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      ASCL1 represses a SOX9 + neural crest stem-like state in small cell lung cancer

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          Abstract

          In this study, Olsen et al. show that alterations in Rb1/Trp53/Myc in the mouse lung induce an ASCL1 + state of small cell lung cancer (SCLC) in multiple cells of origin. Their findings show that in a MYC-driven SCLC model, ASCL1 promotes neuroendocrine fate and represses the emergence of a SOX9 + nonendodermal stem-like fate that resembles neural crest.

          Abstract

          ASCL1 is a neuroendocrine lineage-specific oncogenic driver of small cell lung cancer (SCLC), highly expressed in a significant fraction of tumors. However, ∼25% of human SCLC are ASCL1-low and associated with low neuroendocrine fate and high MYC expression. Using genetically engineered mouse models (GEMMs), we show that alterations in Rb1/Trp53/Myc in the mouse lung induce an ASCL1 + state of SCLC in multiple cells of origin. Genetic depletion of ASCL1 in MYC-driven SCLC dramatically inhibits tumor initiation and progression to the NEUROD1 + subtype of SCLC. Surprisingly, ASCL1 loss promotes a SOX9 + mesenchymal/neural crest stem-like state and the emergence of osteosarcoma and chondroid tumors, whose propensity is impacted by cell of origin. ASCL1 is critical for expression of key lineage-related transcription factors NKX2-1, FOXA2, and INSM1 and represses genes involved in the Hippo/Wnt/Notch developmental pathways in vivo. Importantly, ASCL1 represses a SOX9/RUNX1/RUNX2 program in vivo and SOX9 expression in human SCLC cells, suggesting a conserved function for ASCL1. Together, in a MYC-driven SCLC model, ASCL1 promotes neuroendocrine fate and represses the emergence of a SOX9 + nonendodermal stem-like fate that resembles neural crest.

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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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              Cutadapt removes adapter sequences from high-throughput sequencing reads

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                Author and article information

                Journal
                Genes Dev
                Genes Dev
                genesdev
                genesdev
                GAD
                Genes & Development
                Cold Spring Harbor Laboratory Press
                0890-9369
                1549-5477
                June 2021
                June 2021
                : 35
                : 11-12
                : 847-869
                Affiliations
                [1 ]Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, Utah 84112, USA;
                [2 ]Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37212, USA;
                [3 ]Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA;
                [4 ]Department of Pathology, University of Utah, Salt Lake City, Utah 84112, USA;
                [5 ]ARUP Laboratories at University of Utah, Salt Lake City, Utah 84108, USA;
                [6 ]Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, Texas 75390, USA
                Author notes
                Author information
                http://orcid.org/0000-0002-2749-9353
                http://orcid.org/0000-0003-2082-2397
                Article
                8711660
                10.1101/gad.348295.121
                8168563
                34016693
                a60ec66f-3bbe-4b1d-b1b3-e79de0ec9e29
                © 2021 Olsen et al.; Published by Cold Spring Harbor Laboratory Press

                This article, published in Genes & Development, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 20 January 2021
                : 12 April 2021
                Page count
                Pages: 23
                Funding
                Funded by: Cancer Institute
                Funded by: National Institutes of Health , open-funder-registry 10.13039/100000002;
                Award ID: P30CA042014
                Funded by: NIH , open-funder-registry 10.13039/100000002;
                Funded by: NCI , open-funder-registry 10.13039/100000054;
                Award ID: R21-CA216504-01A1
                Award ID: U01-CA231844
                Award ID: U01-CA213338
                Award ID: U24-CA213274
                Categories
                Research Paper

                sclc,ascl1,mouse models,lung cancer,cell of origin,plasticity,neuroendocrine,small cell lung cancer

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