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      SMARCA4 controls state plasticity in small cell lung cancer through regulation of neuroendocrine transcription factors and REST splicing

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          Abstract

          Introduction

          Small Cell Lung Cancer (SCLC) can be classified into transcriptional subtypes with distinct degrees of neuroendocrine (NE) differentiation. Recent evidence supports plasticity among subtypes with a bias toward adoption of low-NE states during disease progression or upon acquired chemotherapy resistance. Here, we identify a role for SMARCA4, the catalytic subunit of the SWI/SNF complex, as a regulator of subtype shift in SCLC.

          Methods

          ATACseq and RNAseq experiments were performed in SCLC cells after pharmacological inhibition of SMARCA4. DNA binding of SMARCA4 was characterized by ChIPseq in high-NE SCLC patient derived xenografts (PDXs). Enrichment analyses were applied to transcriptomic data. Combination of FHD-286 and afatinib was tested in vitro and in a set of chemo-resistant SCLC PDXs in vivo.

          Results

          SMARCA4 expression positively correlates with that of NE genes in both SCLC cell lines and patient tumors. Pharmacological inhibition of SMARCA4 with FHD-286 induces the loss of NE features and downregulates neuroendocrine and neuronal signaling pathways while activating non-NE factors. SMARCA4 binds to gene loci encoding NE-lineage transcription factors ASCL1 and NEUROD1 and alters chromatin accessibility, enhancing NE programs. Enrichment analysis applied to high-confidence SMARCA4 targets confirmed neuron related pathways as the top GO Biological processes regulated by SMARCA4 in SCLC. In parallel, SMARCA4 also controls REST, a known suppressor of the NE phenotype, by regulating SRRM4-dependent REST transcript splicing. Furthermore, SMARCA4 inhibition drives ERBB pathway activation in SCLC, rendering SCLC tumors sensitive to afatinib.

          Conclusions

          This study nominates SMARCA4 as a key regulator of the NE state plasticity and defines a novel therapeutic strategy for SCLC.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13045-024-01572-3.

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          Most cited references67

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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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              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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                Author and article information

                Contributors
                quintaa@mskcc.org
                rudinc@mskcc.org
                Journal
                J Hematol Oncol
                J Hematol Oncol
                Journal of Hematology & Oncology
                BioMed Central (London )
                1756-8722
                30 July 2024
                30 July 2024
                2024
                : 17
                : 58
                Affiliations
                [1 ]Department of Medicine, Memorial Sloan Kettering Cancer Center, ( https://ror.org/02yrq0923) New York, NY USA
                [2 ]Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, ( https://ror.org/02yrq0923) New York, NY USA
                [3 ]Precision Pathology Center, Memorial Sloan Kettering Cancer Center, ( https://ror.org/02yrq0923) New York, NY USA
                [4 ]Antitumor Assessment Core, Memorial Sloan Kettering Cancer Center, ( https://ror.org/02yrq0923) New York, NY USA
                [5 ]GRID grid.5386.8, ISNI 000000041936877X, Weill Cornell Medicine Graduate School of Medical Sciences, ; New York, NY USA
                [6 ]Applied Bioinformatics Core, Weill Cornell Medicine, ( https://ror.org/02r109517) New York, NY 10065 USA
                [7 ]Division of Hematology and Oncology, Department of Medicine, Weill Cornell Medicine, ( https://ror.org/02r109517) New York, NY 10065 USA
                [8 ]Department of Physiology, Biophysics and Systems Biology, Institute for Computational Biomedicine, Weill Cornell Medicine, ( https://ror.org/02r109517) New York, NY 10065 USA
                Article
                1572
                10.1186/s13045-024-01572-3
                11290012
                39080761
                4ff34093-761e-4639-88d3-fba3e8ebfcb0
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 28 April 2024
                : 3 July 2024
                Funding
                Funded by: Ramon Areces Foundation
                Funded by: Druckenmiller Center for Lung Cancer Research
                Funded by: P30 CA008748
                Funded by: NIH T32 CA1600001
                Funded by: American Lung Association
                Funded by: Robert J. and Helen C. Kleberg Foundation
                Funded by: Van Andel Research Institute
                Award ID: Stand Up to Cancer Epigenetics Dream Team
                Award Recipient :
                Funded by: NCI R35 CA263816
                Funded by: U24 CA213274
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Oncology & Radiotherapy
                lung cancer,sclc,plasticity,epigenetics,targeted therapies
                Oncology & Radiotherapy
                lung cancer, sclc, plasticity, epigenetics, targeted therapies

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