Outcome signature genes in breast cancer: is there a unique set?
  Motivation: Predicting the metastatic potential of primary malignant tissues has direct bearing on choice of therapy. Several microarray studies yielded gene sets whose expression profiles successfully predicted survival (Ramaswamy et al 2003; Sorlie et al 2001; van't Veer et al 2003). Nevertheless, the overlap between these gene sets is almost zero. Such small overlaps were observed also in other complex diseases (Lossos et al 2003; Miklos and Maleszka 2004), and the variables that could account for the differences had evoked a wide interest. 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. Results: To answer this question we concentrated on one single breast cancer dataset, and analyzed it by one single method, the one which was used by van't Veer et al to produce a set of outcome predictive genes. We showed 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: (a) many genes are correlated with survival; (b) the differences between these correlations are small; (c) the correlations fluctuate strongly when measured over different subsets of patients. A possible biological explanation for these properties is discussed. 
Liat Ein-Dor, Itai Kela, Gad Getz, David Givol and Eytan Domany