Simulation of DSB yield for high LET radiation

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 ofD 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 aD 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.

Spectral composition of secondary electrons based on the Kiefer-Straaten ion track structure model

The major part of energy deposition of ionizing radiation is caused by secondary electrons, independent of the primary radiation type. However, their spatial concentration and their spectral properties strongly depend on the primary radiation type and finally determine the pattern of molecular damage e.g. to biological targets as the DNA, and thus the final effect of the radiation exposure. To describe the physical and to predict the biological consequences of charged ion irradiation, amorphous track structure approaches have proven to be pragmatic and helpful. There, the local dose deposition in the ion track is equated by considering the emission and slowing down of the secondary electrons from the primary particle track. In the present work we exploit the model of Kiefer and Straaten and derive the spectral composition of secondary electrons as function of the distance to the track center. The spectral composition indicates differences to spectra of low linear energy transfer (LET) photon radiation, which we confirm by a comparison with Monte Carlo studies. We demonstrate that the amorphous track structure approach provides a simple tool for evaluating the spectral electron properties within the track structure. Predictions of the LET of electrons across the track structure as well as the electronic dose build-up effect are derived. Implications for biological effects and corresponding predicting models based on amorphous track structure are discussed.

Multiple levels of stochasticity accounted for in different radiation biophysical models: from physics to biology

PURPOSE

In the present paper we investigate how some stochastic effects are included in a class of radiobiological models with particular emphasis on how such randomnesses reflect into the predicted cell survival curve.

MATERIALS AND METHODS

We consider four different models, namely the Generalized Stochastic Microdosimetric Model GSM², in its original full form, the Dirac GSM² the Poisson GSM² and the Repair-Misrepair Model (RMR). While GSM² and the RMR models are known in literature, the Dirac and the Poisson GSM²  have been newly introduced in this work. We further numerically investigate via Monte Carlo simulation of four different particle beams, how the proposed stochastic approximations reflect into the predicted survival curves. To achieve these results, we consider different ion species at energies of interest for therapeutic applications, also including a mixed field scenario.

RESULTS

We show how the Dirac GSM², the Poisson GSM² and the RMR can be obtained from the GSM² under suitable approximations on the stochasticity considered. We analytically derive the cell survival curve predicted by the four models, characterizing rigorously the high and low dose limits. We further study how the theoretical findings emerge also using Monte Carlo numerical simulations.

CONCLUSIONS

We show how different models include different levels of stochasticity in the description of cellular response to radiation. This translates into different cell survival predictions depending on the radiation quality.

A matter of space: how the spatial heterogeneity in energy deposition determines the biological outcome of radiation exposure

The outcome of the exposure of living organisms to ionizing radiation is determined by the distribution of the associated energy deposition at different spatial scales. Radiation proceeds through ionizations and excitations of hit molecules with an ~ nm spacing. Approaches such as nanodosimetry/microdosimetry and Monte Carlo track-structure simulations have been successfully adopted to investigate radiation quality effects: they allow to explore correlations between the spatial clustering of such energy depositions at the scales of DNA or chromosome domains and their biological consequences at the cellular level. Physical features alone, however, are not enough to assess the entity and complexity of radiation-induced DNA damage: this latter is the result of an interplay between radiation track structure and the spatial architecture of chromatin, and further depends on the chromatin dynamic response, affecting the activation and efficiency of the repair machinery. The heterogeneity of radiation energy depositions at the single-cell level affects the trade-off between cell inactivation and induction of viable mutations and hence influences radiation-induced carcinogenesis. In radiation therapy, where the goal is cancer cell inactivation, the delivery of a homogenous dose to the tumour has been the traditional approach in clinical practice. However, evidence is accumulating that introducing heterogeneity with spatially fractionated beams (mini- and microbeam therapy) can lead to significant advantages, particularly in sparing normal tissues. Such findings cannot be explained in merely physical terms, and their interpretation requires considering the scales at play in the underlying biological mechanisms, suggesting a systemic response to radiation.

A double-strand-break model for the relative biological effectiveness of electrons based on ionization clustering

BACKGROUND

The effectiveness of ionizing radiation regarding DNA damage induction depends on its spatial energy deposition pattern. For electrons an increased effectiveness is observed at low kinetic energies due to the enhanced density of energy deposition events at electron track ends.

