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      Integrated profiling of human pancreatic cancer organoids reveals chromatin accessibility features associated with drug sensitivity

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          Abstract

          Chromatin accessibility plays an essential role in controlling cellular identity and the therapeutic response of human cancers. However, the chromatin accessibility landscape and gene regulatory network of pancreatic cancer are largely uncharacterized. Here, we integrate the chromatin accessibility profiles of 84 pancreatic cancer organoid lines with whole-genome sequencing data, transcriptomic sequencing data and the results of drug sensitivity analysis of 283 epigenetic-related chemicals and 5 chemotherapeutic drugs. We identify distinct transcription factors that distinguish molecular subtypes of pancreatic cancer, predict numerous chromatin accessibility peaks associated with gene regulatory networks, discover regulatory noncoding mutations with potential as cancer drivers, and reveal the chromatin accessibility signatures associated with drug sensitivity. These results not only provide the chromatin accessibility atlas of pancreatic cancer but also suggest a systematic approach to comprehensively understand the gene regulatory network of pancreatic cancer in order to advance diagnosis and potential personalized medicine applications.

          Abstract

          The chromatin accessibility landscape and gene regulatory network of pancreatic cancer has not been fully characterised. Here, the authors perform multi-omics analysis of 84 pancreatic cancer organoid lines and reveal gene regulatory networks and distinct molecular subtypes.

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          Most cited references74

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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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
                lnchen@sibcb.ac.cn
                ywang@amss.ac.cn
                jingang@smmu.edu.cn
                dong.gao@sibcb.ac.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                21 April 2022
                21 April 2022
                2022
                : 13
                : 2169
                Affiliations
                [1 ]GRID grid.73113.37, ISNI 0000 0004 0369 1660, Department of Hepatobiliary Pancreatic Surgery, Changhai Hospital, , Second Military Medical University (Naval Medical University), ; Shanghai, China
                [2 ]GRID grid.9227.e, ISNI 0000000119573309, State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, , Chinese Academy of Sciences, ; Shanghai, 200031 China
                [3 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, University of Chinese Academy of Sciences, ; Beijing, 100049 China
                [4 ]GRID grid.9227.e, ISNI 0000000119573309, CEMS, NCMIS, HCMS, MDIS, Academy of Mathematics and Systems Science, , Chinese Academy of Sciences, ; Beijing, 100080 China
                [5 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, School of Mathematical Sciences, , University of Chinese Academy of Sciences, ; Beijing, 100049 China
                [6 ]GRID grid.9227.e, ISNI 0000000119573309, Center for Excellence in Animal Evolution and Genetics, , Chinese Academy of Sciences, ; Kunming, 650223 China
                [7 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, , University of Chinese Academy of Sciences, Chinese Academy of Sciences, ; Hangzhou, China
                [8 ]Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai, Guangdong 519031 China
                [9 ]GRID grid.9227.e, ISNI 0000000119573309, Institute for Stem Cell and Regeneration, , Chinese Academy of Sciences, ; Beijing, 100101 China
                Author information
                http://orcid.org/0000-0003-4638-1462
                http://orcid.org/0000-0002-1305-9656
                http://orcid.org/0000-0002-8022-0374
                http://orcid.org/0000-0003-1020-0236
                http://orcid.org/0000-0002-3064-219X
                http://orcid.org/0000-0002-5539-5893
                http://orcid.org/0000-0001-9532-4582
                http://orcid.org/0000-0002-3960-0068
                http://orcid.org/0000-0003-0695-5273
                http://orcid.org/0000-0001-6713-1185
                http://orcid.org/0000-0003-1821-2741
                Article
                29857
                10.1038/s41467-022-29857-6
                9023604
                35449156
                8ee5d892-50bb-4442-82a1-5cd11391d8ec
                © The Author(s) 2022

                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
                : 12 March 2021
                : 31 March 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                cancer epigenetics,high-throughput screening,cancer genomics
                Uncategorized
                cancer epigenetics, high-throughput screening, cancer genomics

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