Cell autonomous cancer dependencies are now routinely identified using CRISPR loss-of-function viability screens. However, a bias exists that makes it difficult to assess the true essentiality of genes located in amplicons, since the entire amplified region can exhibit lethal scores. These false-positive hits can either be discarded from further analysis, which in cancer models can represent a significant number of hits, or methods can be developed to rescue the true-positives within amplified regions. We propose two methods to rescue true positive hits in amplified regions by correcting for this copy number artefact. The Local Drop Out (LDO) method uses the relative lethality scores within genomic regions to assess true essentiality and does not require additional orthogonal data (e.g. copy number value). LDO is meant to be used in screens covering a dense region of the genome (e.g. a whole chromosome or the whole genome). The General Additive Model (GAM) method models the screening data as a function of the known copy number values and removes the systematic effect from the measured lethality. GAM does not require the same density as LDO, but does require prior knowledge of the copy number values. Both methods have been developed with single sample experiments in mind so that the correction can be applied even in smaller screens. Here we demonstrate the efficacy of both methods at removing the copy number effect and rescuing hits from some of the amplified regions. We estimate a 70–80% decrease of false positive hits with either method in regions of high copy number compared to no correction.
Cancer vulnerabilities have been identified by systematically disrupting individual genes in cancer cells and observing the resulting effect on cell proliferation. In recent years, a new gene editing technique called CRISPR has made it easier and cheaper to disrupt genes by precisely and completely suppressing the function of individual genes by cutting through its DNA. However, an artefact of the approach yields false positives: using CRISPR to target genes in regions of the genome which are abnormally repeated, called copy number alterations (CNA), has been shown to kill the investigated cells irrespective of the true essentiality of the amplified genes. This artefact is a particular issue when studying tumours, since CNAs are common in cancer. Additionally cancer-specific genes are known to selectively drive amplification, making the ability to assess the essentiality of genes in these regions even more important. Here we describe and provide the code to computationally correct for this artefact and recover the true essentiality of CNA genes.