Reliability of the transverse acetabular ligament as a landmark for functional cup anteversion in total hip arthroplasty.

BACKGROUND: The transverse acetabular ligament (TAL) can be a useful and reproducible landmark in the orientation of the acetabular cup in total hip arthroplasty (THA). Its role in guiding cup orientation when aiming to implant in a functional anteversion orientation is unclear. The aim of this study was to assess the relationship between the TAL and the planned acetabular cup anteversion when implanted in a function orientation. MATERIAL AND METHODS: In a retrospective study the anteversion of the TAL in the contralateral un-replaced hip was measured in CT scans of patients undergoing THA and compared to the functional cup anteversion using the patient-specific spinopelvic parameters. Comparative measurements of the native acetabular version were made from the superior rim to the inferior rim and at 10 mm intervals between the 2, all in reference to the anterior pelvic plane. RESULTS: 96 hips were measured. The mean TAL anteversion angle was 17.2° ± 4.5°. The mean planned acetabular cup anteversion angle was 26.3° ± 4.7°. Pearson's correlation coefficient of this measurement with the TAL was -0.03 (p = 0.769). There was a significant difference between the planned acetabular cup anteversion and the measurement of the TAL (p < 0.0001). CONCLUSIONS: If cups are implanted parallel to the TAL, almost 80% will be >5° different to targeted functional cup version. It should be aimed to implant cups with more anteversion than the TAL indicates.

Benchmarking proton RBE models.

Objective.To biologically optimise proton therapy, models which can accurately predict variations in proton relative biological effectiveness (RBE) are essential. Current phenomenological models show large disagreements in RBE predictions, due to different model assumptions and differences in the data to which they were fit. In this work, thirteen RBE models were benchmarked against a comprehensive proton RBE dataset to evaluate predictions when all models are fit using the same data and fitting techniques, and to assess the statistical robustness of the models.Approach.Model performance was initially evaluated by fitting to the full dataset, and then a cross-validation approach was applied to assess model generalisability and robustness. The impact of weighting the fit and the choice of biological endpoint (either single or multiple survival levels) was also evaluated.Main results.Fitting the models to a common dataset reduced differences between their predictions, however significant disagreements remained due to different underlying assumptions. All models performed poorly under cross-validation in the weighted fits, suggesting that some uncertainties on the experimental data were significantly underestimated, resulting in over-fitting and poor performance on unseen data. The simplest model, which depends linearly on the LET but has no tissue or dose dependence, performed best for a single survival level. However, when fitting to multiple survival levels simultaneously, more complex models with tissue dependence performed better. All models had significant residual uncertainty in their predictions compared to experimental data.Significance.This analysis highlights that poor quality of error estimation on the dose response parameters introduces substantial uncertainty in model fitting. The significant residual error present in all approaches illustrates the challenges inherent in fitting to large, heterogeneous datasets and the importance of robust statistical validation of RBE models.

Response of Cancer Stem Cells and Human Skin Fibroblasts to Picosecond-Scale Electron Irradiation at 1010 to 1011 Gy/s.

PURPOSE: This study aimed to demonstrate for the first time the possibility of irradiating biological cells with gray (Gy)-scale doses delivered over single bursts of picosecond-scale electron beams, resulting in unprecedented dose rates of 1010 to 1011 Gy/s. METHODS AND MATERIALS: Cancer stem cells and human skin fibroblasts were irradiated with MeV-scale electron beams from a laser-driven source. Doses up to 3 Gy per pulse with a high spatial uniformity (coefficient of variance, 3%-6%) and within a timescale range of 10 to 20 picoseconds were delivered. Doses were characterized during irradiation and were found to be in agreement with Monte Carlo simulations. Cell survival and DNA double-strand break repair dynamics were studied for both cell lines using clonogenic assay and 53BP1 foci formation. The results were compared with reference x-rays at a dose rate of 0.49 Gy/min. RESULTS: Results from clonogenic assays of both cell lines up to 3 Gy were well fitted by a linear quadratic model with α = (0.68 ± 0.08) Gy-1 and ß = (0.01 ± 0.01) Gy-2 for human skin fibroblasts and α = (0.51 ± 0.14) Gy-1 and ß = (0.01 ± 0.01) Gy-2 for cancer stem cells. Compared with irradiation at 0.49 Gy/min, our experimental results indicate no statistically significant difference in cell survival rate for doses up to 3 Gy despite a significant increase in the α parameter, which may reflect more complex damage. Foci measurements showed no significant difference between irradiation at 1011 Gy/s and at 0.49 Gy/min. CONCLUSIONS: This study demonstrates the possibility of performing radiobiological studies with picosecond-scale laser-generated electron beams at ultrahigh dose rates of 1010 to1011 Gy/s. Preliminary results indicate, within statistical uncertainties, a significant increase of the α parameter, a possible indication of more complex damage induced by a higher density of ionizing tracks.

