Equivalency of the quality of sublethal lesions after photons and high-linear energy transfer ion beams

Interferon signaling is enhanced by ATR inhibition in glioblastoma cells irradiated with X-rays, protons or carbon ions

BACKGROUND AND PURPOSE

Interferon (IFN) signaling plays an important role in antitumor immune responses. Inhibitors of the DNA damage response, such as ATR inhibitors, can increase IFN signaling upon conventional radiotherapy with X-rays. However, it is not known whether such inhibitors also enhance IFN signaling after irradiation with high linear energy transfer (LET) particles.

MATERIALS AND METHODS

Human glioblastoma U-251 and T98G cells were irradiated with X-rays, protons (LET: 4.8 and 41.9 keV/µm) and carbon ions (LET: 28 and 73 keV/µm), with and without ATR inhibitor (VE-822) or ATM inhibitor (AZD1390). DNA damage signaling and cell cycle distribution were analyzed by immunoblotting and flow cytometry, and radiosensitivity was assessed by clonogenic survival assay. IFN-β secretion was measured by ELISA, and STAT1 activation was examined by immunoblotting.

RESULTS

High-LET protons and carbon ions caused stronger activation of the DNA damage response compared to low-LET protons and X-rays at similar radiation doses. G2 checkpoint arrest was abrogated by the ATR inhibitor and prolonged by the ATM inhibitor after all radiation types. The inhibitors increased radiosensitivity, as measured after X- and carbon ion irradiation. ATR inhibition increased IFN signaling following both low-LET and high-LET irradiation. ATM inhibition also increased IFN signaling, but to a lesser extent. Notably, both cell lines secreted significantly more IFN-β when the inhibitors were combined with high-LET compared to low-LET irradiation.

CONCLUSION

These findings indicate that DNA damage response inhibitors can enhance IFN signaling following X-, proton and carbon ion irradiation, with a strong positive dependency on LET.

Potential Benefits of Combining Proton or Carbon Ion Therapy with DNA Damage Repair Inhibitors

The use of charged particle radiotherapy is currently increasing, but combination therapy with DNA repair inhibitors remains to be exploited in the clinic. The high-linear energy transfer (LET) radiation delivered by charged particles causes clustered DNA damage, which is particularly effective in destroying cancer cells. Whether the DNA damage response to this type of damage is different from that elicited in response to low-LET radiation, and if and how it can be targeted to increase treatment efficacy, is not fully understood. Although several preclinical studies have reported radiosensitizing effects when proton or carbon ion irradiation is combined with inhibitors of, e.g., PARP, ATR, ATM, or DNA-PKcs, further exploration is required to determine the most effective treatments. Here, we examine what is known about repair pathway choice in response to high- versus low-LET irradiation, and we discuss the effects of inhibitors of these pathways when combined with protons and carbon ions. Additionally, we explore the potential effects of DNA repair inhibitors on antitumor immune signaling upon proton and carbon ion irradiation. Due to the reduced effect on healthy tissue and better immune preservation, particle therapy may be particularly well suited for combination with DNA repair inhibitors.

Ionization Detail Parameters for DNA Damage Evaluation in Charged Particle Radiotherapy: Simulation Study Based on Cell Survival Database

Details of excitation and ionization acts hide a description of the biological effects of charged particle traversal through living tissue. Nanodosimetry enables the introduction of novel quantities that characterize and quantify the particle track structure while also serving as a foundation for assessing biological effects based on this quantification. This presents an opportunity to enhance the planning of charged particle radiotherapy by taking into account the ionization detail. This work uses Monte Carlo simulations with Geant4-DNA code for a wide variety of charged particles and their radiation qualities to analyze the distribution of ionization cluster sizes within nanometer-scale volumes, similar to DNA diameter. By correlating these results with biological parameters extracted from the PIDE database for the V79 cell line, a novel parameter R2 based on ionization details is proposed for the evaluation of radiation quality in terms of biological consequences, i.e., radiobiological cross section for inactivation. By incorporating the probability p of sub-lethal damage caused by a single ionization, we address limitations associated with the usually proposed nanodosimetric parameter Fk for characterizing the biological effects of radiation. We show that the new parameter R2 correlates well with radiobiological data and can be used to predict biological outcomes.

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.

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.

