A perspective on tumor radiation resistance following high-LET radiation treatment.

High-linear energy transfer (LET) radiation is a promising alternative to conventional low-LET radiation for therapeutic gain against cancer owing to its ability to induce complex and clustered DNA lesions. However, the development of radiation resistance poses a significant barrier. The potential molecular mechanisms that could confer resistance development are translesion synthesis (TLS), replication gap suppression (RGS) mechanisms, autophagy, epithelial-mesenchymal transition (EMT) activation, release of exosomes, and epigenetic changes. This article will discuss various types of complex clustered DNA damage, their repair mechanisms, mutagenic potential, and the development of radiation resistance strategies. Furthermore, it highlights the importance of careful consideration and patient selection when employing high-LET radiotherapy in clinical settings.

Dose and DNA damage modelling of diffusing alpha-emitters radiation therapy using Geant4.

PURPOSE: Diffusing alpha-emitters radiation therapy (DaRT) is a brachytherapy technique using α-particles to treat solid tumours. The high linear energy transfer (LET) and short range of α-particles make them good candidates for the targeted treatment of cancer. Treatment planning of DaRT requires a good understanding of the dose from α-particles and the other particles released in the 224Ra decay chain. METHODS: The Geant4 Monte Carlo toolkit has been used to simulate a DaRT seed to better understand the dose contribution from all particles and simulate the DNA damage due to this treatment. RESULTS: Close to the seed α-particles deliver the majority of dose, however at radial distances greater than 4 mm, the contribution of ß-particles is greater. The RBE has been estimated as a function of number of double strand breaks (DSBs) and complex DSBs. A maximum seed spacing of 5.5 mm and 6.5 mm was found to deliver at least 20 Gy RBE weighted dose between the seeds for RBEDSB and RBEcDSB respectively. CONCLUSIONS: The DNA damage changes with radial distance from the seed and has been found to become less complex with distance, which is potentially easier for the cell to repair. Close to the seed α-particles contribute the majority of dose, however the contribution from other particles cannot be neglected and may influence the choice of seed spacing.

Modeling Radiation-Induced Epithelial Cell Injury in Murine Three-Dimensional Esophageal Organoids.

Esophageal squamous cell carcinoma (ESCC) is a deadly consequence of radiation exposure to the esophagus. ESCC arises from esophageal epithelial cells that undergo malignant transformation and features a perturbed squamous cell differentiation program. Understanding the dose- and radiation quality-dependence of the esophageal epithelium response to radiation may provide insights into the ability of radiation to promote ESCC. We have explored factors that may play a role in esophageal epithelial radiosensitivity and their potential relationship to ESCC risk. We have utilized a murine three-dimensional (3D) organoid model that recapitulates the morphology and functions of the stratified squamous epithelium of the esophagus to study persistent dose- and radiation quality-dependent changes. Interestingly, although high-linear energy transfer (LET) Fe ion exposure induced a more intense and persistent alteration of squamous differentiation and 53BP1 DNA damage foci levels as compared to Cs, the MAPK/SAPK stress pathway signaling showed similar altered levels for most phospho-proteins with both radiation qualities. In addition, the lower dose of high-LET exposure also revealed nearly the same degree of morphological changes, even though only ~36% of the cells were predicted to be hit at the lower 0.1 Gy dose, suggesting that a bystander effect may be induced. Although p38 and ERK/MAPK revealed the highest levels following high-LET exposure, the findings reveal that even a low dose (0.1 Gy) of both radiation qualities can elicit a persistent stress signaling response that may critically impact the differentiation gradient of the esophageal epithelium, providing novel insights into the pathogenesis of radiation-induced esophageal injury and early stage esophageal carcinogenesis.

Dose averaged linear energy transfer optimization for large sacral chordomas in carbon ion therapy.

