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胃癌、肺癌和口腔癌病例研究将病因流行与癌症发病率联系起来。

Case Studies of Gastric, Lung, and Oral Cancer Connect Etiologic Agent Prevalence to Cancer Incidence.

机构信息

Department of Epidemiology, University of Michigan, Ann Arbor, Michigan.

出版信息

Cancer Res. 2018 Jun 15;78(12):3386-3396. doi: 10.1158/0008-5472.CAN-17-3467.

Abstract

Obtaining detailed individual-level data on both exposure and cancer outcomes is challenging, and it is difficult to understand and characterize how temporal aspects of exposures translate into cancer risk. We show that, in lieu of individual-level information, population-level data on cancer incidence and etiologic agent prevalence can be leveraged to investigate cancer mechanisms and to better characterize and predict cancer trends. We use mechanistic carcinogenesis models [multistage clonal expansion (MSCE) models] and data on smoking, (), and HPV infection prevalence to investigate trends of lung, gastric, and HPV-related oropharyngeal cancers. MSCE models are based on the initiation-promotion-malignant conversion paradigm and allow for interpretation of trends in terms of general biological mechanisms. We assumed the rates of initiation depend on the prevalence of the corresponding risk factors. We performed two types of analysis, using the agent prevalence and cancer incidence data to estimate the model parameters and using cancer incidence data to infer the etiologic agent prevalence as well as the model parameters. By including risk factor prevalence, MSCE models with as few as three parameters closely reproduced 40 years of age-specific cancer incidence data. We recovered trends of prevalence in the United States and demonstrated that cohort effects can explain the observed bimodal, age-specific pattern of oral HPV prevalence in men. Our results demonstrate the potential for joint analyses of population-level cancer and risk factor data through mechanistic modeling. This approach can be a first step in systematically testing relationships between exposures and cancer risk when individual-level data is lacking. Analysis of trends in risk-factor prevalence and cancer incidence can shed light on cancer mechanisms and the way that carcinogen exposure through time shapes the risk of cancer at different ages. http://cancerres.aacrjournals.org/content/canres/78/12/3386/F1.large.jpg .

摘要

获取暴露和癌症结局的详细个体水平数据具有挑战性,并且难以理解和描述暴露的时间方面如何转化为癌症风险。我们表明,在没有个体水平信息的情况下,可以利用癌症发病率和病因agent 流行率的人群水平数据来研究癌症机制,并更好地描述和预测癌症趋势。我们使用机制致癌发生模型(多阶段克隆扩张(MSCE)模型)和关于吸烟、()和 HPV 感染流行率的数据来研究肺癌、胃癌和 HPV 相关口咽癌的趋势。MSCE 模型基于起始-促进-恶性转化范式,允许根据一般生物学机制解释趋势。我们假设起始率取决于相应危险因素的流行率。我们进行了两种类型的分析,使用代理流行率和癌症发病率数据来估计模型参数,以及使用癌症发病率数据推断病因agent 流行率以及模型参数。通过包含风险因素流行率,具有三个参数的 MSCE 模型可以很好地重现 40 年的年龄特异性癌症发病率数据。我们恢复了美国的 流行趋势,并证明了队列效应可以解释观察到的男性口腔 HPV 流行率的双峰、年龄特异性模式。我们的结果表明,通过机制建模联合分析人群水平的癌症和风险因素数据具有潜力。当缺乏个体水平数据时,这种方法可以作为系统测试暴露与癌症风险之间关系的第一步。分析风险因素流行率和癌症发病率的趋势可以揭示癌症机制以及随着时间的推移致癌剂暴露对不同年龄癌症风险的影响方式。http://cancerres.aacrjournals.org/content/canres/78/12/3386/F1.large.jpg。

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