Abstract 667: Genomic and transcriptomic profiling of malignant mesothelioma patients identifies gene signatures predictive of survival and response to immuno and chemotherapy

Abstract Background Malignant mesothelioma (MM) is an aggressive cancer with limited treatment options and poor prognosis. Malignant pleural mesothelioma comprises 80% of the cases and has worse outcome than malignant peritoneal mesothelioma. An in-depth knowledge of genetic, transcriptomic and immunogenic events involved in MM is critical for successful development of prognostics and therapeutic modalities. Methods We performed whole-exome sequencing of germline and tumors of 122 patients with pleural (n=59), peritoneal (n=61) and tunica vaginalis (n=2) mesothelioma, and RNA-sequencing of 100 tumors to identify pathogenic variants, somatic mutational signatures, and prognostic gene expression signatures, predictive of patient survival and tumor response to therapies. We validated our findings using the TCGA and Bueno et al. mesothelioma datasets. Results The important findings from this study include: a) Key somatic mutational signatures are associated with DNA repair pathways and BRCA1 associated protein-1 (BAP1) is the most commonly mutated gene (~13% with germline mutation). b) We identified a set of 48 genes, a “mesothelioma prognostic signature”, whose high expression level is associated with poor survival (Cox regression, FDR < 0.1). These genes are enriched for genes related to cell cycle and DNA repair. This signature is highly predictive of patient survival in two other independent, pleural mesothelioma cohorts: TCGA (Hazard ratio (HR) = 2.6, P = 6.94e-10) and Bueno et al. mesothelioma dataset (HR = 1.49, P = 4.34e-07), after controlling for age and gender. c) Among the 48 genes, the expression of CCNB1 is highly predictive of patient survival suggesting its important role in MM, possibly via its involvement in the CDK1-CCNB1-CCNF complex (HR = 2.54, P = 1.89e-08 for TCGA; HR = 1.40, P = 1.65e-05 for Bueno et al. dataset). d) Using a synthetic lethality (SL) based precision-oncology computational framework for analyzing the patients' transcriptomic data, we were able to accurately predict response to an anti-PD1 immune checkpoint inhibitor and combination therapies with pemetrexed (chemotherapy) in mesothelioma patients. The SL profiles successfully predicted the overall patient-response observed across targeted, immuno- and chemotherapies in 11 independent mesothelioma clinical trials (Spearman's ρ = 0.64, P = 0.0348). This is the first analysis shown to successfully predict overall patient-response for various treatments within a cancer type. Conclusions By analyzing the tumor genomic and transcriptomics data of a large cohort of MM patients, we identify gene expression prognostic markers predictive of patient survival and response to therapy, both as independent signatures and via their SL interactions. These findings lay a basis for the future development of personalized therapy approaches for mesothelioma patients. Citation Format: Nishanth Ulhas Nair, Qun Jiang, Jun S. Wei, Vikram A. Misra, Betsy Morrow, Leandro C. Hermida, Joo Sang Lee, Idrees Mian, Jingli Zhang, Manjistha Sengupta, Javed Khan, Eytan Ruppin, Raffit Hassan. Genomic and transcriptomic profiling of malignant mesothelioma patients identifies gene signatures predictive of survival and response to immuno and chemotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 667.