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

被引:7
作者
Brouwer, Andrew F. [1 ]
Eisenberg, Marisa C. [1 ]
Meza, Rafael [1 ]
机构
[1] Univ Michigan, Dept Epidemiol, 1415 Washington Hts, Ann Arbor, MI 48109 USA
关键词
BIOLOGICALLY-BASED ANALYSIS; CLONAL EXPANSION MODEL; STEM-CELL DIVISIONS; UNITED-STATES; MULTISTAGE CARCINOGENESIS; TEMPORAL VARIATION; COLORECTAL-CANCER; INCIDENCE TRENDS; COHORT MODELS; HPV INFECTION;
D O I
10.1158/0008-5472.CAN-17-3467
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Obtaining detailed individual-level data on butte exposure and cancer outcomes is challenging, and it is difficult to understand and characterize hove 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, Helicobacter pylori (H. pylori), 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. Fly 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 H. pylori prevalence in the United Slates and demonstrated that cohort effects can explain the observed bimodal, age-specific pattern of oral HPV prevalence in men. Our re alts 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. Significance: 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. (C)2018 AACR.
引用
收藏
页码:3386 / 3396
页数:11
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