Oct 30, 2018

PRODIGY: personalized prioritization of driver genes

BioRxiv : the Preprint Server for Biology
Gal Dinstag, Ron Shamir

Abstract

Background: Evolution of cancer is driven by few somatic mutations that disrupt cellular processes, causing abnormal proliferation and tumor development, while most somatic mutations have no impact on progression. Distinguishing those mutated genes that drive tumorigenesis in a patient is a primary goal in cancer therapy: Knowledge of these genes and the pathways on which they operate can illuminate disease mechanisms and indicate potential therapies and drug targets. Current research focuses mainly on cohort-level driver gene identification, but patient-specific driver gene identification remains a challenge. Methods: We developed a new algorithm for patient-specific ranking of driver genes. The algorithm, called PRODIGY, analyzes the expression and mutation profiles of the patient along with data on known pathways and protein-protein interactions. Prodigy quantifies the impact of each mutated gene on every deregulated pathway using the prize collecting Steiner tree model. Mutated genes are ranked by their aggregated impact on all deregulated pathways. Results: In testing on five TCGA cancer cohorts spanning >2500 patients and comparison to validated driver genes, Prodigy outperformed extant methods and ranking based on networ...Continue Reading

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Mentioned in this Paper

Biochemical Pathway
Research
Cellular Process
Oncologist
Genes
Somatic Mutation
Aggregation
Neoplasms
Gene Mutation
Cell Proliferation

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