DIAGNOSTICS AND METHODS FOR PROGNOSING RESPONSE TO IMMUNOTHERAPY BASED ON THE METHYLATION STATUS OF IMMUNE SYNAPSE GENE SIGNATURE

Information

  • Patent Application
  • 20220275461
  • Publication Number
    20220275461
  • Date Filed
    August 21, 2020
    4 years ago
  • Date Published
    September 01, 2022
    2 years ago
Abstract
Disclosed are methods for using the methylation status of a cancerous tissue to assess the susceptibility of a cancer to immunotherapy and determine new treatment regimens. Disclosed are methods related to producing an immunotherapeutic regimen based on the amount of methylation in co-stimulatory genes and/or immune checkpoint genes, as well as, methods of treating an immunogenic cancer based on the same.
Description
III. BACKGROUND

Cancer Immunotherapy has emerged as newest weapon in the arsenal to combat cancer. Nevertheless, the immunosuppressive effect induced by the tumor microenvironment represents a major obstacle for the success of promising T cell-based immunotherapies, including tumor-expanded T cells, chimeric antigen receptors (CAR)-T cells, and chimeric endocrine receptor (CER)-T cells. One of the obstacles in this field is the inability to predict treatment efficacy and patient response to immunotherapy. Thus, what are needed are new methods to determine the suitability of a subject for an immunotherapy prior to administration of the therapy.


IV. SUMMARY

Disclosed are methods related to producing an immunotherapeutic regimen based on the amount of methylation in co-stimulatory genes and/or immune checkpoint genes, as well as, methods of treating an immunogenic cancer based on the same.


In one aspect, disclosed herein are methods of treating, inhibiting, reducing, ameliorating, and/or preventing an immunogenic cancer or metastasis (such as, for example, adenocarcinoma, breast cancer, bladder cancer, cervical cancer, colon cancer, lymphoma, esophageal cancer, renal cancer, lung cancer, mesothelioma, head and neck cancer, cholangiocarcinoma, liver cancer, ovarian cancer, pancreatic cancer, prostate cancer, adrenal gland cancer, nerve cell cancer, rectal cancer, melanoma, sarcoma, testicular cancer, thyroid cancer, uterine cancer, or ocular cancer, such cancers including, but not limited to adrenocortical carcinoma, bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma, lymphoid neoplasm diffuse large B-cell lymphoma, esophageal carcinoma, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectum adenocarcinoma, sarcoma, skin cutaneous melanoma, stomach adenocarcinoma, testicular germ sell tumors, thyroid carcinoma, thymoma, uterine corpus endometrial carcinoma, uterine carcinosarcoma, or uveal melanoma) in a subject comprising a) obtaining a tissue sample from the subject; b) assaying the amount of methylation of one or more co-stimulatory genes (such as, for example, cluster of differentiation (CD) 40 (CD40), CD70, homologous to lymphotoxin, exhibits inducible expression and competes with HSV glycoprotein D for binding to herpesvirus entry mediator, a receptor expressed on T lymphocytes (LIGHT), OX40L, CD137L (4-1BBL), glucocorticoid-induced tumour-necrosis-factor-receptor-related protein (GITR) ligand (GITRL), B7 related protein 1 (B7RP1), and/or human leukocyte antigen (HLA)-A (HLA-A)) and/or one or more immune checkpoint genes (such as, for example, carcinoembryonic antigen-related adhesion molecule (CEACAM) 1 (CEACAM1), Galectin 9, programmed death ligand (PDL) 1 (PDL1), PDL2, V-domain Ig suppressor of T cell activation (VISTA), B7-H3, B7-H4, B7-2 (CD86), B7-1 (CD80), HHLA2, CD155, and/or Galectin 3) in the tissue sample; and c) administering to the subject an immunotherapy wherein an increase in the methylation of one or more co-stimulatory genes relative to a normal control tissue is detected and/or a decrease in the methylation of one or more immune checkpoint genes relative to a normal control tissue is detected.


Also disclosed herein are methods treating, inhibiting, reducing, ameliorating, and/or preventing an immunogenic cancer or metastasis of any preceding aspect, wherein the immunotherapy comprises an antibody, cytokine, natural killer (NK) cell, chimeric antigen receptor (CAR) T cell, CAR NK cell, tumor infiltrating lymphocyte (TIL), marrow infiltrating lymphocyte (MIL), and/or tumor infiltrating NK cell (TINK), for example, an immune checkpoint inhibitor blockade.


In one aspect, disclosed herein are method treating, inhibiting, reducing, ameliorating, and/or preventing an immunogenic cancer or metastasis of any preceding aspect, further comprising administering to the subject an inhibitor of methylation (such as, for example, azacytidine, decitabine, and/or zebularine) when the amount of methylation of the one or more co-stimulatory genes is increased relative to a normal tissue control or not administering to the subject an inhibitor of methylation (such as, for example, azacytidine, decitabine, and/or zebularine) when the amount of methylation of the one or more co-stimulatory genes is decreased relative to a normal tissue control or the amount of methylation of the one or more immune checkpoint genes is increased relative to a normal tissue control.


Also disclosed herein are methods of treating, inhibiting, reducing, ameliorating, and/or preventing an immunogenic cancer or metastasis of any preceding aspect, wherein methylation is measured by performing principal component (PC) analysis (PCA) of the one or more co-stimulatory genes and/or one or more immune checkpoint genes; wherein PChigh indicates an increase in methylation and PClow indicates a decrease in methylation.


In one aspect, disclosed herein are methods of assessing the suitability of an immunotherapy treatment regimen for the treatment an immunogenic cancer or metastasis (such as, for example, adenocarcinoma, breast cancer, bladder cancer, cervical cancer, colon cancer, lymphoma, esophageal cancer, renal cancer, lung cancer, mesothelioma, head and neck cancer, cholangiocarcinoma, liver cancer, ovarian cancer, pancreatic cancer, prostate cancer, adrenal gland cancer, nerve cell cancer, rectal cancer, melanoma, sarcoma, testicular cancer, thyroid cancer, uterine cancer, or ocular cancer, such cancers including, but not limited to adrenocortical carcinoma, bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma, lymphoid neoplasm diffuse large B-cell lymphoma, esophageal carcinoma, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectum adenocarcinoma, sarcoma, skin cutaneous melanoma, stomach adenocarcinoma, testicular germ sell tumors, thyroid carcinoma, thymoma, uterine corpus endometrial carcinoma, uterine carcinosarcoma, or uveal melanoma) in a subject comprising a) obtaining a tissue sample from the subject; and b) assaying the amount of methylation of one or more co-stimulatory genes (such as, for example, cluster of differentiation (CD) 40 (CD40), CD70, homologous to lymphotoxin, exhibits inducible expression and competes with HSV glycoprotein D for binding to herpesvirus entry mediator, a receptor expressed on T lymphocytes (LIGHT), OX40L, CD137L (4-1BBL), glucocorticoid-induced tumour-necrosis-factor-receptor-related protein (GITR) ligand (GITRL), B7 related protein 1(B7RP1), and/or human leukocyte antigen (HLA)-A (HLA-A)) and/or one or more immune checkpoint genes (such as, for example, carcinoembryonic antigen-related adhesion molecule (CEACAM) 1 (CEACAM1), Galectin 9, programmed death ligand (PDL) 1 (PDL1), PDL2, V-domain Ig suppressor of T cell activation (VISTA), B7-H3, B7-H4, B7-2 (CD86), B7-1 (CD80), HHLA2, CD155, and/or Galectin 3) in the tissue sample; wherein an increase in the methylation of one or more co-stimulatory genes relative to a normal control tissue and/or a decrease in the methylation of one or more immune checkpoint genes relative to a normal control tissue indicates that immunotherapy is suitable for treatment of the cancer in the subject.


Also disclosed herein are methods of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of any preceding aspect, wherein the immunotherapy comprises an antibody, cytokine, natural killer (NK) cell, chimeric antigen receptor (CAR) T cell, CAR NK cell, tumor infiltrating lymphocyte (TIL), marrow infiltrating lymphocyte (MIL), and/or tumor infiltrating NK cell (TINK)), for example, an immune checkpoint inhibitor blockade.


In one aspect, disclosed herein are methods of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of any preceding aspect, wherein a decrease in the methylation of one or more co-stimulatory genes relative to a normal control tissue or an increase in the methylation of one or more immune checkpoint genes relative to a normal control indicates that an inhibitor of methylation should not be administered to the subject.


Also disclosed herein are methods of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of any preceding aspect, wherein an increase in the methylation of one or more co-stimulatory genes relative to a normal control tissue indicates that an inhibitor of methylation can be administered to the subject.


In one aspect, disclosed herein are methods of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of any preceding aspect, wherein the assessment is conducted prior to the commencement of any immunotherapy regimen; wherein an increase in the methylation of one or more co-stimulatory genes relative to a normal control tissue and/or a decrease in the methylation of one or more immune checkpoint genes relative to a normal control tissue indicates that the subject can start an immunotherapy regimen; and wherein a decrease in the methylation or same amount of methylation of one or more co-stimulatory genes relative to a normal control tissue and/or an increase in the methylation of one or more immune checkpoint genes relative to a normal control tissue indicates that the subject should start an anti-cancer regimen that is not an immunotherapy.


Also disclosed herein are methods of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of any preceding aspect, wherein the assessment is conducted after to the commencement of an immunotherapy regimen; wherein an increase in the methylation of one or more co-stimulatory genes relative to a normal control tissue and/or a decrease in the methylation of one or more immune checkpoint genes relative to a normal control tissue indicates that the subject can continue an immunotherapy regimen; and wherein a decrease or same amount of methylation of one or more co-stimulatory genes relative to a normal control tissue and/or an increase or same amount of methylation of one or more immune checkpoint genes relative to a normal control tissue indicates that the subject should discontinue an anti-cancer regimen that is not an immunotherapy.


In one aspect, disclosed herein are methods of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of any preceding aspect, wherein methylation is measured by performing principal component analysis of the one or more co-stimulatory genes and/or one or more immune checkpoint genes; wherein PChigh indicates an increase in methylation and PClow indicates a decrease in methylation.





V. BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate several embodiments and together with the description illustrate the disclosed compositions and methods.



FIGS. 1A, 1B, 1C, and 1D show the distinct pattern of immune synapse gene methylation depends on tumor histology. FIG. 1A shows the schematic of immune synapse between the antigen presenting cells/tumor and T-cells is demonstrated. FIG. 1B shows T-SNE analysis was performed on 8,186 solid tumors and 745 normal adjacent tissues based on the β-values for methylation levels on all probes for CSGs and ICGs from (1A) contrasting tumor (blue) vs. normal adjacent tissue (red). FIG. 1C shows the spatial relationship between distinct tumor types is depicted with breast tumors in blue- and normal adjacent tissue samples black-dotted boxes. FIG. 1D shows unbiased hierarchical clustering analysis is shown.



FIGS. 2A, 2B, 2C, 2D, 2F, 2G, and 2H show the polarity of methylation patterns for co-stimulatory and immune checkpoint ligands. FIGS. 2A and 2B show β-values of methylation probes for TSS1500, TSS200, 5′UTR, body, and 3′UTR of HHLA2 gene (2A), an example of ICG, or CD40 (2B), an example of CSG, derived from all tumor samples (blue) and normal adjacent tissues (red) are depicted. The methylation level for each probe is represented by a box-plot. The left most column indicates the presence of CpG-island, while the second column colors indicate where on the gene the probe is located. The genomic location is listed on the left y-axis, probe id on the right y-axis and probes selected for further analysis is marked with blue color. FIGS. 2C and 2D show a heatmap of the correlation coefficient between all the probes within a gene, HHLA2 (2C) or CD40 (2D) across all tumor types. FIGS. 2E and 2F show a box plot of average β-values for selected probes HHLA2 (2E) or CD40 (2F) from tumor (blue) and normal adjacent tissue (red) are shown. FIGS. 2G and 2H show the average β-values for selected probes within TSS1500, TSS200, and 5′UTR are plotted against gene expression for HHLA2 (2C) or CD40 (2F). Each circle represents an individual tissue sample.



FIGS. 3A, 3B, 3C, and 3D show the methylation pattern for CEACAM1. FIG. 3A shows β-values of methylation probes for TSS1500, TSS200, 5′UTR, body, and 3′UTR of CEACAM1 gene derived from all tumor samples (blue) and normal adjacent tissues (red) are depicted. The methylation level for each probe is represented by a box-plots. The left most column indicates the presence of CpG-island, while the second column colors indicate where on the gene the probe is located. The genomic location is listed on the left y-axis and the probe id is shown on the right y-axis the probes selected for further analysis is marked with blue color. FIG. 3B shows a heatmap of the correlation coefficient between all the probes within a gene across all tumor types. FIG. 3C show the average β-values for selected probes within TSS1500, TSS200, and 5′UTR are plotted against gene expression. Each circle represents an individual tissue sample. FIG. 3D shows a box plot of average β-values for selected probes from tumor (blue) and normal adjacent tissue (red) are shown.



FIGS. 4A, 4B, 4C, and 4D show the methylation pattern for LGALS9 (Galectin9). FIG. 4A shows β-values of methylation probes for TSS1500, TSS200, 5′UTR, body, and 3′UTR of LGALS9 (Galectin9) gene derived from all tumor samples (blue) and normal adjacent tissues (red) are depicted. The methylation level for each probe is represented by a box-plots. The left most column indicates the presence of CpG-island, while the second column colors indicate where on the gene the probe is located. The genomic location is listed on the left y-axis and the probe id is shown on the right y-axis the probes selected for further analysis is marked with blue color. FIG. 4B shows a heatmap of the correlation coefficient between all the probes within a gene across all tumor types. FIG. 4C shows the average β-values for selected probes within TSS1500, TSS200, and 5′UTR are plotted against gene expression. Each circle represents an individual tissue sample. FIG. 4D shows a box plot of average β-values for selected probes from tumor (blue) and normal adjacent tissue (red) are shown.



FIGS. 5A, 5B, 5C, and 5D show the methylation pattern for CD274 (PDL1). FIG. 5A shows β-values of methylation probes for TSS1500, TSS200, 5′UTR, body, and 3′UTR of CD274 (PDL1) gene derived from all tumor samples (blue) and normal adjacent tissues (red) are depicted. The methylation level for each probe is represented by a box-plots. The left most column indicates the presence of CpG-island, while the second column colors indicate where on the gene the probe is located. The genomic location is listed on the left y-axis and the probe id is shown on the right y-axis the probes selected for further analysis is marked with blue color. FIG. 5B shows a heatmap of the correlation coefficient between all the probes within a gene across all tumor types. FIG. 5C shows the average β-values for selected probes within TSS1500, TSS200, and 5′UTR are plotted against gene expression. Each circle represents an individual tissue sample. FIG. 5D shows a box plot of average β-values for selected probes from tumor (blue) and normal adjacent tissue (red) are shown.



FIGS. 6A, 6B, 6C, and 6D show the methylation pattern for PDCD1LG2 (PDL2). FIG. 6A shows β-values of methylation probes for TSS1500, TSS200, 5′UTR, body, and 3′UTR of PDCD1LG2 (PDL2) gene derived from all tumor samples (blue) and normal adjacent tissues (red) are depicted. The methylation level for each probe is represented by a box-plots. The left most column indicates the presence of CpG-island, while the second column colors indicate where on the gene the probe is located. The genomic location is listed on the left y-axis and the probe id is shown on the right y-axis the probes selected for further analysis is marked with blue color. FIG. 6B shows a heatmap of the correlation coefficient between all the probes within a gene across all tumor types. FIG. 6C shows the average β-values for selected probes within TSS1500, TSS200, and 5′UTR are plotted against gene expression. Each circle represents an individual tissue sample. FIG. 6D shows a box plot of average β-values for selected probes from tumor (blue) and normal adjacent tissue (red) are shown.



FIGS. 7A, 7B, 7C, and 7D show the methylation pattern for C10orf54 (VISTA). FIG. 7A shows β-values of methylation probes for TSS1500, TSS200, 5′UTR, body, and 3′UTR of C10orf54 (VISTA) gene derived from all tumor samples (blue) and normal adjacent tissues (red) are depicted. The methylation level for each probe is represented by a box-plots. The left most column indicates the presence of CpG-island, while the second column colors indicate where on the gene the probe is located. The genomic location is listed on the left y-axis and the probe id is shown on the right y-axis the probes selected for further analysis is marked with blue color. FIG. 7B shows a heatmap of the correlation coefficient between all the probes within a gene across all tumor types. FIG. 7C shows the average β-values for selected probes within TSS1500, TSS200, and 5′UTR are plotted against gene expression. Each circle represents an individual tissue sample. FIG. 7D shows a box plot of average β-values for selected probes from tumor (blue) and normal adjacent tissue (red) are shown.



FIGS. 8A, 8B, 8C, and 8D show the methylation pattern for CD276 (B7-H3). FIG. 8A shows β-values of methylation probes for TSS1500, TSS200, 5′UTR, body, and 3′UTR of CD276 (B7-H3) gene derived from all tumor samples (blue) and normal adjacent tissues (red) are depicted. The methylation level for each probe is represented by a box-plots. The left most column indicates the presence of CpG-island, while the second column colors indicate where on the gene the probe is located. The genomic location is listed on the left y-axis and the probe id is shown on the right y-axis the probes selected for further analysis is marked with blue color. FIG. 8B shows a heatmap of the correlation coefficient between all the probes within a gene across all tumor types. FIG. 8C shows the average β-values for selected probes within TSS1500, TSS200, and 5′UTR are plotted against gene expression. Each circle represents an individual tissue sample. FIG. 8D shows a box plot of average β-values for selected probes from tumor (blue) and normal adjacent tissue (red) are shown.



FIGS. 9A, 9B, 9C, and 9D show the methylation pattern for VTCN1 (B7-H4). FIG. 9A shows β-values of methylation probes for TSS1500, TSS200, 5′UTR, body, and 3′UTR of VTCN1 (B7-H4) gene derived from all tumor samples (blue) and normal adjacent tissues (red) are depicted. The methylation level for each probe is represented by a box-plots. The left most column indicates the presence of CpG-island, while the second column colors indicate where on the gene the probe is located. The genomic location is listed on the left y-axis and the probe id is shown on the right y-axis the probes selected for further analysis is marked with blue color. FIG. 9B shows a heatmap of the correlation coefficient between all the probes within a gene across all tumor types. FIG. 9C shows the average β-values for selected probes within TSS1500, TSS200, and 5′UTR are plotted against gene expression. Each circle represents an individual tissue sample. FIG. 9D shows a box plot of average β-values for selected probes from tumor (blue) and normal adjacent tissue (red) are shown.



