Objective:

To investigate the mechanisms of immune evasion and resistance in estrogen receptor-positive breast cancer through regulatory network analysis and spatial modeling, highlighting the clinical significance of these mechanisms.

Key Findings:

• The GRN revealed antagonistic epithelial and mesenchymal modules that drive phenotypic heterogeneity, impacting treatment strategies.
• Resistance to tamoxifen is associated with EMT and increased PD-L1 expression, suggesting a link between these factors.
• Mesenchymal-like groups show significantly poorer survival outcomes, indicating the need for targeted therapies.
• Combination strategies can potentially enhance immune accessibility and constrain malignant diversification, offering new therapeutic avenues.

Interpretation:

The study highlights the interconnectedness of EMT, resistance mechanisms, and immune evasion in ER+ breast cancer, suggesting that targeting these pathways could improve therapeutic outcomes through innovative treatment strategies.

Limitations:

• The model's predictions require validation in clinical settings, which may affect the applicability of the findings.
• The complexity of tumor microenvironments may not be fully captured in the simulations, potentially limiting the model's accuracy.

Conclusion:

This research provides a computational framework for understanding resistance mechanisms in ER+ breast cancer and identifies potential therapeutic strategies to overcome these challenges, emphasizing the importance of further exploration in clinical contexts.