chromosome 4 Open Reading Frame 3), CLIC4, DPYSL3, DUSP5 (Dual Specificity Phosphatase 5), FAM168B, FKBP9, FSCN1, FSTL1, GJA1, HLA- DMB (Major Histocompatibility Complex, Class II, DM Beta), HLA-F, HTRA1, IGFBP4, IGFBP5, ITGB1 (Integrin Subunit Beta 1), ITGB5 (Integrin Subunit Beta 5), LRRC8A (Leucine Rich Repeat Containing 8 VRAC Subunit A), MARVELD1, MRC2 (Mannose Receptor C-Type 2), PDGFRB (Platelet Derived Growth Factor Receptor Beta), PSMB9, RTN4, SPIN1 (Spindlin 1), TOB1 (Transducer Of ERBB2, 1), TRBC1, VIM, BST2 (Bone Marrow Stromal Cell Antigen 2), CALD1 (Caldesmon 1), CAVIN1, CCN2 (Cellular Communication Network Factor 2), CTSS, DTX3L, HLA-B, HLA-DMA, HLA-DOA (Major Histocompatibility Complex, Class II, DO Alpha), IFI27, IFI35, IFI44, IFI6, IFIT3, ISG15, LGALS3BP, OAS1, OAS3, PARP14, PSMB8, PSME2, SERPINE1, SLC50A1 (Solute Carrier Family 50 Member 1), STAT1, STAT2, TRAC, TRAFD1 (TRAF-Type Zinc Finger Domain Containing 1), and UBE2L6. In some embodiments, the second set of genes comprise one or more genes selected from Table 16. In some embodiments, the second set of genes is capable of identifying an immune evasive lung cancer. In some embodiments, the second set of genes is capable of identifying immune permissive lung cancer. In some embodiments, the expression levels of the second set of genes can classify if the lung cancer is immune evasive or immune permissive. sf-5975534Docket No.: 220232001840

[0105] In some embodiments, the second set of genes comprise COL1A2, CPE, CTNNB1 (Beta-catenin), DSTN (Destrin), EIF1B, EMILIN1, FBLN1, GJA1, GLG1, GPC1, IGFBP4, KDELR2 (KDEL Endoplasmic Reticulum Protein Retention Receptor 2), LAMC1, LAP3, P4HA1, PLOD3, PSMB9, PSME2, RHBDD2 (Rhomboid Domain Containing 2), SNAI2 (Snail Family Transcriptional Repressor 2), ST13 (Suppression of Tumorigenicity 13), TAP1, TGFBI (Transforming Growth Factor Beta Induced), TMED4 (Transmembrane P24 Trafficking Protein 4), TRAC, VEGFB (Vascular Endothelial Growth Factor B), YKT6, CCL5, CD53 (CD53 Molecule), CD74, CHPF (Chondroitin Polymerizing Factor), CTSS, CYB5R3, DTX3L, GBP1, GNA12 (G Protein Subunit Alpha 12), HLA-DMA, HLA-F, IFI27, IFI35, IFI44, IFIT3, ISG15, LGALS3BP, NT5DC2 (5'-Nucleotidase Domain Containing 2), PCOLCE (Procollagen C- Endopeptidase Enhancer), STAT1, STAT2, TRBC1, and UBE2L6. In some embodiments, the second set of genes comprise one or more genes selected from Table 20. In some embodiments, the second set of genes is capable of identifying an immune evasive lung cancer. In some embodiments, the second set of genes is capable of identifying immune permissive lung cancer. In some embodiments, the expression levels of the second set of genes can classify if the lung cancer is immune evasive or immune permissive.

[0106] Also provided herein are methods of calculating an immune evasion score for a cancer sample. In some embodiments, the cancer sample is identified to be immune evasive high or immune evasive low based on the calculated immune evasion score in comparison to a reference value. In some embodiments, the immune evasion score is calculated based on the expression levels of one or more genes selected from any one of Table 1, Table 2, Table 6, Table 8, Table 12, Table 16, and Table 20.

[0107] In some embodiments, elevated expression of a subset of genes from the second set of genes indicates the CRC is immune evasive. In some embodiments, the subset of genes in which elevated expression indicates an immune evasive CRC are selected from the group consisting of CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, and VIM.

[0108] In some embodiments, reduced expression of a subset of genes from the second set of genes indicates the CRC is immune evasive. In some embodiments, the subset of genes in which reduced expression indicates an immune evasive CRC are selected from the group consisting of sf-5975534Docket No.: 220232001840 DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6.

[0109] In some embodiments, the immune evasion score is calculated based on the normalized measured RNA expression level of the marker genes within the second set of genes. In some embodiments, the immune evasion score based on the second set of genes is calculated with following expression (Equation 2):where i=gene, N=28, a = coefficient associated with the gene, and expression = normalized RNA expression level

[0110] In some embodiments, the immune evasion score is calculated with Equation 2 based on the expression levels of the genes in Table 2.

[0111] In some embodiments, the coefficient comprises a value between about -1.0 and 1.0. In some embodiments, the coefficient comprises a value of about -0.20, about -0.19, about -0.18, about -0.17, about -0.16, about -0.15, about -0.14, about -0.13, about -0.12, about -0.11, about - 0.10, about -0.09, about -0.08, about -0.07, about -0.06, about -0.05, about -0.04, about -0.03, about -0.02, about -0.01, about 0.01, about 0.02, about 0.03, about 0.04, about 0.05, about 0.06, about 0.07, about 0.08, about 0.09, about 0.10, about 0.11, about 0.12, about 0.13, about 0.14, about 0.15, about 0.16, about 0.17, about 0.18, about 0.19, about 0.20, about 0.21, about 0.22, about 0.23, about 0.24, about 0.25, about 0.26, about 0.27, about 0.28, about 0.29, about 0.30, about 0.31, about 0.32, about 0.33, about 0.34, or about 0.35. In some embodiments, the coefficient associated with the gene has a value that is about the coefficient value shown in Table 5, such as about ± 15%, ± 10%, ± 5%, or ± 1% the coefficient value shown in Table 5. In some embodiments, the coefficient associated with the gene has a value as shown in Table 5.

[0112] In some embodiments, elevated expression of a subset of genes from the second set of genes indicates the CRC is immune evasive. In some embodiments, the subset of genes in which elevated expression indicates an immune evasive CRC are selected from the group consisting of CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, and VIM. sf-5975534Docket No.: 220232001840

[0113] In some embodiments, reduced expression of a subset of genes from the second set of genes indicates the CRC is immune evasive. In some embodiments, the subset of genes in which reduced expression indicates an immune evasive CRC are selected from the group consisting of DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6.

[0114] In some embodiments, the immune evasion score based on the second set of genes is calculated with following expression (Equation 3):where i=gene, N=23, a = coefficient associated with the gene, and expression = normalized RNA expression level

[0115] In some embodiments, the immune evasion score is calculated with Equation 3 based on the expression levels of the genes in Table 6.

[0116] In some embodiments, the coefficient comprises a value between about -1.0 and 1.0. In some embodiments, the coefficient comprises a value of about -0.20, about -0.19, about -0.18, about -0.17, about -0.16, about -0.15, about -0.14, about -0.13, about -0.12, about -0.11, about - 0.10, about -0.09, about -0.08, about -0.07, about -0.06, about -0.05, about -0.04, about -0.03, about -0.02, about -0.01, about 0.01, about 0.02, about 0.03, about 0.04, about 0.05, about 0.06, about 0.07, about 0.08, about 0.09, about 0.10, about 0.11, about 0.12, about 0.13, about 0.14, about 0.15, about 0.16, about 0.17, about 0.18, about 0.19, about 0.20, about 0.21, about 0.22, about 0.23, about 0.24, about 0.25, about 0.26, about 0.27, about 0.28, about 0.29, about 0.30, about 0.31, about 0.32, about 0.33, about 0.34, or about 0.35. In some embodiments, the coefficient associated with the gene has a value that is about the coefficient value shown in Table 7, such as about ± 15%, ± 14%, ± 13%, ± 12%, ± 11%, ± 10%, ± 9%, ± 8%, ± 7%, ± 6%, ± 5%, ± 4%, ± 3%, ± 2%, or ± 1% the coefficient value shown in Table 7. In some embodiments, the coefficient associated with the gene has a value as shown in Table 7.

[0117] In some embodiments, the immune evasive and immune permissive cancers include lung cancers. In some embodiments, the lung cancer is selected from the group consisting of non-small cell lung cancer (NSCLC), NSCLC not having genetic mutations in ALK, EGFR, or KRAS, lung adenocarcinoma, and lung squamous cell carcinoma (LUSC). sf-5975534Docket No.: 220232001840 IV. Methods of Using Immune Evasion Signatures

[0118] Also provided herein are methods of identifying targets for cancer therapy, comprising the steps of: a) detecting expression levels of a first set of genes in cancer samples containing cancer cells, wherein the cancer samples are of the same cancer type; b) determining an immune evasion score based on the expression levels of the first set of genes for each sample in step a; c) selecting samples comprising immune evasion scores in about the top 33rd percentile (‘immune evasive high’) and about the bottom 33rd percentile (‘immune evasive low’) among the cancer samples; d) detecting genes that are differentially expressed between the immune evasive high and the immune evasive low samples; e) identifying a second set of genes from the differentially expressed genes detected in step d, wherein the expression levels of the second set of genes can classify if the cancer is immune evasive; and f) identifying one or more cancer treatment targets from the second set of genes.

[0119] The second set of genes identified using the methods described herein are useful for identifying an individual having immune evasive high cancer, identifying an individual having immune evasive low cancer, and for treating an individual with cancer. In some embodiments, the genes shared between two or more of any of Table 1, Table 2, Table 6, Table 8, Table 12, Table 16, and Table 20 are useful for identifying an individual having immune evasive high cancer, identifying an individual having immune evasive low cancer, and for treating an individual with cancer.

[0120] Provided herein are methods of identifying an individual having immune evasive high colorectal cancer, comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step (a); and c) identifying the colorectal cancer is likely to be immune evasive high if the immune evasion score in step (b) is higher than a reference value. In some embodiments, the set of genes comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, or at least 20 or more genes selected from any one of Table 1, Table 2, and Table 6.