Most DNA repair defects do not modify the relationship between relative biological effectiveness and linear energy transfer in CRISPR-edited cells.

BACKGROUND: Cancer is a highly heterogeneous disease, driven by frequent genetic alterations which have significant effects on radiosensitivity. However, radiotherapy for a given cancer type is typically given with a standard dose determined from population-level trials. As a result, a proportion of patients are under- or over-dosed, reducing the clinical benefit of radiotherapy. Biological optimization would not only allow individual dose prescription but also a more efficient allocation of limited resources, such as proton and carbon ion therapy. Proton and ion radiotherapy offer an advantage over photons due to their elevated Relative Biological Effectiveness (RBE) resulting from their elevated Linear Energy Transfer (LET). Despite significant interest in optimizing LET by tailoring radiotherapy plans, RBE's genetic dependence remains unclear. PURPOSE: The aim of this study is to better define the RBE/LET relationship in a panel of cell lines with different defects in DSB repair pathways, but otherwise identical biological features and genetic background to isolate these effects. METHODS: Normal human cells (RPE1), genetically modified to introduce defects in DNA double-strand break (DSB) repair genes, ATM, BRCA1, DCLRE1C, LIG4, PRKDC and TP53, were used to map the RBE-LET relationship. Cell survival was measured with clonogenic assays after exposure to photons, protons (LET 1 and 12 keV/µm) and alpha particles (129 keV/µm). Gene knockout sensitizer enhancement ratio (SER) values were calculated as the ratio of the mean inactivation dose (MID) of wild-type cells to repair-deficient cells, and RBE values were calculated as the ratio of the MID of X-ray and particle irradiated cells. 53BP1 foci were used to quantify radiation-induced DSBs and their repair following irradiation. RESULTS: Deletion of NHEJ genes had the greatest impact on photon sensitivity (ATM-/- SER = 2.0 and Lig4-/- SER = 1.8), with genes associated with HR having smaller effects (BRCA1-/- SER = 1.2). Wild-type cells showed RBEs of 1.1, 1.3, 5.0 for low- and high-LET protons and alpha particles respectively. SERs for different genes were independent of LET, apart from NHEJ knockouts which proved to be markedly hypersensitive across all tested LETs. Due to this hypersensitivity, the impact of high LET was reduced in cell models lacking the NHEJ repair pathway. HR-defective cells had moderately increased sensitivity across all tested LETs, but, notably, the contribution of HR pathway to survival appeared independent of LET. Analysis of 53BP1 foci shows that NHEJ-defective cells had the least DSB repair capacity after low LET exposure, and no visible repair after high LET exposure. HR-defective cells also had slower repair kinetics, but the impact of HR defects is not as severe as NHEJ defects. CONCLUSIONS: DSB repair defects, particularly in NHEJ, conferred significant radiosensitivity across all LETs. This sensitization appeared independent of LET, suggesting that the contribution of different DNA repair pathways to survival does not depend on radiation quality.

Patient reported outcome measures correlate with step-count in total hip arthroplasty.