PURPOSE

A model is presented, which enables the calculation of the double-strand-break (DSB) yield and the relative biological effectiveness (RBE) for DSB induction of electrons.

METHODS

The model applies the mean free path between two ionizations and the assumption that two ionizations within a certain threshold distance are necessary to potentially lead to a DSB. Next to an expression for the electron RBE according to its common definition, a local RBE is determined, which describes the electrons' local effectiveness at a defined point on their track.

RESULTS

This local RBE allows a better understanding of microscopic processes resulting from radiation and can be used, for instance, to describe the mean effectiveness of the mixed electron radiation field as a function of the radial distance to the center of an ion track.

CONCLUSIONS

The presented model reflects the experimentally observed increased effectiveness of low-energetic electrons. It will be used in a future work to improve RBE predictions for ions performed with the local effect model.

Impact of DNA Repair Kinetics and Dose Rate on RBE Predictions in the UNIVERSE

Accurate knowledge of the relative biological effectiveness (RBE) and its dependencies is crucial to support modern ion beam therapy and its further development. However, the influence of different dose rates of the reference radiation and ion beam are rarely considered. The ion beam RBE-model within our "UNIfied and VERSatile bio response Engine" (UNIVERSE) is extended by including DNA damage repair kinetics to investigate the impact of dose-rate effects on the predicted RBE. It was found that dose-rate effects increase with dose and biological effects saturate at high dose-rates, which is consistent with data- and model-based studies in the literature. In a comparison with RBE measurements from a high dose in-vivo study, the predictions of the presented modification were found to be improved in comparison to the previous version of UNIVERSE and existing clinical approaches that disregard dose-rate effects. Consequently, DNA repair kinetics and the different dose rates applied by the reference and ion beams might need to be considered in biophysical models to accurately predict the RBE. Additionally, this study marks an important step in the further development of UNIVERSE, extending its capabilities in giving theoretical guidance to support progress in ion beam therapy.

Comprehensive comparison of local effect model IV predictions with the particle irradiation data ensemble

PURPOSE

The increased relative biological effectiveness (RBE) of ions is one of the key benefits of ion radiotherapy compared to conventional radiotherapy with photons. To account for the increased RBE of ions during the process of ion radiotherapy treatment planning, a robust model for RBE predictions is indispensable. Currently, at several ion therapy centers the local effect model I (LEM I) is applied to predict the RBE, which varies with biological and physical impacting factors. After the introduction of LEM I, several model improvements were implemented, leading to the current version, LEM IV, which is systematically tested in this study.

METHODS

As a comprehensive RBE model should give consistent results for a large variety of ion species and energies, the particle irradiation data ensemble (PIDE) is used to systematically validate the LEM IV. The database covers over 1100 photon and ion survival experiments in form of their linear-quadratic parameters for a wide range of ion types and energies. This makes the database an optimal tool to challenge the systematic dependencies of the RBE model. After appropriate filtering of the database, 571 experiments were identified and used as test data.

RESULTS

The study confirms that the LEM IV reflects the RBE systematics observed in measurements well. It is able to reproduce the dependence of RBE on the linear energy transfer (LET) as well as on the α_γ /β_γ ratio for several ion species in a wide energy range. Additionally, the systematic quantitative analysis revealed precision capabilities and limits of the model. At lower LET values, the LEM IV tends to underestimate the RBE with an increasing underestimation with increasing atomic number of the ion. At higher LET values, the LEM IV overestimates the RBE for protons or helium ions, whereas the predictions for heavier ions match experimental data well.

CONCLUSIONS

The LEM IV is able to predict general RBE characteristics for several ion species in a broad energy range. The accuracy of the predictions is reasonable considering the small number of input parameters needed by the model. The detailed quantification of possible systematic deviations, however, enables to identify not only strengths but also limitations of the model. The gained knowledge can be used to develop model adjustments to further improve the model accuracy, which is on the way.

Combined DNA Damage Repair Interference and Ion Beam Therapy: Development, Benchmark, and Clinical Implications of a Mechanistic Biological Model

PURPOSE

Our purpose was to develop a mechanistic model that describes and predicts radiation response after combined DNA damage repair interference (DDRi) and particle radiation therapy.