[0121] Also provided herein are methods of identifying an individual having immune evasive low colorectal cancer, comprising the steps of a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step (a), and c) identifying 40 sf-5975534Docket No.: 220232001840 the colorectal cancer is likely to be immune evasive low if the immune evasion score in step (b) is lower than a reference value. In some embodiments, the set of genes comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, or at least 20 or more genes selected from any one of Table 1, Table 2, and Table 6.

[0122] In some embodiments, the genes used in the set of genes are up-regulated genes, such as any of the up-regulated genes of any one of Table 1, Table 2, and Table 6. In some embodiments, the genes used in the set of genes are down-regulated genes, such as any of the down-regulated genes of any one of Table 1, Table 2, and Table 6. In some embodiments, the genes used are a combination of up-regulated and down-regulated genes, such as any of the genes of Table 1, Table 2, and Table 6.

[0123] Also provided herein are methods of identifying an individual having immune evasive high colorectal cancer, comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises i) one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; ii) one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii) ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b) determining an immune evasion score for the colorectal cancer based on the sf-5975534Docket No.: 220232001840 expression levels of the set of genes in step a; and c) identifying the colorectal cancer is likely to be immune evasive high if the immune evasion score in step b is higher than a reference value. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in Equation 1. In some embodiments, the reference value is about 0.8, about 0.9, about 1.0, about 1.1, about 1.2, about 1.3, about 1.4, about 1.5, about 1.6, about 1.7, about 1.8, about 1.9, or about 2.0. In some embodiments, the reference value is about 1.0.

[0124] Also provided herein are methods of identifying an individual having immune evasive low colorectal cancer, comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises i) one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; ii) one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii) ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; and c) identifying the colorectal cancer is likely to be immune evasive low if the immune evasion score in step b is lower than a reference value. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in Equation 1. In some embodiments, the reference value is about -0.8, about -0.9, about - sf-5975534Docket No.: 220232001840 1.0, about -1.1, about -1.2, about -1.3, about -1.4, about -1.5, about -1.6, about -1.7, about -1.8, about -1.9, or about -2.0. In some embodiments, the reference value is about -1.0.

[0125] Also provided herein are methods of identifying an individual having immune evasive high colorectal cancer, comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; and c) identifying the colorectal cancer is likely to be immune evasive high if the immune evasion score in step b is higher than a reference value. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in Equation 2. In some embodiments, the coefficient associated with the gene has a value as shown in Table 5. In some embodiments, the reference value is about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, about 0.9, about 1.0, about 1.1, about 1.2, about 1.3, about 1.4, about 1.5, about 1.6, about 1.7, about 1.8, about 1.9, or about 2.0. In some embodiments, the reference value is about 1.0. In some embodiments, the reference value is about 0.195.

[0126] Also provided herein are methods of identifying an individual having immune evasive low colorectal cancer, comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; and c) identifying the colorectal cancer is likely to be immune evasive low if the immune evasion score in step b is lower than a reference value. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in Equation 2. In some embodiments, the coefficient associated with the gene has a value as shown in Table 5. In some embodiments, the reference value is about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, about -0.9, about -1.0, about -1.1, about -1.2, about -1.3, about -1.4, about -1.5, about -1.6, about sf-5975534Docket No.: 220232001840 -1.7, about -1.8, about -1.9, or about -2.0. In some embodiments, the reference value is about - 1.0. In some embodiments, the reference value is about -0.256.

[0127] Also provided herein are methods of identifying an individual having immune evasive high colorectal cancer, comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; and c) identifying the colorectal cancer is likely to be immune evasive high if the immune evasion score in step b is higher than a reference value. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in Equation 3. In some embodiments, the coefficient associated with the gene has a value as shown in Table 7. In some embodiments, the reference value is about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, about 0.9, about 1.0, about 1.1, about 1.2, about 1.3, about 1.4, about 1.5, about 1.6, about 1.7, about 1.8, about 1.9, or about 2.0. In some embodiments, the reference value is about 1.0. In some embodiments, the reference value is about 0.209.

[0128] Also provided herein are methods of identifying an individual having immune evasive low colorectal cancer, comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; and c) identifying the colorectal cancer is likely to be immune evasive low if the immune evasion score in step b is lower than a reference value. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in Equation 3. In some embodiments, the coefficient associated with the gene has a value as shown in Table 7. In some embodiments, the reference value is about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, about - 0.9, about -1.0, about -1.1, about -1.2, about -1.3, about -1.4, about -1.5, about -1.6, about -1.7, sf-5975534Docket No.: 220232001840 about -1.8, about -1.9, or about -2.0. In some embodiments, the reference value is about -1.0. In some embodiments, the reference value is about -0.237.

[0129] Also provided herein are methods of treating an individual with colorectal cancer (CRC), comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises i) one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; ii) one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii) ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; c) identifying the colorectal cancer is likely to be immune suppressive (or immune evasive high) if the immune evasion score in step b is higher than a reference value; and d) administering a cancer therapy to the individual that comprises a therapeutic targeting one or more targets selected from the group consisting of ADORA2A, ADRA2C, ADSSL1, AGT, AHCY, AKR1B10, ALOX15, AMZ1, ANO1, ANXA3, AQP1, ARHGAP6, ASGR1, BDNF, BMP2, C12orf49, CA1, CA4, CACNA2D1, CADM1, CALU, CAVIN1, CCBE1, CCL5, CCL21, CCN2, CCNE1, CD177, CHST11, CLBA1, CLIC4, COL6A2, COL27A1, CPE, CTSB, DDX58, DTX3L, DUSP9, EMB, EML5, ENPP4, ESD, sf-5975534Docket No.: 220232001840 FLNA, FN1, GDF15, GDPD1, GEM, GOT1, GPC1, GRIN2D, GPRC5B, GREB1, GREM1, GRIK2, HAP1, HAVCR1, HAVCR2, HGF, HLA-DQB2, HMGA2, HTRA1, IFI27, IFI27L2, IFI35, IFIT2, IGFBP4, IGFBP5, IL11, IRF9, ISG15, ISM1, ITGB3, ITPR1, JAM2, KAT2A, KDM5A, KHK, KIF17, LAP3, LAMC1, LBH, LGALS1, LGALS3BP, LGALS7, MFAP4, MMP2, MPP6, MTCL1, MYADM, NPFFR1, OAS1, OAS2, OAS3, PADI2, PARP14, PCDH19, PHF11, PIN1, PIP5K2A, PIP5K2B, PIP5K2C, PKN3, PLK1, PLXNA4, PMAIP1, POSTN, PPM1L, PRSS12, PSMB9, PSME2, PTGES, PTGS1, PVR, RAB15, RTL8C, SAMHD1, SCARB1, SCD, SERPINE1, SFRP2, SH3BP5, SHC2, SIGLEC15, SLC2A1, SLC14A1, SLC6A8, SLC7A5, SLFN13, SLITRK6, SPARCL1, SPP1, SRPX2, STAT1, STAT2, ST3GAL5, SULF1, SYN1, TAP1, TCF7L2, THBS1, TIMP2, TIMD4, TMEM97, TMEM176B, TMEM179, TNC, TOR3A, TRAP1, TRIM5, TRIM21, TRIM34, TUBA1A, TWIST2, UBA7, UBE2L6, USP11, VAT1, VIM, WNT10A, and ZNFX1. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in Equation 1. In some embodiments, the reference value is about 0.8, about 0.9, about 1.0, about 1.1, about 1.2, about 1.3, about 1.4, about 1.5, about 1.6, about 1.7, about 1.8, about 1.9, or about 2.0. In some embodiments, the reference value is about 1.0.

[0130] Also provided herein are methods of treating an individual with colorectal cancer (CRC), comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises i) one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; ii) one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii) ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv) sf-5975534Docket No.: 220232001840 CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v) CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; c) identifying the colorectal cancer is likely to be immune evasive low if the immune evasion score in step b is lower than a reference value; and d) administering a cancer therapy to the individual that comprises a therapeutic targeting one or more targets selected from the group consisting of AXL, C10orf54, CD112, CD137, CD152, CD155, CD172a, CD223, CD27, CD272, CD274, CD38, CD40, CD47, CD96, cGAMP, cGAS, CSF1, CSF1R, CXCR4, ENPP1, ENTPD1, ENTPD2, GITR, HPK1, ICOS, ICOSLG, IDO, IFNB1, IKZF1, IKZF2, IL-1B, IL-6, IRF3, IRF7, LSGAL3, LSGAL9, MAVS,MERTK, NKG2A, NKG2D, NT5E, PDCD1, PDCD2, RIG-I, SAMHD1, SHP2, STAT3, STING, TANK, TBK1, TGFB1,TIGIT, TLR7, TLR9, TNFA, TNFRSF4, TREX1, TYRO3, USP1, USP18, and VEGF. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in Equation 1. In some embodiments, the reference value is about -0.8, about - 0.9, about -1.0, about -1.1, about -1.2, about -1.3, about -1.4, about -1.5, about -1.6, about -1.7, about -1.8, about -1.9, or about -2.0. In some embodiments, the reference value is about -1.0.

