A reaction-diffusion model for radiation-induced bystander effects

Radiation-induced bystander effect and its clinical implications

For many years, targeted DNA damage caused by radiation has been considered the main cause of various biological effects. Based on this paradigm, any small amount of radiation is harmful to the organism. Epidemiological studies of Japanese atomic bomb survivors have proposed the linear-non-threshold model as the dominant standard in the field of radiation protection. However, there is increasing evidence that the linear-non-threshold model is not fully applicable to the biological effects caused by low dose radiation, and theories related to low dose radiation require further investigation. In addition to the cell damage caused by direct exposure, non-targeted effects, which are sometimes referred to as bystander effects, abscopal effects, genetic instability, etc., are another kind of significant effect related to low dose radiation. An understanding of this phenomenon is crucial for both basic biomedical research and clinical application. This article reviews recent studies on the bystander effect and summarizes the key findings in the field. Additionally, it offers a cross-sectional comparison of bystander effects caused by various radiation sources in different cell types, as well as an in-depth analysis of studies on the potential biological mechanisms of bystander effects. This review aims to present valuable information and provide new insights on the bystander effect to enlighten both radiobiologists and clinical radiologists searching for new ways to improve clinical treatments.

Datasets of in vitro clonogenic assays showing low dose hyper-radiosensitivity and induced radioresistance

Low dose hyper-radiosensitivity and induced radioresistance are primarily observed in surviving fractions of cell populations exposed to ionizing radiation, plotted as the function of absorbed dose. Several biophysical models have been developed to quantitatively describe these phenomena. However, there is a lack of raw, openly available experimental data to support the development and validation of quantitative models. The aim of this study was to set up a database of experimental data from the public literature. Using Google Scholar search, 46 publications with 101 datasets on the dose-dependence of surviving fractions, with clear evidence of low dose hyper-radiosensitivity, were identified. Surviving fractions, their uncertainties, and the corresponding absorbed doses were digitized from graphs of the publications. The characteristics of the cell line and the irradiation were also recorded, along with the parameters of the linear-quadratic model and/or the induced repair model if they were provided. The database is available in STORE^DB, and can be used for meta-analysis, for comparison with new experiments, and for development and validation of biophysical models.

Measurement(s)	surviving fraction of cells
Technology Type(s)	optical microscopy
Factor Type(s)	absorbed dose
Sample Characteristic - Organism	Homo sapiens • Chinese hamster • Rattus sp.
Sample Characteristic - Environment	cell culture

Personalized Virus Load Curves for Acute Viral Infections

We introduce an explicit function that describes virus-load curves on a patient-specific level. This function is based on simple and intuitive model parameters. It allows virus load analysis of acute viral infections without solving a full virus load dynamic model. We validate our model on data from mice influenza A, human rhinovirus data, human influenza A data, and monkey and human SARS-CoV-2 data. We find wide distributions for the model parameters, reflecting large variability in the disease outcomes between individuals. Further, we compare the virus load function to an established target model of virus dynamics, and we provide a new way to estimate the exponential growth rates of the corresponding infection phases. The virus load function, the target model, and the exponential approximations show excellent fits for the data considered. Our virus-load function offers a new way to analyze patient-specific virus load data, and it can be used as input for higher level models for the physiological effects of a virus infection, for models of tissue damage, and to estimate patient risks.

The Role and Mechanism of ATM-Mediated Autophagy in the Transition From Hyper-Radiosensitivity to Induced Radioresistance in Lung Cancer Under Low-Dose Radiation

Objective: This study aimed to investigate the effect of ataxia telangiectasia mutated (ATM)–mediated autophagy on the radiosensitivity of lung cancer cells under low-dose radiation and to further investigate the role of ATM and its specific mechanism in the transition from hyper-radiosensitivity (HRS) to induced radioresistance (IRR).

