BCCRI-Smoking (BCCRI) British Columbia Cancer Research Institute

Overview

The British Columbia Cancer Research Institute Lung Cancer Natural History and Smoking Model (BCCRI-Smoking) was formerly known as the UM-LCSm Model (University of Michigan). The BCCRI-Smoking is an effective tool for evaluating lung cancer trends in the U.S. population and the effects of possible interventions.¹ The model was developed in two steps. First, a natural history model that describes lung cancer incidence or mortality as a function of smoking history and age was derived, estimating the model parameters by fitting it to prospective cohort data of smoking histories and lung cancer incidence/mortality.^2-4 Second, the natural history model was embedded in an Age-Period-Cohort (APC) population model,⁵ and relative lung cancer mortality rates by calendar year and birth cohort (period and cohort effects, respectively) were estimated by calibration against U.S. lung cancer mortality. The BCCRI-Smoking has been used to estimate the number of lung cancer deaths prevented in the U.S. due to tobacco control efforts implemented since the first Surgeon General’s Report (SGR) on smoking and health was issued in 1964.^1,4

The BCCRI-Smoking model is based on the biologically based two-stage clonal expansion (TSCE) model that relates individual smoking histories to the age-specific risk of lung cancer incidence or mortality.⁶ The TSCE model is a stochastic model that represents the process of carcinogenesis in three phases. In the first phase (initiation), a susceptible stem cell acquires one or more mutations, resulting in an initiated cell that has partially escaped growth control. In the second phase (promotion), initiated cells undergo clonal expansion, either spontaneously or in response to endogenous or exogenous promoters. Finally, in the third phase (malignant transformation), one of the initiated cells acquires further mutational changes leading to a malignant cell.

To model the effects of smoking on lung cancer risk, the model initiation, promotion, and malignant transformation parameters are assumed to be altered during periods of smoking exposure through parametric dose-response relationships. This dose-response relationship links the individual smoking history to the cell kinetic parameters in the TSCE model.

Model calibration consists of estimating dose-response parameters that best represent the effects of individual smoking histories in relation to lung cancer initiation, promotion, malignant transformation, and in turn, incidence or mortality. Using likelihood-based methods, the TSCE model was calibrated to lung cancer data in four U.S. smoking cohorts: lung cancer mortality in the American Cancer Society Cancer Prevention Studies I and II (CPS-I and CPS-II),² and lung cancer incidence and mortality in the Nurses’ Health Study (NHS) and Health Professionals’ Follow-up Study (HPFS). ^3,4

As mentioned above, the calibrated TSCE models were then embedded into an APC model, replacing the non-parametric age effects of the traditional APC models with the TSCE model hazard (i.e., modeling of age-specific incidence). Period and cohort effects were then estimated by calibrating the model predictions to lung cancer mortality in the U.S. population, using microsimulation of individual smoking histories in the U.S. from the Smoking History Generator (SHG) developed by the CISNET Lung Working Group.⁷

References

1. Moolgavkar SH, Holford TR, Levy DT, et al. Impact of reduced tobacco smoking on lung cancer mortality in the United States during 1975-2000. J Natl Cancer Inst. 2012;104(7):541-548.
2. Hazelton WD, Clements MS, Moolgavkar SH. Multistage Carcinogenesis and Lung Cancer Mortality in Three Cohorts. Cancer Epidemiol Biomarkers Prev. 2005;14(5):1171-1181.
3. Meza R, Hazelton WD, Colditz GA, Moolgavkar SH. Analysis of lung cancer incidence in the Nurses' Health and the Health Professionals' Follow-Up Studies using a multistage carcinogenesis model. Cancer Causes Control. 2008;19(3):317-328.
4. Hazelton WD, Jeon J, Meza R, Moolgavkar SH. The FHCRC lung cancer model. Risk Analysis. 2012;32(S1):s99-s116.
5. Holford TR. The estimation of age, period and cohort effects for vital rates. Biometrics. 1983;39(2):311-324.
6. Moolgavkar SH, Knudson AG, Jr. Mutation and cancer: a model for human carcinogenesis. J Natl Cancer Inst. 1981;66(6):1037-1052.
7. Jeon J, Meza R, Krapcho M, Clarke LD, Byrne J, Levy DT. Chapter 5: Actual and counterfactual smoking prevalence rates in the U.S. population via microsimulation. Risk Anal. 2012;32 Suppl 1:S51-68.