A model for radiation-induced bystander effects, with allowance for spatial position and the effects of cell turnover

Modelling radiobiology

Radiotherapy has played an essential role in cancer treatment for over a century, and remains one of the best-studied methods of cancer treatment. Because of its close links with the physical sciences, it has been the subject of extensive quantitative mathematical modelling, but a complete understanding of the mechanisms of radiotherapy has remained elusive. In part this is because of the complexity and range of scales involved in radiotherapy-from physical radiation interactions occurring over nanometres to evolution of patient responses over months and years. This review presents the current status and ongoing research in modelling radiotherapy responses across these scales, including basic physical mechanisms of DNA damage, the immediate biological responses this triggers, and genetic- and patient-level determinants of response. Finally, some of the major challenges in this field and potential avenues for future improvements are also discussed.

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.

Computational Biophysical Modeling of the Radiation Bystander Effect in Irradiated Cells

It is well known that ionizing radiation can cause damages to cells that interact with it directly. However, many studies have shown that damages also occur in cells that have not experienced direct interaction. This is due to the so-called bystander effect, which is observed when the irradiated cell sends signals that can damage neighboring cells. Due to the complexity of this effect, it is not easy to strictly describe it biophysically, and thus it is also difficult to simulate. This article reviews various approaches to modeling and simulating the bystander effect from the point of view of radiation biophysics. In particular, the last model presented within this article is part of a larger project of modeling the response of a group of cells to ionizing radiation using Monte Carlo methods. The new approach presented here is based on the probability tree, the Poisson distribution of signals and the saturated dose-related probability distribution of the bystander effect’s appearance, which makes the model very broad and universal.

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.

Development of a coupled simulation toolkit for computational radiation biology based on Geant4 and CompuCell3D

Understanding and designing clinical radiation therapy is one of the most important areas of state-of-the-art oncological treatment regimens. Decades of research have gone into developing sophisticated treatment devices and optimization protocols for schedules and dosages. In this paper, we presented a comprehensive computational platform that facilitates building of the sophisticated multi-cell-based model of how radiation affects the biology of living tissue. We designed and implemented a coupled simulation method, including a radiation transport model, and a cell biology model, to simulate the tumor response after irradiation. The radiation transport simulation was implemented through Geant4 which is an open-source Monte Carlo simulation platform that provides many flexibilities for users, as well as low energy DNA damage simulation physics, Geant4-DNA. The cell biology simulation was implemented using CompuCell3D (CC3D) which is a cell biology simulation platform. In order to couple Geant4 solver with CC3D, we developed a 'bridging' module, RADCELL, that extracts tumor cellular geometry of the CC3D simulation (including specification of the individual cells) and ported it to the Geant4 for radiation transport simulation. The cell dose and cell DNA damage distribution in multicellular system were obtained using Geant4. The tumor response was simulated using cell-based tissue models based on CC3D, and the cell dose and cell DNA damage information were fed back through RADCELL to CC3D for updating the cell properties. By merging two powerful and widely used modeling platforms, CC3D and Geant4, we delivered a novel tool that can give us the ability to simulate the dynamics of biological tissue in the presence of ionizing radiation, which provides a framework for quantifying the biological consequences of radiation therapy. In this introductory methods paper, we described our modeling platform in detail and showed how it can be applied to study the application of radiotherapy to a vascularized tumor.

Inflammation Mediates the Development of Aggressive Breast Cancer Following Radiotherapy

PURPOSE

Women treated with radiotherapy before 30 years of age have increased risk of developing breast cancer at an early age. Here, we sought to investigate mechanisms by which radiation promotes aggressive cancer.

EXPERIMENTAL DESIGN

The tumor microenvironment (TME) of breast cancers arising in women treated with radiotherapy for Hodgkin lymphoma was compared with that of sporadic breast cancers. To investigate radiation effects on carcinogenesis, we analyzed tumors arising from Trp53-null mammary transplants after irradiation of the target epithelium or host using immunocompetent and incompetent mice, some of which were treated with aspirin.

