Study of exposures and biomarkers in cancer epidemiology

癌症流行病学中的暴露和生物标志物研究

• 批准号: 9106742
• 负责人: JAYA M SATAGOPAN
• 金额: $ 41.53万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2020-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9106742
• 关键词: Address; American Association of Cancer Research; Bayesian Method; Biological Markers; Breast Cancer Patient; Cancer Intervention; Candidate Disease Gene; Chemoprevention; Child; Clinical; Cognitive; Communities; Data; Data Set; Disease; Disease Outcome; Ensure; Environment; Environmental Exposure; Epidemiology; Etiology; Evolution; Foundations; Funding; Genes; Genetic; Human; Impaired cognition; Individual; Individual Differences; Inherited; Intervention; Laboratory Organism; Libraries; Malignant Neoplasms; Malignant neoplasm of brain; Measures; Memorial Sloan-Kettering Cancer Center; Methodology; Methods; Modeling; Neuropsychological Tests; Nevus; Outcome; Outcome Measure; Patients; Physicians; Play; Preventive Intervention; Process; Professional Organizations; Programming Languages; Research; Risk Factors; Role; Sample Size; Statistical Methods; Statistical Models; Sun Exposure; Techniques; Testing; Therapeutic; Time; Treatment Factor; United States Food and Drug Administration; United States National Institutes of Health; Validation; Variant; Writing; cancer epidemiology; cancer therapy; carcinogenesis; curative treatments; disorder risk; epidemiology study; gene interaction; improved; individualized medicine; innovation; insight; malignant breast neoplasm; oncology; programs; public health relevance; success; treatment effect; treatment strategy

 DESCRIPTION (provided by applicant): It is well recognized that different individuals respond in different ways to the same treatment, and inherited genetic factors play a role on these inter-individual differences. Such genetic factors, referred to as predictive genetic factors, are beginning to enable physicians to make informed therapeutic decisions by tailoring treatments and interventions according to the genetic profiles of patients. When there is an interaction between a genetic factor and treatment or intervention, it means that treatment benefits vary according to the level of the genetic factor. Therefore, epidemiology studies increasingly try to investigate gene-treatment, gene-exposure, and gene-gene interactions in statistical models to identify promising predictive genetic factors. Despite remark- able progress in the identification of etiologic risk factors for cancer, the success rate of identifying interactions and predictive genetic factors remains low. While sample size limitations may partly contribute to this challenge, some significant interactions cannot be replicated because they may be biologically implausible. Therefore, improving the power to detect interactions and developing methodologies to identify practically interpretable interactions and predictive genetic factors are among the critical needs of the field. While there is a large and growing body of work on evaluating interactions for binary outcomes, other richer data types are also be- coming available, and analytic methods to evaluate predictive genetic factors are urgently needed for these settings. The overarching objective of our proposal is to develop formal statistical and mathematical foundations to address these needs. In this R01 project, we propose to show that interactions arising in statistical models corresponding to quantitative expressions for carcinogenesis can be written in a parsimonious manner that can provide insights into the rate at which disease outcome increases in relation to the risk factors. We propose to develop innovative and powerful frequentist and Bayesian statistical techniques to evaluate interactions by harnessing the significant potential of model parsimony. We propose to use these powerful methods to develop well-calibrated models to identify clinically interpretable predictive genetic factors. We also propose to develop and disseminate R libraries that implement our proposed methods. We focus on developing methodologies for count outcomes (measured at a single time point and at two time points) and multiple continuous outcomes measured at a single time point. We will apply our proposed methods to data from three collaborative studies - the study of nevi in children, and cognitive studies of brain and breast cancer patients - and confirm our results using validation data sets.

 描述（由申请人提供）：众所周知，不同的个体对相同的治疗有不同的反应，遗传因素对这些个体间差异起作用。这些遗传因素，被称为预测性遗传因素，开始使医生能够根据患者的遗传特征定制治疗和干预措施，从而做出明智的治疗决定。当遗传因素与治疗或干预之间存在相互作用时，这意味着治疗贝内根据遗传因素的水平而变化。因此，流行病学研究越来越多地尝试在统计模型中调查基因治疗，基因暴露和基因-基因相互作用，以确定有希望的预测遗传因素。尽管在识别方面取得了显著进展 在癌症的病因风险因素中，识别相互作用和预测遗传因素的成功率仍然很低。虽然样本量限制可能部分导致了这一挑战，但一些重要的相互作用无法复制，因为它们在生物学上可能是不可信的。因此，提高检测相互作用的能力并开发方法来识别实际上可解释的相互作用和预测遗传因素是非常重要的。 在外地的关键需求。虽然在评估二元结果的相互作用方面有大量且不断增长的工作，但其他更丰富的数据类型也即将可用，并且迫切需要评估预测遗传因素的分析方法。我们提案的首要目标是开发正式的统计和数学基础来满足这些需求。在这个R 01项目中，我们建议表明，在对应于致癌作用的定量表达的统计模型中产生的相互作用可以以一种简约的方式编写，这种方式可以提供对疾病结局与风险因素相关的增加率的见解。我们建议开发创新和强大的频率论和贝叶斯统计技术，通过利用模型简约性的巨大潜力来评估相互作用。我们建议使用这些强大的方法来开发校准良好的模型，以确定临床上可解释的预测遗传因素。我们还建议开发和传播实现我们提出的方法的R库。我们专注于开发计数结果（在单个时间点和两个时间点测量）和在单个时间点测量的多个连续结果的方法。我们将把我们提出的方法应用于三项合作研究的数据--儿童痣的研究，以及脑癌和乳腺癌患者的认知研究--并使用验证数据集来确认我们的结果。