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Harnessing synthetic lethality to predict the response to cancer treatment

Author

Listed:
  • Joo Sang Lee

    (University of Maryland
    National Institute of Health)

  • Avinash Das

    (University of Maryland)

  • Livnat Jerby-Arnon

    (Tel Aviv University)

  • Rand Arafeh

    (Weizmann Institute)

  • Noam Auslander

    (University of Maryland
    National Institute of Health)

  • Matthew Davidson

    (Beatson Institute)

  • Lynn McGarry

    (Beatson Institute)

  • Daniel James

    (Beatson Institute)

  • Arnaud Amzallag

    (Massachusetts General Hospital Center for Cancer Research
    Harvard Medical School
    PatientsLikeMe)

  • Seung Gu Park

    (University of Maryland)

  • Kuoyuan Cheng

    (University of Maryland
    National Institute of Health)

  • Welles Robinson

    (University of Maryland
    National Institute of Health)

  • 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
    Tel Aviv University)

  • Sridhar Hannenhalli

    (University of Maryland)

  • Eyal Gottlieb

    (Beatson Institute)

  • Cyril H. Benes

    (Massachusetts General Hospital Center for Cancer Research
    Harvard Medical School)

  • Yardena Samuels

    (Weizmann Institute)

  • Emma Shanks

    (Beatson Institute)

  • Eytan Ruppin

    (University of Maryland
    National Institute of Health
    Tel Aviv University
    Tel Aviv University)

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.

Suggested Citation

  • 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 & Di, 2018. "Harnessing synthetic lethality to predict the response to cancer treatment," Nature Communications, Nature, vol. 9(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04647-1
    DOI: 10.1038/s41467-018-04647-1
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    Cited by:

    1. Sumana Srivatsa & Hesam Montazeri & Gaia Bianco & Mairene Coto-Llerena & Mattia Marinucci & Charlotte K. Y. Ng & Salvatore Piscuoglio & Niko Beerenwinkel, 2022. "Discovery of synthetic lethal interactions from large-scale pan-cancer perturbation screens," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Nishanth Ulhas Nair & Patricia Greninger & Xiaohu Zhang & Adam A. Friedman & Arnaud Amzallag & Eliane Cortez & Avinash Das Sahu & Joo Sang Lee & Anahita Dastur & Regina K. Egan & Ellen Murchie & Miche, 2023. "A landscape of response to drug combinations in non-small cell lung cancer," Nature Communications, Nature, vol. 14(1), pages 1-19, December.

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