RESULTS

Compared with age-matched specimens of sporadic breast cancer, radiation-preceded breast cancers (RP-BC) were characterized by TME rich in TGFβ, cyclooxygenase 2, and myeloid cells, indicative of greater immunosuppression, even when matched for triple-negative status. The mechanism by which radiation impacts TME construction was investigated in carcinomas arising in mice bearing Trp53-null mammary transplants. Immunosuppressive TMEs (iTME) were recapitulated in mice irradiated before transplantation, which implicated systemic immune effects. In nu/nu mice lacking adaptive immunity irradiated before Trp53-null mammary transplantation, cancers also established an iTME, which pointed to a critical role for myeloid cells. Consistent with this, irradiated mammary glands contained more macrophages and human cells cocultured with polarized macrophages underwent dysplastic morphogenesis mediated by IFNγ. Treating mice with low-dose aspirin for 6 months postirradiation prevented establishment of an iTME and resulted in less aggressive tumors.

CONCLUSIONS

These data show that radiation acts via nonmutational mechanisms to promote markedly immunosuppressive features of aggressive, RP-BCs.

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.

Cell death and survival due to cytotoxic exposure modelled as a two-state Ising system

Cancer chemotherapy agents are assessed for their therapeutic utility primarily by their ability to cause apoptosis of cancer cells and their potency is given by an IC50 value. Chemotherapy uses both target-specific and systemic-action drugs and drug combinations to treat cancer. It is important to judiciously choose a drug type, its dosage and schedule for optimized drug selection and administration. Consequently, the precise mathematical formulation of cancer cells' response to chemotherapy may assist in the selection process. In this paper, we propose a mathematical description of the cancer cell response to chemotherapeutic agent exposure based on a time-tested physical model of two-state multiple-component systems near criticality. We describe the Ising model methodology and apply it to a diverse panel of cytotoxic drugs administered against numerous cancer cell lines in a dose-response manner. The analysed dataset was generated by the Netherlands Translational Research Center B.V. (Oncolines). This approach allows for an accurate and consistent analysis of cytotoxic agents' effects on cancer cell lines and reveals the presence or absence of the bystander effect through the interaction constant. By calculating the susceptibility function, we see the value of IC50 coinciding with the peak of this measure of the system's sensitivity to external perturbations.

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.

Bystander effects and their implications for clinical radiation therapy: Insights from multiscale in silico experiments

Radiotherapy is a commonly used treatment for cancer and is usually given in varying doses. At low radiation doses relatively few cells die as a direct response to radiation but secondary radiation effects, such as DNA mutation or bystander phenomena, may affect many cells. Consequently it is at low radiation levels where an understanding of bystander effects is essential in designing novel therapies with superior clinical outcomes. In this paper, we use a hybrid multiscale mathematical model to study the direct effects of radiation as well as radiation-induced bystander effects on both tumour cells and normal cells. We show that bystander responses play a major role in mediating radiation damage to cells at low-doses of radiotherapy, doing more damage than that due to direct radiation. The survival curves derived from our computational simulations showed an area of hyper-radiosensitivity at low-doses that are not obtained using a traditional radiobiological model.

Potential implications of the bystander effect on TCP and EUD when considering target volume dose heterogeneity

PURPOSE

In light of in vitro evidence suggesting that radiation-induced bystander effects may enhance non-local cell killing, there is potential for impact on radiotherapy treatment planning paradigms such as the goal of delivering a uniform dose throughout the clinical target volume (CTV). This work applies a bystander effect model to calculate equivalent uniform dose (EUD) and tumor control probability (TCP) for external beam prostate treatment and compares the results with a more common model where local response is dictated exclusively by local absorbed dose. The broad assumptions applied in the bystander effect model are intended to place an upper limit on the extent of the results in a clinical context.

MATERIALS AND METHODS

EUD and TCP of a prostate cancer target volume under conditions of increasing dose heterogeneity were calculated using two models: One incorporating bystander effects derived from previously published in vitro bystander data ( McMahon et al. 2012 , 2013a); and one using a common linear-quadratic (LQ) response that relies exclusively on local absorbed dose. Dose through the CTV was modelled as a normal distribution, where the degree of heterogeneity was then dictated by changing the standard deviation (SD). Also, a representative clinical dose distribution was examined as cold (low dose) sub-volumes were systematically introduced.

RESULTS

The bystander model suggests a moderate degree of dose heterogeneity throughout a target volume will yield as good or better outcome compared to a uniform dose in terms of EUD and TCP. For a typical intermediate risk prostate prescription of 78 Gy over 39 fractions maxima in EUD and TCP as a function of increasing SD occurred at SD ∼ 5 Gy. The plots only dropped below the uniform dose values for SD ∼ 10 Gy, almost 13% of the prescribed dose. Small, but potentially significant differences in the outcome metrics between the models were identified in the clinically-derived dose distribution as cold sub-volumes were introduced.