FIGS. 10A, 10B, 10C, and 10D show the methylation pattern for CD86. FIG. 10A shows β-values of methylation probes for TSS1500, TSS200, 5′UTR, body, and 3′UTR of CD86 gene derived from all tumor samples (blue) and normal adjacent tissues (red) are depicted. The methylation level for each probe is represented by a box-plots. The left most column indicates the presence of CpG-island, while the second column colors indicate where on the gene the probe is located. The genomic location is listed on the left y-axis and the probe id is shown on the right y-axis the probes selected for further analysis is marked with blue color. FIG. 10B shows a heatmap of the correlation coefficient between all the probes within a gene across all tumor types. FIG. 10C shows the average β-values for selected probes within TSS1500, TSS200, and 5′UTR are plotted against gene expression. Each circle represents an individual tissue sample. FIG. 10D shows a box plot of average β-values for selected probes from tumor (blue) and normal adjacent tissue (red) are shown.



FIGS. 11A, 11B, 11C, and 11D show the methylation pattern for CD80. FIG. 11A shows β-values of methylation probes for TSS1500, TSS200, 5′UTR, body, and 3′UTR of CD80 gene derived from all tumor samples (blue) and normal adjacent tissues (red) are depicted. The methylation level for each probe is represented by a box-plots. The left most column indicates the presence of CpG-island, while the second column colors indicate where on the gene the probe is located. The genomic location is listed on the left y-axis and the probe id is shown on the right y-axis the probes selected for further analysis is marked with blue color. FIG. 11B shows a heatmap of the correlation coefficient between all the probes within a gene across all tumor types. FIG. 11C shows the average β-values for selected probes within TSS1500, TSS200, and 5′UTR are plotted against gene expression. Each circle represents an individual tissue sample. FIG. 11D shows a box plot of average β-values for selected probes from tumor (blue) and normal adjacent tissue (red) are shown.



FIGS. 12A, 12B, 12C, and 12D show the methylation pattern for PVR. FIG. 12A shows β-values of methylation probes for TSS1500, TSS200, 5′UTR, body, and 3′UTR of PVR gene derived from all tumor samples (blue) and normal adjacent tissues (red) are depicted. The methylation level for each probe is represented by a box-plots. The left most column indicates the presence of CpG-island, while the second column colors indicate where on the gene the probe is located. The genomic location is listed on the left y-axis and the probe id is shown on the right y-axis the probes selected for further analysis is marked with blue color. FIG. 12B shows a heatmap of the correlation coefficient between all the probes within a gene across all tumor types. FIG. 12C shows the average β-values for selected probes within TSS1500, TSS200, and 5′UTR are plotted against gene expression. Each circle represents an individual tissue sample. FIG. 12D shows a box plot of average β-values for selected probes from tumor (blue) and normal adjacent tissue (red) are shown.



FIGS. 13A, 13B, 13C, and 13D show the methylation pattern for LGALS3 (Galectin3). FIG. 13A shows β-values of methylation probes for TSS1500, TSS200, 5′UTR, body, and 3′UTR of LGALS3 (Galectin3) gene derived from all tumor samples (blue) and normal adjacent tissues (red) are depicted. The methylation level for each probe is represented by a box-plots. The left most column indicates the presence of CpG-island, while the second column colors indicate where on the gene the probe is located. The genomic location is listed on the left y-axis and the probe id is shown on the right y-axis the probes selected for further analysis is marked with blue color. FIG. 13B shows a heatmap of the correlation coefficient between all the probes within a gene across all tumor types. FIG. 13C shows the average β-values for selected probes within TSS1500, TSS200, and 5′UTR are plotted against gene expression. Each circle represents an individual tissue sample. FIG. 13D shows a box plot of average β-values for selected probes from tumor (blue) and normal adjacent tissue (red) are shown.



FIGS. 14A, 14B, 14C, and 14D show the methylation pattern for TNFSF14 (LIGHT). FIG. 14A shows β-values of methylation probes for TSS1500, TSS200, 5′UTR, body, and 3′UTR of TNFSF14 (LIGHT) gene derived from all tumor samples (blue) and normal adjacent tissues (red) are depicted. The methylation level for each probe is represented by a box-plots. The left most column indicates the presence of CpG-island, while the second column colors indicate where on the gene the probe is located. The genomic location is listed on the left y-axis and the probe id is shown on the right y-axis the probes selected for further analysis is marked with blue color. FIG. 14B shows a heatmap of the correlation coefficient between all the probes within a gene across all tumor types. FIG. 14C shows the average β-values for selected probes within TSS1500, TSS200, and 5′UTR are plotted against gene expression. Each circle represents an individual tissue sample. FIG. 14D shows a box plot of average β-values for selected probes from tumor (blue) and normal adjacent tissue (red) are shown.



FIGS. 15A, 15B, 15C, and 15D show the methylation pattern for TNFSF4 (OX40L). FIG. 15A shows β-values of methylation probes for TSS1500, TSS200, 5′UTR, body, and 3′UTR of TNFSF4 (OX40L) gene derived from all tumor samples (blue) and normal adjacent tissues (red) are depicted. The methylation level for each probe is represented by a box-plots. The left most column indicates the presence of CpG-island, while the second column colors indicate where on the gene the probe is located. The genomic location is listed on the left y-axis and the probe id is shown on the right y-axis the probes selected for further analysis is marked with blue color. FIG. 15B shows a heatmap of the correlation coefficient between all the probes within a gene across all tumor types. FIG. 15C shows the average β-values for selected probes within TSS1500, TSS200, and 5′UTR are plotted against gene expression. Each circle represents an individual tissue sample. FIG. 15D shows a box plot of average β-values for selected probes from tumor (blue) and normal adjacent tissue (red) are shown.



FIGS. 16A, 16B, 16C, and 16D show the methylation pattern for TNFSF9 (CD173L). FIG. 16A shows β-values of methylation probes for TSS1500, TSS200, 5′UTR, body, and 3′UTR of TNFSF9 (CD173L) gene derived from all tumor samples (blue) and normal adjacent tissues (red) are depicted. The methylation level for each probe is represented by a box-plots. The left most column indicates the presence of CpG-island, while the second column colors indicate where on the gene the probe is located. The genomic location is listed on the left y-axis and the probe id is shown on the right y-axis the probes selected for further analysis is marked with blue color. FIG. 16B shows a heatmap of the correlation coefficient between all the probes within a gene across all tumor types. FIG. 16C shows the average β-values for selected probes within TSS1500, TSS200, and 5′UTR are plotted against gene expression. Each circle represents an individual tissue sample. FIG. 16D shows a box plot of average β-values for selected probes from tumor (blue) and normal adjacent tissue (red) are shown.



FIGS. 17A, 17B, 17C, and 17D show the methylation pattern for HLA-A. FIG. 17A shows β-values of methylation probes for TSS1500, TSS200, 5′UTR, body, and 3′UTR of HLA-A gene derived from all tumor samples (blue) and normal adjacent tissues (red) are depicted. The methylation level for each probe is represented by a box-plots. The left most column indicates the presence of CpG-island, while the second column colors indicate where on the gene the probe is located. The genomic location is listed on the left y-axis and the probe id is shown on the right y-axis the probes selected for further analysis is marked with blue color. FIG. 17B shows a heatmap of the correlation coefficient between all the probes within a gene across all tumor types. FIG. 17C shows the average β-values for selected probes within TSS1500, TSS200, and 5′UTR are plotted against gene expression. Each circle represents an individual tissue sample. FIG. 17D shows a box plot of average β-values for selected probes from tumor (blue) and normal adjacent tissue (red) are shown.



FIGS. 18A, 18B, 18C, 18D, 18E, and 18F show the principal component analysis (PCA) segregates co-stimulatory and immune checkpoint ligands. FIG. 18A shows two-dimensional plot of PC1 and PC2 scores for all tumor types (blue) and normal adjacent tissues (red) is shown. FIG. 18B shows the importance of each variable, CpG-probes, for PC1 and PC2 are depicted. A box plot of PC1 (18C) and PC2 (18D) scores for tumor (blue) and normal adjacent tissue (red) compared across histologic types. FIG. 18E shows PC1 scores of mock- or 5-azacitidine-treated epithelial cancer cell lines. FIGS. 18F shows the methylation status of CD40 gene in mock- or azacytidine-treated CAMA1 cell line. * p<0.05, ** p<0.01, *** p<0.001, **** p<0.0001 by t-test.



FIGS. 19A, 19B, 19C, 19D, and 19E show the correlation of PC1 and PC2 with CSG and ICG. FIG. 19A shows a scatter plot of PC1 score vs. average β-values of CSG probes is shown. FIG. 19B shows a scatter plot of PC2 score vs. average β-values of ICG probes is shown. FIGS. 19C shows the PCA loadings of each variable, CpG-probes, for CSG probes (Blue circles) and ICG probes (Red squares) are depicted. A box plot of average β-values of CSG probes (19D) and average β-values of ICG probes (19E) for tumor (blue) and normal adjacent tissue (red) are compared across histologic types.



FIGS. 20A, 20B, 20C, 20D, 20E, 20F, 20G, and 20H show the methylation status of co-stimulatory ligands is prognostic in melanoma. FIG. 20A shows Kaplan-Meier curves for DSS of melanoma patients with high, intermediate, and low tertials of PC1 score are shown. Higher PC1 score represents hypermethylation of CSGs. FIG. 20B shows Box-plot of PC1 score distribution based on melanoma patient staging. FIGS. 20C and 20D show Kaplan-Meier curves for DSS of UCEC patients with MSI (20C) or without MSI (20D) with high, intermediate, and low tertiles of PC1 score are shown. FIG. 20E shows T-cell recruitment in PC1high and PC1low melanoma patients is approximated by gene expression of CD3E, CD4 and CD8B. FIG. 20H shows T effector functions in PC1high and PC1low melanoma patients is approximated by gene expression of CD3ζ(CD247), Granzyme B (GZMB), Perforin (PRF1), and IFNγ. FIG. 20G shows chemokines for immune cell trafficking in PC1high and PC1low melanoma patients is approximated by gene expression of CCL2, CCL3, CCL4, CCLS, CCL9 and CCL10. FIG. 20H shows immunogenicity of PC1high and PC1low melanoma patients is approximated by gene expression of cGAS. p-value in panel 20A, 20C and 20D is a log rank test between High and Low group. ****, p<0.0001 by t-test.



FIG. 21 shows a PLS regression model in melanoma. The PLS model was developed by analysis of patients with longer DSS vs. shorter DSS in the training set in melanoma. Kaplan-Meier curves for DSS of the validation melanoma cohort is depicted based on the high vs. low predicted response by median.



FIGS. 22A, 22B, 22C, 22D, 22E, 22F, and 22G show that PC1 predicts OS and DSS in immunogenic cancers. FIG. 22A shows Kaplan-Meier curves for OS of melanoma patients with high, intermediate, and low tertile of PC1 score are shown. FIGS. 22B and 22C show Kaplan-Meier curves for OS (22B) and DSS (22C) of lung squamous cell carcinoma with high, intermediate and low tertile of PC1 score are shown. FIGS. 22D and 22E show Kaplan-Meier curves for OS (22D) and DSS (22E) of lung adenocarcinoma patients with high, intermediate, and low tertile of PC1 score are shown. FIG. 22F and 22G show Kaplan-Meier curves for OS of uterine cancer patients with MSI (22F) or without MSI (22G) with high, intermediate, and low tertials of PC1 score are shown.



FIGS. 23A, 23B, 23C, 23D, 23E, and 23F show that PC2 predicts OS and DSS in immunogenic cancers. FIGS. 23A and 23B show Kaplan-Meier curves for OS (23A) and DSS (23B) of head and neck squamous cell carcinoma with high, intermediate and low tertile of PC2 score are shown. FIGS. 23C and 23D show Kaplan-Meier curves for OS (23C) and DSS (23D) of renal clear cell carcinoma patients with high, intermediate, and low tertile of PC1 score are shown. FIGS. 23E and 23F show Kaplan-Meier curves for OS (23E) and DSS (23F) of renal papillary carcinoma patients with high, intermediate, and low tertile of PC1 score are shown.



FIGS. 24A and 24B show that PC1 can predict response to immunotherapy in melanoma.



FIG. 25 shows a cartoon showing the co-stimulatory and checkpoint interactions of a T cell and a tumor cell.





VI. DETAILED DESCRIPTION

Before the present compounds, compositions, articles, devices, and/or methods are disclosed and described, it is to be understood that they are not limited to specific synthetic methods or specific recombinant biotechnology methods unless otherwise specified, or to particular reagents unless otherwise specified, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting.


A. Definitions

As used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a pharmaceutical carrier” includes mixtures of two or more such carriers, and the like.


Ranges can be expressed herein as from “about” one particular value, and/or to “about” another particular value. When such a range is expressed, another embodiment includes from the one particular value and/or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms another embodiment. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint. It is also understood that there are a number of values disclosed herein, and that each value is also herein disclosed as “about” that particular value in addition to the value itself. For example, if the value “10” is disclosed, then “about 10” is also disclosed. It is also understood that when a value is disclosed that “less than or equal to” the value, “greater than or equal to the value” and possible ranges between values are also disclosed, as appropriately understood by the skilled artisan. For example, if the value “10” is disclosed the “less than or equal to 10”as well as “greater than or equal to 10” is also disclosed. It is also understood that the throughout the application, data is provided in a number of different formats, and that this data, represents endpoints and starting points, and ranges for any combination of the data points. For example, if a particular data point “10” and a particular data point 15 are disclosed, it is understood that greater than, greater than or equal to, less than, less than or equal to, and equal to 10 and 15 are considered disclosed as well as between 10 and 15. It is also understood that each unit between two particular units are also disclosed. For example, if 10 and 15 are disclosed, then 11, 12, 13, and 14 are also disclosed.


In this specification and in the claims which follow, reference will be made to a number of terms which shall be defined to have the following meanings:


“Optional” or “optionally” means that the subsequently described event or circumstance may or may not occur, and that the description includes instances where said event or circumstance occurs and instances where it does not.


An “increase” can refer to any change that results in a greater amount of a symptom, disease, composition, condition or activity. An increase can be any individual, median, or average increase in a condition, symptom, activity, composition in a statistically significant amount. Thus, the increase can be a 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100% increase so long as the increase is statistically significant.


A “decrease” can refer to any change that results in a smaller amount of a symptom, disease, composition, condition, or activity. A substance is also understood to decrease the genetic output of a gene when the genetic output of the gene product with the substance is less relative to the output of the gene product without the substance. Also for example, a decrease can be a change in the symptoms of a disorder such that the symptoms are less than previously observed. A decrease can be any individual, median, or average decrease in a condition, symptom, activity, composition in a statistically significant amount. Thus, the decrease can be a 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100% decrease so long as the decrease is statistically significant.


“Inhibit,” “inhibiting,” and “inhibition” mean to decrease an activity, response, condition, disease, or other biological parameter. This can include but is not limited to the complete ablation of the activity, response, condition, or disease. This may also include, for example, a 10% reduction in the activity, response, condition, or disease as compared to the native or control level. Thus, the reduction can be a 10, 20, 30, 40, 50, 60, 70, 80, 90, 100%, or any amount of reduction in between as compared to native or control levels.


By “reduce” or other forms of the word, such as “reducing” or “reduction,” is meant lowering of an event or characteristic (e.g., tumor growth). It is understood that this is typically in relation to some standard or expected value, in other words it is relative, but that it is not always necessary for the standard or relative value to be referred to. For example, “reduces tumor growth” means reducing the rate of growth of a tumor relative to a standard or a control.


By “prevent” or other forms of the word, such as “preventing” or “prevention,” is meant to stop a particular event or characteristic, to stabilize or delay the development or progression of a particular event or characteristic, or to minimize the chances that a particular event or characteristic will occur. Prevent does not require comparison to a control as it is typically more absolute than, for example, reduce. As used herein, something could be reduced but not prevented, but something that is reduced could also be prevented. Likewise, something could be prevented but not reduced, but something that is prevented could also be reduced. It is understood that where reduce or prevent are used, unless specifically indicated otherwise, the use of the other word is also expressly disclosed.


The term “subject” refers to any individual who is the target of administration or treatment. The subject can be a vertebrate, for example, a mammal. In one aspect, the subject can be human, non-human primate, bovine, equine, porcine, canine, or feline. The subject can also be a guinea pig, rat, hamster, rabbit, mouse, or mole. Thus, the subject can be a human or veterinary patient. The term “patient” refers to a subject under the treatment of a clinician, e.g., physician.


The term “therapeutically effective” refers to the amount of the composition used is of sufficient quantity to ameliorate one or more causes or symptoms of a disease or disorder. Such amelioration only requires a reduction or alteration, not necessarily elimination.


The term “treatment” refers to the medical management of a patient with the intent to cure, ameliorate, stabilize, or prevent a disease, pathological condition, or disorder. This term includes active treatment, that is, treatment directed specifically toward the improvement of a disease, pathological condition, or disorder, and also includes causal treatment, that is, treatment directed toward removal of the cause of the associated disease, pathological condition, or disorder. In addition, this term includes palliative treatment, that is, treatment designed for the relief of symptoms rather than the curing of the disease, pathological condition, or disorder; preventative treatment, that is, treatment directed to minimizing or partially or completely inhibiting the development of the associated disease, pathological condition, or disorder; and supportive treatment, that is, treatment employed to supplement another specific therapy directed toward the improvement of the associated disease, pathological condition, or disorder.


“Biocompatible” generally refers to a material and any metabolites or degradation products thereof that are generally non-toxic to the recipient and do not cause significant adverse effects to the subject.


“Comprising” is intended to mean that the compositions, methods, etc. include the recited elements, but do not exclude others. “Consisting essentially of” when used to define compositions and methods, shall mean including the recited elements, but excluding other elements of any essential significance to the combination. Thus, a composition consisting essentially of the elements as defined herein would not exclude trace contaminants from the isolation and purification method and pharmaceutically acceptable carriers, such as phosphate buffered saline, preservatives, and the like. “Consisting of” shall mean excluding more than trace elements of other ingredients and substantial method steps for administering the compositions provided and/or claimed in this disclosure. Embodiments defined by each of these transition terms are within the scope of this disclosure.


A “control” is an alternative subject or sample used in an experiment for comparison purposes. A control can be “positive” or “negative.” A normal control can refer to a tissue sample that is disease and/or cancer free either obtained from a subject with a cancer (such as a neighboring disease free tissue) or a subject without a cancer.


“Effective amount” of an agent refers to a sufficient amount of an agent to provide a desired effect. The amount of agent that is “effective” will vary from subject to subject, depending on many factors such as the age and general condition of the subject, the particular agent or agents, and the like. Thus, it is not always possible to specify a quantified “effective amount.” However, an appropriate “effective amount” in any subject case may be determined by one of ordinary skill in the art using routine experimentation. Also, as used herein, and unless specifically stated otherwise, an “effective amount” of an agent can also refer to an amount covering both therapeutically effective amounts and prophylactically effective amounts. An “effective amount” of an agent necessary to achieve a therapeutic effect may vary according to factors such as the age, sex, and weight of the subject. Dosage regimens can be adjusted to provide the optimum therapeutic response. For example, several divided doses may be administered daily or the dose may be proportionally reduced as indicated by the exigencies of the therapeutic situation.


