Extension of a generalized state-vector model of radiation carcinogenesis to consideration of dose rate

A computational model for radiation-induced cellular transformation to in vitro irradiation of cells by acute doses of X-rays

This research incorporates new biological concepts to improve the predictive ability of a state-vector model with respect to dose-response data on in vitro oncogenic transformation, including mechanisms of DNA damage, DNA repair, cell death, cell proliferation and intercellular communication. Experimentally recognized biological processes, including background transformation, compensatory proliferation and bystander cell-killing effect were formulated mathematically and included as model parameters. These were then adjusted with an optimization method to reproduce in vitro transformation frequency data from C3H10T1/2 mouse cells exposed to acute doses of X-rays. A plateau observed in the data at low doses is reproduced well and a dose-dependent increase above 1 Gy is predicted almost precisely. Extension of the model predictions to the dose range 0-100 mGy indicates that transformation frequencies are practically constant over this low dose region. Results suggest a protective, rather than detrimental, bystander cell-killing effect. Further analysis of model sensitivity to this bystander parameter, though, revealed uncertainties with respect to its biological plausibility in the model.

Modeling Intercellular Interactions during Carcinogenesis

By modulating the microenvironment of malignant or premalignant cells, inhibitory or stimulatory signals from nearby cells can play a key role in carcinogenesis. However, current commonly used quantitative models for induction of cancers by ionizing radiation focus on single cells and their progeny. Intercellular interactions are neglected or assumed to be confined to unidirectional radiation bystander effect signals from cells of the same tissue type. We here formulate a parsimoniously parameterized two-stage logistic (TSL) carcinogenesis model that incorporates some effects of intercellular interactions during the growth of premalignant cells. We show that for baseline tumor rates, involving no radiation apart from background radiation, this TSL model gives acceptable fits to a number of data sets. Specifically, it gives the same baseline hazard function, using the same number of adjustable parameters, as does the commonly used two-stage clonal expansion (TSCE) model, so it is automatically applicable to the many data sets on baseline cancer that have been analyzed using the TSCE model. For perturbations of baseline rates due to radiation, the models differ. We argue from epidemiological and laboratory evidence, especially results for the atomic bomb survivors, that for radiation carcinogenesis the TSL model gives results at least as realistic as the TSCE or similar models, despite involving fewer adjustable parameters in many cases.

Mechanistic basis for nonlinear dose-response relationships for low-dose radiation-induced stochastic effects

The linear nonthreshold (LNT) model plays a central role in low-dose radiation risk assessment for humans. With the LNT model, any radiation exposure is assumed to increase one's risk of cancer. Based on the LNT model, others have predicted tens of thousands of deaths related to environmental exposure to radioactive material from nuclear accidents (e.g., Chernobyl) and fallout from nuclear weapons testing. Here, we introduce a mechanism-based model for low-dose, radiation-induced, stochastic effects (genomic instability, apoptosis, mutations, neoplastic transformation) that leads to a LNT relationship between the risk for neoplastic transformation and dose only in special cases. It is shown that nonlinear dose-response relationships for risk of stochastic effects (problematic nonlethal mutations, neoplastic transformation) should be expected based on known biological mechanisms. Further, for low-dose, low-dose rate, low-LET radiation, large thresholds may exist for cancer induction. We summarize previously published data demonstrating large thresholds for cancer induction. We also provide evidence for low-dose-radiation-induced, protection (assumed via apoptosis) from neoplastic transformation. We speculate based on work of others (Chung 2002) that such protection may also be induced to operate on existing cancer cells and may be amplified by apoptosis-inducing agents such as dietary isothiocyanates.

Analysis of radon-induced lung cancer risk by a stochastic state-vector model of radiation carcinogenesis

A biologically based state-vector model (SVM) of radiation carcinogenesis has been extended to incorporate stochasticity of cellular transitions and specific in vivo irradiation conditions in the lungs. Dose-rate-dependent cellular transitions related to the formation of double-stranded DNA breaks, repair of breaks, interactions (translocations) between breaks, fixation of breaks, cellular inactivation, stimulated mitosis and promotion through loss of intercellular communication are simulated by Monte Carlo methods. The stochastic SVM has been applied to the analysis of lung cancer incidence in uranium miners exposed to alpha-emitting radon progeny. When incorporating in vivo features of cell differentiation, stimulated cell division and heterogeneity of cellular doses into the model, excellent agreement between epidemiological data and modelling results could be obtained. At low doses, the model predicts a nonlinear dose-response relationship; e.g., computed lung cancerrisk at 20WLM is about half of current lung cancer estimates based on the linear hypothesis. The model also predicts a slight dose rate effect; e.g., at a cumulative exposure of 20 WLM, calculated lung cancer incidence for an exposure rate 0.27 WLM/year (assuming an exposure time of 73 years) is smaller by a factor of 1.2 than that for an exposure rate of 10 WLM/year.

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.

Modeling energy deposition and cellular radiation effects in human bronchial epithelium by radon progeny alpha particles

Energy deposition and cellular radiation effects arising from the interaction of single 218Po and 214Po alpha particles with basal and secretory cell nuclei were simulated for different target cell depths in the bronchial epithelium of human airway generations 2, 4, 6, and 10. To relate the random chord lengths of alpha particle tracks through spherical cell nuclei to the resulting biological endpoints, probabilities per unit track length for different cellular radiation effects as functions of LET were derived from in vitro experiments. The radiobiological data employed in the present study were inactivation and mutation (mutant frequency at the HPRT gene) in V79 Chinese hamster cells and inactivation and transformation in C3H 10T1/2 cells. Based on computed LET spectra and relative frequencies of target cells, probabilities for transformation, mutation, and cell killing in basal and secretory cells were computed for a lifetime exposure of 20 WLM. While predicted transformation probabilities were about two orders of magnitude higher than mutation probabilities, they were still about two orders of magnitude lower than inactivation probabilities. Furthermore transformation probabilities for basal cells are generally higher than those for secretory cells, and 214Po alpha particles are primarily responsible for transformations in bronchial target cells.

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.