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      Toward an integrated map of genetic interactions in cancer cells

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          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.

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          Most cited references 69

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          Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.

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            The genetic landscape of a cell.

            A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for approximately 75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.
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              Epistasis--the essential role of gene interactions in the structure and evolution of genetic systems.

              Epistasis, or interactions between genes, has long been recognized as fundamentally important to understanding the structure and function of genetic pathways and the evolutionary dynamics of complex genetic systems. With the advent of high-throughput functional genomics and the emergence of systems approaches to biology, as well as a new-found ability to pursue the genetic basis of evolution down to specific molecular changes, there is a renewed appreciation both for the importance of studying gene interactions and for addressing these questions in a unified, quantitative manner.

                Author and article information

                Mol Syst Biol
                Mol. Syst. Biol
                Molecular Systems Biology
                John Wiley and Sons Inc. (Hoboken )
                21 February 2018
                February 2018
                : 14
                : 2 ( doiID: 10.1002/msb.v14.2 )
                [ 1 ] Division of Signaling and Functional Genomics German Cancer Research Center (DKFZ) Heidelberg Germany
                [ 2 ] Department of Cell and Molecular Biology Medical Faculty Mannheim Heidelberg University Heidelberg Germany
                [ 3 ] Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany
                Author notes
                [* ]Corresponding author. Tel: +49 6221 421950; Fax: +49 6221 421959; E‐mail: m.boutros@ 123456dkfz.de
                © 2018 The Authors. Published under the terms of the CC BY 4.0 license

                This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 9, Tables: 0, Pages: 17, Words: 15673
                Funded by: H2020 | H2020 Priority Excellent Science | H2020 European Research Council (ERC)
                Funded by: BMBF‐funded Heidelberg Center for Human Bioinformatics (HD‐HuB) within the German Network for Bioinformatics Infrastructure (de.NBI)
                Award ID: 031A537A
                Custom metadata
                February 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:version= mode:remove_FC converted:21.02.2018


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