ArticleBMC systems biology2009
Modeling ERBB receptor-regulated G1/S transition to find novel targets for de novo trastuzumab resistance.
Article in BMC systems biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers.
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Who cites it
73 citing papers in PubMed, 347 citations in OpenAlex.
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- Contribution of synergism between PHF8 and HER2 signalling to breast cancer development and drug resistance.EBioMedicine · 2020Article
- Dynamics of gene regulatory networks and their dependence on network topology and quantitative parameters - the case of phage λ.BMC bioinformatics · 2019Article
- Incorporating Time Delays in Process Hitting Framework for Dynamical Modeling of Large Biological Regulatory Networks.Frontiers in physiology · 2019Article
- Fitting mathematical models of biochemical pathways to steady state perturbation response data without simulating perturbation experiments.Scientific reports · 2018Article
- Predicting neuroblastoma using developmental signals and a logic-based model.Biophysical chemistry · 2018Article
- Discrete dynamic network modeling of oncogenic signaling: Mechanistic insights for personalized treatment of cancer.Current opinion in systems biology · 2018Review
- Regulation of dual specificity phosphatases in breast cancer during initial treatment with Herceptin: a Boolean model analysis.BMC systems biology · 2018Article
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- Boolean network modeling in systems pharmacology.Journal of pharmacokinetics and pharmacodynamics · 2018Review
- Logical Modeling and Analysis of Cellular Regulatory Networks With GINsim 3.0.Frontiers in physiology · 2018Article
13 more citing papers are in PubMed but not listed here.
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Authors and funding
14 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn breast cancer, overexpression of the transmembrane tyrosine kinase ERBB2 is an adverse prognostic marker, and occurs in almost 30% of the patients. For therapeutic intervention, ERBB2 is targeted by monoclonal antibody trastuzumab in adjuvant settings; however, de novo resistance to this antibody is still a serious issue, requiring the identification of additional targets to overcome resistance. In this study, we have combined computational simulations, experimental testing of simulation results, and finally reverse engineering of a protein interaction network to define potential therapeutic strategies for de novo trastuzumab resistant breast cancer.
resultsFirst, we employed Boolean logic to model regulatory interactions and simulated single and multiple protein loss-of-functions. Then, our simulation results were tested experimentally by producing single and double knockdowns of the network components and measuring their effects on G1/S transition during cell cycle progression. Combinatorial targeting of ERBB2 and EGFR did not affect the response to trastuzumab in de novo resistant cells, which might be due to decoupling of receptor activation and cell cycle progression. Furthermore, examination of c-MYC in resistant as well as in sensitive cell lines, using a specific chemical inhibitor of c-MYC (alone or in combination with trastuzumab), demonstrated that both trastuzumab sensitive and resistant cells responded to c-MYC perturbation.
conclusionIn this study, we connected ERBB signaling with G1/S transition of the cell cycle via two major cell signaling pathways and two key transcription factors, to model an interaction network that allows for the identification of novel targets in the treatment of trastuzumab resistant breast cancer. Applying this new strategy, we found that, in contrast to trastuzumab sensitive breast cancer cells, combinatorial targeting of ERBB receptors or of key signaling intermediates does not have potential for treatment of de novo trastuzumab resistant cells. Instead, c-MYC was identified as a novel potential target protein in breast cancer cells.
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