Abstract
Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and create vulnerabilities for potential therapeutic exploitation. To identify genotype‐dependent vulnerabilities, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework to integrate CRISPR/Cas9 screens originating from different libraries building on approaches pioneered for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cells combining functional data with information on genetic variants to explore more than 2.1 million gene‐background relationships. In addition to known dependencies, we identified new genotype‐specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities identified GANAB and PRKCSH as new positive regulators of Wnt/β‐catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data are included.
Highlights
Genes rarely function in isolation to affect phenotypes at the cellular or organismal level
Integrating CRISPR/Cas9 phenotypes from different studies In order to systematically predict interactions between genes knocked out by CRISPR/Cas9 and genes functionally impaired by mutations in cancer cells, we reanalyzed a set of 85 CRISPR/Cas9 viability screens in 60 cell lines (Figure 1A, Supplementary Table 3)
Since the cell lines screened with this library are derived from various different tissues and cancer types and a common resistance to cyclindependent kinase 7 (CDK7) knockout seems unlikely
Summary
Genes rarely function in isolation to affect phenotypes at the cellular or organismal level. A loss of genetic buffering can result in the emergence of diseases such as cancer (Hartman et al 2001; Hartwell et al 1997). A buffering (or alleviating) interaction is observed when the double mutant’s measured phenotype is weaker than expected. Methods of pairwise gene perturbation were later extended using combinatorial RNA interference (RNAi) to map genetic interactions in cultured metazoan cells (Horn et al 2011; Laufer et al 2013; Fischer et al 2015; Byrne et al 2007; Snijder et al 2013; Srivas et al 2016). Screening of all pairwise gene combinations scales poorly with increasing genome size and novel approaches are necessary to facilitate the generation of large genetic interaction maps of complex organisms while minimizing cost and experimental effort
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