Mathematical mechanistic model representing the cancer immunity cycle under radiation effects
Abstract Combining radiotherapy with immune checkpoint inhibitors is a promising approach to improve the effectiveness of cancer treatment. However, the success rates of these clinical studies are limited. It is essential to determine the optimal irradiation scheme that maximizes the therapeutic eff...
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-04715-9 |
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| Summary: | Abstract Combining radiotherapy with immune checkpoint inhibitors is a promising approach to improve the effectiveness of cancer treatment. However, the success rates of these clinical studies are limited. It is essential to determine the optimal irradiation scheme that maximizes the therapeutic effect by taking into account the balance between the positive and negative effects of radiation on immunity. In this context, we developed a mathematical mechanistic model that simulates (1) the balance between effector and exhausted cytotoxic T-lymphocytes (CTLs), (2) the number of neoantigens released by high-dose irradiation, and (3) the impact of radiation on draining lymph nodes (DLNs) for systemic anti-tumor immunity, and tested whether this mathematic model fits in several animal experiments. Our mechanistic model reproduced the anti-tumor effects of several cancer treatment models for combination therapies with radiation, immune checkpoint inhibitors, and/or a metabolic modulator. Furthermore, this mechanistic model simulated that tumor suppression in distant metastatic foci, known as the abscopal effect, was dysregulated by hypofractionated high-dose irradiation or by the direct radiation exposure on DLN. The mechanistic model successfully reproduced tumor control under various treatment conditions with appropriate parameters, indicating that it may be useful for optimizing immunoradiotherapy prescriptions. |
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| ISSN: | 2045-2322 |