Research article

Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer

Elin Karlsson¹ , Ulla Delle¹ , Anna Danielsson¹ , Björn Olsson² , Frida Abel³ , Per Karlsson⁴ and Khalil Helou¹

¹  Department of Oncology, Institute of Clinical Sciences, Blå stråket 2, University of Gothenburg, SE-413 45, Göteborg, Sweden

²  School of Life Sciences, University College of Skövde, Box 408, SE-541 28, Skövde, Sweden

³  Genomics Core Facility, Medicinaregatan 5A, Box 413, SE-405 30, Göteborg Sweden

⁴  Oncology section, Department of Oncology, Sahlgrenska University Hospital, Blå, stråket 2, University of Gothenburg, SE-413 45, Göteborg, Sweden

author email corresponding author email

BMC Cancer 2008, 8:254doi:10.1186/1471-2407-8-254

Published:	8 September 2008

Abstract

Background

It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated.

Methods

46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used for evaluation of the gene lists, a correlation-based classifier and a Voting Features Interval (VFI) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients. They had both developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material.

Results

A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively).

Conclusion

The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients may have benefited from different adjuvant treatment than they recieved.

Trial registration

The research on these tumours was approved by the Medical Faculty Research Ethics Committee (Medicinska fakultetens forskningsetikkommitté, Göteborg, Sweden (S164-02)).