CONCLUSIONS

In terms of EUD and TCP, the bystander model demonstrates the potential to deviate from the common local LQ model predictions as dose heterogeneity through a prostate CTV varies. The results suggest, at least in a limiting sense, the potential for allowing some degree of dose heterogeneity within a CTV, although further investigation of the assumptions of the bystander model are warranted.

Potential implications on TCP for external beam prostate cancer treatment when considering the bystander effect in partial exposure scenarios

PURPOSE

This work investigated the potential implications on tumour control probability (TCP) for external beam prostate cancer treatment when considering the bystander effect in partial exposure scenarios.

MATERIALS AND METHODS

The biological response of a prostate cancer target volume under conditions where a sub-volume of the target volume was not directly irradiated was modelled in terms of surviving fraction (SF) and Poisson-based TCP. A direct comparison was made between the linear-quadratic (LQ) response model, and a response model that incorporates bystander effects as derived from published in vitro data by McMahon et al. in 2012 and 2013. Scenarios of random and systematic misses were considered.

RESULTS

Our results suggested the potential for the bystander effect to deviate from LQ predictions when even very small (< 1%) sub-volumes of the target volume were directly irradiated. Under conditions of random misses for each fraction, the bystander model predicts a 3% and 1% improvement in tumour control compared to that predicted by an LQ model when only 90% and 95% of the prostate cells randomly receive the intended dose. Under conditions of systematic miss, if even a small portion of the target volume is not directly exposed, the LQ model predicts a TCP approaching zero, whereas the bystander model suggests TCP will improve starting at exposed volumes of around 85%.

CONCLUSIONS

The bystander model, when applied to clinically relevant scenarios, demonstrates the potential to deviate from the TCP predictions of the common local LQ model when sub-volumes of a target volume are randomly or systematically missed over a course of fractionated radiation therapy.

The complex interactions between radiation induced non-targeted effects and cancer

Radiation induced non-targeted effects have been widely investigated in the last two decades for their potential impact on low dose radiation risk. In this paper we will give an overview of the most relevant aspects related to these effects, starting from the definition of the low dose scenarios. We will underline the role of radiation quality, both in terms of mechanisms of interaction with the biological matter and for the importance of charged particles as powerful tools for low dose effects investigation. We will focus on cell communication, representing a common feature of non-targeted effects, giving also an overview of cancer models that have explicitly considered such effects.

Bystander signalling: exploring clinical relevance through new approaches and new models

Classical radiation biology research has centred on nuclear DNA as the main target of radiation-induced damage. Over the past two decades, this has been challenged by a significant amount of scientific evidence clearly showing radiation-induced cell signalling effects to have important roles in mediating overall radiobiological response. These effects, generally termed radiation-induced bystander effects (RIBEs) have challenged the traditional DNA targeted theory in radiation biology and highlighted an important role for cells not directly traversed by radiation. The multiplicity of experimental systems and exposure conditions in which RIBEs have been observed has hindered precise definitions of these effects. However, RIBEs have recently been classified for different relevant human radiation exposure scenarios in an attempt to clarify their role in vivo. Despite significant research efforts in this area, there is little direct evidence for their role in clinically relevant exposure scenarios. In this review, we explore the clinical relevance of RIBEs from classical experimental approaches through to novel models that have been used to further determine their potential implications in the clinic.

Non-targeted effects of ionising radiation--implications for low dose risk

Non-DNA targeted effects of ionising radiation, which include genomic instability, and a variety of bystander effects including abscopal effects and bystander mediated adaptive response, have raised concerns about the magnitude of low-dose radiation risk. Genomic instability, bystander effects and adaptive responses are powered by fundamental, but not clearly understood systems that maintain tissue homeostasis. Despite excellent research in this field by various groups, there are still gaps in our understanding of the likely mechanisms associated with non-DNA targeted effects, particularly with respect to systemic (human health) consequences at low and intermediate doses of ionising radiation. Other outstanding questions include links between the different non-targeted responses and the variations in response observed between individuals and cell lines, possibly a function of genetic background. Furthermore, it is still not known what the initial target and early interactions in cells are that give rise to non-targeted responses in neighbouring or descendant cells. This paper provides a commentary on the current state of the field as a result of the non-targeted effects of ionising radiation (NOTE) Integrated Project funded by the European Union. Here we critically examine the evidence for non-targeted effects, discuss apparently contradictory results and consider implications for low-dose radiation health effects.

