Integrated cross-study datasets of genetic dependencies in cancer

Clare Pacini, Joshua M. Dempster, Isabella Boyle, Emanuel Gonçalves, Hanna Najgebauer, Emre Karakoc, Dieudonne Meer, Andrew Barthorpe, Howard Lightfoot, Patricia Jaaks, James M. McFarland, Mathew J. Garnett, Aviad Tsherniak and Francesco Iorio ()
Additional contact information
Clare Pacini: Wellcome Genome Campus, Hinxton
Joshua M. Dempster: Broad Institute of MIT and Harvard
Isabella Boyle: Broad Institute of MIT and Harvard
Emanuel Gonçalves: Wellcome Genome Campus, Hinxton
Hanna Najgebauer: Wellcome Genome Campus, Hinxton
Emre Karakoc: Wellcome Genome Campus, Hinxton
Dieudonne Meer: Wellcome Genome Campus, Hinxton
Andrew Barthorpe: Wellcome Genome Campus, Hinxton
Howard Lightfoot: Wellcome Genome Campus, Hinxton
Patricia Jaaks: Wellcome Genome Campus, Hinxton
James M. McFarland: Broad Institute of MIT and Harvard
Mathew J. Garnett: Wellcome Genome Campus, Hinxton
Aviad Tsherniak: Broad Institute of MIT and Harvard
Francesco Iorio: Wellcome Genome Campus, Hinxton

Nature Communications, 2021, vol. 12, issue 1, 1-14

Abstract: Abstract CRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated the two largest public independent CRISPR-Cas9 screens performed to date (at the Broad and Sanger institutes) by assessing, comparing, and selecting methods for correcting biases due to heterogeneous single-guide RNA efficiency, gene-independent responses to CRISPR-Cas9 targeting originated from copy number alterations, and experimental batch effects. Our integrated datasets recapitulate findings from the individual datasets, provide greater statistical power to cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. We provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets.

Date: 2021
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DOI: 10.1038/s41467-021-21898-7

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