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HGG-41. STRUCTURAL VARIANT DRIVERS IN PEDIATRIC HIGH-GRADE GLIOMA

BACKGROUND: Driver single nucleotide variants (SNV) and somatic copy number aberrations (SCNA) of pediatric high-grade glioma (pHGGs), including Diffuse Midline Gliomas (DMGs) are characterized. However, structural variants (SVs) in pHGGs and the mechanisms through which they contribute to glioma fo...

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Autores principales: Dubois, Frank, Shapira, Ofer, Greenwald, Noah, Zack, Travis, Tsai, Jessica W, Harutyunyan, Ashot S, Kumar, Kiran, Sinai, Claire, Malkin, Hayley, Jones, Robert, Ho, Patricia, O’Rourke, Ryan, Kang, Kyung S, Jabado, Nada, Kieran, Mark W, Ligon, Keith, Beroukhim, Rameen, Bandopadhayay, Pratiti
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7715247/
http://dx.doi.org/10.1093/neuonc/noaa222.322
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author Dubois, Frank
Shapira, Ofer
Greenwald, Noah
Zack, Travis
Tsai, Jessica W
Harutyunyan, Ashot S
Kumar, Kiran
Sinai, Claire
Malkin, Hayley
Jones, Robert
Ho, Patricia
O’Rourke, Ryan
Kang, Kyung S
Jabado, Nada
Kieran, Mark W
Ligon, Keith
Beroukhim, Rameen
Bandopadhayay, Pratiti
author_facet Dubois, Frank
Shapira, Ofer
Greenwald, Noah
Zack, Travis
Tsai, Jessica W
Harutyunyan, Ashot S
Kumar, Kiran
Sinai, Claire
Malkin, Hayley
Jones, Robert
Ho, Patricia
O’Rourke, Ryan
Kang, Kyung S
Jabado, Nada
Kieran, Mark W
Ligon, Keith
Beroukhim, Rameen
Bandopadhayay, Pratiti
author_sort Dubois, Frank
collection PubMed
description BACKGROUND: Driver single nucleotide variants (SNV) and somatic copy number aberrations (SCNA) of pediatric high-grade glioma (pHGGs), including Diffuse Midline Gliomas (DMGs) are characterized. However, structural variants (SVs) in pHGGs and the mechanisms through which they contribute to glioma formation have not been systematically analyzed genome-wide. METHODS: Using SvABA for SVs as well as the latest pipelines for SCNAs and SNVs we analyzed whole-genome sequencing from 174 patients. This includes 60 previously unpublished samples, 43 of which are DMGs. Signature analysis allowed us to define pHGG groups with shared SV characteristics. Significantly recurring SV breakpoints and juxtapositions were identified with algorithms we recently developed and the findings were correlated with RNAseq and H3K27ac ChIPseq. RESULTS: The SV characteristics in pHGG showed three groups defined by either complex, intermediate or simple signature activities. These associated with distinct combinations of known driver oncogenes. Our statistical analysis revealed recurring SVs in the topologically associating domains of MYCN, MYC, EGFR, PDGFRA & MET. These correlated with increased mRNA expression and amplification of H3K27ac peaks. Complex recurring amplifications showed characteristics of extrachromosomal amplicons and were enriched in coding SVs splitting protein regulatory from effector domains. Integrative analysis of all SCNAs, SNVs & SVs revealed patterns of characteristic combinations between potential drivers and signatures. This included two distinct groups of H3K27M DMGs with either complex or simple signatures and different combinations of associated variants. CONCLUSION: Recurrent SVs associate with signatures shaped by an underlying process, which can lead to distinct mechanisms to activate the same oncogene.
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spelling pubmed-77152472020-12-09 HGG-41. STRUCTURAL VARIANT DRIVERS IN PEDIATRIC HIGH-GRADE GLIOMA Dubois, Frank Shapira, Ofer Greenwald, Noah Zack, Travis Tsai, Jessica W Harutyunyan, Ashot S Kumar, Kiran Sinai, Claire Malkin, Hayley Jones, Robert Ho, Patricia O’Rourke, Ryan Kang, Kyung S Jabado, Nada Kieran, Mark W Ligon, Keith Beroukhim, Rameen Bandopadhayay, Pratiti Neuro Oncol High Grade Glioma BACKGROUND: Driver single nucleotide variants (SNV) and somatic copy number aberrations (SCNA) of pediatric high-grade glioma (pHGGs), including Diffuse Midline Gliomas (DMGs) are characterized. However, structural variants (SVs) in pHGGs and the mechanisms through which they contribute to glioma formation have not been systematically analyzed genome-wide. METHODS: Using SvABA for SVs as well as the latest pipelines for SCNAs and SNVs we analyzed whole-genome sequencing from 174 patients. This includes 60 previously unpublished samples, 43 of which are DMGs. Signature analysis allowed us to define pHGG groups with shared SV characteristics. Significantly recurring SV breakpoints and juxtapositions were identified with algorithms we recently developed and the findings were correlated with RNAseq and H3K27ac ChIPseq. RESULTS: The SV characteristics in pHGG showed three groups defined by either complex, intermediate or simple signature activities. These associated with distinct combinations of known driver oncogenes. Our statistical analysis revealed recurring SVs in the topologically associating domains of MYCN, MYC, EGFR, PDGFRA & MET. These correlated with increased mRNA expression and amplification of H3K27ac peaks. Complex recurring amplifications showed characteristics of extrachromosomal amplicons and were enriched in coding SVs splitting protein regulatory from effector domains. Integrative analysis of all SCNAs, SNVs & SVs revealed patterns of characteristic combinations between potential drivers and signatures. This included two distinct groups of H3K27M DMGs with either complex or simple signatures and different combinations of associated variants. CONCLUSION: Recurrent SVs associate with signatures shaped by an underlying process, which can lead to distinct mechanisms to activate the same oncogene. Oxford University Press 2020-12-04 /pmc/articles/PMC7715247/ http://dx.doi.org/10.1093/neuonc/noaa222.322 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle High Grade Glioma
Dubois, Frank
Shapira, Ofer
Greenwald, Noah
Zack, Travis
Tsai, Jessica W
Harutyunyan, Ashot S
Kumar, Kiran
Sinai, Claire
Malkin, Hayley
Jones, Robert
Ho, Patricia
O’Rourke, Ryan
Kang, Kyung S
Jabado, Nada
Kieran, Mark W
Ligon, Keith
Beroukhim, Rameen
Bandopadhayay, Pratiti
HGG-41. STRUCTURAL VARIANT DRIVERS IN PEDIATRIC HIGH-GRADE GLIOMA
title HGG-41. STRUCTURAL VARIANT DRIVERS IN PEDIATRIC HIGH-GRADE GLIOMA
title_full HGG-41. STRUCTURAL VARIANT DRIVERS IN PEDIATRIC HIGH-GRADE GLIOMA
title_fullStr HGG-41. STRUCTURAL VARIANT DRIVERS IN PEDIATRIC HIGH-GRADE GLIOMA
title_full_unstemmed HGG-41. STRUCTURAL VARIANT DRIVERS IN PEDIATRIC HIGH-GRADE GLIOMA
title_short HGG-41. STRUCTURAL VARIANT DRIVERS IN PEDIATRIC HIGH-GRADE GLIOMA
title_sort hgg-41. structural variant drivers in pediatric high-grade glioma
topic High Grade Glioma
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7715247/
http://dx.doi.org/10.1093/neuonc/noaa222.322
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