New biological insights on the link between radiation exposure and breast cancer risk

Radiation exposure is a well-documented risk factor for breast cancer in women. Compelling epidemiological evidence in different exposed populations around the world demonstrate that excess breast cancer increases with radiation doses above 10 cGy. Both frequency and type of breast cancer are affected by prior radiation exposure. Many epidemiological studies suggest that radiation risk is inversely related to age at exposure; exposure during puberty poses the greatest risk while exposures past the menopause appear to carry very low risk. These observations are supported by experimental studies in mice and rats, which together provide the basis for the pubertal 'window of susceptibility' hypothesis for carcinogenic exposure. One line of experimental investigation suggests that the pubertal epithelium is more sensitive because DNA damage responses are less efficient, an other suggests that radiation affects stem cells self-renewal. A recent line of investigation suggests that the irradiated microenvironment mediates cancer risk. Studying the biological basis for radiation effects provides potential routes for protection in vulnerable populations, which include survivors of childhood cancers, as well as insights into the biology for certain types of sporadic cancer.

A kinetic-based model of radiation-induced intercellular signalling

It is now widely accepted that intercellular communication can cause significant variations in cellular responses to genotoxic stress. The radiation-induced bystander effect is a prime example of this effect, where cells shielded from radiation exposure see a significant reduction in survival when cultured with irradiated cells. However, there is a lack of robust, quantitative models of this effect which are widely applicable. In this work, we present a novel mathematical model of radiation-induced intercellular signalling which incorporates signal production and response kinetics together with the effects of direct irradiation, and test it against published data sets, including modulated field exposures. This model suggests that these so-called “bystander” effects play a significant role in determining cellular survival, even in directly irradiated populations, meaning that the inclusion of intercellular communication may be essential to produce robust models of radio-biological outcomes in clinically relevant in vivo situations.

Murine microenvironment metaprofiles associate with human cancer etiology and intrinsic subtypes

PURPOSE

Ionizing radiation is a well-established carcinogen in rodent models and a risk factor associated with human cancer. We developed a mouse model that captures radiation effects on host biology by transplanting unirradiated Trp53-null mammary tissue to sham or irradiated hosts. Gene expression profiles of tumors that arose in irradiated mice are distinct from those that arose in naïve hosts. We asked whether expression metaprofiles could discern radiation-preceded human cancer or be informative in sporadic breast cancers.

EXPERIMENTAL DESIGN

Affymetrix microarray gene expression data from 56 Trp53-null mammary tumors were used to define gene profiles and a centroid that discriminates tumors arising in irradiated hosts. These were applied to publicly available human cancer datasets.

RESULTS

Host irradiation induces a metaprofile consisting of gene modules representing stem cells, cell motility, macrophages, and autophagy. Human orthologs of the host irradiation metaprofile discriminated between radiation-preceded and sporadic human thyroid cancers. An irradiated host centroid was strongly associated with estrogen receptor-negative breast cancer. When applied to sporadic human breast cancers, the irradiated host metaprofile strongly associated with basal-like and claudin-low breast cancer intrinsic subtypes. Comparing host irradiation in the context of TGF-β levels showed that inflammation was robustly associated with claudin-low tumors.

CONCLUSIONS

Detection of radiation-preceded human cancer by the irradiated host metaprofile raises possibilities of assessing human cancer etiology. Moreover, the association of the irradiated host metaprofiles with estrogen receptor-negative status and claudin-low subtype suggests that host processes similar to those induced by radiation underlie sporadic cancers.

Investigation of the mechanisms underpinning IL-6 cytokine release in bystander responses: the roles of radiation dose, radiation quality and specific ROS/RNS scavengers

PURPOSE

To investigate the mechanisms regulating the pathways of the bystander transmission in vitro, focusing on the radiation-perturbed signalling (via Interleukine 6, IL-6) of the irradiated cells after exposure to low doses of different radiation types.

