Mutation patterns in genes encoding interferon signaling and antigen presentation: A pan-cancer survey with implications for the use of immune checkpoint inhibitors.

Abstract

Blockade of immune checkpoints has become a powerful tool in cancer medicine, which is effective across various solid cancer types and hematologic malignancies. While immunohistochemical detection of PD-L1 expression in tumor cells, immune cells, or both has been introduced as predictive biomarker in several clinical trials, shortcomings and limitations of this approach were quickly recognized. As a single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer, various genetic determinants of therapy success, including microsatellite instability, mutational burden, and PD-L1 amplification, are being investigated. Very recent work indicates that mutations in B2M, JAK1, and JAK2 render melanoma resistant to immune checkpoint blockade, thus serving as negative response predictors. Using the TCGA dataset, we performed a pan-cancer analysis of potentially damaging mutations in key genes implicated in antigen presentation and interferon-gamma signaling and investigated associations with transcript levels of immune checkpoint genes, cytolytic activity, and mutational burden. For B2M, JAK1, and JAK2, we observed overall mutation frequencies of 1.8%, 2%, and 2.6%, respectively, and found significant associations with mutational burden. On pathway level, melanoma as well as bladder, gastric, and lung cancer were most frequently affected by putative resistance mutations with mutation rates of 27%-50% in the antigen presentation pathway and of 16%-21% in the interferon signaling pathway. Our analysis suggests that a significant number of tumors harbor mutations that may negatively interfere with immune checkpoint inhibition, or confer a higher likelihood of resistance for which a second hit is ultimately required. Since these mutations are prevalent in treatment-naïve tumors, genetic screening prior to therapy might complement current approaches at predicting response to immune checkpoint blockade.