Novel methods for identifying genetic interactions in cancer prognosis

识别癌症预后中遗传相互作用的新方法

• 批准号: 9079917
• 负责人: Shuangge Ma
• 金额: $ 38.28万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9079917
• 关键词: Address; Adopted; Attention; Benchmarking; Bioinformatics; Cancer Prognosis; Clinical; Computer software; Data; Data Analyses; Data Collection; Development; Educational workshop; Environment; Evaluation; Exploratory/Developmental Grant for Diagnostic Cancer Imaging; Future; Genes; Genetic; Individual; Investigation; Joints; Lead; Malignant Neoplasms; Malignant neoplasm of lung; Medical Records; Methods; Modeling; Non-Hodgkin' s Lymphoma; Paper; Pattern; Play; Property; Publishing; Resources; Risk Factors; Role; Statistical Data Interpretation; Stress; Techniques; Time; United States National Institutes of Health; Work; base; biomarker identification; cancer genetics; cancer type; case control; experience; frontier; gene interaction; melanoma; method development; novel; outcome forecast; prognostic; public health relevance; simulation; success; web site

 DESCRIPTION (provided by applicant): Project Summary In cancer prognosis, beyond the main effects of environmental/clinical (E) and genetic (G) risk factors, the interactions between G and E factors (G*E interactions) and those between G and G factors (G*G interactions) also play critical roles. The existing findings are insufficient, and there is a strong need for identifing more prognostic interactions. Most of the existing effort has been focused on data collection. In contrast, the development of effective analysis methods has been lagging behind. Compared to data collection, methodological development takes much less resources but is equally critical in making reliable findings. Most of the existing interaction analysis methods share the limitation of lacking robustness properties. In practice, data contamination and model mis-specification are not uncommon and can lead to severely biased model parameter estimation and false marker identification. The development of robust genetic interaction analysis methods is very limited. There are a few methods for case-control data, but they are not applicable to prognosis data. For prognosis data and interaction analysis, there is some very recent progress in quantile regression and rank-based methods, but the development has been limited and unsystematic. Last but not least, the existing robust methods have the common drawback of adopting ineffective marker selection techniques. Our group has been at the frontier of developing robust interaction analysis methods. Our statistical investigations and simulations have provided convincing evidences that the robust methods using the penalization technique outperform alternatives with significantly more accurate marker identification and model parameter estimation. In data analysis, important interactions missed by the existing analyses have been identified for multiple cancer types. However, we have also found that the scope of the existing studies needs to be significantly expanded in terms of both methodological development and data analysis. This project has been motivated by the importance of interactions in cancer prognosis and limitations of the existing studies. Our objectives are as follows. (Aim 1) Develop novel marginal analysis methods that are robust to data contamination and model mis-specification for identifying important interactions. (Aim 2) Develop novel joint analysis methods that are robust to data contamination and model mis-specification for identifying important interactions. (Aim 3) Develop tailored inference approaches to draw more definitive conclusions on the identified interactions. (Aim 4) Develop public R software and a dynamic project website. Identify prognostic interactions for multiple cancers. For the identified interactions, we will conduct extensive bioinformatic and statistical analysis, evaluations, and comparisons. With our unique expertise, extensive experiences, and promising preliminary studies, this project has a high likelihood of success.

 在癌症预后中，除了环境/临床（E）和遗传（G）风险因素的主要影响外，G和E因素之间的相互作用（G*E相互作用）以及G和G因素之间的相互作用（G*G相互作用）也起着关键作用。现有的研究结果是不够的，有一个强烈的需要，以确定更多的预后相互作用。现有的工作大部分集中在数据收集方面。相比之下，有效分析方法的发展一直比较滞后。与数据收集相比，方法制定所需资源少得多，但对于得出可靠的结论同样重要。 现有的交互分析方法大多缺乏鲁棒性。在实践中，数据污染和模型错误指定并不罕见，并可能导致严重偏倚的模型参数估计和错误的标记识别。稳健的遗传互作分析方法的发展是非常有限的。病例对照资料的方法有几种，但不适用于预后资料。对于预后数据和相互作用分析，最近在分位数回归和基于秩的方法方面取得了一些进展，但发展有限且不系统。最后但并非最不重要的是，现有的鲁棒方法具有采用无效标记选择技术的共同缺点。 我们的团队一直处于开发强大的相互作用分析方法的前沿。我们的统计调查和模拟提供了令人信服的证据，使用惩罚技术的鲁棒方法优于替代品，具有更准确的标记识别和模型参数估计。在数据分析中，已经确定了多种癌症类型中现有分析遗漏的重要相互作用。然而，我们也发现，现有研究的范围需要显着扩大的方法发展和数据分析方面。 该项目的动机是相互作用在癌症预后中的重要性和现有研究的局限性。我们的目标如下。(Aim 1）开发新的边际分析方法，这些方法对数据污染和模型错误规范具有鲁棒性，用于识别重要的相互作用。(Aim 2）开发新的联合分析方法，对数据污染和模型错误规范具有鲁棒性，以识别重要的相互作用。(Aim 3）开发定制的推理方法，以得出更明确的结论，对已确定的相互作用。(Aim 4）开发公共R软件和动态项目网站。确定多种癌症的预后相互作用。对于确定的相互作用，我们将进行广泛的生物信息学和统计分析，评估和比较。凭借我们独特的专业知识，丰富的经验和有希望的初步研究，该项目成功的可能性很高。