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      Integrated chromatin and transcriptomic profiling of patient-derived colon cancer organoids identifies personalized drug targets to overcome oxaliplatin resistance

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          Abstract

          Colorectal cancer is a leading cause of cancer deaths. Most colorectal cancer patients eventually develop chemoresistance to the current standard-of-care therapies. Here, we used patient-derived colorectal cancer organoids to demonstrate that resistant tumor cells undergo significant chromatin changes in response to oxaliplatin treatment. Integrated transcriptomic and chromatin accessibility analyses using ATAC-Seq and RNA-Seq identified a group of genes associated with significantly increased chromatin accessibility and upregulated gene expression. CRISPR/Cas9 silencing of fibroblast growth factor receptor 1 (FGFR1) and oxytocin receptor (OXTR) helped overcome oxaliplatin resistance. Similarly, treatment with oxaliplatin in combination with an FGFR1 inhibitor (PD166866) or an antagonist of OXTR (L-368,899) suppressed chemoresistant organoids. However, oxaliplatin treatment did not activate either FGFR1 or OXTR expression in another resistant organoid, suggesting that chromatin accessibility changes are patient-specific. The use of patient-derived cancer organoids in combination with transcriptomic and chromatin profiling may lead to precision treatments to overcome chemoresistance in colorectal cancer.

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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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            Fast gapped-read alignment with Bowtie 2.

            As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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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
                Journal
                Genes Dis
                Genes Dis
                Genes & Diseases
                Chongqing Medical University
                2352-4820
                2352-3042
                29 October 2019
                March 2021
                29 October 2019
                : 8
                : 2
                : 203-214
                Affiliations
                [a ]Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, 14853, USA
                [b ]Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, NC, 27708, USA
                [c ]Center for Genomics and Computational Biology, Duke University, Durham, NC, 27708, USA
                [d ]Department of Medical Oncology, Duke University Medical Center, Durham, NC, 27708, USA
                [e ]Department of Pediatrics, Division of Medical Genetics, Duke University, Durham, NC, 27708, USA
                [f ]Duke Cancer Institute, Duke University, Durham, NC, 27708, USA
                Author notes
                []Corresponding author. xiling.shen@ 123456duke.edu
                [1]

                These authors contributed equally to this work.

                Article
                S2352-3042(19)30099-6
                10.1016/j.gendis.2019.10.012
                8099686
                33997167
                899acac1-d18e-4eb1-91de-2922b25cee3e
                © 2019 Chongqing Medical University. Production and hosting by Elsevier B.V.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 28 August 2019
                : 20 October 2019
                : 21 October 2019
                Categories
                Full Length Article

                chromatin accessibility,drug screening,patient-derived organoids,personalized medicine,target discovery

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