BACKGROUND: Carbon ion beams are well accepted as densely ionizing radiation with a high linear energy transfer (LET). However, the current clinical practice does not fully exploit the highest possible dose-averaged LET (LETd) and, consequently, the biological potential in the target. This aspect becomes worse in larger tumors for which inferior clinical outcomes and corresponding lower LETd was reported. PURPOSE: The vicinity to critical organs in general and the inferior overall survival reported for larger sacral chordomas treated with carbon ion radiotherapy (CIRT), makes the treatment of such tumors challenging. In this work it was aimed to increase the LETd in large volume tumors while maintaining the relative biological effectiveness (RBE)-weighted dose, utilizing the LETd optimization functions of a commercial treatment planning system (TPS). METHODS: Ten reference sequential boost carbon ion treatment plans, designed to mimic clinical plans for large sacral chordoma tumors, were generated. High dose clinical target volumes (CTV-HD) larger than 250 cm 3 $250 \,{\rm cm}^{3}$ were considered as large targets. The total RBE-weighted median dose prescription with the local effect model (LEM) was D RBE , 50 % = 73.6 Gy $\textrm {D}_{\rm RBE, 50\%}=73.6 \,{\rm Gy}$ in 16 fractions (nine to low dose and seven to high dose planning target volume). No LETd optimization was performed in the reference plans, while LETd optimized plans used the minimum LETd (Lmin) optimization function in RayStation 2023B. Three different Lmin values were investigated and specified for the seven boost fractions: L min = 60 keV / µ m $\textrm {L}_{\rm min}=60 \,{\rm keV}/{\umu }{\rm m}$ , L min = 80 keV / µ m $\textrm {L}_{\rm min}=80 \,{\rm keV}/{\umu }{\rm m}$ and L min = 100 keV / µ m $\textrm {L}_{\rm min}=100 \,{\rm keV}/{\umu }{\rm m}$ . To compare the LETd optimized against reference plans, LETd and RBE-weighted dose based goals similar to and less strict than clinical ones were specified for the target. The goals for the organs at risk (OAR) remained unchanged. Robustness evaluation was studied for eight scenarios ( ± 3.5 % $\pm 3.5\%$ range uncertainty and ± 3 mm $\pm 3 \,{\rm mm}$ setup uncertainty along the main three axes). RESULTS: The optimization method with L min = 60 keV / µ m $\textrm {L}_{\rm min}=60 \,{\rm keV}/{\umu }{\rm m}$ resulted in an optimal LETd distribution with an average increase of LET d , 98 % ${\rm {LET}}_{{\rm {d,}}98\%}$ (and LET d , 50 % ${\rm {LET}}_{{\rm {d,}}50\%}$ ) in the CTV-HD by 8.9 ± 1.5 keV / µ m $8.9\pm 1.5 \,{\rm keV}/{\umu }{\rm m}$ ( 27 % $27\%$ ) (and 6.9 ± 1.3 keV / µ m $6.9\pm 1.3 \,{\rm keV}/{\umu }{\rm m}$ ( 17 % $17\%$ )), without significant difference in the RBE-weighted dose. By allowing ± 5 % $\pm 5\%$ over- and under-dosage in the target, the LET d , 98 % ${\rm {LET}}_{{\rm {d,}}98\%}$ (and LET d , 50 % ${\rm {LET}}_{{\rm {d,}}50\%}$ ) can be increased by 11.3 ± 1.2 keV / µ m $11.3\pm 1.2 \,{\rm keV}/{\umu }{\rm m}$ ( 34 % $34\%$ ) (and 11.7 ± 3.4 keV / µ m $11.7\pm 3.4 \,{\rm keV}/{\umu }{\rm m}$ ( 29 % $29\%$ )), using the optimization parameters L min = 80 keV / µ m $\textrm {L}_{\rm min}=80 \,{\rm keV}/{\umu }{\rm m}$ . The pass rate for the OAR goals in the LETd optimized plans was in the same level as the reference plans. LETd optimization lead to less robust plans compared to reference plans. CONCLUSIONS: Compared to conventionally optimized treatment plans, the LETd in the target was increased while maintaining the RBE-weighted dose using TPS LETd optimization functionalities. Regularly assessing RBE-weighted dose robustness and acquiring more in-room images remain crucial and inevitable aspects during treatment.

Deep learning based linear energy transfer calculation for proton therapy.