MATERIALS AND METHODS

An integrated 'systems radiation biology' approach was adopted. Experimentally the level of the secreted cytokine from human fibroblasts was detected with ELISA (Enzyme-Linked ImmunoSorbent Assay) method and subsequently the data were analyzed and coupled with a phenomenological model based on differential equations to evaluate the single-cell release mechanisms.

RESULTS

The data confirmed the important effect of radiation on the IL-6 pathway, clearly showing a crucial role of the ROS (Reactive Oxygen Species) in transducing the effect of initial radiation exposure and the subsequent long-term release of IL-6. Furthermore, a systematic investigation of radiation dose/radiation quality dependence seems to indicate an increasing efficiency of high LET (Linear Energy Transfer) irradiation in the release of the cytokine. Basic hypotheses were tested, on the correlation between direct radiobiological damage and signal release and on the radiation target for this endpoint (secretion of IL-6).

CONCLUSIONS

The results demonstrate the role of reactive oxygen and nitrogen species in the signaling pathways of IL-6. Furthermore the systems radiation biology approach here adopted, allowed us to test and verify hypotheses on the behavior of the single cell in the release of cytokine, after the exposure to different doses and different qualities of ionizing radiation.

Computational modeling of cellular effects post-irradiation with low- and high-let particles and different absorbed doses

The use of computational methods to improve the understanding of biological responses to various types of radiation is an approach where multiple parameters can be modelled and a variety of data is generated. This study compares cellular effects modelled for low absorbed doses against high absorbed doses. The authors hypothesized that low and high absorbed doses would contribute to cell killing via different mechanisms, potentially impacting on targeted tumour radiotherapy outcomes. Cellular kinetics following irradiation with selective low- and high-linear energy transfer (LET) particles were investigated using the Virtual Cell (VC) radiobiology algorithm. Two different cell types were assessed using the VC radiobiology algorithm: human fibroblasts and human crypt cells. The results showed that at lower doses (0.01 to 0.2 Gy), all radiation sources used were equally able to induce cell death (p>0.05, ANOVA). On the other hand, at higher doses (1.0 to 8.0 Gy), the radiation response was LET and dose dependent (p<0.05, ANOVA). The data obtained suggests that the computational methods used might provide some insight into the cellular effects following irradiation. The results also suggest that it may be necessary to re-evaluate cellular radiation-induced effects, particularly at low doses that could affect therapeutic effectiveness.

Computational modeling of signaling pathways mediating cell cycle checkpoint control and apoptotic responses to ionizing radiation-induced DNA damage

The shape of dose response of ionizing radiation (IR) induced cancer at low dose region, either linear non-threshold or J-shaped, has been a debate for a long time. This dose response relationship can be influenced by built-in capabilities of cells that minimize the fixation of IR-mediated DNA damage as pro-carcinogenic mutations. Key capabilities include sensing of damage, activation of cell cycle checkpoint arrests that provide time needed for repair of the damage as well as apoptosis. Here we describe computational modeling of the signaling pathways that link sensing of DNA damage and checkpoint arrest activation/apoptosis to investigate how these molecular-level interactions influence the dose response relationship for IR induced cancer. The model provides qualitatively accurate descriptions of the IR-mediated activation of cell cycle checkpoints and the apoptotic pathway, and of time-course activities and dose response of relevant regulatory proteins (e.g. p53 and p21). Linking to a two-stage clonal growth cancer model, the model described here successfully captured a monotonically increasing to a J-shaped dose response curve and identified one potential mechanism leading to the J-shape: the cell cycle checkpoint arrest time saturates with the increase of the dose.

Radiation-induced bystander effects: what are they, and how relevant are they to human radiation exposures?

The term radiation-induced bystander effect is used to describe radiation-induced biological changes that manifest in unirradiated cells remaining within an irradiated cell population. Despite their failure to fit into the framework of classical radiobiology, radiation-induced bystander effects have entered the mainstream and have become established in the radiobiology vocabulary as a bona fide radiation response. However, there is still no consensus on a precise definition of radiation-induced bystander effects, which currently encompasses a number of distinct signal-mediated effects. These effects are classified here into three classes: bystander effects, abscopal effects and cohort effects. In this review, the data have been evaluated to define, where possible, various features specific to radiation-induced bystander effects, including their timing, range, potency and dependence on dose, dose rate, radiation quality and cell type. The weight of evidence supporting these defining features is discussed in the context of bystander experimental systems that closely replicate realistic human exposure scenarios. Whether the manifestation of bystander effects in vivo is intrinsically limited to particular radiation exposure scenarios is considered. The conditions under which radiation-induced bystander effects are induced in vivo will ultimately determine their impact on radiation-induced carcinogenic risk.

