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      A First-Generation Pediatric Cancer Dependency Map

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          Abstract

          Exciting therapeutic targets are emerging from CRISPR-based screens of high mutational burden adult cancers. A key question, however, is whether functional genomic approaches will yield new targets in pediatric cancers, known for remarkably few mutations which often encode proteins considered challenging drug targets. To address this, we created a first-generation Pediatric Cancer Dependency Map representing 13 pediatric solid and brain tumor types. Eighty-two pediatric cancer cell lines were subjected to genome-scale CRISPR-Cas9 loss-of-function screening to identify genes required for cell survival. In contrast to the finding that pediatric cancers harbor fewer somatic mutations, we found a similar complexity of genetic dependencies in pediatric cancer cell lines compared to adult models. Findings from the Pediatric Cancer Dependency Map provide pre-clinical support for ongoing precision medicine clinical trials. The vulnerabilities seen in pediatric cancers were often distinct from adult, indicating that repurposing adult oncology drugs will be insufficient to address childhood cancers.

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          Most cited references74

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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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            The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

            Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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              Is Open Access

              The Ensembl Variant Effect Predictor

              The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.
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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat Genet
                Nature genetics
                1061-4036
                1546-1718
                10 March 2021
                22 March 2021
                April 2021
                22 September 2021
                : 53
                : 4
                : 529-538
                Affiliations
                [1 ]Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
                [2 ]Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA, USA
                [3 ]Broad Institute of MIT and Harvard, Cambridge, MA, USA
                [4 ]Harvard Medical School, Boston, MA, USA
                [5 ]Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
                [6 ]Department of Oncology, Comprehensive Cancer Center, St. Jude Children’s Research Hospital, Memphis, TN, USA
                Author notes
                [#]

                Current address: Keck School of Medicine of the University of Southern California, Los Angeles, CA, USA

                [*]

                Current address: St. Jude Children’s Research Hospital, Memphis, TN, USA

                [@]

                Current address: Department of Pediatrics, Emory University and Department of Hematology and Oncology, Children’s Healthcare of Atlanta, Atlanta, GA, USA

                [%]

                Current address: University of Maryland, College Park, MD, USA

                Author contributions

                JSB, WCH, CWMR, AT, TRG, FV and KS conceptualized the study. NVD, GK, LMG, CFM, ADD, ALH, TPH, PB, ACW, JMD, JMKB, BRP, JMM, AT, TRG, FV and KS devised the study methodology. NVD, GK, ACW, JMD and JMKB performed computational analyses. LMG and CSW validated the MCL1 inhibitor. IF managed the project. PM, NJ, AT and PM created the companion website for the project. NVD, TRG, FV and KS wrote the original draft. GK, LMG, CFM, ADD, ALH, TPH, PB, CSW, IF, ACW, JMD, JMKB, BRP, PM, AT, PM, JSB, WCH, CWMR, JMM and AT reviewed and edited the manuscript. CWMR, AT, TRG, FV and KS supervised the study. JSB, WCH, CWMR, TRG and KS acquired the funding.

                [$ ] Corresponding authors: Kimberly Stegmaier Kimberly_Stegmaier@ 123456dfci.harvard.edu , Francisca Vazquez vazquez@ 123456broadinstitute.org
                Article
                NIHMS1674737
                10.1038/s41588-021-00819-w
                8049517
                33753930
                24c63c99-b5d8-4271-b3f4-2a1bea96e54c

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                Genetics
                Genetics

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