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Integrated genomic analyses of de novo pathways underlying atypical meningiomas [miRNA-seq]

ABSTRACT: Meningiomas are mostly benign brain tumors, with a potential for becoming atypical or malignant. Based on comprehensive genomic, transcriptomic and epigenomic analyses of meningiomas, we compared benign tumors to atypical ones. We show that the vast majority of primary (de novo atypical meningiomas display loss of NF2, which co-occurs either with genomic instability or recurrent mutations in SMARCB1. These tumors harbor increased H3K27me3 repressive signal and a hypermethylated phenotype, mainly occupying the polycomb repressive complex 2 (PRC2 binding sites in human embryonic stem cells (hESCs, thereby phenocopying a more primitive cellular state. Consistent with this observation, and based on differential gene expression analysis as well as correlation of mRNA:miRNA regulatory networks, atypical meningiomas exhibit up-regulation of EZH2, the catalytic subunit of the PRC2 complex, well as the E2F2 and FOXM1 transcriptional networks that promote proliferation through activation of the cell cycle pathways. In addition, based on H3K27ac ChIP-seq analysis, we show atypical tumors to display an activated super-enhancer near the meningeal identity transcription factor ZIC1, leading to its transcriptional upregulation. Importantly, these primary atypical meningiomas do not harbor activating TERT promoter mutations, which have been reported in atypical tumors that progressed from benign ones. Our results establish the genomic landscape of primary atypical meningiomas, differentiating their profile from benign and progressed tumors and establishing novel therapeutic targets.

ORGANISM(S): Homo sapiens

PROVIDER: GSE93519 | GEO | 2017/01/12

SECONDARY ACCESSION(S): PRJNA360980

REPOSITORIES: GEO

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Integrated genomic analyses of de novo pathways underlying atypical meningiomas [H3k27me3 ChIP-Seq]

Project description:Meningiomas are mostly benign brain tumors, with a potential for becoming atypical or malignant. Based on comprehensive genomic, transcriptomic and epigenomic analyses of meningiomas, we compared benign tumors to atypical ones. We show that the vast majority of primary (de novo) atypical meningiomas display loss of NF2, which co-occurs either with genomic instability or recurrent mutations in SMARCB1. These tumors harbor increased H3K27me3 repressive signal and a hypermethylated phenotype, mainly occupying the polycomb repressive complex 2 (PRC2) binding sites in human embryonic stem cells (hESCs), thereby phenocopying a more primitive cellular state. Consistent with this observation, and based on differential gene expression analysis as well as correlation of mRNA:miRNA regulatory networks, atypical meningiomas exhibit up-regulation of EZH2, the catalytic subunit of the PRC2 complex, well as the E2F2 and FOXM1 transcriptional networks that promote proliferation through activation of the cell cycle pathways. In addition, based on H3K27ac ChIP-seq analysis, we show atypical tumors to display an activated super-enhancer near the meningeal identity transcription factor ZIC1, leading to its transcriptional upregulation. Importantly, these primary atypical meningiomas do not harbor activating TERT promoter mutations, which have been reported in atypical tumors that progressed from benign ones. Our results establish the genomic landscape of primary atypical meningiomas, differentiating their profile from benign and progressed tumors and establishing novel therapeutic targets.

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Integrated genomic analyses of de novo pathways underlying atypical meningiomas [ChIP-Seq]