BACKGROUND: Passive smartphone-based apps are becoming more common for measuring patient progress after total hip arthroplasty (THA). Optimum activity levels during early THA recovery have not been well documented. OBJECTIVES: Correlations between step-count and patient reported outcome measures (PROMs) during early recovery were explored. This study also investigated how demographics impact step-count during early post-operative recovery. METHODS: Smartphone captured step-count data from 666 THA patients was retrospectively reviewed. Mean age was 64 ± 11 years. 55% were female. Mean BMI was 29 ± 8kg/m2. Mean daily step-count was calculated for each patient over four time-windows: 60 days prior to surgery (preop), 42-49 days postop (6 weeks), 91-98 days postop (3 months), and 183-197 days postop (6 months). Spearman correlation coefficients and linear regression were used to assess the association between PROMs (HOOS-12, HOOS-Jr, and UCLA) were performed. Patients were separated into three step-count levels: low (< 2500 steps/day), medium (2500-5500 steps/day), and high (> 5500 steps/day). Age > 65 years, BMI > 35 kg/m2, and sex were used for demographic comparisons. Post hoc analyses were performed using Welch's unequal variances t-tests, or Wilcoxon rank-sum tests, both with Bonferroni corrections, where appropriate when comparing between groups. Chi-squared analyses were also used when comparing categorical variables. RESULTS: UCLA correlated with step-count at all time-windows (p< 0.001). HOOS12-Function correlated with step-count preoperatively, at 6 weeks, and at 3 months (p< 0.001). High step-count individuals had improved UCLA scores compared to low step-count individuals preoperatively (Δ1.5, p< 0.001), at 6 weeks (Δ0.9, p< 0.001), at 3 months (Δ1.4, p< 0.001), and at 6 months (Δ1.4, p< 0.001). High step-count individuals had improved HOOS12-Function scores compared to low step-count individuals preoperatively (Δ9.6, p< 0.001), at 6 weeks (Δ5.3, p< 0.001), and at 3 months (Δ6.1, p< 0.001). Males had greater step-count at all time points (p< 0.001). Younger patients and low BMI patients had greater step-count across all time points (p< 0.001). CONCLUSION: High step-count improved PROMs scores compared to low step-count. Early post-operative step-count was significantly impacted by age, sex, and BMI. Generic recovery profiles may not be appropriate across diverse populations.

Effects of Differing Underlying Assumptions in In Silico Models on Predictions of DNA Damage and Repair.

The induction and repair of DNA double-strand breaks (DSBs) are critical factors in the treatment of cancer by radiotherapy. To investigate the relationship between incident radiation and cell death through DSB induction many in silico models have been developed. These models produce and use custom formats of data, specific to the investigative aims of the researchers, and often focus on particular pairings of damage and repair models. In this work we use a standard format for reporting DNA damage to evaluate combinations of different, independently developed, models. We demonstrate the capacity of such inter-comparison to determine the sensitivity of models to both known and implicit assumptions. Specifically, we report on the impact of differences in assumptions regarding patterns of DNA damage induction on predicted initial DSB yield, and the subsequent effects this has on derived DNA repair models. The observed differences highlight the importance of considering initial DNA damage on the scale of nanometres rather than micrometres. We show that the differences in DNA damage models result in subsequent repair models assuming significantly different rates of random DSB end diffusion to compensate. This in turn leads to disagreement on the mechanisms responsible for different biological endpoints, particularly when different damage and repair models are combined, demonstrating the importance of inter-model comparisons to explore underlying model assumptions.

Validation of In Vitro Trained Transcriptomic Radiosensitivity Signatures in Clinical Cohorts.

Transcriptomic personalisation of radiation therapy has gained considerable interest in recent years. However, independent model testing on in vitro data has shown poor performance. In this work, we assess the reproducibility in clinical applications of radiosensitivity signatures. Agreement between radiosensitivity predictions from published signatures using different microarray normalization methods was assessed. Control signatures developed from resampled in vitro data were benchmarked in clinical cohorts. Survival analysis was performed using each gene in the clinical transcriptomic data, and gene set enrichment analysis was used to determine pathways related to model performance in predicting survival and recurrence. The normalisation approach impacted calculated radiosensitivity index (RSI) values. Indeed, the limits of agreement exceeded 20% with different normalisation approaches. No published signature significantly improved on the resampled controls for prediction of clinical outcomes. Functional annotation of gene models suggested that many overlapping biological processes are associated with cancer outcomes in RT treated and non-RT treated patients, including proliferation and immune responses. In summary, different normalisation methods should not be used interchangeably. The utility of published signatures remains unclear given the large proportion of genes relating to cancer outcome. Biological processes influencing outcome overlapped for patients treated with or without radiation suggest that existing signatures may lack specificity.

High-LET radiation induces large amounts of rapidly-repaired sublethal damage.

