A consensus prognostic gene expression classifier for ER positive breast cancer

Andrew E Teschendorff^*¹ , Ali Naderi^*¹ , Nuno L Barbosa-Morais¹^,2 , Sarah E Pinder³ , Ian O Ellis⁴ , Sam Aparicio¹^,5 , James D Brenton¹ and Carlos Caldas¹

¹Cancer Genomics Program, Department of Oncology, University of Cambridge, Hutchison/MRC Research Center, Hills Road, Cambridge CB2 2XZ, UK

²Institute of Molecular Medicine, Faculty of Medicine, University of Lisbon, 1649-028 Lisbon, Portugal

³Cancer Genomics Program, Department of Pathology, University of Cambridge, Hutchison/MRC Research Center, Hills Road, Cambridge CB2 2XZ, UK

⁴Histopathology, Nottingham City Hospital NHS Trust and University of Nottingham, Nottingham NG5 1PB, UK

⁵Molecular Oncology and Breast Cancer Program, the BC Cancer Research Centre, West 10th Avenue, Vancouver BC, V5Z 1L3, Canada

author email corresponding author email* Contributed equally

Genome Biology 2006, 7:R101doi:10.1186/gb-2006-7-10-r101


Published:	31 October 2006

Subject areas: Bioinformatics, Cancer

Abstract

Background

A consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer.

Results

Here we perform a combined analysis of three major breast cancer microarray data sets to hone in on a universally valid prognostic molecular classifier in estrogen receptor (ER) positive tumors. Using a recently developed robust measure of prognostic separation, we further validate the prognostic classifier in three external independent cohorts, confirming the validity of our molecular classifier in a total of 877 ER positive samples. Furthermore, we find that molecular classifiers may not outperform classical prognostic indices but that they can be used in hybrid molecular-pathological classification schemes to improve prognostic separation.

Conclusion

The prognostic molecular classifier presented here is the first to be valid in over 877 ER positive breast cancer samples and across three different microarray platforms. Larger multi-institutional studies will be needed to fully determine the added prognostic value of molecular classifiers when combined with standard prognostic factors.