» Articles » PMID: 37459862

Radio-immune Response Modelling for Spatially Fractionated Radiotherapy

Overview
Journal Phys Med Biol
Publisher IOP Publishing
Date 2023 Jul 17
PMID 37459862
Authors
Affiliations
Soon will be listed here.
Abstract

Radiation-induced cell death is a complex process influenced by physical, chemical and biological phenomena. Although consensus on the nature and the mechanism of the bystander effect were not yet made, the immune process presumably plays an important role in many aspects of the radiotherapy including the bystander effect. A mathematical model of immune response during and after radiation therapy is presented.Immune response of host body and immune suppression of tumor cells are modelled with four compartments in this study; viable tumor cells, T cell lymphocytes, immune triggering cells, and doomed cells. The growth of tumor was analyzed in two distinctive modes of tumor status (immune limited and immune escape) and its bifurcation condition.Tumors in the immune limited mode can grow only up to a finite size, named as terminal tumor volume analytically calculated from the model. The dynamics of the tumor growth in the immune escape mode is much more complex than the tumors in the immune limited mode especially when the status of tumor is close to the bifurcation condition. Radiation can kill tumor cells not only by radiation damage but also by boosting immune reaction.The model demonstrated that the highly heterogeneous dose distribution in spatially fractionated radiotherapy (SFRT) can make a drastic difference in tumor cell killing compared to the homogeneous dose distribution. SFRT cannot only enhance but also moderate the cell killing depending on the immune response triggered by many factors such as dose prescription parameters, tumor volume at the time of treatment and tumor characteristics. The model was applied to the lifted data of 67NR tumors on mice and a sarcoma patient treated multiple times over 1200 days for the treatment of tumor recurrence as a demonstration.

Citing Articles

Linking spatial drug heterogeneity to microbial growth dynamics in theory and experiment.

Hu Z, Wu Y, Freire T, Gjini E, Wood K bioRxiv. 2024; .

PMID: 39605592 PMC: 11601811. DOI: 10.1101/2024.11.21.624783.


Designing combination therapies for cancer treatment: application of a mathematical framework combining CAR T-cell immunotherapy and targeted radionuclide therapy.

Adhikarla V, Awuah D, Caserta E, Minnix M, Kuznetsov M, Krishnan A Front Immunol. 2024; 15:1358478.

PMID: 38698840 PMC: 11063284. DOI: 10.3389/fimmu.2024.1358478.

References
1.
Zhong H, Chetty I . A note on modeling of tumor regression for estimation of radiobiological parameters. Med Phys. 2014; 41(8):081702. PMC: 4105966. DOI: 10.1118/1.4884019. View

2.
Davidson T, Zhang H, Dong H, Grams M, Park S, Yan Y . Overcoming Immunotherapy Resistance With Radiation Therapy and Dual Immune Checkpoint Blockade. Adv Radiat Oncol. 2022; 7(4):100931. PMC: 8971589. DOI: 10.1016/j.adro.2022.100931. View

3.
STEEL G, LAMERTON L . The growth rate of human tumours. Br J Cancer. 1966; 20(1):74-86. PMC: 2008056. DOI: 10.1038/bjc.1966.9. View

4.
Serre R, Benzekry S, Padovani L, Meille C, Andre N, Ciccolini J . Mathematical Modeling of Cancer Immunotherapy and Its Synergy with Radiotherapy. Cancer Res. 2016; 76(17):4931-40. DOI: 10.1158/0008-5472.CAN-15-3567. View

5.
Peng V, Suchowerska N, Esteves A, Rogers L, Claridge Mackonis E, Toohey J . Models for the bystander effect in gradient radiation fields: Range and signalling type. J Theor Biol. 2018; 455:16-25. DOI: 10.1016/j.jtbi.2018.06.027. View