Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

Open Access Highly Accessed Research article

Comparison of prognostic gene expression signatures for breast cancer

Benjamin Haibe-Kains12, Christine Desmedt1, Fanny Piette3, Marc Buyse3, Fatima Cardoso15, Laura van't Veer45, Martine Piccart1, Gianluca Bontempi2 and Christos Sotiriou15*

Author Affiliations

1 Functional Genomics Unit, Jules Bordet Institute, Université Libre de Bruxelles, Brussels, Belgium

2 Machine Learning Group, Université Libre de Bruxelles, Brussels, Belgium

3 International Drug Development Institute (IDDI), Louvain-La-Neuve, Belgium

4 Netherlands Cancer Institute, Amsterdam, Netherlands

5 TRANSBIG consortium, Jules Bordet Institute, Brussels, Belgium

For all author emails, please log on.

BMC Genomics 2008, 9:394  doi:10.1186/1471-2164-9-394

Published: 21 August 2008

Abstract

Background

During the last years, several groups have identified prognostic gene expression signatures with apparently similar performances. However, signatures were never compared on an independent population of untreated breast cancer patients, where risk assessment was computed using the original algorithms and microarray platforms.

Results

We compared three gene expression signatures, the 70-gene, the 76-gene and the Gene expression Grade Index (GGI) signatures, in terms of predicting distant metastasis free survival (DMFS) for the individual patient. To this end, we used the previously published TRANSBIG independent validation series of node-negative untreated primary breast cancer patients. We observed agreement in prediction for 135 of 198 patients (68%) when considering the three signatures. When comparing the signatures two by two, the agreement in prediction was 71% for the 70- and 76-gene signatures, 76% for the 76-gene signature and the GGI, and 88% for the 70-gene signature and the GGI. The three signatures had similar capabilities of predicting DMFS and added significant prognostic information to that provided by the classical parameters.

Conclusion

Despite the difference in development of these signatures and the limited overlap in gene identity, they showed similar prognostic performance, adding to the growing evidence that these prognostic signatures are of clinical relevance.