Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

By C. Winter, G. Kristiansen, S. Kersting, Janine Roy, D. Aust, T. Knösel, P. Rümmele, Beatrix Jahnke, Vera Hentrich, F. Rückert and 11 more. 2012

196 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Oncology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

EgoNet: identification of human disease ego-network modules (2014, methodology): “To date, many computational methods have been developed to integrate gene expression profiles with protein-protein interaction maps or pathway databases, with the goal of identifying significant subnetwork markers for predicting biological or clinical outcomes [10-18].”
Random walks and diffusion on networks (2016, methodology): “For example, seven new genes that predict the survival of patients in a type of pancreatic cancer were identified using PageRank [293].”
PageRank beyond the Web (2014, background): “Because of this power, teleportation behaviors are much more varied for these localized applications.”