There is agreement that high-LET radiation has a high Relative Biological Effectiveness (RBE) when delivered as a single treatment, but how it interacts with radiations of different qualities, such as X-rays, is less clear. We sought to clarify these effects by quantifying and modelling responses to X-ray and alpha particle combinations. Cells were exposed to X-rays, alpha particles, or combinations, with different doses and temporal separations. DNA damage was assessed by 53BP1 immunofluorescence, and radiosensitivity assessed using the clonogenic assay. Mechanistic models were then applied to understand trends in repair and survival. 53BP1 foci yields were significantly reduced in alpha particle exposures compared to X-rays, but these foci were slow to repair. Although alpha particles alone showed no inter-track interactions, substantial interactions were seen between X-rays and alpha particles. Mechanistic modelling suggested that sublethal damage (SLD) repair was independent of radiation quality, but that alpha particles generated substantially more sublethal damage than a similar dose of X-rays, [Formula: see text]. This high RBE may lead to unexpected synergies for combinations of different radiation qualities which must be taken into account in treatment design, and the rapid repair of this damage may impact on mechanistic modelling of radiation responses to high LETs.

Characterization of Intrinsic Radiation Sensitivity in a Diverse Panel of Normal, Cancerous and CRISPR-Modified Cell Lines.

Intrinsic radiosensitivity is a major determinant of radiation response. Despite the extensive amount of radiobiological data available, variability among different studies makes it very difficult to produce high-quality radiosensitivity biomarkers or predictive models. Here, we characterize a panel of 27 human cell lines, including those derived from lung cancer, prostate cancer, and normal tissues. In addition, we used CRISPR-Cas9 to generate a panel of lines with known DNA repair defects. These cells were characterised by measuring a range of biological features, including the induction and repair of DNA double-strand breaks (DSBs), cell cycle distribution, ploidy, and clonogenic survival following X-ray irradiation. These results offer a robust dataset without inter-experimental variabilities for model development. In addition, we used these results to explore correlations between potential determinants of radiosensitivity. There was a wide variation in the intrinsic radiosensitivity of cell lines, with cell line Mean Inactivation Doses (MID) ranging from 1.3 to 3.4 Gy for cell lines, and as low as 0.65 Gy in Lig4-/- cells. Similar substantial variability was seen in the other parameters, including baseline DNA damage, plating efficiency, and ploidy. In the CRISPR-modified cell lines, residual DSBs were good predictors of cell survival (R2 = 0.78, p = 0.009), as were induced levels of DSBs (R2 = 0.61, p = 0.01). However, amongst the normal and cancerous cells, none of the measured parameters correlated strongly with MID (R2 < 0.45), and the only metrics with statistically significant associations are plating efficiency (R2 = 0.31, p = 0.01) and percentage of cell in S phase (R2 = 0.37, p = 0.005). While these data provide a valuable dataset for the modelling of radiobiological responses, the differences in the predictive power of residual DSBs between CRISPR-modified and other subgroups suggest that genetic alterations in other pathways, such as proliferation and metabolism, may have a greater impact on cellular radiation response. These pathways are often neglected in response modelling and should be considered in the future.

Modelling oxygen effects on the in- and out-of-field radiosensitivity of cells exposed to intensity-modulated radiation fields.

Objective. The delivery of intensity-modulated radiation fields has improved the conformity of dose to tumour targets during radiotherapy (RT). Previously, it has been shown that intercellular communication between cells positioned in- and outside of the radiation field impacts cellular radiosensitivity under hypoxic and normoxic conditions. However, the mechanism of intercellular communication in hypoxia remains to be fully understood. In this study, the cell-killing effects of intercellular communication in hypoxia were modelled in an effort to better understand the underlying mechanisms of response.Approach. By irradiating a 50% area of the culture dish (half-field exposure), experimental dose-response curves for cell survival and residual DNA double-strand breaks (DSBs) were generated in prostate (DU145) and non-small cell lung cancer (H1299) cells. The oxygen enhancement ratio (OER) was determined from early DSB yields (corresponding to relative direct damage) and used to model the in- and out-of-field radiosensitivity.Main results. The developed integrated microdosimetric-kinetic (IMK) model successfully predicted the experimental dose responses for survival and lethal lesions, and provides a mechanistic interpretation that the probability of hits for releasing cell-killing signals is dependent on oxygen. This experimental and modelling study also suggests that residual DSBs correspond to logarithmic survival fraction (meaning lethal lesions) for in- and out-of-field cells. Our data suggest that the OER value determined using uniform-field exposure can be applied to predict the in- and out-of-field radiosensitivity of cells following exposure to intensity modulated beams.Significance. The developed IMK model facilitates a more precise understanding of intercellular signalling following exposure to intensity-modulated radiation fields.

