STUDY OF EXPOSURES, BEHAVIORS, AND BIOMARKERS IN CANCER EPIDEMIOLOGY

癌症流行病学中的暴露、行为和生物标志物研究

• 批准号: 8256518
• 负责人: JAYA M SATAGOPAN
• 金额: $ 38.16万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2014-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8256518
• 关键词: Address; Behavior; Behavioral; Biologic Characteristic; Biological Markers; Biological Process; Blood; Candidate Disease Gene; Complex; Data; Data Analyses; Diagnostic Procedure; Disease; Dose; Environmental Exposure; Environmental Risk Factor; Etiology; Evaluation; Exhibits; Exposure to; Frequencies; Genes; Genetic; Goals; Guidelines; Health; Individual; Intervention; Investigation; Linkage Disequilibrium; Logistics; Measures; Methods; Motivation; Nature; Pathway interactions; Pattern; Population; Predisposition; Property; Proteins; Public Health; Questionnaires; Relative (related person); Research; Research Design; Research Methodology; Residual state; Risk Factors; Route; Sampling; Surveys; Time; To specify; Variant; cancer epidemiology; cohort; cost; design; epidemiology study; follow-up; gene environment interaction; genetic variant; improved; insight; novel; novel strategies; public health relevance; response; simulation; trait

DESCRIPTION (provided by applicant): It is now widely accepted that both environmental exposures and genetic factors contribute to most diseases. Cancer epidemiology studies often ascertain exposures and behavioral factors (referred to generally as exposure factors) through survey instruments (for example, questionnaires), and typically examine variants or blood levels of exposures or markers of exposures (biomarkers) in candidate genes or pathways to identify risk factors for disease or for disease precursors. The responses to questionnaire items may be correlated. Similarly, the biomarkers may be correlated due to common underlying biological function or linkage disequilibrium and may occur at a low frequency. These render data analysis a challenging task. Although analytic methods have been developed to address this challenge, there continues to be a need to investigate novel approaches for efficient analyses of exposure factors and biomarkers, and to develop optimal study designs for follow-up research. The long-term goals of this proposal are to address these needs through the following four specific aims. In Aim 1 we will examine how to conduct efficient analysis of correlated exposure factors and biomarkers. In Aim 2 we will examine how to evaluate the effects of routes of exposures and underlying biological characteristics in an efficient manner. The etiologic effects of the risk factors are likely to be complex due to a number of reasons, including gene-exposure interactions. Aim 3 will examine parsimonious analytic methods for evaluating interactions. In Aim 4 we will develop a novel study design for sampling an informative sub-population from an established cohort to evaluate additional risk factors, when evaluation of the full cohort is not feasible due to logistic or cost considerations. Our proposal is motivated by several collaborative studies. We will conduct simulations to investigate the properties of the methods proposed under each aim, and derive guidelines for their use in practical settings. We will conduct an empirical evaluation of the methods and guidelines using data from the collaborative studies. Our proposal has two major objectives. First, we intend to develop and improve research designs and methodologies for evaluating correlated risk factors in cancer epidemiology studies. Second, we intend to assess the practical relevance of these methods by conducting empirical investigations using data from various studies. We anticipate that the insights gained from these investigations will have a significant impact on our understanding of analysis and study designs for identifying risk factors of public health relevance in an efficient and parsimonious manner. PUBLIC HEALTH RELEVANCE: It is widely accepted that both environmental exposures and genetic factors contribute to most diseases. Cancer epidemiology studies often ascertain exposures and behavioral factors through survey instruments (for example, questionnaires), and examine susceptibility variants in candidate genes or blood levels of exposures or markers of exposures to identify risk factors for disease traits. The responses to questionnaire items may be correlated. Similarly, the genetic variants may exhibit correlation due to their common underlying biological function and may occur at a low frequency. These render data analysis a challenging task. The long-term goals of this proposal are to investigate novel methods for efficient analyses of correlated risk factors, and to develop optimal study designs for follow-up research.

描述（由申请人提供）：现在人们普遍认为，环境暴露和遗传因素都会导致大多数疾病。癌症流行病学研究通常通过调查工具（例如问卷）确定暴露和行为因素（通常称为暴露因素），并通常检查候选基因或途径中的暴露或暴露标记物（生物标记物）的变体或血液水平，以确定疾病或疾病前兆的风险因素。对调查表项目的答复可能是相互关联的。类似地，生物标志物可能由于共同的潜在生物学功能或连锁不平衡而相关，并且可能以低频率发生。这些使得数据分析成为一项具有挑战性的任务。虽然已经开发了分析方法来应对这一挑战，但仍然需要研究有效分析暴露因素和生物标志物的新方法，并为后续研究开发最佳研究设计。本提案的长期目标是通过以下四个具体目标满足这些需求。在目标1中，我们将研究如何对相关暴露因素和生物标志物进行有效分析。在目标2中，我们将研究如何以有效的方式评估接触途径和基本生物特征的影响。由于许多原因，包括基因暴露的相互作用，危险因素的病因学影响可能是复杂的。目标3将检验用于评估相互作用的简约分析方法。在目标4中，我们将开发一种新的研究设计，用于从已建立的队列中抽取信息性亚群，以评价由于逻辑或成本考虑而无法进行全队列评价时的其他风险因素。我们的建议是由几个合作研究的动机。我们将进行模拟，以调查在每个目标下提出的方法的属性，并推导出在实际环境中使用的指导方针。我们将使用合作研究的数据对方法和指南进行实证评估。我们的建议有两个主要目标。首先，我们打算开发和改进研究设计和方法，以评估癌症流行病学研究中的相关风险因素。其次，我们打算通过使用各种研究的数据进行实证调查，评估这些方法的实际意义。我们预计，从这些调查中获得的见解将对我们的分析和研究设计的理解产生重大影响，以有效和节俭的方式确定公共卫生相关的风险因素。公共卫生相关性：人们普遍认为，环境暴露和遗传因素都会导致大多数疾病。癌症流行病学研究通常通过调查工具（例如问卷）确定暴露和行为因素，并检查候选基因中的易感性变体或暴露的血液水平或暴露的标志物，以确定疾病特征的风险因素。对调查表项目的答复可能是相互关联的。类似地，遗传变体可能由于其共同的潜在生物学功能而表现出相关性，并且可能以低频率发生。这些使得数据分析成为一项具有挑战性的任务。该提案的长期目标是研究有效分析相关风险因素的新方法，并为后续研究开发最佳研究设计。

项目成果

Sun exposure, vitamin D receptor genetic variants, and risk of breast cancer in the Agricultural Health Study.

• DOI: 10.1289/ehp.1206274
• 发表时间: 2014-02
• 期刊: Environmental health perspectives
• 影响因子: 10.4
• 作者: Engel LS;Satagopan J;Sima CS;Orlow I;Mujumdar U;Coble J;Roy P;Yoo S;Sandler DP;Alavanja MC
• 通讯作者: Alavanja MC

Quantifying Treatment Benefit in Molecular Subgroups to Assess a Predictive Biomarker.

• DOI: 10.1158/1078-0432.ccr-15-2517
• 发表时间: 2016-05-01
• 期刊: Clinical cancer research : an official journal of the American Association for Cancer Research
• 作者: Iasonos A;Chapman PB;Satagopan JM
• 通讯作者: Satagopan JM