Do Low Dose-Rate Bystander Effects Influence Domestic Radon Risks?

David J. Brenner, in collaboration with Rainer K. Sachs1

Radon risks derive from exposure of bronchio-epithelial cells to high-LET alpha particles. Alpha particle exposure can result in bystander effects, where irradiated cells emit signals resulting in damage to nearby unirradiated bystander cells. This can result in non-linear dose-response relations, and inverse dose-rate effects. Domestic radon risk estimates are currently extrapolated from miner data which are at both higher doses and higher dose rates, so bystander effects on unhit cells could play a large role in the extrapolation of risks from mines to homes. We therefore extend an earlier quantitative mechanistic model of bystander effects to include protracted exposure, with the aim of quantifying the significance of the bystander effect for very prolonged exposures.

A model of high-LET bystander effects, originally developed to analyze oncogenic transformation in vitro (1), has been extended to low dose rates. The model considers radiation response as a superposition of bystander and linear direct effects. It attributes bystander effects to a small subpopulation of hypersensitive cells, with the bystander contribution dominating the direct contribution at very low acute doses but saturating as the dose increases. Inverse dose-rate effects are attributed to replenishment of the hypersensitive subpopulation during prolonged irradiation. The essential features of the approach are summarized in Figure 1.

Fig. 1. Cartoon illustrating the main results regarding the interplay of risk between dose and dose rate: The small boxes represent collective, supra-cellular targets, defined by the property that a hit on any target cell nucleus in the collective target results in bystander signal to all cells in that collective target. Our estimates suggest ~50 target cells / collective target, but, for clarity, each collective target is shown as containing just two cells. In a few cases, a collective target may contain a hypersensitive cell, shown here as solid. The average number of alpha particle hits is labeled D in this cartoon to emphasize its proportionality to dose, and the bystander response – number of hit hypersensitive cells, is labeled R.

A. Panel represents a dose which is "very low" in the sense that most collective targets are not hit, and the chance for two alpha particles in one collective target is negligible. The bystander response is 1.

B. To illustrate the effect of dose rate on very-low dose risks, we split the same very low dose into two separate fractions. The pattern of hypersensitive cells can change between fractions, but it is seen that a very low total dose will produce the same average response, R. Thus at very low doses, inverse dose-rate effects are negligible.

C and D. Here the dose is twice as large, but it remains low in the sense that the chance of 2 hits per collective target is negligible. In agreement with general microdosimetric arguments, the response is also doubled, i.e. is linearly proportional to dose, and dose-rate effects remain negligible.

E. At a high acute dose, the chance of more than one alpha particle per collective target is no longer negligible and this panel represents the case where an average of 4 hits occurs per collective target. For acute doses, 4 alpha particles in one collective target are no more effective, in terms of the bystander response, than one alpha particle; the bystander response therefore increases less rapidly than linearly with dose because of "saturation" – some of the alpha particles are "wasted."

F. If the high dose is split into two fractions separated by a time interval (long enough for hypersensitive cells to be replaced), the response is doubled, i.e. there is an inverse dose-rate effect at high doses.

Overall, comparing panel E with panel B shows that a linear extrapolation of risk from a high acute dose to low dose and low dose rate may underestimate this risk, in this schematic case by a factor of 4, due to saturation and to inverse dose-rate effects in the bystander response.

The model was fitted to dose- and dose-rate dependent radon-exposed miner data (2), and gives a reasonable fit to the data (Fig. 2). Parameters from the fit suggest that one directly-hit target bronchio-epithelial cell can send bystander signals to about 50 neighboring target cells.

Fig. 2

The estimated parameter values from this fit were used to extrapolate the miner data to lower doses, and for a 60-year exposure period. The results are shown in Fig. 3: For the comparatively short miner exposures (solid curve; for illustrative purposes, we use a duration of 6 y, the average time of miner exposure in the data), the dose-response relation is linear at very high doses (where the direct effect dominates). It can be seen, however, that at intermediate doses, where the bystander response starts to become important, the 6-yr exposure (solid) curve become non linear and curves downwards. At these intermediate doses the risks from a 6-yr exposure (dashed line) are larger than for a 60-yr exposure (solid line) – the inverse dose-rate effect. At still lower doses, dose rate effects become small, so the 6-yr exposure and the 60-yr-exposure produce the same risk.

Fig. 3

Figure 3 also shows a linear extrapolation of the miner data in which the effects of dose rate are ignored. It can be seen that ignoring dose-rate effect and simply using a linear extrapolation from the miner to the domestic situation would result (using our estimated parameters) in an underestimation of the low-dose radon risk by about a factor of 4.5. This underestimation is comparable to the corresponding empirically estimated factor in the BEIR-VI report of ~3.7.

For comparison, also plotted in Fig. 3 are results from various domestic radon case-control studies. The spread and uncertainties of the results are such that they are consistent with both the current mechanistically-based low-dose-rate / low-dose extrapolation, the BEIR-VI phenomenological low-dose-rate / low-dose extrapolation, and also the "naïve" low-dose extrapolation from miner data which ignores the effect of dose rate. It is important, however, to note that these data typically represent above-average cumulative radon exposures, and that, assuming low-dose linearity, most radon-related deaths will be at still lower cumulative exposures.

The main conclusions of this analysis are as follows:

  1. At high doses, the model predicts saturation effects and inverse dose-rate effects in the bystander response. At sufficiently low doses, in agreement with general microdosimetric arguments, the predicted response is linear in dose and independent of dose-rate.

  2. Parameter estimates based on applying the model to dose- and dose-rate dependent miner data suggest that a single directly-hit target bronchial basal cell can send bystander signals to about 50 neighboring cells.

  3. The model parameter values obtained from this analysis of epidemiological data, in as much as they can be compared with parameter values obtained from in-vitro analyses, are significantly different. Thus model parameters estimated from analysis of in-vitro studies cannot necessarily be applied to the in-vivo situation.

  4. The high-dose saturation and inverse dose-rate effects in the bystander response suggest that a linear extrapolation from miner data which does not properly take into account dose rate effects would underestimate the domestic radon risk by about a factor of four – a value comparable to the empirical estimate applied in the recent BEIR-VI report on radon risk estimation.

It is important to stress that we have in no sense "proven" the relevance of bystander phenomena to low-dose radon risks; rather we have described a mechanistic model which is parsimonious in its number of parameters (four parameters, making the model potentially highly testable), and which is consistent with a large body of epidemiological and laboratory data.

In conclusion, bystander effects represent a plausible quantitative and mechanistic explanation of inverse dose-rate effects by high-LET radiation, resulting in dose-response relations which are non linear and which feature a complex interplay between the effects of dose and exposure time. The model presented here provides a potential mechanistic underpinning for the empirical exposure-time correction factors applied in the recent BEIR-VI report on domestic radon risk estimation.

References

  1. Brenner DJ, Little JB, Sachs RK, The bystander effect in radiation oncogenesis, II. A quantitative model, Radiation Research 155:402-408, 2001.

  2. Lubin JH et al, Radon-exposed underground miners and inverse dose-rate (protraction enhancement) effects, Health Physics 69:494-500, 1995.


1. University of California, Berkeley, Ca.

 
 

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