OmniPath: guidelines and gateway for literature-curated signaling pathway resources

Türei, Dénes; Korcsmáros, Tamás; Sáez-Rodríguez, Julio

doi:10.1038/nmeth.4077

Cited by 558 publications

(596 citation statements)

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“…Higher performance (average Pearson correlation 0.37 vs 0.25; P=0.008, Figure 4B) was observed for combinations inhibiting the PI3K/AKT pathway together with MAPK pathway, or apoptosis pathway with metabolism or receptor tyrosine kinases. Assessment of the interactions between drug targets and neighbouring proteins from OmniPath, a comprehensive compendium of literature-based pathway resources 17 , revealed no differences in the somatic alteration frequency for targets or their first neighbors between the poorly and well predicted combinations (Supp. Fig.…”

Section: Increasing Performance Using Biologically Meaningful Featuresmentioning

confidence: 99%

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

Wang

Guan

et al. 2017

Preprint

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In the last decade advances in genomics, uptake of targeted therapies, and the advent of personalized treatments have fueled a dramatic change in cancer care. However, the effectiveness of most targeted therapies is short lived, as tumors evolve and develop resistance. Combinations of drugs offer the potential to overcome resistance. The space of possible combinations is vast, and significant advances are required to effectively find optimal treatment regimens tailored to a patient's tumor. DREAM and AstraZeneca hosted a Challenge open to the scientific community aimed at computational prediction of synergistic drug combinations and predictive biomarkers associated to these combinations. We released a data set comprising ~11,500 experimentally tested drug combinations, coupled to deep molecular characterization of the respective 85 cancer cell lines. Among 150 submitted approaches, those that incorporated prior knowledge of putative drug targets showed superior performance predicting drug synergy across independent data. Genomic features of best-performing models revealed putative mechanisms of drug synergy for multiple drugs in combination with PI3K/AKT pathway inhibitors.All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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Section: Increasing Performance Using Biologically Meaningful Featuresmentioning

confidence: 99%

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

Wang

Guan

et al. 2017

Preprint

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“…The steps include the building of the signaling network, its improvement using available data, and its simulation and analysis geared towards obtaining useful biological insights and predictions. We also present several tools that are useful for such a workflow, namely, Omnipath 48 to construct the signaling network, CellNOpt 49 to build a model trained to data, MaBoSS 50 to simulate and predict treatment response, and Cytoscape 51 to visualize some results of the simulations. It should be noted that many other excellent tools to model and analyze signaling networks exist, such as BoolSim,52 BoolNet,53 GINsim,54 etc.…”

Section: Biological Applications Of Logic Modelingmentioning

confidence: 99%

“…In this tutorial we use Omnipath 48 for signaling database mining, CellNOpt 49 for model fitting, MaBoSS 50 for simulations, and Cytoscape 51 for visualization and network analysis. Different steps of the pipeline include 1) selecting a system and a question of interest and building a first version of the network, 2) choosing a modeling formalism and improving the model with data, and 3) analyzing the model, making predictions and comparing them to experimental data.…”

Section: Biological Applications Of Logic Modelingmentioning

confidence: 99%

“…However, the modeler should keep in mind the limits of such resources: because they are not always up‐to‐date, specific but important information not included in the databases might be missed. To facilitate the retrieval of information from all these databases, we use Omnipath ,48 a comprehensive collection of existing manually curated pathway databases. Omnipath comes with a Python tool called pypath , developed to query its content, and manipulate and filter it easily.…”

Section: Biological Applications Of Logic Modelingmentioning

confidence: 99%

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Logic Modeling in Quantitative Systems Pharmacology

Traynard

Tobalina

Eduati

et al. 2017

CPT Pharmacometrics Syst. Pharmacol.

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Here we present logic modeling as an approach to understand deregulation of signal transduction in disease and to characterize a drug's mode of action. We discuss how to build a logic model from the literature and experimental data and how to analyze the resulting model to obtain insights of relevance for systems pharmacology. Our workflow uses the free tools OmniPath (network reconstruction from the literature), CellNOpt (model fit to experimental data), MaBoSS (model analysis), and Cytoscape (visualization).

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“…However, like other pathway maps 17,18 , the ACSN lacks kinetic rate laws and rate constants, preventing numerical simulation and quantitative prediction. This might be addressed in the future by using comprehensive databases 13,19,20 in combination with semi-automatic [21][22][23] or automatic reconstruction methods 8,24,25 .…”

Section: Introductionmentioning

confidence: 99%

Efficient parameterization of large-scale mechanistic models enables drug response prediction for cancer cell lines

Fröhlich

Keßler

Weindl³

et al. 2017

Preprint

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The response of cancer cells to drugs is determined by various factors, including the cells' mutations and gene expression levels. These factors can be assessed using next-generation sequencing. Their integration with vast prior knowledge on signaling pathways is, however, limited by the availability of mathematical models and scalable computational methods. Here, we present a computational framework for the parameterization of large-scale mechanistic models and its application to the prediction of drug response of cancer cell lines from exome and transcriptome sequencing data. With this framework, we parameterized a mechanistic model describing major cancer-associated signaling pathways (>1200 species and >2600 reactions) using drug response data. For the parameterized mechanistic model, we found a prediction accuracy, which exceeds that of the considered statistical approaches. Our results demonstrate for the first time the massive integration of heterogeneous datasets using large-scale mechanistic models, and how these models facilitate individualized predictions of drug response. We anticipate our parameterized model to be a starting point for the development of more comprehensive, curated models of signaling pathways, accounting for additional pathways and drugs. IntroductionPersonalized tumor therapy relies on our ability to predict the drug response of cancer cells from genomic data 1 . This requires the integration of genomic data with available prior knowledge, and its interpretation in the context of cancer-associated processes 2 . At the heart of this endeavor are statistical and mechanistic mathematical models 3 . In patient . CC-BY-NC-ND 4.0 International license not peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was . http://dx.doi.org/10.1101/174094 doi: bioRxiv preprint first posted online Aug. 9, 2017; 2 stratification, statistical models are used to derive prognostic and predictive signatures of tumor subtypes 4,5 . Linear and nonlinear regression, machine learning methods and related approaches have been used to obtain such signatures 6 . Yet, purely statistical models do not provide mechanistic insights or information about actionable targets. High-quality mechanistic models of cancer signaling are thus of interest to researchers and clinicians in systems biology and systems medicine.Mechanistic models aim to quantitatively describe biological processes. Consequently, they facilitate the systematic integration of prior knowledge on signaling pathways, as well as the effect of somatic mutations and gene expression. These models have been used for the identification of drug targets 7 as well as the development of prognostic signatures 8,9 .Furthermore, mechanistic modeling has facilitated the study of oncogene addiction 10 , After the construction of a mechanistic model, parameterization from experimental data is necessary to render the model predictive. Optimization methods achieve this by iteratively minimizing the objective f...

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OmniPath: guidelines and gateway for literature-curated signaling pathway resources

Cited by 558 publications

References 6 publications

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction

Logic Modeling in Quantitative Systems Pharmacology

Efficient parameterization of large-scale mechanistic models enables drug response prediction for cancer cell lines

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