Project description:Meningiomas are mostly benign brain tumors, with a potential for becoming atypical or malignant. Based on comprehensive genomic, transcriptomic and epigenomic analyses of meningiomas, we compared benign tumors to atypical ones. We show that the vast majority of primary (de novo) atypical meningiomas display loss of NF2, which co-occurs either with genomic instability or recurrent mutations in SMARCB1. These tumors harbor increased H3K27me3 repressive signal and a hypermethylated phenotype, mainly occupying the polycomb repressive complex 2 (PRC2) binding sites in human embryonic stem cells (hESCs), thereby phenocopying a more primitive cellular state. Consistent with this observation, and based on differential gene expression analysis as well as correlation of mRNA:miRNA regulatory networks, atypical meningiomas exhibit up-regulation of EZH2, the catalytic subunit of the PRC2 complex, well as the E2F2 and FOXM1 transcriptional networks that promote proliferation through activation of the cell cycle pathways. In addition, based on H3K27ac ChIP-seq analysis, we show atypical tumors to display an activated super-enhancer near the meningeal identity transcription factor ZIC1, leading to its transcriptional upregulation. The H3k27ac ChIP-seq data for 15 benign meningiomas and 2 dura samples listed below were created by Dr. Justin Cotney in Dr. James Noonan’s lab at Yale and previously published in a paper by our group with Drs. Cotney and Noonan as co-authors (Clark et al. Science, 2013). Sample IDs: MN-297, MN-288, MN-292, MN-163, MN-1037, MN-105, MN-201, MN-249, MN-191, MN-1066, MN-169, MN-291, MN-24, MN-79, MN-1044, CONTROL1, CONTROL2. In this study, we used these benign meningioma H3k27ac ChIP-seq data as controls and compared them to the newly created ChIP-seq data. Sample IDs: MN-54, MN-97 and MN-171. This GEO entry contains ChIP-seq results for both data sets.

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2014-05-29 | E-GEOD-58037 | biostudies-arrayexpress

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Project description:Meningiomas, named for their cell of origin, are the most common intracranial tumors in adults, representing 39% of all primary adult central nervous system tumors. These tumors originate in the meninges, which are the outer three layers of tissue between the skull and the brain that cover and protect the brain just under the skull. Most meningioma tumors (85-90 percent) are categorized as benign, with the remaining 10-15 percent being atypical meningioma or malignant meningioma (cancerous). The word “benign” can be misleading for meningiomas. Depending on location and growth rate, a benign meningioma brain tumor may impinge on vital nerves or compress the brain, causing disability. They may even become life threatening. We describe transcriptional signatures of four most common groups of benign meningiomas. Each subgroup of meningiomas displayed a unique gene expression program identifying signaling pathways potentially implicated in the tumorigenesis. These findings will improve our understanding of meningioma tumorigenesis. Objective: To define gene expression signatures of the most common subtypes of meningiomas to better understand cellular processes and signaling pathways specific for each tumor genotype.

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Genomic analysis of non-NF2 meningiomas reveals mutations in TRAF7, KLF4, AKT1, and SMO.

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High-grade NF2 mutant meningiomas downregulate oncogenic YAP signaling.

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Metabolomic and Lipidomic Characterization of Meningioma Grades Using LC-HRMS and Machine Learning

Project description:Meningiomas are among the most common brain tumors that arise from the leptomeningeal cover of the brain and spinal cord and account for around 37% of all central nervous system tumors. According to the World Health Organization, meningiomas are classified into three histological subtypes: benign, atypical, and anaplastic. Sometimes, meningiomas with a histological diagnosis of benign tumors show clinical characteristics and behavior of aggressive tumors. In this study, we examined the metabolomic and lipidomic profiles of meningioma tumors, focusing on comparing low-grade and high-grade tumors and identifying potential markers that can discriminate between benign and malignant tumors. High-resolution mass spectrometry coupled to liquid chromatography was used for untargeted metabolomics and lipidomics analyses of 85 tumor biopsy samples with different meningioma grades. We then applied feature selection and machine learning techniques to find the features with the highest information to aid in the diagnosis of meningioma grades. Three biomarkers were identified to differentiate low- and high-grade meningioma brain tumors. The use of mass-spectrometry-based metabolomics and lipidomics combined with machine learning analyses to prospect and characterize biomarkers associated with meningioma grades may pave the way for elucidating potential therapeutic and prognostic targets.

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