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      AR coactivators, CBP/p300, are critical mediators of DNA repair in prostate cancer

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          Abstract

          Castration resistant prostate cancer (CRPC) remains an incurable disease stage with ineffective treatments options. Here, the androgen receptor (AR) coactivators CBP/p300, which are histone acetyltransferases, were identified as critical mediators of DNA damage repair (DDR) to potentially enhance therapeutic targeting of CRPC. Key findings demonstrate that CBP/p300 expression increases with disease progression and selects for poor prognosis in metastatic disease. CBP/p300 bromodomain inhibition enhances response to standard of care therapeutics. Functional studies, CBP/p300 cistrome mapping, and transcriptome in CRPC revealed that CBP/p300 regulates DDR. Further mechanistic investigation showed that CBP/p300 attenuation via therapeutic targeting and genomic knockdown decreases homologous recombination (HR) factors in vitro, in vivo, and in human prostate cancer (PCa) tumors ex vivo. Similarly, CBP/p300 expression in human prostate tissue correlates with HR factors. Lastly, targeting CBP/p300 impacts HR-mediate repair and patient outcome. Collectively, these studies identify CBP/p300 as drivers of PCa tumorigenesis and lay the groundwork to optimize therapeutic strategies for advanced PCa via CBP/p300 inhibition, potentially in combination with AR-directed and DDR therapies.

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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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            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
                Role: ValidationRole: Formal AnalysisRole: WritingRole: Writing – Review & EditingRole: Visualization
                Role: ConceptualizationRole: MethodologyRole: SoftwareRole: ValidationRole: Formal AnalysisRole: ResourcesRole: Writing – Review & EditingRole: Visualization
                Role: Validation
                Role: Formal Analysis
                Role: SoftwareRole: Formal AnalysisRole: Visualization
                Role: SoftwareRole: Formal AnalysisRole: Visualization
                Role: Formal Analysis
                Role: ConceptualizationRole: ResourcesRole: Writing – Review & Editing
                Role: Investigation
                Role: InvestigationRole: ResourcesRole: Writing – Review & Editing
                Role: Formal Analysis
                Role: Formal Analysis
                Role: Conceptualization
                Role: ConceptualizationRole: Formal AnalysisRole: ResourcesRole: Writing – Review & EditingRole: Visualization
                Role: ConceptualizationRole: ResourcesRole: Writing – Review & EditingRole: Visualization
                Role: ResourcesRole: Writing – Review & Editing
                Role: ResourcesRole: Writing – Review & Editing
                Role: ResourcesRole: Writing – Review & Editing
                Role: ConceptualizationRole: MethodologyRole: ResourcesRole: Writing – Review & EditingRole: Funding Acquisition
                Role: ConceptualizationRole: MethodologyRole: ValidationRole: Formal AnalysisRole: InvestigationRole: ResourcesRole: WritingRole: Writing – Review & EditingRole: VisualizationRole: Funding Acquisition
                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                07 May 2024
                : 2024.05.07.592966
                Affiliations
                [1 ]Center for Prostate Disease Research, Murtha Cancer Center Research Program, Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, Maryland, 20817, USA
                [2 ]The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, Maryland, 20817 USA
                [3 ]Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania, 19107, USA
                [4 ]The Institute of Cancer Research, London, United Kingdom
                [5 ]The Royal Marsden Hospital, London, United Kingdom
                [6 ]South Australian Immunogenomics Cancer Institute, The University of Adelaide, Australia
                [7 ]South Australian Health and Medical Research Institute, Adelaide, Australia
                [8 ]David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
                [9 ]CellCentric Ltd., Cambridge, United Kingdom
                [10 ]The American Cancer Society, Philadelphia, Pennsylvania, 19103, USA
                Author notes
                [#]

                Equal contribution

                [* ]Corresponding Author: Ayesha A. Shafi PhD, Center for Prostate Disease Research (CPDR), 6720A Rockledge Drive, Suite 300, Bethesda, MD, 20817, ashafi@ 123456cpdr.org
                Author information
                http://orcid.org/0000-0002-2325-4228
                http://orcid.org/0000-0003-0335-638X
                Article
                10.1101/2024.05.07.592966
                11100730
                38766099
                f2a881ef-21cf-433f-bf91-23f65e323761

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

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                prostate cancer,cbp/p300,androgen receptor (ar),dna repair,novel therapeutics

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