Investigating the potential contribution of inter-track interactions within ultra-high dose-rate proton therapy.

Objective. Laser-accelerated protons offer an alternative delivery mechanism for proton therapy. This technique delivers dose-rates of ≥109Gy s-1, many orders of magnitude greater than used clinically. Such ultra-high dose-rates reduce delivery time to nanoseconds, equivalent to the lifetime of reactive chemical species within a biological medium. This leads to the possibility of inter-track interactions between successive protons within a pulse, potentially altering the yields of damaging radicals if they are in sufficient spatial proximity. This work investigates the temporal evolution of chemical species for a range of proton energies and doses to quantify the circumstances required for inter-track interactions, and determine any relevance within ultra-high dose-rate proton therapy.Approach. The TOPAS-nBio Monte Carlo toolkit was used to investigate possible inter-track interactions. Firstly, protons between 0.5 and 100 MeV were simulated to record the radial track dimensions throughout the chemical stage from 1 ps to 1µs. Using the track areas, the geometric probability of track overlap was calculated for various exposures and timescales. A sample of irradiations were then simulated in detail to compare any change in chemical yields for independently and instantaneously delivered tracks, and validate the analytic model.Main results. Track overlap for a clinical 2 Gy dose was negligible for biologically relevant timepoints for all energies. Overlap probability increased with time after irradiation, proton energy and dose, with a minimum 23 Gy dose required before significant track overlap occurred. Simulating chemical interactions confirmed these results with no change in radical yields seen up to 8 Gy for independently and instantaneously delivered tracks.Significance. These observations suggest that the spatial separation between incident protons is too large for physico-chemical inter-track interactions, regardless of the delivery time, indicating such interactions would not play a role in any potential changes in biological response between laser-accelerated and conventional proton therapy.

Proposing a Clinical Model for RBE Based on Proton Track-End Counts.

PURPOSE: In proton therapy, the clinical application of linear energy transfer (LET) optimization remains contentious, in part because of challenges associated with the definition and calculation of LET and its exact relationship with relative biological effectiveness (RBE) because of large variation in experimental in vitro data. This has raised interest in other metrics with favorable properties for biological optimization, such as the number of proton track ends in a voxel. In this work, we propose a novel model for clinical calculations of RBE, based on proton track end counts. METHODS AND MATERIALS: We developed an effective dose concept to translate between the total proton track-end count per unit mass in a voxel and a proton RBE value. Dose, track end, and dose-averaged LET (LETd) distributions were simulated using Monte Carlo models for a series of water phantoms, in vitro radiobiological studies, and patient treatment plans. We evaluated the correlation between track ends and regions of elevated biological effectiveness in comparison to LETd-based models of RBE. RESULTS: Track ends were found to correlate with biological effects in in vitro experiments with an accuracy comparable to LETd. In patient simulations, our track end model identified the same biological hotspots as predicted by LETd-based radiobiological models of proton RBE. CONCLUSIONS: These results suggest that, for clinical optimization and evaluation, an RBE model based on proton track end counts may match LETd-based models in terms of information provided while also offering superior statistical properties.

Cellular irradiations with laser-driven carbon ions at ultra-high dose rates.

