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bioRxiv · 10.1101/2025.11.17.688869

A Hybrid Multiscale Model for Predicting CAR-T Therapy Outcomes in SolidTumors

Nikmaneshi, M.·Munn, L. L.
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Abstract

T cell distribution within tumors ("tumor hotness") critically determines immunotherapy success. However, despite numerous strategies to enhance intratumoral T cell accumulation--such as multi-target CAR-Ts and combinatorial approaches--limited mechanistic understanding of T cell-microenvironment interactions has constrained progress. To address this, we developed a physiological mechanistic model of the 3D tumor microenvironment (TME) to evaluate CAR-T performance under environmental fluctuations and different infusion strategies. The model integrates key vascular (rolling, adhesion, endothelial suppression) and interstitial (ECM density, metabolic competition, chemokine sensitivity) barriers. Our simulations reveal that collagen density and metabolic competition dominate CAR-T efficacy. Enhancing vascular adhesion improves infiltration but remains limited by collagen and metabolism. Endothelial suppression markedly reduces tumor hotness, while its alleviation enhances response. Systemic infusion yields higher tumor hotness than intratumoral delivery, but combined routes or reduced collagen restore efficacy even in dense tumors. This mechanistic framework enables rational optimization of CAR-T strategies. Significance StatementThe success of immunotherapies such as CAR-T cells depends on their ability to infiltrate and persist within solid tumors, yet the mechanisms that govern this process remain poorly understood. Using a mechanistic 3D model of the tumor microenvironment, we quantitatively dissected how vascular and interstitial barriers--including endothelial suppression, collagen density, metabolic competition, and chemokine cues--shape CAR-T distribution ("tumor hotness"). Our results reveal that stromal and metabolic constraints, rather than vascular adhesion alone, dominate CAR-T efficacy. This framework bridges molecular, cellular, and tissue-scale mechanisms, providing a quantitative foundation for optimizing CAR-T design and delivery strategies to overcome resistance in solid tumors.

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Nikmaneshi, M., Munn, L. L.. 2025-11-18. A Hybrid Multiscale Model for Predicting CAR-T Therapy Outcomes in SolidTumors. https://doi.org/10.1101/2025.11.17.688869

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