Skip to main content

Advertisement

Outcome signature genes in breast cancer: is there a unique set?

Article metrics

  • 950 Accesses

  • 1 Citations

Predicting the metastatic potential of primary malignant tissues has direct bearing on the choice of therapy. Several microarray studies yielded gene sets whose expression profiles successfully predicted survival. Nevertheless, the overlap between these gene sets is almost zero. One of the main open questions in this context is whether the disparity can be attributed only to trivial reasons such as different technologies, different patients and different types of analysis. To answer this question we concentrated on one single breast cancer dataset, and analyzed it by one single method, that used by van 't Veer and colleagues [1], to produce an outcome predictive signature set of 70 genes. We show that in fact the resulting set of genes is not unique; it is strongly influenced by the subset of patients used for gene selection. Many equally predictive lists could have been produced from the same analysis. Three main properties of the data explain this sensitivity: many genes are correlated with survival; the differences between these correlations are small; and the correlations fluctuate strongly when measured over different subsets of patients. A possible correlation of this finding and the complexity of gene expression in cancer is discussed.

References

  1. 1.

    van 't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, et al: Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002, 415: 530-536. 10.1038/415530a.

Download references

Author information

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Kela, I., Ein-Dor, L., Getz, G. et al. Outcome signature genes in breast cancer: is there a unique set?. Breast Cancer Res 7, P4.38 (2005) doi:10.1186/bcr1168

Download citation

Keywords

  • Breast Cancer
  • Metastatic Potential
  • Gene Selection
  • Microarray Study
  • Malignant Tissue