The Significance of the Bystander Effect: Modeling, Experiments, and More Modeling

Brenner, David J.

Columbia University Medical Center, Center for Radiological Research

关键词: Proliferation;    Dna Damages;    98 Nuclear Disarmament, Safeguards, And Physical Protection;    Lungs;    Cell Proliferation;   

DOI  :  10.2172/960234
RP-ID  :  DOE/ER/63226
RP-ID  :  FG02-01ER63226
RP-ID  :  960234

美国|英语

来源: UNT Digital Library

【 摘 要 】

Non-targeted (bystander) effects of ionizing radiation are caused by intercellular signaling; they include production of DNA damage and alterations in cell fate (i.e. apoptosis, differentiation, senescence or proliferation). Biophysical models capable of quantifying these effects may improve cancer risk estimation at radiation doses below the epidemiological detection threshold. Understanding the spatial patterns of bystander responses is important, because it provides estimates of how many bystander cells are affected per irradiated cell. In a first approach to modeling of bystander spatial effects in a three-dimensional artificial tissue, we assumed the following: (1) The bystander phenomenon results from signaling molecules (S) that rapidly propagate from irradiated cells and decrease in concentration (exponentially in the case of planar symmetry) as distance increases. (2) These signals can convert cells to a long-lived epigenetically activated state, e.g. a state of oxidative stress; cells in this state are more prone to DNA damage and behavior alterations than normal and therefore exhibit an increased response (R) for many end points (e.g. apoptosis, differentiation, micronucleation). These assumptions were implemented by a mathematical formalism and computational algorithms. The model adequately described data on bystander responses in the 3D system using a small number of adjustable parameters. Mathematical models of radiation carcinogenesis are important for understanding mechanisms and for interpreting or extrapolating risk. There are two classes of such models: (1) long-term formalisms that track pre-malignant cell numbers throughout an entire lifetime but treat initial radiation dose-response simplistically and (2) short-term formalisms that provide a detailed initial dose-response even for complicated radiation protocols, but address its modulation during the subsequent cancer latency period only indirectly. We argue that integrating short- and long-term models is needed. As an example of this novel approach, we integrated a stochastic short-term initiation/inactivation/repopulation model with a deterministic two-stage long-term model. Within this new formalism, the following assumptions are implemented: radiation initiates, promotes, or kills pre-malignant cells; a pre-malignant cell generates a clone, which, if it survives, quickly reaches a size limitation; the clone subsequently grows more slowly and can eventually generate a malignant cell; the carcinogenic potential of pre-malignant cells decreases with age. The effectiveness of high-LET radiation per unit dose increases as dose rate decreases. This “inverse dose rate effect” is seen in radon-induced lung carcinogenesis. We suggest a biologically-motivated mechanism based on radiation-induced direct and bystander-effect-related risks: During radon exposure, only a fraction of cells are traversed by alpha particles. These irradiated cells have an increased probability of being initiated into a pre-malignant state. They release signals, which convert some nearby unirradiated cells to an activated state. When already pre-malignant cells are activated, their proliferation (promotion) rate increases. If a radiation dose is sufficient to activate most susceptible cells, protracting the exposure does not substantially decrease the number of activated cells, but prolongs the activated state during which pre-malignant cell proliferation is accelerated. This mechanism is implemented in a low-dose-rate extension of our carcinogenesis model, which integrates both short- and long-term modeling approaches, and was applied to radiotherapy-induced second cancer risk estimation. Model predictions adequately describe the data on radon-induced lung carcinogenesis in humans and rats, using few adjustable parameters. Conclusions about the relative importance of promotion vs. initiation for radon carcinogenesis are similar to those reported with the two-stage clonal expansion model, but a mechanistic explanation for promotion is provided.