TY - JOUR T1 - Community Assessment of the Predictability of Cancer Protein and Phosphoprotein Levels from Genomics and Transcriptomics. JF - Cell Syst Y1 - 2020 A1 - Yang, Mi A1 - Petralia, Francesca A1 - Li, Zhi A1 - Li, Hongyang A1 - Ma, Weiping A1 - Song, Xiaoyu A1 - Kim, Sunkyu A1 - Lee, Heewon A1 - Yu, Han A1 - Lee, Bora A1 - Bae, Seohui A1 - Heo, Eunji A1 - Kaczmarczyk, Jan A1 - Stępniak, Piotr A1 - Warchoł, Michał A1 - Yu, Thomas A1 - Calinawan, Anna P A1 - Boutros, Paul C A1 - Payne, Samuel H A1 - Reva, Boris A1 - Boja, Emily A1 - Rodriguez, Henry A1 - Stolovitzky, Gustavo A1 - Guan, Yuanfang A1 - Kang, Jaewoo A1 - Wang, Pei A1 - Fenyö, David A1 - Saez-Rodriguez, Julio KW - Crowdsourcing KW - Female KW - Genomics KW - Humans KW - Machine Learning KW - Male KW - Neoplasms KW - Phosphoproteins KW - Proteins KW - Proteomics KW - Transcriptome AB -

Cancer is driven by genomic alterations, but the processes causing this disease are largely performed by proteins. However, proteins are harder and more expensive to measure than genes and transcripts. To catalyze developments of methods to infer protein levels from other omics measurements, we leveraged crowdsourcing via the NCI-CPTAC DREAM proteogenomic challenge. We asked for methods to predict protein and phosphorylation levels from genomic and transcriptomic data in cancer patients. The best performance was achieved by an ensemble of models, including as predictors transcript level of the corresponding genes, interaction between genes, conservation across tumor types, and phosphosite proximity for phosphorylation prediction. Proteins from metabolic pathways and complexes were the best and worst predicted, respectively. The performance of even the best-performing model was modest, suggesting that many proteins are strongly regulated through translational control and degradation. Our results set a reference for the limitations of computational inference in proteogenomics. A record of this paper's transparent peer review process is included in the Supplemental Information.

VL - 11 IS - 2 U1 - https://www.ncbi.nlm.nih.gov/pubmed/32710834?dopt=Abstract ER - TY - JOUR T1 - Mechanistic Models of Signaling Pathways Reveal the Drug Action Mechanisms behind Gender-Specific Gene Expression for Cancer Treatments. JF - Cells Y1 - 2020 A1 - Cubuk, Cankut A1 - Can, Fatma E A1 - Peña-Chilet, Maria A1 - Dopazo, Joaquin KW - Female KW - Gene Expression Regulation, Neoplastic KW - Humans KW - Male KW - Neoplasms KW - Signal Transduction AB -

Despite the existence of differences in gene expression across numerous genes between males and females having been known for a long time, these have been mostly ignored in many studies, including drug development and its therapeutic use. In fact, the consequences of such differences over the disease mechanisms or the drug action mechanisms are completely unknown. Here we applied mechanistic mathematical models of signaling activity to reveal the ultimate functional consequences that gender-specific gene expression activities have over cell functionality and fate. Moreover, we also used the mechanistic modeling framework to simulate the drug interventions and unravel how drug action mechanisms are affected by gender-specific differential gene expression. Interestingly, some cancers have many biological processes significantly affected by these gender-specific differences (e.g., bladder or head and neck carcinomas), while others (e.g., glioblastoma or rectum cancer) are almost insensitive to them. We found that many of these gender-specific differences affect cancer-specific pathways or in physiological signaling pathways, also involved in cancer origin and development. Finally, mechanistic models have the potential to be used for finding alternative therapeutic interventions on the pathways targeted by the drug, which lead to similar results compensating the downstream consequences of gender-specific differences in gene expression.

VL - 9 IS - 7 U1 - https://www.ncbi.nlm.nih.gov/pubmed/32610626?dopt=Abstract ER - TY - JOUR T1 - Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. JF - Nat Commun Y1 - 2019 A1 - Menden, Michael P A1 - Wang, Dennis A1 - Mason, Mike J A1 - Szalai, Bence A1 - Bulusu, Krishna C A1 - Guan, Yuanfang A1 - Yu, Thomas A1 - Kang, Jaewoo A1 - Jeon, Minji A1 - Wolfinger, Russ A1 - Nguyen, Tin A1 - Zaslavskiy, Mikhail A1 - Jang, In Sock A1 - Ghazoui, Zara A1 - Ahsen, Mehmet Eren A1 - Vogel, Robert A1 - Neto, Elias Chaibub A1 - Norman, Thea A1 - Tang, Eric K Y A1 - Garnett, Mathew J A1 - Veroli, Giovanni Y Di A1 - Fawell, Stephen A1 - Stolovitzky, Gustavo A1 - Guinney, Justin A1 - Dry, Jonathan R A1 - Saez-Rodriguez, Julio KW - ADAM17 Protein KW - Antineoplastic Combined Chemotherapy Protocols KW - Benchmarking KW - Biomarkers, Tumor KW - Cell Line, Tumor KW - Computational Biology KW - Datasets as Topic KW - Drug Antagonism KW - Drug Resistance, Neoplasm KW - Drug Synergism KW - Genomics KW - Humans KW - Molecular Targeted Therapy KW - mutation KW - Neoplasms KW - pharmacogenetics KW - Phosphatidylinositol 3-Kinases KW - Phosphoinositide-3 Kinase Inhibitors KW - Treatment Outcome AB -

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.