Methods: The changes in the HRS/IRR phenomenon in A549 and H460 cells were verified by colony formation assay. Changes to ATM phosphorylation and cell autophagy in A549 and H460 cells under different low doses of radiation were examined by western blot, polymerase chain reaction (PCR), and electron microscopy. ATM expression was knocked down by short interfering RNA (siRNA) transfection, and ATM-regulated molecules related to autophagy pathways were screened by transcriptome sequencing analysis. The detection results were verified by PCR and western blot. The differential metabolites were screened by transcriptome sequencing and verified by colony formation assay and western blot. The nude mouse xenograft model was used to verify the results of the cell experiments.

Results: (1) A549 cells with high expression of ATM showed positive HRS/IRR, whereas H460 cells with low expression of ATM showed negative HRS/IRR. After the expression of ATM decreased, the HRS phenomenon in A549 cells increased, and the radiosensitivity of H460 cells also increased. This phenomenon was associated with the increase in the autophagy-related molecules phosphorylated c-Jun N-terminal kinase (p-JNK) and autophagy/Beclin 1 regulator 1 (AMBRA1). (2) DL-Norvaline, a product of carbon metabolism in cells, inhibited autophagy in A549 cells under low-dose radiation. DL-Norvaline increased the expression levels of ATM, JNK, and AMBRA1 in A549 cells. (3) Mouse experiments confirmed the regulatory role of ATM in autophagy and metabolism and its function in HRS/IRR.

Conclusion: ATM may influence autophagy through p-JNK and AMBRA1 to participate in the regulation of the HRS/IRR phenomenon. Autophagy interacts with the cellular carbon metabolite DL-Norvaline to participate in regulating the low-dose radiosensitivity of cells.

Modeling space radiation induced cognitive dysfunction using targeted and non-targeted effects

Radiation-induced cognitive dysfunction is increasingly recognized as an important risk for human exploration of distant planets. Mechanistically-motivated mathematical modeling helps to interpret and quantify this phenomenon. Here we considered two general mechanisms of ionizing radiation-induced damage: targeted effects (TE), caused by traversal of cells by ionizing tracks, and non-targeted effects (NTE), caused by responses of other cells to signals released by traversed cells. We compared the performances of 18 dose response model variants based on these concepts, fitted by robust nonlinear regression to a large published data set on novel object recognition testing in rats exposed to multiple space-relevant radiation types (H, C, O, Si, Ti and Fe ions), covering wide ranges of linear energy transfer (LET) (0.22-181 keV/µm) and dose (0.001-2 Gy). The best-fitting model (based on Akaike information criterion) was an NTE + TE variant where NTE saturate at low doses (~ 0.01 Gy) and occur at all tested LETs, whereas TE depend on dose linearly with a slope that increases with LET. The importance of NTE was also found by additional analyses of the data using quantile regression and random forests. These results suggest that NTE-based radiation effects on brain function are potentially important for astronaut health and for space mission risk assessments.

REVIEW OF QUANTITATIVE MECHANISTIC MODELS OF RADIATION-INDUCED NON-TARGETED EFFECTS (NTE)

Quantitative mechanistic modeling of the biological effects of ionizing radiation has a long rich history. Initially, it was dominated by target theory, which quantifies damage caused by traversal of cellular targets like DNA by ionizing tracks. The discovery that mutagenesis, death and/or altered behavior sometimes occur in cells that were not themselves traversed by any radiation tracks but merely interacted with traversed cells was initially seen as surprising. As more evidence of such 'non-targeted' or 'bystander' effects accumulated, the importance of their contribution to radiation-induced damage became more recognized. Understanding and modeling these processes is important for quantifying and predicting radiation-induced health risks. Here we review the variety of mechanistic mathematical models of nontargeted effects that emerged over the past 2-3 decades. This review is not intended to be exhaustive, but focuses on the main assumptions and approaches shared or distinct between models, and on identifying areas for future research.