[0131] Also provided herein are methods of treating an individual with colorectal cancer (CRC), comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; c) identifying the colorectal cancer is likely to be immune suppressive (or immune evasive high) if the immune evasion score in step b is higher than a reference value; and d) administering a cancer therapy to the individual that comprises a therapeutic targeting one or more targets selected from the group consisting of ADORA2A, ADRA2C, ADSSL1, AGT, AHCY, AKR1B10, ALOX15, AMZ1, ANO1, ANXA3, AQP1, ARHGAP6, ASGR1, BDNF, BMP2, C12orf49, CA1, CA4, sf-5975534Docket No.: 220232001840 CACNA2D1, CADM1, CALU, CAVIN1, CCBE1, CCL5, CCL21, CCN2, CCNE1, CD177, CHST11, CLBA1, CLIC4, COL6A2, COL27A1, CPE, CTSB, DDX58, DTX3L, DUSP9, EMB, EML5, ENPP4, ESD, FLNA, FN1, GDF15, GDPD1, GEM, GOT1, GPC1, GRIN2D, GPRC5B, GREB1, GREM1, GRIK2, HAP1, HAVCR1, HAVCR2, HGF, HLA-DQB2, HMGA2, HTRA1, IFI27, IFI27L2, IFI35, IFIT2, IGFBP4, IGFBP5, IL11, IRF9, ISG15, ISM1, ITGB3, ITPR1, JAM2, KAT2A, KDM5A, KHK, KIF17, LAP3, LAMC1, LBH, LGALS1, LGALS3BP, LGALS7, MFAP4, MMP2, MPP6, MTCL1, MYADM, NPFFR1, OAS1, OAS2, OAS3, PADI2, PARP14, PCDH19, PHF11, PIN1, PIP5K2A, PIP5K2B, PIP5K2C, PKN3, PLK1, PLXNA4, PMAIP1, POSTN, PPM1L, PRSS12, PSMB9, PSME2, PTGES, PTGS1, PVR, RAB15, RTL8C, SAMHD1, SCARB1, SCD, SERPINE1, SFRP2, SH3BP5, SHC2, SIGLEC15, SLC2A1, SLC14A1, SLC6A8, SLC7A5, SLFN13, SLITRK6, SPARCL1, SPP1, SRPX2, STAT1, STAT2, ST3GAL5, SULF1, SYN1, TAP1, TCF7L2, THBS1, TIMP2, TIMD4, TMEM97, TMEM176B, TMEM179, TNC, TOR3A, TRAP1, TRIM5, TRIM21, TRIM34, TUBA1A, TWIST2, UBA7, UBE2L6, USP11, VAT1, VIM, WNT10A, and ZNFX1. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in Equation 2. In some embodiments, the coefficient associated with the gene has a value as shown in Table 5. In some embodiments, the reference value is about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, about 0.9, about 1.0, about 1.1, about 1.2, about 1.3, about 1.4, about 1.5, about 1.6, about 1.7, about 1.8, about 1.9, or about 2.0. In some embodiments, the reference value is about 1.0. In some embodiments, the reference value is about 0.195.

[0132] Also provided herein are methods of treating an individual with colorectal cancer (CRC), comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; c) identifying the colorectal cancer is likely to be immune evasive low if the immune evasion score in step b is lower than a reference value; and d) administering a cancer therapy to the individual that comprises a therapeutic targeting one or more targets selected from the group consisting of AXL, C10orf54, CD112, CD137, CD152, CD155, CD172a, CD223, CD27, CD272, CD274, sf-5975534Docket No.: 220232001840 CD38, CD40, CD47, CD96, cGAMP, cGAS, CSF1, CSF1R, CXCR4, ENPP1, ENTPD1, ENTPD2, GITR, HPK1, ICOS, ICOSLG, IDO, IFNB1, IKZF1, IKZF2, IL-1B, IL-6, IRF3, IRF7, LSGAL3, LSGAL9, MAVS,MERTK, NKG2A, NKG2D, NT5E, PDCD1, PDCD2, RIG-I, SAMHD1, SHP2, STAT3, STING, TANK, TBK1, TGFB1,TIGIT, TLR7, TLR9, TNFA, TNFRSF4, TREX1, TYRO3, USP1, USP18, and VEGF. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in Equation 2. In some embodiments, the coefficient associated with the gene has a value as shown in Table 5. In some embodiments, the reference value is about -0.2, about -0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, about -0.9, about -1.0, about -1.1, about -1.2, about -1.3, about -1.4, about -1.5, about -1.6, about -1.7, about -1.8, about -1.9, or about -2.0. In some embodiments, the reference value is about -1.0. In some embodiments, the reference value is about -0.256.

[0133] Also provided herein are methods of treating an individual with colorectal cancer (CRC), comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; c) identifying the colorectal cancer is likely to be immune suppressive (or immune evasive high) if the immune evasion score in step b is higher than a reference value; and d) administering a cancer therapy to the individual that comprises a therapeutic targeting one or more targets selected from the group consisting of ADORA2A, ADRA2C, ADSSL1, AGT, AHCY, AKR1B10, ALOX15, AMZ1, ANO1, ANXA3, AQP1, ARHGAP6, ASGR1, BDNF, BMP2, C12orf49, CA1, CA4, CACNA2D1, CADM1, CALU, CAVIN1, CCBE1, CCL5, CCL21, CCN2, CCNE1, CD177, CHST11, CLBA1, CLIC4, COL6A2, COL27A1, CPE, CTSB, DDX58, DTX3L, DUSP9, EMB, EML5, ENPP4, ESD, FLNA, FN1, GDF15, GDPD1, GEM, GOT1, GPC1, GRIN2D, GPRC5B, GREB1, GREM1, GRIK2, HAP1, HAVCR1, HAVCR2, HGF, HLA-DQB2, HMGA2, HTRA1, IFI27, IFI27L2, IFI35, IFIT2, IGFBP4, IGFBP5, IL11, IRF9, ISG15, ISM1, ITGB3, ITPR1, JAM2, KAT2A, KDM5A, KHK, KIF17, LAP3, LAMC1, LBH, LGALS1, LGALS3BP, LGALS7, MFAP4, MMP2, MPP6, MTCL1, MYADM, NPFFR1, OAS1, OAS2, OAS3, PADI2, PARP14, PCDH19, PHF11, PIN1, PIP5K2A, PIP5K2B, PIP5K2C, PKN3, PLK1, PLXNA4, PMAIP1, POSTN, PPM1L, PRSS12, PSMB9, PSME2, sf-5975534Docket No.: 220232001840 PTGES, PTGS1, PVR, RAB15, RTL8C, SAMHD1, SCARB1, SCD, SERPINE1, SFRP2, SH3BP5, SHC2, SIGLEC15, SLC2A1, SLC14A1, SLC6A8, SLC7A5, SLFN13, SLITRK6, SPARCL1, SPP1, SRPX2, STAT1, STAT2, ST3GAL5, SULF1, SYN1, TAP1, TCF7L2, THBS1, TIMP2, TIMD4, TMEM97, TMEM176B, TMEM179, TNC, TOR3A, TRAP1, TRIM5, TRIM21, TRIM34, TUBA1A, TWIST2, UBA7, UBE2L6, USP11, VAT1, VIM, WNT10A, and ZNFX1. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in Equation 3. In some embodiments, the coefficient associated with the gene has a value as shown in Table 7. In some embodiments, the reference value is about 0.2, about 0.3, about 0.4, about 0.5, about 0.6, about 0.7, about 0.8, about 0.9, about 1.0, about 1.1, about 1.2, about 1.3, about 1.4, about 1.5, about 1.6, about 1.7, about 1.8, about 1.9, or about 2.0. In some embodiments, the reference value is about 1.0. In some embodiments, the reference value is about 0.209.

[0134] Also provided herein are methods of treating an individual with colorectal cancer (CRC), comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; c) identifying the colorectal cancer is likely to be immune evasive low if the immune evasion score in step b is lower than a reference value; and d) administering a cancer therapy to the individual that comprises a therapeutic targeting one or more targets selected from the group consisting of AXL, C10orf54, CD112, CD137, CD152, CD155, CD172a, CD223, CD27, CD272, CD274, CD38, CD40, CD47, CD96, cGAMP, cGAS, CSF1, CSF1R, CXCR4, ENPP1, ENTPD1, ENTPD2, GITR, HPK1, ICOS, ICOSLG, IDO, IFNB1, IKZF1, IKZF2, IL-1B, IL-6, IRF3, IRF7, LSGAL3, LSGAL9, MAVS,MERTK, NKG2A, NKG2D, NT5E, PDCD1, PDCD2, RIG-I, SAMHD1, SHP2, STAT3, STING, TANK, TBK1, TGFB1,TIGIT, TLR7, TLR9, TNFA, TNFRSF4, TREX1, TYRO3, USP1, USP18, and VEGF. In some embodiments, the immune evasion score is determined using normalized RNA expression levels in Equation 3. In some embodiments, the coefficient associated with the gene has a value as shown in Table 7. In some embodiments, the reference value is about -0.2, about - 0.3, about -0.4, about -0.5, about -0.6, about -0.7, about -0.8, about -0.9, about -1.0, about -1.1, sf-5975534Docket No.: 220232001840 about -1.2, about -1.3, about -1.4, about -1.5, about -1.6, about -1.7, about -1.8, about -1.9, or about -2.0. In some embodiments, the reference value is about -1.0. In some embodiments, the reference value is about -0.237.

[0135] Provided herein are methods of treating an individual with colorectal cancer, comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sample from the individual, b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step (a); c) identifying the colorectal cancer is likely to be immune suppressive if the immune evasion score in step (b) is higher than a reference value; and d) administering a cancer therapy to the individual. In some embodiments, the set of genes comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, or at least 20 or more genes selected from any one of Table 1, Table 2, and Table 6. In some embodiments, the cancer therapy comprises a therapeutic targeting one or more targets. In some embodiments, the therapeutic targets one or more targets selected from the group consisting of ADORA2A, ADRA2C, ADSSL1, AGT, AHCY, AKR1B10, ALOX15, AMZ1, ANO1, ANXA3, AQP1, ARHGAP6, ASGR1, BDNF, BMP2, C12orf49, CA1, CA4, CACNA2D1, CADM1, CALU, CAVIN1, CCBE1, CCL5, CCL21, CCN2, CCNE1, CD177, CHST11, CLBA1, CLIC4, COL6A2, COL27A1, CPE, CTSB, DDX58, DTX3L, DUSP9, EMB, EML5, ENPP4, ESD, FLNA, FN1, GDF15, GDPD1, GEM, GOT1, GPC1, GRIN2D, GPRC5B, GREB1, GREM1, GRIK2, HAP1, HAVCR1, HAVCR2, HGF, HLA-DQB2, HMGA2, HTRA1, IFI27, IFI27L2, IFI35, IFIT2, IGFBP4, IGFBP5, IL11, IRF9, ISG15, ISM1, ITGB3, ITPR1, JAM2, KAT2A, KDM5A, KHK, KIF17, LAP3, LAMC1, LBH, LGALS1, LGALS3BP, LGALS7, MFAP4, MMP2, MPP6, MTCL1, MYADM, NPFFR1, OAS1, OAS2, OAS3, PADI2, PARP14, PCDH19, PHF11, PIN1, PIP5K2A, PIP5K2B, PIP5K2C, PKN3, PLK1, PLXNA4, PMAIP1, POSTN, PPM1L, PRSS12, PSMB9, PSME2, PTGES, PTGS1, PVR, RAB15, RTL8C, SAMHD1, SCARB1, SCD, SERPINE1, SFRP2, SH3BP5, SHC2, SIGLEC15, SLC2A1, SLC14A1, SLC6A8, SLC7A5, SLFN13, SLITRK6, SPARCL1, SPP1, SRPX2, STAT1, STAT2, ST3GAL5, SULF1, SYN1, TAP1, TCF7L2, THBS1, TIMP2, TIMD4, TMEM97, TMEM176B, TMEM179, TNC, TOR3A, TRAP1, TRIM5, TRIM21, TRIM34, TUBA1A, TWIST2, UBA7, UBE2L6, USP11, VAT1, VIM, WNT10A, and ZNFX1.

