Defining a Cancer Dependency Map

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From: Cell(Vol. 170, Issue 3)
Publisher: Elsevier B.V.
Document Type: Report
Length: 415 words

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To access, purchase, authenticate, or subscribe to the full-text of this article, please visit this link: Byline: Aviad Tsherniak (1,6), Francisca Vazquez (1,2,6), Phil G. Montgomery (1), Barbara A. Weir (1,2), Gregory Kryukov (1,2), Glenn S. Cowley (1), Stanley Gill (1,2), William F. Harrington (1), Sasha Pantel (1), John M. Krill-Burger (1), Robin M. Meyers (1), Levi Ali (1), Amy Goodale (1), Yenarae Lee (1), Guozhi Jiang (1), Jessica Hsiao (1), William F.J. Gerath (1), Sara Howell (1), Erin Merkel (1), Mahmoud Ghandi (1), Levi A. Garraway (1,2,3,4,5), David E. Root (1,7), Todd R. Golub (1,2,4,5,7), Jesse S. Boehm (1,7), William C. Hahn [] (1,2,3,4,7,8,*) Keywords cancer dependencies; cancer targets; genetic vulnerabilities; precision medicine; predictive modeling; shRNA; seed effects; RNAi screens; genomic biomarkers Highlights * The DEMETER computational model segregates on- from off-target effects of RNAi * 769 strong differential dependencies were identified in 501 cancer cell lines * Predictive models for 426 dependencies were found using 66,646 molecular features * This cancer dependency map facilitates the prioritization of therapeutic targets Summary Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on- from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six SDs from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets. Author Affiliation: (1) Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA, USA (2) Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, USA (3) Department of Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, USA (4) Harvard Medical School, 25 Shattuck Street, Boston, MA, USA (5) Howard Hughes Medical Institute, 4000 Jones Bridge Road, Chevy Chase, MD, USA * Corresponding author Article History: Received 12 January 2017; Revised 9 April 2017; Accepted 7 June 2017 (miscellaneous) Published: July 27, 2017 (footnote)6 These authors contributed equally (footnote)7 Senior author (footnote)8 Lead Contact

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Gale Document Number: GALE|A521933553