A “pharmaceutically acceptable” component can refer to a component that is not biologically or otherwise undesirable, i.e., the component may be incorporated into a pharmaceutical formulation provided by the disclosure and administered to a subject as described herein without causing significant undesirable biological effects or interacting in a deleterious manner with any of the other components of the formulation in which it is contained. When used in reference to administration to a human, the term generally implies the component has met the required standards of toxicological and manufacturing testing or that it is included on the Inactive Ingredient Guide prepared by the U.S. Food and Drug Administration.


“Pharmaceutically acceptable carrier” (sometimes referred to as a “carrier”) means a carrier or excipient that is useful in preparing a pharmaceutical or therapeutic composition that is generally safe and non-toxic and includes a carrier that is acceptable for veterinary and/or human pharmaceutical or therapeutic use. The terms “carrier” or “pharmaceutically acceptable carrier” can include, but are not limited to, phosphate buffered saline solution, water, emulsions (such as an oil/water or water/oil emulsion) and/or various types of wetting agents. As used herein, the term “carrier” encompasses, but is not limited to, any excipient, diluent, filler, salt, buffer, stabilizer, solubilizer, lipid, stabilizer, or other material well known in the art for use in pharmaceutical formulations and as described further herein.


“Pharmacologically active” (or simply “active”), as in a “pharmacologically active” derivative or analog, can refer to a derivative or analog (e.g., a salt, ester, amide, conjugate, metabolite, isomer, fragment, etc.) having the same type of pharmacological activity as the parent compound and approximately equivalent in degree.


“Therapeutic agent” refers to any composition that has a beneficial biological effect. Beneficial biological effects include both therapeutic effects, e.g., treatment of a disorder or other undesirable physiological condition, and prophylactic effects, e.g., prevention of a disorder or other undesirable physiological condition (e.g., a non-immunogenic cancer). The terms also encompass pharmaceutically acceptable, pharmacologically active derivatives of beneficial agents specifically mentioned herein, including, but not limited to, salts, esters, amides, proagents, active metabolites, isomers, fragments, analogs, and the like. When the terms “therapeutic agent” is used, then, or when a particular agent is specifically identified, it is to be understood that the term includes the agent per se as well as pharmaceutically acceptable, pharmacologically active salts, esters, amides, proagents, conjugates, active metabolites, isomers, fragments, analogs, etc.


“Therapeutically effective amount” or “therapeutically effective dose” of a composition (e.g. a composition comprising an agent) refers to an amount that is effective to achieve a desired therapeutic result. In some embodiments, a desired therapeutic result is the control of type I diabetes. In some embodiments, a desired therapeutic result is the control of obesity. Therapeutically effective amounts of a given therapeutic agent will typically vary with respect to factors such as the type and severity of the disorder or disease being treated and the age, gender, and weight of the subject. The term can also refer to an amount of a therapeutic agent, or a rate of delivery of a therapeutic agent (e.g., amount over time), effective to facilitate a desired therapeutic effect, such as pain relief. The precise desired therapeutic effect will vary according to the condition to be treated, the tolerance of the subject, the agent and/or agent formulation to be administered (e.g., the potency of the therapeutic agent, the concentration of agent in the formulation, and the like), and a variety of other factors that are appreciated by those of ordinary skill in the art. In some instances, a desired biological or medical response is achieved following administration of multiple dosages of the composition to the subject over a period of days, weeks, or years.


Throughout this application, various publications are referenced. The disclosures of these publications in their entireties are hereby incorporated by reference into this application in order to more fully describe the state of the art to which this pertains. The references disclosed are also individually and specifically incorporated by reference herein for the material contained in them that is discussed in the sentence in which the reference is relied upon.


B. Methods of Treating a Cancer and Assessing a Cancer Treatment Regimen

Cancer immune evasion is achieved through multiple layers of immune tolerance mechanisms including immune editing, recruitment of tolerogenic immune cells, and secretion of immune suppressive cytokines. Recent success with immune checkpoint inhibitors in cancer immunotherapy indicates a dysfunctional immune synapse as a pivotal tolerogenic mechanism. Tumor cells express immune synapse proteins to suppress the immune system, which is often modulated by epigenetic mechanisms. When the methylation status of key immune synapse genes was interrogated, a disproportionately hyper-methylated co-stimulatory genes and hypo-methylation of immune checkpoint genes was observed, which were negatively associated with functional T-cell recruitment to the tumor microenvironment. Therefore, the methylation status of immune synapse genes reflects tumor immunogenicity and correlates with survival.


In one aspect, disclosed herein are methods of treating, inhibiting, reducing, ameliorating, and/or preventing an immunogenic cancer or metastasis (such as, for example, adenocarcinoma, breast cancer, bladder cancer, cervical cancer, colon cancer, lymphoma, esophageal cancer, renal cancer, lung cancer, mesothelioma, head and neck cancer, cholangiocarcinoma, liver cancer, ovarian cancer, pancreatic cancer, prostate cancer, adrenal gland cancer, nerve cell cancer, rectal cancer, melanoma, sarcoma, testicular cancer, thyroid cancer, uterine cancer, or ocular cancer, such cancers including, but not limited to adrenocortical carcinoma, bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma, lymphoid neoplasm diffuse large B-cell lymphoma, esophageal carcinoma, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectum adenocarcinoma, sarcoma, skin cutaneous melanoma, stomach adenocarcinoma, testicular germ sell tumors, thyroid carcinoma, thymoma, uterine corpus endometrial carcinoma, uterine carcinosarcoma, or uveal melanoma) in a subject comprising a) obtaining a tissue sample from the subject; b) assaying the amount of methylation of one or more co-stimulatory genes (such as, for example, cluster of differentiation (CD) 40 (CD40), CD70, homologous to lymphotoxin, exhibits inducible expression and competes with HSV glycoprotein D for binding to herpesvirus entry mediator, a receptor expressed on T lymphocytes (LIGHT), OX40L, CD137L (4-1BBL), glucocorticoid-induced tumour-necrosis-factor-receptor-related protein (GITR) ligand (GITRL), B7 related protein 1 (B7RP1), and/or human leukocyte antigen (HLA)-A (HLA-A)) and/or one or more immune checkpoint genes (such as, for example, carcinoembryonic antigen-related adhesion molecule (CEACAM) 1 (CEACAM1), Galectin 9, programmed death ligand (PDL) 1 (PDL1), PDL2, V-domain Ig suppressor of T cell activation (VISTA), B7-H3, B7-H4, B7-2 (CD86), B7-1 (CD80), HHLA2, CD155, and/or Galectin 3) in the tissue sample; and c) administering to the subject an immunotherapy wherein an increase in the methylation of one or more co-stimulatory genes relative to a normal control tissue is detected and/or a decrease in the methylation of one or more immune checkpoint genes relative to a normal control tissue is detected.


As disclosed herein methylation, and in particular, hypermethylation (i.e., an increase in methylation relative to a normal tissue control) can lead to a decrease in gene expression of co-stimulatory genes which reduces an immune response to a cancer. Thus, decreasing methylation in an hypermethylated of co-stimulatory genes through, for example, the administration of any inhibitor of methylation (such as, for example, azacytidine, decitabine, and/or zebularine) can alone or in combination with an immunotherapy (including, any of the immune checkpoint inhibitor blockades disclosed herein) decrease, inhibit, ameliorate, reduce, treat, and/or prevent a cancer or metastasis. However, administration of an inhibitor of methylation when co-stimulatory genes are hypomethylated and/or immune checkpoint genes are hypermethylated can have a detrimental effect. Thus, in one aspect, disclosed herein are method treating, inhibiting, reducing, ameliorating, and/or preventing an immunogenic cancer or metastasis, further comprising administering to the subject an inhibitor of methylation (such as, for example, azacytidine, decitabine, and/or zebularine) when the amount of methylation of the one or more co-stimulatory genes is increased relative to a normal tissue control or not administering to the subject an inhibitor of methylation (such as, for example, azacytidine, decitabine, and/or zebularine) when the amount of methylation of the one or more co-stimulatory genes is decreased relative to a normal tissue control or the amount of methylation of the one or more immune checkpoint genes is increased relative to a normal tissue control.


As disclosed herein methylation of co-stimulatory genes and/or immune checkpoint genes can have a significant effect on the efficacy of immunotherapy. Applying immunotherapy to a cancer in a subject that has the wrong methylation profile will not only not be effective, but ultimately decreases the likelihood of successful treatment as the cancer will have an opportunity to grow and spread while the ineffective immunotherapy is being applied. Knowing that a cancer is not susceptible to immunotherapy before administration or being able to assess a cancer and stope an immunotherapy treatment in a subject has profound benefit to the patient as more successful treatment options could be used instead. Alternatively, knowing that a cancer is susceptible to immunotherapy can guide the caregiver to administer an immunotherapy early or stay the course if already implemented. Thus, in one aspect, disclosed herein are methods of assessing the suitability of an immunotherapy treatment regimen for the treatment an immunogenic cancer or metastasis (such as, for example, adenocarcinoma, breast cancer, bladder cancer, cervical cancer, colon cancer, lymphoma, esophageal cancer, renal cancer, lung cancer, mesothelioma, head and neck cancer, cholangiocarcinoma, liver cancer, ovarian cancer, pancreatic cancer, prostate cancer, adrenal gland cancer, nerve cell cancer, rectal cancer, melanoma, sarcoma, testicular cancer, thyroid cancer, uterine cancer, or ocular cancer, such cancers including, but not limited to adrenocortical carcinoma, bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma, lymphoid neoplasm diffuse large B-cell lymphoma, esophageal carcinoma, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectum adenocarcinoma, sarcoma, skin cutaneous melanoma, stomach adenocarcinoma, testicular germ sell tumors, thyroid carcinoma, thymoma, uterine corpus endometrial carcinoma, uterine carcinosarcoma, or uveal melanoma) in a subject comprising a) obtaining a tissue sample from the subject; and b) assaying the amount of methylation of one or more co-stimulatory genes (such as, for example, cluster of differentiation (CD) 40 (CD40), CD70, homologous to lymphotoxin, exhibits inducible expression and competes with HSV glycoprotein D for binding to herpesvirus entry mediator, a receptor expressed on T lymphocytes (LIGHT), OX40L, CD137L (4-1BBL), glucocorticoid-induced tumour-necrosis-factor-receptor-related protein (GITR) ligand (GITRL), B7 related protein 1 (B7RP1), and/or human leukocyte antigen (HLA)-A (HLA-A)) and/or one or more immune checkpoint genes (such as, for example, carcinoembryonic antigen-related adhesion molecule (CEACAM) 1 (CEACAM1), Galectin 9, programmed death ligand (PDL) 1 (PDL1), PDL2, V-domain Ig suppressor of T cell activation (VISTA), B7-H3, B7-H4, B7-2 (CD86), B7-1 (CD80), HHLA2, CD155, and/or Galectin 3) in the tissue sample; wherein an increase in the methylation of one or more co-stimulatory genes relative to a normal control tissue and/or a decrease in the methylation of one or more immune checkpoint genes relative to a normal control tissue indicates that immunotherapy is suitable for treatment of the cancer in the subject.


It is understood and herein contemplated that the disclosed immunotherapy treatment assessment methods are useful both prior to any administration of an immunotherapy to tell of the immunotherapy should or should not be administered and also after commencement of an immunotherapy to determine if said immunotherapy should be continued. Knowing the methylation status allows the treating physician to avoid wasting valuable treatment time or unnecessarily subjecting the patient to an ineffective therapy by discontinuing an immunotherapy or never starting immunotherapy if the cancer does not have the correct methylation profile for the immunotherapy to be efficacious. Alternatively, where the methylation profile is appropriate, immunotherapy can be initiated or continued. Thus, in one aspect, disclosed herein are methods of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject, wherein the assessment is conducted prior to the commencement of any immunotherapy regimen; wherein an increase in the methylation of one or more co-stimulatory genes relative to a normal control tissue and/or a decrease in the methylation of one or more immune checkpoint genes relative to a normal control tissue indicates that the subject can start an immunotherapy regimen; and wherein a decrease in the methylation or same amount of methylation of one or more co-stimulatory genes relative to a normal control tissue and/or an increase in the methylation of one or more immune checkpoint genes relative to a normal control tissue indicates that the subject should start an anti-cancer regimen that is not an immunotherapy. Also disclosed herein are methods of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject, wherein the assessment is conducted after to the commencement of an immunotherapy regimen; wherein an increase in the methylation of one or more co-stimulatory genes relative to a normal control tissue and/or a decrease in the methylation of one or more immune checkpoint genes relative to a normal control tissue indicates that the subject can continue an immunotherapy regimen; and wherein a decrease or same amount of methylation of one or more co-stimulatory genes relative to a normal control tissue and/or an increase or same amount of methylation of one or more immune checkpoint genes relative to a normal control tissue indicates that the subject should discontinue an anti-cancer regimen that is not an immunotherapy.


As noted above, methylation, and in particular, hypermethylation (i.e., an increase in methylation relative to a normal tissue control) can lead to a decrease in gene expression of co-stimulatory genes which reduces an immune response to a cancer. Thus, decreasing methylation in an hypermethylated of co-stimulatory genes through, for example, the administration of any inhibitor of methylation (such as, for example, azacytidine, decitabine, and/or zebularine) can alone or in combination with an immunotherapy (including, any of the immune checkpoint inhibitor blockades disclosed herein) decrease, inhibit, ameliorate, reduce, treat, and/or prevent a cancer or metastasis. However, administration of an inhibitor of methylation when co-stimulatory genes are hypomethylated and/or immune checkpoint genes are hypermethylated can have a detrimental effect. Accordingly, disclosed herein are methods of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of any preceding aspect, wherein a decrease in the methylation of one or more co-stimulatory genes relative to a normal control tissue or an increase in the methylation of one or more immune checkpoint genes relative to a normal control indicates that an inhibitor of methylation should not be administered to the subject. Also disclosed herein are methods of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject, wherein an increase in the methylation of one or more co-stimulatory genes relative to a normal control tissue indicates that an inhibitor of methylation can be administered to the subject.


The disclosed treatment methods and assessment methods are suitable for any immunotherapy known to those of skill in the art, including, but not limited to the administration of antibodies, cytokines, natural killer (NK) cells, chimeric antigen receptor (CAR) T cells, CAR NK cells, tumor infiltrating lymphocytes (TILs), marrow infiltrating lymphocytes (MILs), and/or tumor infiltrating NK cells (TINKs) that target or modulate immune response. This includes immune checkpoint inhibitor blockades (such as, for example, antibodies that block PD-1 (Nivolumab (BMS-936558 or MDX1106), CT-011, MK-3475), PD-L1 (MDX-1105 (BMS-936559), Durvalumab, Avelumab, Atezolizumab), MPDL3280A, or MSB0010718C), PD-L2 (rHIgM12B7), CTLA-4 (Ipilimumab (MDX-010), Tremelimumab (CP-675,206)), IDO, B7-H3 (MGA271), B7-H4, TIM3, LAG-3 (BMS-986016)). In particular, where hypermethylation (i.e., an increase in methylation relative to a normal tissue control) of a co-stimulatory gene or hypomethylation (i.e., an decrease in methylation relative to a normal tissue control) of an immune checkpoint gene is detected, the treatment methods can include or further include the administration of immunotherapy, including, but not limited to checkpoint inhibitors. Examples of checkpoint inhibitors include, but are not limited to antibodies that block PD-1 (Nivolumab (BMS-936558 or MDX1106), CT-011, MK-3475), PD-L1 (MDX-1105 (BMS-936559), Durvalumab, Avelumab, Atezolizumab), MPDL3280A, or MSB0010718C), PD-L2 (rHIgM12B7), CTLA-4 (Ipilimumab (MDX-010), Tremelimumab (CP-675,206)), IDO, B7-H3 (MGA271), B7-H4, TIM3, LAG-3 (BMS-986016). Similarly, disclosed herein are methods of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject, wherein the immunotherapy comprises an antibody, cytokine, natural killer (NK) cell, chimeric antigen receptor (CAR) T cell, CAR NK cell, tumor infiltrating lymphocyte (TIL), marrow infiltrating lymphocyte (MIL), and/or tumor infiltrating NK cell (TINK)), for example, an immune checkpoint inhibitor blockade.


It is understood and herein contemplated that the assessment of methylation used in the disclosed methods of treating and assessing a treatment regimen can be accomplished by any means known in the art, including, but not limited to principal component analysis, mass spectrometry, High Performance Liquid Chromatography (HPLC), Enzyme-Linked Immunosorbant Assay (ELISA), PCR, bead array, methylation specific PCR, pyrosequencing, bisulfite sequencing, digestion based assay followed by PCR, and/or LUMA. Thus, also disclosed herein are methods of treating, inhibiting, reducing, ameliorating, and/or preventing an immunogenic cancer or metastasis, as well as, methods of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis, wherein methylation is measured by performing principal component (PC) analysis (PCA) of the one or more co-stimulatory genes and/or one or more immune checkpoint genes; wherein PChigh indicates an increase in methylation and PClow indicates a decrease in methylation.


The disclosed methods comprise obtaining tissue samples. As used herein, “tissue sample” can comprise any solid or liquid tissue from a subject including, but not limited to biopsy, blood, urine, sputum, saliva, tissue lavage. Tissue samples can be obtained by any means known in the art including but not limited to swab, catch collection, tissue resection, biopsy phlebotomy, and/or core biopsy.


The disclosed methods can be used to treat or assess the treatment of any disease where uncontrolled cellular proliferation occurs such as cancers. A non-limiting list of different types of cancers comprises lymphoma, B cell lymphoma, T cell lymphoma, mycosis fungoides, Hodgkin's Disease, myeloid leukemia, bladder cancer, brain cancer, nervous system cancer, head and neck cancer, squamous cell carcinoma of head and neck, lung cancers such as small cell lung cancer and non-small cell lung cancer, neuroblastoma/glioblastoma, ovarian cancer, skin cancer, liver cancer, melanoma, squamous cell carcinomas of the mouth, throat, larynx, and lung, cervical cancer, cervical carcinoma, breast cancer, and epithelial cancer, renal cancer, genitourinary cancer, pulmonary cancer, esophageal carcinoma, head and neck carcinoma, large bowel cancer, hematopoietic cancers; testicular cancer; colon cancer, rectal cancer, prostatic cancer, or pancreatic cancer. For example, the cancer can comprise adrenocortical carcinoma, bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma, lymphoid neoplasm diffuse large B-cell lymphoma, esophageal carcinoma, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectum adenocarcinoma, sarcoma, skin cutaneous melanoma, stomach adenocarcinoma, testicular germ sell tumors, thyroid carcinoma, thymoma, uterine corpus endometrial carcinoma, uterine carcinosarcoma, or uveal melanoma.