Objective.Carbon is an ion species of significant radiobiological interest, particularly in view of its use in cancer radiotherapy, where its large Relative Biological Efficiency is often exploited to overcome radio resistance. A growing interest in highly pulsed carbon delivery has arisen in the context of the development of the FLASH radiotherapy approach, with recent studies carried out at dose rates of 40 Gy s-1. Laser acceleration methods, producing ultrashort ion bursts, can now enable the delivery of Gy-level doses of carbon ions at ultra-high dose rates (UHDRs), exceeding 109Gy s-1. While studies at such extreme dose rate have been carried out so far using low LET particles such as electrons and protons, the radiobiology of high-LET, UHDR ions has not yet been explored. Here, we report the first application of laser-accelerated carbon ions generated by focussing 1020W cm-2intense lasers on 10-25 nm carbon targets, to irradiate radioresistant patient-derived Glioblastoma stem like cells (GSCs).Approach.We exposed GSCs to 1 Gy of 9.5 ± 0.5 MeV/n carbon ions delivered in a single ultra-short (∼400-picosecond) pulse, at a dose rate of 2 × 109Gy s-1, generated using the ASTRA GEMINI laser of the Central Laser Facility at the Rutherford Appleton Laboratory, Didcot, Oxfordshire, UK. We quantified carbon ion-induced DNA double strand break (DSB) damage using the 53BP1 foci formation assay and used 225 kVp x-rays as a reference radiation.Main Results.Laser-accelerated carbon ions induced complex DNA DSB damage, as seen through persistent 53BP1 foci (11.5 ± 0.4 foci/cell/Gy) at 24 h and significantly larger foci (1.69 ± 0.07µm2) than x-rays induced ones (0.63 ± 0.02µm2). The relative foci induction value for laser-driven carbon ions relative to conventional x-rays was 3.2 ± 0.3 at 24 h post-irradiation also confirming the complex nature of the induced damage.Significance.Our study demonstrates the feasibility of radiobiology investigations at unprecedented dose rates using laser-accelerated high-LET carbon ions in clinically relevant models.

DNA Repair Inhibitors Potentiate Fractionated Radiotherapy More Than Single-Dose Radiotherapy in Breast Cancer Cells.

Pharmacological inhibitors of DNA damage response (DDR) proteins, such as the ataxia-telangiectasia mutated (ATM) and ataxia-telangiectasia and Rad3-related (ATR) kinases and poly (ADP-ribose) polymerase (PARP), have been developed to overcome tumor radioresistance. Despite demonstrating radiosensitization preclinically, they have performed suboptimally in clinical trials, possibly due to an incomplete understanding of the influence of DDR inhibition on ionizing radiation (IR) dose fractionation and sublethal damage repair. Hence, this study aimed to evaluate the radiosensitizing ability under fractionation of ATM inhibitor AZD0156, ATR inhibitor AZD6738 and PARP inhibitor AZD2281 (olaparib), utilizing MDA-MB-231 and MCF-7 human breast cancer cells. Clonogenic assays were performed to assess cell survival and sublethal damage repair after treatment with DDR inhibitors and either single-dose or fractionated IR. Immunofluorescence microscopy was utilized to evaluate DNA double-strand break repair kinetics. Cell cycle distributions were investigated using flow cytometry. All inhibitors showed significant radiosensitization, which was significantly greater following fractionated IR than single-dose IR. They also led to more unrepaired DNA double-strand breaks at 24 h post-IR. This study provides preclinical evidence for the role of AZD0156, AZD6738 and olaparib as radiosensitizing agents. Still, it highlights the need to evaluate these drugs in fractionated settings mirroring clinical practice to optimize the trial design.

Differential responses to 223Ra and Alpha-particles exposure in prostate cancer driven by mitotic catastrophe.

Introduction: Radium-223 (223Ra) has been shown to have an overall survival benefit in metastatic castration-resistant prostate cancer (mCRPC) involving bone. Despite its increased clinical usage, relatively little is known regarding the mechanism of action of 223Ra at the cellular level. Methods: We evaluated the effects of 223Ra irradiation in a panel of cell lines and then compared them with standard X-ray and external alpha-particle irradiation, with a particular focus on cell survival and DNA damage repair kinetics. Results: 223Ra exposures had very high, cell-type-dependent RBE50% ranging from 7 to 15. This was significantly greater than external alpha irradiations (RBE50% from 1.4 to 2.1). These differences were shown to be partially related to the volume of 223Ra solution added, independent of the alpha-particle dose rate, suggesting a radiation-independent mechanism of effect. Both external alpha particles and 223Ra exposure were associated with delayed DNA repair, with similar kinetics. Additionally, the greater treatment efficacy of 223Ra was associated with increased levels of residual DNA damage and cell death by mitotic catastrophe. Conclusions: These results suggest that 223Ra exposure may be associated with greater biological effects than would be expected by direct comparison with a similar dose of external alpha particles, highlighting important challenges for future therapeutic optimization.

A computational approach to quantifying miscounting of radiation-induced double-strand break immunofluorescent foci.

