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.
Competing Interest Statement
MJG, and FI receive funding from Open Targets, a public-private initiative involving academia and industry. MJG receives funding from AstraZeneca and performs consultancy for Sanofi. FI performs consultancy for the joint CRUK - AstraZeneca Functional Genomics Centre. AT is a consultant `for Tango Therapeutics and Cedilla Therapeutics. JMD, JM and AT receive funding from the Cancer Dependency Map Consortium, but no consortium member was involved in or influenced this study. All the other authors declare no competing interests.
Footnotes
We have substantially restructured the Results section of our manuscript, reporting outcomes from a number of use case scenarios and highlighting pros and cons of each benchmarked computational method. This provides a results-driven decision making process that underpins our presentation of two distinct final integrated datasets of cancer dependencies, and demonstrates that this will be advantageous for the computational biology community. Additionally, we have carefully addressed all the other reviewers' points and also included a final novel analysis estimating the minimal required size of overlapping cell lines for integrating future CRISPR datasets.
