Fernanda Villegas

Proton and light ion RBE for the induction of direct DNA double strand breaks

PURPOSE To present and characterize a Monte Carlo (MC) tool for the simulation of the relative biological effectiveness for the induction of direct DNA double strand breaks (RBEDSB (direct)) for protons and light ions. METHODS The MC tool uses a pregenerated event-by-event tracks library of protons and light ions that are overlaid on a cell nucleus model. The cell nucleus model is a cylindrical arrangement of nucleosome structures consisting of 198 DNA base pairs. An algorithm relying on k-dimensional trees and cylindrical symmetries is used to search coincidences of energy deposition sites with volumes corresponding to the sugar-phosphate backbone of the DNA molecule. Strand breaks (SBs) are scored when energy higher than a threshold is reached in these volumes. Based on the number of affected strands, they are categorized into either single strand break (SSB) or double strand break (DSB) lesions. The number of SBs composing each lesion (i.e., its size) is also recorded. RBEDSB (direct) is obtained by taking the ratio of DSB yields of a given radiation field to a (60)Co field. The MC tool was used to obtain SSB yields, DSB yields, and RBEDSB (direct) as a function of linear energy transfer (LET) for protons ((1)H(+)), (4)He(2+), (7)Li(3+), and (12)C(6+) ions. RESULTS For protons, the SSB yields decreased and the DSB yields increased with LET. At ≈24.5 keV μm(-1), protons generated 15% more DSBs than (12)C(6+) ions. The RBEDSB (direct) varied between 1.24 and 1.77 for proton fields between 8.5 and 30.2 keV μm(-1), and it was higher for iso-LET ions with lowest atomic number. The SSB and DSB lesion sizes showed significant differences for all radiation fields. Generally, the yields of SSB lesions of sizes ≥2 and the yields of DSB lesions of sizes ≥3 increased with LET and increased for iso-LET ions of lower atomic number. On the other hand, the ratios of SSB to DSB lesions of sizes 2-4 did not show variability with LET nor projectile atomic number, suggesting that these metrics are independent of the radiation quality. Finally, a variance of up to 8% in the DSB yields was observed as a function of the particle incidence angle on the cell nucleus. This simulation effect is due to the preferential alignment of ion tracks with the DNA nucleosomes at specific angles. CONCLUSIONS The MC tool can predict SSB and DSB yields for light ions of various LET and estimate RBEDSB (direct). In addition, it can calculate the frequencies of different DNA lesion sizes, which is of interest in the context of biologically relevant absolute dosimetry of particle beams.

Cluster pattern analysis of energy deposition sites for the brachytherapy sources 103Pd, 125I, 192Ir, 137Cs, and 60Co

Analysing the pattern of energy depositions may help elucidate differences in the severity of radiation-induced DNA strand breakage for different radiation qualities. It is often claimed that energy deposition (ED) sites from photon radiation form a uniform random pattern, but there is indication of differences in RBE values among different photon sources used in brachytherapy. The aim of this work is to analyse the spatial patterns of EDs from 103Pd, 125I, 192Ir, 137Cs sources commonly used in brachytherapy and a 60Co source as a reference radiation. The results suggest that there is both a non-uniform and a uniform random component to the frequency distribution of distances to the nearest neighbour ED. The closest neighbouring EDs show high spatial correlation for all investigated radiation qualities, whilst the uniform random component dominates for neighbours with longer distances for the three higher mean photon energy sources (192Ir, 137Cs, and 60Co). The two lower energy photon emitters (103Pd and 125I) present a very small uniform random component. The ratio of frequencies of clusters with respect to 60Co differs up to 15% for the lower energy sources and less than 2% for the higher energy sources when the maximum distance between each pair of EDs is 2 nm. At distances relevant to DNA damage, cluster patterns can be differentiated between the lower and higher energy sources. This may be part of the explanation to the reported difference in RBE values with initial DSB yields as an endpoint for these brachytherapy sources.

Reply to the comment on ‘Monte Carlo calculated microdosimetric spread for cell nucleus-sized targets exposed to brachytherapy 125I and 192Ir sources and 60Co cell irradiation’

A discrepancy between the Monte Carlo derived relative standard deviation [Formula: see text] (microdosimetric spread) and experimental data was reported by Villegas et al (2013 Phys. Med. Biol. 58 6149-62) suggesting wall effects as a plausible explanation. The comment by Lindborg et al (2015 Phys. Med. Biol. 60 8621-4) concludes that this is not a likely explanation. A thorough investigation of the Monte Carlo (MC) transport code used for track simulation revealed a critical bug. The corrected MC version yielded [Formula: see text] values that are now within experimental uncertainty. Other microdosimetric findings are hereby communicated.

Microdosimetric spread for cell-sized targets exposed to 60Co, 192Ir and 125I sources

The magnitude of the spread in specific energy deposition per cell may be a confounding factor in dose-response analysis motivating derivation of explicit data for the most common brachytherapy isotopes (125)I and (192)Ir, and for (60)Co radiation frequently used as reference in RBE studies. The aim of this study is to analyse the microdosimetric spread as given by the frequency distribution of specific energy for a range of doses imparted by (125)I, (192)Ir and (60)Co sources. An upgraded version of the Monte Carlo code PENELOPE was used for scoring energy deposition distributions in liquid water for each of the radiation qualities. Frequency distributions of specific energy were calculated according to the formalism of Kellerer and Chmelevsky. Results indicate that the magnitude of the microdosimetric spread increases with decreasing target size and decreasing energy of the radiation quality. Within the clinical relevant dose range (1 to 100 Gy), the spread does not exceed 4 % for (60)Co, 5 % for (192)Ir and 6 % for (125)I. The frequency distributions can be accurately approximated with symmetrical normal distributions at doses down to 0.2 Gy for (60)Co, 0.1 Gy for (192)Ir and 0.08 Gy for (125)I.

Energy deposition clustering as a functional radiation quality descriptor for modeling relative biological effectiveness.

PURPOSE To explore the use of the frequency of the energy deposition (ED) clusters of different sizes (cluster order, CO) as a surrogate (instead of, e.g., LET) classification of the physical characteristics of ionizing radiation at a nanometer scale, to construct a framework for the calculation of relative biological effectiveness (RBE) with cell survival as endpoint. METHODS The frequency of cluster order fCO is calculated by sorting the ED sites generated with the Monte Carlo track structure code LIonTrack into clusters based on a single parameter called the cluster distance dC being the maximum allowed distance between two neighboring EDs belonging to a cluster. Published cell survival data parameterized with the linear-quadratic (LQ) model for V79 cells exposed to 15 different radiation qualities (including brachytherapy sources, proton, and carbon ions) were used as input to a fitting procedure, designed to determine a weighting function wCO that describes the capacity of a cluster of a certain CO to damage the cells sensitive volume. The proposed framework uses both fCO and wCO to construct surrogate based functions for the LQ parameters α and β from which RBE values can be derived. RESULTS The results demonstrate that radiation quality independent weights wCO exist for both the α and β parameters. This enables the calculation of α values that correlate to their experimental counterparts within experimental uncertainties (relative residual of 15% for dC = 2.5 nm). The combination of both α and β surrogate based functions, despite the higher relative residuals for β values, yielded an RBE function that correlated to experimentally derived RBE values (relative residual of 16.5% for dC = 2.5 nm) for all radiation qualities included in this work. CONCLUSIONS The fCO cluster characterization of ionizing radiation at a nanometer scale can effectively be used to calculate particle and energy dependent α and β values to predict RBE values with potential applications to, e.g., treatment planning systems in radiotherapy.