Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells

Abstract

CERES is a new computational method to estimate gene-dependency levels from CRISPR–Cas9 essentiality screens while accounting for copy number effects and variable sgRNA activity. Applying CERES to new genome-scale CRISPR–Cas9 essentiality screen data from 342 cancer cell lines and other published data sets shows that CERES decreases false-positive results and provides consistent estimates of sgRNA activity. The CRISPR–Cas9 system has revolutionized gene editing both at single genes and in multiplexed loss-of-function screens, thus enabling precise genome-scale identification of genes essential for proliferation and survival of cancer cells1,2. However, previous studies have reported that a gene-independent antiproliferative effect of Cas9-mediated DNA cleavage confounds such measurement of genetic dependency, thereby leading to false-positive results in copy number–amplified regions3,4. We developed CERES, a computational method to estimate gene-dependency levels from CRISPR–Cas9 essentiality screens while accounting for the copy number–specific effect. In our efforts to define a cancer dependency map, we performed genome-scale CRISPR–Cas9 essentiality screens across 342 cancer cell lines and applied CERES to this data set. We found that CERES decreased false-positive results and estimated sgRNA activity for both this data set and previously published screens performed with different sgRNA libraries. We further demonstrate the utility of this collection of screens, after CERES correction, for identifying cancer-type-specific vulnerabilities.