[0136] Provided herein are methods of treating an individual with colorectal cancer, comprising the steps of: a) detecting expression levels of a set of genes in a colorectal cancer sf-5975534Docket No.: 220232001840 sample from the individual, b) determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step (a); c) identifying the colorectal cancer is likely to be immune evasive low if the immune evasion score in step (b) is lower than a reference value; and d) administering a cancer therapy to the individual. In some embodiments, the set of genes comprises at least 1, at least 2, at least 3, at least 4, at least 5, at least 10, at least 15, or at least 20 or more genes selected from any one of Table 1, Table 2, and Table 6. In some embodiments, the cancer therapy comprises a therapeutic targeting one or more targets. In some embodiments, the therapeutic targets one or more targets selected from the group consisting of AXL, C10orf54, CD112, CD137, CD152, CD155, CD172a, CD223, CD27, CD272, CD274, CD38, CD40, CD47, CD96, cGAMP, cGAS, CSF1, CSF1R, CXCR4, ENPP1, ENTPD1, ENTPD2, GITR, HPK1, ICOS, ICOSLG, IDO, IFNB1, IKZF1, IKZF2, IL-1B, IL-6, IRF3, IRF7, LSGAL3, LSGAL9, MAVS, MERTK, NKG2A, NKG2D, NT5E, PDCD1, PDCD2, RIG- I, SAMHD1, SHP2, STAT3, STING, TANK, TBK1, TGFB1,TIGIT, TLR7, TLR9, TNFA, TNFRSF4, TREX1, TYRO3, USP1, USP18, and VEGF.

[0137] In some embodiments, the reference value for the immune evasion score is selected so that one third of the samples are present in the immune evasive high, immune evasive medium and immune evasive low categories. In some embodiments, the individual is a human.

[0138] In some embodiments, the cancer sample is selected from the group consisting of a cancer biopsy sample, a liquid biopsy, a cancer cell, and a cancer organoid. In some embodiments, the cancer cell or the cancer organoid is patient derived. In some embodiments, the cancer cell or the cancer organoid is not patient derived. In some embodiments, the cancer sample contains immune evasive cancer cells. In some embodiments, the cancer sample contains immune permissive cancer cells.

[0139] In some embodiments, the cancer sample is a colorectal cancer sample. In some embodiments, the colorectal cancer is a CMS1 subtype colorectal cancer, a CMS2 subtype colorectal cancer, a CMS3 subtype colorectal cancer, or a CMS4 subtype colorectal cancer.

[0140] In some embodiments, the expression levels are mRNA expression levels. In some embodiments, mRNA expression levels are detected by quantitative PCR or nanostring. In some embodiments, the mRNA expression levels are detected by bulk RNA sequencing. In some embodiments, the mRNA expression levels are detected by single cell RNA sequencing. In some sf-5975534Docket No.: 220232001840 embodiments, the expression levels are normalized to a reference level. In some embodiments, the reference level is the expression level of one or more housekeeping genes. In some embodiments, the expression levels are normalized to log2(TPM+1) values. EXAMPLES

[0141] The following examples are included for illustrative purposes only and are not intended to limit the scope of the present disclosure. Example 1: Development of the Immune Evasion Signature 2

[0142] This example describes the development of an immune evasive gene signature for colorectal cancer (CRC). A summary of the method to develop an immune evasive gene signature is provided in FIG. 1.

[0143] A 97 gene signature derived from immune evasive melanoma cancer cells (the immune evasion signature 1, Table 1) was applied to The Cancer Genome Atlas (TCGA) CRC dataset comprising bulk RNA expression data from a multitude of individual CRC samples. Surprisingly, Immune Evasion Signature 1 was able to enrich the Consensus Molecular Subtype 4 (CMS4) subtype of CRC from CMS subtypes 1, 2, and 3 (FIGs. 2A-2B). As shown in FIG. 2B, a high proportion of CMS4 subtype tumors were enriched using the Immune Evasion Signature 1.

[0144] This result is significant, as CMS4 CRC is characterized by having worse relapse-free and worse overall survival characteristics when compared to other CRC subtypes. CMS4 subtype CRC is characterized by high levels of stromal cells and their products (desmoplasia) and this is thought to suppress T cell function in tumors, consistent with enrichment of a signature that marks active, gain-of-function tumor driven suppression of the immune system. CMS4 CRC is also known to respond poorly to immuno-oncology therapies. CRC is a genetically and transcriptomically heterogenous disease. The ability of the 97 gene signature developed using melanoma cancer cells to enrich for the CMS4 subtype of CRC is surprising, and points to specific shared immunosuppressive mechanisms between this cancer subtype and melanoma. The 97 gene signature may therefore be applicable to a more diverse range of immune- suppressive cancer types than previously thought. sf-5975534Docket No.: 220232001840 Table 1: Immune Evasion Signature 1

[0145] The Immune Evasion Signature 1 (Table 1) was used to classify CRC tumor samples from TCGA into three immune evasive categories: immune evasive - high, immune evasive – medium and immune evasive – low. A differentially expressed gene (DEG) analysis comparing the immune evasive – high and immune evasive – low tumor samples identified 1019 genes with statistically significant differences in gene expression levels between the two different populations (FIG. 3).

[0146] A model was developed using an elastic net regression algorithm that reduced the 1019 gene set to a 28 gene signature that can classify CRC immune evasive tumors (Table 2). The model was generated using the RNA expression level of the 1019 DEG genes from 513 TCGA CRC samples and the Immune Evasion Signature 1 score as desired output. The 1019 gene expression data was pre-processed by centering and scaling the transcripts per million (TPM) values. The resulting models were resampled by 10-fold cross-validation and repeating five times. To compare the Immune Evasion Signature 2 model to the Immune Evasion Signature 1, the Immune Evasion Signature 1 score and Immune Evasion Signature 2 score were compared for each TCGA CRC sample used to build the model. As shown in FIG. 4A an R2of 0.84 was sf-5975534Docket No.: 220232001840 observed, indicating that the Immune Evasion Signature 1 score and Immune Evasion Signature 2 score were highly correlated. Table 2: Immune Evasion Signature 2

[0147] The generation of the Immune Evasion Signature 2 was a two-step process. The first step involved identifying the best elastic net regression model that minimized the number of descriptors and provided the lowest root mean square estimate (RMSE) between training sets. The input for the elastic net regression was the RNA expression levels of the 1019 genes identified in the DEG analysis from 513 TCGA CRC samples, pre-processed to be centered and scaled. The outcome to predicted by the model was the Immune Evasion Signature 1 score. The resulting models were re-sampled five times by 10-fold cross-validation to calculate RMSE (Table 3). The best model from this analysis was composed of 209 genes with elastic net regression tuning parameters alpha = 0.36 and lambda = 0.018. Table 3: Resampling results across the tuning parameterssf-5975534Docket No.: 220232001840

[0148] The second step of the process was to optimize between capturing the Immune Evasion Signature 1 score and using the minimal number of genes for the signature. This was done by performing net regression analyses starting with the 209 genes identified from the previous best model as the descriptors, keeping the alpha parameter held constant, and allowing the lambda parameter to be adjusted. The optimal model was selected by the combination of the lowest number of genes in the model and the coefficient of determination (R2) between the model prediction and Immune Evasion Signature 1. Since models lose some predictive power when applied to data outside of the training and test sets, the criteria of the model having a R2> 0.8 was required for model selection. As shown in Table 4 and FIG. 4A, a 28 gene-signature model was identified that met the criteria of having the least number of genes and having R2= 0.84, and thus was selected as Immune Evasion Signature 2. The 28 gene-signature model was re-trained with fixed selected genes and generalized linear regression (no regularization applied) to increase model accuracy. As shown in FIG. 4B, Immune Evasion Signature 2 was optimized to better predict Immune Evasion Signature 1, resulting in a R2value of 0.87. Table 4: Optimization of Immune Evasion Signature 2

[0149] The resulting equation to calculate an Immune Evasion Signature 2 score is: sf-5975534Docket No.: 220232001840where i=gene, N=28, a = coefficient associated with gene (Table 5) and expression = normalized RNA expression level

[0150] Using the equation and the coefficient values shown in Table 5, immune evasion score for a colorectal cancer can be calculated. For example, if the immune evasion score is higher than a reference value of about 0.195, the colorectal cancer is likely to be immune evasive high; and if the immune evasion score is lower than a reference value of about -0.256, the colorectal cancer is likely to be immune evasive low. Table 5: Coefficients for the 28 Gene Immune Evasion Signature 2sf-5975534Docket No.: 220232001840

[0151] To test the robustness of the Immune Evasion Signature 2 across CRC tumors, the model performance was assessed against three independent CRC data sets available at NCBI Sequence Read Archive (SRA) and Gene Expression Omnibus (GEO) data repositories. As shown in FIGs. 5A-5C, the Immune Evasion Signature 2 score maintains its correlation with the Immune Evasion Signature 1 score in the PRJNA387172 dataset (R2of 0.81) (FIG. 5A), the GSE152430 dataset (R2of 0.77) (FIG. 5B), and the GSE20067 dataset (R2of 0.74) (FIG. 5C). These results suggest that the Immune Evasion Signature 2 can identify immune evasiveness across a wide range of CRC samples. As shown in FIGs. 6A-6B, the Immune Evasion Signature 2 improves the distinction between immune evasive and immune infiltrating tumors compared to the Immune Evasion Signature 1. The Immune Evasion Signature 2 offers substantial clinical utility to assist in the identification of individuals who would benefit from immuno-oncology based therapeutics to treat CRC.

