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Strategy to Find Molecular Signatures in a Small Series of Rare Cancers: Validation for Radiation-Induced Breast and Thyroid Tumors

Methods of classification using transcriptome analysis for case-by-case tumor diagnosis could be limited by tumor heterogeneity and masked information in the gene expression profiles, especially as the number of tumors is small. We propose a new strategy, EMts_2PCA, based on: 1) The identification o...

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Autores principales: Ugolin, Nicolas, Ory, Catherine, Lefevre, Emilie, Benhabiles, Nora, Hofman, Paul, Schlumberger, Martin, Chevillard, Sylvie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3154936/
https://www.ncbi.nlm.nih.gov/pubmed/21853153
http://dx.doi.org/10.1371/journal.pone.0023581
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author Ugolin, Nicolas
Ory, Catherine
Lefevre, Emilie
Benhabiles, Nora
Hofman, Paul
Schlumberger, Martin
Chevillard, Sylvie
author_facet Ugolin, Nicolas
Ory, Catherine
Lefevre, Emilie
Benhabiles, Nora
Hofman, Paul
Schlumberger, Martin
Chevillard, Sylvie
author_sort Ugolin, Nicolas
collection PubMed
description Methods of classification using transcriptome analysis for case-by-case tumor diagnosis could be limited by tumor heterogeneity and masked information in the gene expression profiles, especially as the number of tumors is small. We propose a new strategy, EMts_2PCA, based on: 1) The identification of a gene expression signature with a great potential for discriminating subgroups of tumors (EMts stage), which includes: a) a learning step, based on an expectation-maximization (EM) algorithm, to select sets of candidate genes whose expressions discriminate two subgroups, b) a training step to select from the sets of candidate genes those with the highest potential to classify training tumors, c) the compilation of genes selected during the training step, and standardization of their levels of expression to finalize the signature. 2) The predictive classification of independent prospective tumors, according to the two subgroups of interest, by the definition of a validation space based on a two-step principal component analysis (2PCA). The present method was evaluated by classifying three series of tumors and its robustness, in terms of tumor clustering and prediction, was further compared with that of three classification methods (Gene expression bar code, Top-scoring pair(s) and a PCA-based method). Results showed that EMts_2PCA was very efficient in tumor classification and prediction, with scores always better that those obtained by the most common methods of tumor clustering. Specifically, EMts_2PCA permitted identification of highly discriminating molecular signatures to differentiate post-Chernobyl thyroid or post-radiotherapy breast tumors from their sporadic counterparts that were previously unsuccessfully classified or classified with errors.
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spelling pubmed-31549362011-08-18 Strategy to Find Molecular Signatures in a Small Series of Rare Cancers: Validation for Radiation-Induced Breast and Thyroid Tumors Ugolin, Nicolas Ory, Catherine Lefevre, Emilie Benhabiles, Nora Hofman, Paul Schlumberger, Martin Chevillard, Sylvie PLoS One Research Article Methods of classification using transcriptome analysis for case-by-case tumor diagnosis could be limited by tumor heterogeneity and masked information in the gene expression profiles, especially as the number of tumors is small. We propose a new strategy, EMts_2PCA, based on: 1) The identification of a gene expression signature with a great potential for discriminating subgroups of tumors (EMts stage), which includes: a) a learning step, based on an expectation-maximization (EM) algorithm, to select sets of candidate genes whose expressions discriminate two subgroups, b) a training step to select from the sets of candidate genes those with the highest potential to classify training tumors, c) the compilation of genes selected during the training step, and standardization of their levels of expression to finalize the signature. 2) The predictive classification of independent prospective tumors, according to the two subgroups of interest, by the definition of a validation space based on a two-step principal component analysis (2PCA). The present method was evaluated by classifying three series of tumors and its robustness, in terms of tumor clustering and prediction, was further compared with that of three classification methods (Gene expression bar code, Top-scoring pair(s) and a PCA-based method). Results showed that EMts_2PCA was very efficient in tumor classification and prediction, with scores always better that those obtained by the most common methods of tumor clustering. Specifically, EMts_2PCA permitted identification of highly discriminating molecular signatures to differentiate post-Chernobyl thyroid or post-radiotherapy breast tumors from their sporadic counterparts that were previously unsuccessfully classified or classified with errors. Public Library of Science 2011-08-11 /pmc/articles/PMC3154936/ /pubmed/21853153 http://dx.doi.org/10.1371/journal.pone.0023581 Text en Ugolin et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Ugolin, Nicolas
Ory, Catherine
Lefevre, Emilie
Benhabiles, Nora
Hofman, Paul
Schlumberger, Martin
Chevillard, Sylvie
Strategy to Find Molecular Signatures in a Small Series of Rare Cancers: Validation for Radiation-Induced Breast and Thyroid Tumors
title Strategy to Find Molecular Signatures in a Small Series of Rare Cancers: Validation for Radiation-Induced Breast and Thyroid Tumors
title_full Strategy to Find Molecular Signatures in a Small Series of Rare Cancers: Validation for Radiation-Induced Breast and Thyroid Tumors
title_fullStr Strategy to Find Molecular Signatures in a Small Series of Rare Cancers: Validation for Radiation-Induced Breast and Thyroid Tumors
title_full_unstemmed Strategy to Find Molecular Signatures in a Small Series of Rare Cancers: Validation for Radiation-Induced Breast and Thyroid Tumors
title_short Strategy to Find Molecular Signatures in a Small Series of Rare Cancers: Validation for Radiation-Induced Breast and Thyroid Tumors
title_sort strategy to find molecular signatures in a small series of rare cancers: validation for radiation-induced breast and thyroid tumors
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3154936/
https://www.ncbi.nlm.nih.gov/pubmed/21853153
http://dx.doi.org/10.1371/journal.pone.0023581
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