University of Pittsburgh's Biomedical Informatics Lecture Series

Speaker: Nishanth Nair, PhD

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

Description: Malignant mesothelioma is an aggressive cancer with limited treatment options and poor prognosis. Better understanding of mesothelioma genetics, transcriptomics and tumor immune micro-environment is critical for successful development of prognostics markers and novel therapies for these patients. 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 of these tumors to identify pathogenic variants, prognostic gene expression signatures, as well as predictors of patient survival and response to therapies. We tested and validated our findings using the TCGA and Bueno et al. independent mesothelioma datasets. We identified a 48 gene-set prognostic signature whose high expression level is associated with poor survival. This signature is highly predictive of patient survival in two other independent, pleural mesothelioma cohorts (the TCGA and Bueno et al. mesothelioma datasets), after controlling for age and gender. Among the 48 genes, the expression of CCNB1 is highly predictive of patient survival suggesting it has an important role in mesothelioma, possibly via its involvement in the CDK1-CCNB1-CCNF complex. Using a synthetic lethality (SL) based precision-oncology computational framework for analyzing the patients’ transcriptomic data, we were able to identify SL-based signatures that are predictive of response to an anti-PD1 immune checkpoint inhibitor and combination therapies with chemotherapy (pemetrexed) in mesothelioma patients. The SL profiles successfully predicted the overall patient-response observed across targeted, immuno- and chemotherapies in 11 independent mesothelioma clinical trials spanning 7 different treatments. By analyzing the tumor genomic and transcriptomics data of a large cohort of mesothelioma patients, we identified gene expression prognostic markers predictive of patient survival and SL-based signatures predictive of response to therapy. These findings lay a basis for the future development of personalized therapy approaches for mesothelioma patients.

Zoom link: https://pitt.zoom.us/j/97285938194

For information on this event, see https://www.dbmi.pitt.edu/node/54457

For information on other Department of Biomedical Informatics lectures, see https://www.dbmi.pitt.edu/seminars/spring2022