Integrated cross-study datasets of genetic dependencies in cancer

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. The integration of independent pan-cancer CRISPR-Cas9 datasets allows better representation of genomic heterogeneity across different cancer types. Here, the authors propose a strategy for the integration of two large CRISPR-Cas9 screens and report increased coverage of molecular diversity and genetic dependencies.

Automated summary

This study integrates two large CRISPR-Cas9 screens to correct biases, provide greater statistical power, identify additional biomarkers, and improve the detection of common essential genes, enhancing cancer-specific analyses.