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Published in: Breast Cancer Research 6/2008

Open Access 01-12-2008 | Research article

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

Authors: Fabien Reyal, Martin H van Vliet, Nicola J Armstrong, Hugo M Horlings, Karin E de Visser, Marlen Kok, Andrew E Teschendorff, Stella Mook, Laura van 't Veer, Carlos Caldas, Remy J Salmon, Marc J van de Vijver, Lodewyk FA Wessels

Published in: Breast Cancer Research | Issue 6/2008

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Abstract

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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Metadata
Title
A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the Proliferation, Immune response and RNA splicing modules in breast cancer
Authors
Fabien Reyal
Martin H van Vliet
Nicola J Armstrong
Hugo M Horlings
Karin E de Visser
Marlen Kok
Andrew E Teschendorff
Stella Mook
Laura van 't Veer
Carlos Caldas
Remy J Salmon
Marc J van de Vijver
Lodewyk FA Wessels
Publication date
01-12-2008
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 6/2008
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/bcr2192

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