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Updated: Sep 25, 2025
A Melanoma Patient-Derived Xenograft Model
Published on: May 20, 2019
Computational Model of Heterogeneity in Melanoma: Designing Therapies and Predicting Outcomes
Arran Hodgkinson1, Dumitru Trucu2, Matthieu Lacroix3,4
1Living Systems Institute, University of Exeter, Exeter, United Kingdom.
Abstract:
Cutaneous melanoma is a highly invasive tumor and, despite the development of recent therapies, most patients with advanced metastatic melanoma have a poor clinical outcome. The most frequent mutations in melanoma affect the BRAF oncogene, a protein kinase of the MAPK signaling pathway. Therapies targeting both BRAF and MEK are effective for only 50% of patients and, almost systematically, generate drug resistance. Genetic and non-genetic mechanisms associated with the strong heterogeneity and plasticity of melanoma cells have been suggested to favor drug resistance but are still poorly understood. Recently, we have introduced a novel mathematical formalism allowing the representation of the relation between tumor heterogeneity and drug resistance and proposed several models for the development of resistance of melanoma treated with BRAF/MEK inhibitors. In this paper, we further investigate this relationship by using a new computational model that copes with multiple cell states identified by single cell mRNA sequencing data in melanoma treated with BRAF/MEK inhibitors. We use this model to predict the outcome of different therapeutic strategies. The reference therapy, referred to as "continuous" consists in applying one or several drugs without disruption. In "combination therapy", several drugs are used sequentially. In "adaptive therapy" drug application is interrupted when the tumor size is below a lower threshold and resumed when the size goes over an upper threshold. We show that, counter-intuitively, the optimal protocol in combination therapy of BRAF/MEK inhibitors with a hypothetical drug targeting cell states that develop later during the tumor response to kinase inhibitors, is to treat first with this hypothetical drug. Also, even though there is little difference in the timing of emergence of the resistance between continuous and adaptive therapies, the spatial distribution of the different melanoma subpopulations is more zonated in the case of adaptive therapy.
Insights
New computational models reveal that adaptive therapy strategies can better manage melanoma drug resistance. Counter-intuitively, initiating treatment with a novel drug targeting specific cell states optimizes outcomes for BRAF/MEK inhibitor-resistant melanoma.
Area of Science:
- Oncology
- Computational Biology
- Cancer Therapeutics
Background:
- Cutaneous melanoma is an aggressive cancer with poor outcomes in advanced stages.
- BRAF/MEK inhibitor therapies are limited by rapid drug resistance due to tumor heterogeneity and plasticity.
- Understanding resistance mechanisms is crucial for developing effective melanoma treatments.
Purpose of the Study:
- To investigate the relationship between tumor heterogeneity and drug resistance in melanoma.
- To develop and utilize a computational model incorporating single-cell mRNA sequencing data to predict therapeutic outcomes.
- To evaluate different therapeutic strategies, including continuous, combination, and adaptive therapies.
Main Methods:
- Developed a novel computational model integrating multi-state cell populations identified by single-cell mRNA sequencing.
- Simulated continuous, combination, and adaptive therapy protocols for BRAF/MEK inhibitor-treated melanoma.
- Analyzed the impact of therapeutic strategies on drug resistance emergence and spatial distribution of melanoma subpopulations.
Main Results:
- A counter-intuitive optimal treatment sequence was identified for combination therapy: initiating with a hypothetical drug targeting later-emerging cell states.
- Adaptive therapy resulted in a more zonated spatial distribution of melanoma subpopulations compared to continuous therapy.
- Minimal differences were observed in the timing of resistance emergence between continuous and adaptive therapies.
Conclusions:
- Computational modeling provides insights into managing melanoma drug resistance by considering tumor heterogeneity.
- Adaptive therapy strategies may influence the spatial organization of melanoma subpopulations, potentially impacting treatment efficacy.
- Novel therapeutic sequencing, including early intervention with specific targeting drugs, shows promise for overcoming resistance in metastatic melanoma.