[0152] As shown in FIG. 7A-7C, the Immune Evasion Signature 2 can be applied to additional indications than TCGA CRC tumors (FIG. 7A), including melanoma (FIG. 7B) and lung adenocarcinoma (FIG. 7C). Example 2: Development of the Immune Evasion Signature 3

[0153] This example describes the refinement of immune evasive gene signature 2 for colorectal cancer (CRC).

[0154] A model was developed using an elastic net regression algorithm that reduced the 28 gene set from Immune Evasion Signature 2 to a 23 gene signature that can classify CRC immune evasive tumors (Table 6). The model was generated using the RNA expression level of the 28 genes from 513 TCGA CRC samples and the Immune Evasion Signature 1 score as desired output. The 28 gene expression data was pre-processed by centering and scaling the TPM values. The resulting models were resampled by 10-fold cross-validation and repeating five times. To compare the Immune Evasion Signature 3 model to the Immune Evasion Signature 1, the sf-5975534Docket No.: 220232001840 Immune Evasion Signature 1 score and Immune Evasion Signature 3 score were compared for each TCGA CRC sample used to build the model. As shown in FIG. 8A an R2of 0.86 was observed, indicating that the Immune Evasion Signature 1 score and Immune Evasion Signature 3 score were highly correlated. Table 6: Immune Evasion Signature 3

[0155] The generation of the Immune Evasion Signature 3 was a one-step process. The first step involved identifying the best elastic net regression model that minimized the number of descriptors and provided the lowest root mean square estimate (RMSE) between training sets. The input for the elastic net regression was the RNA expression levels of the 1019 genes identified in the DEG analysis from 513 TCGA CRC samples, pre-processed to be centered and scaled. The outcome to be predicted by the model was the Immune Evasion Signature 1 score. The resulting models were re-sampled five times by 10-fold cross-validation to calculate RMSE (Table 3). The best model from this analysis was composed of 209 genes with elastic net regression tuning parameters alpha = 0.36 and lambda = 0.018.

[0156] The resulting equation to calculate an Immune Evasion Signature 3 score is:where i=gene, N=23, a = coefficient associated with gene (Table 7) and expression = normalized RNA expression level sf-5975534Docket No.: 220232001840

[0157] Using the equation and the coefficient values shown in Table 7, immune evasion score for a colorectal cancer can be calculated. For example, if the immune evasion score is higher than a reference value of about 0.209, the colorectal cancer is likely to be immune evasive high; and if the immune evasion score is lower than a reference value of about -0.237, the colorectal cancer is likely to be immune evasive low. Table 7: Coefficients for the 23 Gene Immune Evasion Signature 3

[0158] As shown in FIGs. 8B-8D, the Immune Evasion Signature 3 can be applied to independent datasets beyond TCGA including GSE152430 (FIG. 8B), Sidra-LUMC AC-ICAM (FIG. 8C) and GSE179351 (FIG. 8D). In all three independent datasets, a significant positive correlation was detected between Immune Evasion Signature 1 score and Immune Evasion 60 sf-5975534Docket No.: 220232001840 Signature 3 score, demonstrating the model’s ability to accurately identify whether a tumor is immune evasive or immune permissive in independent data sets. Notably, the correlation between Immune Evasion Signature 1 score and Immune Evasion Signature 3 score was seen in both primary, untreated tumors (Fig. 8B-8C) and metastatic tumors subjected to multiple treatments (Fig. 8D). This suggests that Immune Evasion Signature 3 may be stable across CRC stage and treatment regimens. Example 3: Development of the Immune Evasion Signature 4

[0159] This example describes the refinement of immune evasive gene signature 4 for non- small cell lung cancer (NSCLC).

[0160] A 97 gene signature derived from immune evasive melanoma cancer cells (the immune evasion signature 1, Table 1) was applied to The Cancer Genome Atlas (TCGA) LUAD and LUSC datasets comprising bulk RNA expression data from a multitude of individual NSCLC samples. This step was taken after applying Immune Evasion Signature 1 to the GSE135222 dataset comprising bulk RNA expression data from 60 advanced NSCLC patients who were treated with anti-PD-1 or anti-PD-L1, which showed that higher levels of Immune Evasion Signature 1 are present in patients that did not benefit from PD-1 blockade immunotherapy (FIG. 9).

[0161] This result is significant, as the ability to identify lung cancers not responding to PD-1 and thus guide treatment decisions has remained a challenge. The ability of the 97 gene signature developed using melanoma cancer cells to enrich for PD-1 blockade non-responders in lung cancer points to specific shared immunosuppressive mechanisms between this cancer subtype and melanoma. The 97 gene signature may therefore be applicable to a more diverse range of immune-suppressive cancer types than previously thought.

[0162] Similar to Example 1, the Immune Evasion Signature 1 (Table 1) was used to classify NSCLC tumor samples from TCGA (LUAD + LUSC) into 3 immune evasive categories: immune evasive - high, immune evasive – medium and immune evasive – low. A DEG analysis comparing the immune evasive – high and immune evasive – low tumor samples identified 2006 genes with statistically significant differences in gene expression levels between the two different populations (FIG. 10A). sf-5975534Docket No.: 220232001840

[0163] A model was developed using an elastic net regression algorithm that reduced the 2006 gene set to a 58 gene signature that can classify NSCLC immune evasive tumors (Table 8). The model was generated using the RNA expression level of the 2006 genes from 1041 TCGA LUAD and LUSC samples and the Immune Evasion Signature 1 score as desired output. The 2006 gene expression data was pre-processed by centering and scaling the TPM values. The resulting models were resampled by 10-fold cross-validation and repeating five times. To compare the Immune Evasion Signature 4 model to the Immune Evasion Signature 1, the Immune Evasion Signature 1 score and Immune Evasion Signature 4 score were compared for each TCGA LUAD and LUSC sample used to build the model. As shown in FIG. 10B an R2of 0.784 was observed, indicating that the Immune Evasion Signature 1 score and Immune Evasion Signature 4 score were highly correlated. Table 8. Immune Evasion Signature 4sf-5975534Docket No.: 220232001840

[0164] The generation of the Immune Evasion Signature 4 was a two-step process. The first step involved identifying the best elastic net regression model that minimized the number of descriptors and provided the lowest root mean square estimate (RMSE) between training sets. The input for the elastic net regression was the RNA expression levels of the 2006 genes identified in the DEG analysis from 1041 TCGA LUSC and LUAD samples, pre-processed to be centered and scaled. The outcome to be predicted by the model was the Immune Evasion Signature 1 score. The resulting models were re-sampled five times by 10-fold cross-validation to calculate RMSE (Table 9). The best model from this analysis was composed of 280 genes with elastic net regression tuning parameters alpha = 0.29 and lambda = 0.04. Table 9. Resampling results across the tuning parameters

[0165] The second step of the process was to optimize between capturing the Immune Evasion Signature 1 score and using the minimal number of genes for the Immune Evasion Signature 4. This was done by performing net regression analyses starting with the 280 genes identified from 63 sf-5975534Docket No.: 220232001840 the previous best model as the descriptors, keeping the alpha parameter held constant, and allowing the lambda parameter to be adjusted. The optimal model was selected by the combination of the lowest number of genes in the model and the coefficient of determination (R2) between the model prediction and Immune Evasion Signature 1. Since models lose some predictive power when applied to data outside of the training and test sets, the criteria of the model having a R2> 0.8 was required for model selection. As shown in Table 10, a 58 gene- signature model was identified that met the criteria of having the least number of genes and having R2= 0.81, and thus was selected as Immune Evasion Signature 4. Table 10. Optimization of immune evasion signature 4

[0166] The resulting equation to calculate an Immune Evasion Signature 4 score is:where i=gene, N=58, a = coefficient associated with gene (Table 11) and expression = normalized RNA expression level Table 11. Coefficients for the 58 Gene Immune Evasion Signature 4sf-5975534Docket No.: 220232001840 TNC 0.06335 FKBP9 -0.01651 DPYSL3 0.059682 IFIT3 -0.023370.055332GPC1 0.049007 HLA-E -0.03663 FSCN1 0.045474 HLA-B -0.03841 FBLN1 0.043303 DTX3L -0.04016 GJA1 0.041276 MMP14 -0.04659 RTN4 0.037854PLP2 0.036792 -0.06082 FSTL10.035293 -0.06169 YKT6 0.033918 -0.06367 LAMC1 0.033815-0.07198TRAC 0.024076 -0.07667 LAP3 0.023454-0.08112 PSMB8 0.021862IFI35 -0.08124SLC38A2 0.016901 STAT2 -0.10799 FAM168B 0.014032 OAS3 -0.14323 P4HA1 0.011651 YWHAG 0.011243 IRF7 0.00253PALLD0.001502 Example 4: Development of the Immune Evasion Signature 5

[0167] This example describes the refinement of immune evasive gene signature 5 for non- small cell lung cancer (NSCLC) not having genetic mutations in ALK, EGFR, or KRAS.

[0168] A 97 gene signature derived from immune evasive melanoma cancer cells (the immune evasion signature 1, Table 1) was applied to The Cancer Genome Atlas (TCGA) LUAD and LUSC datasets comprising bulk RNA expression data from a multitude of individual NSCLC samples, excluding samples containing mutations in ALK, EGFR, or KRAS. This step was taken after applying Immune Evasion Signature 1 to the GSE135222 dataset (Jung, Hyunchul et al. Nature communications vol. 10,14278. 19 Sep. 2019) comprising bulk RNA expression data from 60 advanced NSCLC patients who were treated with anti-PD-1 or anti-PD-L1, which sf-5975534Docket No.: 220232001840 showed that higher levels of Immune Evasion Signature 1 are present in patients that did not benefit from PD-1 blockade immunotherapy (FIG. 9). This result is significant, as the ability to identify lung cancers not responding to PD-1 and thus guide treatment decisions has remained a challenge. The ability of the 97 gene signature developed using melanoma cancer cells to enrich for PD-1 blockade non-responders in lung cancer points to specific shared immunosuppressive mechanisms between this cancer subtype and melanoma. The 97 gene signature may therefore be applicable to a more diverse range of immune-suppressive cancer types than previously thought.

