Cardiovascular disease mortality of A-bomb survivors and the healthy survivor selection effect

The latest A-bomb survivor data for cardiovascular diseases are analysed to investigate whether in the first years after the bombings the baseline rates of proximal survivors were markedly different compared with those of the distal survivors. This phenomenon relates to a healthy survivor selection effect. This question is important for the decision whether to include or exclude the early years of follow-up when analysing the biological effects from acute low and high dose exposures following the nuclear weapons explosions in Hiroshima and Nagasaki. The present study shows that for cerebrovascular diseases and heart diseases the baseline rates are not significantly different in the first two decades of follow-up. Thus, for these two detrimental health outcomes, there is no need to exclude distal survivors and the first decades of follow-up time when investigating the shapes of the related dose-responses.

Dose-responses from multi-model inference for the non-cancer disease mortality of atomic bomb survivors

The non-cancer mortality data for cerebrovascular disease (CVD) and cardiovascular diseases from Report 13 on the atomic bomb survivors published by the Radiation Effects Research Foundation were analysed to investigate the dose-response for the influence of radiation on these detrimental health effects. Various parametric and categorical models (such as linear-no-threshold (LNT) and a number of threshold and step models) were analysed with a statistical selection protocol that rated the model description of the data. Instead of applying the usual approach of identifying one preferred model for each data set, a set of plausible models was applied, and a sub-set of non-nested models was identified that all fitted the data about equally well. Subsequently, this sub-set of non-nested models was used to perform multi-model inference (MMI), an innovative method of mathematically combining different models to allow risk estimates to be based on several plausible dose-response models rather than just relying on a single model of choice. This procedure thereby produces more reliable risk estimates based on a more comprehensive appraisal of model uncertainties. For CVD, MMI yielded a weak dose-response (with a risk estimate of about one-third of the LNT model) below a step at 0.6 Gy and a stronger dose-response at higher doses. The calculated risk estimates are consistent with zero risk below this threshold-dose. For mortalities related to cardiovascular diseases, an LNT-type dose-response was found with risk estimates consistent with zero risk below 2.2 Gy based on 90% confidence intervals. The MMI approach described here resolves a dilemma in practical radiation protection when one is forced to select between models with profoundly different dose-responses for risk estimates.

Systems biological and mechanistic modelling of radiation-induced cancer

This paper summarises the five presentations at the First International Workshop on Systems Radiation Biology that were concerned with mechanistic models for carcinogenesis. The mathematical description of various hypotheses about the carcinogenic process, and its comparison with available data is an example of systems biology. It promises better understanding of effects at the whole body level based on properties of cells and signalling mechanisms between them. Of these five presentations, three dealt with multistage carcinogenesis within the framework of stochastic multistage clonal expansion models, another presented a deterministic multistage model incorporating chromosomal aberrations and neoplastic transformation, and the last presented a model of DNA double-strand break repair pathways for second breast cancers following radiation therapy.

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.

Low-LET-induced radioprotective mechanisms within a stochastic two-stage cancer model

A stochastic two-stage cancer model with clonal expansion was used to investigate the potential impact on human lung cancer incidence of some aspects of the hormesis mechanisms suggested by Feinendegen (Health Phys. 52 663-669, 1987). The model was applied to low doses of low-LET radiation delivered at low dose rates. Non-linear responses arise in the model because radiologically induced adaptations in radical scavenging and DNA repair may reduce the biological consequences of DNA damage formed by endogenous processes and ionizing radiation. Sensitivity studies were conducted to identify critical model inputs and to help define the changes in cellular defense mechanisms necessary to produce a lifetime probability for lung cancer that deviates from a linear no-threshold (LNT) type of response. Our studies suggest that lung cancer risk predictions may be very sensitive to the induction of DNA damage by endogenous processes. For doses comparable to background radiation levels, endogenous DNA damage may account for as much as 50 to 80% of the predicted lung cancers. For an additional lifetime dose of 1 Gy from low-LET radiation, endogenous processes may still account for as much as 20% of the predicted cancers (Fig. 2). When both repair and scavengers are considered as inducible, radiation must enhance DNA repair and radical scavenging in excess of 30 to 40% of the baseline values to produce lifetime probabilities for lung cancer outside the range expected for endogenous processes and background radiation.

