Quantitative landscapes reveal trajectories of cell-state transitions associated with drug resistance in melanoma

Abstract

Summary: Drug resistance and tumor relapse in patients with melanoma is attributed to a combination of genetic and non-genetic mechanisms. Dedifferentiation, a common mechanism of non-genetic resistance in melanoma is characterized by the loss of melanocytic markers. While various molecular attributes of de-differentiation have been identified, the transition dynamics remain poorly understood. Here, we construct cell-state transition landscapes, to quantify the stochastic dynamics driving phenotypic switching in melanoma based on its underlying regulatory network. These landscapes reveal the existence of multiple alternative paths to resistance—de-differentiation and transition to a hyper-pigmented phenotype. Finally, by visualizing the changes in the landscape during in silico molecular perturbations, we identify combinatorial strategies that can lead to the most optimal outcome—a landscape with the minimum occupancy of the two drug-resistant states. Therefore, we present these landscapes as platforms to screen possible therapeutic interventions in terms of their ability to lead to the most favorable patient outcomes.

Keywords

• Computational bioinformatics
• Cancer systems biology
• Cancer