Molecular correlates and therapeutic targets in T cell-inflamed versus non-T cell-inflamed tumors across cancer types

• Published in: Genome medicine Vol. 12; no. 1; pp. 1 - 90
• Main Authors: Bao, Riyue, Stapor, Daniel, Luke, Jason J
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 27.10.2020; BioMed Central; BMC
• Subjects: Analysis; Biological response modifiers; Cancer; Cancer immunotherapy; Cancer treatment; Care and treatment; CD11c antigen; CD8 antigen; Clinical trials; Consortia; Datasets; DNA binding proteins; Drug development; Editing; Gene expression; Genes; Genetic aspects; Genomes; Genomics; Health aspects; Hedgehog protein; Immune evasion; Immunosuppression; Immunosuppressive agents; Immunotherapy; Inflammation; Interferon; Lymphocytes; Lymphocytes T; Medical research; Melanoma; Metastasis; Mutation; Myc protein; Nominations; PD-1 protein; Phenotypes; Proteins; Proteomes; Signal transduction; T cell-inflamed; T cells; TCGA; Transcription (Genetics); Transcription factors; Transcriptomics; Tumor suppressor genes; Tumors; VHL protein; Wnt protein; β-Catenin
• ISSN: 1756-994X; 1756-994X
• DOI: 10.1186/s13073-020-00787-6

Background The T cell-inflamed tumor microenvironment, characterized by CD8 T cells and type I/II interferon transcripts, is an important cancer immunotherapy biomarker. Tumor mutational burden (TMB) may also dictate response, and some oncogenes (i.e., WNT/[beta]-catenin) are known to mediate immunosuppression. Methods We performed an integrated multi-omic analysis of human cancer including 11,607 tumors across multiple databases and patients treated with anti-PD1. After adjusting for TMB, we correlated the T cell-inflamed gene expression signature with somatic mutations, transcriptional programs, and relevant proteome for different immune phenotypes, by tumor type and across cancers. Results Strong correlations were noted between mutations in oncogenes and tumor suppressor genes and non-T cell-inflamed tumors with examples including IDH1 and GNAQ as well as less well-known genes including KDM6A, CD11c, and genes with unknown functions. Conversely, we observe genes associating with the T cell-inflamed phenotype including VHL and PBRM1. Analyzing gene expression patterns, we identify oncogenic mediators of immune exclusion across cancer types (HIF1A and MYC) as well as novel examples in specific tumors such as sonic hedgehog signaling, hormone signaling and transcription factors. Using network analysis, somatic and transcriptomic events were integrated. In contrast to previous reports of individual tumor types such as melanoma, integrative pan-cancer analysis demonstrates that most non-T cell-inflamed tumors are influenced by multiple signaling pathways and that increasing numbers of co-activated pathways leads to more highly non-T cell-inflamed tumors. Validating these analyses, we observe highly consistent inverse relationships between pathway protein levels and the T cell-inflamed gene expression across cancers. Finally, we integrate available databases for drugs that might overcome or augment the identified mechanisms. Conclusions These results nominate molecular targets and drugs potentially available for further study and potential immediate translation into clinical trials for patients with cancer. Keywords: T cell-inflamed, Immune evasion, Genomics, Transcriptomics, TCGA