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- W2808283971 abstract "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, 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. 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 H. pylori 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. 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. Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/78/12/3386/F1.large.jpg. Cancer Res; 78(12); 3386–96. ©2018 AACR." @default.
- W2808283971 created "2018-06-21" @default.
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- W2808283971 date "2018-06-14" @default.
- W2808283971 modified "2023-09-24" @default.
- W2808283971 title "Case Studies of Gastric, Lung, and Oral Cancer Connect Etiologic Agent Prevalence to Cancer Incidence" @default.
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- W2808283971 doi "https://doi.org/10.1158/0008-5472.can-17-3467" @default.
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!["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, 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. 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 H. pylori 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. 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. Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/78/12/3386/F1.large.jpg. Cancer Res; 78(12); 3386–96. ©2018 AACR."](?object=%22Abstract%20Obtaining%20detailed%20individual-level%20data%20on%20both%20exposure%20and%20cancer%20outcomes%20is%20challenging%2C%20and%20it%20is%20difficult%20to%20understand%20and%20characterize%20how%20temporal%20aspects%20of%20exposures%20translate%20into%20cancer%20risk.%20We%20show%20that%2C%20in%20lieu%20of%20individual-level%20information%2C%20population-level%20data%20on%20cancer%20incidence%20and%20etiologic%20agent%20prevalence%20can%20be%20leveraged%20to%20investigate%20cancer%20mechanisms%20and%20to%20better%20characterize%20and%20predict%20cancer%20trends.%20We%20use%20mechanistic%20carcinogenesis%20models%20%5Bmultistage%20clonal%20expansion%20(MSCE)%20models%5D%20and%20data%20on%20smoking%2C%20Helicobacter%20pylori%20(H.%20pylori)%2C%20and%20HPV%20infection%20prevalence%20to%20investigate%20trends%20of%20lung%2C%20gastric%2C%20and%20HPV-related%20oropharyngeal%20cancers.%20MSCE%20models%20are%20based%20on%20the%20initiation%E2%80%93promotion%E2%80%93malignant%20conversion%20paradigm%20and%20allow%20for%20interpretation%20of%20trends%20in%20terms%20of%20general%20biological%20mechanisms.%20We%20assumed%20the%20rates%20of%20initiation%20depend%20on%20the%20prevalence%20of%20the%20corresponding%20risk%20factors.%20We%20performed%20two%20types%20of%20analysis%2C%20using%20the%20agent%20prevalence%20and%20cancer%20incidence%20data%20to%20estimate%20the%20model%20parameters%20and%20using%20cancer%20incidence%20data%20to%20infer%20the%20etiologic%20agent%20prevalence%20as%20well%20as%20the%20model%20parameters.%20By%20including%20risk%20factor%20prevalence%2C%20MSCE%20models%20with%20as%20few%20as%20three%20parameters%20closely%20reproduced%2040%20years%20of%20age-specific%20cancer%20incidence%20data.%20We%20recovered%20trends%20of%20H.%20pylori%20prevalence%20in%20the%20United%20States%20and%20demonstrated%20that%20cohort%20effects%20can%20explain%20the%20observed%20bimodal%2C%20age-specific%20pattern%20of%20oral%20HPV%20prevalence%20in%20men.%20Our%20results%20demonstrate%20the%20potential%20for%20joint%20analyses%20of%20population-level%20cancer%20and%20risk%20factor%20data%20through%20mechanistic%20modeling.%20This%20approach%20can%20be%20a%20first%20step%20in%20systematically%20testing%20relationships%20between%20exposures%20and%20cancer%20risk%20when%20individual-level%20data%20is%20lacking.%20Significance%3A%20Analysis%20of%20trends%20in%20risk-factor%20prevalence%20and%20cancer%20incidence%20can%20shed%20light%20on%20cancer%20mechanisms%20and%20the%20way%20that%20carcinogen%20exposure%20through%20time%20shapes%20the%20risk%20of%20cancer%20at%20different%20ages.%20Graphical%20Abstract%3A%20http%3A%2F%2Fcancerres.aacrjournals.org%2Fcontent%2Fcanres%2F78%2F12%2F3386%2FF1.large.jpg.%20Cancer%20Res%3B%2078(12)%3B%203386%E2%80%9396.%20%C2%A92018%20AACR.%22){kind=link}