Objective.This study aims to address the limitations of traditional methods for calculating linear energy transfer (LET), a critical component in assessing relative biological effectiveness (RBE). Currently, Monte Carlo (MC) simulation, the gold-standard for accuracy, is resource-intensive and slow for dose optimization, while the speedier analytical approximation has compromised accuracy. Our objective was to prototype a deep-learning-based model for calculating dose-averaged LET (LETd) using patient anatomy and dose-to-water (DW) data, facilitating real-time biological dose evaluation and LET optimization within proton treatment planning systems.Approach. 275 4-field prostate proton Stereotactic Body Radiotherapy plans were analyzed, rendering a total of 1100 fields. Those were randomly split into 880, 110, and 110 fields for training, validation, and testing. A 3D Cascaded UNet model, along with data processing and inference pipelines, was developed to generate patient-specific LETddistributions from CT images and DW. The accuracy of the LETdof the test dataset was evaluated against MC-generated ground truth through voxel-based mean absolute error (MAE) and gamma analysis.Main results.The proposed model accurately inferred LETddistributions for each proton field in the test dataset. A single-field LETdcalculation took around 100 ms with trained models running on a NVidia A100 GPU. The selected model yielded an average MAE of 0.94 ± 0.14 MeV cm-1and a gamma passing rate of 97.4% ± 1.3% when applied to the test dataset, with the largest discrepancy at the edge of fields where the dose gradient was the largest and counting statistics was the lowest.Significance.This study demonstrates that deep-learning-based models can efficiently calculate LETdwith high accuracy as a fast-forward approach. The model shows great potential to be utilized for optimizing the RBE of proton treatment plans. Future efforts will focus on enhancing the model's performance and evaluating its adaptability to different clinical scenarios.

RadPhysBio: A Radiobiological Database for the Prediction of Cell Survival upon Exposure to Ionizing Radiation.

Based on the need for radiobiological databases, in this work, we mined experimental ionizing radiation data of human cells treated with X-rays, γ-rays, carbon ions, protons and α-particles, by manually searching the relevant literature in PubMed from 1980 until 2024. In order to calculate normal and tumor cell survival α and ß coefficients of the linear quadratic (LQ) established model, as well as the initial values of the double-strand breaks (DSBs) in DNA, we used WebPlotDigitizer and Python programming language. We also produced complex DNA damage results through the fast Monte Carlo code MCDS in order to complete any missing data. The calculated α/ß values are in good agreement with those valued reported in the literature, where α shows a relatively good association with linear energy transfer (LET), but not ß. In general, a positive correlation between DSBs and LET was observed as far as the experimental values are concerned. Furthermore, we developed a biophysical prediction model by using machine learning, which showed a good performance for α, while it underscored LET as the most important feature for its prediction. In this study, we designed and developed the novel radiobiological 'RadPhysBio' database for the prediction of irradiated cell survival (α and ß coefficients of the LQ model). The incorporation of machine learning and repair models increases the applicability of our results and the spectrum of potential users.

Health Effects of Ionizing Radiation on the Human Body.

Radioactivity is a process in which the nuclei of unstable atoms spontaneously decay, producing other nuclei and releasing energy in the form of ionizing radiation in the form of alpha (α) and beta (ß) particles as well as the emission of gamma (γ) electromagnetic waves. People may be exposed to radiation in various forms, as casualties of nuclear accidents, workers in power plants, or while working and using different radiation sources in medicine and health care. Acute radiation syndrome (ARS) occurs in subjects exposed to a very high dose of radiation in a very short period of time. Each form of radiation has a unique pathophysiological effect. Unfortunately, higher organisms-human beings-in the course of evolution have not acquired receptors for the direct "capture" of radiation energy, which is transferred at the level of DNA, cells, tissues, and organs. Radiation in biological systems depends on the amount of absorbed energy and its spatial distribution, particularly depending on the linear energy transfer (LET). Photon radiation with low LET leads to homogeneous energy deposition in the entire tissue volume. On the other hand, radiation with a high LET produces a fast Bragg peak, which generates a low input dose, whereby the penetration depth into the tissue increases with the radiation energy. The consequences are mutations, apoptosis, the development of cancer, and cell death. The most sensitive cells are those that divide intensively-bone marrow cells, digestive tract cells, reproductive cells, and skin cells. The health care system and the public should raise awareness of the consequences of ionizing radiation. Therefore, our aim is to identify the consequences of ARS taking into account radiation damage to the respiratory system, nervous system, hematopoietic system, gastrointestinal tract, and skin.

