The effect of non-targeted cellular mechanisms on lung cancer risk for chronic, low level radon exposures

Author: Truta-Popa Lucia A.   Hofmann Werner   Fakir Hatim   Cosma Constantin  

Publisher: Informa Healthcare

ISSN: 1362-3095

Source: International Journal of Radiation Biology, Vol.87, Iss.9, 2011-09, pp. : 944-953

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Abstract

AbstractPurpose: The goal of the present study was to investigate the effect of non-targeted mechanisms on the shape of the lung cancer risk function at chronic, low level radon exposures relative to direct cellular radiation effects. This includes detrimental and protective bystander effects, radio-adaptive bystander response, genomic instability and induction of apoptosis by surrounding cells.Methods: To quantify the dependence of these mechanisms on dose, analytical functions were derived from the experimental evidence presently available. Alpha particle intersections of bronchial target cells during a given exposure period were simulated by a Transformation Frequency-Tissue Response (TF-TR) model, formulated in terms of cellular hits within the cycle time of the cell and then integrated over the whole exposure period.Results: In general, non-targeted effects like genomic instability and bystander effects amplify the biological effectiveness of a given radiation dose, while induction of apoptosis and adaptive response will decrease the risk values. While these observations are related to the absolute number of lung cancer cases, normalization to the epidemiologically observed risk at 0.675 Gy suggests that the effect of such mechanisms on the shape of the dose-response relationship may be different. Indeed, genomic instability and adaptive response cause a substantial reduction of the risk at low doses, while induction of apoptosis and detrimental bystander effects slightly increase the risk.Conclusions: Predictions of lung cancer risk, including these mechanisms, exhibit a distinct sublinear dose-response relationship at low exposures, particularly for very low exposure rates. However, the relatively large error bars of the epidemiological data do not currently allow the prediction of a statistically significant deviation from the Linear – No Threshold (LNT) assumption.

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