Defining a Cancer Dependency Map

By Aviad Tsherniak, F. Vazquez, Phillip G Montgomery, B. Weir, G. Kryukov, G. Cowley, S. Gill, W. Harrington, S. Pantel, J. Krill-Burger and 15 more. Cell, 2017

2,742 citations recorded by the source corpus.

SUMMARY 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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/20dff6e75555ec21fe95d1d6844df5bb1680d214

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Citations in the corpus

Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma (2020, background): “, 2016) (Figure 4G) and annotated outliers for the degree to which short hairpin RNA (shRNA)- or CRISPR-mediated depletion reduced survival and proliferation in lung cancer cell lines (Barretina et al., 2012; Tsherniak et al., 2017).”
How Machine Learning will Transform Biomedicine (2020, neutral): “For example, the Cancer Dependency Map (Tsherniak et al., 2017) has collected multimodal molecular profiles, drug response, and genetic viability data on more than 1,000 cancer cell lines.”
Proteolysis Targeting Chimeras as Therapeutics and Tools for Biological Discovery (2020, neutral): “, phenotypic genome-wide CRISPR screens or RNAi) or for cell type-specific dependencies during target selection to gather information about the target protein (Tsherniak et al., 2017).”
Computational correction of copy-number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells (2017, positive): “To confirm this finding using a complementary assay, we analyzed this set of genes in a dataset derived from genome-scale RNAi screens across 501 cancer cell lines 20 .”
Benchmark and integration of resources for the estimation of human transcription factor activities (2018, background): “2) (Tsherniak et al. 2017) and ATARiS scores from the project DRIVE (McDonald et al.”
Open Community Challenge Reveals Molecular Network Modules with Key Roles in Diseases (2018, background): “from oncology (Tsherniak et al., 2017) to cell differentiation (Cahan et al.”