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Cell. 2017 Jul 27;170(3):564-576.e16. doi: 10.1016/j.cell.2017.06.010.

Defining a Cancer Dependency Map.

Author information

1
Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA, USA.
2
Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA, USA; Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, USA.
3
Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA, USA; Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Howard Hughes Medical Institute, 4000 Jones Bridge Road, Chevy Chase, MD, USA.
4
Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA, USA; Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA, USA; Howard Hughes Medical Institute, 4000 Jones Bridge Road, Chevy Chase, MD, USA.
5
Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA, USA; Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA, USA. Electronic address: william_hahn@dfci.harvard.edu.

Abstract

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.

KEYWORDS:

RNAi screens; cancer dependencies; cancer targets; genetic vulnerabilities; genomic biomarkers; precision medicine; predictive modeling; seed effects; shRNA

PMID:
28753430
PMCID:
PMC5667678
DOI:
10.1016/j.cell.2017.06.010
[Indexed for MEDLINE]
Free PMC Article

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