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

  • Nat Commun. 2017 May 31;8:15580. doi: 10.1038/ncomms15580.
Subarna Sinha  1 ,  Daniel Thomas  2 ,  Steven Chan  3 ,  Yang Gao  4 ,  Diede Brunen  5 ,  Damoun Torabi  2 ,  Andreas Reinisch  2 ,  David Hernandez  2 ,  Andy Chan  6 ,  Erinn B Rankin  6  7 ,  Rene Bernards  5 ,  Ravindra Majeti  2 ,  David L Dill  1
Affiliations
  • 1. Department of Computer Science, Stanford University, Stanford, California 94305, USA.
  • 2. Department of Medicine, Division of Hematology, Cancer Institute, and Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, California 94305, USA.
  • 3. Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada M5G 2M9.
  • 4. Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, California 94720, USA.
  • 5. Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Plesmanlaan 121, Amsterdam 1066 CX, The Netherlands.
  • 6. Division of Radiation and Cancer Biology, Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California 94305, USA.
  • 7. Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, California 94305, USA.
PMID: 28561042 DOI: 10.1038/ncomms15580
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.

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