Radiobiological damage by space radiation: extension of the BIANCA model to heavy ions up to iron, and pilot application to cosmic ray exposure

Space research seems to be object of a renewed interest, also considering that human missions to the Moon, and possibly Mars, are being planned. Among the risks affecting such missions, astronauts' exposure to space radiation is a major concern. In this work, the question of the evaluation of biological damage by Galactic Cosmic Rays (GCR) was addressed by a biophysical model called BIophysical ANalysis of Cell death and chromosome Aberrations (BIANCA), which simulates the induction of cell death and chromosome aberrations by different ions. While previously BIANCA has been validated for calculating cell death along hadrontherapy beams up to oxygen, herein the approach was extended up to Fe ions. Specifically, experimental survival curves available in literature for V79 cells irradiated by Si-, Ne-, Ar- and Fe-ions were reproduced, and a reference radiobiological database describing V79 cell survival as a function of ion type (1 ⩽Z⩽ 26), energy and dose was constructed. Analogous databases were generated for Chinese hamster ovary hamster cells and human skin fibroblasts, finding good agreement between simulations and data. Concerning chromosome aberrations, which are regarded as radiation risk biomarkers, dicentric data in human lymphocytes irradiated by heavy ions up to iron were reproduced, and a radiobiological database allowing calculation of lymphocyte dicentric yields as a function of dose, ion type (1 ⩽Z⩽ 26) and energy was constructed. Following interface between BIANCA and the FLUKA Monte Carlo transport code, a feasibility study was performed to calculate the relative biological effectiveness (RBE) of different GCR spectrum components, for both dicentrics and cell death. Fe-ions, although representing only 10% of the total absorbed dose, were found to be responsible for about 35%-40% of the RBE-weighted dose. Interestingly, the RBE for dicentrics was higher than that for cell survival. More generally, this work shows that BIANCA can calculate RBE values for cell death and lymphocyte dicentrics not only for ion therapy, but also for space radiation.

Single-Cell RNA-Sequencing Identifies Activation of TP53 and STAT1 Pathways in Human T Lymphocyte Subpopulations in Response to Ex Vivo Radiation Exposure

Detrimental health consequences from exposure to space radiation are a major concern for long-duration human exploration missions to the Moon or Mars. Cellular responses to radiation are expected to be heterogeneous for space radiation exposure, where only high-energy protons and other particles traverse a fraction of the cells. Therefore, assessing DNA damage and DNA damage response in individual cells is crucial in understanding the mechanisms by which cells respond to different particle types and energies in space. In this project, we identified a cell-specific signature for radiation response by using single-cell transcriptomics of human lymphocyte subpopulations. We investigated gene expression in individual human T lymphocytes 3 h after ex vivo exposure to 2-Gy gamma rays while using the single-cell sequencing technique (10X Genomics). In the process, RNA was isolated from ~700 irradiated and ~700 non-irradiated control cells, and then sequenced with ~50 k reads/cell. RNA in each of the cells was distinctively barcoded prior to extraction to allow for quantification for individual cells. Principal component and clustering analysis of the unique molecular identifier (UMI) counts classified the cells into three groups or sub-types, which correspond to CD4+, naïve, and CD8+/NK cells. Gene expression changes after radiation exposure were evaluated using negative binomial regression. On average, BBC3, PCNA, and other TP53 related genes that are known to respond to radiation in human T cells showed increased activation. While most of the TP53 responsive genes were upregulated in all groups of cells, the expressions of IRF1, STAT1, and BATF were only upregulated in the CD4+ and naïve groups, but were unchanged in the CD8+/NK group, which suggests that the interferon-gamma pathway does not respond to radiation in CD8+/NK cells. Thus, single-cell RNA sequencing technique was useful for simultaneously identifying the expression of a set of genes in individual cells and T lymphocyte subpopulation after gamma radiation exposure. The degree of dependence of UMI counts between pairs of upregulated genes was also evaluated to construct a similarity matrix for cluster analysis. The cluster analysis identified a group of TP53-responsive genes and a group of genes that are involved in the interferon gamma pathway, which demonstrate the potential of this method for identifying previously unknown groups of genes with similar expression patterns.

DOSE-RATE AND CELL-KILLING SENSITIVITY OF HIGH-LINEAR ENERGY TRANSFER ION BEAM

It is believed that the dose-rate of radiation will have an influence on cell sensitivity. The dose-rate effects on cell survival can be expressed by the change of the β term in the linear quadratic model. The value at a high-dose-rate decreases below 60 Gy/h and reaches zero at 0.2 Gy/h or less for photons. However, the effect for a high-LET ion-beam is not well known. At HIMAC, cells were exposed to 70 keV/μm carbon-ion beams at different dose-rates between 0.5 and 600 Gy/h at room temperature. The β values for all survival curves show no significant differences among the dose-rates tested for HSG, V79 and CHO cells. Changing the ion-beam dose-rate had no effect on cell survival. This suggests that high-LET particle beams, such as galactic cosmic rays, may not exhibit a dose-rate effect on cell survival. Low-dose-rate radiation showed an effect similar to high-dose-rate radiation.

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.