In one aspect, it is understood and herein contemplated that successful treatment of a cancer in a subject is important and doing so may include the administration of additional treatments. This is particular true where hypermethylation (i.e., an increase in methylation relative to a normal tissue control) of a co-stimulatory gene or hypomethylation (i.e., an decrease in methylation relative to a normal tissue control) of an immune checkpoint gene is not detected as the cancer is less susceptible and/or resistant to immunotherapy (including, but not limited to immune checkpoint inhibitor blockade). Thus, the disclosed treatments can include and/or further include any anti-cancer therapy known in the art including, but not limited to Abemaciclib, Abiraterone Acetate, Abitrexate (Methotrexate), Abraxane (Paclitaxel Albumin-stabilized Nanoparticle Formulation), ABVD, ABVE, ABVE-PC, AC, AC-T, Adcetris (Brentuximab Vedotin), ADE, Ado-Trastuzumab Emtansine, Adriamycin (Doxorubicin Hydrochloride), Afatinib Dimaleate, Afinitor (Everolimus), Akynzeo (Netupitant and Palonosetron Hydrochloride), Aldara (Imiquimod), Aldesleukin, Alecensa (Alectinib), Alectinib, Alemtuzumab, Alimta (Pemetrexed Disodium), Aliqopa (Copanlisib Hydrochloride), Alkeran for Injection (Melphalan Hydrochloride), Alkeran Tablets (Melphalan), Aloxi (Palonosetron Hydrochloride), Alunbrig (Brigatinib), Ambochlorin (Chlorambucil), Amboclorin Chlorambucil), Amifostine, Aminolevulinic Acid, Anastrozole, Aprepitant, Aredia (Pamidronate Disodium), Arimidex (Anastrozole), Aromasin (Exemestane), Arranon (Nelarabine), Arsenic Trioxide, Arzerra (Ofatumumab), Asparaginase Erwinia chrysanthemi, Atezolizumab, Avastin (Bevacizumab), Avelumab, Axitinib, Azacitidine, Bavencio (Avelumab), BEACOPP, Becenum (Carmustine), Beleodaq (Belinostat), Belinostat, Bendamustine Hydrochloride, BEP, Besponsa (Inotuzumab Ozogamicin), Bevacizumab, Bexarotene, Bexxar (Tositumomab and Iodine I 131 Tositumomab), Bicalutamide, BiCNU (Carmustine), Bleomycin, Blinatumomab, Blincyto (Blinatumomab), Bortezomib, Bosulif (Bosutinib), Bosutinib, Brentuximab Vedotin, Brigatinib, BuMel, Busulfan, Busulfex (Busulfan), Cabazitaxel, Cabometyx (Cabozantinib-S-Malate), Cabozantinib-S-Malate, CAF, Campath (Alemtuzumab), Camptosar, (Irinotecan Hydrochloride), Capecitabine, CAPDX, Carac (Fluorouracil--Topical), Carboplatin, CARBOPLATIN-TAXOL, Carfilzomib, Carmubris (Carmustine), Carmustine, Carmustine Implant, Casodex (Bicalutamide), CEM, Ceritinib, Cerubidine (Daunorubicin Hydrochloride), Cervarix (Recombinant HPV Bivalent Vaccine), Cetuximab, CEV, Chlorambucil, CHLORAMBUCIL-PREDNISONE, CHOP, Cisplatin, Cladribine, Clafen (Cyclophosphamide), Clofarabine, Clofarex (Clofarabine), Clolar (Clofarabine), CMF, Cobimetinib, Cometriq (Cabozantinib-S-Malate), Copanlisib Hydrochloride, COPDAC, COPP, COPP-ABV, Cosmegen (Dactinomycin), Cotellic (Cobimetinib), Crizotinib, CVP, Cyclophosphamide, Cyfos (Ifosfamide), Cyramza (Ramucirumab), Cytarabine, Cytarabine Liposome, Cytosar-U (Cytarabine), Cytoxan (Cyclophosphamide), Dabrafenib, Dacarbazine, Dacogen (Decitabine), Dactinomycin, Daratumumab, Darzalex (Daratumumab), Dasatinib, Daunorubicin Hydrochloride, Daunorubicin Hydrochloride and Cytarabine Liposome, Decitabine, Defibrotide Sodium, Defitelio (Defibrotide Sodium), Degarelix, Denileukin Diftitox, Denosumab, DepoCyt (Cytarabine Liposome), Dexamethasone, Dexrazoxane Hydrochloride, Dinutuximab, Docetaxel, Doxil (Doxorubicin Hydrochloride Liposome), Doxorubicin Hydrochloride, Doxorubicin Hydrochloride Liposome, Dox-SL (Doxorubicin Hydrochloride Liposome), DTIC-Dome (Dacarbazine), Durvalumab, Efudex (Fluorouracil—Topical), Elitek (Rasburicase), Ellence (Epirubicin Hydrochloride), Elotuzumab, Eloxatin (Oxaliplatin), Eltrombopag Olamine, Emend (Aprepitant), Empliciti (Elotuzumab), Enasidenib Mesylate, Enzalutamide, Epirubicin Hydrochloride, EPOCH, Erbitux (Cetuximab), Eribulin Mesylate, Erivedge (Vismodegib), Erlotinib Hydrochloride, Erwinaze (Asparaginase Erwinia chrysanthemi), Ethyol (Amifostine), Etopophos (Etoposide Phosphate), Etoposide, Etoposide Phosphate, Evacet (Doxorubicin Hydrochloride Liposome), Everolimus, Evista, (Raloxifene Hydrochloride), Evomela (Melphalan Hydrochloride), Exemestane, 5-FU (Fluorouracil Injection), 5-FU (Fluorouracil—Topical), Fareston (Toremifene), Farydak (Panobinostat), Faslodex (Fulvestrant), FEC, Femara (Letrozole), Filgrastim, Fludara (Fludarabine Phosphate), Fludarabine Phosphate, Fluoroplex (Fluorouracil—Topical), Fluorouracil Injection, Fluorouracil—Topical, Flutamide, Folex (Methotrexate), Folex PFS (Methotrexate), FOLFIRI, FOLFIRI-BEVACIZUMAB, FOLFIRI-CETUXIMAB, FOLFIRINOX, FOLFOX, Folotyn (Pralatrexate), FU-LV, Fulvestrant, Gardasil (Recombinant HPV Quadrivalent Vaccine), Gardasil 9 (Recombinant HPV Nonavalent Vaccine), Gazyva (Obinutuzumab), Gefitinib, Gemcitabine Hydrochloride, GEMCITABINE-CISPLATIN, GEMCITABINE-OXALIPLATIN, Gemtuzumab Ozogamicin, Gemzar (Gemcitabine Hydrochloride), Gilotrif (Afatinib Dimaleate), Gleevec (Imatinib Mesylate), Gliadel (Carmustine Implant), Gliadel wafer (Carmustine Implant), Glucarpidase, Goserelin Acetate, Halaven (Eribulin Mesylate), Hemangeol (Propranolol Hydrochloride), Herceptin (Trastuzumab), HPV Bivalent Vaccine, Recombinant, HPV Nonavalent Vaccine, Recombinant, HPV Quadrivalent Vaccine, Recombinant, Hycamtin (Topotecan Hydrochloride), Hydrea (Hydroxyurea), Hydroxyurea, Hyper-CVAD, Ibrance (Palbociclib), Ibritumomab Tiuxetan, Ibrutinib, ICE, Iclusig (Ponatinib Hydrochloride), Idamycin (Idarubicin Hydrochloride), Idarubicin Hydrochloride, Idelalisib, Idhifa (Enasidenib Mesylate), Ifex (Ifosfamide), Ifosfamide, Ifosfamidum (Ifosfamide), IL-2 (Aldesleukin), Imatinib Mesylate, Imbruvica (Ibrutinib), Imfinzi (Durvalumab), Imiquimod, Imlygic (Talimogene Laherparepvec), Inlyta (Axitinib), Inotuzumab Ozogamicin, Interferon Alfa-2b, Recombinant, Interleukin-2 (Aldesleukin), Intron A (Recombinant Interferon Alfa-2b), Iodine I 131 Tositumomab and Tositumomab, Ipilimumab, Iressa (Gefitinib), Irinotecan Hydrochloride, Irinotecan Hydrochloride Liposome, Istodax (Romidepsin), Ixabepilone, Ixazomib Citrate, Ixempra (Ixabepilone), Jakafi (Ruxolitinib Phosphate), JEB, Jevtana (Cabazitaxel), Kadcyla (Ado-Trastuzumab Emtansine), Keoxifene (Raloxifene Hydrochloride), Kepivance (Palifermin), Keytruda (Pembrolizumab), Kisqali (Ribociclib), Kymriah (Tisagenlecleucel), Kyprolis (Carfilzomib), Lanreotide Acetate, Lapatinib Ditosylate, Lartruvo (Olaratumab), Lenalidomide, Lenvatinib Mesylate, Lenvima (Lenvatinib Mesylate), Letrozole, Leucovorin Calcium, Leukeran (Chlorambucil), Leuprolide Acetate, Leustatin (Cladribine), Levulan (Aminolevulinic Acid), Linfolizin (Chlorambucil), LipoDox (Doxorubicin Hydrochloride Liposome), Lomustine, Lonsurf (Trifluridine and Tipiracil Hydrochloride), Lupron (Leuprolide Acetate), Lupron Depot (Leuprolide Acetate), Lupron Depot-Ped (Leuprolide Acetate), Lynparza (Olaparib), Marqibo (Vincristine Sulfate Liposome), Matulane (Procarbazine Hydrochloride), Mechlorethamine Hydrochloride, Megestrol Acetate, Mekinist (Trametinib), Melphalan, Melphalan Hydrochloride, Mercaptopurine, Mesna, Mesnex (Mesna), Methazolastone (Temozolomide), Methotrexate, Methotrexate LPF (Methotrexate), Methylnaltrexone Bromide, Mexate (Methotrexate), Mexate-AQ (Methotrexate), Midostaurin, Mitomycin C, Mitoxantrone Hydrochloride, Mitozytrex (Mitomycin C), MOPP, Mozobil (Plerixafor), Mustargen (Mechlorethamine Hydrochloride), Mutamycin (Mitomycin C), Myleran (Busulfan), Mylosar (Azacitidine), Mylotarg (Gemtuzumab Ozogamicin), Nanoparticle Paclitaxel (Paclitaxel Albumin-stabilized Nanoparticle Formulation), Navelbine (Vinorelbine Tartrate), Necitumumab, Nelarabine, Neosar (Cyclophosphamide), Neratinib Maleate, Nerlynx (Neratinib Maleate), Netupitant and Palonosetron Hydrochloride, Neulasta (Pegfilgrastim), Neupogen (Filgrastim), Nexavar (Sorafenib Tosylate), Nilandron (Nilutamide), Nilotinib, Nilutamide, Ninlaro (Ixazomib Citrate), Niraparib Tosylate Monohydrate, Nivolumab, Nolvadex (Tamoxifen Citrate), Nplate (Romiplostim), Obinutuzumab, Odomzo (Sonidegib), OEPA, Ofatumumab, OFF, Olaparib, Olaratumab, Omacetaxine Mepesuccinate, Oncaspar (Pegaspargase), Ondansetron Hydrochloride, Onivyde (Irinotecan Hydrochloride Liposome), Ontak (Denileukin Diftitox), Opdivo (Nivolumab), OPPA, Osimertinib, Oxaliplatin, Paclitaxel, Paclitaxel Albumin-stabilized Nanoparticle Formulation, PAD, Palbociclib, Palifermin, Palonosetron Hydrochloride, Palonosetron Hydrochloride and Netupitant, Pamidronate Disodium, Panitumumab, Panobinostat, Paraplat (Carboplatin), Paraplatin (Carboplatin), Pazopanib Hydrochloride, PCV, PEB, Pegaspargase, Pegfilgrastim, Peginterferon Alfa-2b, PEG-Intron (Peginterferon Alfa-2b), Pembrolizumab, Pemetrexed Disodium, Perjeta (Pertuzumab), Pertuzumab, Platinol (Cisplatin), Platinol-AQ (Cisplatin), Plerixafor, Pomalidomide, Pomalyst (Pomalidomide), Ponatinib Hydrochloride, Portrazza (Necitumumab), Pralatrexate, Prednisone, Procarbazine Hydrochloride , Proleukin (Aldesleukin), Prolia (Denosumab), Promacta (Eltrombopag Olamine), Propranolol Hydrochloride, Provenge (Sipuleucel-T), Purinethol (Mercaptopurine), Purixan (Mercaptopurine), Radium 223 Dichloride, Raloxifene Hydrochloride, Ramucirumab, Rasburicase, R-CHOP, R-CVP, Recombinant Human Papillomavirus (HPV) Bivalent Vaccine, Recombinant Human Papillomavirus (HPV) Nonavalent Vaccine, Recombinant Human Papillomavirus (HPV) Quadrivalent Vaccine, Recombinant Interferon Alfa-2b, Regorafenib, Relistor (Methylnaltrexone Bromide), R-EPOCH, Revlimid (Lenalidomide), Rheumatrex (Methotrexate), Ribociclib, R-ICE, Rituxan (Rituximab), Rituxan Hycela (Rituximab and Hyaluronidase Human), Rituximab, Rituximab and Hyaluronidase Human, Rolapitant Hydrochloride, Romidepsin, Romiplostim, Rubidomycin (Daunorubicin Hydrochloride), Rubraca (Rucaparib Camsylate), Rucaparib Camsylate, Ruxolitinib Phosphate, Rydapt (Midostaurin), Sclerosol Intrapleural Aerosol (Talc), Siltuximab, Sipuleucel-T, Somatuline Depot (Lanreotide Acetate), Sonidegib, Sorafenib Tosylate, Sprycel (Dasatinib), STANFORD V, Sterile Talc Powder (Talc), Steritalc (Talc), Stivarga (Regorafenib), Sunitinib Malate, Sutent (Sunitinib Malate), Sylatron (Peginterferon Alfa-2b), Sylvant (Siltuximab), Synribo (Omacetaxine Mepesuccinate), Tabloid (Thioguanine), TAC, Tafinlar (Dabrafenib), Tagrisso (Osimertinib), Talc, Talimogene Laherparepvec, Tamoxifen Citrate, Tarabine PFS (Cytarabine), Tarceva (Erlotinib Hydrochloride), Targretin (Bexarotene), Tasigna (Nilotinib), Taxol (Paclitaxel), Taxotere (Docetaxel), Tecentriq, (Atezolizumab), Temodar (Temozolomide), Temozolomide, Temsirolimus, Thalidomide, Thalomid (Thalidomide), Thioguanine, Thiotepa, Tisagenlecleucel, Tolak (Fluorouracil—Topical), Topotecan Hydrochloride, Toremifene, Torisel (Temsirolimus), Tositumomab and Iodine I 131 Tositumomab, Totect (Dexrazoxane Hydrochloride), TPF, Trabectedin, Trametinib, Trastuzumab, Treanda (Bendamustine Hydrochloride), Trifluridine and Tipiracil Hydrochloride, Trisenox (Arsenic Trioxide), Tykerb (Lapatinib Ditosylate), Unituxin (Dinutuximab), Uridine Triacetate, VAC, Vandetanib, VAMP, Varubi (Rolapitant Hydrochloride), Vectibix (Panitumumab), VeIP, Velban (Vinblastine Sulfate), Velcade (Bortezomib), Velsar (Vinblastine Sulfate), Vemurafenib, Venclexta (Venetoclax), Venetoclax, Verzenio (Abemaciclib), Viadur (Leuprolide Acetate), Vidaza (Azacitidine), Vinblastine Sulfate, Vincasar PFS (Vincristine Sulfate), Vincristine Sulfate, Vincristine Sulfate Liposome, Vinorelbine Tartrate, VIP, Vismodegib, Vistogard (Uridine Triacetate), Voraxaze (Glucarpidase), Vorinostat, Votrient (Pazopanib Hydrochloride), Vyxeos (Daunorubicin Hydrochloride and Cytarabine Liposome), Wellcovorin (Leucovorin Calcium), Xalkori (Crizotinib), Xeloda (Capecitabine), XELIRI, XELOX, Xgeva (Denosumab), Xofigo (Radium 223 Dichloride), Xtandi (Enzalutamide), Yervoy (Ipilimumab), Yondelis (Trabectedin), Zaltrap (Ziv-Aflibercept), Zarxio (Filgrastim), Zejula (Niraparib Tosylate Monohydrate), Zelboraf (Vemurafenib), Zevalin (Ibritumomab Tiuxetan), Zinecard (Dexrazoxane Hydrochloride), Ziv-Aflibercept, Zofran (Ondansetron Hydrochloride), Zoladex (Goserelin Acetate), Zoledronic Acid, Zolinza (Vorinostat), Zometa (Zoledronic Acid), Zydelig (Idelalisib), Zykadia (Ceritinib), and/or Zytiga (Abiraterone Acetate).


1. Pharmaceutical Carriers/Delivery of Pharmaceutical Products


As described above, the compositions can also be administered in vivo in a pharmaceutically acceptable carrier. By “pharmaceutically acceptable” is meant a material that is not biologically or otherwise undesirable, i.e., the material may be administered to a subject, along with the nucleic acid or vector, without causing any undesirable biological effects or interacting in a deleterious manner with any of the other components of the pharmaceutical composition in which it is contained. The carrier would naturally be selected to minimize any degradation of the active ingredient and to minimize any adverse side effects in the subject, as would be well known to one of skill in the art.


The compositions may be administered orally, parenterally (e.g., intravenously), by intramuscular injection, by intraperitoneal injection, transdermally, extracorporeally, topically or the like, including topical intranasal administration or administration by inhalant. As used herein, “topical intranasal administration” means delivery of the compositions into the nose and nasal passages through one or both of the nares and can comprise delivery by a spraying mechanism or droplet mechanism, or through aerosolization of the nucleic acid or vector. Administration of the compositions by inhalant can be through the nose or mouth via delivery by a spraying or droplet mechanism. Delivery can also be directly to any area of the respiratory system (e.g., lungs) via intubation. The exact amount of the compositions required will vary from subject to subject, depending on the species, age, weight and general condition of the subject, the severity of the allergic disorder being treated, the particular nucleic acid or vector used, its mode of administration and the like. Thus, it is not possible to specify an exact amount for every composition. However, an appropriate amount can be determined by one of ordinary skill in the art using only routine experimentation given the teachings herein.


Parenteral administration of the composition, if used, is generally characterized by injection. Injectables can be prepared in conventional forms, either as liquid solutions or suspensions, solid forms suitable for solution of suspension in liquid prior to injection, or as emulsions. A more recently revised approach for parenteral administration involves use of a slow release or sustained release system such that a constant dosage is maintained. See, e.g., U.S. Pat. No. 3,610,795, which is incorporated by reference herein.


