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.
Synopsis
A novel computational framework integrates diverse CRISPR/Cas9 screens to map genetic interactions in human cells. This application of MINGLE demonstrates how concepts developed for model organisms can be transferred to create comprehensive maps of genetic interactions in cancer cells.
MINGLE combines large sets of CRISPR/Cas9 screens in genetically diverse cell lines across multiple sgRNA libraries to increase statistical power for detection and decrease bias for selection of genetic interactions.
Significant genetic interactions can be inferred systematically from the combination of integrated genetic variants and differential phenotypes of specific CRISPR/Cas9 induced mutant cells.
We applied the computational framework to create a genetic interaction network and predicted novel functional components of Wnt signaling.
Benchmarking against known protein complex data and spatial enrichment analysis revealed clustering of functionally related genes by similarity of their inferred genetic interaction profiles.
MINGLE can be applied iteratively to a growing dataset to systematically extend and refine a global map of gene function in cancer cells.
Graphical Abstract
A novel computational framework integrates diverse CRISPR/Cas9 screens to map genetic interactions in human cells. This application of MINGLE demonstrates how concepts developed for model organisms can be transferred to create comprehensive maps of genetic interactions in cancer cells.