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      Mutant SF3B1 promotes AKT- and NF-κB–driven mammary tumorigenesis

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          Abstract

          Mutations in the core RNA splicing factor SF3B1 are prevalent in leukemias and uveal melanoma, but hotspot SF3B1 mutations are also seen in epithelial malignancies such as breast cancer. Although hotspot mutations in SF3B1 alter hematopoietic differentiation, whether SF3B1 mutations contribute to epithelial cancer development and progression is unknown. Here, we identify that SF3B1 mutations in mammary epithelial and breast cancer cells induce a recurrent pattern of aberrant splicing leading to activation of AKT and NF-κB, enhanced cell migration, and accelerated tumorigenesis. Transcriptomic analysis of human cancer specimens, MMTV -cre Sf3b1 K700E/WT mice, and isogenic mutant cell lines identified hundreds of aberrant 3′ splice sites (3′ss) induced by mutant SF3B1. Consistently between mouse and human tumors, mutant SF3B1 promoted aberrant splicing (dependent on aberrant branchpoints as well as pyrimidines downstream of the cryptic 3′ss) and consequent suppression of PPP2R5A and MAP3K7, critical negative regulators of AKT and NF-κB. Coordinate activation of NF-κB and AKT signaling was observed in the knockin models, leading to accelerated cell migration and tumor development in combination with mutant PIK3CA but also hypersensitizing cells to AKT kinase inhibitors. These data identify hotspot mutations in SF3B1 as an important contributor to breast tumorigenesis and reveal unique vulnerabilities in cancers harboring them.

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          Most cited references 48

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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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            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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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

               Y. Liao,  G Smyth,  W Shi (2014)
              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
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                Journal
                Journal of Clinical Investigation
                American Society for Clinical Investigation
                0021-9738
                1558-8238
                January 4 2021
                January 4 2021
                January 4 2021
                January 4 2021
                January 4 2021
                January 4 2021
                : 131
                : 1
                Article
                10.1172/JCI138315
                7773370
                33031100
                © 2021
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