Abstract 1671: Characterization of a gene signature predictive of cancer patient survival prognosis and chemo-response

Abstract

Abstract Precision oncology aims to match cancer patients with drugs based on their unique molecular profiles. The MATCH trial results indicate ~80% of cancer patients do not benefit from screening cancer patients for actionable genomic aberrations. We aim to identify potentially druggable gene signatures that are molecular drivers of poor survival outcome for these cancer patients. We analyze data from The Cancer Genome Atlas (TCGA) to identify patient groups with differential survival characteristics using gene expression data. We applied a univariate Cox regression model to identify genes associated with patient survival and identified a 67 gene signature of highly correlated genes significant for cell cycle related gene ontologies on over-representation analysis. Patients from the TCGA dataset whose tumors have a higher gene expression score are more likely to have worse survival prognosis compared to tumors with a lower gene expression score, demonstrated through Kaplan-Meier survival analysis and univariate and multivariate Cox proportional hazard models. This suggests we have identified a co-regulated gene expression network associated with cellular proliferation which predicts overall and recurrence-free survival in many of the cancers in the TCGA dataset. We further analyzed data from the NCI-60 Human Tumor Cell Lines Screen and found the cellular proliferation gene signature expression is correlated with response to cell cycle inhibitors. We then validated this finding using resistant cell lines and gene knockdown models in vitro. This gene signature may provide an important tool for selecting patients for chemotherapy administration. Citation Format: Paul Tran, Shuchun Li, Hai-Tao Liu, Lynn Tran, Sharad Purohit, Boying Dun, Jin-Xiong She. Characterization of a gene signature predictive of cancer patient survival prognosis and chemo-response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1671.