[0169] Similar to Example 1, the Immune Evasion Signature 1 (Table 1) was used to classify NSCLC tumor samples from TCGA (LUAD + LUSC, excluding samples containing mutations in ALK, EGFR, or KRAS) into three immune evasive categories: immune evasive - high, immune evasive – medium and immune evasive – low. A DEG analysis comparing the immune evasive – high and immune evasive – low tumor samples identified 1869 genes with statistically significant differences in gene expression levels between the two different populations (FIG. 11A).

[0170] A model was developed using an elastic net regression algorithm that reduced the 1869 gene set to a 54 gene signature that can classify NSCLC immune evasive tumors (Table 12). The model was generated using the RNA expression level of the 1869 genes from 751 TCGA LUAD and LUSC samples (excluding samples containing mutations in ALK, EGFR, or KRAS) and the Immune Evasion Signature 1 score as desired output. The 1869 gene expression data was pre- processed by centering and scaling the TPM values. The resulting models were resampled by 10- fold cross-validation and repeating five times. To compare the Immune Evasion Signature 5 model to the Immune Evasion Signature 1, the Immune Evasion Signature 1 score and Immune Evasion Signature 5 score were compared for each TCGA LUAD and LUSC (tumors without mutations in EGRF, ALK, and KRAS) sample used to build the model. As shown in FIG. 11B an R2of 0.85 was observed, indicating that the Immune Evasion Signature 1 score and Immune Evasion Signature 5 score were highly correlated. Table 12. Immune Evasion Signature 5sf-5975534Docket No.: 220232001840

[0171] The generation of the Immune Evasion Signature 5 was a two-step process. The first step involved identifying the best elastic net regression model that minimized the number of descriptors and provided the lowest root mean square estimate (RMSE) between training sets. The input for the elastic net regression was the RNA expression levels of the 1869 genes identified in the DEG analysis from 751 TCGA CRC samples, pre-processed to be centered and scaled. The outcome to be predicted by the model was the Immune Evasion Signature 1 score. The resulting models were re-sampled five times by 10-fold cross-validation to calculate RMSE (Table 13). The best model from this analysis was composed of 280 genes with elastic net regression tuning parameters alpha = 0.29 and lambda = 0.04. sf-5975534Docket No.: 220232001840 Table 13. Resampling results across the tuning parameters

[0172] The second step of the process was to optimize between capturing the Immune Evasion Signature 1 score and using the minimal number of genes for the signature. This was done by performing net regression analyses starting with the 257 genes identified from the previous best model as the descriptors, keeping the alpha parameter held constant, and allowing the lambda parameter to be adjusted. The optimal model was selected by the combination of the lowest number of genes in the model and the coefficient of determination (R2) between the model prediction and Immune Evasion Signature 1. Since models lose some predictive power when applied to data outside of the training and test sets, the criteria of the model having a R2> 0.8 was required for model selection. As shown in Table 14, a 54 gene-signature model was identified that met the criteria of having the least number of genes and having R2= 0.80, and thus was selected as Immune Evasion Signature 5. Table 14. Optimization of Immune Evasion Signature 5 Lambda nGenes R² 0.0371 257 0.05565 1920.083475 150 0.125213 110 0.187819 86 0.281728 66 0.35 54 0.633888 34 0.950832 21 1.42624911

[0173] The resulting equation to calculate an Immune Evasion Signature 5 score is: sf-5975534Docket No.: 220232001840where i=gene, N=54, a = coefficient associated with gene (Table 15) and expression = normalized RNA expression level Table 15. Coefficients for the 54 Gene Immune Evasion Signature 5sf-5975534Docket No.: 220232001840

[0174] As shown in FIGs. 11C-11D, which assess RNA expression of human lung (NSCLC) tumors of different stages and after treatment with PD-1 blockers (nivolumab or pembrolizumab), the Immune Evasion Signature 5 can be applied to additional indications other than TCGA LUAD & LUSC tumors (tumors without mutations in EGRF, ALK, and KRAS) (FIG. 11B), including the GSE109265 dataset (mixed treatment lines, n=43) (FIG. 11C) and the GSE181820 dataset (multiple stages and driver mutations, n=22) (FIG. 11D). Example 5: Development of the Immune Evasion Signature 6

[0175] This example describes the refinement of immune evasive gene signature 6 for lung adenocarcinoma (LUAD).

[0176] A 97 gene signature derived from immune evasive melanoma cancer cells (the immune evasion signature 1, Table 1) was applied to The Cancer Genome Atlas (TCGA) LUAD dataset comprising bulk RNA expression data from a multitude of individual samples of the LUAD type of NSCLC.

[0177] Similar to Example 1, the Immune Evasion Signature 1 (Table 1) was used to classify NSCLC tumor samples from TCGA LUAD into three immune evasive categories: immune evasive - high, immune evasive – medium and immune evasive – low. A DEG analysis comparing the immune evasive – high and immune evasive – low tumor samples identified 714 genes with statistically significant differences in gene expression levels between the two different populations (FIG. 12A).

[0178] A model was developed using an elastic net regression algorithm that reduced the 714 gene set to a 55 gene signature that can classify NSCLC immune evasive tumors (Table 16). The model was generated using the RNA expression level of the 714 genes from 539 TCGA LUAD samples and the Immune Evasion Signature 1 score as desired output. The 714 gene expression data was pre-processed by centering and scaling the TPM values. The resulting models were resampled by 10-fold cross-validation and repeating five times. To compare the Immune Evasion Signature 6 model to the Immune Evasion Signature 1, the Immune Evasion Signature 1 score and Immune Evasion Signature 6 score were compared for each TCGA LUAD sample used to build the model. As shown in FIG. 12B an R2of 0.842 was observed, indicating that the Immune Evasion Signature 1 score and Immune Evasion Signature 6 score were highly correlated. sf-5975534Docket No.: 220232001840 Table 16. Immune Evasion Signature 6

[0179] The generation of the Immune Evasion Signature 6 was a two-step process. The first step involved identifying the best elastic net regression model that minimized the number of descriptors and provided the lowest root mean square estimate (RMSE) between training sets. The input for the elastic net regression was the RNA expression levels of the 714 genes identified in the DEG analysis from 539 TCGA CRC samples, pre-processed to be centered and sf-5975534Docket No.: 220232001840 scaled. The outcome to predicted by the model was the Immune Evasion Signature 1 score. The resulting models were re-sampled five times by 10-fold cross-validation to calculate RMSE (Table 17). The best model from this analysis was composed of 194 genes with elastic net regression tuning parameters alpha = 0.29 and lambda = 0.04. Table 17. Resampling results across the tuning parameters

[0180] The second step of the process was to optimize between capturing the Immune Evasion Signature 1 score and using the minimal number of genes for the signature. This was done by performing net regression analyses starting with the 194 genes identified from the previous best model as the descriptors, keeping the alpha parameter held constant, and allowing the lambda parameter to be adjusted. The optimal model was selected by the combination of the lowest number of genes in the model and the coefficient of determination (R2) between the model prediction and Immune Evasion Signature 1. Since models lose some predictive power when applied to data outside of the training and test sets, the criteria of the model having a R2> 0.8 was required for model selection. As shown in Table 18, a 55 gene-signature model was identified that met the criteria of having the least number of genes and having R2= 0.80, and thus was selected as Immune Evasion Signature 6. Table 18. Optimization of Immune Evasion Signature 6sf-5975534Docket No.: 220232001840

[0181] The resulting equation to calculate an Immune Evasion Signature 6 score is:where i=gene, N=55, a = coefficient associated with gene (Table 19) and expression = normalized RNA expression level Table 19. Coefficients for the 55 Gene Immune Evasion Signature 673 sf-5975534Docket No.: 220232001840Example 6: Development of the Immune Evasion Signature 7

[0182] This example describes the refinement of immune evasive gene signature 7 for lung squamous cell carcinoma (LUSC).

[0183] A 97 gene signature derived from immune evasive melanoma cancer cells (the immune evasion signature 1, Table 1) was applied to The Cancer Genome Atlas (TCGA) LUSC dataset comprising bulk RNA expression data from a multitude of individual NSCLC samples.

[0184] Similar to Example 1, the Immune Evasion Signature 1 (Table 1) was used to classify NSCLC tumor samples from TCGA LUSC into three immune evasive categories: immune evasive - high, immune evasive – medium and immune evasive – low. A DEG analysis comparing the immune evasive – high and immune evasive – low tumor samples identified 1103 genes with statistically significant differences in gene expression levels between the two different populations (FIG. 13A).

[0185] A model was developed using an elastic net regression algorithm that reduced the 1103 gene set to a 50 gene signature that can classify NSCLC LUSC immune evasive tumors (Table 20). The model was generated using the RNA expression level of the 1103 genes from 502TCGA LUSC samples and the Immune Evasion Signature 1 score as desired output. The 1103 gene expression data was pre-processed by centering and scaling the TPM values. The resulting models were resampled by 10-fold cross-validation and repeating five times. To compare the Immune Evasion Signature 7 model to the Immune Evasion Signature 1, the Immune Evasion Signature 1 score and Immune Evasion Signature 7 score were compared for each TCGA LUSC sample used to build the model. As shown in FIG. 13B an R2of 0.664 was observed, indicating that the Immune Evasion Signature 1 score and Immune Evasion Signature 7 score were highly correlated. Table 20. Immune Evasion Signature 7sf-5975534Docket No.: 220232001840

[0186] The generation of the Immune Evasion Signature 7 was a two-step process. The first step involved identifying the best elastic net regression model that minimized the number of descriptors and provided the lowest root mean square estimate (RMSE) between training sets. The input for the elastic net regression was the RNA expression levels of the 1103 genes identified in the DEG analysis from 502 TCGA CRC samples, pre-processed to be centered and scaled. The outcome to predicted by the model was the Immune Evasion Signature 1 score. The resulting models were re-sampled five times by 10-fold cross-validation to calculate RMSE (Table 21). The best model from this analysis was composed of 215 genes with elastic net regression tuning parameters alpha = 0.29 and lambda = 0.04. sf-5975534Docket No.: 220232001840 Table 21. Resampling results across the tuning parameters

