Mesothelioma survival prediction based on a six-gene transcriptomic signature

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Background: Mesothelioma is an aggressive, fatal cancer that is inextricably linked to asbestos exposure. Recent trials using a combination of the immune checkpoint inhibitors ipilimumab and nivolumab has significantly improved treatment outcomes, however durable treatment responses remain restricted to a subset of patients (15-20%), highlighting the need to identify strategies that better predict treatment response. Method: We performed RNAseq on a large tumor biobank (n=167) from genetically diverse mouse model, CC-MexTAg model to compare gene expression profiles of tumors from mice with different overall survival to develop a prognostic gene signature. Results: while the variation in gene expression data of tumors did not associate with 3-fold variation in overall survival of CC-MexTAg mice, we identified two distinct tumor clusters characterized with immune and non-immune phenotypes, in which immune cluster tumours showed the better potential of response to cancer therapies. We used 20 hub genes associated with this tumor phenotype to develop a 6-gene signature that could predict survival in four independent mesothelioma datasets (Bueno, NCI, TCGA and Creaney) and showed a potential to respond to cancer immunotherapy. Here, the shared data include R markdown files to perform Gene set enrichment analysis (GSEA), CIBERSORT and WGCNA on RNAseq data from CCMT mouse model (CCMT data analysis_part 1 and 2). Folder (Gene_signature_development_validation_part 3) include the R markdown file for developing and validating the 6-gene signature via interrogating five independent human mesothelioma datasets.