整合机制建模与机器学习研究肿瘤抗原调控下的 CD4+/CD8+ CAR-T 细胞动力学

Chimeric antigen receptor (CAR) T cell therapy has shown remarkable success in hematological malignancies, yet patient responses remain highly variable and the roles of CD4 + and CD8 + subsets are not fully understood.

We present an extended mathematical framework of CAR-T cell dynamics that explicitly models CD4 + helper and CD8 + cytotoxic lineages and their interactions with tumor antigen burden. Building on a recent model of antigen-regulated memory-effector-exhaustion transitions in CAR-T cells, our system of differential equations incorporates CD4 + -mediated modulation of CD8 + proliferation, cytotoxicity, and memory regeneration through biologically grounded, saturating interactions.

Sensitivity analyses identify effector proliferation, antigen turnover, and CD8 + expansion rates as dominant drivers of treatment outcome. Virtual patient simulations recover reported qualitative trends in CAR-T composition, including enhanced expansion and tumor clearance for defined CD4:CD8 products relative to CD8-only formulations, while also revealing inter-patient variability and time-dependent effects.

To assess the practical limits of patient-level prediction under parameter uncertainty, we introduce controlled noise into key parameters and show that direct mechanistic classification rapidly degrades.

We then demonstrate that a simple feed-forward neural network can partially recover predictive signal from noisy inputs, outperforming a na ve baseline while remaining consistent with mechanistic sensitivities. This work positions the extended model as a hypothesis generator, and illustrates how data-driven methods can complement mechanistic modeling when parameter uncertainty constrains predictive confidence.