Abstract
Summary 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.
Full text
1,346 characters
· extracted from
oa-doi-fallback
· click to expand
Summary
Cell state plasticity drives metastasis and therapy resistance in cancers. In melanoma, these behaviors map onto a melanocytic-to-mesenchymal-like continuum regulated by AP-1 transcription factors. However, how the AP-1 network encodes a limited set of discrete states, why their distributions vary across tumors, and what drives phenotypically consequential AP-1 state transitions remain unclear. We develop a mechanistic ODE model of the AP-1 network capturing their dimerization-controlled, co-regulated, competitive interactions. Calibrated to heterogeneous single-cell data across genetically 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 activity across tumor lines and with variability within clones, linking MAPK signaling to AP-1 states. The model predicts and experiments validate adaptive AP-1 reconfiguration under MAPK inhibition, inducing a dedifferentiated, therapy-resistant state that can be blocked by model-guided AP-1 perturbations. These results establish AP-1 as a configurable network and provide a computational framework for predicting and modulating AP-1 driven cell state plasticity.
Competing Interest Statement
The authors have declared no competing interest.
Text is read by the "Ask this paper" AI Q&A widget below.
Extraction quality varies by source — PMC NXML preserves structure
cleanly, OA-HTML may include some navigation residue, and OA-PDF can
have broken hyphenation. The publisher copy
(via DOI)
is the canonical version.