reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer. Supplementary data can be downloaded from <http://homepages.cwi.nl/~staiger/supplement.pdf> .

Introduction {#introduction .unnumbered}
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Modern high-throughput methods provide the means to observe genome wide changes in gene expression patterns in breast cancer samples. Gene expression signatures have been proposed [@Veer2002; @Wang2005] to predict prognosis in breast cancer patients, but were shown to vary substantially between data sets. One possible explanation for this effect is that the data sets on which the predictors are trained are typically poorly dimensioned, consisting of many more genes than samples. Integrating secondary data sources like, for example, protein-protein interaction (PPI) networks, co-expression networks or pathways from databases such as KEGG, has recently been proposed to overcome variability of prognostic signatures and to increase their prognostic performance [@Chuang2007; @Lee2008; @Taylor2009; @Ma2010; @Abraham2010]. Many of these studies claim that combining gene expression data with secondary data sources to construct composite features results in higher accuracy in outcome prediction and higher stability of the obtained signatures. In addition, inclusion of the secondary sources raises the hope that the obtained signatures will be more interpretable and thus provide more insight into the 