Network quantification of EGFR signaling unveils potential for targeted combination therapy

Klinger, Bertram; Sieber, Anja; Fritsche‐Guenther, Raphaela; Witzel, Franziska; Berry, Leanne; Schumacher, Dirk; Yan, Yibing; Durek, Pawel; Merchant, Mark; Schäfer, Reinhold; Sers, Christine; Blüthgen, Nils

doi:10.1038/msb.2013.29

Cited by 158 publications

(276 citation statements)

References 68 publications

(117 reference statements)

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“…A notable absence from the Challenge was the use of mathematical, boolean or logic based mechanistic pathway modelling approaches [25][26][27][28][29] , likely due to the intensity of model creation. The dynamic nature of mechanistic models may offer an advantage by enabling consideration of the heterogeneity that exists across even apparently 'clonal' cell line populations 21 .…”

Section: Discussionmentioning

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 No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.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: Discussionmentioning

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 particular, a number of recent studies have successfully studied logic models to investigate signaling pathways and suggest effective drug combinations which were then validated in vitro and/or in vivo 18, 19, 20, 45. Mathematical models calibrated using cell lines have also been proved effective in predicting clinical patient outcomes 82…”

Section: Resultsmentioning

confidence: 99%

“…For example, comparing signaling pathways of healthy and diseased cells allows investigating which mechanisms are deregulated as a cause or an effect of the disease 16, 17. Similarly, the comparison of pathway activity in different cells from the same cancer type allows investigating mechanism of resistance to drugs, which can be exploited to suggest personalized therapies 18, 19, 20. If experimental data are available, optimization procedures can be used to refine the initial networks to be cell line‐ or context‐specific.…”

Section: Regulatory Networkmentioning

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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“…It also contributed to robustness to a MEK inhibitor during medical treatment (Sturm et al, 2010b;Fritsche-Guenther et al, 2011). For colorectal cancer cells, it was shown that the negative feedback in EGFR signaling led to cross-talk between ERK and AKT signaling; inhibition of ERK resulted in activation of AKT, via EGFR, and combined inhibition of ERK and EGFR inhibited Ras, ERK, and AKT (Sturm et al, 2010b;Klinger et al, 2013). These predictions were first made with a mathematical model before being validated with a xenograft model.…”

Section: Negative Feedback In Signal Transduction Cascades: Oscillatimentioning

confidence: 99%

G Protein–Coupled Receptor Signaling Networks from a Systems Perspective

Roth¹,

Kholodenko²,

Smit³

et al. 2015

Mol Pharmacol

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The signal-transduction network of a mammalian cell integrates internal and external cues to initiate adaptive responses. Among the cell-surface receptors are the G protein-coupled receptors (GPCRs), which have remarkable signal-integrating capabilities. Binding of extracellular signals stabilizes intracellular-domain conformations that selectively activate intracellular proteins. Hereby, multiple signaling routes are activated simultaneously to degrees that are signal-combination dependent. Systems-biology studies indicate that signaling networks have emergent processing capabilities that go far beyond those of single proteins. Such networks are spatiotemporally organized and capable of gradual, oscillatory, all-or-none, and subpopulation-generating responses. Proteinprotein interactions, generating feedback and feedforward circuitry, are generally required for these spatiotemporal phenomena. Understanding of information processing by signaling networks therefore requires network theories in addition to biochemical and biophysical concepts. Here we review some of the key signaling systems behaviors that have been discovered recurrently across signaling networks. We emphasize the role of GPCRs, so far underappreciated receptors in systems-biology research.

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Network quantification of EGFR signaling unveils potential for targeted combination therapy

Abstract: Analysis of the signaling response of colon cancer cells to systematic perturbations reveals an EGF receptor-mediated cross-talk between the MAPK and AKT pathways. Accordingly, the predicted combinatorial treatment is shown to inhibit tumor growth in vivo.

Cited by 158 publications

References 68 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

G Protein–Coupled Receptor Signaling Networks from a Systems Perspective

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