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

Area of Science:

• Oncology
• Computational Biology
• Molecular Biology

Background:

• Cell state plasticity is a key driver of melanoma metastasis and therapy resistance.
• The AP-1 transcription factor network is implicated in regulating these plastic behaviors along a melanocytic-to-mesenchymal-like continuum.
• The precise mechanisms by which the AP-1 network encodes discrete cell states, their population-specific distributions, and the drivers of state transitions remain incompletely understood.

Purpose of the Study:

• To develop a mechanistic model of the AP-1 network to understand its role in cell state plasticity in melanoma.
• To elucidate how AP-1 network features explain observed distributions of cell states across diverse melanoma populations.
• To investigate the link between MAPK signaling, AP-1 states, and the emergence of therapy resistance.

Main Methods:

• Developed a mechanistic ordinary differential equation (ODE) model of the AP-1 network, accounting for dimerization, co-regulation, and competitive interactions.
• Calibrated the model using heterogeneous single-cell data from genetically diverse melanoma populations.
• Integrated the model with statistical learning techniques and performed experimental validation of model predictions.

Main Results:

• The model identified specific AP-1 network features that explain variations in AP-1 state distributions across melanoma populations.
• These network features were found to correlate with MAPK activity and intra-clonal variability, establishing a link between MAPK signaling and AP-1 states.
• The model predicted, and experiments confirmed, that MAPK inhibition induces a dedifferentiated, therapy-resistant state, which can be mitigated by AP-1 targeted interventions.

Conclusions:

• The AP-1 network acts as a configurable system capable of driving cell state plasticity in melanoma.
• The study provides a computational framework for predicting and modulating AP-1 driven cell state transitions.
• These findings offer potential new strategies for overcoming therapy resistance in melanoma by targeting the AP-1 network.