Robust rank-based methods and detection of GXE in cancer etiology and survival

稳健的基于排名的方法以及 GXE 在癌症病因学和生存中的检测

• 批准号: 8216973
• 负责人: Shuangge Ma
• 金额: $ 14.41万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-03-13 至 2015-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8216973
• 关键词: Behavioral; Biological; Cancer Etiology; Cancer Prognosis; Computer software; Connecticut; Data Analyses; Detection; Development; Environment; Environmental Exposure; Etiology; Family; Future; Genes; Genetic; Genetic Predisposition to Disease; Individual; Intervention; Joints; Lasso; Lead; Linux; Malignant Neoplasms; Masks; Methodology; Methods; Modeling; Modification; Non-Hodgkin' s Lymphoma; Outcome; Pathway interactions; Performance; Property; Reproducibility; Research; Risk Factors; Time; base; burden of illness; cancer genomics; clinical practice; cost; forest; gene environment interaction; genetic profiling; high risk; insight; novel; outcome forecast; predictive modeling; prevent; simulation; tumor progression; user friendly software; user-friendly; validation studies; web site

DESCRIPTION (provided by applicant): It is now well recognized that both environmental exposures and genetic susceptibility contribute to the development and progression of cancer. In order to identify individuals at a higher risk for developing cancer or with poor prognosis based on their environmental exposures and genetic profiles and to inform potential environmental modifications or behavioral change interventions that can be implemented to prevent or reduce disease burden, it is essential to understand gene-environment (G 4 E) interactions. While considerable effort has been made to study G4E interactions, existing methods suffer serious limitations, which may mask the detection of genetic effects, lead to inconsistent results across studies, and result in suboptimal predictive models. As such, there is an urgent need for novel methodologies that can effectively analyze data and identify important, reproducible G4E interactions for cancer etiology and survival. In this study, we will develop novel rank-based methods for analyzing G4E interactions in cancer etiology and survival studies. The proposed methods have the much desired robustness and consistency properties not shared by existing methods. They can accommodate the joint effects of a large number of markers, conduct both individual marker-level and pathway-level analyses, and are computationally affordable. We will comprehensively evaluate the proposed methods using simulation studies and compare with existing methods. In addition, we will apply the proposed methods and identify G4E interactions in NHL (non-Hodgkin Lymphoma) etiology and survival. Particularly, we will first analyze the Connecticut study. The findings will be comprehensively evaluated and then validated using the NCI-SEER study. The specific aims are as follows. (Aim 1) Develop robust rank-based methods and detect environmental, genetic, and G4E risk factors marginally associated with etiology and survival. (Aim 2) Develop robust rank- based penalization methods and detect environmental, genetic, and G4E risk factors with important joint effects for etiology and survival. (Aim 3) Develop user-friendly software and project website. (Aim 4) Analyze the Connecticut NHL study and identify important G4E interactions. The findings will be comprehensively evaluated and then validated using the NCI-SEER study. The proposed methods will provide a way to more effectively identify G 4 E interactions in the development and prognosis of cancer. They will have superior statistical properties and identify important markers missed by existing methods. The identified markers will provide important insights into the biological mechanisms underlying NHL and serve as basis for future validation studies and clinical practice. PUBLIC HEALTH RELEVANCE: This study will be among the first to systematically develop and implement novel rank-based methods for the analysis of gene-environment interactions in cancer. The proposed methods will enrich the family of analytic approaches for studying gene-environment interactions and cancer genomics. They will be used to identify markers of etiology and survival of non-Hodgkin lymphoma.

描述(由申请人提供)：现已公认，环境暴露和遗传易感性都有助于癌症的发展和进展。为了根据环境暴露和基因特征识别癌症高危个体或预后不良的个体，并为潜在的环境改变或行为改变干预提供信息，以预防或减轻疾病负担，了解基因-环境(G4E)相互作用是至关重要的。虽然已经做出了相当大的努力来研究G4E相互作用，但现有的方法存在严重的局限性，这可能会掩盖对遗传效应的检测，导致跨研究的结果不一致，并导致次优预测模型。因此，迫切需要新的方法来有效地分析数据，并识别重要的、可重复的G4E相互作用，以确定癌症病因学和存活率。在这项研究中，我们将开发新的基于等级的方法来分析癌症病因和生存研究中G4E的相互作用。提出的方法具有现有方法所不具备的非常理想的稳健性和一致性。它们可以适应大量标记的联合影响，进行单独的标记水平和路径水平的分析，并且在计算上是负担得起的。我们将通过仿真研究对所提出的方法进行全面评估，并与现有方法进行比较。此外，我们将应用所提出的方法，并确定G4E相互作用在NHL(非霍奇金淋巴瘤)病因和生存中的作用。特别是，我们将首先分析康涅狄格州的研究。这些发现将得到全面评估，然后使用NCI-SEER研究进行验证。具体目标如下。(目标1)开发可靠的基于等级的方法，并检测与病因学和存活率略有关联的环境、遗传和G4E风险因素。(目的2)发展稳健的基于等级的惩罚方法，并检测环境、遗传和G4E危险因素，这些因素对病因和生存具有重要的联合影响。(目标3)开发用户友好的软件和项目网站。(目的4)分析康涅狄格州非霍奇金淋巴瘤研究并确定重要的G4E相互作用。这些发现将得到全面评估，然后使用NCI-SEER研究进行验证。所提出的方法将提供一种更有效地识别G4E相互作用在癌症发展和预后中的方法。它们将具有优越的统计特性，并识别现有方法遗漏的重要标记。已确定的标记将为NHL的生物学机制提供重要的见解，并为未来的验证研究和临床实践提供基础。 公共卫生相关性：这项研究将是第一个系统地开发和实施新的基于等级的方法来分析癌症中基因-环境相互作用的方法之一。所提出的方法将丰富研究基因-环境相互作用和癌症基因组学的分析方法家族。它们将被用来确定非霍奇金淋巴瘤的病因学和存活率的标志物。