The materials may be in solution, suspension (for example, incorporated into microparticles, liposomes, or cells). These may be targeted to a particular cell type via antibodies, receptors, or receptor ligands. The following references are examples of the use of this technology to target specific proteins to tumor tissue (Senter, et al., Bioconjugate Chem., 2:447-451, (1991); Bagshawe, K. D., Br. J. Cancer, 60:275-281, (1989); Bagshawe, et al., Br. J. Cancer, 58:700-703, (1988); Senter, et al., Bioconjugate Chem., 4:3-9, (1993); Battelli, et al., Cancer Immunol. Immunother., 35:421-425, (1992); Pietersz and McKenzie, Immunolog. Reviews, 129:57-80, (1992); and Roffler, et al., Biochem. Pharmacol, 42:2062-2065, (1991)). Vehicles such as “stealth” and other antibody conjugated liposomes (including lipid mediated drug targeting to colonic carcinoma), receptor mediated targeting of DNA through cell specific ligands, lymphocyte directed tumor targeting, and highly specific therapeutic retroviral targeting of murine glioma cells in vivo. The following references are examples of the use of this technology to target specific proteins to tumor tissue (Hughes et al., Cancer Research, 49:6214-6220, (1989); and Litzinger and Huang, Biochimica et Biophysica Acta, 1104:179-187, (1992)). In general, receptors are involved in pathways of endocytosis, either constitutive or ligand induced. These receptors cluster in clathrin-coated pits, enter the cell via clathrin-coated vesicles, pass through an acidified endosome in which the receptors are sorted, and then either recycle to the cell surface, become stored intracellularly, or are degraded in lysosomes. The internalization pathways serve a variety of functions, such as nutrient uptake, removal of activated proteins, clearance of macromolecules, opportunistic entry of viruses and toxins, dissociation and degradation of ligand, and receptor-level regulation. Many receptors follow more than one intracellular pathway, depending on the cell type, receptor concentration, type of ligand, ligand valency, and ligand concentration. Molecular and cellular mechanisms of receptor-mediated endocytosis has been reviewed (Brown and Greene, DNA and Cell Biology 10:6, 399-409 (1991)).


a) Pharmaceutically Acceptable Carriers


The compositions, including antibodies, can be used therapeutically in combination with a pharmaceutically acceptable carrier.


Suitable carriers and their formulations are described in Remington: The Science and Practice of Pharmacy (19th ed.) ed. A.R. Gennaro, Mack Publishing Company, Easton, Pa. 1995. Typically, an appropriate amount of a pharmaceutically-acceptable salt is used in the formulation to render the formulation isotonic. Examples of the pharmaceutically-acceptable carrier include, but are not limited to, saline, Ringer's solution and dextrose solution. The pH of the solution is preferably from about 5 to about 8, and more preferably from about 7 to about 7.5. Further carriers include sustained release preparations such as semipermeable matrices of solid hydrophobic polymers containing the antibody, which matrices are in the form of shaped articles, e.g., films, liposomes or microparticles. It will be apparent to those persons skilled in the art that certain carriers may be more preferable depending upon, for instance, the route of administration and concentration of composition being administered.


Pharmaceutical carriers are known to those skilled in the art. These most typically would be standard carriers for administration of drugs to humans, including solutions such as sterile water, saline, and buffered solutions at physiological pH. The compositions can be administered intramuscularly or subcutaneously. Other compounds will be administered according to standard procedures used by those skilled in the art.


Pharmaceutical compositions may include carriers, thickeners, diluents, buffers, preservatives, surface active agents and the like in addition to the molecule of choice. Pharmaceutical compositions may also include one or more active ingredients such as antimicrobial agents, antiinflammatory agents, anesthetics, and the like.


The pharmaceutical composition may be administered in a number of ways depending on whether local or systemic treatment is desired, and on the area to be treated. Administration may be topically (including ophthalmically, vaginally, rectally, intranasally), orally, by inhalation, or parenterally, for example by intravenous drip, subcutaneous, intraperitoneal or intramuscular injection. The disclosed antibodies can be administered intravenously, intraperitoneally, intramuscularly, subcutaneously, intracavity, or transdermally.


Preparations for parenteral administration include sterile aqueous or non-aqueous solutions, suspensions, and emulsions. Examples of non-aqueous solvents are propylene glycol, polyethylene glycol, vegetable oils such as olive oil, and injectable organic esters such as ethyl oleate. Aqueous carriers include water, alcoholic/aqueous solutions, emulsions or suspensions, including saline and buffered media. Parenteral vehicles include sodium chloride solution, Ringer's dextrose, dextrose and sodium chloride, lactated Ringer's, or fixed oils. Intravenous vehicles include fluid and nutrient replenishers, electrolyte replenishers (such as those based on Ringer's dextrose), and the like. Preservatives and other additives may also be present such as, for example, antimicrobials, anti-oxidants, chelating agents, and inert gases and the like.


Formulations for topical administration may include ointments, lotions, creams, gels, drops, suppositories, sprays, liquids and powders. Conventional pharmaceutical carriers, aqueous, powder or oily bases, thickeners and the like may be necessary or desirable.


Compositions for oral administration include powders or granules, suspensions or solutions in water or non-aqueous media, capsules, sachets, or tablets. Thickeners, flavorings, diluents, emulsifiers, dispersing aids or binders may be desirable.


Some of the compositions may potentially be administered as a pharmaceutically acceptable acid- or base-addition salt, formed by reaction with inorganic acids such as hydrochloric acid, hydrobromic acid, perchloric acid, nitric acid, thiocyanic acid, sulfuric acid, and phosphoric acid, and organic acids such as formic acid, acetic acid, propionic acid, glycolic acid, lactic acid, pyruvic acid, oxalic acid, malonic acid, succinic acid, maleic acid, and fumaric acid, or by reaction with an inorganic base such as sodium hydroxide, ammonium hydroxide, potassium hydroxide, and organic bases such as mono-, di-, trialkyl and aryl amines and substituted ethanolamines


b) Therapeutic Uses


Effective dosages and schedules for administering the compositions may be determined empirically, and making such determinations is within the skill in the art. The dosage ranges for the administration of the compositions are those large enough to produce the desired effect in which the symptoms of the disorder are effected. The dosage should not be so large as to cause adverse side effects, such as unwanted cross-reactions, anaphylactic reactions, and the like. Generally, the dosage will vary with the age, condition, sex and extent of the disease in the patient, route of administration, or whether other drugs are included in the regimen, and can be determined by one of skill in the art. The dosage can be adjusted by the individual physician in the event of any counterindications. Dosage can vary, and can be administered in one or more dose administrations daily, for one or several days. Guidance can be found in the literature for appropriate dosages for given classes of pharmaceutical products. For example, guidance in selecting appropriate doses for antibodies can be found in the literature on therapeutic uses of antibodies, e.g., Handbook of Monoclonal Antibodies, Ferrone et al., eds., Noges Publications, Park Ridge, N.J., (1985) ch. 22 and pp. 303-357; Smith et al., Antibodies in Human Diagnosis and Therapy, Haber et al., eds., Raven Press, New York (1977) pp. 365-389. A typical daily dosage of the antibody used alone might range from about 1 μg/kg to up to 100 mg/kg of body weight or more per day, depending on the factors mentioned above.


C. Examples

The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how the compounds, compositions, articles, devices and/or methods claimed herein are made and evaluated, and are intended to be purely exemplary and are not intended to limit the disclosure. Efforts have been made to ensure accuracy with respect to numbers (e.g., amounts, temperature, etc.), but some errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, temperature is in ° C. or is at ambient temperature, and pressure is at or near atmospheric.


1. Example 1: Methylation Modulates the Tumor Immune Synapse


Unprecedented clinical success with immune checkpoint inhibitors alludes to the pivotal importance of the immune synapse that forms between the antigen presenting cells and the effector T-cells. Professional antigen presenting cells such as dendritic cells present tumor-associated antigens via human leukocyte antigen complex (HLA) to the cognate T-cells to elicit tumor-specific immune responses. This high-fidelity recognition of tumor antigen by effector T-cells is either augmented by concomitant interaction of co-stimulatory molecules leading to a functional immune response, or interrupted by engagement of immune checkpoint molecules mediating T-cell anergy or exhaustion.


While professional antigen presenting cells are deemed critical for elicitation of a competent immune response, the immune synapse also forms between the tumor and the effector T-cells; thus, the tumor cells may evade the effector T-cells by neutralizing this interaction. In fact, the interaction between tumor cells and immune cells can shape the immune-suppressive landscape within the tumor microenvironment via mechanisms involved in downregulation of expression of both HLA and a wide array of immune checkpoint and co-stimulatory ligands to modulate T-cell responses. Indeed, the role of tumor in the immune synapse is best illustrated by a tendency of superior efficacy of PD1 blocking antibodies against tumors expressing high levels of PDL1.


Expression of HLA and co-stimulatory/immune checkpoint molecules is intricately modulated at transcription, translation and post-translational levels. In particular, DNA methylation is a crucial epigenetic mechanism of immune regulation with critical roles in T-cell development and differentiation, antigen presentation, effector function and immunologic memory. Because cancer cells frequently utilize epigenetic dysregulation to silence tumor suppressors or activate oncogenes, we hypothesized that tumor progression requires epigenetic reprogramming of immune synapse genes to evade immune killing.


a) Results and Discussions


Tumor evolution to evade immune-surveillance is a hallmark of carcinogenesis, and modulation of the immune synapse between antigen presenting cells and effector T-cells directly impacts tumor-specific immunity. As APCs and tumor modulate effector T-cells via ligands for co-stimulatory and immune checkpoint pathways, we focused on the methylation status of these ligands in tumor (FIG. 1A). The Cancer Genome Atlas (TCGA) Level 1 methylation data from 30 solid tumor types were studied (Table 1). Twenty selected genes were divided into two groups, immune checkpoint genes (ICG; i.e., inhibitory) and co-stimulatory genes (CSG; i.e., stimulatory), (Table 2). Of note, CD80 and CD86 have dual roles as both stimulatory when interacting with CD28 or inhibitory as a ligand for CTLA-4. Their affinity is stronger for CTLA-4 and thus likely to mediate inhibitory signals when expressed in low levels, as is generally the case in tumors. Therefore, these two genes were categorized as inhibitory genes in the tumor-immune synapse.









TABLE 1







List of 30 TCGA tumor types










TCGA_ID
Description







ACC
Adrenocortical carcinoma



BLCA
Bladder Urothelial Carcinoma



BRCA
Breast invasive carcinoma



CESC
Cervical squamous cell carcinoma




and endocervical adenocarcinoma



CHOL
Cholangiocarcinoma



COAD
Colon adenocarcinoma



DLBC
Lymphoid Neoplasm Diffuse




Large B-cell Lymphoma



ESCA
Esophageal carcinoma



HNSC
Head and Neck squamous cell carcinoma



KICH
Kidney Chromophobe



KIRC
Kidney renal clear cell carcinoma



KIRP
Kidney renal papillary cell carcinoma



LIHC
Liver hepatocellular carcinoma



LUAD
Lung adenocarcinoma



LUSC
Lung squamous cell carcinoma



MESO
Mesothelioma



OV
Ovarian serous cystadenocarcinoma



PAAD
Pancreatic adenocarcinoma



PCPG
Pheochromocytoma and Paraganglioma



PRAD
Prostate adenocarcinoma



READ
Rectum adenocarcinoma



SARC
Sarcoma



SKCM
Skin Cutaneous Melanoma



STAD
Stomach adenocarcinoma



TGCT
Testicular Germ Cell Tumors



THCA
Thyroid carcinoma



THYM
Thymoma



UCEC
Uterine Corpus Endometrial Carcinoma



UCS
Uterine Carcinosarcoma



UVM
Uveal Melanoma

















TABLE 2







List of all Immune synapse genes













Gene



Alternative name
Type
Symbol







CEACAM1
Inhibitory
CEACAM1



Galectin 9
Inhibitory
LGALS9



PDL1
Inhibitory
CD274



PDL2
Inhibitory
PDCD1LG2



VISTA
Inhibitory
C10orf54



B7-H3
Inhibitory
CD276



B7-H4
Inhibitory
VTCN1



B7-2 (CD86)
Inhibitory
CD86



B7-1 (CD80)
Inhibitory
CD80



HHLA2
Inhibitory
HHLA2



CD155
Inhibitory
PVR



Galectin 3
Inhibitory
LGALS3



CD40
Stimulatory
CD40



CD70
Stimulatory
CD70



LIGHT
Stimulatory
TNFSF14



OX40L
Stimulatory
TNFSF4



CD137L (4-1BBL)
Stimulatory
TNFSF9



GITRL
Stimulatory
TNFSF18



B7RP1
Stimulatory
ICOSLG



HLA-A
Stimulatory
HLA-A










We first investigated whether distinct tumor types were identifiable based on the methylation status of the immune synapse genes using two dimensional t-distributed stochastic neighbor embedding (t-SNE) and unbiased hierarchical clustering analysis. Strikingly, patients with the same tumor type clustered together regardless of other clinical characteristics including age, sex or stage (FIG. 1B-D). This finding indicates the methylation status of immune synapse genes is heavily imprinted by the tissue of origin. By contrast, normal adjacent tissue of the same histology differentially segregated within the cluster highlighting the epigenetic evolution of tumors during carcinogenesis (FIG. 1B-D). For instance, breast cancer (inverted pink triangle) is clearly separated from its counterpart normal adjacent tissue.


Unbiased t-SNE and hierarchical clustering analysis demonstrated that the methylation status of immune synapse genes alone can distinguish tumor vs. normal tissue and histologic subtypes opening up an intriguing possibility that the methylation status of immune synapse genes can be utilized for early detection of cancer.


Next, we endeavored to understand the biologic basis of separation between the tumor and the normal adjacent tissue by the methylation status of ICG and CSG by analyzing the methylation pattern of individual genes and their CpG-probes on the 450K chip. A full list of the genes and their probes is given in Table 3. Recent studies have demonstrated that DNA methylation of gene bodies also contribute to transcriptional regulation, however, the probes targeting the putative promoter region of the genes within TSS1500, TSS200, and 5′UTR were evaluated. Interestingly, ICGs and CSGs demonstrated inverse methylation patterns reflecting their opposite immunomodulatory functions (FIG. 2-17). For instance, the β-values of probes within the CD40 gene locus, a prominent CSG, have demonstrated profound hypermethylation in the tumor while the HHLA2 gene locus, an ICG, demonstrated hypomethylation in the tumor in comparison to the normal adjacent tissue (FIG. 2A-B). By contrast, the opposite phenomenon was observed for the CSG genes with an increased methylation in tumor vs. normal adjacent tissue. The correlation between probes within the same gene is high, indicating the consistence of the methylation level measurements (FIG. 2C). Similarly, the CD40 gene locus demonstrated a concordant methylation status with the exception of two probes located in the body and 3′UTR regions of the gene unlikely to be involved in transcriptional control of the gene (FIG. 2D). The average methylation level was calculated using probes located in the TSS1500, TSS200 or 5′UTR region of the gene and with a r<−0.2 (Table 3). Further, the average β-value of the selected probes within the HHLA2 and CD40 gene loci demonstrated consistent methylation patterns across disease sites (FIG. 2E-F): hypermethylation of CD40 and hypomethylation of HHLA2 in comparison to the normal adjacent tissue. Additionally, for both HHLA2 and CD40, the tumor samples demonstrated a larger variance in the methylation levels in tumor vs. normal tissue across disease sites (FIG. 2E-F). Because the known epigenetic mechanism of gene methylation is transcriptional suppression, we interrogated the relationship between the methylation status and its gene expression. As anticipated, an inverse correlation between methylation and gene expression was manifest among tumor and normal adjacent tissue (FIG. 2G-H). Such inverse relationship however was confined to tumor samples with detectable gene expression (i.e. log2 expression>4) (FIG. 2G).









TABLE 3







List of Illumina 450 probes for immune synapse genes.























In 75









Alternative



selected


#
name
Type
Num
Gene Symbol
probes
ProbeId
Chr
ChrPos
Islandtype
GeneGroup
r Pearson





















1
CEACAM1
Inhibitory
1
CEACAM1;
No
cg08174715
19
43012255

3′UTR;
0.218935






CEACAM1





3′UTR


2
CEACAM1
Inhibitory
2
CEACAM1;
Yes
cg14904363
19
43032587

5′UTR;
−0.416157






CEACAM1;





1stExon;






CEACAM1;





1stExon;






CEACAM1





5′UTR


3
CEACAM1
Inhibitory
3
CEACAM1;
Yes
cg11811510
19
43032683

TSS200;
−0.58287






CEACAM1





TSS200


4
CEACAM1
Inhibitory
4
CEACAM1;
Yes
cg20657383
19
43033362

TSS1500;
−0.374552






CEACAM1





TSS1500


5
CEACAM1
Inhibitory
5
CEACAM1;
No
cg19776453
19
43033801

TSS1500;
−0.196006






CEACAM1





TSS1500


6
Galectin 9
Inhibitory
1
LGALS9;
No
cg19654781
17
25957331

TSS1500;
−0.114948






LGALS9;





TSS1500;






LGALS9





TSS1500


7
Galectin 9
Inhibitory
2
LGALS9;
No
cg10699049
17
25957771

TSS1500;
0.0411219






LGALS9;





TSS1500;






LGALS9





TSS1500


8
Galectin 9
Inhibitory
3
LGALS9;
Yes
cg27625456
17
25958267

Body;
−0.501265






LGALS9;





1stExon;






LGALS9;





1stExon;






LGALS9;





5′UTR;






LGALS9





5′UTR


9
Galectin 9
Inhibitory
4
LGALS9;
Yes
cg21157094
17
25958282

Body;
−0.542128






LGALS9;





1stExon;






LGALS9;





1stExon;






LGALS9;





5′UTR;






LGALS9





5′UTR


10
Galectin 9
Inhibitory
5
LGALS9;
No
cg23290146
17
25958303

Body;
−0.507165






LGALS9;





1stExon;






LGALS9





1stExon


11
Galectin 9
Inhibitory
6
LGALS9;
No
cg05105919
17
25958673

Body;
−0.522717






LGALS9;





Body;






LGALS9





Body


12
Galectin 9
Inhibitory
7
LGALS9;
No
cg03909504
17
25959847

Body;
0.0871213






LGALS9;





Body;






LGALS9





Body


13
Galectin 9
Inhibitory
8
LGALS9;
No
cg06852032
17
25976191

Body;
0.116474






LGALS9;





3′UTR;






