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Neuroblastoma signalling models unveil combination therapies targeting feedback-mediated resistance

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Item Type:Article
Title:Neuroblastoma signalling models unveil combination therapies targeting feedback-mediated resistance
Creators Name:Dorel, M. and Klinger, B. and Mari, T. and Toedling, J. and Blanc, E. and Messerschmidt, C. and Nadler-Holly, M. and Ziehm, M. and Sieber, A. and Hertwig, F. and Beule, D. and Eggert, A. and Schulte, J.H. and Selbach, M. and Blüthgen, N.
Abstract:Very high risk neuroblastoma is characterised by increased MAPK signalling, and targeting MAPK signalling is a promising therapeutic strategy. We used a deeply characterised panel of neuroblastoma cell lines and found that the sensitivity to MEK inhibitors varied drastically between these cell lines. By generating quantitative perturbation data and mathematical modelling, we determined potential resistance mechanisms. We found that negative feedbacks within MAPK signalling and via the IGF receptor mediate re-activation of MAPK signalling upon treatment in resistant cell lines. By using cell-line specific models, we predict that combinations of MEK inhibitors with RAF or IGFR inhibitors can overcome resistance, and tested these predictions experimentally. In addition, phospho-proteomic profiling confirmed the cell-specific feedback effects and synergy of MEK and IGFR targeted treatment. Our study shows that a quantitative understanding of signalling and feedback mechanisms facilitated by models can help to develop and optimise therapeutic strategies. Our findings should be considered for the planning of future clinical trials introducing MEKi in the treatment of neuroblastoma.
Keywords:MAPK Signaling Cascades, Neuroblastoma, Signal Inhibition, Neuroblastoma Cells, Signaling Networks, Principal Component Analysis, ERK Signaling Cascade, Feedback Regulation
Source:PLoS Computational Biology
Publisher:Public Library of Science
Page Range:e1009515
Date:4 November 2021
Official Publication:https://doi.org/10.1371/journal.pcbi.1009515
PubMed:View item in PubMed
Related to:
https://edoc.mdc-berlin.de/20363/Preprint version

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