OSLD nanoDot characterization for carbon radiotherapy dosimetry.

Objective. This study characterized optically-stimulated luminescent dosimeter (OSLD) nanoDots for use in a therapeutic carbon beam using the Imaging and Radiation Oncology Core (IROC) framework for remote output verification.Approach. The absorbed dose correction factors for OSLD (fading, linearity, beam quality, angularity, and depletion), as defined by AAPM TG 191, were characterized for carbon beams. For the various correction factors, the effect of linear energy transfer (LET) was examined by characterizing in both a low and high LET setting.Main results. Fading was not statistically different between reference photons and carbon, nor between low and high LET beams; thus, the standard IROC-defined exponential function could be used to characterize fading. Dose linearity was characterized with a linear fit; while low and high LET carbon linearity was different, these differences were small and could be rolled into the uncertainty budget if using a single linearity correction. A linear fit between beam quality and dose-averaged LET was determined. The OSLD response at various angles of incidence was not statistically different, thus a correction factor need not be applied. There was a difference in depletion between low and high LET irradiations in a primary carbon beam, but this difference was small over the standard five readings. The largest uncertainty associated with the use of OSLDs in carbon was because of thekQcorrection factor, with an uncertainty of 6.0%. The overall uncertainty budget was 6.3% for standard irradiation conditions.Significance. OSLD nanoDot response was characterized in a therapeutic carbon beam. The uncertainty was larger than for traditional photon applications. These findings enable the use of OSLDs for carbon absorbed dose measurements, but with less accuracy than conventional OSLD audit programs.

Monte carlo simulation study on the dose and dose-averaged linear energy transfer distributions in carbon ion radiotherapy.

Dose-averaged linear energy transfer (LETd) is conventionally evaluated from the relative biological effectiveness (RBE)-LETd fitted function used in the treatment planning system. In this study, we calculated the physical doses and their linear energy transfer (LET) distributions for patterns of typical CIRT beams using Monte Carlo (MC) simulation. The LETd was then deduced from the MC simulation and compared with that obtained from the conventional method. The two types of LETd agreed well with each other, except around the distal end of the spread-out Bragg peak. Furthermore, an MC simulation was conducted with the material composition of water and realistic materials. The profiles of physical dose and LETd were in good agreement for both techniques. These results indicate that the previous studies to analyze the minimum LETd in CIRT cases are valid for practical situations, and the material composition conversion to water little affects the dose distribution in the irradiation field.

NF-κB in the Radiation Response of A549 Non-Small Cell Lung Cancer Cells to X-rays and Carbon Ions under Hypoxia.

Cellular hypoxia, detectable in up to 80% of non-small cell lung carcinoma (NSCLC) tumors, is a known cause of radioresistance. High linear energy transfer (LET) particle radiation might be effective in the treatment of hypoxic solid tumors, including NSCLC. Cellular hypoxia can activate nuclear factor κB (NF-κB), which can modulate radioresistance by influencing cancer cell survival. The effect of high-LET radiation on NF-κB activation in hypoxic NSCLC cells is unclear. Therefore, we compared the effect of low (X-rays)- and high (12C)-LET radiation on NF-κB responsive genes' upregulation, as well as its target cytokines' synthesis in normoxic and hypoxic A549 NSCLC cells. The cells were incubated under normoxia (20% O2) or hypoxia (1% O2) for 48 h, followed by irradiation with 8 Gy X-rays or 12C ions, maintaining the oxygen conditions until fixation or lysis. Regulation of NF-κB responsive genes was evaluated by mRNA sequencing. Secretion of NF-κB target cytokines, IL-6 and IL-8, was quantified by ELISA. A greater fold change increase in expression of NF-κB target genes in A549 cells following exposure to 12C ions compared to X-rays was observed, regardless of oxygenation status. These genes regulate cell migration, cell cycle, and cell survival. A greater number of NF-κB target genes was activated under hypoxia, regardless of irradiation status. These genes regulate cell migration, survival, proliferation, and inflammation. X-ray exposure under hypoxia additionally upregulated NF-κB target genes modulating immunosurveillance and epithelial-mesenchymal transition (EMT). Increased IL-6 and IL-8 secretion under hypoxia confirmed NF-κB-mediated expression of pro-inflammatory genes. Therefore, radiotherapy, particularly with X-rays, may increase tumor invasiveness in surviving hypoxic A549 cells.