LGALS9





3′UTR


14
PDL1
Inhibitory
1
CD274
Yes
cg15837913
9
5449890
N_Shore
TSS1500
−0.218126


15
PDL1
Inhibitory
2
CD274
No
cg02823866
9
5450410
Island
TSS200
0.0544956


16
PDL1
Inhibitory
3
CD274
No
cg14305799
9
5450535
Island
TSS200
0.00970366


17
PDL1
Inhibitory
4
CD274
No
cg13474877
9
5450724
S_Shore
5′UTR
−0.122665


18
PDL1
Inhibitory
5
CD274
Yes
cg19724470
9
5450936
S_Shore
5′UTR
−0.378707


19
PDL2
Inhibitory
1
PDCD1LG2
No
cg14440664
9
5509642

TSS1500
−0.110612


20
PDL2
Inhibitory
2
PDCD1LG2
Yes
cg07211259
9
5510497

TSS200
−0.492481


21
PDL2
Inhibitory
3
PDCD1LG2
No
cg14351952
9
5515324

5′UTR
0.289053


22
PDL2
Inhibitory
4
PDCD1LG2
No
cg14133064
9
5530115

Body
0.0263889


23
PDL2
Inhibitory
5
PDCD1LG2
No
cg14374994
9
5543782

Body
0.111178


24
VISTA
Inhibitory
1
C10orf54;
No
cg05440642
10
73507806

3′UTR;
−0.331883






CDH23





Body


25
VISTA
Inhibitory
2
CDH23;
No
cg04179740
10
73516760

Body;
−0.116387






C10orf54





Body


26
VISTA
Inhibitory
3
CDH23;
No
cg19227382
10
73521606

Body;
0.143279






C10orf54





Body


27
VISTA
Inhibitory
4
CDH23;
No
cg23968456
10
73521631

Body;
0.149368






C10orf54





Body


28
VISTA
Inhibitory
5
CDH23;
No
cg09895190
10
73521645

Body;
0.0618893






C10orf54





Body


29
VISTA
Inhibitory
6
CDH23;
No
cg14916175
10
73529624
N_Shelf
Body;
−0.168602






C10orf54





Body


30
VISTA
Inhibitory
7
CDH23;
No
cg06768251
10
73533035
N_Shore
Body;
−0.301248






C10orf54





Body


31
VISTA
Inhibitory
8
CDH23;
No
cg13954090
10
73533106
Island
Body;
−0.270241






C10orf54





Body


32
VISTA
Inhibitory
9
C10orf54;
No
cg13957721
10
73533304
Island
5′UTR;
−0.133001






C10orf54;





1stExon;






CDH23





Body


33
VISTA
Inhibitory
10
CDH23;
No
cg12568633
10
73533407
Island
Body;
−0.14949






C10orf54





TSS200


34
VISTA
Inhibitory
11
CDH23;
No
cg04083751
10
73533414
Island
Body;
−0.139335






C10orf54





TSS200


35
VISTA
Inhibitory
12
CDH23;
No
cg08840836
10
73533441
Island
Body;
−0.189989






C10orf54





TSS200


36
VISTA
Inhibitory
13
CDH23;
No
cg09810750
10
73533449
Island
Body;
−0.188456






C10orf54





TSS200


37
VISTA
Inhibitory
14
CDH23;
No
cg14522427
10
73533468
Island
Body;
−0.171435






C10orf54





TSS200


38
VISTA
Inhibitory
15
CDH23;
No
cg17411913
10
73533483
Island
Body;
−0.177352






C10orf54





TSS200


39
VISTA
Inhibitory
16
C10orf54;
Yes
cg06655361
10
73533561
S_Shore
TSS1500;
−0.217879






CDH23





Body


40
VISTA
Inhibitory
17
C10orf54;
No
cg24499627
10
73533891
S_Shore
TSS1500;
0.0382118






CDH23





Body


41
VISTA
Inhibitory
18
C10orf54;
No
cg23282441
10
73533927
S_Shore
TSS1500;
0.0509555






CDH23





Body


42
VISTA
Inhibitory
19
C10orf54;
No
cg06372475
10
73534286
S_Shore
TSS1500;
−0.132819






CDH23





Body


43
VISTA
Inhibitory
20
C10orf54;
No
cg11633461
10
73534338
S_Shore
TSS1500;
−0.089301






CDH23





Body


44
B7-H3
Inhibitory
1
CD276;
No
cg04289575
15
73975176
N_Shore
TSS1500;
0.0521741






CD276





TSS1500


45
B7-H3
Inhibitory
2
CD276;
No
cg12524179
15
73976229
N_Shore
TSS1500;
−0.10806






CD276





TSS1500


46
B7-H3
Inhibitory
3
CD276;
No
cg24688248
15
73976389
N_Shore
TSS1500;
−0.153876






CD276





TSS1500


47
B7-H3
Inhibitory
4
CD276;
No
cg13497475
15
73976511
N_Shore
TSS200;
−0.136537






CD276





TSS200


48
B7-H3
Inhibitory
5
CD276;
No
cg20856453
15
73976537
N_Shore
TSS200;
−0.121453






CD276





TSS200


49
B7-H3
Inhibitory
6
CD276;
No
cg04094107
15
73976560
Island
TSS200;
−0.112064






CD276





TSS200


50
B7-H3
Inhibitory
7
CD276;
No
cg14868530
15
73976679
Island
5′UTR;
−0.165773






CD276;





1stExon;






CD276;





1stExon;






CD276





5′UTR


51
B7-H3
Inhibitory
8
CD276;
No
cg13907424
15
73976968
Island
5′UTR;
−0.193746






CD276





5′UTR


52
B7-H3
Inhibitory
9
CD276;
No
cg00133909
15
73977201
Island
5′UTR;
−0.185099






CD276





5′UTR


53
B7-H3
Inhibitory
10
CD276;
No
cg15484899
15
73977283
Island
5′UTR;
−0.169522






CD276





5′UTR


54
B7-H3
Inhibitory
11
CD276;
Yes
cg10586317
15
73979250
S_Shore
5′UTR;
−0.336759






CD276





5′UTR


55
B7-H3
Inhibitory
12
CD276;
Yes
cg14910296
15
73980827
S_Shelf
5′UTR;
−0.272279






CD276





5′UTR


56
B7-H3
Inhibitory
13
CD276;
Yes
cg09706277
15
73984094

5′UTR;
−0.288548






CD276





5′UTR


57
B7-H3
Inhibitory
14
CD276;
No
cg02161084
15
73989526

5′UTR;
−0.11153






CD276





5′UTR


58
B7-H3
Inhibitory
15
CD276;
No
cg06478102
15
73992274

Body;
0.0802598






CD276





Body


59
B7-H3
Inhibitory
16
CD276;
No
cg25868793
15
73996194

Body;
0.0405871






CD276





Body


60
B7-H3
Inhibitory
17
CD276;
No
cg19698416
15
73996268

Body;
0.0843344






CD276





Body


61
B7-H3
Inhibitory
18
CD276;
No
cg00117012
15
73996358

Body;
0.0777926






CD276





Body


62
B7-H3
Inhibitory
19
CD276;
No
cg27388966
15
74006718

3′UTR;
0.0909163






CD276





3′UTR


63
B7-H4
Inhibitory
1
VTCN1
No
cg27055365
1
117689356

3′UTR
0.213829


64
B7-H4
Inhibitory
2
VTCN1
No
cg19309752
1
117695016

Body
0.21785


65
B7-H4
Inhibitory
3
VTCN1
No
cg17494585
1
117705743

Body
0.0228648


66
B7-H4
Inhibitory
4
VTCN1
No
cg08936501
1
117722652

Body
−0.236615


67
B7-H4
Inhibitory
5
VTCN1
No
cg09035152
1
117730902

Body
0.0214917


68
B7-H4
Inhibitory
6
VTCN1
No
cg00350642
1
117740190

Body
0.139957


69
B7-H4
Inhibitory
7
VTCN1
No
cg22424746
1
117753313

Body
−0.311247


70
B7-H4
Inhibitory
8
VTCN1
Yes
cg16408593
1
117753591

TSS200
−0.42456


71
B7-H4
Inhibitory
9
VTCN1
Yes
cg15597855
1
117753602

TSS200
−0.458988


72
B7-H4
Inhibitory
10
VTCN1
Yes
cg24006253
1
117753616

TSS200
−0.398822


73
B7-H4
Inhibitory
11
VTCN1
Yes
cg04718492
1
117753741

TSS200
−0.435105


74
B7-H4
Inhibitory
12
VTCN1
No
cg27446185
1
117753965

TSS1500
−0.198633


75
B7-H4
Inhibitory
13
VTCN1
Yes
cg20821424
1
117754304

TSS1500
−0.308691


76
B7-2
Inhibitory
1
CD86
No
cg11874272
3
121773564

TSS1500
0.0168761


77
B7-2
Inhibitory
2
CD86
No
cg01878435
3
121774218

TSS200
−0.19246


78
B7-2
Inhibitory
3
CD86
No
cg04387658
3
121775080

Body
0.0958312


79
B7-2
Inhibitory
4
CD86;
No
cg00697440
3
121795768

TSS1500;
0.0457929






CD86





Body


80
B7-2
Inhibitory
5
CD86;
No
cg06327732
3
121795811

TSS1500;
0.00947203






CD86





Body


81
B7-2
Inhibitory
6
CD86;
No
cg09644952
3
121796071

TSS1500;
−0.128381






CD86





Body


82
B7-2
Inhibitory
7
CD86;
Yes
cg01436254
3
121796580

TSS200;
−0.264535






CD86





Body


83
B7-2
Inhibitory
8
CD86;
Yes
cg16331599
3
121796627

TSS200;
−0.287409






CD86





Body


84
B7-2
Inhibitory
9
CD86;
Yes
cg13617155
3
121796719

TSS200;
−0.247929






CD86





Body


85
B7-2
Inhibitory
10
CD86;
No
cg13069531
3
121796767

5′UTR;
−0.194598






CD86;





Body;






CD86





1stExon


86
B7-2
Inhibitory
11
CD86;
No
cg12323361
3
121798553

5′UTR;
0.0256737






CD86





Body


87
B7-2
Inhibitory
12
CD86;
No
cg09410271
3
121820727

Body;
0.0553987






CD86





Body


88
B7-2
Inhibitory
13
CD86;
No
cg09838701
3
121839600

3′UTR;
0.094376






CD86





3′UTR


89
B7-1
Inhibitory
1
CD80
No
cg21139795
3
119243933

3′UTR
−0.012676


90
B7-1
Inhibitory
2
CD80
No
cg06045968
3
119273355

Body
0.0786748


91
B7-1
Inhibitory
3
CD80
Yes
cg13458803
3
119276917

5′UTR
−0.368677


92
B7-1
Inhibitory
4
CD80
Yes
cg21572897
3
119277821

5′UTR
−0.207401


93
B7-1
Inhibitory
5
CD80
No
cg13913728
3
119278596

TSS200
0.0524523


94
B7-1
Inhibitory
6
CD80
Yes
cg02470871
3
119278637

TSS200
−0.213288


95
B7-1
Inhibitory
7
CD80
Yes
cg12978275
3
119278956

TSS1500
−0.33574


96
B7-1
Inhibitory
8
CD80
No
cg06300880
3
119279147

TSS1500
0.0958884


97
HHLA2
Inhibitory
1
HHLA2
Yes
cg02124498
3
108020451

TSS1500
−0.313458


98
HHLA2
Inhibitory
2
HHLA2
Yes
cg08817540
3
108020727

TSS1500
−0.272822


99
HHLA2
Inhibitory
3
HHLA2
Yes
cg02059214
3
108021106

TSS1500
−0.387769


100
HHLA2
Inhibitory
4
HHLA2
Yes
cg10431989
3
108021214

TSS200
−0.380847


101
HHLA2
Inhibitory
5
HHLA2
No
cg22926869
3
108021293

TSS200
−0.192687


102
HHLA2
Inhibitory
6
HHLA2
No
cg00915092
3
108031411

5′UTR
0.0827543


103
HHLA2
Inhibitory
7
HHLA2
No
cg24769830
3
108041508

5′UTR
0.0668664


104
HHLA2
Inhibitory
8
HHLA2
No
cg14703454
3
108065259

5′UTR
0.165865


105
HHLA2
Inhibitory
9
HHLA2
No
cg27229097
3
108096637

3′UTR
0.254071


106
CD155
Inhibitory
1
PVR;
No
cg02415834
19
45146246
N_Shore
TSS1500;
0.108791






PVR;





TSS1500;






PVR;





TSS1500;






PVR





TSS1500


107
CD155
Inhibitory
2
PVR;
No
cg21521892
19
45146289
N_Shore
TSS1500;
0.0640571






PVR;





TSS1500;






PVR;





TSS1500;






PVR





TSS1500


108
CD155
Inhibitory
3
PVR;
Yes
cg01396723
19
45146828
N_Shore
TSS1500;
−0.274669






PVR;





TSS1500;






PVR;





TSS1500;






PVR





TSS1500


109
CD155
Inhibitory
4
PVR;
No
cg22580353
19
45146900
N_Shore
TSS200;
0.0505606






PVR;





TSS200;






PVR;





TSS200;






PVR





TSS200


110
CD155
Inhibitory
5
PVR;
Yes
cg04566018
19
45146967
N_Shore
TSS200;
−0.267189






PVR;





TSS200;






PVR;





TSS200;






PVR





TSS200


111
CD155
Inhibitory
6
PVR;
Yes
cg14538146
19
45146976
N_Shore
TSS200;
−0.232072






PVR;





TSS200;






PVR;





TSS200;






PVR





TSS200


112
CD155
Inhibitory
7
PVR;
Yes
cg10777702
19
45147005
N_Shore
TSS200;
−0.247643






PVR;





TSS200;






PVR;





TSS200;






PVR





TSS200


113
CD155
Inhibitory
8
PVR;
Yes
cg07917289
19
45147056
N_Shore
TSS200;
−0.229176






PVR;





TSS200;






PVR;





TSS200;






PVR





TSS200


114
CD155
Inhibitory
9
PVR;
Yes
cg05012825
19
45147078
Island
TSS200;
−0.225405






PVR;





TSS200;






PVR;





TSS200;






PVR





TSS200


115
CD155
Inhibitory
10
PVR;
No
cg05878558
19
45147316
Island
1stExon;
−0.195013






PVR;





5′UTR;






PVR;





5′UTR;






PVR;





1stExon;






PVR;





5′UTR;






PVR;





1stExon;






PVR;





1stExon;






PVR





5′UTR


116
CD155
Inhibitory
11
PVR;
No
cg13906416
19
45147440
Island
1stExon;
−0.175206






PVR;





1stExon;






PVR;





1stExon;






PVR





1stExon


117
CD155
Inhibitory
12
PVR;
No
cg01496416
19
45147715
Island
Body;
−0.203545






PVR;





Body;






PVR;





Body;






PVR





Body


118
CD155
Inhibitory
13
PVR;
No
cg07455685
19
45150513
Island
Body;
0.0921031






PVR;





Body;






PVR;





Body;






PVR





Body


119
CD155
Inhibitory
14
PVR;
No
cg01865721
19
45150552
Island
Body;
0.0166044






PVR;





Body;






PVR;





Body;






PVR





Body


120
CD155
Inhibitory
15
PVR;
No
cg23696432
19
45150725
Island
Body;
0.0350335






PVR;





Body;






PVR;





Body;






PVR





Body


121
CD155
Inhibitory
16
PVR;
No
cg24098859
19
45152536
S_Shore
Body;
−0.211099






PVR;





Body;






PVR;





Body;






PVR





Body


122
CD155
Inhibitory
17
PVR;
No
cg27077673
19
45153485
S_Shelf
Body;
0.0260456






PVR;





Body;






PVR;





Body;






PVR





Body


123
CD155
Inhibitory
18
PVR;
No
cg25328384
19
45165815

3′UTR;
0.0426888






PVR;





3′UTR;






PVR





3′UTR


124
Galectin 3
Inhibitory
1
LGALS3
Yes
cg04306507
14
55594613
N_Shore
TSS1500
−0.265989


125
Galectin 3
Inhibitory
2
LGALS3
Yes
cg20008101
14
55595227
N_Shore
TSS1500
−0.421086


126
Galectin 3
Inhibitory
3
LGALS3
Yes
cg26335127
14
55595666
N_Shore
TSS1500
−0.532083


127
Galectin 3
Inhibitory
4
LGALS3
Yes
cg02183170
14
55595698
Island
TSS200
−0.613505


128
Galectin 3
Inhibitory
5
LGALS3
Yes
cg18996663
14
55595768
Island
TSS200
−0.462405


129
Galectin 3
Inhibitory
6
LGALS3;
Yes
cg13185030
14
55595949
Island
1stExon;
−0.369518






LGALS3





5′UTR


130
Galectin 3
Inhibitory
7
LGALS3;
Yes
cg19099850
14
55595958
Island
1stExon;
−0.378208






LGALS3





5′UTR


131
Galectin 3
Inhibitory
8
LGALS3
Yes
cg13570982
14
55596240
Island
5′UTR
−0.368171


132
Galectin 3
Inhibitory
9
LGALS3
Yes
cg17403875
14
55596356
Island
5′UTR
−0.407798


133
Galectin 3
Inhibitory
10
LGALS3
Yes
cg09939831
14
55596647
Island
5′UTR
−0.338851


134
Galectin 3
Inhibitory
11
LGALS3
Yes
cg19899505
14
55596862
S_Shore
5′UTR
−0.366875


135
Galectin 3
Inhibitory
12
LGALS3
Yes
cg18273401
14
55600338
S_Shelf
5′UTR
−0.23157


136
Galectin 3
Inhibitory
13
LGALS3;
Yes
cg04260307
14
55602634

TSS1500;
−0.217037






LGALS3





5′UTR


137
Galectin 3
Inhibitory
14
LGALS3;
Yes
cg14871010
14
55603225

TSS1500;
−0.365055






LGALS3





5′UTR


138
Galectin 3
Inhibitory
15
LGALS3;
Yes
cg23575099
14
55603874

TSS200;
−0.275906






LGALS3





5′UTR


139
Galectin 3
Inhibitory
16
LGALS3;
No
cg01051098
14
55604454

Body;
−0.294323






LGALS3





Body


140
CD40
Stimulatory
1
CD40;
No
cg01149415
20
44745522
N_Shore
TSS1500;
0.22303






CD40





TSS1500


141
CD40
Stimulatory
2
CD40;
Yes
cg09053081
20
44746392
N_Shore
TSS1500;
−0.538294






CD40





TSS1500


142
CD40
Stimulatory
3
CD40;
Yes
cg19839655
20
44746499
N_Shore
TSS1500;
−0.525082






CD40





TSS1500


143
CD40
Stimulatory
4
CD40;
Yes
cg19785066
20
44746655
N_Shore
TSS1500;
−0.560293






CD40





TSS1500


144
CD40
Stimulatory
5
CD40;
Yes
cg17929951
20
44746681
N_Shore
TSS1500;
−0.545697






CD40





TSS1500


145
CD40
Stimulatory
6
CD40;
Yes
cg11841529
20
44746751
N_Shore
TSS200;
−0.527903






CD40





TSS200


146
CD40
Stimulatory
7
CD40;
Yes
cg25239996
20
44746767
N_Shore
TSS200;
−0.501244






CD40





TSS200


147
CD40
Stimulatory
8
CD40;
Yes
cg06571407
20
44746823
Island
TSS200;
−0.541914






CD40





TSS200


148
CD40
Stimulatory
9
CD40;
Yes
cg22232207
20
44746825
Island
TSS200;
−0.540844






CD40





TSS200


149
CD40
Stimulatory
10
CD40;
Yes
cg24575067
20
44746902
Island
TSS200;
−0.225347






CD40





TSS200


150
CD40
Stimulatory
11
CD40;
Yes
cg01943874
20
44746944
Island
1stExon;
−0.548976






CD40;





1stExon;






CD40;





5′UTR;






