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A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the Proliferation, Immune response and RNA splicing modules in breast cancer

INTRODUCTION: Several gene expression signatures have been proposed and demonstrated to be predictive of outcome in breast cancer. In the present article we address the following issues: Do these signatures perform similarly? Are there (common) molecular processes reported by these signatures? Can b...

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Autores principales: Reyal, Fabien, van Vliet, Martin H, Armstrong, Nicola J, Horlings, Hugo M, de Visser, Karin E, Kok, Marlen, Teschendorff, Andrew E, Mook, Stella, van 't Veer, Laura, Caldas, Carlos, Salmon, Remy J, Vijver, Marc J van de, Wessels, Lodewyk FA
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2656909/
https://www.ncbi.nlm.nih.gov/pubmed/19014521
http://dx.doi.org/10.1186/bcr2192
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author Reyal, Fabien
van Vliet, Martin H
Armstrong, Nicola J
Horlings, Hugo M
de Visser, Karin E
Kok, Marlen
Teschendorff, Andrew E
Mook, Stella
van 't Veer, Laura
Caldas, Carlos
Salmon, Remy J
Vijver, Marc J van de
Wessels, Lodewyk FA
author_facet Reyal, Fabien
van Vliet, Martin H
Armstrong, Nicola J
Horlings, Hugo M
de Visser, Karin E
Kok, Marlen
Teschendorff, Andrew E
Mook, Stella
van 't Veer, Laura
Caldas, Carlos
Salmon, Remy J
Vijver, Marc J van de
Wessels, Lodewyk FA
author_sort Reyal, Fabien
collection PubMed
description INTRODUCTION: Several gene expression signatures have been proposed and demonstrated to be predictive of outcome in breast cancer. In the present article we address the following issues: Do these signatures perform similarly? Are there (common) molecular processes reported by these signatures? Can better prognostic predictors be constructed based on these identified molecular processes? METHODS: We performed a comprehensive analysis of the performance of nine gene expression signatures on seven different breast cancer datasets. To better characterize the functional processes associated with these signatures, we enlarged each signature by including all probes with a significant correlation to at least one of the genes in the original signature. The enrichment of functional groups was assessed using four ontology databases. RESULTS: The classification performance of the nine gene expression signatures is very similar in terms of assigning a sample to either a poor outcome group or a good outcome group. Nevertheless the concordance in classification at the sample level is low, with only 50% of the breast cancer samples classified in the same outcome group by all classifiers. The predictive accuracy decreases with the number of poor outcome assignments given to a sample. The best classification performance was obtained for the group of patients with only good outcome assignments. Enrichment analysis of the enlarged signatures revealed 11 functional modules with prognostic ability. The combination of the RNA-splicing and immune modules resulted in a classifier with high prognostic performance on an independent validation set. CONCLUSIONS: The study revealed that the nine signatures perform similarly but exhibit a large degree of discordance in prognostic group assignment. Functional analyses indicate that proliferation is a common cellular process, but that other functional categories are also enriched and show independent prognostic ability. We provide new evidence of the potentially promising prognostic impact of immunity and RNA-splicing processes in breast cancer.
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spelling pubmed-26569092009-03-17 A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the Proliferation, Immune response and RNA splicing modules in breast cancer Reyal, Fabien van Vliet, Martin H Armstrong, Nicola J Horlings, Hugo M de Visser, Karin E Kok, Marlen Teschendorff, Andrew E Mook, Stella van 't Veer, Laura Caldas, Carlos Salmon, Remy J Vijver, Marc J van de Wessels, Lodewyk FA Breast Cancer Res Research Article INTRODUCTION: Several gene expression signatures have been proposed and demonstrated to be predictive of outcome in breast cancer. In the present article we address the following issues: Do these signatures perform similarly? Are there (common) molecular processes reported by these signatures? Can better prognostic predictors be constructed based on these identified molecular processes? METHODS: We performed a comprehensive analysis of the performance of nine gene expression signatures on seven different breast cancer datasets. To better characterize the functional processes associated with these signatures, we enlarged each signature by including all probes with a significant correlation to at least one of the genes in the original signature. The enrichment of functional groups was assessed using four ontology databases. RESULTS: The classification performance of the nine gene expression signatures is very similar in terms of assigning a sample to either a poor outcome group or a good outcome group. Nevertheless the concordance in classification at the sample level is low, with only 50% of the breast cancer samples classified in the same outcome group by all classifiers. The predictive accuracy decreases with the number of poor outcome assignments given to a sample. The best classification performance was obtained for the group of patients with only good outcome assignments. Enrichment analysis of the enlarged signatures revealed 11 functional modules with prognostic ability. The combination of the RNA-splicing and immune modules resulted in a classifier with high prognostic performance on an independent validation set. CONCLUSIONS: The study revealed that the nine signatures perform similarly but exhibit a large degree of discordance in prognostic group assignment. Functional analyses indicate that proliferation is a common cellular process, but that other functional categories are also enriched and show independent prognostic ability. We provide new evidence of the potentially promising prognostic impact of immunity and RNA-splicing processes in breast cancer. BioMed Central 2008 2008-11-13 /pmc/articles/PMC2656909/ /pubmed/19014521 http://dx.doi.org/10.1186/bcr2192 Text en Copyright © 2008 Reyal et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Reyal, Fabien
van Vliet, Martin H
Armstrong, Nicola J
Horlings, Hugo M
de Visser, Karin E
Kok, Marlen
Teschendorff, Andrew E
Mook, Stella
van 't Veer, Laura
Caldas, Carlos
Salmon, Remy J
Vijver, Marc J van de
Wessels, Lodewyk FA
A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the Proliferation, Immune response and RNA splicing modules in breast cancer
title A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the Proliferation, Immune response and RNA splicing modules in breast cancer
title_full A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the Proliferation, Immune response and RNA splicing modules in breast cancer
title_fullStr A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the Proliferation, Immune response and RNA splicing modules in breast cancer
title_full_unstemmed A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the Proliferation, Immune response and RNA splicing modules in breast cancer
title_short A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the Proliferation, Immune response and RNA splicing modules in breast cancer
title_sort comprehensive analysis of prognostic signatures reveals the high predictive capacity of the proliferation, immune response and rna splicing modules in breast cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2656909/
https://www.ncbi.nlm.nih.gov/pubmed/19014521
http://dx.doi.org/10.1186/bcr2192
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