A mathematical framework for separating the direct and bystander components of cellular radiation response

A mathematical model for fractional tumor cell survival was developed incorporating components of cell killing due to direct radiation interactions and bystander signals resulting from non-local dose deposition.

MATERIAL AND METHODS

Three possible mechanisms for signal production were tested by fitting predictions to available experimental results for tumor cells (non-small cell lung cancer NCI-H460 and melanoma MM576) exposed to gradient x-ray fields. The parameter fitting allowed estimation of the contribution of bystander signaling to cell death (20-50% for all models). Separation of the two components of cell killing allowed determination of the α and β parameters of the linear-quadratic model both with and without the presence of bystander signaling.

RESULTS AND DISCUSSION

For both cell lines, cell death from bystander signaling and direct radiation interactions were comparable. For NCI-H460 cells, the values for α and β were 0.18 Gy⁻¹ and 0.10 Gy⁻² respectively when direct and bystander effects were combined, and 0.053 Gy⁻¹ and 0.061 Gy⁻² respectively when the signaling component was removed. For MM576, the corresponding respective values were 0.09 Gy⁻¹ and 0.011 Gy⁻² for the combined response, and 0.014 Gy⁻¹ and 0.002 Gy⁻² for the isolated direct radiation response. The bystander component in cell death was found to be significant and should not be ignored. Further experimental evidence is required to determine how these results translate to the in vivo situation where tumor control probability (TCP) models that currently assume cellular independence may need to be revised.

Effects of ionizing radiation on cell-to-cell communication

Cell-to-cell signaling has become a significant issue in radiation biology due to experimental evidence, accumulated primarily since the early 1990s, of radiation-induced bystander effects. Several candidate mediators involved in cell-to-cell communication have been investigated and proposed as being responsible for this phenomenon, but the current investigation techniques (both theoretical and experimental) of the mechanisms involved, due to the particular set-up of each experiment, result in experimental data that often are not directly comparable. In this study, a comprehensive approach was adopted to describe cell-to-cell communication (focusing on cytokine signaling) and its modulation by external agents such as ionizing radiation. The aim was also to provide integrated theoretical instruments and experimental data to help in understanding the peculiarities of in vitro experiments. Theoretical/modeling activities were integrated with experimental measurements by (1) redesigning a cybernetic model (proposed in its original form in the 1950s) to frame cell-to-cell communication processes, (2) implementing and developing a mathematical model, and (3) designing and carrying out experiments to quantify key parameters involved in intercellular signaling (focusing as a pilot study on the release and decay of IL-6 molecules and their modulation by radiation). This formalization provides an interpretative framework for understanding the intercellular signaling and in particular for focusing on the study of cell-to-cell communication in a "step-by-step" approach. Under this model, the complex phenomenon of signal transmission was reduced where possible into independent processes to investigate them separately, providing an evaluation of the role of cell communication to guarantee and maintain the robustness of the in vitro experimental systems against the effects of perturbations.

Non-targeted effects of ionising radiation and radiotherapy

Modern radiobiology is undergoing rapid change due to new discoveries contradicting the target concept which is currently used to predict dose-response relationships. Thus relatively recently discovered radiation-induced bystander effects (RIBEs), that include additional death, mutation and radio-adaptation in non-irradiated cells, change our understanding of the target concept and broadens its boundaries. This can be significant from a radioprotection point of view and also has the potential to reassess radiation damage models currently used in radiotherapy. This article reviews briefly the general concepts of RIBEs such as the proposed underlying mechanisms of signal induction and propagation, experimental approaches and biological end points used to investigate these phenomena. It also summarises several mathematical models currently proposed in an attempt to quantify RIBE. The main emphasis of this article is to review and highlight the potential impact of the bystander phenomena in radiotherapy.

Cellular automaton model of cell response to targeted radiation

It has been shown that the response of cells to low doses of radiation is not linear and cannot be accurately extrapolated from the high dose response. To investigate possible mechanisms involved in the behaviour of cells under very low doses of radiation, a cellular automaton (CA) model was created. The diffusion and consumption of glucose in the culture dish were computed in parallel to the growth of cells. A new model for calculating survival probability was introduced; the communication between targeted and non-targeted cells was also included. Early results on the response of non-confluent cells to targeted irradiation showed the capability of the model to take account for the non-linear response in the low-dose domain.

