Protein subnetwork markers improve prediction of cancer outcome

Mol Syst Biol. 3: 141The reliability of gene predictors of cancer outcome has been recently questioned, pointing to deficiencies in experimental design, insufficient statistical power due to small sample size, and flaws in predictor generation and performance assessment, with proposed guidelines to overcome these limitations (Ntzani and Ioannidis, 2003; Michiels et al , 2005, Dupuy and Simon, 2007). Now, in a recent article published in Molecular Systems Biology (Chuang et al , 2007), a complementary strategy has been proposed based on integration of expression profiles with protein interactions, demonstrating that more reproducible and robust predictors can be generated with the additional benefit of including mutated genes which are excluded in the classical analyses, and also providing models for the molecular mechanisms involved in metastasis formation. This is achieved through combination of mRNA expression profiles with curated protein–protein interaction data, which became recently available (Rual et al , 2005), leveraging methods for modular subnetwork identification and biological validation (Segal et al , 2003; Poyatos and Hurst, 2004).During the past decade, transcriptome analysis has been used increasingly to monitor expression profiles of extensive collections of genes in cancer samples, providing insights into the molecular mechanisms underlying cancer development …