METHODS AND MATERIALS

The heterogeneous dose distributions of protons and ⁴He ions were implemented into the "UNIfied and VERSatile bio-response Engine" (UNIVERSE). Predictions for monoenergetic and mixed fields over clinically relevant dose and linear energy transfer range were compared with experimental in vitro survival data measured in this work as well as data available in the literature, including different cell lines and DDR interferences. Ultimately, UNIVERSE predictions were investigated in a patient plan.

RESULTS

UNIVERSE accurately predicts survival of cell lines with and without DDRi in clinical settings of ion beam therapy based only on 3 parameters derived from photon data. With increasing dose or linear energy transfer, the radiosensitizing effect of DDRi decreases, resulting in diminished relative biological effect of ion beam radiation for cells subjected to DDRi in comparison to cells that are not. Similar trends were observed in patient plan recalculations; however, this analysis also suggests that DDRi + particle radiation therapy may better preserve the therapeutic window in comparison to DDRi + photon radiation therapy.

CONCLUSIONS

The presented framework represents the first mechanistic model of combined DDRi and particle radiation therapy comprehensively benchmarked in clinically relevant scenarios and a step toward more personalized treatment. It reveals potential differences between DDRi + photon radiation therapy versus DDRi + particle radiation therapy, which have not been described so far. UNIVERSE could aid in appraising the clinical viability of combined administration of radiosensitizing drugs and charged particle therapy, as well as the identification of patients with known DDR deficiencies in the tumor who might benefit from therapy with light ions, freeing limited space at heavy ion therapy centers.

Reprint of: Deciphering and simulating models of radiation genotoxicity with CRISPR/Cas9 systems

This short review explores the utility and applications of CRISPR/Cas9 systems in radiobiology. Specifically, in the context of experimentally simulating genotoxic effects of Ionizing Radiation (IR) to determine the contributions from DNA targets and 'Complex Double-Stranded Breaks' (complex DSBs) to the IR response. To elucidate this objective, this review considers applications of CRISPR/Cas9 on nuclear DNA targets to recognize the respective 'nucleocentric' response. The article also highlights contributions from mitochondrial DNA (mtDNA) - an often under-recognized target in radiobiology. This objective requires accurate experimental simulation of IR-like effects and parameters with the CRISPR/Cas9 systems. Therefore, the role of anti-CRISPR proteins in modulating enzyme activity to simulate dose rate - an important factor in radiobiology experiments is an important topic of this review. The applications of auxiliary domains on the Cas9 nuclease to simulate oxidative base damage and multiple stressor experiments are also topics of discussion. Ultimately, incorporation of CRISPR/Cas9 experiments into computational parameters in radiobiology models of IR damage and shortcomings to the technology are discussed as well. Altogether, the simulation of IR parameters and lack of damage to non-DNA targets in the CRISPR/Cas9 system lends this rapidly emerging tool as an effective model of IR induced DNA damage. Therefore, this literature review ultimately considers the relevance of complex DSBs to radiobiology with respect to using the CRISPR/Cas9 system as an effective experimental tool in models of IR induced effects.

Deciphering and simulating models of radiation genotoxicity with CRISPR/Cas9 systems

This short review explores the utility and applications of CRISPR/Cas9 systems in radiobiology. Specifically, in the context of experimentally simulating genotoxic effects of Ionizing Radiation (IR) to determine the contributions from DNA targets and 'Complex Double-Stranded Breaks' (complex DSBs) to the IR response. To elucidate this objective, this review considers applications of CRISPR/Cas9 on nuclear DNA targets to recognize the respective 'nucleocentric' response. The article also highlights contributions from mitochondrial DNA (mtDNA) - an often under-recognized target in radiobiology. This objective requires accurate experimental simulation of IR-like effects and parameters with the CRISPR/Cas9 systems. Therefore, the role of anti-CRISPR proteins in modulating enzyme activity to simulate dose rate - an important factor in radiobiology experiments is an important topic of this review. The applications of auxiliary domains on the Cas9 nuclease to simulate oxidative base damage and multiple stressor experiments are also topics of discussion. Ultimately, incorporation of CRISPR/Cas9 experiments into computational parameters in radiobiology models of IR damage and shortcomings to the technology are discussed as well. Altogether, the simulation of IR parameters and lack of damage to non-DNA targets in the CRISPR/Cas9 system lends this rapidly emerging tool as an effective model of IR induced DNA damage. Therefore, this literature review ultimately considers the relevance of complex DSBs to radiobiology with respect to using the CRISPR/Cas9 system as an effective experimental tool in models of IR induced effects.

