Refining photon- and particle-induced chromosome aberration predictions for simplified and detailed radiation damage...

Objective.Besides preferential tumour targeting, particle therapy has an increased relative biological effectiveness compared to x-rays, but uncertainties in this effectiveness prevent full exploitation of its clinical benefits. Mechanistic radiation response models can predict the effects of different radiation qualities but the model detail required to capture experimental data remains unclear. In this work, key DNA damage and chromosomal aberration endpoints were simulated and compared to experimental literature.Approach.The TOPAS-nBio and Medras models were used to simulate DNA double strand break (DSB) damage for different radiation exposures. The repair of this damage was simulated, modelling misrepair and chromosome aberration formation in an updated Medras DNA repair model. The characteristic rejoining range of DSB ends in the repair model was re-optimised against experimental photon dose data and tested against ion exposures.Main results.For DSBs, predictions were higher than experimental observations, attributed to the assay resolution limits. Predicted photon-induced chromosome aberrations were higher than observed, with a root mean square deviation (RMSD) of 1.28 and 1.41 for the Medras and TOPAS-nBio models respectively against the experimental data. The RMSD against the experimental data was lowered by over 70% for both models by re-optimisation of the analytically predicted characteristic DSB end rejoining range to a value of 0.0335 + -0.0034 (80% of the previous value). This optimisation also performed well when predicting the dependence on ion linear energy transfer, reducing the proton RMSD by 40% to 0.43 and 0.69 for the Medras and TOPAS-nBio models respectively.Significance.The Medras biological response model was updated and predicted good agreement in aberration yields with the experimental data for both the detailed TOPAS-nBio and less detailed Medras damage models. This highlights how simple mechanistic models, with the guidance of robust experimental data, can be used to explore the effects of radiation quality and guide future experiments.