The simultaneous measurement of thousands of genes gives the opportunity to personalize and improve cancer therapy. In addition, the integration of meta-data such as protein-protein interaction (PPI) information into the analyses helps in the identification and prioritization of genes from these screens. Here, we describe a computational approach that identifies genes prognostic for outcome by combining gene profiling data from any source with a network of known relationships between genes.