Abstract PR04: A CRISPR-based genetic interaction map identifies synergistic drug combinations for cancer

Abstract

Abstract Identification of effective combination therapies is critical to address the emergence of drug-resistant cancers. Although millions of drug combinations might be created by repurposing existing drugs, direct screening of these combinations is infeasible. Here, we designed a scalable CRISPR-based double knockout (CDKO) system to generate a mammalian genetic interaction (GI) map at unprecedented scale, comprised of 490,000 double-sgRNAs directed against 21,321 pairs of drug targets. We first developed an efficient strategy for cloning and sequencing the libraries, as well as a robust statistical scoring method for calculating GIs from CRISPR-deleted gene pairs. We then extensively validated this system by identifying known and novel genetic interactions in the ricin pathway, and compared the GIs to known protein-protein interactions (PPIs). Using this validated system, we searched for rare synthetic lethal drug target pairs in K562 leukemia cells and identified a number of potent combinations for which corresponding drugs exhibit synergistic killing. Together, this work demonstrates an effective strategy to screen synergistic drug combinations in high throughput, and a powerful CRISPR-based tool to dissect functional genetic interaction networks. Citation Format: Kyuho Han, Edwin Jeng, Gaelen Hess, David Morgens, Amy Li, Michael Bassik. A CRISPR-based genetic interaction map identifies synergistic drug combinations for cancer [abstract]. In: Proceedings of the AACR Precision Medicine Series: Opportunities and Challenges of Exploiting Synthetic Lethality in Cancer; Jan 4-7, 2017; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2017;16(10 Suppl):Abstract nr PR04.