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Neuroblastoma signalling models unveil combination therapies targeting feedback-mediated resistance
Mathurin Dorel1,2, Bertram Klinger1,2,3,4, Tommaso Mari5
1Institute of Pathology, Charité-Universitätsmedizin Berlin, Berlin, Germany.
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.
Insights
Targeting the MAPK pathway in neuroblastoma shows promise, but resistance varies. Mathematical models revealed feedback loops causing resistance, suggesting combination therapies with MEK inhibitors and RAF or IGFR inhibitors can overcome this.
Area of Science:
- Oncology
- Molecular Biology
- Systems Biology
Background:
- Very high-risk neuroblastoma exhibits elevated MAPK signaling, making it a therapeutic target.
- MEK inhibitors (MEKi) are a potential treatment, but their efficacy is limited by variable sensitivity in neuroblastoma cell lines.
Purpose of the Study:
- To investigate the mechanisms of resistance to MEK inhibitors in neuroblastoma.
- To develop predictive models for optimizing combination therapies against neuroblastoma.
Main Methods:
- Quantitative perturbation data generation and mathematical modeling of signaling pathways.
- Phospho-proteomic profiling to analyze feedback mechanisms.
- Experimental validation of predicted synergistic drug combinations.
Main Results:
- Identified negative feedback loops within MAPK signaling and via the IGF receptor as key resistance mechanisms.
- MEK inhibitor sensitivity varied significantly across neuroblastoma cell lines.
- Predicted and experimentally validated that combining MEK inhibitors with RAF or IGFR inhibitors overcomes resistance.
Conclusions:
- Quantitative modeling of signaling networks is crucial for understanding and overcoming drug resistance in neuroblastoma.
- Combination therapy with MEK inhibitors and RAF or IGFR inhibitors shows potential for treating neuroblastoma.
- Findings support the strategic planning of future clinical trials for MEKi in neuroblastoma treatment.
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