CD40





5′UTR


151
CD40
Stimulatory
12
CD40;
No
cg21601405
20
44747006
Island
1stExon;
−0.63227






CD40





1stExon


152
CD40
Stimulatory
13
CD40;
No
cg16686951
20
44747351
S_Shore
Body;
−0.616846






CD40





Body


153
CD40
Stimulatory
14
CD40;
No
cg06218285
20
44751033
S_Shelf
Body;
0.505468






CD40





Body


154
CD40
Stimulatory
15
CD40;
No
cg07222575
20
44757985

3′UTR;
0.481161






CD40





3′UTR


155
CD70
Stimulatory
1
CD70
No
cg26737640
19
6587584
N_Shelf
Body
0.356726


156
CD70
Stimulatory
2
CD70
No
cg15679532
19
6588838
N_Shore
Body
0.174149


157
CD70
Stimulatory
3
CD70
No
cg27335924
19
6588898
N_Shore
Body
0.0327536


158
CD70
Stimulatory
4
CD70
No
cg18475039
19
6590106
N_Shore
Body
0.0265233


159
CD70
Stimulatory
5
CD70
No
cg25949886
19
6590516
Island
Body
0.0366861


160
CD70
Stimulatory
6
CD70
No
cg14870229
19
6590801
Island
Body
−0.275658


161
CD70
Stimulatory
7
CD70
No
cg24778383
19
6590895
Island
1stExon
−0.141889


162
CD70
Stimulatory
8
CD70
No
cg23263923
19
6591204
S_Shore
TSS200
−0.168459


163
CD70
Stimulatory
9
CD70
No
cg22633597
19
6591674
S_Shore
TSS1500
0.0722559


164
CD70
Stimulatory
10
CD70
No
cg11904429
19
6592554
S_Shore
TSS1500
0.0906074


165
LIGHT
Stimulatory
1
TNFSF14;
No
cg01432753
19
6664926

3′UTR;
0.199538






TNFSF14





3′UTR


166
LIGHT
Stimulatory
2
TNFSF14;
No
cg23071186
19
6669849

Body;
−0.246688






TNFSF14





Body


167
LIGHT
Stimulatory
3
TNFSF14;
Yes
cg10362335
19
6670109

5′UTR;
−0.338743






TNFSF14





5′UTR


168
LIGHT
Stimulatory
4
TNFSF14;
Yes
cg04891836
19
6670390

1stExon;
−0.216417






TNFSF14;





5′UTR;






TNFSF14;





5′UTR;






TNFSF14





1stExon


169
LIGHT
Stimulatory
5
TNFSF14;
No
cg16043888
19
6670865

TSS1500;
0.0925605






TNFSF14





TSS1500


170
LIGHT
Stimulatory
6
TNFSF14;
No
cg05348870
19
6671045

TSS1500;
0.15579






TNFSF14





TSS1500


171
OX40L
Stimulatory
1
TNFSF4
No
cg15112923
1
173155033

3′UTR
0.0275687


172
OX40L
Stimulatory
2
TNFSF4
No
cg24633390
1
173159746

Body
0.316404


173
OX40L
Stimulatory
3
TNFSF4
No
cg21876925
1
173175328

Body
−0.196511


174
OX40L
Stimulatory
4
TNFSF4;
Yes
cg16517394
1
173176362

5′UTR;
−0.323261






TNFSF4





1stExon


175
OX40L
Stimulatory
5
TNFSF4;
Yes
cg21439763
1
173176366

5′UTR;
−0.365141






TNFSF4





1stExon


176
OX40L
Stimulatory
6
TNFSF4
Yes
cg26315984
1
173176501

TSS200
−0.259014


177
OX40L
Stimulatory
7
TNFSF4
Yes
cg10861599
1
173176523

TSS200
−0.307549


178
CD137L
Stimulatory
1
TNFSF9
No
cg15451045
19
6529614
N_Shore
TSS1500
0.0524961


179
CD137L
Stimulatory
2
TNFSF9
No
cg26444348
19
6530187
N_Shore
TSS1500
0.0171351


180
CD137L
Stimulatory
3
TNFSF9;
Yes
cg01186777
19
6531016
Island
5′UTR;
−0.351878






TNFSF9





1stExon


181
CD137L
Stimulatory
4
TNFSF9
No
cg05670459
19
6531335
Island
Body
−0.15091


182
CD137L
Stimulatory
5
TNFSF9
No
cg00169167
19
6531539
Island
Body
−0.189517


183
CD137L
Stimulatory
6
TNFSF9
No
cg24342464
19
6532277
S_Shore
Body
0.0195071


184
CD137L
Stimulatory
7
TNFSF9
No
cg20617093
19
6532849
N_Shore
Body
0.158907


185
CD137L
Stimulatory
8
TNFSF9
No
cg03907016
19
6534760
Island
Body
0.442548


186
CD137L
Stimulatory
9
TNFSF9
No
cg14995475
19
6534774
Island
Body
0.46594


187
CD137L
Stimulatory
10
TNFSF9
No
cg10632765
19
6534916
Island
Body
0.493373


188
CD137L
Stimulatory
11
TNFSF9
No
cg21759280
19
6534936
Island
Body
0.466407


189
CD137L
Stimulatory
12
TNFSF9
No
cg10818587
19
6535078
Island
3′UTR
0.391184


190
GITRL
Stimulatory
1
TNFSF18
No
cg18879481
1
1

Body
0.0672151


191
GITRL
Stimulatory
2
TNFSF18
No
cg05335876
1
173018989

Body
0.0667748


192
GITRL
Stimulatory
3
TNFSF18
No
cg19589427
1
173019720

Body
0.0586345


193
GITRL
Stimulatory
4
TNFSF18
No
cg05936800
1
173020492

TSS1500
0.0327653


194
GITRL
Stimulatory
5
TNFSF18
No
cg11532054
1
173021195

TSS1500
0.0303499


195
B7RP1
Stimulatory
1
ICOSLG
No
cg23813082
21
45648328

3′UTR
0.22599


196
B7RP1
Stimulatory
2
ICOSLG
No
cg25726128
21
45656979
N_Shelf
Body
0.19852


197
B7RP1
Stimulatory
3
ICOSLG
No
cg03048921
21
45658538
N_Shore
Body
0.160248


198
B7RP1
Stimulatory
4
ICOSLG
No
cg06033443
21
45658683
N_Shore
Body
0.193176


199
B7RP1
Stimulatory
5
ICOSLG
No
cg13520931
21
45660251
Island
Body
0.0226509


200
B7RP1
Stimulatory
6
ICOSLG
No
cg17249942
21
45660288
Island
Body
0.0275158


201
B7RP1
Stimulatory
7
ICOSLG;
No
cg06173626
21
45660806
Island
1stExon;
0.0348772






ICOSLG





5′UTR


202
B7RP1
Stimulatory
8
ICOSLG;
No
cg04256691
21
45660826
Island
1stExon;
0.0359561






ICOSLG





5′UTR


203
B7RP1
Stimulatory
9
ICOSLG
No
cg24327461
21
45661066
Island
TSS1500
0.0161379


204
B7RP1
Stimulatory
10
ICOSLG
No
cg04112169
21
45661261
Island
TSS1500
−0.122396


205
B7RP1
Stimulatory
11
ICOSLG
No
cg00993674
21
45661322
Island
TSS1500
−0.13978


206
B7RP1
Stimulatory
12
ICOSLG
No
cg09800026
21
45662262
Island
TSS1500
0.0685446


207
HLA-A
Stimulatory
1
HLA-A
No
cg11086883
6
29908891
N_Shore
TSS1500
−0.135477


208
HLA-A
Stimulatory
2
HLA-A
Yes
cg00390191
6
29908976
N_Shore
TSS1500
−0.251201


209
HLA-A
Stimulatory
3
HLA-A
No
cg07163603
6
29910051
N_Shore
TSS1500
−0.137586


210
HLA-A
Stimulatory
4
HLA-A
Yes
cg05523662
6
29910101
N_Shore
TSS1500
−0.334206


211
HLA-A
Stimulatory
5
HLA-A
Yes
cg19151378
6
29910146
N_Shore
TSS200
−0.308148


212
HLA-A
Stimulatory
6
HLA-A
Yes
cg20142377
6
29910206
Island
TSS200
−0.238395


213
HLA-A
Stimulatory
7
HLA-A
Yes
cg20879959
6
29910208
Island
TSS200
−0.254067


214
HLA-A
Stimulatory
8
HLA-A
Yes
cg25291387
6
29910237
Island
TSS200
−0.290732


215
HLA-A
Stimulatory
9
HLA-A
Yes
cg15319255
6
29910269
Island
TSS200
−0.259475


216
HLA-A
Stimulatory
10
HLA-A
Yes
cg17678719
6
29910273
Island
TSS200
−0.261888


217
HLA-A
Stimulatory
11
HLA-A
Yes
cg23489273
6
29910292
Island
TSS200
−0.206572


218
HLA-A
Stimulatory
12
HLA-A
No
cg05157171
6
29910411
Island
Body
−0.125061


219
HLA-A
Stimulatory
13
HLA-A
No
cg24580035
6
29910525
Island
Body
−0.219747


220
HLA-A
Stimulatory
14
HLA-A
No
cg11808100
6
29910755
Island
Body
−0.186328


221
HLA-A
Stimulatory
15
HLA-A
No
cg25548869
6
29910776
Island
Body
−0.392067


222
HLA-A
Stimulatory
16
HLA-A
No
cg19748509
6
29910778
Island
Body
−0.28115


223
HLA-A
Stimulatory
17
HLA-A
No
cg09803951
6
29910796
Island
Body
−0.104342


224
HLA-A
Stimulatory
18
HLA-A
No
cg22951229
6
29910912
Island
Body
−0.31346


225
HLA-A
Stimulatory
19
HLA-A
No
cg21591486
6
29910994
Island
Body
−0.299429


226
HLA-A
Stimulatory
20
HLA-A
No
cg05738749
6
29911004
Island
Body
−0.352269


227
HLA-A
Stimulatory
21
HLA-A
No
cg11722179
6
29911011
Island
Body
−0.234728


228
HLA-A
Stimulatory
22
HLA-A
No
cg18106971
6
29911028
Island
Body
−0.254845


229
HLA-A
Stimulatory
23
HLA-A
No
cg10886493
6
29911036
Island
Body
−0.234664


230
HLA-A
Stimulatory
24
HLA-A
No
cg18599206
6
29911087
Island
Body
−0.471095


231
HLA-A
Stimulatory
25
HLA-A
No
cg19045970
6
29911091
Island
Body
−0.415368


232
HLA-A
Stimulatory
26
HLA-A
No
cg05839762
6
29911095
Island
Body
−0.436049


233
HLA-A
Stimulatory
27
HLA-A
No
cg14772439
6
29911104
Island
Body
−0.419053


234
HLA-A
Stimulatory
28
HLA-A
No
cg10018004
6
29911261
Island
Body
−0.113211


235
HLA-A
Stimulatory
29
HLA-A
No
cg14018363
6
29911265
Island
Body
−0.144475


236
HLA-A
Stimulatory
30
HLA-A
No
cg09535358
6
29911295
Island
Body
−0.231793


237
HLA-A
Stimulatory
31
HLA-A
No
cg08039587
6
29911334
Island
Body
−0.414116


238
HLA-A
Stimulatory
32
HLA-A
No
cg00082981
6
29911339
Island
Body
−0.309379


239
HLA-A
Stimulatory
33
HLA-A
No
cg16742075
6
29911366
Island
Body
−0.499466


240
HLA-A
Stimulatory
34
HLA-A
No
cg20408505
6
29911494
S_Shore
Body
−0.518677


241
HLA-A
Stimulatory
35
HLA-A
No
cg25637655
6
29911542
S_Shore
Body
−0.520002


242
HLA-A
Stimulatory
36
HLA-A
No
cg17608381
6
29911550
S_Shore
Body
−0.494328


243
HLA-A
Stimulatory
37
HLA-A
No
cg11946459
6
29911558
S_Shore
Body
−0.503476


244
HLA-A
Stimulatory
38
HLA-A
No
cg23303505
6
29911836
S_Shore
Body
−0.238288


245
HLA-A
Stimulatory
39
HLA-A
No
cg18349863
6
29912713
S_Shore
Body
0.0608357


246
HLA-A
Stimulatory
40
HLA-A
No
cg20221094
6
29913098
S_Shore
Body
0.0494603


247
HLA-A
Stimulatory
41
HLA-A
No
cg19585676
6
29913343
S_Shore
3′UTR
0.0965819





The probeID, the location within the gene locus, the R correlation coefficient between probe β-value and gene expression for all probes used in the study are summarized.






These results indicate that the tumor-immune synapse is regulated by methylation in cancer. Sporadic evidence for regulation of HLA, CD40, or CD80 by methylation in select tumor types now appears a more generalized phenomenon in the majority of co-stimulatory and immune checkpoint genes across tumor types. Interestingly, two probes within the promoter region were negatively correlated with the gene expression. However, a clear trend for hypomethylation of PD-L1 locus in comparison to normal adjacent tissue was not observed, indicating competing mechanisms governing PD-L1 expression (FIG. 5).


Next, we conducted a principal component analysis (PCA) to summarize the methylation pattern across all genes and their CpG-probes. To minimize noise and enrich for biologically relevant signal, only the CSGs and ICGs CpG-probes that demonstrated negative correlation (r<−0.2) between the methylation status and their corresponding gene expression and located in the TSS1500, TSS200, 5′UTR regions were selected for further analysis; in total 75 probes. (FIG. 3-17, Table 3-4). PCA revealed two major principal components (PCs), explaining 22.6% and 16.6% of the variation, respectively. A two-dimensional representation of PC1 and PC2 for 8,186 solid tumors and 745 normal adjacent tissues clearly showed that many tumors have an abnormal methylation pattern (FIG. 18A). Strikingly, the dominant components of PC1 were CSGs, in particular CD40 and HLA-A. By contrast, PC2 was mainly driven by ICGs including VTCN1, HHLA2, PDL1, CEACAM1, CD80, and CD86 (FIG. 18B, 19). Consequently, PC1 and PC2 were highly correlated with average β-values of CSG probes and ICG probes respectively (FIG. 18B, 19A-C). Probes from the same gene generally clustered together further confirming robustness of this analysis (FIG. 18B). It should be noted that all CpG-probes contribute to both PCA components with variable contributions, some with a negative weight for a specific PCA component. The total score for a sample is thus a weighted average of all variables. Consistent with the methylation patterns observed with individual CSG and ICG, primary tumor exhibited higher PC1 and lower PC2 scores in comparison to the normal adjacent tissue score across disease sites (FIG. 18C-D), which was also replicated in the average β-values of CSG and ICG probes (FIG. 19D-E). Importantly, we observed reversal of hypermethylation of CSGs by 5-azacytidine in the dataset of 26 epithelial cancer cell lines with a significant decrease in PC1 scores (FIG. 18E). At an individual gene level, demethylation of CD40 by azacytidine was also evident (FIG. 18F) underscoring that the methylation status of CSGs is therapeutically actionable.









TABLE 4





List of 75 selected probes for PCA.


ProbeId







cg14904363


cg11811510


cg20657383


cg27625456


cg21157094


cg15837913


cg19724470


cg07211259


cg06655361


cg10586317


cg14910296


cg09706277


cg16408593


cg15597855


cg24006253


cg04718492


cg20821424


cg01436254


cg16331599


cg13617155


cg13458803


cg21572897


cg02470871


cg12978275


cg02124498


cg08817540


cg02059214


cg10431989


cg01396723


cg04566018


cg14538146


cg10777702


cg07917289


cg05012825


cg04306507


cg20008101


cg26335127


cg02183170


cg18996663


cg13185030


cg19099850


cg13570982


cg17403875


cg09939831


cg19899505


cg18273401


cg04260307


cg14871010


cg23575099


cg09053081


cg19839655


cg19785066


cg17929951


cg11841529


cg25239996


cg06571407


cg22232207


cg24575067


cg01943874


cg10362335


cg04891836


cg16517394


cg21439763


cg26315984


cg10861599


cg01186777


cg00390191


cg05523662


cg19151378


cg20142377


cg20879959


cg25291387


cg15319255


cg17678719


cg23489273





The probes located within TSS1500, TSS200, 5’UTR within a gene with negative correlation to its expression are selected for PCA.






Two-dimensional evaluation of CSG and ICG methylation status revealed that normal tissues generally exhibit relative hypermethylation of ICGs and hypomethylation of CSGs, demonstrating absence of epigenetic brake to suppress immune response. Indeed, highly efficient central tolerance mechanisms governing clonal deletion of self-reactive T-cells allows normal tissues to remain highly immunogenic to any abnormal presence of foreign antigens, which usually represent infection. By contrast, tumor tissues manifest either hypermethylation of CSGs and/or hypomethylation of ICGs, effectively employing epigenetic mechanisms to deliberately suppress the immune system. Because of neo-antigens, oncogenic viral antigens, or cancer testis antigens, tumor specific immune responses ensue. Therefore, altered methylation status reflects tumor adaptation to evolutionary pressure exerted by immune-surveillance. Relatively consistent methylation phenotype between early stage and late stage melanoma indicates such epigenetic adaptation occurs early during carcinogenesis, which explains in part the markedly consistent methylation phenotype of immune synapse genes across tumor types. While expression of HLA and co-stimulatory/immune checkpoint molecules is frequently dysregulated in cancer via multiple mechanisms, heritable changes to impact the entire tumor tissue as a whole require the initial cascade of tolerogenic signal to involve genetic or epigenetic changes. Because germline or somatic mutations of these immune synapse genes are rare events, the immune status of tumor manifest on the epigenetic footprints of immune synapse genes.


Because immune evasion is critical for cancer progression and survival, we hypothesized that the differential methylation status of the immune synapse genes can determine clinical outcome. Therefore, we investigated the clinical relevance of the PCA model in melanoma, a prototypic immunogenic cancer. PC1 was a determinant of disease specific survival (DSS) in melanoma with significant survival advantage in PC1low patients characterized by hypomethylation of CSGs (FIG. 20A). An alternate approach with partial least squares (PLS) modeling using the outcome as response variable also confirmed differences in survival outcome based on CSGs (FIG. 21). Interestingly, the PC1 score was relatively consistent among early and late stage melanoma patients, and thus, the survival difference was independent of patient staging (FIG. 20B).


The methylation status of immune synapse genes was prognostic only in immunogenic tumors indicating that modulation of tumor-immune synapse by methylation can become clinically relevant only in the presence of active anti-tumor immune responses. For instance, PC1 was prognostic for DSS in uterine corpus endometrial carcinoma (UCEC) with microsatellite instability (MSI-H) (FIG. 20C). By contrast, no differences in survival was noted based on PC1 in UCEC without MSI (WT) (FIG. 20D). Consistently, the methylation status correlated with overall survival (OS) and DSS also in other relatively immunogenic cancers, including non-small cell lung cancer (NSCLC), renal cell carcinoma (RCC) and head and neck cancer (HNSC). Similar to the findings with melanoma, NSCLC patients with lower PC1 score demonstrated improved survival (FIG. 22). By contrast, prognosis for head and neck squamous cell carcinoma and renal cell carcinoma correlated with PC2 (FIG. 23).


