You are here

Cell DOI:10.1016/j.cell.2017.06.010

Defining a Cancer Dependency Map.

Publication TypeJournal Article
Year of Publication2017
AuthorsTsherniak, 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, Root, DE, Golub, TR, Boehm, JS, Hahn, WC
JournalCell
Volume170
Issue3
Pages564-576.e16
Date Published2017 Jul 27
ISSN1097-4172
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.

DOI10.1016/j.cell.2017.06.010
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/28753430?dopt=Abstract

Alternate JournalCell
PubMed ID28753430