Detrimental and protective bystander effects: a model approach

This work integrates two important cellular responses to low doses, detrimental bystander effects and apoptosis-mediated protective bystander effects, into a multistage model for chromosome aberrations and in vitro neoplastic transformation: the State-Vector Model. The new models were tested on representative data sets that show supralinear or U-shaped dose responses. The original model without the new low-dose features was also tested for consistency with LNT-shaped dose responses. Reductions of in vitro neoplastic transformation frequencies below the spontaneous level have been reported after exposure of cells to low doses of low-LET radiation. In the current study, this protective effect is explained with bystander-induced apoptosis. An important data set that shows a low-dose detrimental bystander effect for chromosome aberrations was successfully fitted by additional terms within the cell initiation stage. It was found that this approach is equivalent to bystander-induced clonal expansion of initiated cells. This study is an important step toward a comprehensive model that contains all essential biological mechanisms that can influence dose-response curves at low doses.

Microdosimetric Model for the Induction of Cell Killing through Medium‐Borne Signals

Microbeam, medium-transfer and low-dose experiments have demonstrated that intercellular signals can initiate many of the same biological events and processes as direct exposure to ionizing radiation. These phenomena cast doubt on cell-autonomous modes of action and the linear, no-threshold carcinogenesis paradigm. To account for the effects of intercellular signals, new approaches are needed to relate dosimetric quantities to the emission and processing of signals by irradiated and unirradiated cells. In this paper, microdosimetric principles are used to develop a stochastic model to relate absorbed dose to the emission and processing of cell death signals by unirradiated cells. Our analyses of published results of medium transfer experiments performed using HPV-G human keratinocytes suggest that the emission of death signals is a bi-exponential function of dose with a distinct plateau in the 5- to 100-mGy range. However, the emission of death signals by HPV-G cells may not become fully saturated until the absorbed dose becomes larger than 0.6 Gy. Similar saturation effects have been observed in microbeam and medium-transfer experiments with other mammalian cell lines. The model predicts that the cell-killing effect of medium-borne death signals decreases exponentially as the absorbed dose becomes small compared to the frequency-mean specific energy per radiation event.

A model for the induction of chromosome aberrations through direct and bystander mechanisms

A state vector model (SVM) for chromosome aberrations and neoplastic transformation has been adapted to describe detrimental bystander effects. The model describes initiation (formation of translocations) and promotion (clonal expansion and loss of contact inhibition of initiated cells). Additional terms either in the initiation model or in the rate of clonal expansion of initiated cells, describe detrimental bystander effects for chromosome aberrations as reported in the scientific literature. In the present study, the SVM with bystander effects is tested on a suitable dataset. In addition to the simulation of non-linear effects, a classical dataset for neoplastic transformation in C3H 10T1/2 cells after alpha particle irradiation is used to show that the model without bystander features can also describe LNT-like dose responses. A published model for bystander induced neoplastic transformation was adapted for chromosome aberration induction and used to compare the results obtained with the different models.

Modelling radiation-induced bystander effect and cellular communication

In the last 10 years evidence has accumulated on the so-called radiation-induced 'non-targeted effects' and in particular on bystander effects, consisting of damage induction in non-irradiated cells most likely following the release of soluble factors by the irradiated ones. These phenomena were observed for different biological endpoints, both lethal and non-lethal for the cell. Although the underlying mechanisms are largely unknown, it is now widely recognised that two types of cellular communication (i.e. via gap junctions and/or release of molecular messengers into the extracellular environment) play a pivotal role. Furthermore, the effects can be significantly modulated by parameters such as cell type and cell-cycle stage, cell density, time after irradiation etc. Theoretical models and simulation codes can be of help to improve our knowledge of the mechanisms, as well as to investigate the possible role of these effects in determining deviations from the linear relationship between dose and risk which is generally applied in radiation protection. In this paper three models, including an approach under development at the University of Pavia, will be presented in detail. The focus will be on the various adopted assumptions, together with their implications in terms of non-targeted radiobiological damage and, more generally, low-dose radiation risk. Comparisons with experimental data will also be discussed.