TY - JOUR T1 - CAERUS: Predicting CAncER oUtcomeS Using Relationship between Protein Structural Information, Protein Networks, Gene Expression Data, and Mutation Data A1 - Zhang, Kelvin Xi A1 - Ouellette, B. F. Francis Y1 - 2011/03/31 N2 - Author Summary It is widely known that cancer is a complex process in which a large number of genes appear to be involved. Through experimental approaches, some oncogenes and tumor suppressors have been identified as playing important roles in the signaling and the regulatory pathways. However, we have not fully understood the complete mechanism of how cancer develops and how it leads to different disease outcomes (aggressive/dangerous or non-aggressive/less-dangerous). In order to identify a list of gene signatures and better predict cancer outcome, we developed an integrated and systematical approach by investigating gene expression profiling alternation caused by disruptions between protein-protein interactions and domain-domain interactions in the human interactome. Our approach achieves the favorable predictive performance if tested on a set of well-documented breast cancer patients, which suggests that the disrupted interactome is important to determine patient prognosis. Our approach is robust if tested on other independent data sets. This work provides a promising prognostic tool to classify different cancer outcomes. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 7 IS - 3 UR - https://doi.org/10.1371/journal.pcbi.1001114 SP - e1001114 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1001114 ER -