@article {434, title = {High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes.}, journal = {Oncotarget}, volume = {8}, year = {2017}, month = {2017 Jan 17}, pages = {5160-5178}, abstract = {

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.

}, keywords = {Computational Biology, gene expression, Gene Regulatory Networks, Humans, mutation, Neoplasms, Precision Medicine, Sequence Analysis, RNA, Signal Transduction}, issn = {1949-2553}, doi = {10.18632/oncotarget.14107}, author = {Hidalgo, Marta R and Cubuk, Cankut and Amadoz, Alicia and Salavert, Francisco and Carbonell-Caballero, Jos{\'e} and Dopazo, Joaquin} } @article {474, title = {Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity.}, journal = {Sci Rep}, volume = {5}, year = {2015}, month = {2015 Dec 18}, pages = {18494}, abstract = {

Many complex traits, as drug response, are associated with changes in biological pathways rather than being caused by single gene alterations. Here, a predictive framework is presented in which gene expression data are recoded into activity statuses of signal transduction circuits (sub-pathways within signaling pathways that connect receptor proteins to final effector proteins that trigger cell actions). Such activity values are used as features by a prediction algorithm which can efficiently predict a continuous variable such as the IC50 value. The main advantage of this prediction method is that the features selected by the predictor, the signaling circuits, are themselves rich-informative, mechanism-based biomarkers which provide insight into or drug molecular mechanisms of action (MoA).

}, keywords = {Algorithms, Antineoplastic Agents, biomarkers, Cell Line, Tumor, Cell Survival, gene expression, Humans, Lethal Dose 50, Neoplasms, Phosphorylation, Proteins, Signal Transduction}, issn = {2045-2322}, doi = {10.1038/srep18494}, author = {Amadoz, Alicia and Sebasti{\'a}n-Leon, Patricia and Vidal, Enrique and Salavert, Francisco and Dopazo, Joaquin} } @article {500, title = {Inferring the functional effect of gene expression changes in signaling pathways.}, journal = {Nucleic Acids Res}, volume = {41}, year = {2013}, month = {2013 Jul}, pages = {W213-7}, abstract = {

Signaling pathways constitute a valuable source of information that allows interpreting the way in which alterations in gene activities affect to particular cell functionalities. There are web tools available that allow viewing and editing pathways, as well as representing experimental data on them. However, few methods aimed to identify the signaling circuits, within a pathway, associated to the biological problem studied exist and none of them provide a convenient graphical web interface. We present PATHiWAYS, a web-based signaling pathway visualization system that infers changes in signaling that affect cell functionality from the measurements of gene expression values in typical expression microarray case-control experiments. A simple probabilistic model of the pathway is used to estimate the probabilities for signal transmission from any receptor to any final effector molecule (taking into account the pathway topology) using for this the individual probabilities of gene product presence/absence inferred from gene expression values. Significant changes in these probabilities allow linking different cell functionalities triggered by the pathway to the biological problem studied. PATHiWAYS is available at: http://pathiways.babelomics.org/.

}, keywords = {Animals, Humans, Internet, Mice, Models, Statistical, Receptors, Cell Surface, Signal Transduction, Software, Transcriptome}, issn = {1362-4962}, doi = {10.1093/nar/gkt451}, author = {Sebasti{\'a}n-Leon, Patricia and Carbonell, Jos{\'e} and Salavert, Francisco and S{\'a}nchez, Rub{\'e}n and Medina, Ignacio and Dopazo, Joaquin} }