Novel Methods for Identifying Genetic Interactions for Cancer Prognosis

识别癌症预后基因相互作用的新方法

• 批准号: 10451680
• 负责人: Shuangge Ma
• 金额: $ 38.99万
• 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 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10451680
• 关键词: Architecture; Award; Benchmarking; Biological; Cancer Biology; Cancer Prognosis; Characteristics; Clinical; Computer software; Custom; Data; Data Analyses; Development; Environment; Environmental Risk Factor; Foundations; Gene Expression; Genes; Genetic; Goals; Image; Inherited; Malignant Neoplasms; Malignant neoplasm of lung; Measurement; Methodology; Methods; Methylation; MicroRNAs; Modeling; Molecular; Mutation; Noise; Non-Hodgkin' s Lymphoma; Outcome; Performance; Prevention; Prognosis; Proteins; Role; Series; Signal Transduction; Tail; Techniques; The Cancer Genome Atlas; Training; Translational Research; analysis pipeline; base; clinical decision-making; clinical practice; cost; deep learning; gene environment interaction; high dimensionality; improved; innovation; learning strategy; melanoma; molecular imaging; multidimensional data; neural network architecture; novel; novel marker; prognostic model; screening; sound; statistics; therapy development

Project Summary For the prognosis of melanoma, lung cancer, and many other cancers, G-E (gene-environment) interactions have important implications. Through a series of studies, our group has taken a unique robustness perspective and a leading role in developing the foundation of G-E interaction analysis using cutting-edge high-dimensional and regularized statistics. Recently, our group pioneered I-E (histopathological imaging-environment) interaction analysis and significantly expanded the scope of cancer analytics. We have made important discoveries for NHL, melanoma, and lung cancer, impactfully advancing their translational research and clinical practice. Our overarching goal is to construct more powerful prognosis models and more accurately identify G-E/I- E interactions so as to truthfully describe cancer biology and informatively guide clinical decision-making. In this project, we will be the first to develop paradigm-shifting SDL (statistically principled deep learning) techniques tailored to G-E/I-E interaction analysis for cancer prognosis. The proposed methods will inherit strengths from the existing deep learning and regression techniques and be superior to both. We will continue analyzing data on melanoma and lung cancer, further enhancing the high translational and clinical impact of our study. We will: (Aim 1) Develop foundational SDL techniques tailored to G-E/I-E interaction analysis. We will first develop “benchmark” nonrobust losses and then innovatively advance to losses that are robust to model mis-specification and long-tailed distribution/contamination. A novel penalization technique will be applied for architecture construction, which will accommodate the unique characteristics of the main G/I effects, main E effects, and their interactions in a customized manner, screen out noises, and respect the “main effects, interactions” hierarchy. (Aim 2) Boost performance by incorporating additional information. We will cost- effectively improve SDL performance by incorporating additional information on (a) the interconnections between prognosis and G-E/I-E interactions as well as main G/I effects, and (b) the interconnections among G/I variables. (Aim 3) Expand analysis scope and integrate multiple types of G/I measurements. Motivated by their overlapping but also independent information for prognosis, we will develop novel SDL methods and be the first to integrate multiple types of molecular and imaging measurements in interaction analysis. (Aim 4) Analyze the Yale SPORE and TCGA data on melanoma and lung cancer. Analysis will be conducted on multiple prognosis outcomes. Demographic/clinical/environmental risk factors, multiple types of molecular measurements (protein, gene expression, mutation, methylation, and microRNA), and histopathological imaging features will be analyzed. The analysis results will be thoroughly and rigorously evaluated, extensively compared to those using alternatives, and validated in multiple ways.

项目概要 对于黑色素瘤、肺癌和许多其他癌症的预后，G-E（基因-环境）相互作用 通过一系列的研究，我们的团队采取了独特的稳健性观点。 并在利用尖端高维技术开发 G-E 相互作用分析基础方面发挥主导作用 最近，我们小组首创了 I-E（组织病理学成像-环境）交互。 分析并显着扩展了癌症分析的范围，我们为 NHL 做出了重要发现， 黑色素瘤和肺癌，有效地推进了它们的转化研究和临床实践。 我们的首要目标是构建更强大的预后模型并更准确地识别 G-E/I- E 相互作用，从而真实地描述癌症生物学并为临床决策提供信息。 项目中，我们将率先开发范式转换的 SDL（统计原理深度学习）技术 针对癌症预后的 G-E/I-E 相互作用分析量身定制的方法将继承以下方法的优点。 现有的深度学习和回归技术，并优于两者，我们将继续分析数据。 对黑色素瘤和肺癌的研究，进一步增强了我们研究的高转化和临床影响。 我们将：（目标 1）开发适合 G-E/I-E 交互分析的基础 SDL 技术。 首先开发“基准”非稳健损失，然后创新地推进到可稳健建模的损失 错误规范和长尾分布/污染将被应用。 建筑结构，将适应主要 G/I 效果、主要 E 的独特特征 影响及其相互作用以定制的方式，屏蔽噪音，并尊重“主要影响， （目标 2）通过纳入额外信息来提高绩效。 通过纳入以下方面的附加信息，有效提高 SDL 性能：(a) 之间的互连 预后和 G-E/I-E 相互作用以及主要 G/I 效应，以及 (b) G/I 变量之间的相互关系。 （目标 3）扩大分析范围并整合多种类型的 G/I 测量的重叠。 而且还有用于预后的独立信息，我们将开发新颖的 SDL 方法并成为第一个集成的方法 （目标 4）分析耶鲁孢子 将对黑色素瘤和肺癌的 TCGA 数据进行分析。 人口统计/临床/环境风险因素，多种类型的分子测量（蛋白质、基因 将分析表达、突变、甲基化和 microRNA）和组织病理学成像特征。 分析结果将经过彻底、严格的评估，主要是与使用替代方案的结果进行比较， 并通过多种方式进行验证。