TY - JOUR T1 - Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes A1 - Winter, Christof A1 - Kristiansen, Glen A1 - Kersting, Stephan A1 - Roy, Janine A1 - Aust, Daniela A1 - Knösel, Thomas A1 - Rümmele, Petra A1 - Jahnke, Beatrix A1 - Hentrich, Vera A1 - Rückert, Felix A1 - Niedergethmann, Marco A1 - Weichert, Wilko A1 - Bahra, Marcus A1 - Schlitt, Hans J. A1 - Settmacher, Utz A1 - Friess, Helmut A1 - Büchler, Markus A1 - Saeger, Hans-Detlev A1 - Schroeder, Michael A1 - Pilarsky, Christian A1 - Grützmann, Robert Y1 - 2012/05/17 N2 - Author Summary Why do some people with the same type of cancer die early and some live long? Apart from influences from the environment and personal lifestyle, we believe that differences in the individual tumor genome account for different survival times. Recently, powerful methods have become available to systematically read genomic information of patient samples. The major remaining challenge is how to spot, among the thousands of changes, those few that are relevant for tumor aggressiveness and thereby affecting patient survival. Here, we make use of the fact that genes and proteins in a cell never act alone, but form a network of interactions. Finding the relevant information in big networks of web documents and hyperlinks has been mastered by Google with their PageRank algorithm. Similar to PageRank, we have developed an algorithm that can identify genes that are better indicators for survival than genes found by traditional algorithms. Our method can aid the clinician in deciding if a patient should receive chemotherapy or not. Reliable prediction of survival and response to therapy based on molecular markers bears a great potential to improve and personalize patient therapies in the future. JF - PLOS Computational Biology JA - PLOS Computational Biology VL - 8 IS - 5 UR - https://doi.org/10.1371/journal.pcbi.1002511 SP - e1002511 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pcbi.1002511 ER -