Abstract
Background: Mature T-cell lymphomas (MTCLs) are considered rare and comprise a small percentage of all lymphomas, with varying incidence rates globally. T-cell receptor (TCR) repertoire polyclonality may be an innate tumor-suppressive mechanism functioning via homeostatic clonal competition of antigen-presenting cell (APC) niches. However, the application of computational models to formalize the principle of clonal competition for lymphoma risk prediction or gene therapy safety monitoring has been limited.
Objective: To suggest and analytically benchmark a seven-module AI/ML framework that puts the clonal competition results into perspective and extrapolates them to TCR repertoire-based lymphoma risk prediction, competitive dynamics simulation, integration site oncogenicity scoring, automated ALCL histopathology classification, and a regulatory-grade gene therapy safety index.
Framework: Seven AI/ML modules are suggested: (1) DeepTCR/BERT-based TCR diversity scorer with an AUROC ≥0.93; (2) a reinforcement learning agent-based model (RL-ABM) with an index of tipping-point polyclonality (PI ≈ 0.30); (3) a graph neural network TCR-MHC niche affinity predictor; (4) an XGBoost transformation susceptibility classifier (F1 ≥0.93, MCC ≥0.84); (5) a CNN-based retroviral integration site oncogenicity predictor; (6) a Vision Transformer ALCL histopathology classifier (AUROC ≥0.97); and (7) a multi-modal ensemble Gene Therapy Safety Index (AUROC ≥0.98, F1 ≥0.96) that generates RED/AMBER/GREEN surveillance alerts.
Conclusion: The suggested framework will convert TCR diversity-mediated lymphoma suppression into a quantitative, predictive AI pipeline with direct regulatory implications to safe design of adoptive TCR therapy and CAR-T cell products.