Literature DB >> 20507257

A mathematical framework for separating the direct and bystander components of cellular radiation response.

Martin A Ebert1, Natalka Suchowerska, Michael A Jackson, David R McKenzie.   

Abstract

UNLABELLED: A mathematical model for fractional tumor cell survival was developed incorporating components of cell killing due to direct radiation interactions and bystander signals resulting from non-local dose deposition.
MATERIAL AND METHODS: Three possible mechanisms for signal production were tested by fitting predictions to available experimental results for tumor cells (non-small cell lung cancer NCI-H460 and melanoma MM576) exposed to gradient x-ray fields. The parameter fitting allowed estimation of the contribution of bystander signaling to cell death (20-50% for all models). Separation of the two components of cell killing allowed determination of the α and β parameters of the linear-quadratic model both with and without the presence of bystander signaling. RESULTS AND DISCUSSION: For both cell lines, cell death from bystander signaling and direct radiation interactions were comparable. For NCI-H460 cells, the values for α and β were 0.18 Gy⁻¹ and 0.10 Gy⁻² respectively when direct and bystander effects were combined, and 0.053 Gy⁻¹ and 0.061 Gy⁻² respectively when the signaling component was removed. For MM576, the corresponding respective values were 0.09 Gy⁻¹ and 0.011 Gy⁻² for the combined response, and 0.014 Gy⁻¹ and 0.002 Gy⁻² for the isolated direct radiation response. The bystander component in cell death was found to be significant and should not be ignored. Further experimental evidence is required to determine how these results translate to the in vivo situation where tumor control probability (TCP) models that currently assume cellular independence may need to be revised.

Entities:  

Mesh:

Year:  2010        PMID: 20507257     DOI: 10.3109/0284186X.2010.487874

Source DB:  PubMed          Journal:  Acta Oncol        ISSN: 0284-186X            Impact factor:   4.089


  7 in total

1.  Modelling responses to spatially fractionated radiation fields using preclinical image-guided radiotherapy.

Authors:  Karl Terence Butterworth; Mihaela Ghita; Stephen J McMahon; Conor K Mcgarry; Robert J Griffin; Alan R Hounsell; Kevin M Prise
Journal:  Br J Radiol       Date:  2016-09-15       Impact factor: 3.039

2.  On the Inclusion of Short-distance Bystander Effects into a Logistic Tumor Control Probability Model.

Authors:  David G Tempel; N Patrik Brodin; Wolfgang A Tomé
Journal:  Cureus       Date:  2018-01-01

Review 3.  REVIEW OF QUANTITATIVE MECHANISTIC MODELS OF RADIATION-INDUCED NON-TARGETED EFFECTS (NTE).

Authors:  Igor Shuryak; David J Brenner
Journal:  Radiat Prot Dosimetry       Date:  2020-12-30       Impact factor: 0.972

4.  A kinetic-based model of radiation-induced intercellular signalling.

Authors:  Stephen J McMahon; Karl T Butterworth; Colman Trainor; Conor K McGarry; Joe M O'Sullivan; Giuseppe Schettino; Alan R Hounsell; Kevin M Prise
Journal:  PLoS One       Date:  2013-01-22       Impact factor: 3.240

5.  A simulation study of the radiation-induced bystander effect: modeling with stochastically defined signal reemission.

Authors:  Kohei Sasaki; Kosuke Wakui; Kaori Tsutsumi; Akio Itoh; Hiroyuki Date
Journal:  Comput Math Methods Med       Date:  2012-11-11       Impact factor: 2.238

6.  Cellular automaton-based model for radiation-induced bystander effects.

Authors:  Yuya Hattori; Akinari Yokoya; Ritsuko Watanabe
Journal:  BMC Syst Biol       Date:  2015-12-07

Review 7.  Enhancing the Bystander and Abscopal Effects to Improve Radiotherapy Outcomes.

Authors:  Virgínea de Araújo Farias; Isabel Tovar; Rosario Del Moral; Francisco O'Valle; José Expósito; Francisco Javier Oliver; José Mariano Ruiz de Almodóvar
Journal:  Front Oncol       Date:  2020-01-08       Impact factor: 6.244

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.