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.

 描述（适用提供）：癌症提示中的项目摘要，超出环境/临床（E）和遗传（G）风险因素的主要影响，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（G*G相互作用）之间的相互作用（G*G相互作用）也起着关键作用。现有发现不足，并且非常需要识别更多的预后相互作用。现有的大多数工作都集中在数据收集上。相反，有效分析方法的发展一直落后。与数据收集相比，方法论开发的资源要少得多，但对于做出可靠的发现同样至关重要。大多数现有的相互作用分析方法都具有缺乏鲁棒性能的局限性。实际上，数据污染和模型错误指定并不少见，并且可能导致严重偏见的模型参数估计和错误标记识别。强大的遗传相互作用分析方法的发展非常有限。病例对照数据有几种方法，但它们不适用于预后数据。对于预后数据和相互作用分析，分位数回归和基于等级的方法有一些最新的进展，但是发展是有限且非系统性的。最后但并非最不重要的一点是，现有的鲁棒方法具有采用无效标记选择技术的共同缺点。我们的小组一直在开发强大的相互作用分析方法的前沿。我们的统计研究和模拟提供了令人信服的证据，即使用惩罚技术的鲁棒方法优于替代方案，其标记识别和模型参数估计明显更准确。在数据分析中，已经确定了多种癌症类型的现有分析所错过的重要相互作用。但是，我们还发现，现有研究的范围需要在方法论开发和数据分析方面显着扩大。该项目的激励性是由于相互作用在癌症预后和现有研究的局限性中的重要性所激发的。我们的目标如下。 （AIM 1）开发新型的边缘分析方法，这些方法对数据污染和模型错误指定具有可靠性，以识别重要的相互作用。 （AIM 2）开发新型的联合分析方法，这些方法可用于数据污染和模型错误指定，以识别重要的相互作用。 （AIM 3）开发量身定制的推理方法，以得出有关已确定相互作用的更明确的结论。 （AIM 4）开发公共R软件和动态项目网站。确定多个候选者的原型相互作用。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，丰富的经验和有前途的初步研究，该项目的成功可能性很大。