Abstract SY45-02: Harnessing genetic interactions to advance whole-exome precision cancer treatment

Abstract

Abstract Much of the current focus in cancer research is on studying cancer driver genes. In search for new and effective cancer drugs, this has been translated into searching for ‘actionable’ mutations in these genes, aiming at their therapeutic targeting. However, identifying novel genetic interactions occurring between cancer genes may open new drug treatment opportunities across the whole cancer genome. This talk will focus on studying the utility of two fundamental types of genetic interactions: The first are the well-known Synthetic Lethal interactions, describing the relationship between two genes whose combined inactivation is lethal to the cell. The second type are the much less studied Synthetic Rescues interactions, where a change in the activity of one gene is lethal to the cell but an alteration of its SR partner gene can rescue cell viability. I shall describe a new approach for the data-driven identification of these two types of genetic interactions by directly mining patients’ tumor data. Applying it to analyze the Cancer Genome Atlas (TCGA) data, we have identified the first pan-cancer genetic interaction networks shared across many types of cancer, which we then validated via existing and new experimental in vitro and in vivo screens. We find that: (a) Synthetic Lethal interactions offer an exciting venue for personalized selective anticancer treatments enabling the prediction of patients’ drug response and providing new selective drug target candidates, and (b) targeting Synthetic Rescue genes can mitigate resistance emerging to primary cancer therapy, including both targeted and immunotherapy. (c) Finally, I shall present recent unpublished results showing that we can quite accurately predict the context specificity of emerging synthetic-lethal based combination therapies, both in terms of the cancer types that are sensitive to them and re. the specific sensitivity of individual patients within a cancer type. Importantly, these results are obtained without the need for specific training on the datasets predicted and are derived directly from patients data, thus increasing the likelihood of their translational relevance. Citation Format: Eytan Ruppin. Harnessing genetic interactions to advance whole-exome precision cancer treatment [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 SY45-02.