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Defining a Cancer Dependency Map.

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.

journal_name

Cell

journal_title

Cell

authors

Tsherniak A,Vazquez F,Montgomery PG,Weir BA,Kryukov G,Cowley GS,Gill S,Harrington WF,Pantel S,Krill-Burger JM,Meyers RM,Ali L,Goodale A,Lee Y,Jiang G,Hsiao J,Gerath WFJ,Howell S,Merkel E,Ghandi M,Garraway LA,Roo

doi

10.1016/j.cell.2017.06.010

subject

Has Abstract

pub_date

2017-07-27 00:00:00

pages

564-576.e16

issue

3

eissn

0092-8674

issn

1097-4172

pii

S0092-8674(17)30651-7

journal_volume

170

pub_type

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