Immunofluorescent tagging of DNA double-strand break (DSB) markers, such as γ-H2AX and other DSB repair proteins, are powerful tools in understanding biological consequences following irradiation. However, whilst the technique is widespread, there are many uncertainties related to its ability to resolve and reliably deduce the number of foci when counting using microscopy. We present a new tool for simulating radiation-induced foci in order to evaluate microscope performance within in silico immunofluorescent images. Simulations of the DSB distributions were generated using Monte Carlo track-structure simulation. For each DSB distribution, a corresponding DNA repair process was modelled and the un-repaired DSBs were recorded at several time points. Corresponding microscopy images for both a DSB and (γ-H2AX) fluorescent marker were generated and compared for different microscopes, radiation types and doses. Statistically significant differences in miscounting were found across most of the tested scenarios. These inconsistencies were propagated through to repair kinetics where there was a perceived change between radiation-types. These changes did not reflect the underlying repair rate and were caused by inconsistencies in foci counting. We conclude that these underlying uncertainties must be considered when analysing images of DNA damage markers to ensure differences observed are real and are not caused by non-systematic miscounting.

Impact of DNA Geometry and Scoring on Monte Carlo Track-Structure Simulations of Initial Radiation-Induced Damage.

Track structure Monte Carlo simulations are a useful tool to investigate the damage induced to DNA by ionizing radiation. These simulations usually rely on simplified geometrical representations of the DNA subcomponents. DNA damage is determined by the physical and physicochemical processes occurring within these volumes. In particular, damage to the DNA backbone is generally assumed to result in strand breaks. DNA damage can be categorized as direct (ionization of an atom part of the DNA molecule) or indirect (damage from reactive chemical species following water radiolysis). We also consider quasi-direct effects, i.e., damage originated by charge transfers after ionization of the hydration shell surrounding the DNA. DNA geometries are needed to account for the damage induced by ionizing radiation, and different geometry models can be used for speed or accuracy reasons. In this work, we use the Monte Carlo track structure tool TOPAS-nBio, built on top of Geant4-DNA, for simulation at the nanometer scale to evaluate differences among three DNA geometrical models in an entire cell nucleus, including a sphere/spheroid model specifically designed for this work. In addition to strand breaks, we explicitly consider the direct, quasi-direct, and indirect damage induced to DNA base moieties. We use results from the literature to determine the best values for the relevant parameters. For example, the proportion of hydroxyl radical reactions between base moieties was 80%, and between backbone, moieties was 20%, the proportion of radical attacks leading to a strand break was 11%, and the expected ratio of base damages and strand breaks was 2.5-3. Our results show that failure to update parameters for new geometric models can lead to significant differences in predicted damage yields.

Development of a portable hypoxia chamber for ultra-high dose rate laser-driven proton radiobiology applications.

BACKGROUND: There is currently significant interest in assessing the role of oxygen in the radiobiological effects at ultra-high dose rates. Oxygen modulation is postulated to play a role in the enhanced sparing effect observed in FLASH radiotherapy, where particles are delivered at 40-1000 Gy/s. Furthermore, the development of laser-driven accelerators now enables radiobiology experiments in extreme regimes where dose rates can exceed 109 Gy/s, and predicted oxygen depletion effects on cellular response can be tested. Access to appropriate experimental enviroments, allowing measurements under controlled oxygenation conditions, is a key requirement for these studies. We report on the development and application of a bespoke portable hypoxia chamber specifically designed for experiments employing laser-driven sources, but also suitable for comparator studies under FLASH and conventional irradiation conditions. MATERIALS AND METHODS: We used oxygen concentration measurements to test the induction of hypoxia and the maintenance capacity of the chambers. Cellular hypoxia induction was verified using hypoxia inducible factor-1α immunostaining. Calibrated radiochromic films and GEANT-4 simulations verified the dosimetry variations inside and outside the chambers. We irradiated hypoxic human skin fibroblasts (AG01522B) cells with laser-driven protons, conventional protons and reference 225 kVp X-rays to quantify DNA DSB damage and repair under hypoxia. We further measured the oxygen enhancement ratio for cell survival after X-ray exposure in normal fibroblast and radioresistant patient- derived GBM stem cells. RESULTS: Oxygen measurements showed that our chambers maintained a radiobiological hypoxic environment for at least 45 min and pathological hypoxia for up to 24 h after disconnecting the chambers from the gas supply. We observed a significant reduction in the 53BP1 foci induced by laser-driven protons, conventional protons and X-rays in the hypoxic cells compared to normoxic cells at 30 min post-irradiation. Under hypoxic irradiations, the Laser-driven protons induced significant residual DNA DSB damage in hypoxic AG01522B cells compared to the conventional dose rate protons suggesting an important impact of these extremely high dose-rate exposures. We obtained an oxygen enhancement ratio (OER) of 2.1 ± 0.1 and 2.5 ± 0.1 respectively for the AG01522B and patient-derived GBM stem cells for X-ray irradiation using our hypoxia chambers. CONCLUSION: We demonstrated the design and application of portable hypoxia chambers for studying cellular radiobiological endpoints after exposure to laser-driven protons at ultra-high dose, conventional protons and X-rays. Suitable levels of reduced oxygen concentration could be maintained in the absence of external gassing to quantify hypoxic effects. The data obtained provided indication of an enhanced residual DNA DSB damage under hypoxic conditions at ultra-high dose rate compared to the conventional protons or X-rays.

