|Publication Type||Journal Article|
|Year of Publication||2017|
|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, Root, DE, Golub, TR, Boehm, JS, Hahn, WC|
|Date Published||2017 Jul 27|
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.