Increased tumor infiltration by CD4+ and CD8+ T-cells was evident in PC1low patients (FIG. 20E). Further, increased levels of CD3ξ (CD247), Granzyme B (GZMB), Perforin (PRF1), and IFNγin PC1low patients indicate superior effector functions by these T-cells (FIG. 20F). Interestingly, key chemokines that drive T-cell recruitment and trafficking in melanoma, CCL2, CCL3, CCL4, CCL5, CXCL9, and CXCL10, were elevated in PC1low patients (FIG. 20G). More recently, STING/cGAS pathway has been critically implicated in tumor immunogenicity. A significant increase in cGAS expression was also manifest in PC1low patients (FIG. 20H). Therefore, hypomethylation of CSGs in melanoma was associated with improved survival as well as enhanced tumor immunogenicity and recruitment of effector T-cells.


In summary, we report methylation of immune synapse genes as a crucial driver of tolerogenic immune landscapes in cancer. Notably, preclinical studies have demonstrated the efficacy of demethylating agents to augment immunotherapy. Based on this study, we show that the subset of patients with hypermethylated CSGs (PC1high) benefit from combination therapy of PD1 blockade with 5-azacitidine, while conversely, patients with hypermethylated ICGs (PC2high) can be adversely impacted. Given negative findings from the phase II randomized clinical trial of oral 5-azacitidine plus pembrolizumab vs pembrolizumab plus placebo, patient selection can be crucial to overcome resistance to PD1 blockade. Alternatively, targeted editing of tumor methylation of immune synapse genes by TET1 or DNMT3a via CRISPR-cas allows for a personalized approach to augment immunotherapy. Notably, the methylation status of immune synapse genes can be utilized to predict response to immunotherapy. The major advantage to the use of the methylation status is that DNA is stable and degradation is less likely in Formalin-Fixed Paraffin-Embedded tissues, and thus anticipated to be more robust than RNA based or histology based approaches.


b) Methods


(1) Analysis of TCGA Methylation Database


TCGA Level 1 IDAT files for the selected tumor types was downloaded between April and May of 2016 using the Data Matrix. Preprocessing the data included normalization via internal controls probe followed by background subtraction using the methylumi R package from Bioconductor. The calculated β-values were then extracted from the MethyLumiSet object following preprocessing.


(2) Analysis of TCGA RNAseq Database


The TCGA RNAseq samples was extracted from the “EBPlusPlusAdjustPANCAN_IlluminaHiSeq_RNASeqV2.geneExp.tsv” file and log2 transformed, log2(x+1).


(3) GSE57342 5-azacitidine Treated Cancer Cell Lines


The GSE57342 processed dataset was downloaded and cell lines with more than three Mock- and three 5-azacitidine-treated samples was selected for analysis.


(4) T-SNE Analysis


T-SNE was calculated using all 247 probes for the selected 20 genes across all TCGA samples. The 50 first PCA-components was used as input with perplexity=50 and Euclidian distance as implemented in MATLAB.


(5) Correlation Coefficient Heatmap


The Pearson's correlation coefficients between all the probes within a gene were calculated and displayed as a heatmap.


(6) Principal Component Analysis


We used the first and second principal component (a weighted average β-values among the CSG and ICG probes), as they account for the largest variability in the data, to represent the overall methylation status for 8,931 tumor and normal samples in the TCGA database. That is, PC=Σwixi, a weighted average β-values among the selected CSG and ICG probes, where xi represents gene i β-value, wi is the corresponding weight (loading coefficient) with Σwi2=1, and the wi values maximize the variance of Σwixi. For each gene, a set of probes were selected using the following criteria to minimize noise, r<−0.2 (methylation vs gene expression) located in the TSS1500, TSS200 or the 5′UTR (Table 4). Each probe was centered but not scaled before PCA calculations.


(7) Survival Analysis


Tertiles was used to define high, intermediate (Int) and low PC1 or PC2 for melanoma, NSCLC, HNSC, RCC, UCEC MSIhi and wild type patients. Kaplan-Meier curves were then plotted based on tertile scores.


(8) Partial Least Squares (PLS) Modelling


A PLS model was derived using melanoma poor survivors (DSS Dead<12 months, 0) and long survivors (DSS Alive>120 months, 1) as a binary response using the CSG-probes. Cross validation indicated two significant PLS components. The PLS model was then applied to the melanoma samples not used in training. Samples with a predicted response>0.5 was compared to samples with a predicted response<0.5 using a log rank test.


(9) MSI Status


Samples with a MANTIS score larger than 0.4 was considered MSI positive.


(10) Statistics


T-SNE, PCA, PLS, Pearson's correlation statistics, and two-sided Student's t-tests were done in MATLAB R2018B. Survival analysis was done using MatSurv.


D. References





    • Bonneville R, Krook M A, Kautto E A, Miya J, Wing M R, Chen H Z, et al. Landscape of Microsatellite Instability Across 39 Cancer Types. JCO precision oncology. 2017; 2017.

    • Chen L, and Flies D B. Molecular mechanisms of T-cell co-stimulation and co-inhibition. Nature reviews Immunology. 2013; 13(4):227-42.

    • Chiappinelli K B, Strissel P L, Desrichard A, Li H, Henke C, Akman B, et al. Inhibiting DNA Methylation Causes an Interferon Response in Cancer via dsRNA Including Endogenous Retroviruses. Cell. 2017; 169(2):361.

    • Davis S D P, Bilke S, Triche T, Bootwalla M. Handle Illumina methylation data. R package version 2.12.0. 2014.

    • Ehrlich M. DNA methylation and cancer-associated genetic instability. Adv Exp Med Biol. 2005;570:363-92.

    • Fitzpatrick D R, and Wilson C B. Methylation and demethylation in the regulation of genes, cells, and responses in the immune system. Clin Immunol. 2003; 109(1):37-45.

    • Harlin H, Meng Y, Peterson A C, Zha Y, Tretiakova M, Slingluff C, et al. Chemokine expression in melanoma metastases associated with CD8+ T-cell recruitment. Cancer Res. 2009; 69(7):3077-85.

    • Kluger H M, Zito C R, Turcu G, Baine M K, Zhang H, Adeniran A, et al. PD-L1 Studies Across Tumor Types, Its Differential Expression and Predictive Value in Patients Treated with Immune Checkpoint Inhibitors. Clinical cancer research: an official journal of the American Association for Cancer Research. 2017; 23(15):4270-9.

    • Kowanetz M, Zou W, Gettinger S N, Koeppen H, Kockx M, Schmid P, et al. Differential regulation of PD-L1 expression by immune and tumor cells in NSCLC and the response to treatment with atezolizumab (anti-PD-L1). Proc Natl Acad Sci USA. 2018; 115(43):E10119-E26.

    • Li H, Chiappinelli K B, Guzzetta A A, Easwaran H, Yen R W, Vatapalli R, et al Immune regulation by low doses of the DNA methyltransferase inhibitor 5-azacitidine in common human epithelial cancers. Oncotarget. 2014; 5(3):587-98.

    • Micevic G, Thakral D, McGeary M, and Bosenberg M. PD-L1 methylation regulates PD-L1 expression and is associated with melanoma survival. PigmenT-cell & melanoma research. 2018.

    • Pardon D M. The blockade of immune checkpoints in cancer immunotherapy. Nature reviews. 2012; 12(4):252-64.

    • Sade-Feldman M, Jiao Y J, Chen J H, Rooney M S, Barzily-Rokni M, Eliane J P, et al. Resistance to checkpoint blockade therapy through inactivation of antigen presentation. Nature communications. 2017; 8(1):1136.

    • Scarpa M, Scarpa M, Castagliuolo I, Erroi F, Basato S, Brun P, et al. CD80 down-regulation is associated to aberrant DNA methylation in non-inflammatory colon carcinogenesis. BMC cancer. 2016; 16:388.

    • Serrano A, Tanzarella S, Lionello I, Mendez R, Traversari C, Ruiz-Cabello F, et al. Rexpression of HLA class I antigens and restoration of antigen-specific CTL response in melanoma cells following 5-aza-2′-deoxycytidine treatment. International journal of cancer Journal international du cancer. 2001; 94(2):243-51.

    • Suzuki M M, and Bird A. DNA methylation landscapes: provocative insights from epigenomics. Nature reviews Genetics. 2008; 9(6):465-76.

    • Thorsson V, Gibbs D L, Brown S D, Wolf D, Bortone D S, Ou Yang T H, et al. The Immune Landscape of Cancer. Immunity. 2018; 48(4):812-30 e14.

    • Tirapu I, Huarte E, Guiducci C, Arina A, Zaratiegui M, Murillo O, et al. Low surface expression of B7-1 (CD80) is an immunoescape mechanism of colon carcinoma. Cancer Res. 2006; 66(4):2442-50.

    • Van der Maaten L. Visualizing Data using t-SNE. Journal of Machine Learning Research. 2008; 9:2579-605.

    • Wrangle J, Wang W, Koch A, Easwaran H, Mohammad H P, Vendetti F, et al. Alterations of immune response of Non-Small Cell Lung Cancer with Azacytidine. Oncotarget. 2013; 4(11):2067-79.




Claims
  • 1. A method of treating, inhibiting, reducing, ameliorating, and/or preventing an immunogenic cancer or metastasis in a subject comprising: a. obtaining a tissue sample from the subject;b. assaying the amount of methylation of one or more co-stimulatory genes and/or one or more immune checkpoint genes in the tissue sample; andc. administering to the subject an immunotherapy wherein an increase in the methylation of one or more co-stimulatory genes relative to a normal control tissue is detected and/or a decrease in the methylation of one or more immune checkpoint genes relative to a normal control tissue is detected.
  • 2. The method of treating, inhibiting, reducing, ameliorating, and/or preventing an immunogenic cancer or metastasis claim 1, wherein the one or more co-stimulatory gene comprises cluster of differentiation (CD) 40 (CD40), CD70, homologous to lymphotoxin, exhibits inducible expression and competes with HSV glycoprotein D for binding to herpesvirus entry mediator, a receptor expressed on T lymphocytes (LIGHT), OX40L, CD137L (4-1BBL), glucocorticoid-induced tumour-necrosis-factor-receptor-related protein (GITR) ligand (GITRL), B7 related protein 1 (B7RP1), and/or human leukocyte antigen (HLA)-A (HLA-A).
  • 3. The method of treating, inhibiting, reducing, ameliorating, and/or preventing an immunogenic cancer or metastasis claim 1, wherein the one or more immune checkpoint gene comprises carcinoembryonic antigen-related adhesion molecule (CEACAM) 1 (CEACAM1), Galectin 9, programmed death ligand (PDL) 1 (PDL1), PDL2, V-domain Ig suppressor of T cell activation (VISTA), B7-H3, B7-H4, B7-2 (CD86), B7-1 (CD80), HHLA2, CD155, and/or Galectin 3.
  • 4. The method treating, inhibiting, reducing, ameliorating, and/or preventing an immunogenic cancer or metastasis of claim 1, wherein the immunotherapy comprises an antibody, cytokine, natural killer (NK) cell, chimeric antigen receptor (CAR) T cell, CAR NK cell, tumor infiltrating lymphocyte (TIL), marrow infiltrating lymphocyte (MIL), and/or tumor infiltrating NK cell (TINK).
  • 5. The method treating, inhibiting, reducing, ameliorating, and/or preventing an immunogenic cancer or metastasis of claim 1, wherein the antibody comprises an immune checkpoint inhibitor blockade.
  • 6. The method treating, inhibiting, reducing, ameliorating, and/or preventing an immunogenic cancer or metastasis of claim 1, further comprising administering to the subject an inhibitor of methylation when the amount of methylation of the one or more co-stimulatory genes is increased relative to a control.
  • 7. The method treating, inhibiting, reducing, ameliorating, and/or preventing an immunogenic cancer or metastasis of claim 6, wherein the inhibitor of methylation comprises azacytidine, decitabine, and/or zebularine.
  • 8. The method of treating, inhibiting, reducing, ameliorating, and/or preventing an immunogenic cancer or metastasis of claim 1, wherein the cancer comprises adenocarcinoma, breast cancer, bladder cancer, cervical cancer, colon cancer, lymphoma, esophageal cancer, renal cancer, lung cancer, mesothelioma, head and neck cancer, cholangiocarcinoma, liver cancer, ovarian cancer, pancreatic cancer, prostate cancer, adrenal gland cancer, nerve cell cancer, rectal cancer, melanoma, sarcoma, testicular cancer, thyroid cancer, uterine cancer, or ocular cancer.
  • 9. The method of treating, inhibiting, reducing, ameliorating, and/or preventing an immunogenic cancer or metastasis of claim 8, wherein the cancer comprises adrenocortical carcinoma, bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma, lymphoid neoplasm diffuse large B-cell lymphoma, esophageal carcinoma, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectum adenocarcinoma, sarcoma, skin cutaneous melanoma, stomach adenocarcinoma, testicular germ sell tumors, thyroid carcinoma, thymoma, uterine corpus endometrial carcinoma, uterine carcinosarcoma, or uveal melanoma.
  • 10. The method of treating, inhibiting, reducing, ameliorating, and/or preventing an immunogenic cancer or metastasis of claim 1, wherein methylation is measured by performing principal component (PC) analysis (PCA) of the one or more co-stimulatory genes and/or one or more immune checkpoint genes; wherein PChigh indicates an increase in methylation and PClow indicates a decrease in methylation.
  • 11. A method of assessing the suitability of an immunotherapy treatment regimen for the treatment an immunogenic cancer or metastasis in a subject comprising: a. obtaining a tissue sample from the subject; andb. assaying the amount of methylation of one or more co-stimulatory genes and/or one or more immune checkpoint genes in the tissue sample;wherein an increase in the methylation of one or more co-stimulatory genes relative to a normal control tissue and/or a decrease in the methylation of one or more immune checkpoint genes relative to a normal control tissue indicates that immunotherapy is suitable for treatment of the cancer in the subject.
  • 12. The method of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of claim 11, wherein the one or more co-stimulatory gene comprises cluster of differentiation (CD) 40 (CD40), CD70, homologous to lymphotoxin, exhibits inducible expression and competes with HSV glycoprotein D for binding to herpesvirus entry mediator, a receptor expressed on T lymphocytes (LIGHT), OX40L, CD137L (4-1BBL), glucocorticoid-induced tumour-necrosis-factor-receptor-related protein (GITR) ligand (GITRL), B7 related protein 1 (B7RP1), and/or human leukocyte antigen (HLA)-A (HLA-A)
  • 13. The method of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of claim 11, wherein the one or more immune checkpoint gene comprises carcinoembryonic antigen-related adhesion molecule (CEACAM) 1 (CEACAM1), Galectin 9, programmed death ligand (PDL) 1 (PDL1), PDL2, V-domain Ig suppressor of T cell activation (VISTA), B7-H3, B7-H4, B7-2 (CD86), B7-1 (CD80), HHLA2, CD155, and/or Galectin 3.
  • 14. The method of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of claim 11, wherein the immunotherapy comprises an antibody, cytokine, natural killer (NK) cell, chimeric antigen receptor (CAR) T cell, CAR NK cell, tumor infiltrating lymphocyte (TIL), marrow infiltrating lymphocyte (MIL), and/or tumor infiltrating NK cell (TINK).
  • 15. The method of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of claim 11, wherein the antibody comprises an immune checkpoint inhibitor blockade.
  • 16. The method of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of claim 11, wherein a decrease in the methylation of one or more co-stimulatory genes relative to a normal control tissue or an increase in the methylation of one or more immune checkpoint genes relative to a normal control indicates that an inhibitor of methylation should not be administered to the subject.
  • 17. The method of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of claim 11, wherein an increase in the methylation of one or more co-stimulatory genes relative to a normal control tissue indicates that an inhibitor of methylation can be administered to the subject.
  • 18. The method of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of claim 11, wherein the cancer comprises adenocarcinoma, breast cancer, bladder cancer, cervical cancer, colon cancer, lymphoma, esophageal cancer, renal cancer, lung cancer, mesothelioma, head and neck cancer, cholangiocarcinoma, liver cancer, ovarian cancer, pancreatic cancer, prostate cancer, adrenal gland cancer, nerve cell cancer, rectal cancer, melanoma, sarcoma, testicular cancer, thyroid cancer, uterine cancer, or ocular cancer.
  • 19. The method of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of claim 18, wherein the cancer comprises adrenocortical carcinoma, bladder urothelial carcinoma, breast invasive carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma, lymphoid neoplasm diffuse large B-cell lymphoma, esophageal carcinoma, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, mesothelioma, ovarian serous cystadenocarcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma, prostate adenocarcinoma, rectum adenocarcinoma, sarcoma, skin cutaneous melanoma, stomach adenocarcinoma, testicular germ sell tumors, thyroid carcinoma, thymoma, uterine corpus endometrial carcinoma, uterine carcinosarcoma, or uveal melanoma
  • 20. The method of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of claim 11, wherein the assessment is conducted prior to the commencement of any immunotherapy regimen; wherein an increase in the methylation of one or more co-stimulatory genes relative to a normal control tissue and/or a decrease in the methylation of one or more immune checkpoint genes relative to a normal control tissue indicates that the subject can start an immunotherapy regimen; and wherein a decrease in the methylation or same amount of methylation of one or more co-stimulatory genes relative to a normal control tissue and/or an increase in the methylation of one or more immune checkpoint genes relative to a normal control tissue indicates that the subject should start an anti-cancer regimen that is not an immunotherapy.
  • 21. The method of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of claim 11, wherein the assessment is conducted after to the commencement of an immunotherapy regimen; wherein an increase in the methylation of one or more co-stimulatory genes relative to a normal control tissue and/or a decrease in the methylation of one or more immune checkpoint genes relative to a normal control tissue indicates that the subject can continue an immunotherapy regimen; and wherein a decrease or same amount of methylation of one or more co-stimulatory genes relative to a normal control tissue and/or an increase or same amount of methylation of one or more immune checkpoint genes relative to a normal control tissue indicates that the subject should discontinue an anti-cancer regimen that is not an immunotherapy.
  • 22. The method of assessing the suitability of an immunotherapy treatment regimen for the treatment of an immunogenic cancer or metastasis in a subject of claim 11, wherein methylation is measured by performing principal component analysis of the one or more co-stimulatory genes and/or one or more immune checkpoint genes; wherein PChigh indicates an increase in methylation and PClow indicates a decrease in methylation.
II. PRIORITY CLAIMS

This application claims the benefit of U.S. Provisional Application No. 62/889,981, filed on Aug. 21, 2019, which is incorporated herein by reference in its entirety.

I. STATEMENT OF GOVERNMENT SUPPORT

This invention was made with government support under Grant No. CA076292 awarded by National Cancer Institute. The government has certain rights in the invention.

PCT Information
Filing Document Filing Date Country Kind
PCT/US2020/047475 8/21/2020 WO
Provisional Applications (1)
Number Date Country
62889981 Aug 2019 US