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

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

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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