Analysis of epidemiological cohort data on smoking effects and lung cancer with a multi-stage cancer model

A stochastic two-stage cancer model is used to analyse the relation between lung cancer and cigarette smoking. The model contains the main rate-limiting stages of carcinogenesis, which include initiation, promotion (clonal expansion of initiated cells), malignant transformation and a lag time for tumour formation. Various data sets were used to test the model. These include the data of a large prospective collaborative project carried out in 10 different European countries, the European Prospective Investigation into Cancer and Nutrition (EPIC). This new data set has not been modelled before. The model is also tested on other published data from CPS-II (Cancer Prevention Study II) of the American Cancer Society and the British doctors' study. The analyses indicate that the EPIC data are best described with smoking dependence on the rates of malignant transformation and clonal expansion. With increasing smoking rates, saturation effects in the two exposure rate-dependent model parameters were observed. The results find confirmation in the biological literature, where both mutational effects and promotional effects of cigarette smoke are documented.

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.

A model for low dose effects of low-LET radiation delivered at high dose rates

In vitro studies show that protective tumour-reducing effects occur for low dose rates (mGy per minute). To account for these phenomena, we have previously developed stochastic and deterministic multi-stage cancer models that include radiation-induced adaptations in DNA repair processes and radical scavenging. Here, these models are extended to account for the induction of radioprotective mechanisms for low doses of low LET radiation delivered at high dose rates. Cellular adaptations in DNA repair are related to temporal changes in the amount of DNA damage in a cell. The combined effects of endogenous DNA damage, background radiation and artificial irradiation are considered.

An examination of radiation hormesis mechanisms using a multistage carcinogenesis model

A multistage cancer model that describes the putative rate-limiting steps in carcinogenesis is developed and used to investigate the potential impact on cumulative lung cancer incidence of the hormesis mechanisms suggested by Feinendegen and Pollycove. In the model, radiation and endogenous processes damage the DNA of target cells in the lung. Some fraction of the misrepaired or unrepaired DNA damage induces genomic instability and, ultimately, leads to the accumulation of malignant cells. The model explicitly accounts for cell birth and death processes, the clonal expansion of initiated cells, malignant conversion, and a lag period for tumor formation. Radioprotective mechanisms are incorporated into the model by postulating dose and dose-rate-dependent radical scavenging. The accuracy of DNA damage repair also depends on dose and dose rate. As currently formulated, the model is most applicable to low-linear-energy-transfer (LET) radiation delivered at low dose rates. Sensitivity studies are conducted to identify critical model inputs and to help define the shapes of the cumulative lung cancer incidence curves that may arise when dose and dose-rate-dependent cellular defense mechanisms are incorporated into a multistage cancer model. For lung cancer, both linear no-threshold (LNT-), and non-LNT-shaped responses can be obtained. If experiments demonstrate that the effects of DNA damage repair and radical scavenging are enhanced at least three-fold under low-dose conditions, our studies would support the existence of U-shaped responses. The overall fidelity of the DNA damage repair process may have a large impact on the cumulative incidence of lung cancer. The reported studies also highlight the need to know whether or not (or to what extent) multiply damaged DNA sites are formed by endogenous processes. Model inputs that give rise to U-shaped responses are consistent with an effective cumulative lung cancer incidence threshold that may be as high as 300 mGy (4 mGy per year for 75 years) for low-LET radiation.