Evaluating Iodine-125 DNA Damage Benchmarks of Monte Carlo DNA Damage Models.

A wide range of Monte Carlo models have been applied to predict yields of DNA damage based on nanoscale track structure calculations. While often similar on the macroscopic scale, these models frequently employ different assumptions which lead to significant differences in nanoscale dose deposition. However, the impact of these differences on key biological readouts remains unclear. A major challenge in this area is the lack of robust datasets which can be used to benchmark models, due to a lack of resolution at the base pair level required to deeply test nanoscale dose deposition. Studies investigating the distribution of strand breakage in short DNA strands following the decay of incorporated 125I offer one of the few benchmarks for model predictions on this scale. In this work, we have used TOPAS-nBio to evaluate the performance of three Geant4-DNA physics models at predicting the distribution and yield of strand breaks in this irradiation scenario. For each model, energy and OH radical distributions were simulated and used to generate predictions of strand breakage, varying energy thresholds for strand breakage and OH interaction rates to fit to the experimental data. All three models could fit well to the observed data, although the best-fitting strand break energy thresholds ranged from 29.5 to 32.5 eV, significantly higher than previous studies. However, despite well describing the resulting DNA fragment distribution, these fit models differed significantly with other endpoints, such as the total yield of breaks, which varied by 70%. Limitations in the underlying data due to inherent normalisation mean it is not possible to distinguish clearly between the models in terms of total yield. This suggests that, while these physics models can effectively fit some biological data, they may not always generalise in the same way to other endpoints, requiring caution in their extrapolation to new systems and the use of multiple different data sources for robust model benchmarking.

RadSigBench: a framework for benchmarking functional genomics signatures of cancer cell radiosensitivity.

Multiple transcriptomic predictors of tumour cell radiosensitivity (RS) have been proposed, but they have not been benchmarked against one another or to control models. To address this, we present RadSigBench, a comprehensive benchmarking framework for RS signatures. The approach compares candidate models to those developed from randomly resampled control signatures and from cellular processes integral to the radiation response. Robust evaluation of signature accuracy, both overall and for individual tissues, is performed. The NCI60 and Cancer Cell Line Encyclopaedia datasets are integrated into our workflow. Prediction of two measures of RS is assessed: survival fraction after 2 Gy and mean inactivation dose. We apply the RadSigBench framework to seven prominent published signatures of radiation sensitivity and test for equivalence to control signatures. The mean out-of-sample R2 for the published models on test data was very poor at 0.01 (range: -0.05 to 0.09) for Cancer Cell Line Encyclopedia and 0.00 (range: -0.19 to 0.19) in the NCI60 data. The accuracy of both published and cellular process signatures investigated was equivalent to the resampled controls, suggesting that these signatures contain limited radiation-specific information. Enhanced modelling strategies are needed for effective prediction of intrinsic RS to inform clinical treatment regimes. We make recommendations for methodological improvements, for example the inclusion of perturbation data, multiomics, advanced machine learning and mechanistic modelling. Our validation framework provides for robust performance assessment of ongoing developments in intrinsic RS prediction.