consisting of six breast cancer cohorts [@Reyal2008] including the cohort from the Netherlands Cancer Institute[@Vijver2002].

In contrast to earlier findings we find that when we apply a proper correction for the number of genes appearing in the composite features employed by the composite feature classifier, the stability of single gene feature sets is comparable to the stability of composite feature sets. Much to our surprise, and in contrast to other studies, we also find that integrating secondary data, as done in the evaluated methods, does *not* lead to increased classification accuracy when compared to simple single gene based methods. Our findings are partly consistent with the findings of Abraham [*et al.*]{}[@Abraham2010], where the authors show that averaging over gene sets does not increase the prediction performance over a single gene classifier.

We investigated several possible factors that may explain the disappointing performance of approaches incorporating secondary data. First, we looked into the effect of the way the number of features is selected. Second, we looked into the effect of the exact size and composition of the starting gene set. This factor could play a role since not all genes are included in secondary data sources, hence classifiers employing secondary data 