Cell state plasticity emerging from co-regulated, competitive, and configurable interactions within the AP-1 network

Abstract

AP-1 transcription factors have been implicated in cellular plasticity, differentiation-state heterogeneity, and phenotype switching in response to cancer therapies. Although AP-1 states, defined by combinatorial expression of AP-1 proteins, are heterogeneous within cell populations, only a subset of possible states is observed. How these states are constrained, why their distributions vary across cell populations, and what drives their phenotypically consequential transitions remain unclear. We develop a mechanistic ODE model of the AP-1 network, capturing dimerization-dependent, co-regulated, and competitive interactions. Calibrated to single-cell protein measurements across diverse melanoma populations and combined with statistical learning, the model reveals network features explaining population-specific AP-1 state distributions. These features correlate with MAPK signaling across tumor lines and individual cells. The model predicts and experiments validate adaptive AP-1 reconfiguration following MAPK inhibition, driving a dedifferentiated, therapy-resistant state that is attenuated through model-guided perturbations. These findings establish AP-1 as a configurable network and provide a quantitative framework for modulating AP-1 driven cell-state plasticity.