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

Fabien Reyal^1,2,3 , Martin H van Vliet^4,5 , Nicola J Armstrong⁴ , Hugo M Horlings¹ , Karin E de Visser⁶ , Marlen Kok¹ , Andrew E Teschendorff⁷ , Stella Mook¹ , Laura van 't Veer¹ , Carlos Caldas⁷ , Remy J Salmon³ , Marc J van de Vijver^1,8 and Lodewyk FA Wessels^4,5

¹  Department of Pathology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands

²  Department of Surgery, Institut Curie, 6 rue d'Ulm, 75005 Paris, France

³  UMR 144, CNRS-Institut Curie, Molecular Oncology Team, 12 rue Lhomond, 75005 Paris, France

⁴  Bioinformatics and Statistics Group, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands

⁵  Faculty of EEMCS, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands

⁶  Department of Molecular Biology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands

⁷  Cancer Research UK, Cambridge Research Institute and Department of Oncology, Li Ka Shing Centre, University of Cambridge, Cambridge CB2 ORE, UK

⁸  Department of Pathology, Academic Medical Center, Meibergdreef 9, 1100 DD Amsterdam The Netherlands

author email corresponding author email

Breast Cancer Research 2008, 10:R93doi:10.1186/bcr2192

Published:	13 November 2008

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.