Normal tissue content impact on the GBM molecular classification.

Molecular classification of glioblastoma has enabled a deeper understanding of the disease. The four-subtype model (including Proneural, Classical, Mesenchymal and Neural) has been replaced by a model that discards the Neural subtype, found to be associated with samples with a high content of normal...

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Autores: Madurga, Rodrigo, García-Romero, Noemí, Jiménez, Beatriz, Collazo, Ana, Pérez Rodríguez, Francisco, Hernández Laín, Aurelio, Fernández Carballal, Carlos, Prat Acín, Ricardo, Zanin, Massimiliano, Menasalvas, Ernestina, Ayuso-Sacido, Ángel
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad Francisco de Vitoria
Repositorio:DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
Idioma:inglés
OAI Identifier:oai:ddfv.ufv.es:10641/2351
Acceso en línea:http://hdl.handle.net/10641/2351
Access Level:acceso abierto
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spelling Normal tissue content impact on the GBM molecular classification.Madurga, RodrigoGarcía-Romero, NoemíJiménez, BeatrizCollazo, AnaPérez Rodríguez, FranciscoHernández Laín, AurelioFernández Carballal, CarlosPrat Acín, RicardoZanin, MassimilianoMenasalvas, ErnestinaAyuso-Sacido, ÁngelMolecular classification of glioblastoma has enabled a deeper understanding of the disease. The four-subtype model (including Proneural, Classical, Mesenchymal and Neural) has been replaced by a model that discards the Neural subtype, found to be associated with samples with a high content of normal tissue. These samples can be misclassified preventing biological and clinical insights into the different tumor subtypes from coming to light. In this work, we present a model that tackles both the molecular classification of samples and discrimination of those with a high content of normal cells. We performed a transcriptomic in silico analysis on glioblastoma (GBM) samples (n = 810) and tested different criteria to optimize the number of genes needed for molecular classification. We used gene expression of normal brain samples (n = 555) to design an additional gene signature to detect samples with a high normal tissue content. Microdissection samples of different structures within GBM (n = 122) have been used to validate the final model. Finally, the model was tested in a cohort of 43 patients and confirmed by histology. Based on the expression of 20 genes, our model is able to discriminate samples with a high content of normal tissue and to classify the remaining ones. We have shown that taking into consideration normal cells can prevent errors in the classification and the subsequent misinterpretation of the results. Moreover, considering only samples with a low content of normal cells, we found an association between the complexity of the samples and survival for the three molecular subtypes.20212021-01-0120212021-01-01journal articlehttp://purl.org/coar/resource_type/c_6501SMURhttp://purl.org/coar/version/c_71e4c1898caa6e32info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10641/2351reponame:DDFV. Repositorio Institucional de la Universidad Francisco de Vitoriainstname:Universidad Francisco de VitoriaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2Atribución-NoComercial-SinDerivadas 3.0 Españahttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:ddfv.ufv.es:10641/23512026-06-11T12:44:57Z
dc.title.none.fl_str_mv Normal tissue content impact on the GBM molecular classification.
title Normal tissue content impact on the GBM molecular classification.
spellingShingle Normal tissue content impact on the GBM molecular classification.
Madurga, Rodrigo
title_short Normal tissue content impact on the GBM molecular classification.
title_full Normal tissue content impact on the GBM molecular classification.
title_fullStr Normal tissue content impact on the GBM molecular classification.
title_full_unstemmed Normal tissue content impact on the GBM molecular classification.
title_sort Normal tissue content impact on the GBM molecular classification.
dc.creator.none.fl_str_mv Madurga, Rodrigo
García-Romero, Noemí
Jiménez, Beatriz
Collazo, Ana
Pérez Rodríguez, Francisco
Hernández Laín, Aurelio
Fernández Carballal, Carlos
Prat Acín, Ricardo
Zanin, Massimiliano
Menasalvas, Ernestina
Ayuso-Sacido, Ángel
author Madurga, Rodrigo
author_facet Madurga, Rodrigo
García-Romero, Noemí
Jiménez, Beatriz
Collazo, Ana
Pérez Rodríguez, Francisco
Hernández Laín, Aurelio
Fernández Carballal, Carlos
Prat Acín, Ricardo
Zanin, Massimiliano
Menasalvas, Ernestina
Ayuso-Sacido, Ángel
author_role author
author2 García-Romero, Noemí
Jiménez, Beatriz
Collazo, Ana
Pérez Rodríguez, Francisco
Hernández Laín, Aurelio
Fernández Carballal, Carlos
Prat Acín, Ricardo
Zanin, Massimiliano
Menasalvas, Ernestina
Ayuso-Sacido, Ángel
author2_role author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv
description Molecular classification of glioblastoma has enabled a deeper understanding of the disease. The four-subtype model (including Proneural, Classical, Mesenchymal and Neural) has been replaced by a model that discards the Neural subtype, found to be associated with samples with a high content of normal tissue. These samples can be misclassified preventing biological and clinical insights into the different tumor subtypes from coming to light. In this work, we present a model that tackles both the molecular classification of samples and discrimination of those with a high content of normal cells. We performed a transcriptomic in silico analysis on glioblastoma (GBM) samples (n = 810) and tested different criteria to optimize the number of genes needed for molecular classification. We used gene expression of normal brain samples (n = 555) to design an additional gene signature to detect samples with a high normal tissue content. Microdissection samples of different structures within GBM (n = 122) have been used to validate the final model. Finally, the model was tested in a cohort of 43 patients and confirmed by histology. Based on the expression of 20 genes, our model is able to discriminate samples with a high content of normal tissue and to classify the remaining ones. We have shown that taking into consideration normal cells can prevent errors in the classification and the subsequent misinterpretation of the results. Moreover, considering only samples with a low content of normal cells, we found an association between the complexity of the samples and survival for the three molecular subtypes.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01
2021
2021-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
SMUR
http://purl.org/coar/version/c_71e4c1898caa6e32
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10641/2351
url http://hdl.handle.net/10641/2351
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 3.0 España
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
instname:Universidad Francisco de Vitoria
instname_str Universidad Francisco de Vitoria
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collection DDFV. Repositorio Institucional de la Universidad Francisco de Vitoria
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