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      shinyDepMap, a tool to identify targetable cancer genes and their functional connections from Cancer Dependency Map data

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          Abstract

          Individual cancers rely on distinct essential genes for their survival. The Cancer Dependency Map (DepMap) is an ongoing project to uncover these gene dependencies in hundreds of cancer cell lines. To make this drug discovery resource more accessible to the scientific community, we built an easy-to-use browser, shinyDepMap ( https://labsyspharm.shinyapps.io/depmap). shinyDepMap combines CRISPR and shRNA data to determine, for each gene, the growth reduction caused by knockout/knockdown and the selectivity of this effect across cell lines. The tool also clusters genes with similar dependencies, revealing functional relationships. shinyDepMap can be used to (1) predict the efficacy and selectivity of drugs targeting particular genes; (2) identify maximally sensitive cell lines for testing a drug; (3) target hop, that is, navigate from an undruggable protein with the desired selectivity profile, such as an activated oncogene, to more druggable targets with a similar profile; and (4) identify novel pathways driving cancer cell growth and survival.

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          Most cited references33

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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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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              An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation

              Gene set enrichment analysis is a widely used tool for analyzing gene expression data. However, current implementations are slow due to a large number of required samples for the analysis to have a good statistical power. In this paper we present a novel algorithm, that efficiently reuses one sample multiple times and thus speeds up the analysis. We show that it is possible to make hundreds of thousands permutations in a few minutes, which leads to very accurate p-values. This, in turn, allows applying standard FDR correction procedures, which are more accurate than the ones currently used. The method is implemented in a form of an R package and is freely available at \url{https://github.com/ctlab/fgsea}.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                08 February 2021
                2021
                : 10
                : e57116
                Affiliations
                [1]Laboratory of Systems Pharmacology and Department of Systems Biology, Harvard Medical School BostonUnited States
                Fox Chase Cancer Center United States
                The Wistar Institute United States
                Fox Chase Cancer Center United States
                Author information
                https://orcid.org/0000-0001-8540-9785
                https://orcid.org/0000-0001-6095-2466
                http://orcid.org/0000-0002-0811-637X
                http://orcid.org/0000-0001-7781-1897
                Article
                57116
                10.7554/eLife.57116
                7924953
                33554860
                3acad2d0-b0ca-43d5-aa43-848174c2e374
                © 2021, Shimada et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 20 March 2020
                : 06 February 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001691, Japan Society for the Promotion of Science;
                Award ID: H29-814
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: P50GM107618
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: R35GM131753
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000185, Defense Advanced Research Projects Agency;
                Award ID: W911NF-15-1-0544
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000054, National Cancer Institute;
                Award ID: U54-CA225088
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Tools and Resources
                Cancer Biology
                Computational and Systems Biology
                Custom metadata
                shinyDepMap helps users explore the essentiality, selectivity, and function of the genes across hundreds of cancer cell lines and identify cancer drug targets.

                Life sciences
                depmap,shinydepmap,precision medicine,essential genes,synthetic lethality,selectivity,human
                Life sciences
                depmap, shinydepmap, precision medicine, essential genes, synthetic lethality, selectivity, human

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