Lung Cancer Model: Risk, Progression and Intervention

描述（由申请人提供）： 在上一个CISNET资助期间，我们开发了两个互补的肺癌模型：（i）致癌模型，扩展到包括遗传易感性和吸烟模式的影响，（ii）基于随机肿瘤生长和随机阶段转换的进展，检测和治疗模型。计划中的研究主要集中在两个目标上：目标1。确定干预措施对人群的影响，例如：（a）戒烟和预防开始吸烟，（B）在高危人群中通过使用螺旋CT定期筛查早期发现肺癌，随后进行治疗，（c）生活方式干预（例如，饮食），消除暴露（ETS，石棉，氡）。目标二。 预测尚未开发的新型干预措施对人口的影响，例如对重度吸烟者和其他高危人群进行基因筛查，使用新的生物标志物进行检测，新的治疗方式等。虽然吸烟对肺癌的影响通常是很好理解的，但这种建模的某些方面仍然是一个重大挑战，例如，更好地了解戒烟者的致癌过程，并了解非吸烟者肺癌的趋势。这就是我们的目标3。在既往吸烟者和从不吸烟者中建立肺癌发生和自然史模型。 建模是唯一的方法，允许外推控制癌症干预研究的结果，以估计美国人口和社区的有效性。从上述综述中可以看出，目前的模型没有解决易感性、自然史、治疗反应等方面的个体间差异。我们在本应用程序中采用的基于个人的建模方法将允许解决这种可变性。基于个体的方法也适用于对尚不存在的干预措施（如新疗法）进行建模。

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.