TY - JOUR
T1 - Harnessing synthetic lethality to predict the response to cancer treatment
AU - Lee, Joo Sang
AU - Das, Avinash
AU - Jerby-Arnon, Livnat
AU - Arafeh, Rand
AU - Auslander, Noam
AU - Davidson, Matthew
AU - McGarry, Lynn
AU - James, Daniel
AU - Amzallag, Arnaud
AU - Park, Seung Gu
AU - Cheng, Kuoyuan
AU - Robinson, Welles
AU - Atias, Dikla
AU - Stossel, Chani
AU - Buzhor, Ella
AU - Stein, Gidi
AU - Waterfall, Joshua J.
AU - Meltzer, Paul S.
AU - Golan, Talia
AU - Hannenhalli, Sridhar
AU - Gottlieb, Eyal
AU - Benes, Cyril H.
AU - Samuels, Yardena
AU - Shanks, Emma
AU - Ruppin, Eytan
N1 - Funding Information:
The authors thank Ze’ev Ronai, Max Leiserson, Allon Wagner for their helpful comments. J.S.L., N.A., W.R. and E.R. are partially supported by a grant from the Israeli Science Foundation (ISF) 41/11 and R33-CA225291-01. N.A. and W.R. are supported by the NCI-UMD partnership for integrative cancer research. Y.S. is supported by the ISF 696/17 and R.A. is supported by Clore foundation. S.H. is supported by NSF 1564785, and C.H.B. is supported by Welcome Trust award 102696. D.A., C.S., E.B. and T.G. are partially supported by Canadian friends Alex U. Soyka Foundation. M.D., L.M., D.J., and E.S. is supported by Cancer Research UK. L.J.-A. is supported by Cancer Research Institute Irvington Fellowship and Eric and Wendy Schmidt Postdoctoral program.
Publisher Copyright:
© 2018 The Author(s).
PY - 2018/12/1
Y1 - 2018/12/1
N2 - 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.
AB - 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.
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U2 - 10.1038/s41467-018-04647-1
DO - 10.1038/s41467-018-04647-1
M3 - Article
C2 - 29959327
AN - SCOPUS:85049349747
SN - 2041-1723
VL - 9
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 2546
ER -