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

描述（由申请人提供）：众所周知，环境暴露和遗传易感性都有助于癌症的发生和发展。为了根据环境暴露和遗传特征确定患癌症风险较高或预后不良的个体，并告知可能实施的环境改变或行为改变干预措施，以预防或减轻疾病负担，了解基因-环境（g4e）相互作用至关重要。虽然在研究G4E相互作用方面已经做出了相当大的努力，但现有的方法存在严重的局限性，这可能会掩盖遗传效应的检测，导致研究结果不一致，并导致次优预测模型。因此，迫切需要一种新的方法来有效地分析数据，并确定重要的、可重复的G4E相互作用对癌症病因和生存的影响。在这项研究中，我们将开发新的基于排名的方法来分析G4E在癌症病因学和生存研究中的相互作用。所提出的方法具有现有方法所不具有的鲁棒性和一致性。它们可以适应大量标记的联合效应，进行单个标记水平和路径水平的分析，并且在计算上是负担得起的。我们将利用模拟研究全面评估建议的方法，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在NHL（非霍奇金淋巴瘤）病因和生存中的相互作用。特别是，我们将首先分析康涅狄格的研究。研究结果将进行全面评估，然后使用NCI-SEER研究进行验证。具体目的如下。（目标1）开发稳健的基于等级的方法，检测与病因和生存相关的环境、遗传和G4E风险因素。（目标2）发展稳健的基于等级的惩罚方法，检测对病因和生存有重要联合影响的环境、遗传和G4E风险因素。（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NHL研究并确定重要的G4E相互作用。研究结果将进行全面评估，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在癌症发展和预后中的相互作用的方法。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。确定的标记物将为NHL的生物学机制提供重要见解，并作为未来验证研究和临床实践的基础。

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.