Abstract A26: Prediction of synergistic anticancer drug combinations by integrating chemical and genetic screens

Identification of effective drug combinations inhibiting multiple target genes that are essential for cancer cell survival is seen as an effective strategy to overcome drug resistance. Cellular pathways are often redundant, and network rewiring in the face of an assault promotes the emergence of resistant cells that contribute to clinical relapse. However, the number of possible drug-target combinations to investigate by conducting experiments is exponentially large and impractical, therefore warranting the need for systematic approaches to prioritize the most effective drug combinations. Here, we aimed to integrate large-scale drug screening, drug-target affinity knowledge and public RNAi screening datasets on a huge compendium of cancer cell lines to predict synergistic drug combinations. In a case study in an individual breast cancer cell line, MDA-MB-231, we identified drugs selectively active in this cell line compared to the background distribution. We find that target genes of active drugs and other genes involved in related cellular processes were also differentially essential in RNAi screening data. Further, we observe that the drug combinations of these selectively active drugs were synergistic. We propose a systematic approach for integrating chemical screening, drug target and cellular processes knowledge, and functional genetic screens to optimize drug combination testing efforts. Citation Format: Alok Jaiswal, Prson Gautam, Jing Tang, Krister Wennerberg, Tero Aittokallio. Prediction of synergistic anticancer drug combinations by integrating chemical and genetic screens [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 A26.