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      Integrated genomic analyses of de novo pathways underlying atypical meningiomas

      research-article
      1 , 2 , 1 , 2 , 3 , 1 , 2 , 3 , 1 , 2 , 1 , 2 , 3 , 4 , 5 , 1 , 2 , 1 , 2 , 1 , 2 , 6 , 6 , 7 , 8 , 8 , 8 , 1 , 2 , 1 , 2 , 3 , 1 , 2 , 1 , 2 , 1 , 3 , 1 , 2 , 3 , 4 , 5 , 7 , 1 , 2 , 9 , 1 , 3 , 10 , 1 , 2 , 6 , 11 , a , 1 , 2 , 3 , 4 , 5 , 12
      Nature Communications
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          Abstract

          Meningiomas are mostly benign brain tumours, with a potential for becoming atypical or malignant. On the basis of comprehensive genomic, transcriptomic and epigenomic analyses, we compared benign meningiomas to atypical ones. Here, we show that the majority of primary ( de novo) atypical meningiomas display loss of NF2, which co-occurs either with genomic instability or recurrent SMARCB1 mutations. These tumours harbour increased H3K27me3 signal and a hypermethylated phenotype, mainly occupying the polycomb repressive complex 2 (PRC2) binding sites in human embryonic stem cells, thereby phenocopying a more primitive cellular state. Consistent with this observation, atypical meningiomas exhibit upregulation of EZH2, the catalytic subunit of the PRC2 complex, as well as the E2F2 and FOXM1 transcriptional networks. Importantly, these primary atypical meningiomas do not harbour TERT promoter mutations, which have been reported in atypical tumours that progressed from benign ones. Our results establish the genomic landscape of primary atypical meningiomas and potential therapeutic targets.

          Abstract

          Meningiomas are mostly benign brain tumours with the potential for becoming atypical or malignant. Here, the authors show that primary atypical meningiomas are epigenetically and genetically distinct from benign and progressed tumours, highlighting possible therapeutic targets such as PRC2.

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          Most cited references28

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          A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

          Heng Li (2011)
          Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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            Stampy: a statistical algorithm for sensitive and fast mapping of Illumina sequence reads.

            High-volume sequencing of DNA and RNA is now within reach of any research laboratory and is quickly becoming established as a key research tool. In many workflows, each of the short sequences ("reads") resulting from a sequencing run are first "mapped" (aligned) to a reference sequence to infer the read from which the genomic location derived, a challenging task because of the high data volumes and often large genomes. Existing read mapping software excel in either speed (e.g., BWA, Bowtie, ELAND) or sensitivity (e.g., Novoalign), but not in both. In addition, performance often deteriorates in the presence of sequence variation, particularly so for short insertions and deletions (indels). Here, we present a read mapper, Stampy, which uses a hybrid mapping algorithm and a detailed statistical model to achieve both speed and sensitivity, particularly when reads include sequence variation. This results in a higher useable sequence yield and improved accuracy compared to that of existing software.
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              Epidemiology and etiology of meningioma

              Although most meningiomas are encapsulated and benign tumors with limited numbers of genetic aberrations, their intracranial location often leads to serious and potentially lethal consequences. They are the most frequently diagnosed primary brain tumor accounting for 33.8% of all primary brain and central nervous system tumors reported in the United States between 2002 and 2006. Inherited susceptibility to meningioma is suggested both by family history and candidate gene studies in DNA repair genes. People with certain mutations in the neurofibromatosis gene (NF2) have a very substantial increased risk for meningioma. High dose ionizing radiation exposure is an established risk factor for meningioma, and lower doses may also increase risk, but which types and doses are controversial or understudied. Because women are twice as likely as men to develop meningiomas and these tumors harbor hormone receptors, an etiologic role for hormones (both endogenous and exogenous) has been hypothesized. The extent to which immunologic factors influence meningioma etiology has been largely unexplored. Growing emphasis on brain tumor research coupled with the advent of new genetic and molecular epidemiologic tools in genetic and molecular epidemiology promise hope for advancing knowledge about the causes of intra-cranial meningioma. In this review, we highlight current knowledge about meningioma epidemiology and etiology and suggest future research directions.
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group
                2041-1723
                14 February 2017
                2017
                : 8
                : 14433
                Affiliations
                [1 ]Yale Program in Brain Tumor Research, Yale School of Medicine , New Haven, Connecticut 06510, USA
                [2 ]Department of Neurosurgery, Yale School of Medicine , New Haven, Connecticut 06510, USA
                [3 ]Department of Genetics, Yale School of Medicine , New Haven, Connecticut 06510, USA
                [4 ]Department of Neurobiology, Yale School of Medicine , New Haven, Connecticut 06510, USA
                [5 ]Yale Program on Neurogenetics, Yale School of Medicine , New Haven, Connecticut 06510, USA
                [6 ]Whitehead Institute for Biomedical Research , 9 Cambridge Center, Cambridge, Massachusetts 02142, USA
                [7 ]Department of Neurosurgery, University of Bonn Medical School , Bonn 53105, Germany
                [8 ]Department of General Neurosurgery, University Hospital of Cologne , Cologne 50937, Germany
                [9 ]Department of Pathology, Yale School of Medicine , New Haven, Connecticut 06510, USA
                [10 ]Yale Center for Genome Analysis, Yale School of Medicine , Orange, Connecticut 06477, USA
                [11 ]Department of Biology, Massachusetts Institute of Technology , Cambridge, Massachusetts 02139, USA
                [12 ]Yale Comprehensive Cancer Center, Yale School of Medicine , New Haven, Connecticut 06510, USA
                Author notes
                Author information
                http://orcid.org/0000-0001-6888-252X
                http://orcid.org/0000-0002-8473-5279
                http://orcid.org/0000-0002-3606-532X
                http://orcid.org/0000-0001-8855-8647
                Article
                ncomms14433
                10.1038/ncomms14433
                5316884
                28195122
                1f39e0a5-0e7f-44e4-829e-48b443349816
                Copyright © 2017, The Author(s)

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 02 August 2016
                : 28 December 2016
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