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

ABSTRACT

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.