Integrated cross-study datasets of genetic dependencies in cancer

Abstract

CRISPR-Cas9 viability screens are being 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 that might be exploited therapeutically. Here, we integrated the two largest independent CRISPR-Cas9 screens performed to date (at Broad and Sanger institutes), by assessing and selecting methods for correcting technology-specific biases and batch effects arising from differences in the underlying experimental protocols. Our integrated datasets recapitulate findings from the individual ones, provide larger statistical power allowing novel cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. Finally, we provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets and assembling large cross-study cancer dependency maps.