Beyond two-stage models for lung carcinogenesis in the Mayak workers: Implications for Plutonium risk

Mechanistic multi-stage models are used to analyze lung-cancer mortality after Plutonium exposure in the Mayak-workers cohort, with follow-up until 2008. Besides the established two-stage model with clonal expansion, models with three mutation stages as well as a model with two distinct pathways to cancer are studied. The results suggest that three-stage models offer an improved description of the data. The best-fitting models point to a mechanism where radiation increases the rate of clonal expansion. This is interpreted in terms of changes in cell-cycle control mediated by bystander signaling or repopulation following cell killing. No statistical evidence for a two-pathway model is found. To elucidate the implications of the different models for radiation risk, several exposure scenarios are studied. Models with a radiation effect at an early stage show a delayed response and a pronounced drop-off with older ages at exposure. Moreover, the dose-response relationship is strongly nonlinear for all three-stage models, revealing a marked increase above a critical dose

Key parameter estimates for the two-stage model, including 95%-level uncertainties.

<p>For definitions and interpretation of the parameters used, see text. (Note that only certain rate combinations can be determined from the hazard.)</p

Same as Table 3, but for the parameters of the best three-stage models.

<p>Same as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126238#pone.0126238.t003" target="_blank">Table 3</a>, but for the parameters of the best three-stage models.</p

Schematic structure of a <i>k</i>-stage model (top) and the two-path model studied in this paper (below).

<p>Here, <i>X</i>_<i>j</i> denotes the stochastic number of cells at stage <i>j</i>, with arrows indicating transitions at rates <i>μ</i>_<i>j</i>, etc. (see text). Cancer is assumed to occur once the first malignant cell appears, with latency period <i>t</i>_lag ∼ 5 years.</p

Age-dependent excess relative risk (ERR/<i>D</i>) of different multi-stage models, for smokers with constant exposure between ages 25 and 60 (<i>D</i> = 0.5Gy).

<p>For comparison, the non-significant trend in the descriptive model (thin-dotted line) is also shown. (All error bars are at 95% confidence level.)</p

Observed numbers of lung-cancer cases by dose categories, compared with those predicted by the descriptive and multi-stage models (in brackets: excess cases).

<p>As a reference, we also give the person years (py), i.e., the number of years spent in the respective (sub)cohort summed over all persons.</p

Synopsis of the best models in this study, along with figures of merit for their goodness of fit (see text for details).

<p>The columns labeled <i>d</i> and <i>s</i> indicate the model’s parametric dependence on dose(rate) and smoking-related confounders; e.g., <i>γ</i> = <i>γ</i>⁽⁰⁾(<i>s</i>) + <i>δγ</i>(<i>d</i>) for the TSCE model.</p

Dependence of the proliferation rate, <i>γ</i>(<i>d</i>), on internal (lung) dose rate in the TSCE model.

<p>For comparison, the dots illustrate the results of a categorical fit (with 95%-level error bars).</p