VL - 10 IS - 1 U1 - https://www.ncbi.nlm.nih.gov/pubmed/31209238?dopt=Abstract ER - TY - JOUR T1 - Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models. JF - NPJ Syst Biol Appl Y1 - 2019 A1 - Cubuk, Cankut A1 - Hidalgo, Marta R A1 - Amadoz, Alicia A1 - Rian, Kinza A1 - Salavert, Francisco A1 - Pujana, Miguel A A1 - Mateo, Francesca A1 - Herranz, Carmen A1 - Carbonell-Caballero, José A1 - Dopazo, Joaquin KW - Computational Biology KW - Computer Simulation KW - Drug discovery KW - Gene Regulatory Networks KW - Humans KW - Internet KW - Metabolic Networks and Pathways KW - Models, Biological KW - Neoplasms KW - Phenotype KW - Software KW - Transcriptome AB -

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.

VL - 5 U1 - https://www.ncbi.nlm.nih.gov/pubmed/30854222?dopt=Abstract ER - TY - JOUR T1 - Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape. JF - Cancer Res Y1 - 2018 A1 - Cubuk, Cankut A1 - Hidalgo, Marta R A1 - Amadoz, Alicia A1 - Pujana, Miguel A A1 - Mateo, Francesca A1 - Herranz, Carmen A1 - Carbonell-Caballero, José A1 - Dopazo, Joaquin KW - Cell Line, Tumor KW - Cluster Analysis KW - Disease Progression KW - Gene Expression Profiling KW - Gene Expression Regulation, Neoplastic KW - Gene Regulatory Networks KW - Humans KW - Kaplan-Meier Estimate KW - Metabolome KW - mutation KW - Neoplasms KW - Oncogenes KW - Phenotype KW - Prognosis KW - RNA, Small Interfering KW - Sequence Analysis, RNA KW - Transcriptome KW - Treatment Outcome AB -

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. .

VL - 78 IS - 21 U1 - https://www.ncbi.nlm.nih.gov/pubmed/30135189?dopt=Abstract ER - TY - JOUR T1 - High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes. JF - Oncotarget Y1 - 2017 A1 - Hidalgo, Marta R A1 - Cubuk, Cankut A1 - Amadoz, Alicia A1 - Salavert, Francisco A1 - Carbonell-Caballero, José A1 - Dopazo, Joaquin KW - Computational Biology KW - gene expression KW - Gene Regulatory Networks KW - Humans KW - mutation KW - Neoplasms KW - Precision Medicine KW - Sequence Analysis, RNA KW - Signal Transduction AB -

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.

VL - 8 IS - 3 U1 - https://www.ncbi.nlm.nih.gov/pubmed/28042959?dopt=Abstract ER - TY - JOUR T1 - Integrated gene set analysis for microRNA studies. JF - Bioinformatics Y1 - 2016 A1 - Garcia-Garcia, Francisco A1 - Panadero, Joaquin A1 - Dopazo, Joaquin A1 - Montaner, David KW - Computational Biology KW - Gene Expression Profiling KW - Gene ontology KW - Gene Regulatory Networks KW - High-Throughput Nucleotide Sequencing KW - Humans KW - MicroRNAs KW - Neoplasms KW - Reproducibility of Results AB -

MOTIVATION: Functional interpretation of miRNA expression data is currently done in a three step procedure: select differentially expressed miRNAs, find their target genes, and carry out gene set overrepresentation analysis Nevertheless, major limitations of this approach have already been described at the gene level, while some newer arise in the miRNA scenario.Here, we propose an enhanced methodology that builds on the well-established gene set analysis paradigm. Evidence for differential expression at the miRNA level is transferred to a gene differential inhibition score which is easily interpretable in terms of gene sets or pathways. Such transferred indexes account for the additive effect of several miRNAs targeting the same gene, and also incorporate cancellation effects between cases and controls. Together, these two desirable characteristics allow for more accurate modeling of regulatory processes.

RESULTS: We analyze high-throughput sequencing data from 20 different cancer types and provide exhaustive reports of gene and Gene Ontology-term deregulation by miRNA action.

