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A general mechanistic model enables predictions of the biological effectiveness of different qualities of radiation

Predicting the responses of biological systems to ionising radiation is extremely challenging, particularly when comparing X-rays and heavy charged particles, due to the uncertainty in their Relative Biological Effectiveness (RBE). Here we assess the power of a novel mechanistic model of DNA damage...

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Autores principales: McMahon, Stephen J., McNamara, Aimee L., Schuemann, Jan, Paganetti, Harald, Prise, Kevin M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5589818/
https://www.ncbi.nlm.nih.gov/pubmed/28883414
http://dx.doi.org/10.1038/s41598-017-10820-1
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author McMahon, Stephen J.
McNamara, Aimee L.
Schuemann, Jan
Paganetti, Harald
Prise, Kevin M.
author_facet McMahon, Stephen J.
McNamara, Aimee L.
Schuemann, Jan
Paganetti, Harald
Prise, Kevin M.
author_sort McMahon, Stephen J.
collection PubMed
description Predicting the responses of biological systems to ionising radiation is extremely challenging, particularly when comparing X-rays and heavy charged particles, due to the uncertainty in their Relative Biological Effectiveness (RBE). Here we assess the power of a novel mechanistic model of DNA damage repair to predict the sensitivity of cells to X-ray, proton or carbon ion exposures in vitro against over 800 published experiments. By specifying the phenotypic characteristics of cells, the model was able to effectively stratify X-ray radiosensitivity (R (2) = 0.74) without the use of any cell-specific fitting parameters. This model was extended to charged particle exposures by integrating Monte Carlo calculated dose distributions, and successfully fit to cellular proton radiosensitivity using a single dose-related parameter (R (2) = 0.66). Using these parameters, the model was also shown to be predictive of carbon ion RBE (R (2) = 0.77). This model can effectively predict cellular sensitivity to a range of radiations, and has the potential to support developments of personalised radiotherapy independent of radiation type.
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spelling pubmed-55898182017-09-13 A general mechanistic model enables predictions of the biological effectiveness of different qualities of radiation McMahon, Stephen J. McNamara, Aimee L. Schuemann, Jan Paganetti, Harald Prise, Kevin M. Sci Rep Article Predicting the responses of biological systems to ionising radiation is extremely challenging, particularly when comparing X-rays and heavy charged particles, due to the uncertainty in their Relative Biological Effectiveness (RBE). Here we assess the power of a novel mechanistic model of DNA damage repair to predict the sensitivity of cells to X-ray, proton or carbon ion exposures in vitro against over 800 published experiments. By specifying the phenotypic characteristics of cells, the model was able to effectively stratify X-ray radiosensitivity (R (2) = 0.74) without the use of any cell-specific fitting parameters. This model was extended to charged particle exposures by integrating Monte Carlo calculated dose distributions, and successfully fit to cellular proton radiosensitivity using a single dose-related parameter (R (2) = 0.66). Using these parameters, the model was also shown to be predictive of carbon ion RBE (R (2) = 0.77). This model can effectively predict cellular sensitivity to a range of radiations, and has the potential to support developments of personalised radiotherapy independent of radiation type. Nature Publishing Group UK 2017-09-07 /pmc/articles/PMC5589818/ /pubmed/28883414 http://dx.doi.org/10.1038/s41598-017-10820-1 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
McMahon, Stephen J.
McNamara, Aimee L.
Schuemann, Jan
Paganetti, Harald
Prise, Kevin M.
A general mechanistic model enables predictions of the biological effectiveness of different qualities of radiation
title A general mechanistic model enables predictions of the biological effectiveness of different qualities of radiation
title_full A general mechanistic model enables predictions of the biological effectiveness of different qualities of radiation
title_fullStr A general mechanistic model enables predictions of the biological effectiveness of different qualities of radiation
title_full_unstemmed A general mechanistic model enables predictions of the biological effectiveness of different qualities of radiation
title_short A general mechanistic model enables predictions of the biological effectiveness of different qualities of radiation
title_sort general mechanistic model enables predictions of the biological effectiveness of different qualities of radiation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5589818/
https://www.ncbi.nlm.nih.gov/pubmed/28883414
http://dx.doi.org/10.1038/s41598-017-10820-1
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