Abstract 168: Computational model for prediction of actionable drug combinations in cancer

Abstract

Abstract Synthetic essentiality presents an effective approach towards finding connected genetic targets in cancer by identifying genes that produce an inviable phenotype upon joint inactivation. However, it is less studied how synthetic lethal gene pairs may be contextualized with respect to deregulation of different cell functions and how these pairs act in different cell types and tissues. To study cancer-specific synthetic essentiality, we devised an integrated network-based algorithm to analyze relationships between tumor gene expression and patient survival in The Cancer Genome Atlas (TCGA) and expression data from the Genotype-Tissue Expression (GTEx) project. In particular, we identify gene pairs in each cancer cohort that exhibit upregulation in tumors but are associated with improved survival for patients whose tumors feature reduced expression of the gene pair. Our findings reveal variability in synthetic lethal pair activity across different cancer types, and the identified pairs connect distinct signaling and metabolic cellular pathways to different cancer types. Altogether, our findings present a pan-cancer reference for gene expression-based synthetic lethal gene pairs as well as a nuanced perspective on changes in synthetic essentiality across different cellular and tissue contexts, pointing to a new avenue for drug combination discovery oriented on targeting key pathways based on the cancer type. Citation Format: Sairahul Pentaparthi, Brandon Burgman, S. Stephen Yi. Computational model for prediction of actionable drug combinations in cancer [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 168.