Artificial Intelligence–Enabled Dissection of Regulatory T Cellsin the Tumor Immune Microenvironment:From Mechanistic Insight to Population-Scale Inference
DOI:
https://doi.org/10.71321/s2yhxp11Keywords:
Artificial intelligence, Regulatory T cells, Tumor immune microenvironment, Immunotherapy, Precision oncologyAbstract
Background: Regulatory T cells (Tregs) are central regulators of immune tolerance in the tumor immune microenvironment (TIME) and are increasingly implicated in tumor immune evasion and immunotherapy resistance. While mechanistic studies have delineated pathways governing Treg recruitment and functional stability, extending these insights to spatial organization and population-level heterogeneity remains a major challenge.
Methods: In this correspondence, we integrate recent advances in tumor immunology with emerging artificial intelligence (AI)–based analytical frameworks to highlight how Treg-driven immune programs can be interrogated at scale. We draw on representative experimental mechanisms and AI-enabled multimodal modeling approaches, including virtual tumor microenvironment reconstruction and vision–language foundation models, to illustrate a mechanism-informed computational perspective.
Results: Evidence supports a layered model of tumor-driven Treg regulation, combining chemokine-mediated recruitment with intrinsic transcriptional stabilization within the TIME. AI-enabled approaches enable population-scale inference of Treg abundance, spatial distribution, and functional states, revealing clinically relevant heterogeneity and associations with differential immunotherapy responses that are difficult to capture using conventional experimental strategies alone.
Conclusion: The convergence of mechanistic Treg biology and AI-driven TIME modeling offers a conceptual framework for bridging experimental insight with real-world tumor heterogeneity. Mechanism-informed AI has the potential to refine immune stratification and guide Treg-targeted therapeutic strategies, highlighting a translational path forward for precision immuno-oncology.
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