title: Current composite-feature classification methods do not outperform simple single-genes classifiers in breast cancer prognosis.
abstract: Integrating gene expression data with secondary data such as pathway or protein-protein interaction data has been proposed as a promising approach for improved outcome prediction of cancer patients. Methods employing this approach usually aggregate the expression of genes into new composite features, while the secondary data guide this aggregation. Previous studies were limited to few data sets with a small number of patients. Moreover, each study used different data and evaluation procedures. This makes it difficult to objectively assess the gain in classification performance. Here we introduce the Amsterdam Classification Evaluation Suite (ACES). ACES is a Python package to objectively evaluate classification and feature-selection methods and contains methods for pooling and normalizing Affymetrix microarrays from different studies. It is simple to use and therefore facilitates the comparison of new approaches to best-in-class approaches. In addition to the methods described in our earlier study (Staiger et al., 2012), we have included two prominent prognostic gene signatures specific for breast cancer outcome, one more composite feature selection method and two network-based gene ranking methods. Employing the evaluation pipeline we show that current composite-feature classification methods do not outperform simple single-genes classifiers in predicting outcome in breast cancer. Furthermore, we find that also the stability of features across different data sets is not higher for composite features. Most stunningly, we observe that prediction performances are not affected when extracting features from randomized PPI networks.
authors.affiliation: Life Sciences, Centrum Wiskunde & Informatica Amsterdam, Netherlands ; Computational Cancer Biology, Division of Molecular Carcinogenesis, Netherlands Cancer Institute Amsterdam, Netherlands.
authors.forename: Christine
authors.initials: C
authors.lastname: Staiger
authors.affiliation: Computational Cancer Biology, Division of Molecular Carcinogenesis, Netherlands Cancer Institute Amsterdam, Netherlands.
authors.forename: Sidney
authors.initials: S
authors.lastname: Cadot
authors.affiliation: Research Laboratory of Pediatrics and Nephrology, Hungarian Academy of Sciences Budapest, Hungary.
authors.forename: Balázs
authors.initials: B
authors.lastname: Györffy
authors.affiliation: Computational Cancer Biology, Division of Molecular Carcinogenesis, Netherlands Cancer Institute Amsterdam, Netherlands ; Cancer Systems Biology Center, Netherlands Cancer Institute Amsterdam, Netherlands ; Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, TU Delft Delft, Netherlands.
authors.forename: Lodewyk F A
authors.initials: LF
authors.lastname: Wessels
authors.affiliation: Life Sciences, Centrum Wiskunde & Informatica Amsterdam, Netherlands ; Operations Research and Bioinformatics, Faculty of Sciences, VU University Amsterdam Amsterdam, Netherlands.
authors.forename: Gunnar W
authors.initials: GW
authors.lastname: Klau
keywords: breast cancer; classification; evaluation; feature selection; networks; outcome prediction