Assessment of Radio-Induced Damage in Endothelial Cells Irradiated with 40 kVp, 220 kVp, and 4 MV X-rays by Means of Micro and Nanodosimetric Calculations

The objective of this work was to study the differences in terms of early biological effects that might exist between different X-rays energies by using a mechanistic approach. To this end, radiobiological experiments exposing cell monolayers to three X-ray energies were performed in order to assess the yields of early DNA damage, in particular of double-strand breaks (DSBs). The simulation of these irradiations was set in order to understand the differences in the obtained experimental results. Hence, simulated results in terms of microdosimetric spectra and early DSB induction were analyzed and compared to the experimental data. Human umbilical vein endothelial cells (HUVECs) were irradiated with 40, 220 kVp, and 4 MV X-rays. The Geant4 Monte Carlo simulation toolkit and its extension Geant4-DNA were used for the simulations. Microdosimetric calculations aiming to determine possible differences in the variability of the energy absorbed by the irradiated cell population for those photon spectra were performed on 10,000 endothelial cell nuclei representing a cell monolayer. Nanodosimetric simulations were also carried out using a computation chain that allowed the simulation of physical, physico-chemical, and chemical stages on a single realistic endothelial cell nucleus model including both heterochromatin and euchromatin. DNA damage was scored in terms of yields of prompt DSBs per Gray (Gy) and per giga (10⁹) base pair (Gbp) and DSB complexity was derived in order to be compared to experimental data expressed as numbers of histone variant H2AX (γ-H2AX) foci per cell. The calculated microdosimetric spread in the irradiated cell population was similar when comparing between 40 and 220 kVp X-rays and higher when comparing with 4 MV X-rays. Simulated yields of induced DSB/Gy/Gbp were found to be equivalent to those for 40 and 220 kVp but larger than those for 4 MV, resulting in a relative biological effectiveness (RBE) of 1.3. Additionally, DSB complexity was similar between the considered photon spectra. Simulated results were in good agreement with experimental data obtained by IRSN (Institut de radioprotection et de sûreté nucléaire) radiobiologists. Despite differences in photon energy, few differences were observed when comparing between 40 and 220 kVp X-rays in microdosimetric and nanodosimetric calculations. Nevertheless, variations were observed when comparing between 40/220 kVp and 4 MV X-rays. Thanks to the simulation results, these variations were able to be explained by the differences in the production of secondary electrons with energies below 10 keV.

Prediction of Cell Survival after Exposure to Mixed Radiation Fields with the Local Effect Model

Mixed radiation fields comprise the most common form of radiation exposure. Given their relevance in radiation protection, cancer radiotherapy and space research, accurate predictions of the corresponding radiation effects are essential. The local effect model (LEM) allows the prediction of cell survival after ion irradiation based on the knowledge of the cells' response to photon radiation. The assumption is made that the same spatial DNA double-strand break (DSB) distributions in the cell nucleus lead to the same effects, independent of the radiation quality that produced the DSBs. This makes the LEM an ideal tool for predictions of cell survival after exposure to any mixed radiation field. In this work, the LEM is applied to calculate cell survival for extreme mixed irradiation scenarios, i.e., high-linear energy transfer (LET) ion radiation combined with low-LET photon radiation, which can be understood as a consistency test for the high-LET model. Available experimental data covering several ion species and energies in combination with photon exposure are predicted with the LEM. Furthermore, the results are compared to the microdosimetric model by Zaider and Rossi and the lesion additivity model by Lam, which allow the prediction of cell survival after exposure to mixed radiation fields based on the knowledge of the survival curves of the two radiation components. Although the LEM uses only photon dose-response data as input, it is able to compete with the empirical radiobiological models that additionally require ion dose-response curves as input. Certain experimental scenarios are presented in which the specific consideration of spatial DSB distributions could be essential for an accurate prediction of the effect of mixed radiation fields.

