RT Journal Article
SR Electronic
T1 Quantitative landscapes reveal trajectories of cell-state transitions associated with drug resistance in melanoma
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.04.16.488373
DO 10.1101/2022.04.16.488373
A1 Maalavika Pillai
A1 Zihao Chen
A1 Mohit Kumar Jolly
A1 Chunhe Li
YR 2022
UL http://biorxiv.org/content/early/2022/04/16/2022.04.16.488373.abstract
AB Drug resistance and tumor relapse in melanoma patients is attributed to a combination of genetic and non-genetic mechanisms. Non-genetic mechanisms of drug resistance commonly involve reversible changes in the cell-state or phenotype, i.e., alterations in molecular profiles that can help cells escape being killed by targeted therapeutics. In melanoma, one of the most common mechanisms of non-genetic resistance is dedifferentiation, which is characterized by loss of melanocytic markers. While various molecular attributes of de-differentiation have been identified, the transition dynamics remains poorly understood. Here, we construct cell-state transition landscapes, to quantify the stochastic dynamics driving phenotypic switch 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 minimal 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 most favourable patient outcomes.Competing Interest StatementThe authors have declared no competing interest.