@article {422, title = {Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models.}, journal = {NPJ Syst Biol Appl}, volume = {5}, year = {2019}, month = {2019}, pages = {7}, abstract = {

In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.

}, keywords = {Computational Biology, Computer Simulation, Drug discovery, Gene Regulatory Networks, Humans, Internet, Metabolic Networks and Pathways, Models, Biological, Neoplasms, Phenotype, Software, Transcriptome}, issn = {2056-7189}, doi = {10.1038/s41540-019-0087-2}, author = {Cubuk, Cankut and Hidalgo, Marta R and Amadoz, Alicia and Rian, Kinza and Salavert, Francisco and Pujana, Miguel A and Mateo, Francesca and Herranz, Carmen and Carbonell-Caballero, Jos{\'e} and Dopazo, Joaquin} } @article {405, title = {Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape.}, journal = {Cancer Res}, volume = {78}, year = {2018}, month = {2018 Nov 01}, pages = {6059-6072}, abstract = {

Metabolic reprogramming plays an important role in cancer development and progression and is a well-established hallmark of cancer. Despite its inherent complexity, cellular metabolism can be decomposed into functional modules that represent fundamental metabolic processes. Here, we performed a pan-cancer study involving 9,428 samples from 25 cancer types to reveal metabolic modules whose individual or coordinated activity predict cancer type and outcome, in turn highlighting novel therapeutic opportunities. Integration of gene expression levels into metabolic modules suggests that the activity of specific modules differs between cancers and the corresponding tissues of origin. Some modules may cooperate, as indicated by the positive correlation of their activity across a range of tumors. The activity of many metabolic modules was significantly associated with prognosis at a stronger magnitude than any of their constituent genes. Thus, modules may be classified as tumor suppressors and oncomodules according to their potential impact on cancer progression. Using this modeling framework, we also propose novel potential therapeutic targets that constitute alternative ways of treating cancer by inhibiting their reprogrammed metabolism. Collectively, this study provides an extensive resource of predicted cancer metabolic profiles and dependencies. Combining gene expression with metabolic modules identifies molecular mechanisms of cancer undetected on an individual gene level and allows discovery of new potential therapeutic targets. .

}, keywords = {Cell Line, Tumor, Cluster Analysis, Disease Progression, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Gene Regulatory Networks, Humans, Kaplan-Meier Estimate, Metabolome, mutation, Neoplasms, Oncogenes, Phenotype, Prognosis, RNA, Small Interfering, Sequence Analysis, RNA, Transcriptome, Treatment Outcome}, issn = {1538-7445}, doi = {10.1158/0008-5472.CAN-17-2705}, author = {Cubuk, Cankut and Hidalgo, Marta R and Amadoz, Alicia and Pujana, Miguel A and Mateo, Francesca and Herranz, Carmen and Carbonell-Caballero, Jos{\'e} and Dopazo, Joaquin} } @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 {512, title = {Discovering the hidden sub-network component in a ranked list of genes or proteins derived from genomic experiments.}, journal = {Nucleic Acids Res}, volume = {40}, year = {2012}, month = {2012 Nov 01}, pages = {e158}, abstract = {

Genomic experiments (e.g. differential gene expression, single-nucleotide polymorphism association) typically produce ranked list of genes. We present a simple but powerful approach which uses protein-protein interaction data to detect sub-networks within such ranked lists of genes or proteins. We performed an exhaustive study of network parameters that allowed us concluding that the average number of components and the average number of nodes per component are the parameters that best discriminate between real and random networks. A novel aspect that increases the efficiency of this strategy in finding sub-networks is that, in addition to direct connections, also connections mediated by intermediate nodes are considered to build up the sub-networks. The possibility of using of such intermediate nodes makes this approach more robust to noise. It also overcomes some limitations intrinsic to experimental designs based on differential expression, in which some nodes are invariant across conditions. The proposed approach can also be used for candidate disease-gene prioritization. Here, we demonstrate the usefulness of the approach by means of several case examples that include a differential expression analysis in Fanconi Anemia, a genome-wide association study of bipolar disorder and a genome-scale study of essentiality in cancer genes. An efficient and easy-to-use web interface (available at http://www.babelomics.org) based on HTML5 technologies is also provided to run the algorithm and represent the network.

}, keywords = {Bipolar Disorder, Fanconi Anemia, Gene Regulatory Networks, Genes, Neoplasm, Genome-Wide Association Study, Genomics, Humans, Protein Interaction Mapping}, issn = {1362-4962}, doi = {10.1093/nar/gks699}, author = {Garc{\'\i}a-Alonso, Luz and Alonso, Roberto and Vidal, Enrique and Amadoz, Alicia and De Maria, Alejandro and Minguez, Pablo and Medina, Ignacio and Dopazo, Joaquin} }