Respiratory tract deposition efficiencies: evaluation of effects from smoke released in the Cerro Grande forest fire

Forest-fire smoke inhaled by humans can cause various health effects. This smoke contains toxic chemicals and naturally occurring radionuclides. In northern New Mexico, a large wildfire occurred in May 2000. Known as the Cerro Grande Fire, it devastated the town of Los Alamos and damaged Los Alamos National Laboratory (LANL). Residents were concerned about the possible dissemination of radionuclides from LANL via smoke from the fire. To evaluate potential health effects of inhaling radionuclides contained in the smoke from the Cerro Grande Fire, it was first necessary to evaluate how much smoke would deposit in the human respiratory tract. The purpose of this study was to evaluate respiratory-tract deposition efficiencies of airborne forest-fire smoke for persons of different ages exposed while inside their homes. Potential non-radiological health effects of a forest fire are reviewed. The deposition efficiencies presented can be used to evaluate in-home smoke deposition in the respiratory tract and expected radionuclide intake related to forest fires. The impact of smoke exposure on firemen fighting a forest fire is quantitatively discussed and compared. They primarily inhaled forest-fire smoke while outdoors where the smoke concentration was much higher than inside. Radionuclides released at the LANL site via the Cerro Grande Fire were restricted to naturally occurring radionuclides from burning trees and vegetation. Radiation doses from inhaled airborne radionuclides to individuals inside and outside the Los Alamos area were likely very small.

Interaction of radiation and smoking in lung cancer induction among workers at the Mayak nuclear enterprise

For radiation-related cancer risk evaluation, it is important to assess not only influences of individual risk factors but also their interactive effects (e.g., additive, multiplicative, etc.). Multivariate analysis methods adapted for interactive effects allow such assessments. We have used a multivariate analysis approach to investigate the pair-wise interactions of the previously identified three main etiological factors for lung cancer induction in Russian workers of the Mayak Production Association (PA) nuclear enterprise. These three factors are as follows: (1) body burden of inhaled plutonium-239 (239Pu), an influence on absorbed alpha-radiation dose; (2) cumulative, absorbed external gamma-radiation dose to the lung; and (3) level of cigarette smoking as indicated by a smoking index (SI). The SI represents the cigarettes smoked per day times years smoking. The Mayak PA workers were exposed by inhalation to both soluble and insoluble forms of 239Pu. Based on a cohort of 4,390 persons (77% male), we conducted a nested, case-control study of lung cancer induction using 486 matched cases and controls. Each case was matched to two controls. Matching was based on five factors: sex, year of birth, year work began, profession, and workplace. Three levels of smoking were considered: low (SI = 1 to 499), used as a reference level; middle (SI = 500 to 900); and high (SI = 901 to 2,000). For lung cancer induction, a supra-multiplicative effect was demonstrated for high external gamma-ray doses (> 2.0 Gy) plus high 239Pu intakes (body burden >2.3 kBq). This observation is consistent with the hypothesis of curvilinear dose-response relationships for lung cancer induction by high- and low-LET radiations. The interaction between radiation (external gamma rays or 239Pu body burden) and cigarette smoke was found to depend on the smoking level. For the middle level of smoking in combination with gamma radiation (> 2.0 Gy) or 239Pu body burden (> 2.3 kBq), results were consistent with additive effects. However, for the high level of smoking in combination with gamma radiation (> 2.0 Gy) or 239Pu body burden (> 2.3 kBq), results were consistent with the occurrence of multiplicative effects. These results indicate that low-dose risk estimates for radiation-induced lung cancer derived without adjusting for the influence of cigarette smoking could be greatly overestimated. Further, such systematic error may considerably distort the shape of the risk vs. dose curve and could possibly obscure the presence of a dose threshold for radiation-induced lung cancer.

Explanation of protective effects of low doses of gamma-radiation with a mechanistic radiobiological model

PURPOSE

To test whether data that show protective effects of low doses against spontaneous neoplastic transformation of C3H 10T1/2 cells can be explained with a biomathematical model that includes radioprotective mechanisms. To link important features of the model to known biological processes.

MATERIALS AND METHODS

The model simulates double-strand break formation in transcriptionally active and in bulk DNA, translocation of DNA segments, and the fixation of damage at mitosis; promotion is also included. The model equations were solved numerically using a stiff solver.

RESULTS

The data were successfully simulated by the model: cell transformation-reducing effects of low doses of gamma-radiation delivered at low dose-rates are explained by radiation-inducible DNA repair and enzymatic scavenging.

