High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes
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Marta R. Hidalgo1, Cankut Cubuk1, Alicia Amadoz1,2, Francisco Salavert1,3, José Carbonell-Caballero1, Joaquin Dopazo1,2,3
1Computational Genomics Department, Centro de Investigación Príncipe Felipe (CIPF), Valencia, 46012, Spain
2Functional Genomics Node (INB-ELIXIR-es), Valencia, 46012, Spain
3Bioinformatics in Rare Diseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Valencia, 46012, Spain
Joaquín Dopazo, email: firstname.lastname@example.org
Keywords: signaling pathway, disease mechanism, prognostic, survival, biomarker
Received: September 01, 2016 Accepted: November 21, 2016 Published: December 22, 2016
Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is a main challenge for precision medicine. Here we propose a new method that models cell signaling using biological knowledge on signal transduction. The method recodes individual gene expression values (and/or gene mutations) into accurate measurements of changes in the activity of signaling circuits, which ultimately constitute high-throughput estimations of cell functionalities caused by gene activity within the pathway. Moreover, such estimations can be obtained either at cohort-level, in case/control comparisons, or personalized for individual patients. The accuracy of the method is demonstrated in an extensive analysis involving 5640 patients from 12 different cancer types. Circuit activity measurements not only have a high diagnostic value but also can be related to relevant disease outcomes such as survival, and can be used to assess therapeutic interventions.
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