AVAILABILITY AND IMPLEMENTATION: The proposed methodology was implemented in the Bioconductor library mdgsa http://bioconductor.org/packages/mdgsa For the purpose of reproducibility all of the scripts are available at https://github.com/dmontaner-papers/gsa4mirna

CONTACT: : david.montaner@gmail.com

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

VL - 32 IS - 18 U1 - https://www.ncbi.nlm.nih.gov/pubmed/27324197?dopt=Abstract ER - TY - JOUR T1 - A Pan-Cancer Catalogue of Cancer Driver Protein Interaction Interfaces. JF - PLoS Comput Biol Y1 - 2015 A1 - Porta-Pardo, Eduard A1 - García-Alonso, Luz A1 - Hrabe, Thomas A1 - Dopazo, Joaquin A1 - Godzik, Adam KW - Animals KW - Base Sequence KW - Biomarkers, Tumor KW - Catalogs as Topic KW - Chromosome Mapping KW - Computer Simulation KW - DNA Mutational Analysis KW - Genetic Predisposition to Disease KW - Humans KW - Models, Genetic KW - Molecular Sequence Data KW - mutation KW - Neoplasm Proteins KW - Neoplasms KW - Polymorphism, Single Nucleotide KW - Protein Interaction Mapping KW - Signal Transduction AB -

Despite their importance in maintaining the integrity of all cellular pathways, the role of mutations on protein-protein interaction (PPI) interfaces as cancer drivers has not been systematically studied. Here we analyzed the mutation patterns of the PPI interfaces from 10,028 proteins in a pan-cancer cohort of 5,989 tumors from 23 projects of The Cancer Genome Atlas (TCGA) to find interfaces enriched in somatic missense mutations. To that end we use e-Driver, an algorithm to analyze the mutation distribution of specific protein functional regions. We identified 103 PPI interfaces enriched in somatic cancer mutations. 32 of these interfaces are found in proteins coded by known cancer driver genes. The remaining 71 interfaces are found in proteins that have not been previously identified as cancer drivers even that, in most cases, there is an extensive literature suggesting they play an important role in cancer. Finally, we integrate these findings with clinical information to show how tumors apparently driven by the same gene have different behaviors, including patient outcomes, depending on which specific interfaces are mutated.

VL - 11 IS - 10 U1 - https://www.ncbi.nlm.nih.gov/pubmed/26485003?dopt=Abstract ER - TY - JOUR T1 - Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity. JF - Sci Rep Y1 - 2015 A1 - Amadoz, Alicia A1 - Sebastián-Leon, Patricia A1 - Vidal, Enrique A1 - Salavert, Francisco A1 - Dopazo, Joaquin KW - Algorithms KW - Antineoplastic Agents KW - biomarkers KW - Cell Line, Tumor KW - Cell Survival KW - gene expression KW - Humans KW - Lethal Dose 50 KW - Neoplasms KW - Phosphorylation KW - Proteins KW - Signal Transduction AB -

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).

VL - 5 U1 - https://www.ncbi.nlm.nih.gov/pubmed/26678097?dopt=Abstract ER - TY - JOUR T1 - Evidence for systems-level molecular mechanisms of tumorigenesis. JF - BMC Genomics Y1 - 2007 A1 - Hernández, Pilar A1 - Huerta-Cepas, Jaime A1 - Montaner, David A1 - Al-Shahrour, Fátima A1 - Valls, Joan A1 - Gómez, Laia A1 - Capellà, Gabriel A1 - Dopazo, Joaquin A1 - Pujana, Miguel Angel KW - Cell Transformation, Neoplastic KW - Gene Expression Profiling KW - Gene Expression Regulation, Neoplastic KW - Humans KW - Male KW - Models, Biological KW - Models, Genetic KW - Models, Statistical KW - Neoplasm Proteins KW - Neoplasms KW - Prostatic Neoplasms KW - Protein Interaction Mapping KW - RNA, Messenger KW - Signal Transduction KW - Systems biology AB -

BACKGROUND: Cancer arises from the consecutive acquisition of genetic alterations. Increasing evidence suggests that as a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. Coordinated reprogramming would allow the cell to acquire the capabilities for malignant growth.

RESULTS: Here, we determine the coordinated function of cancer gene products (i.e., proteins encoded by differentially expressed genes in tumors relative to healthy tissue counterparts, hereafter referred to as "CGPs") defined as their topological properties and organization in the interactome network. We show that CGPs are central to information exchange and propagation and that they are specifically organized to promote tumorigenesis. Centrality is identified by both local (degree) and global (betweenness and closeness) measures, and systematically appears in down-regulated CGPs. Up-regulated CGPs do not consistently exhibit centrality, but both types of cancer products determine the overall integrity of the network structure. In addition to centrality, down-regulated CGPs show topological association that correlates with common biological processes and pathways involved in tumorigenesis.

CONCLUSION: Given the current limited coverage of the human interactome, this study proposes that tumorigenesis takes place in a specific and organized way at the molecular systems-level and suggests a model that comprises the precise down-regulation of groups of topologically-associated proteins involved in particular functions, orchestrated with the up-regulation of specific proteins.

VL - 8 U1 - https://www.ncbi.nlm.nih.gov/pubmed/17584915?dopt=Abstract ER -