CONCLUSIONS

The model successfully simulates experimental data. The highly nonlinear features of the data point to a nonlinear dose-effect relationship at low doses and indicate that linear extrapolation from moderate (or high) to low doses and dose-rates may not be justified for in vitro studies of the cell line under consideration.

Testing extrapolation of a biologically based exposure-response model from in vitro to in vivo conditions

Models of carcinogenesis may become so flexible as to preclude the possibility of being falsified by data. This problem is removed in part by stronger biophysical specification of processes and parameters within the model prior to fitting to in vivo data on the relationship between exposure and cancer incidence. This paper explores the use of a biophysical model of chromosomal damage, cellular transformation, repair, mitosis, initiation, promotion, progression, and cytotoxicity in developing exposure-response models for radiation-induced cancer. Many of the aspects of model form and parameter values are developed from in vitro data, and the model then is extrapolated to the in vivo setting using a dosimetric model to account for dose inhomogeneity within the lung tissue of rats exposed to radon progeny in air. The ability of the model to predict cancer incidence in the rats is assessed and is shown to be problematic at higher doses. This calls into question whether a full claim may be made about the ability of first-principle models to fully constrain models applied to in vivo data at present. Possible explanations for the discrepancy, and implications for extrapolation, are provided.

Application of Bayesian inference to characterize risks associated with low doses of low-LET radiation

Improved risk characterization for stochastic biological effects of low doses of low-LET radiation is important for protecting nuclear workers and the public from harm from radiation exposure. Here we present a Bayesian approach to characterize risks of stochastic effects from low doses of low-LET radiation. The stochastic effect considered is neoplastic transformation of cells because it relates closely to cancer induction. We have used a published model of neoplastic transformation called NEOTRANS1. It is based on two different classes of cellular sensitivity for asynchronous, exponentially growing populations (in vitro). One sensitivity class is the hypersensitive cell; the other is the resistant cell. NEOTRANS1 includes the effects of genomic damage accumulation, DNA repair during cell cycle arrest, and DNA misrepair (non-lethal repair errors). The model-associated differential equations are solved for conditions of in vitro irradiation at a fixed rate. Previously published solutions apply only to high dose rates and were incorrectly assumed to apply to only high-LET radiation. Solutions provided here apply to any fixed dose rate and to both high- and low-LET radiations. Markov chain Monte Carlo methods are used to carry out the Bayesian inference of the low-dose risk for neoplastic transformation of aneuploid C3H 10T1/2 cells for X-ray doses from 0 to 1000 mGy. We have assumed that for this low-dose range only the hypersensitive fraction of the cells are affected. Our results indicate that the initial slope of the risk vs dose relationship for neoplastic transformation is as follows: (1) directly proportional to the fraction, f1, of hypersensitive cells; (2) directly proportional to the radiosensitivity of the genomic target; and (3) inversely proportional to the rate at which hypersensitive cells with radiation-induced damage are committed to undergo correct repair of genomic damage. Further, our results indicate that very fast molecular events are associated with the commitment of cells to the correct repair pathway. Results also indicate a relatively large probability for misrepair that leads to genomic instability. Our results are consistent with the view that for very low doses, dose rate is not an important variable for characterizing low-LET radiation risks so long as age-related changes in sensitivity do not occur during irradiation.

Significance of cell-cycle delay, multiple initiation pathways, misrepair and replication errors in a model of radiobiological effects

PURPOSE

To advance a biomathematical model of radiocarcinogenesis by describing multiple pathways for initiation, a radiologically induced cell-cycle delay, misrepair and spontaneous DNA damages caused by replication. It was investigated whether the incorporation of these biological features would improve the fit of the model to data showing plateaus in in vitro irradiations of different cell lines and whether the fit parameters were then more biologically realistic.

MATERIALS AND METHODS

A biomathematical submodel was developed based on a previous State-Vector Model that mathematically described enhanced DNA repair and radical scavenging following irradiation.

RESULTS

With the two initiation pathways and cell-cycle delay, the simulations better explained the mouse data but not the rat data, and for both data sets the fit parameters were biologically more realistic than previously assumed. Inclusion of misrepair and replicational errors did not significantly affect the fit.

