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