Methylation of immune synapse genes modulates tumor immunogenicity
Anders Berglund, … , James Mulé, Sungjune Kim
Anders Berglund, … , James Mulé, Sungjune Kim
Published November 12, 2019
Citation Information: J Clin Invest. 2020;130(2):974-980. https://doi.org/10.1172/JCI131234.
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Concise Communication Immunology

Methylation of immune synapse genes modulates tumor immunogenicity

Abstract

Cancer immune evasion is achieved through multiple layers of immune tolerance mechanisms including immune editing, recruitment of tolerogenic immune cells, and secretion of immunosuppressive cytokines. Recent success with immune checkpoint inhibitors in cancer immunotherapy suggests 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, we observed disproportionately hypermethylated costimulatory genes and hypomethylation of immune checkpoint genes, 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.

Authors

Anders Berglund, Matthew Mills, Ryan M. Putney, Imène Hamaidi, James Mulé, Sungjune Kim

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Figure 1

The distinct pattern of immune synapse gene methylation depends on tumor histology.

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The distinct pattern of immune synapse gene methylation depends on tumor...
(A) Schematic of an immune synapse between the antigen-presenting cells/tumor and T cells. (B) t-SNE analysis was performed on 8,186 solid tumors and 745 normal adjacent tissues based on the β values for methylation levels for all probes for CSGs and ICGs from A, contrasting tumor (blue) versus normal adjacent tissue (red). (C) The spatial relationship between distinct tumor types is depicted, with breast tumors in the blue-dotted box and normal adjacent tissue samples in the black-dotted box. (D) Unbiased hierarchical clustering analysis is shown. 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.