Systematic discovery of mutation-specific synthetic lethals by mining pan-cancer human primary tumor data

Subarna Sinha, Daniel Thomas, Steven Chan, Yang Gao, Diede Brunen, Damoun Torabi, Andreas Reinisch, David Hernandez, Andy Chan, Erinn B. Rankin, Rene Bernards, Ravindra Majeti () and David L. Dill ()
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Subarna Sinha: Stanford University
Daniel Thomas: Cancer Institute, and Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine
Steven Chan: Princess Margaret Cancer Centre, University Health Network
Yang Gao: University of California at Berkeley
Diede Brunen: The Netherlands Cancer Institute
Damoun Torabi: Cancer Institute, and Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine
Andreas Reinisch: Cancer Institute, and Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine
David Hernandez: Cancer Institute, and Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine
Andy Chan: Stanford University School of Medicine
Erinn B. Rankin: Stanford University School of Medicine
Rene Bernards: The Netherlands Cancer Institute
Ravindra Majeti: Cancer Institute, and Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine
David L. Dill: Stanford University

Nature Communications, 2017, vol. 8, issue 1, 1-13

Abstract: Abstract Two genes are synthetically lethal (SL) when defects in both are lethal to a cell but a single defect is non-lethal. SL partners of cancer mutations are of great interest as pharmacological targets; however, identifying them by cell line-based methods is challenging. Here we develop MiSL (Mining Synthetic Lethals), an algorithm that mines pan-cancer human primary tumour data to identify mutation-specific SL partners for specific cancers. We apply MiSL to 12 different cancers and predict 145,891 SL partners for 3,120 mutations, including known mutation-specific SL partners. Comparisons with functional screens show that MiSL predictions are enriched for SLs in multiple cancers. We extensively validate a SL interaction identified by MiSL between the IDH1 mutation and ACACA in leukaemia using gene targeting and patient-derived xenografts. Furthermore, we apply MiSL to pinpoint genetic biomarkers for drug sensitivity. These results demonstrate that MiSL can accelerate precision oncology by identifying mutation-specific targets and biomarkers.

Date: 2017
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DOI: 10.1038/ncomms15580

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