Robustness of carbon-ion radiotherapy against DNA damage repair associated radiosensitivity variation based on a biophysical model.

BACKGROUND: Interpatient variation of tumor radiosensitivity is rarely considered during the treatment planning process despite its known significance for the therapeutic outcome. PURPOSE: To apply our mechanistic biophysical model to investigate the biological robustness of carbon ion radiotherapy (CIRT) against DNA damage repair interference (DDRi) associated patient-to-patient variability in radiosensitivity and its potential clinical advantages against conventional radiotherapy approaches. METHODS AND MATERIALS: The "UNIfied and VERSatile bio response Engine" (UNIVERSE) was extended by carbon ions and its predictions were compared to a panel of in vitro and in vivo data including various endpoints and DDRi settings within clinically relevant dose and linear energy transfer (LET) ranges. The implications of UNIVERSE predictions were then assessed in a clinical patient scenario considering DDRi variance. RESULTS: UNIVERSE tests well against the applied benchmarks. While in vitro survival curves were predicted with an R2 > 0.92, deviations from in vivo RBE data were less than 5.6% The conducted paradigmatic patient plan study implies a markedly reduced significance of DDRi based radiosensitivity variability in CIRT (13% change of D 50 ${{D}_{50}}$ in target) compared to conventional radiotherapy (62%) and that boosting the LET within the target further amplifies this robustness of CIRT (8%). In the case of heightened tumor radiosensitivity, a dose de-escalation strategy for photons allows a reduction of the maximum effective dose within the normal tissue (NT) from a D 2 ${{D}_2}$ of 2.65 to 1.64 Gy, which lies below the level found for CIRT ( D 2 ${{D}_2}$  = 2.41 Gy) for the analyzed plan and parameters. However, even after de-escalation, the integral effective dose in the NT is found to be substantially higher for conventional radiotherapy in comparison to CIRT ( D m e a n ${{D}_{mean}}$ of 0.75, 0.46, and 0.24 Gy for the conventional plan, its de-escalation and CIRT, respectively). CONCLUSIONS: The framework offers adequate predictions of in vitro and in vivo radiation effects of CIRT while allowing the consideration of DRRi based solely on parameters derived from photon data. The results of the patient planning study underline the potential of CIRT to minimize important sources of interpatient divergence in therapy outcome, especially when combined with techniques that allow to maximize the LET within the tumor. Despite the potential of de-escalation strategies for conventional radiotherapy to reduce the maximum effective dose in the NT, CIRT appears to remain a more favorable option due to its ability to reduce the integral effective dose within the NT.

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.

Dose-averaged LET optimized carbon-ion radiotherapy for head and neck cancers.

This feasibility study confirmed the initial safety and efficacy of a novel carbon-ion radiotherapy (CIRT) using linear energy transfer (LET) painting for head and neck cancer. This study is the first step toward establishing CIRT with LET painting in clinical practice and making it a standard practice in the future.

Induction of Chromosomal Aberrations after Exposure to the Auger Electron Emitter Iodine-125, the ß--emitter Tritium and Cesium-137 γ rays.

