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      SMARCB1 loss activates patient-specific distal oncogenic enhancers in malignant rhabdoid tumors

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          Abstract

          Malignant rhabdoid tumor (MRT) is a highly malignant and often lethal childhood cancer. MRTs are genetically defined by bi-allelic inactivating mutations in SMARCB1, a member of the BRG1/BRM-associated factors (BAF) chromatin remodeling complex. Mutations in BAF complex members are common in human cancer, yet their contribution to tumorigenesis remains in many cases poorly understood. Here, we study derailed regulatory landscapes as a consequence of SMARCB1 loss in the context of MRT. Our multi-omics approach on patient-derived MRT organoids reveals a dramatic reshaping of the regulatory landscape upon SMARCB1 reconstitution. Chromosome conformation capture experiments subsequently reveal patient-specific looping of distal enhancer regions with the promoter of the MYC oncogene. This intertumoral heterogeneity in MYC enhancer utilization is also present in patient MRT tissues as shown by combined single-cell RNA-seq and ATAC-seq. We show that loss of SMARCB1 activates patient-specific epigenetic reprogramming underlying MRT tumorigenesis.

          Abstract

          The regulatory landscape of malignant rhabdoid tumor (MRT) due to SMARCB1 loss remains to be explored. Here, the authors perform multi-omics analysis using patient-derived MRT organoids and characterise the epigenetic reprogramming events underlying SMARCB1 loss.

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          Most cited references75

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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                Author and article information

                Contributors
                e.d.wit@nki.nl
                J.Drost@prinsesmaximacentrum.nl
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                1 December 2023
                1 December 2023
                2023
                : 14
                : 7762
                Affiliations
                [1 ]Division of Gene Regulation, Netherlands Cancer Institute, ( https://ror.org/03xqtf034) Amsterdam, the Netherlands
                [2 ]Department of Hematology, Erasmus Medical Center (MC) Cancer Institute, ( https://ror.org/018906e22) Rotterdam, the Netherlands
                [3 ]GRID grid.487647.e, Princess Máxima Center for Pediatric Oncology, ; Utrecht, the Netherlands
                [4 ]Oncode Institute, ( https://ror.org/01n92vv28) Utrecht, the Netherlands
                [5 ]GRID grid.419927.0, ISNI 0000 0000 9471 3191, Hubrecht Institute-KNAW, ; Utrecht, the Netherlands
                [6 ]University Medical Center Utrecht, ( https://ror.org/0575yy874) Utrecht, the Netherlands
                Author information
                http://orcid.org/0000-0002-3151-638X
                http://orcid.org/0000-0002-5587-0439
                http://orcid.org/0000-0002-9442-3551
                http://orcid.org/0000-0003-2883-1415
                http://orcid.org/0000-0002-2941-6179
                Article
                43498
                10.1038/s41467-023-43498-3
                10692191
                38040699
                4c210ca4-d3e1-4fde-a55b-a477711ce730
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 22 January 2023
                : 10 November 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: 850571
                Award ID: 865459
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100006244, Stichting Kinderen Kankervrij (KiKa);
                Award ID: 338
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100003246, Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research);
                Award ID: 203.003
                Award ID: 181.014
                Award ID: 016.16.316
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation);
                Award ID: P2BSP3-174991
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000854, Human Frontier Science Program (HFSP);
                Award ID: LT000209/2018-L
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100010665, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 Marie Skłodowska-Curie Actions (H2020 Excellent Science - Marie Skłodowska-Curie Actions);
                Award ID: 798573
                Award Recipient :
                Categories
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                © Springer Nature Limited 2023

                Uncategorized
                cancer epigenetics,epigenomics,epigenetics
                Uncategorized
                cancer epigenetics, epigenomics, epigenetics

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