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      CDK/cyclin dependencies define extreme cancer cell-cycle heterogeneity and collateral vulnerabilities

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          SUMMARY

          Progression through G1/S phase of the cell cycle is coordinated by cyclin-dependent kinase (CDK) activities. Here, we find that the requirement for different CDK activities and cyclins in driving cancer cell cycles is highly heterogeneous. The differential gene requirements associate with tumor origin and genetic alterations. We define multiple mechanisms for G1/S progression in RB-proficient models, which are CDK4/6 independent and elicit resistance to FDA-approved inhibitors. Conversely, RB-deficient models are intrinsically CDK4/6 independent, but exhibit differential requirements for cyclin E. These dependencies for CDK and cyclins associate with gene expression programs that denote intrinsically different cell-cycle states. Mining therapeutic sensitivities shows that there are reciprocal vulnerabilities associated with RB1 or CCND1 expression versus CCNE1 or CDKN2A. Together, these findings illustrate the complex nature of cancer cell cycles and the relevance for precision therapeutic intervention.

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          In brief

          Knudsen et al. find that there is extensive heterogeneity in the requirement for CDK and cyclins across cancer models. Multiple biochemically distinct mechanisms drive cell division. Divergent cell-cycle states harbor distinct genetic and pharmacological vulnerabilities, suggesting that cell-cycle diversity could be exploited for a precision approach to cancer therapy.

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          Most cited references74

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          Complex heatmaps reveal patterns and correlations in multidimensional genomic data.

          Parallel heatmaps with carefully designed annotation graphics are powerful for efficient visualization of patterns and relationships among high dimensional genomic data. Here we present the ComplexHeatmap package that provides rich functionalities for customizing heatmaps, arranging multiple parallel heatmaps and including user-defined annotation graphics. We demonstrate the power of ComplexHeatmap to easily reveal patterns and correlations among multiple sources of information with four real-world datasets.
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            Defining a Cancer Dependency Map

            Most human epithelial tumors harbor numerous alterations, making it difficult to predict which genes are required for tumor survival. To systematically identify cancer dependencies, we analyzed 501 genome-scale loss-of-function screens performed in diverse human cancer cell lines. We developed DEMETER, an analytical framework that segregates on-from off-target effects of RNAi. 769 genes were differentially required in subsets of these cell lines at a threshold of six standard deviations from the mean. We found predictive models for 426 dependencies (55%) by nonlinear regression modeling considering 66,646 molecular features. Many dependencies fall into a limited number of classes, and unexpectedly, in 82% of models, the top biomarkers were expression-based. We demonstrated the basis behind one such predictive model linking hypermethylation of the UBB ubiquitin gene to a dependency on UBC. Together, these observations provide a foundation for a cancer dependency map that facilitates the prioritization of therapeutic targets.
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              • Record: found
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              Is Open Access

              The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions

              Abstract The BioGRID (Biological General Repository for Interaction Datasets, thebiogrid.org) is an open‐access database resource that houses manually curated protein and genetic interactions from multiple species including yeast, worm, fly, mouse, and human. The ~1.93 million curated interactions in BioGRID can be used to build complex networks to facilitate biomedical discoveries, particularly as related to human health and disease. All BioGRID content is curated from primary experimental evidence in the biomedical literature, and includes both focused low‐throughput studies and large high‐throughput datasets. BioGRID also captures protein post‐translational modifications and protein or gene interactions with bioactive small molecules including many known drugs. A built‐in network visualization tool combines all annotations and allows users to generate network graphs of protein, genetic and chemical interactions. In addition to general curation across species, BioGRID undertakes themed curation projects in specific aspects of cellular regulation, for example the ubiquitin‐proteasome system, as well as specific disease areas, such as for the SARS‐CoV‐2 virus that causes COVID‐19 severe acute respiratory syndrome. A recent extension of BioGRID, named the Open Repository of CRISPR Screens (ORCS, orcs.thebiogrid.org), captures single mutant phenotypes and genetic interactions from published high throughput genome‐wide CRISPR/Cas9‐based genetic screens. BioGRID‐ORCS contains datasets for over 1,042 CRISPR screens carried out to date in human, mouse and fly cell lines. The biomedical research community can freely access all BioGRID data through the web interface, standardized file downloads, or via model organism databases and partner meta‐databases.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                8 April 2022
                01 March 2022
                21 April 2022
                : 38
                : 9
                : 110448
                Affiliations
                [1 ]Department of Molecular and Cellular Biology, Roswell Park Cancer Center, Buffalo, NY 14203, USA
                [2 ]Department of Cancer Genetics and Genomics, Roswell Park Cancer Center, Buffalo, NY 14203, USA
                [3 ]Lunenfeld Tanenbaum Research Institute, Toronto, ON M5G 1X5, Canada
                [4 ]Department of Chemistry and Biochemistry, University of California, Santa Cruz, Santa Cruz, CA 95064, USA
                [5 ]Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
                [6 ]Department of Pathology, Roswell Park Cancer Center, Buffalo, NY 14203, USA
                [7 ]Lead contact
                Author notes

                AUTHOR CONTRIBUTIONS

                Conceptualization, E.S.K., V.K., S.M.R., and A.K.W. Methodology, E.S.K., V.K., R.N., J.D.P., H.R., J.W., K.E., R.B., D.S., S.M.R., A.L.W., and A.K.W. Formal analysis, R.N., V.K., P.V., J.W., and K.E. Investigation, V.K., R.N., P.V., H.R., and J.W. Resources/funding, E.S.K., J.D.P., H.R., R.B., S.M.R., A.L.W., and A.K.W. Writing – original draft, review, & editing, E.S.K., V.K., R.N., J.D.P., H.R., J.W., K.E., R.B., D.S., S.M.R., A.L.W., and A.K.W. Visualization, E.S.K., V.K., R.N., P.V., H.R., J.W., and A.K.W. Supervision, E.S.K., R.B., D.S., S.M.R., A.L.W., and A.K.W.

                Article
                NIHMS1785220
                10.1016/j.celrep.2022.110448
                9022184
                35235778
                a5fdc0db-fb15-4a24-a1af-55b2ce23b69e

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Cell biology
                Cell biology

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