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Google goes cancer: improving outcome prediction for cancer patients by network-based ranking of marker genes
- Source :
- PLoS Computational Biology, Vol 8, Iss 5, p e1002511 (2012), PLoS Computational Biology
- Publication Year :
- 2012
- Publisher :
- Public Library of Science (PLoS), 2012.
-
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.<br />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.
- Subjects :
- Male
Microarrays
Bioinformatics
law.invention
law
Outcome Assessment, Health Care
Pathology
Biology (General)
Ecology
Cancer Risk Factors
Genomics
Primary tumor
Computational Theory and Mathematics
Oncology
Modeling and Simulation
symbols
Medicine
DNA microarray
Immunohistochemical Analysis
Algorithms
Research Article
Genetic Markers
QH301-705.5
Immunology
Genetic Causes of Cancer
Computational biology
Sensitivity and Specificity
Ranking (information retrieval)
Cellular and Molecular Neuroscience
symbols.namesake
Pancreatic Cancer
Text mining
PageRank
Diagnostic Medicine
Pancreatic cancer
Gastrointestinal Tumors
Genetics
medicine
Biomarkers, Tumor
Humans
Genetic Predisposition to Disease
Molecular Biology
Biology
Ecology, Evolution, Behavior and Systematics
Clinical Genetics
business.industry
Personalized Medicine
Cancer
Computational Biology
Cancers and Neoplasms
medicine.disease
Pearson product-moment correlation coefficient
Pancreatic Neoplasms
Computer Science
Immunologic Techniques
Neural Networks, Computer
business
Genome Expression Analysis
Biomarkers
General Pathology
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 8
- Issue :
- 5
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....72cd8fb9d6727e1e35d068a662c501d5