Stochastic modelling of multistage carcinogenesis and progression of human lung cancer

In this paper, we propose a stochastic multistage model that incorporates clonal expansion of premalignant cells and mutational events. Using the age-specific lung cancer as the test system, the proposed model is used to fit the incidence data in the Surveillance, Epidemiology, and End Results (SEER) registry. We first use the model with different numbers of mutations to fit the data of all lung cancer patients. Our results demonstrate that, although from two to six driver mutations in the genome of lung stem cells are reasonable for normal lung stem cells to become a malignant cell, three driver mutations are most likely to occur in the development of lung cancer. In addition, the models are employed to fit the data of female and male patients separately. The interesting result is that, for female patient data the best fit model contains four mutations while that for male patient data is the three-stage model. Finally, robustness analysis suggests that the decrease of cell net proliferation rates is more effective than the decrease of mutation rates in reducing the lung cancer risk.