Speaker
Description
Phenotypic heterogeneity of melanoma cells contributes to drug tolerance, increased metastasis, and immune evasion in patients with progressive disease. However, the dynamics of the co-existence and interconversion among these different phenotypes remains unclear. Here, we integrate dynamical systems modeling with transcriptomic data analysis at bulk and single-cell levels to investigate underlying mechanisms behind phenotypic plasticity in melanoma and its impact on adaptation to different therapies. We construct a minimal core gene regulatory network involving the transcription factors implicated in this process and identify the multiple 'attractors' in phenotypic landscape enabled by this network. The emergent dynamics of this regulatory network comprising MITF, SOX10, SOX9, JUN and ZEB1 can recapitulate experimental observations about the co-existence of diverse phenotypes and reversible cell-state transitions among them, including in response to targeted therapy and immune checkpoint inhibitors. Our model predictions about changes in proliferative to invasive transition and PD-L1 levels as melanoma cells evade targeted therapy and immune checkpoint inhibitors were also validated in multiple RNA-seq data sets from in vitro and in vivo experiments. Our calibrated dynamical model offers a platform to test combinatorial therapies against phenotypic plasticity and provide rational avenues for treating melanoma.
