Harnessing synthetic lethality to predict the response to cancer treatment

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 and Eytan Ruppin ()
Additional contact information
Joo Sang Lee: University of Maryland
Avinash Das: University of Maryland
Livnat Jerby-Arnon: Tel Aviv University
Rand Arafeh: Weizmann Institute
Noam Auslander: University of Maryland
Matthew Davidson: Beatson Institute
Lynn McGarry: Beatson Institute
Daniel James: Beatson Institute
Arnaud Amzallag: Massachusetts General Hospital Center for Cancer Research
Seung Gu Park: University of Maryland
Kuoyuan Cheng: University of Maryland
Welles Robinson: University of Maryland
Dikla Atias: Sheba Medical Center Tel Hashomer
Chani Stossel: Sheba Medical Center Tel Hashomer
Ella Buzhor: Sheba Medical Center Tel Hashomer
Gidi Stein: Tel Aviv University
Joshua J. Waterfall: National Institutes of Health
Paul S. Meltzer: National Institutes of Health
Talia Golan: Sheba Medical Center Tel Hashomer
Sridhar Hannenhalli: University of Maryland
Eyal Gottlieb: Beatson Institute
Cyril H. Benes: Massachusetts General Hospital Center for Cancer Research
Yardena Samuels: Weizmann Institute
Emma Shanks: Beatson Institute
Eytan Ruppin: University of Maryland

Nature Communications, 2018, vol. 9, issue 1, 1-12

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

Date: 2018
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DOI: 10.1038/s41467-018-04647-1

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