A comparison of mechanism-inspired models for particle relative biological effectiveness (RBE)

BACKGROUND AND SIGNIFICANCE

The application of heavy ion beams in cancer therapy must account for the increasing relative biological effectiveness (RBE) with increasing penetration depth when determining dose prescriptions and organ at risk (OAR) constraints in treatment planning. Because RBE depends in a complex manner on factors such as the ion type, energy, cell and tissue radiosensitivity, physical dose, biological endpoint, and position within and outside treatment fields, biophysical models reflecting these dependencies are required for the personalization and optimization of treatment plans.

AIM

To review and compare three mechanism-inspired models which predict the complexities of particle RBE for various ion types, energies, linear energy transfer (LET) values and tissue radiation sensitivities.

METHODS

The review of models and mechanisms focuses on the Local Effect Model (LEM), the Microdosimetric-Kinetic (MK) model, and the Repair-Misrepair-Fixation (RMF) model in combination with the Monte Carlo Damage Simulation (MCDS). These models relate the induction of potentially lethal double strand breaks (DSBs) to the subsequent interactions and biological processing of DSB into more lethal forms of damage. A key element to explain the increased biological effectiveness of high LET ions compared to MV x rays is the characterization of the number and local complexity (clustering) of the initial DSB produced within a cell. For high LET ions, the spatial density of DSB induction along an ion's trajectory is much greater than along the path of a low LET electron, such as the secondary electrons produced by the megavoltage (MV) x rays used in conventional radiation therapy. The main aspects of the three models are introduced and the conceptual similarities and differences are critiqued and highlighted. Model predictions are compared in terms of the RBE for DSB induction and for reproductive cell survival.

RESULTS AND CONCLUSIONS

Comparisons of the RBE for DSB induction and for cell survival are presented for proton (¹ H), helium (⁴ He), and carbon (¹² C) ions for the therapeutically most relevant range of ion beam energies. The reviewed models embody mechanisms of action acting over the spatial scales underlying the biological processing of potentially lethal DSB into more lethal forms of damage. Differences among the number and types of input parameters, relevant biological targets, and the computational approaches among the LEM, MK and RMF models are summarized and critiqued. Potential experiments to test some of the seemingly contradictory aspects of the models are discussed.

DNA damage interactions on both nanometer and micrometer scale determine overall cellular damage

DNA double strand breaks (DSB) play a pivotal role for cellular damage, which is a hazard encountered in toxicology and radiation protection, but also exploited e.g. in eradicating tumors in radiation therapy. It is still debated whether and in how far clustering of such DNA lesions leads to an enhanced severity of induced damage. Here we investigate - using focused spots of ionizing radiation as damaging agent - the spatial extension of DNA lesion patterns causing cell inactivation. We find that clustering of DNA damage on both the nm and µm scale leads to enhanced inactivation compared to more homogeneous lesion distributions. A biophysical model interprets these observations in terms of enhanced DSB production and DSB interaction, respectively. We decompose the overall effects quantitatively into contributions from these lesion formation processes, concluding that both processes coexist and need to be considered for determining the resulting damage on the cellular level.

Modeling Radiation Effects of Ultrasoft X Rays on the Basis of Amorphous Track Structure

There is experimental evidence that ultrasoft X rays (0.1-5 keV) show a higher biological effectiveness than high-energy photons. Similar to high-LET radiation, this is attributed to a rather localized dose distribution associated with a considerably smaller range of secondary electrons, which results in an increasing yield of double-strand breaks (DSBs) and potentially more complex lesions. We previously reported on the application of the Giant LOop Binary LEsion (GLOBLE) model to ultrasoft X rays, in which experimental values of the relative biological effectiveness (RBE) for DSB induction were used to show that this increasing DSB yield was sufficient to explain the enhanced effectiveness in the cell inactivation potential of ultrasoft X rays. Complementary to GLOBLE, we report here on a modeling approach to predict the increased DSB yield of ultrasoft X rays on the basis of amorphous track structure formed by secondary electrons, which was derived from Monte Carlo track structure simulations. This procedure is associated with increased production of single-strand break (SSB) clusters, which are caused by the highly localized energy deposition pattern induced by low-energy photons. From this, the RBE of ultrasoft X rays can be determined and compared to experimental data, showing that the inhomogeneity of the energy deposition pattern represents the key variable to describe the increased biological effectiveness of ultrasoft X rays. Thus, this work demonstrates an extended applicability of the amorphous track structure concept and tests its limits with respect to its predictive power. The employed model mechanism offers a possible explanation for how the cellular response to ultrasoft X rays is directly linked to the energy deposition properties on the nanometric scale.

