title: Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes.
abstract: Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.
authors.affiliation: Department of Bioinformatics, Biotechnology Center, Technische Universität Dresden, Dresden, Germany. winter@biotec.tu-dresden.de
authors.forename: Christof
authors.initials: C
authors.lastname: Winter
authors.forename: Glen
authors.initials: G
authors.lastname: Kristiansen
authors.forename: Stephan
authors.initials: S
authors.lastname: Kersting
authors.forename: Janine
authors.initials: J
authors.lastname: Roy
authors.forename: Daniela
authors.initials: D
authors.lastname: Aust
authors.forename: Thomas
authors.initials: T
authors.lastname: Knösel
authors.forename: Petra
authors.initials: P
authors.lastname: Rümmele
authors.forename: Beatrix
authors.initials: B
authors.lastname: Jahnke
authors.forename: Vera
authors.initials: V
authors.lastname: Hentrich
authors.forename: Felix
authors.initials: F
authors.lastname: Rückert
authors.forename: Marco
authors.initials: M
authors.lastname: Niedergethmann
authors.forename: Wilko
authors.initials: W
authors.lastname: Weichert
authors.forename: Marcus
authors.initials: M
authors.lastname: Bahra
authors.forename: Hans J
authors.initials: HJ
authors.lastname: Schlitt
authors.forename: Utz
authors.initials: U
authors.lastname: Settmacher
authors.forename: Helmut
authors.initials: H
authors.lastname: Friess
authors.forename: Markus
authors.initials: M
authors.lastname: Büchler
authors.forename: Hans-Detlev
authors.initials: HD
authors.lastname: Saeger
authors.forename: Michael
authors.initials: M
authors.lastname: Schroeder
authors.forename: Christian
authors.initials: C
authors.lastname: Pilarsky
authors.forename: Robert
authors.initials: R
authors.lastname: Grützmann
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D005819:Genetic Markers / Q000235:genetics*; D020022:Genetic Predisposition to Disease / Q000453:epidemiology* / Q000235:genetics*; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D017063:Outcome Assessment, Health Care / Q000379:methods*; D010190:Pancreatic Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000401:mortality*; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D005819:Genetic Markers