Implications of immune-mediated metastatic growth on metastatic dormancy, blow-up, early detection, and treatment

Metastatic seeding of distant organs can occur in the very early stages of primary tumor development. Once seeded, these micrometastases may enter a dormant phase that can last decades. Curiously, the surgical removal of the primary tumor can stimulate the accelerated growth of distant metastases, a phenomenon known as metastatic blow-up. Recent clinical evidence has shown that the immune response can have strong tumor promoting effects. In this work, we investigate if the pro-tumor effects of the immune response can have a significant contribution to metastatic dormancy and metastatic blow-up. We develop an ordinary differential equation model of the immune-mediated theory of metastasis. We include both anti- and pro-tumor immune effects, in addition to the experimentally observed phenomenon of tumor-induced immune cell phenotypic plasticity. Using geometric singular perturbation analysis, we derive a rather simple model that captures the main processes and, at the same time, can be fully analyzed. Literature-derived parameter estimates are obtained, and model robustness is demonstrated through a time dependent sensitivity analysis. We determine conditions under which the parameterized model can successfully explain both metastatic dormancy and blow-up. The results confirm the significant active role of the immune system in the metastatic process. Numerical simulations suggest a novel measure to predict the occurrence of future metastatic blow-up in addition to new potential avenues for treatment of clinically undetectable micrometastases.

Hadrontherapy Interactions in Molecular and Cellular Biology

The resistance of cancer cells to radiotherapy is a major issue in the curative treatment of cancer patients. This resistance can be intrinsic or acquired after irradiation and has various definitions, depending on the endpoint that is chosen in assessing the response to radiation. This phenomenon might be strengthened by the radiosensitivity of surrounding healthy tissues. Sensitive organs near the tumor that is to be treated can be affected by direct irradiation or experience nontargeted reactions, leading to early or late effects that disrupt the quality of life of patients. For several decades, new modalities of irradiation that involve accelerated particles have been available, such as proton therapy and carbon therapy, raising the possibility of specifically targeting the tumor volume. The goal of this review is to examine the up-to-date radiobiological and clinical aspects of hadrontherapy, a discipline that is maturing, with promising applications. We first describe the physical and biological advantages of particles and their application in cancer treatment. The contribution of the microenvironment and surrounding healthy tissues to tumor radioresistance is then discussed, in relation to imaging and accurate visualization of potentially resistant hypoxic areas using dedicated markers, to identify patients and tumors that could benefit from hadrontherapy over conventional irradiation. Finally, we consider combined treatment strategies to improve the particle therapy of radioresistant cancers.

A mathematical model for the immune-mediated theory of metastasis

Accumulating experimental and clinical evidence suggest that the immune response to cancer is not exclusively anti-tumor. Indeed, the pro-tumor roles of the immune system  -  as suppliers of growth and pro-angiogenic factors or defenses against cytotoxic immune attacks, for example  -  have been long appreciated, but relatively few theoretical works have considered their effects. Inspired by the recently proposed "immune-mediated" theory of metastasis, we develop a mathematical model for tumor-immune interactions at two anatomically distant sites, which includes both anti- and pro-tumor immune effects, and the experimentally observed tumor-induced phenotypic plasticity of immune cells (tumor "education" of the immune cells). Upon confrontation of our model to experimental data, we use it to evaluate the implications of the immune-mediated theory of metastasis. We find that tumor education of immune cells may explain the relatively poor performance of immunotherapies, and that many metastatic phenomena, including metastatic blow-up, dormancy, and metastasis to sites of injury, can be explained by the immune-mediated theory of metastasis. Our results suggest that further work is warranted to fully elucidate the pro-tumor effects of the immune system in metastatic cancer.

