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      Histone lysine demethylase 4 family proteins maintain the transcriptional program and adrenergic cellular state of MYCN-amplified neuroblastoma

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          Summary

          Neuroblastoma with MYCN amplification (MNA) is a high-risk disease that has a poor survival rate. Neuroblastoma displays cellular heterogeneity, including more differentiated (adrenergic) and more primitive (mesenchymal) cellular states. Here, we demonstrate that MYCN oncoprotein promotes a cellular state switch in mesenchymal cells to an adrenergic state, accompanied by induction of histone lysine demethylase 4 family members (KDM4A-C) that act in concert to control the expression of MYCN and adrenergic core regulatory circulatory (CRC) transcription factors. Pharmacologic inhibition of KDM4 blocks expression of MYCN and the adrenergic CRC transcriptome with genome-wide induction of transcriptionally repressive H3K9me3, resulting in potent anticancer activity against neuroblastomas with MNA by inducing neuroblastic differentiation and apoptosis. Furthermore, a short-term KDM4 inhibition in combination with conventional, cytotoxic chemotherapy results in complete tumor responses of xenografts with MNA. Thus, KDM4 blockade may serve as a transformative strategy to target the adrenergic CRC dependencies in MNA neuroblastomas.

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          Highlights

          • MYCN promotes KDM4 induction and cellular state switch from mesenchymal to adrenergic

          • KDM4 inhibition induces H3K9me3 and blocks MYCN and adrenergic transcriptome

          • KDM4 inhibition leads to significant suppression of neuroblastoma growth

          • KDM4 inhibition in combination with chemotherapy results in complete tumor responses

          Abstract

          Abu-Zaid et al. show that KDM4 inhibition blocks MYCN function in high-risk neuroblastoma models. The combination of a selective KDM4 inhibitor with chemotherapy reduces tumor growth of neuroblastoma xenografts.

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          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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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                Journal
                Cell Rep Med
                Cell Rep Med
                Cell Reports Medicine
                Elsevier
                2666-3791
                21 March 2024
                19 March 2024
                21 March 2024
                : 5
                : 3
                : 101468
                Affiliations
                [1 ]Department of Surgery, St Jude Children’s Research Hospital, Memphis, TN 38105, USA
                [2 ]Center for Applied Bioinformatics, St Jude Children’s Research Hospital, Memphis, TN 38105, USA
                [3 ]Department of Pathology, St Jude Children’s Research Hospital, Memphis, TN 38105, USA
                [4 ]Department of Immunology, St Jude Children’s Research Hospital, Memphis, TN 38105, USA
                [5 ]Department of Biostatistics, St Jude Children’s Research Hospital, Memphis, TN 38105, USA
                [6 ]Department of Molecular Oncology, St Jude Children’s Research Hospital, Memphis, TN 38105, USA
                [7 ]Department of Chemical Biology and Therapeutics, St Jude Children’s Research Hospital, Memphis, TN 38105, USA
                [8 ]Department of Computational Biology, St Jude Children’s Research Hospital, Memphis, TN 38105, USA
                [9 ]Genetics, Genomics & Informatics, The University of Tennessee Health Science Center, Memphis, TN 38103, USA
                [10 ]Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany
                [11 ]Division of Pediatric Surgery, Department of Surgery, University of Tennessee Health Science Center, Memphis, TN 38105, USA
                [12 ]St Jude Graduate School of Biomedical Sciences, St Jude Children’s Research Hospital, Memphis, TN 38105, USA
                [13 ]Department of Pathology and Laboratory Medicine, College of Medicine, The University of Tennessee Health Science Center, 930 Madison Avenue, Suite 500, Memphis, TN 38163, USA
                [14 ]College of Graduate Health Sciences, University of Tennessee Health Science Center, Memphis, TN, USA
                Author notes
                []Corresponding author jun.yang2@ 123456stjude.org
                [15]

                These authors contributed equally

                [16]

                Lead contact

                Article
                S2666-3791(24)00114-9 101468
                10.1016/j.xcrm.2024.101468
                10983111
                38508144
                0f7aab15-c55d-4db6-8fd9-05d12c787ca9
                © 2024 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 22 August 2022
                : 21 December 2023
                : 20 February 2024
                Categories
                Article

                mycn,neuroblastoma,kdm4,qc6352,core regulatory circuitry,cell state switch

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