Theoretical substantiation of biological efficacy enhancement for β-delayed particle decay (9)C beam: A Monte Carlo study in combination with analysis with the local effect model approach

PURPOSE

To improve the efficacy of heavy ion therapy, β-delayed particle decay (9)C beam as a double irradiation source for cancer therapy has been proposed. The authors' previous experiment showed that relative biological effectiveness (RBE) values at the depths around the Bragg peak of a (9)C beam were enhanced and compared to its stable counterpart (12)C beam. The purpose of this study was to explore the nature of the biological efficacy enhancement theoretically.

METHODS

A Monte Carlo simulation study was conducted in this study. First a simplified cell model was established so as to form a tumor tissue. Subsequently, the tumor tissue was imported into the Monte Carlo simulation software package gate and then the tumor cells were virtually irradiated with comparable (9)C and (12)C beams, respectively, in the simulations. The transportation and particle deposition data of the (9)C and (12)C beams, derived from the gate simulations, were analyzed with the authors' local effect model implementation so as to deduce cell survival fractions.

RESULTS

The particles emitted from the decay process of deposited (9)C particles around a cell nucleus increased the dose delivered to the nucleus and elicited clustered damages around the secondary particles' trajectories. Therefore, compared to the (12)C beam, the RBE value of the (9)C beam increased at the depths around their Bragg peaks.

CONCLUSIONS

Collectively, the increased local doses and clustered damages due to the decayed particles emitted from deposited (9)C particles led to the RBE enhancement in contrast with the (12)C beam. Thus, the enhanced RBE effect of a (9)C beam for a simplified tumor model was shown theoretically in this study.

Genetic Analysis of T Cell Lymphomas in Carbon Ion-Irradiated Mice Reveals Frequent Interstitial Chromosome Deletions: Implications for Second Cancer Induction in Normal Tissues during Carbon Ion Radiotherapy

Monitoring mice exposed to carbon ion radiotherapy provides an indirect method to evaluate the potential for second cancer induction in normal tissues outside the radiotherapy target volume, since such estimates are not yet possible from historical patient data. Here, male and female B6C3F1 mice were given single or fractionated whole-body exposure(s) to a monoenergetic carbon ion radiotherapy beam at the Heavy Ion Medical Accelerator in Chiba, Japan, matching the radiation quality delivered to the normal tissue ahead of the tumour volume (average linear energy transfer = 13 keV.μm^-1) during patient radiotherapy protocols. The mice were monitored for the remainder of their lifespan, and a large number of T cell lymphomas that arose in these mice were analysed alongside those arising following an equivalent dose of ¹³⁷Cs gamma ray-irradiation. Using genome-wide DNA copy number analysis to identify genomic loci involved in radiation-induced lymphomagenesis and subsequent detailed analysis of Notch1, Ikzf1, Pten, Trp53 and Bcl11b genes, we compared the genetic profile of the carbon ion- and gamma ray-induced tumours. The canonical set of genes previously associated with radiation-induced T cell lymphoma was identified in both radiation groups. While the pattern of disruption of the various pathways was somewhat different between the radiation types, most notably Pten mutation frequency and loss of heterozygosity flanking Bcl11b, the most striking finding was the observation of large interstitial deletions at various sites across the genome in carbon ion-induced tumours, which were only seen infrequently in the gamma ray-induced tumours analysed. If such large interstitial chromosomal deletions are a characteristic lesion of carbon ion irradiation, even when using the low linear energy transfer radiation to which normal tissues are exposed in radiotherapy patients, understanding the dose-response and tissue specificity of such DNA damage could prove key to assessing second cancer risk in carbon ion radiotherapy patients.