A Mathematical Model for the Effect of Low-Dose Radiation on the G2/M Transition

We develop a mathematical model to study the immediate effect of low-dose radiation on the G2 checkpoint and the G2/M transition of the cell cycle via a radiation pathway (the ATM-Chk2 pathway) of an individual mammalian cell. The model consists of a system of nonlinear differential equations describing the dynamics of a network of regulatory proteins that play key roles in the G2/M transition, cell cycle oscillations, and the radiation pathway. We simulate the application of a single pulse of low-dose radiation at different intensities ([Formula: see text] 0-0.4 Gy) and times during the latter part of the G2-phase. We use bifurcation analysis to characterize the effect of radiation on the G2/M transition via the ATM-Chk2 pathway. We show that radiation between 0.1 and 0.3 Gy can delay the G2/M transition, and radiation higher than 0.3 Gy can fully activate the G2 checkpoint. Also, our results show that radiation can be low enough to neither delay the G2/M transition nor activate the G2 checkpoint ([Formula: see text] 0.1 Gy). Our model supports the idea that the cell response to radiation during G2-phase explains hyper-radiosensitivity and increased radioresistance (HRS/IRR) observed at low dose.

Enhancing low-dose risk assessment using mechanistic mathematical models of radiation effects

Mechanistic mathematical modeling of ionizing radiation (IR) effects has a long history spanning several decades. Models that mathematically represent current knowledge and hypotheses about how radiation damages cells and organs, leading to deleterious outcomes such as carcinogenesis, are particularly useful for estimating radiation risks at doses that are relevant for radiation protection, but are too low to provide a strong 'signal-to-noise ratio' in epidemiological or experimental studies with realistic sample sizes. Here, I discuss examples of models in several relevant areas, including radionuclide biokinetics, non-targeted IR effects, DNA double-strand break (DSB) rejoining and radiation carcinogenesis. I do not provide a detailed review of the vast modeling literature in these fields, but focus on concepts that we have implemented, such as using continuous probability distributions of exponential rates to model radionuclide biokinetics and DSB rejoining, and combining short and long time scales in carcinogenesis models. Improvements in models, including the ability to generate new hypotheses based on model predictions, may come from the introduction of additional novel concepts and from integrating multiple data types.

Effects of G2-checkpoint dynamics on low-dose hyper-radiosensitivity

In experimental studies, it has been found that certain cell lines are more sensitive to low-dose radiation than would be expected from the classical Linear-Quadratic model (LQ model). In fact, it is frequently observed that cells incur more damage at low dose (say 0.3 Gy) than at higher dose (say 1 Gy). This effect has been termed hyper-radiosensitivity (HRS). The effect depends on the type of cells and on their phase in the cell cycle when radiation is applied. Experiments have shown that the G2-checkpoint plays an important role in the HRS effects. Here we design and analyze a differential equation model for the cell cycle that includes G2-checkpoint dynamics and radiation treatment. We fit the model to surviving fraction data for different cell lines including glioma cells, prostate cancer cells, as well as to cell populations that are enriched in certain phases of the cell cycle. The HRS effect is measured in the literature through [Formula: see text], the ratio of slope [Formula: see text] of the surviving fraction curve at zero dose to slope [Formula: see text] of the corresponding LQ model. We derive an explicit formula for this ratio and we show that it corresponds very closely to experimental observations. Finally, we identify the dependence of this ratio on the surviving fraction at 2 Gy. It was speculated in the literature that such dependence exists. Our theoretical analysis will help to more systematically identify the HRS in cell lines, and opens doors to analyze its use in cancer treatment.

Cancer Radiosensitizers

Radiotherapy (RT) is a mainstay treatment for many types of cancer, although it is still a large challenge to enhance radiation damage to tumor tissue and reduce side effects to healthy tissue. Radiosensitizers are promising agents that enhance injury to tumor tissue by accelerating DNA damage and producing free radicals. Several strategies have been exploited to develop highly effective and low-toxicity radiosensitizers. In this review, we highlight recent progress on radiosensitizers, including small molecules, macromolecules, and nanomaterials. First, small molecules are reviewed based on free radicals, pseudosubstrates, and other mechanisms. Second, nanomaterials, such as nanometallic materials, especially gold-based materials that have flexible surface engineering and favorable kinetic properties, have emerged as promising radiosensitizers. Finally, emerging macromolecules have shown significant advantages in RT because these molecules can be combined with biological therapy as well as drug delivery. Further research on the mechanisms of radioresistance and multidisciplinary approaches will accelerate the development of radiosensitizers.