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      Role of specialized composition of SWI/SNF complexes in prostate cancer lineage plasticity

      research-article
      1 , 2 , 1 , 3 , 4 , 5 , 1 , 5 , 6 , 7 , 2 , 8 , 2 , 9 , 10 , 10 , 11 , 12 , 12 , 4 , 9 , 1 , 1 , 13 , 14 , 2 , 8 , 15 , 16 , 12 , 17 , 14 , 18 , 19 , 20 , 21 , 22 , 23 , 1 , 3 , 24 , 14 , 7 , 2 , 5 , 4 , 25 , 26 , 1 , 27 , 28 ,
      Nature Communications
      Nature Publishing Group UK
      Cancer epigenetics, Prostate cancer

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          Abstract

          Advanced prostate cancer initially responds to hormonal treatment, but ultimately becomes resistant and requires more potent therapies. One mechanism of resistance observed in around 10–20% of these patients is lineage plasticity, which manifests in a partial or complete small cell or neuroendocrine prostate cancer (NEPC) phenotype. Here, we investigate the role of the mammalian SWI/SNF (mSWI/SNF) chromatin remodeling complex in NEPC. Using large patient datasets, patient-derived organoids and cancer cell lines, we identify mSWI/SNF subunits that are deregulated in NEPC and demonstrate that SMARCA4 (BRG1) overexpression is associated with aggressive disease. We also show that SWI/SNF complexes interact with different lineage-specific factors in NEPC compared to prostate adenocarcinoma. These data point to a role for mSWI/SNF complexes in therapy-related lineage plasticity, which may also be relevant for other solid tumors.

          Abstract

          The differentiation of prostate adenocarcinoma to neuroendocrine prostate cancer (CRPC-NE) is a mechanism of resistance to androgen deprivation therapy. Here the authors show that SWI/SNF chromatin-remodeling complex is deregulated in CRPC-NE and that the complex interacts with different lineage specific factors throughout prostate cancer transdifferentiation.

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          Most cited references98

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          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

          This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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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
                mark.rubin@dbmr.unibe.ch
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                3 November 2020
                3 November 2020
                2020
                : 11
                : 5549
                Affiliations
                [1 ]GRID grid.5734.5, ISNI 0000 0001 0726 5157, Department for BioMedical Research, , University of Bern, ; 3008 Bern, Switzerland
                [2 ]GRID grid.5386.8, ISNI 000000041936877X, The Caryl and Israel Englander Institute for Precision Medicine, , Weill Cornell Medicine, ; New York, NY 10021 USA
                [3 ]GRID grid.5734.5, ISNI 0000 0001 0726 5157, Department for BioMedical Research, Urology Research Laboratory, , University of Bern, ; 3008 Bern, Switzerland
                [4 ]Institute of Pathology and Medical Genetics, University Hospital Basel, University of Basel, 4051 Basel, Switzerland
                [5 ]GRID grid.11696.39, ISNI 0000 0004 1937 0351, Department of Cellular, Computational and Integrative Biology (CIBIO), , University of Trento, ; 38122 Trento, Italy
                [6 ]GRID grid.430148.a, Bioinformatics Unit, , Hospital of Prato, ; 59100 Prato, Italy
                [7 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Healthcare Policy and Research, Division of Biostatistics and Epidemiology, , Weill Cornell Medicine, ; New York, NY 10021 USA
                [8 ]GRID grid.5386.8, ISNI 000000041936877X, Institute for Computational Biomedicine, , Weill Cornell Medicine, ; New York, NY 10021 USA
                [9 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Laboratory Medicine and Pathology, , Weill Cornell Medicine, ; New York, NY 10021 USA
                [10 ]GRID grid.5386.8, ISNI 000000041936877X, Meyer Cancer Center, , Weill Cornell Medicine, ; New York, NY 10021 USA
                [11 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Biochemistry, , Weill Cornell Medicine, ; New York, NY 10021 USA
                [12 ]GRID grid.5734.5, ISNI 0000 0001 0726 5157, Proteomics Mass Spectrometry Core Facility, , University of Bern, ; 3010 Bern, Switzerland
                [13 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Radiation Oncology, Helen Diller Family Comprehensive Cancer Center, , University of California at San Francisco, ; San Francisco, CA USA
                [14 ]GRID grid.5386.8, ISNI 000000041936877X, Department of Medicine, Division of Medical Oncology, , Weill Cornell Medicine, ; New York, NY 10021 USA
                [15 ]GRID grid.5386.8, ISNI 000000041936877X, HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, , Weill Cornell Medicine, ; New York, NY 10021 USA
                [16 ]GRID grid.5386.8, ISNI 000000041936877X, Meyer Cancer Center, , Weill Cornell Medicine, ; New York, NY 10065 USA
                [17 ]GRID grid.51462.34, ISNI 0000 0001 2171 9952, Human Oncology and Pathogenesis Program and Department of Medicine, , Memorial Sloan-Kettering Cancer Center, ; New York, NY 10065 USA
                [18 ]GRID grid.65499.37, ISNI 0000 0001 2106 9910, Department of Medical Oncology, , Dana Farber Cancer Institute, ; Boston, MA USA
                [19 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Urology, , Johns Hopkins University School of Medicine, ; Baltimore, Maryland USA
                [20 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Pathology, , Johns Hopkins University School of Medicine, ; Baltimore, MD 21205 USA
                [21 ]GRID grid.21107.35, ISNI 0000 0001 2171 9311, Department of Oncology, , Johns Hopkins University School of Medicine, ; Baltimore, MD 21205 USA
                [22 ]GRID grid.415941.c, ISNI 0000 0004 0509 4333, Lindenhofspital Bern, , Prostate Center Bern, ; 3012 Bern, Switzerland
                [23 ]Department of Urology, Essen University Hospital, University of Duisburg-Essen, 47057 Essen, Germany
                [24 ]GRID grid.411656.1, ISNI 0000 0004 0479 0855, Department of Urology, , Inselspital, ; 3010 Bern, Switzerland
                [25 ]GRID grid.6612.3, ISNI 0000 0004 1937 0642, Visceral Surgery Research Laboratory, Clarunis, Department of Biomedicine, , University of Basel, ; 4051 Basel, Switzerland
                [26 ]Clarunis Universitäres Bauchzentrum Basel, 4002 Basel, Switzerland
                [27 ]GRID grid.411656.1, ISNI 0000 0004 0479 0855, Inselspital, ; 3010 Bern, Switzerland
                [28 ]Bern Center for Precision Medicine, 3008 Bern, Switzerland
                Author information
                http://orcid.org/0000-0003-1227-356X
                http://orcid.org/0000-0002-2422-1145
                http://orcid.org/0000-0001-8405-3035
                http://orcid.org/0000-0002-0963-7687
                http://orcid.org/0000-0003-3259-2226
                http://orcid.org/0000-0002-6085-7706
                http://orcid.org/0000-0002-0171-3884
                http://orcid.org/0000-0002-8266-8631
                http://orcid.org/0000-0002-8321-9950
                Article
                19328
                10.1038/s41467-020-19328-1
                7642293
                33144576
                880ea23f-2091-4603-99c8-3287f66aaf28
                © The Author(s) 2020

                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
                : 8 February 2020
                : 7 October 2020
                Funding
                Funded by: NIH/NCI WCM SPORE in Prostate Cancer P50-CA211024.
                Categories
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                © The Author(s) 2020

                Uncategorized
                cancer epigenetics,prostate cancer
                Uncategorized
                cancer epigenetics, prostate cancer

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