Defining a Cancer Dependency Map

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


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.

MeSH terms

  • Cell Line, Tumor
  • Humans
  • Neoplasms / genetics*
  • Neoplasms / pathology*
  • RNA Interference
  • Software
  • Ubiquitin / genetics


  • Ubiquitin