[0187] The second step of the process was to optimize between capturing the Immune Evasion Signature 1 score and using the minimal number of genes for the signature. This was done by performing net regression analyses starting with the 215 genes identified from the previous best model as the descriptors, keeping the alpha parameter held constant, and allowing the lambda parameter to be adjusted. The optimal model was selected by the combination of the lowest number of genes in the model and the coefficient of determination (R2) between the model prediction and Immune Evasion Signature 1. Since models lose some predictive power when applied to data outside of the training and test sets, the criteria of the model having a R2> 0.8 was required for model selection. As shown in Table 22, a 50 gene-signature model was identified that met the criteria of having the least number of genes and having R2= 0.81, and thus was selected as Immune Evasion Signature 7. Table 22. Optimization of Immune Evasion Signature 7

[0188] The resulting equation to calculate an Immune Evasion Signature 7 score is: sf-5975534Docket No.: 220232001840where i=gene, N=50, a = coefficient associated with gene (Table 23) and expression = normalized RNA expression level Table 23. Coefficients for the 50 Gene Immune Evasion Signature 7

[0189] The present disclosure is not intended to be limited in scope to the particular disclosed embodiments, which are provided, for example, to illustrate various aspects of the present sf-5975534Docket No.: 220232001840 disclosure. Various modifications to the compositions and methods described will become apparent from the description and teachings herein. Such variations may be practiced without departing from the true scope and spirit of the disclosure and are intended to fall within the scope of the present disclosure. sf-5975534

Claims

Docket No.: 220232001840 CLAIMS 1. A method of identifying a gene signature for determining if a cancer is immune evasive, comprising the steps of: a. detecting expression levels of a first set of genes in cancer samples containing cancer cells, wherein the cancer samples are of the same cancer type; b. determining an immune evasion score based on the expression levels of the first set of genes for each sample in step a; c. selecting samples comprising immune evasion scores in about the top 33rdpercentile (‘immune evasive high’) and about the bottom 33rdpercentile (‘immune evasive low’) among the cancer samples; d. detecting genes that are differentially expressed between the immune evasive high and the immune evasive low samples; and e. identifying a second set of genes from the differentially expressed genes detected in step d, wherein the expression levels of the second set of genes can classify if the cancer is immune evasive.

2. A method of identifying targets for cancer therapy, comprising the steps of: a. detecting expression levels of a first set of genes in cancer samples containing cancer cells, wherein the cancer samples are of the same cancer type; b. determining an immune evasion score based on the expression levels of the first set of genes for each sample in step a; c. selecting samples comprising immune evasion scores in about the top 33rdpercentile (‘immune evasive high’) and about the bottom 33rdpercentile (‘immune evasive low’) among the cancer samples; d. detecting genes that are differentially expressed between the immune evasive high and the immune evasive low samples; e. identifying a second set of genes from the differentially expressed genes detected in step d, wherein the expression levels of the second set of genes can classify if the cancer is immune evasive; and f. identifying one or more cancer treatment targets from the second set of genes. sf-5975534Docket No.: 220232001840 3. The method of any one of claims 1-2, wherein the first set of genes comprises ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA- DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1.

4. The method of any one of claims 1-3, wherein the cancer cells are colorectal cancer, lung cancer, non-small cell lung cancer, lung adenocarcinoma, lung squamous cell carcinoma, lung squamous cell carcinoma, skin cancer, or melanoma.

5. The method of any one of claims 1-4, wherein the second set of genes comprises one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6.

6. The method of any one of claim 1-4, wherein the second set of genes comprises one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1.

7. The method of any one of claims 1-4, wherein the cancer cells are colorectal cancer cells and the second set of genes comprise CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6. sf-5975534Docket No.: 220232001840 8. The method of any one of claims 1-4, wherein the cancer cells are colorectal cancer cells and the second set of genes comprise CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6.

9. The method of any one of claims 1-4, wherein the cancer cells are non-small cell lung cancer (NSCLC) cells and the second set of genes comprise APP, DPYSL3, EID1, EIF1B, EMILIN1, FAM168B, FBLN1, FSCN1, FSTL1, GJA1, GLG1, GPC1, HLA-F, IGFBP4, IRF7, LAMC1, LAP3, MARVELD1, P4HA1, PALLD, PDPN, PLOD3, PLP2, PSMB8, RTN4, SLC38A2, SLC3A2, TNC, TRAC, YKT6, YWHAG, CCL5, CD74, COL1A2, COL5A1, CTSS, DTX3L, FKBP9, HLA-B, HLA-DMA, HLA-DPA1, HLA-E, IFI27, IFI35, IFI44, IFI6, IFIT3, ISG15, LGALS3BP, MMP14, OAS3, PARP14, PSME2, SLC2A1, STAT1, STAT2, TRBC1, and UBE2L6.

10. The method of any one of claims 1-4, wherein the cancer cells are non-small cell lung cancer (NSCLC) cells not having genetic mutations in ALK, EGFR, or KRAS and the second set of genes comprise APP, C20orf27, CCT5, CPE, CYB5R3, DDX50, DPYSL3, EID1, EMILIN1, EPHB3, FBLN1, FSCN1, FSTL1, GJA1, GPC1, HLA-F, IGFBP4, LAMC1, LAP3, MARVELD1, PDPN, PFN2, PLOD3, PLP2, RTN4, SLC39A6, SLC3A2, TLCD3A, TNC, CCL5, CD74, COL1A2, CTSS, DTX3L, FKBP9, GBP1, GLG1, HLA-B, HLA-DMA, IFI27, IFI35, IFI44, IFI6, IFIT3, ISG15, LGALS3BP, PSME2, SLC2A1, STAT1, STAT2, TRAC, TRBC1, UBE2L6, and YWHAQ.

11. The method of any one of claims 1-4, wherein the cancer cells are lung adenocarcinoma (LUAD) cells and the second set of genes comprise ARL4C, C4orf3, CLIC4, DPYSL3, DUSP5, FAM168B, FKBP9, FSCN1, FSTL1, GJA1, HLA-DMB, HLA-F, HTRA1, IGFBP4, IGFBP5, ITGB1, ITGB5, LRRC8A, MARVELD1, MRC2, PDGFRB, PSMB9, RTN4, SPIN1, TOB1, TRBC1, VIM, BST2, CALD1, CAVIN1, CCN2, CTSS, DTX3L, HLA-B, HLA-DMA, HLA-DOA, IFI27, IFI35, IFI44, IFI6, IFIT3, ISG15, LGALS3BP, OAS1, OAS3, PARP14, PSMB8, PSME2, SERPINE1, SLC50A1, STAT1, STAT2, TRAC, TRAFD1, and UBE2L6. sf-5975534Docket No.: 220232001840 12. The method of any one of claims 1-4, wherein the cancer cells are lung squamous cell carcinoma (LUSC) cells and the second set of genes comprise COL1A2, CPE, CTNNB1, DSTN, EIF1B, EMILIN1, FBLN1, GJA1, GLG1, GPC1, IGFBP4, KDELR2, LAMC1, LAP3, P4HA1, PLOD3, PSMB9, PSME2, RHBDD2, SNAI2, ST13, TAP1, TGFBI, TMED4, TRAC, VEGFB, YKT6, CCL5, CD53, CD74, CHPF, CTSS, CYB5R3, DTX3L, GBP1, GNA12, HLA-DMA, HLA-F, IFI27, IFI35, IFI44, IFIT3, ISG15, LGALS3BP, NT5DC2, PCOLCE, STAT1, STAT2, TRBC1, and UBE2L6.

13. The method of any one of claims 1-12, wherein the second set of genes are identified using elastic net regression analysis.

14. The method of any one of claims 1-13, wherein the immune evasion score is determined using normalized RNA expression levels in the following equation:where i=gene, N=total number of up-regulated genes, j=gene, M=total number of down- regulated genes.

15. A method of identifying an individual having immune evasive high colorectal cancer, comprising the steps of: a. detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises i. one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; ii. one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii. ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, sf-5975534Docket No.: 220232001840 CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv. CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v. CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b. determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; and c. identifying the colorectal cancer is likely to be immune evasive high if the immune evasion score in step b is higher than a reference value.

16. A method of identifying an individual having immune evasive low colorectal cancer, comprising the steps of: a. detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises i. one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; sf-5975534Docket No.: 220232001840 ii. one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii. ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv. CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v. CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b. determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; and c. identifying the colorectal cancer is likely to be immune evasive low if the immune evasion score in step b is lower than a reference value.

17. The method of claim 15 or claim 16, wherein the immune evasion score is determined using normalized RNA expression levels in the following equation: sf-5975534Docket No.: 220232001840where i=gene, N=total number of up-regulated genes, j=gene, and M=total number of down- regulated genes.

18. The method of claim 15, wherein the immune evasion score is determined using normalized RNA expression levels of the set of genes in step a (i-v) in the following equation:where i=gene, N=total number of up-regulated genes, j=gene, M=total number of down- regulated genes; and wherein the reference value is about 1.

0.

19. The method of claim 16, wherein the immune evasion score is determined using normalized RNA expression levels of the set of genes in step a (i-v) in the following equation:where i=gene, N=total number of up-regulated genes, j=gene, M=total number of down- regulated genes;; and wherein the reference value is about -1.

0.

20. The method of claim 15 or claim 16, wherein the immune evasion score is determined using normalized RNA expression levels in the following equation:sf-5975534Docket No.: 220232001840 where i=gene, N=total number of genes, a = coefficient associated with gene, and expression = normalized RNA expression level.

21. The method of claim 15, wherein the immune evasion score is determined using normalized RNA expression levels of the set of genes in step a (iv) in the following equation:where i=gene, N=28, a = coefficient associated with gene, wherein a has a value that is about the coefficient value shown in Table 5, and expression = normalized RNA expression level; and wherein the reference value is about 0.

195.

22. The method of claim 16, wherein the immune evasion score is determined using normalized RNA expression levels of the set of genes in step a (iv) in the following equation:where i=gene, N=28, a = coefficient associated with gene, wherein a has a value that is about the coefficient value shown in Table 5, and expression = normalized RNA expression level; and wherein the reference value is about -0.

256.

