The two-stage clonal expansion model in occupational cancer epidemiology: results from three cohort studies

Abstract

Objectives The objective of this work was to apply the two-stage clonal expansion model, with the intention to expand the literature on epidemiological applications of the model and demonstrate the feasibility of incorporating biologically based modelling methods into the widely used retrospective cohort study. Methods The authors fitted the two-stage clonal expansion model model to three occupational cohort studies: (1) a cohort of textile workers exposed to asbestos and followed for lung cancer mortality; (2) a cohort of diatomaceous earth workers exposed to silica and also followed for lung cancer mortality; and (3) a cohort of automotive manufacturing workers exposed to straight metalworking fluid (MWF) and followed for larynx cancer incidence. The model allowed the authors to estimate exposure effects in three stages: cancer initiation (early effects), promotion or malignant transformation (late effects). Results In the first cohort, the authors found strong evidence for an early effect of asbestos on lung cancer risk. Findings from analyses of the second cohort suggested early and less evidently late effects of silica on lung cancer risk. In the MWF (third) cohort, there was only weak evidence of straight MWF exposure effects on both early and late stages. The authors also observed a late birth cohort effect on larynx cancer risk. Conclusions The findings for asbestos and silica were essentially confirmatory, supporting evidence for their early effects on lung cancer from a large body of literature. The effect of straight MWF on larynx cancer was less clear.

Promotion of lung adenocarcinoma following inhalation exposure to multi-walled carbon nanotubes

“…The mutagenicity of asbestos fibers is correlated with the potency as a carcinogen [73]. Evidence from epidemiological studies has demonstrated that asbestos can act as a tumor promoter at low doses as well as a tumor initiator at longer and/or higher exposure levels [74]. In several human epidemiology studies, smoking exposure and asbestos interact in a more than additive fashion in causing lung cancer [75-79].…”

Risk Analysis Implications of Dose-Response Thresholds for NLRP3 Inflammasome-Mediated Diseases: Respirable Crystalline Silica and Lung Cancer as an Example

“…Parameter values are drawn from the literature, as follows: a 1 = a 2 = 1.4E−7 is taken from McCarthy et al 60 Table 1 for lung cancer initiation and promotion rates, respectively, in nonsmokers. g = g 0 (1 + (1 − exp(−0.373 E ))) is the estimated functional dependency between exposure E and net proliferation rate g for initiated cells specified for RCS and lung cancer by Zeka et al 59 under the assumption that RCS exposure acts only as a promoter, increasing g but not the other parameters. Here, g 0 is the baseline (0-exposure) net proliferation rate for initiated cells, estimated as g 0 = 0.075 by McCarthy et al Table 1.…”

“…This contrasts with the usual modeling assumption that risk increases smoothly with exposure as in the TSCE model in Table 2. 59 However, the following considerations suggest that a discontinuous jump may be more realistic than continuous dependence of risk on exposure. The underlying epidemiological data, 73 shown in Figure 7, do not indicate that lung cancer risk is increased by exposure levels below the highest category (≥5 mg/m 3 -years); to the contrary, they appear more consistent with a U-shaped (hormetic) relationship.…”

“…Figure 8 illustrates the increase in age-specific risk (hazard rate) of lung cancer mortality for workers with and without occupational exposure that induces chronic inflammation of their lungs at age 50, assuming that the effect of the inflammation is to increase g by 20%, from 0.075 to 0.09. (In Figure 8, ages are shifted leftward by 5 years compared to those in Figure 6 because, following Zeka et al, 59 we model the lag [latency] period between creation of a malignant cell and observed lung cancer as negligible. Assuming a positive lag before lung cancer mortality would shift the age axis rightward by the amount of the lag, which is 5 years in Figure 6.)…”

“…It has recently become well understood that chronic inflammation plays an essential role in promoting RCS-induced lung cancer 14,69 and other inflammation-mediated exposure-related lung cancers, in part by promoting cell proliferation and tumor formation. 70,71 However, to our knowledge, this qualitative understanding of the role of chronic inflammation has not yet been incorporated into quantitative mathematical models of carcinogenesis such as the TSCE model (Vineis et al 71 , Zeka et al 59 for silica- and asbestos-associated lung cancer 72 and for joint carcinogenicity of radon and silica). The purpose of this section is to develop a TSCE model for RCS-induced lung cancers that incorporates the promoting role of chronic inflammation.…”