its interactors as features for the classifier. While no claims are made regarding performance improvements, we included this approach in the comparison as it is a recently proposed, novel approach for exploiting secondary data sources to predict outcome in breast cancer.

Table \[tab:methods\_overview\] provides a summary of the characteristics of all methods included in the comparison. It lists a description of each approach, the secondary data sources employed, the types of (composite) features and how the value of a (composite) feature is computed for a single tumor.

All three studies listed above use their own specific cross-validation (CV) protocol and evaluate their method on different (combinations of) data sets. This makes it hard to assess the improvement over other methods. In this work, we therefore employ an unbiased training and validation protocol and present a comprehensive evaluation of cross data set classification performance and stability on six publicly available breast cancer data sets. Given that these classifiers are intended to predict the unknown outcome of a patient, we suggest a cross-validation procedure that does not assume any knowledge about the samples used for testing. Thus, we strictly separate the training data set from the test data set, [*i.e.*]{}composite feature construction, 