23. The method of claim 15, wherein the immune evasion score is determined using normalized RNA expression levels of the set of genes in step a (v) in the following equation:sf-5975534Docket No.: 220232001840 where i=gene, N=23, a = coefficient associated with gene, wherein a has a value that is about the coefficient value shown in Table 7, and expression = normalized RNA expression level; and wherein the reference value is about 0.

209.

24. The method of claim 16, wherein the immune evasion score is determined using normalized RNA expression levels of the set of genes in step a (v) in the following equation:where i=gene, N=23, a = coefficient associated with gene, wherein a has a value that is about the coefficient value shown in Table 7, and expression = normalized RNA expression level; and wherein the reference value is about -0.

237.

25. A method of treating an individual with colorectal cancer (CRC), comprising the steps of: a. detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises i. one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; ii. one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii. ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, 87 sf-5975534Docket No.: 220232001840 DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv. CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v. CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b. determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; c. identifying the colorectal cancer is likely to be immune evasive high if the immune evasion score in step b is higher than a reference value; and d. administering a cancer therapy to the individual that comprises a therapeutic targeting one or more of targets selected from the group consisting of ADORA2A, ADRA2C, ADSSL1, AGT, AHCY, AKR1B10, ALOX15, AMZ1, ANO1, ANXA3, AQP1, ARHGAP6, ASGR1, BDNF, BMP2, C12orf49, CA1, CA4, CACNA2D1, CADM1, CALU, CAVIN1, CCBE1, CCL5, CCL21, CCN2, CCNE1, CD177, CHST11, CLBA1, CLIC4, COL6A2, COL27A1, CPE, CTSB, DDX58, DTX3L, DUSP9, EMB, EML5, ENPP4, ESD, FLNA, FN1, GDF15, GDPD1, GEM, GOT1, GPC1, GRIN2D, GPRC5B, GREB1, GREM1, GRIK2, HAP1, HAVCR1, HAVCR2, HGF, HLA-DQB2, HMGA2, HTRA1, IFI27, IFI27L2, IFI35, IFIT2, IGFBP4, IGFBP5, IL11, IRF9, ISG15, ISM1, ITGB3, ITPR1, JAM2, KAT2A, KDM5A, KHK, KIF17, LAP3, LAMC1, LBH, LGALS1, LGALS3BP, LGALS7, MFAP4, MMP2, MPP6, MTCL1, MYADM, NPFFR1, OAS1, OAS2, OAS3, PADI2, PARP14, PCDH19, PHF11, PIN1, PIP5K2A, PIP5K2B, PIP5K2C, PKN3, PLK1, PLXNA4, PMAIP1, POSTN, sf-5975534Docket No.: 220232001840 PPM1L, PRSS12, PSMB9, PSME2, PTGES, PTGS1, PVR, RAB15, RTL8C, SAMHD1, SCARB1, SCD, SERPINE1, SFRP2, SH3BP5, SHC2, SIGLEC15, SLC2A1, SLC14A1, SLC6A8, SLC7A5, SLFN13, SLITRK6, SPARCL1, SPP1, SRPX2, STAT1, STAT2, ST3GAL5, SULF1, SYN1, TAP1, TCF7L2, THBS1, TIMP2, TIMD4, TMEM97, TMEM176B, TMEM179, TNC, TOR3A, TRAP1, TRIM5, TRIM21, TRIM34, TUBA1A, TWIST2, UBA7, UBE2L6, USP11, VAT1, VIM, WNT10A, and ZNFX1.

26. A method of treating an individual with colorectal cancer (CRC), comprising the steps of: a. detecting expression levels of a set of genes in a colorectal cancer sample from the individual, wherein the set of genes comprises i. one or more of genes selected from the group consisting of DTX3L, IFI27, STAT1, and UBE2L6; ii. one or more genes selected from the group consisting of DTX3L, IFI27, STAT1, UBE2L6, CLIC4, IGFBP5, LAMC1, PSME2, TAP1, VIM, and OAS1; iii. ADRA2A, ADSSL1, AKR1B10, ANO1, ARHGAP6, BDNF, BMP2, C12orf49, CACNA2D1, CCBE1, CHST11, CLBA1, COL27A1, CPE, CTSB, DUSP9, EMB, EML5, ESD, GDPD1, GEM, GOT1, GPC1, GPRC5B, GREB1, GREM1, GRIK2, HGF, HMGA2, IGFBP4, IL11, ISM1, ITGB3, JAM2, KIF17, LBH, LGALS7, MPP6, MTCL1, PKN3, PLXNA4, PMAIP1, PPM1L, PRSS12, PTGES, PTGS1, RAB15, SHC2, SLC14A1, SLC6A8, SLITRK6, SPP1, SRPX2, ST3GAL5, SYN1, TMEM179, TNC, TWIST2, WNT10A, AMZ1, ANXA3, CADM1, CCL5, DDX58, DTX3L, ENPP4, HAP1, HLA-DQB2, IFI27, IFI27L2, IFI35, IFIT2, IRF9, ISG15, ITPR1, LGALS3BP, OAS1, OAS2, OAS3, PARP14, PCDH19, PHF11, SAMHD1, SH3BP5, SLFN13, STAT1, STAT2, TCF7L2, TMEM176B, TOR3A, TRIM5, TRIM21, TRIM34, UBA7, UBE2L6, USP11, and ZNFX1; iv. CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, PSME2, RTL8C, SERPINE1, TAP1, TUBA1A, sf-5975534Docket No.: 220232001840 VAT1, VIM, DTX3L, HTRA1, IFI27, ISG15, LAP3, MMP2, OAS1, PSMB9, SPARCL1, STAT1, TRIM21, and UBE2L6; or v. CALU, CAVIN1, CLIC4, COL6A2, IGFBP5, LAMC1, LGALS1, MFAP4, MYADM, RTL8C, SERPINE1, TUBA1A, VAT1, VIM, DTX3L, IFI27, ISG15, LAP3, OAS1, PSMB9, STAT1, TRIM21, and UBE2L6; and b. determining an immune evasion score for the colorectal cancer based on the expression levels of the set of genes in step a; c. identifying the colorectal cancer is likely to be immune evasive low if the immune evasion score in step b is lower than a reference value; and d. administering to the individual a cancer therapy targeting one or more targets selected from the group consisting of AXL, C10orf54, CD112, CD137, CD152, CD155, CD172a, CD223, CD27, CD272, CD274, CD38, CD40, CD47, CD96, cGAMP, cGAS, CSF1, CSF1R, CXCR4, ENPP1, ENTPD1, ENTPD2, GITR, HPK1, ICOS, ICOSLG, IDO, IFNB1, IKZF1, IKZF2, IL-1B, IL-6, IRF3, IRF7, LSGAL3, LSGAL9, MAVS,MERTK, NKG2A, NKG2D, NT5E, PDCD1, PDCD2, RIG-I, SAMHD1, SHP2, STAT3, STING, TANK, TBK1, TGFB1,TIGIT, TLR7, TLR9, TNFA, TNFRSF4, TREX1, TYRO3, USP1, USP18, and VEGF.

27. The method of claim 25 or claim 26, wherein the immune evasion score is determined using normalized RNA expression levels in the following equation:where i=gene, N=total number of up-regulated genes, j=gene, and M=total number of down- regulated genes. sf-5975534Docket No.: 220232001840 28. The method of claim 25, wherein the immune evasion score is determined using normalized RNA expression levels of the set of genes in step a (i-v) in the following equation:where i=gene, N=total number of up-regulated genes, j=gene, M=total number of down- regulated genes; and wherein the reference value is about 1.

0.

29. The method of claim 26, wherein the immune evasion score is determined using normalized RNA expression levels of the set of genes in step a (i-v) in the following equation:where i=gene, N=total number of up-regulated genes, j=gene, M=total number of down- regulated genes; and wherein the reference value is about -1.

0.

30. The method of claim 25 or claim 26, wherein the immune evasion score is determined using normalized RNA expression levels in the following equation:where i=gene, N=total number of genes, a = coefficient associated with gene, and expression = normalized RNA expression level. sf-5975534Docket No.: 220232001840 31. The method of claim 25, wherein the immune evasion score is determined using normalized RNA expression levels of the set of genes in step a (iv) in the following equation:where i=gene, N=28, a = coefficient associated with gene, wherein a has a value that is about the coefficient value shown in Table 5, and expression = normalized RNA expression level; and wherein the reference value is about 0.

195.

32. The method of claim 26, wherein the immune evasion score is determined using normalized RNA expression levels of the set of genes in step a (iv) in the following equation:where i=gene, N=28, a = coefficient associated with gene, wherein a has a value that is about the coefficient value shown in Table 5, and expression = normalized RNA expression level; and wherein the reference value is about -0.

256.

33. The method of claim 25, wherein the immune evasion score is determined using normalized RNA expression levels of the set of genes in step a (v) in the following equation:where i=gene, N=23, a = coefficient associated with gene, wherein a has a value that is about the coefficient value shown in Table 7, and expression = normalized RNA expression level; and wherein the reference value is about 0.

209. 92 sf-5975534Docket No.: 220232001840 34. The method of claim 26, wherein the immune evasion score is determined using normalized RNA expression levels of the set of genes in step a (v) in the following equation:where i=gene, N=23, a = coefficient associated with gene, wherein a has a value that is about the coefficient value shown in Table 7, and expression = normalized RNA expression level; and wherein the reference value is about -0.

237.

35. The method of any one of claims 1-34, wherein the colorectal cancer sample is selected from the group consisting of a cancer biopsy sample, a liquid biopsy, a cancer cell, and a cancer organoid.

36. The method of claim 35, wherein the cancer cell or the cancer organoid is patient derived.

37. The method of claim 35, wherein the cancer cell or the cancer organoid is not patient derived.

38. The method of any one of claims 15-37, wherein the individual is a human.

39. The method of any one of claims 1-38, wherein the expression levels are mRNA expression levels.

40. The method of claim 39, wherein the mRNA expression levels are detected by quantitative PCR or nanostring.

41. The method of claim 40, wherein the mRNA expression levels are normalized based on the expression level of house-keeping genes. 93 sf-5975534Docket No.: 220232001840 42. The method of claims 1-41, wherein the RNA expression levels are normalized to log2(TPM+1) values. 94 sf-5975534

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