Transcriptomics based prediction of survival and response to therapy in malignant mesothelioma

Abstract

Malignant mesothelioma is an aggressive cancer with limited treatment options and poor prognosis. Better understanding of mesothelioma genomics and transcriptomics could advance novel therapies. We performed whole-exome and RNA-sequencing of germline and tumors of 122 patients with pleural, peritoneal, and tunica-vaginalis mesothelioma. We identify a 48 gene prognostic signature that is highly predictive of mesothelioma patient survival including CCNB1, whose expression is highly predictive of patient survival on its own. Using a synthetic-lethality (SL) based pipeline for analyzing the patients transcriptomic data, we identified SL-based signatures predictive of response to an anti-PD1 immune checkpoint inhibitor and combination therapies with pemetrexed. These SL-profiles successfully predict the overall patient-response observed across targeted, immuno- and chemotherapies in 11 independent mesothelioma clinical trials spanning 7 different treatments. These findings lay a basis for future studies aimed specifically at testing the ability of these SL profiles to serve as treatment biomarkers in mesothelioma.