Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures

Abstract

BackgroundTumor-infiltrating immune cells have been linked to prognosis and response to immunotherapy; however, the levels of distinct immune cell subsets and the signals that draw them into a tumor, such as the expression of antigen presenting machinery genes, remain poorly characterized. Here, we employ a gene expression-based computational method to profile the infiltration levels of 24 immune cell populations in 19 cancer types.ResultsWe compare cancer types using an immune infiltration score and a T cell infiltration score and find that clear cell renal cell carcinoma (ccRCC) is among the highest for both scores. Using immune infiltration profiles as well as transcriptomic and proteomic datasets, we characterize three groups of ccRCC tumors: T cell enriched, heterogeneously infiltrated, and non-infiltrated. We observe that the immunogenicity of ccRCC tumors cannot be explained by mutation load or neo-antigen load, but is highly correlated with MHC class I antigen presenting machinery expression (APM). We explore the prognostic value of distinct T cell subsets and show in two cohorts that Th17 cells and CD8+ T/Treg ratio are associated with improved survival, whereas Th2 cells and Tregs are associated with negative outcomes. Investigation of the association of immune infiltration patterns with the subclonal architecture of tumors shows that both APM and T cell levels are negatively associated with subclone number.ConclusionsOur analysis sheds light on the immune infiltration patterns of 19 human cancers and unravels mRNA signatures with prognostic utility and immunotherapeutic biomarker potential in ccRCC.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1092-z) contains supplementary material, which is available to authorized users.

Upregulated immune checkpoint HHLA2 in clear cell renal cell carcinoma: a novel prognostic biomarker and potential therapeutic target

“…In line with this hypothesis, whole exome sequencing analysis have revealed that the loss-of-function mutation in chromatin remodeler polybromo-1 gene is a key factor causing altered immune signalling pathways in patients with metastatic ccRCC 32. Furthermore, expression of major histocompatability complex class I antigen presenting machinery could reflect the prognosis and immunotherapeutic benefits in ccRCC 31. Given the close relationship between ccRCC and immune signalling, limited evidence has illustrated the function and clinical significance immune checkpoint HHLA2 in ccRCC pathogenesis and prognosis.…”

Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma

“…Melanoma and NSCLC typically harbor 10 to 400 mutations per megabase (Mb) and these genetic variants can generate tumor-specific antigens (neoantigens) that stimulate a strong anti-tumor immune response (1–4). In contrast, ccRCC harbors an average of only 1.1 mutations/Mb (6, 7) yet it ranks highly among tumor types in terms of immune cytolytic activity (8), immune infiltration score, and T cell infiltration score in the tumor microenvironment (9). These observations led us to hypothesize that distinct molecular mechanisms underlie the immunologically active tumor microenvironment and responsiveness to immune checkpoint therapy in patients with ccRCC.…”

“…However, the magnitudes of these differences were small and potentially confounded by differing degrees of tumor-stromal admixture (fig. S7, A to C) (9). We also examined LOF mutations in VHL , the most commonly-mutated gene in the TCGA ccRCC cohort.…”

Mutational Analysis of Gene Fusions Predicts Novel MHC Class I–Restricted T-Cell Epitopes and Immune Signatures in a Subset of Prostate Cancer

“…We explored correlation between total gene fusions, predicted MHC class I binding CASQ epitopes, and immunological parameters using recently published estimates that predicted immune-cell infiltrates using RNA-sequencing data [37]. For immune cell-infiltrates, we used published infiltration estimates for the patients included in this cohort generated, estimated via a single sample gene set enrichment analysis (ssGSEA) approach performed across multiple cancers [37].…”