|Title||High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes.|
|Publication Type||Journal Article|
|Year of Publication||2017|
|Authors||Hidalgo, MR, Cubuk, C, Amadoz, A, Salavert, F, Carbonell-Caballero, J, Dopazo, J|
|Date Published||2017 Jan 17|
|Keywords||Computational Biology; gene expression; Gene Regulatory Networks; Humans; mutation; Neoplasms; Precision Medicine; Sequence Analysis, RNA; Signal Transduction|
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.
|PubMed Central ID||PMC5354899|