Lung Cancer Model: Risk, Progression and Intervention

描述(由申请者提供)：在前一个CISNet资助期，我们开发了两个相辅相成的肺癌模型：(I)致癌模型，扩展到包括遗传易感性和吸烟模式的影响；(Ii)进展、检测和治疗模型，基于随机肿瘤生长和随机阶段转换。计划中的研究将主要信任两个目标：目的1.确定干预措施对人群的影响，如：(A)戒烟和预防吸烟；(B)通过在高危人群中使用螺旋CT进行定期筛查，及早发现肺癌，然后进行治疗，(C)生活方式干预(如饮食干预)，消除暴露(ETS、石棉、氡)。目的2.预测尚未开发的新干预措施对人群的影响，如对重度吸烟者和其他高危群体进行基因筛查，使用新的生物标记物进行检测，新的治疗方式等。虽然吸烟对肺癌的影响一般都很清楚，但该模型的某些方面仍然是一个主要的挑战，例如，对戒烟者更好地了解癌症的发生过程，以及了解非吸烟者中肺癌的趋势。这暗示了我们的目标3。模拟有吸烟史和从不吸烟者的肺癌发生和自然病史。建模是唯一允许将受控癌症干预研究的结果外推到美国人口和社区有效性估计的方法。从上面的综述中可以看出，目前的模型没有解决易感性、自然病史、治疗反应等方面存在的个体间差异。我们在本应用程序中采用的基于个体的建模方法将允许解决这种可变性。以个体为基础的方法也适用于尚不存在的干预措施的建模，例如新的治疗方法。

DESCRIPTION (provided by applicant): In the course of previous CISNET funding period, we developed two complementary models of lung cancer: (i) Model of carcinogenesis, extended to include genetic susceptibility and impact of smoking pattern, (ii) Model of progression, detection and treatment, based on stochastic tumor growth and stochastic stage transitions. The main trust of the research planned will be focused on two Aims: Aim 1. To determine population impact of interventions such as: (a) Smoking cessation and prevention of initiation, (b) Early detection of lung cancer by periodic screening using helical CT, in a high-risk population, followed by therapy, (c) Lifestyle interventions (e.g., dietary), removal of exposures (ETS, asbestos, radon). Aim 2. Predict the population impact of novel interventions, not yet developed, such as genetic screening of heavy smokers and other high-risk groups, detection using new biomarkers, new treatment modalities and so forth..While the impact of smoking on lung cancer is generally well understood, there are certain aspects of this modeling which are still a major challenge, e.g., gaining a better understanding of the process of carcinogenesis for those who have quit smoking and understanding trends in lung cancer among nonsmokers. This implies our Aim 3. To model carcinogenesis and natural history of lung cancer in former smokers and never smokers. Modeling is the only method that allows extrapolation of results of controlled cancer intervention studies to estimates of US population and community effectiveness. Current models, as it is seen from the review above, do not address existing inter-individual variability in susceptibility, natural history, response to treatment, and so forth. The individual-based approach to modeling, which we are taking in this application, will allow addressing this variability. The individual-based approach is also suitable for modeling of interventions, which do not yet exist such as new treatments.