  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.
