A Hybrid Machine Learning Framework for Mechanistically Interpretable Latent Parameter Inference in a Spatiotemporal CAR-T Therapy Model for Solid Tumours

CAR-T cell therapy remains ineffective in most solid tumours because effector cells infiltrate poorly, undergo exhaustion, and face antigen escape within an immunosuppressive microenvironment. To address this, we developed a hybrid framework that combines a mechanistic spatiotemporal model with machine learning for limited patient-specific calibration. At its core, we employed a reaction-diffusion-chemotaxis model describing functional and exhausted CAR-T cells, antigen-positive and antigen-negative tumour subpopulations, a chemoattractant, an immunosuppressive factor, and hypoxia. Gradient boosting combined with nested cross-validation was used as the primary method for parameter inference. Parameters characterising the tumour microenvironment and CAR-T cell exhaustion were recovered most robustly, whereas antigen escape and individualised initial conditions were identified substantially less accurately. As an auxiliary reference point, we also considered a direct empirical baseline for binary clinical outcomes. This baseline indicated that the observed clinical features contained a more stable signal for disease control than for objective response. A favourable response was associated with high CAR-T cell infiltration and cytotoxic potency, whereas resistance was linked to exhaustion, antigen escape, and a suppressive microenvironment. Overall, the proposed approach constitutes an interpretable proof-of-concept platform for limited patient-specific inference of latent parameters and for stratifying the mechanisms underlying response and resistance in CAR-T cell therapy for solid tumours.