CONCLUSIONS

A plateau in the dose-effect relationship for in vitro irradiation of different cell lines can be explained by radioprotective mechanisms. The plateau-type dose-response relationships point to a non-linear dose- effect relationship at low doses and indicate that linear extrapolation from moderate (or high) to low doses may not be justified for in vitro studies of these cell lines.

Introducing Biological Microdosimetry for Ionising Radiation

Microdosimetry is important for radiation protection, for understanding mechanisms of radiation action, and for radiation risk assessment. This article introduces a generic, Monte Carlo based approach to biological microdosimetry for ionising radiation. Our Monte Carlo analyses are carried out with a widely used Crystal Ball software. The approach to biological microdosimetry presented relates to quantal biological effects data (e.g. cell survival, mutagenesis, neoplastic transformation) for which there is an initial linear segment to the dose-response curve. The macroscopic dose data considered were selected such that is could be presumed that the vast majority of cells at risk have radiation dose delivered to their critical target. For cell killing, neoplastic transformation, and mutagenesis, the critical biological target for radiation is presumed to be DNA. Our approach to biological microdosimetry does not require detailed information about the mass, volume, and shape of the critical biological target. Further, one does not have to know what formal distribution function applies to the microdose distribution. However, formal distributions are required for the biological data used to derive the non-parametric microdose distributions. Here, we use the binomial distribution to characterise the variability in the number of cells affected by a fixed macroscopic dose. Assuming this variability to arise from variability in the microscopic dose to the critical biological target, a non-parametric microdose distribution is generated by the standard Monte Carlo method. The non-parametric distribution is then fitted using a set of formal distributions (beta, exponential, extreme value, gamma, logistic, log-normal, normal, Pareto, triangular, uniform, and Weibull). The best fit is then evaluated based on statistical criteria (chi-square test). To demonstrate the application of biological microdosimetry, the standard Monte Carlo method is used with radiobiological data for cell survival after exposure in vitro to alpha radiation in order to generate presumed distributions of the microscopic dose to the critical target (DNA). Results are presented indicating that for a low macroscopic alpha radiation dose to a large group of C3H 10T1/2 cells, the microdose distribution over critical targets of individual cells that receive radiation microdoses may be adequately characterised using a log-normal or gamma distribution.

Adaptive response and dose-response plateaus for initiation in a state-vector model of carcinogenesis

PURPOSE

To investigate whether it is possible to explain dose-response plateaus for in-vitro X-ray irradiation of different cell lines with radioprotective mechanisms such as radiologically induced expression of scavengers and repair enzymes.

MATERIALS AND METHODS

A biomathematical model was developed based on a previous state-vector model. New features of the model are a mathematical description of enhanced repair and radical scavenging as a result of irradiation.

RESULTS

The model produces a plateau in the dose-response for in-vitro tranformations between 0.5 and 1 Gy and for chromosome aberrations and it predicts an inverse-fractionation effect within a selected range of doses.

CONCLUSIONS

Adaptive response mechanisms within a state-vector model provide a coherent explanation of the dose-response characteristics for in-vitro transformations and chromosomal aberrations. These results suggest the need for new experimental studies described in the paper.

Modeling radioprotective mechanisms in the dose effect relation at low doses and low dose rates of ionizing radiation

A new model (Random Coincidence Model--Radiation Adapted (RCM-RA)) is proposed which explains a possible pseudo threshold for stochastic radiation effects. It describes the formation of cancer in the case of multistep fixation of lesions in the critical regions of tumor associated genes such as proto-oncogenes or tumor-suppressor genes. The RCM-RA contains two different possibilities of DNA damage to complementary nucleotides. The damage may be caused either by radiation or by natural processes such as cellular radicals or thermal damage or by chemical cytotoxins. The model is based on the premise that radiation initially is bionegative, damaging organisms at their different levels of organization. The radiation, however, also induces various cellular radioprotective mechanisms which decrease the damage by natural processes. Considering both effects together, the theory explains apparent thresholds in the dose-response relation for radiation carcinogenesis without contradiction to the classical assumption that radiation is predominantly bionegative at doses typically found in occupational exposures.