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PMID: 19014521 Published · ppublish English Comparative Study Journal Article Research Support, Non-U.S. Gov't

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

Breast cancer research : BCR ·Vol. 10 ·No. 6 ·2008-00-00 ·Pages R93

Reyal F, van Vliet MH, Armstrong NJ, Horlings HM, de Visser KE, Kok M, Teschendorff AE, Mook S, van 't Veer L, Caldas C, Salmon RJ, van de Vijver MJ, Wessels LF

Abstract

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? 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. 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. 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.

MeSH Terms
Breast Neoplasms/genetics,metabolism,pathology Cell Proliferation Computational Biology Databases, Genetic Female Gene Expression Profiling Humans Immune System Phenomena/physiology Prognosis RNA Splicing/physiology Survival Rate
Authors & Affiliations
13 authors, click to expand affiliations / ORCID
Reyal Fabien
Department of Pathology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands.
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
van de Vijver Marc J
Wessels Lodewyk F A
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Article Info
Journal
Breast cancer research : BCR
Abbr.
Breast Cancer Res
ISSN
1465-542X
Published
2008-00-00
Epub
2008-00-13
Pages
R93
Language
English
Region
England
NLM ID
100927353
PMCID
PMC2656909
Subset
IM
Analysis Services
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