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      Drug-Induced Epigenomic Plasticity Reprograms Circadian Rhythm Regulation to Drive Prostate Cancer toward Androgen Independence

      , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
      Cancer Discovery
      American Association for Cancer Research (AACR)
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          Abstract

          In prostate cancer, androgen receptor (AR)–targeting agents are very effective in various disease stages. However, therapy resistance inevitably occurs, and little is known about how tumor cells adapt to bypass AR suppression. Here, we performed integrative multiomics analyses on tissues isolated before and after 3 months of AR-targeting enzalutamide monotherapy from patients with high-risk prostate cancer enrolled in a neoadjuvant clinical trial. Transcriptomic analyses demonstrated that AR inhibition drove tumors toward a neuroendocrine-like disease state. Additionally, epigenomic profiling revealed massive enzalutamide-induced reprogramming of pioneer factor FOXA1 from inactive chromatin sites toward active cis-regulatory elements that dictate prosurvival signals. Notably, treatment-induced FOXA1 sites were enriched for the circadian clock component ARNTL. Posttreatment ARNTL levels were associated with patients’ clinical outcomes, and ARNTL knockout strongly decreased prostate cancer cell growth. Our data highlight a remarkable cistromic plasticity of FOXA1 following AR-targeted therapy and revealed an acquired dependency on the circadian regulator ARNTL, a novel candidate therapeutic target.

          Significance:

          Understanding how prostate cancers adapt to AR-targeted interventions is critical for identifying novel drug targets to improve the clinical management of treatment-resistant disease. Our study revealed an enzalutamide-induced epigenomic plasticity toward prosurvival signaling and uncovered the circadian regulator ARNTL as an acquired vulnerability after AR inhibition, presenting a novel lead for therapeutic development.

          See related commentary by Zhang et al., p. 2017.

          This article is highlighted in the In This Issue feature, p. 2007

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          Is Open Access

          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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            Is Open Access

            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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                Journal
                Cancer Discovery
                American Association for Cancer Research (AACR)
                2159-8274
                2159-8290
                September 02 2022
                June 27 2022
                September 02 2022
                June 27 2022
                : 12
                : 9
                : 2074-2097
                Article
                10.1158/2159-8290.CD-21-0576
                35754340
                e3926587-a884-4ecf-8b95-989413ace00a
                © 2022
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