Harnessing synthetic lethality to predict the response to cancer treatment

• DOI: 10.1038/s41467-018-04647-1
• Publication Date: 2018-06-25T11:34:52Z
• Authors: Joo Sang Lee; Avinash Das; Livnat Jerby-Arnon; Rand Arafeh; Noam Auslander; Matthew Davidson; Lynn McGarry; Daniel James; Arnaud Amzallag; Seung Gu Park; Kuoyuan Cheng; Welles Robinson; Dikla Atias; Chani Stossel; Ella Buzhor; Gidi Stein; Joshua J. Waterfall; Paul S. Meltzer; Talia Golan; Sridhar Hannenhalli; Eyal Gottlieb; Cyril H. Benes; Yardena Samuels; Emma Shanks; Eytan Ruppin
• Keywords: Synthetic Lethality; Lethality; Cancer drugs

While synthetic lethality (SL) holds promise in developing effective cancer therapies, SL candidates found via experimental screens often have limited translational value. Here we present a data-driven approach, ISLE (identification of clinically relevant lethality), that mines TCGA cohort to identify the most likely interactions (cSLi) from given candidate set lab-screened SLi. We first validate benchmark large-scale drug response and by predicting efficacy mouse xenograft models. then experimentally test select predicted cSLi new screening experiments, validating their context-specific sensitivity hypoxic vs normoxic conditions demonstrating cSLi's utility synergistic combinations. show can successfully predict patients' treatment provide patient stratification signatures. thus complements existing actionable mutation-based methods for precision therapy, offering an opportunity expand its scope whole genome.