High-LET-type cell survival curves have been observed in cells that were allowed to incorporate 125I-UdR into their DNA. Incorporation of tritiated thymidine into the DNA of cells has also been shown to result in an increase in relative biological effectiveness in cell survival experiments, but the increase is smaller than observed after incorporation of 125I-UdR. These findings are explained in the literature by the overall complexity of the induced DNA damage resulting from energies of the ejected electron(s) during the decay of 3H and 125I. Chromosomal aberrations (CA) are defined as morphological or structural changes of one or more chromosomes, and can be induced by ionizing radiation. Whether the number of CA is associated with the linear energy transfer (LET) of the radiation and/or the actual complexity of the induced DNA double-strand breaks (DSB) remains elusive. In this study, we investigated whether DNA lesions induced at different cell cycle stages and by different radiation types [Auger-electrons (125I), ß- particles (3H), or γ radiation (137Cs)] have an impact on the number of CA induced after induction of the same number of DSB as determined by the γ-H2AX foci assay. Cells were synchronized and pulse-labeled in S phase with low activities of 125I-UdR or tritiated thymidine. For decay accumulation, cells were cryopreserved either after pulse-labeling in S phase or after progression to G2/M or G1 phase. Experiments with γ irradiation (137Cs) were performed with synchronized and cryopreserved cells in S, G2/M or G1 phase. After thawing, a CA assay was performed. All experiments were performed after a similar number of DSB were induced. CA induction after 125I-UdR was incorporated was 2.9-fold and 1.7-fold greater compared to exposure to γ radiation and radiation from incorporated tritiated thymidine, respectively, when measured in G2/M cells. In addition, measurement of CA in G2/M cells after incorporation of 125I-UdR was 2.5-fold greater when compared to cells in G1 phase. In contrast, no differences were observed between the three radiation qualities with respect to exposure after cryopreservation in S or G1 phase. The data indicate that the 3D organization of replicated DNA in G2/M cells seems to be more sensitive to induction of more complex DNA lesions compared to the DNA architecture in S or G1 cells. Whether this is due to the DNA organization itself or differences in DNA repair capability remains unclear.

Radium deposition in human brain tissue: A Geant4-DNA Monte Carlo toolkit study.

NASA has encouraged studies on 226Ra deposition in the human brain to investigate the effects of exposure to alpha particles with high linear energy transfer, which could mimic some of the exposure astronauts face during space travel. However, this approach was criticized, noting that radium is a bone-seeker and accumulates in the skull, which means that the radiation dose from alpha particles emitted by 226Ra would be heavily concentrated in areas close to cranial bones rather than uniformly distributed throughout the brain. In the high background radiation areas of Ramsar, Iran, extremely high levels of 226Ra in soil contribute to a large proportion of the inhabitants' radiation exposure. A prospective study on Ramsar residents with a calcium-rich diet was conducted to improve the dose uniformity due to 226Ra throughout the cerebral and cerebellar parenchyma. The study found that exposure of the human brain to alpha particles did not significantly affect working memory but was significantly associated with increased reaction times. This finding is crucial because astronauts on deep space missions may face similar cognitive impairments due to exposure to high charge and energy particles. The current study was aimed to evaluate the validity of the terrestrial model using the Geant4 Monte Carlo toolkit to simulate the interactions of alpha particles and representative cosmic ray particles, acknowledging that these radiation types are only a subset of the complete space radiation environment.

Targeting OGG1 and PARG radiosensitises head and neck cancer cells to high-LET protons through complex DNA damage persistence.

Complex DNA damage (CDD), containing two or more DNA lesions within one or two DNA helical turns, is a signature of ionising radiation (IR) and contributes significantly to the therapeutic effect through cell killing. The levels and complexity of CDD increases with linear energy transfer (LET), however, the specific cellular response to this type of DNA damage and the critical proteins essential for repair of CDD is currently unclear. We performed an siRNA screen of ~240 DNA damage response proteins to identify those specifically involved in controlling cell survival in response to high-LET protons at the Bragg peak, compared to low-LET entrance dose protons which differ in the amount of CDD produced. From this, we subsequently validated that depletion of 8-oxoguanine DNA glycosylase (OGG1) and poly(ADP-ribose) glycohydrolase (PARG) in HeLa and head and neck cancer cells leads to significantly increased cellular radiosensitivity specifically following high-LET protons, whilst no effect was observed after low-LET protons and X-rays. We subsequently confirmed that OGG1 and PARG are both required for efficient CDD repair post-irradiation with high-LET protons. Importantly, these results were also recapitulated using specific inhibitors for OGG1 (TH5487) and PARG (PDD00017273). Our results suggest OGG1 and PARG play a fundamental role in the cellular response to CDD and indicate that targeting these enzymes could represent a promising therapeutic strategy for the treatment of head and neck cancers following high-LET radiation.

