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 standard deviations 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.

Look up a gene
For each gene, we have estimated the differential dependency using DEMETER and searched for biomarkers which predict that dependency. Selecting a gene will present a report of how differentially dependency profile of that gene across cell lines, other genes with similar dependency profiles, biomarkers and information about the shRNAs the dependency profile was derived from.
What is this scatter plot?
For each differential dependency with a significant predictive model, the predictive power of the best model (y-axis) and its MDP class (color) along with the strength of the dependency in the most dependent cell line (x-axis).
What is a MDP?
A MDP is a Marker-Dependency Pair, with statistically significant accuracy (FDR<0.05; permutation test).
How to see the dependencies of a gene?
In the plot, each point represents a gene dependency which can be predicted with a statistically significant out-of-bag R^2. Clicking on a gene will take to you see the details of that dependency.