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: 10311368
• 负责人: Shuangge Ma
• 金额: $ 39.78万
• 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/10311368
• 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; 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.

项目摘要 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 有着重要的影响。通过一系列研究，我们小组采取了独特的稳健性观点 并在开发使用尖端高维G-E相互作用分析的基础方面发挥了主导作用 和正规化统计数据。最近，我们小组开创了I-E(组织病理成像-环境)交互作用 分析，并显著扩大了癌症分析的范围。我们对NHL有了重要的发现， 黑色素瘤和肺癌，极大地推动了他们的翻译研究和临床实践。 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交互作用，以真实描述癌症生物学，并信息指导临床决策。在这 项目，我们将是第一个开发范式转换SDL(统计原理深度学习)技术的公司 为癌症预后量身定做G-E/I-E交互作用分析。建议的方法将继承以下优点 现有的深度学习和回归技术优于这两种方法。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翻译和临床影响。 我们将：(目标1)开发为G-E/I-E交互分析量身定做的基本SDL技术。我们会 首先建立“基准”的非稳健损失，然后创新地发展到稳健的模型损失 规格错误和长尾分布/污染。一种新的惩罚技术将被应用于 建筑建造，这将适应独一无二的特点，主要的G/I效应，主要E 特效及其交互以定制的方式，屏蔽噪音，并尊重“主效”， 互动“层级结构。(目标2)通过纳入更多信息来提高绩效。我们将花费- 通过整合有关(A)以下各项之间的互连的附加信息，有效提高SDL性能 预后和G-E/I-E相互作用以及主要的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，整合多种类型的G/I测量。受它们重叠的激励 而且是独立的预测信息，我们将开发新的SDL方法，并率先集成 相互作用分析中的多种类型的分子和成像测量。(目标4)分析耶鲁大学的孢子 以及黑色素瘤和肺癌的TCGA数据。将对多种预后结果进行分析。 人口/临床/环境风险因素、多种类型的分子测量(蛋白质、基因 我们将分析这些基因的表达、突变、甲基化和微核糖核糖核酸(MicroRNA)以及组织病理学影像特征。这个 分析结果将得到彻底和严格的评估，与使用替代方案的分析结果相比， 并以多种方式进行验证。