The contribution of high-LET track to DNA damage formation and cell death for Monoenergy and SOBP carbon ion irradiation.

Carbon ion radiotherapy (CIRT) is a heavy ion charge particle therapy with 29 years of prominent use. Despite advantages like high relative biological effectiveness (RBE), improved quality of life, and reduced treatment time, challenges persist, especially regarding heavy nuclear fragments. Our research addresses these challenges in horizontal irradiation, aiming to comprehend Monoenergetic and Spread-Out Bragg peak (SOBP) carbon ion beam trajectories using cell survival analysis and visualizing biological effects through DNA damage (γ-H2AX). This reveals repair-related protein foci near the Bragg peak. CR-39, a plastic nuclear track detector, was explored to understand high-linear energy transfer (LET) tracks and radiation quality near the Bragg peak. Findings unveil high-LET DNA damage signatures through aligned γ-H2AX foci, correlating with LET values in SOBP. CR-39 visualized high-LET particle exposure, indicating comet-type etch-pits at the Bragg peak and suggesting carbon ion fragmentation. Unexpectedly, dot-type etch-pits in irradiated and post-Bragg peak regions indicated high-LET neutron production. This investigation highlights the intricate interplay of carbon ion beams, stressing the importance of understanding LET variations, DNA damage patterns, and undesired secondary exposure.

Deep learning-based synthetic dose-weighted LET map generation for intensity modulated proton therapy.

The advantage of proton therapy as compared to photon therapy stems from the Bragg peak effect, which allows protons to deposit most of their energy directly at the tumor while sparing healthy tissue. However, even with such benefits, proton therapy does present certain challenges. The biological effectiveness differences between protons and photons are not fully incorporated into clinical treatment planning processes. In current clinical practice, the relative biological effectiveness (RBE) between protons and photons is set as constant 1.1. Numerous studies have suggested that the RBE of protons can exhibit significant variability. Given these findings, there is a substantial interest in refining proton therapy treatment planning to better account for the variable RBE. Dose-average linear energy transfer (LETd) is a key physical parameter for evaluating the RBE of proton therapy and aids in optimizing proton treatment plans. Calculating precise LETddistributions necessitates the use of intricate physical models and the execution of specialized Monte-Carlo simulation software, which is a computationally intensive and time-consuming progress. In response to these challenges, we propose a deep learning based framework designed to predict the LETddistribution map using the dose distribution map. This approach aims to simplify the process and increase the speed of LETdmap generation in clinical settings. The proposed CycleGAN model has demonstrated superior performance over other GAN-based models. The mean absolute error (MAE), peak signal-to-noise ratio and normalized cross correlation of the LETdmaps generated by the proposed method are 0.096 ± 0.019 keVµm-1, 24.203 ± 2.683 dB, and 0.997 ± 0.002, respectively. The MAE of the proposed method in the clinical target volume, bladder, and rectum are 0.193 ± 0.103, 0.277 ± 0.112, and 0.211 ± 0.086 keVµm-1, respectively. The proposed framework has demonstrated the feasibility of generating synthetic LETdmaps from dose maps and has the potential to improve proton therapy planning by providing accurate LETdinformation.

A linear energy transfer distributions computation method for inhomogeneous medium by using the water equivalent ratio approximation.

Dose-averaged linear energy transfer (LET), LETd is widely used in proton therapy. Compared with analytical models, Monte Carlo (MC) simulations are more accurate in obtaining LETd distributions, but they are time-consuming. This study used the 3D LETd distributions of proton beam spots in water by MC simulations as a benchmark data set. Subsequently, by combining the water equivalent ratio of various human tissues, the 3D LETd distributions of clinical cases could be quickly obtained. Our method was applied to a single spot of 160 MeV proton beam in a water-bone phantom and a pelvic case. We also computed the 3D LETd distributions for multiple proton beam spots in the pelvic case and a lung case. The results of our method were compared with the results of MC simulations, demonstrating that our method can rapidly provide 3D LETd distributions of clinical cases with acceptable differences from MC simulations.

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.