Figure 2.

AI-integrated liquid-immune feedback architecture linking ctDNA fragmentomics, exosomal PD-L1 vesicular signaling, and T-cell receptor (TCR) repertoire entropy in adaptive immunotherapy

This diagram depicts a closed-loop model in which cfDNA, exosomal PD-L1, and TCR diversity coevolve. These biomarkers help reflect the dynamic immune control in NSCLC (33). Fragmentomic asymmetry and methylation topology of ctDNA provide high-resolution telemetry of tumor genome flux and residual disease; vesicular PD-L1 released via Rab27a/nSMase2-regulated exocytosis broadcasts systemic immunosuppression, attenuating SHP2-mediated TCR signaling; and entropy metrics of the peripheral TCR repertoire quantify adaptive immune plasticity. These orthogonal dimensions converge into an AI-driven digital-twin model that reconstructs longitudinal immune–tumor trajectories, correlating ctDNA variant-allele kinetics with exosomal PD-L1 oscillations and TCR clonal re-expansion (34,89). The resulting composite response index transforms liquid-biopsy snapshots into a continuously learning biosystem capable of forecasting therapeutic resistance, identifying immune rejuvenation windows, and guiding precision-timed checkpoint modulation. Collectively, the figure reframes liquid-immune biomarkers as dynamic network variables within a self-correcting immuno-oncologic cybernetics paradigm. This schematic was conceptually designed and illustrated by the author