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Bystander effects and their implications for clinical radiation therapy : insights from multiscale in silico experiments

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JTB_Bystander_Nohighlight_R1_AAM.pdf (1.661Mb)
Date
21/07/2016
Author
Powathil, Gibin
Munro, Alastair John
Chaplain, Mark Andrew Joseph
Swat, Maciej
Keywords
Multiscale mathematical model
Radiation therapy
Radiation-induced bystander effects
Cell-cycle
RC0254 Neoplasms. Tumors. Oncology (including Cancer)
QA Mathematics
NDAS
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Abstract
Radiotherapy is a commonly used treatment for cancer and is usually given in varying doses. At low radiation doses relatively few cells die as a direct response to radiation but secondary radiation effects, such as DNA mutation or bystander phenomena, may affect many cells. Consequently it is at low radiation levels where an understanding of bystander effects is essential in designing novel therapies with superior clinical outcomes. In this article, we use a hybrid multiscale mathematical model to study the direct effects of radiation as well as radiation-induced bystander effects on both tumour cells and normal cells. We show that bystander responses play a major role in mediating radiation damage to cells at low-doses of radiotherapy, doing more damage than that due to direct radiation. The survival curves derived from our computational simulations showed an area of hyper-radiosensitivity at low-doses that are not obtained using a traditional radiobiological model.
Citation
Powathil , G , Munro , A J , Chaplain , M A J & Swat , M 2016 , ' Bystander effects and their implications for clinical radiation therapy : insights from multiscale in silico experiments ' , Journal of Theoretical Biology , vol. 401 , pp. 1-14 . https://doi.org/10.1016/j.jtbi.2016.04.010
Publication
Journal of Theoretical Biology
Status
Peer reviewed
DOI
https://doi.org/10.1016/j.jtbi.2016.04.010
ISSN
0022-5193
Type
Journal article
Rights
© 2016, Elsevier Ltd. This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at www.sciencedirect.com / https://dx.doi.org/10.1016/j.jtbi.2016.04.010
Description
GGP and MAJC thank University of Dundee, where this research was carried out. The authors gratefully acknowledge the support of the ERC Advanced Investigator Grant 227619, M5CGS - From Mutations to Metastases: Multiscale Mathematical Modelling of Cancer Growth and Spread. AJM Acknowledges support from EU BIOMICS Project DG-CNECT Contract 318202.
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  • University of St Andrews Research
URI
http://hdl.handle.net/10023/10615

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