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 具有重要的含义。通过一系列研究，我们的小组采取了独特的鲁棒性观点 以及使用最先进的高维 和正规统计。最近，我们的小组开创了I-E（组织病理学成像环境）的相互作用 分析并显着扩大了癌症分析的范围。我们已经为NHL做出了重要发现， 黑色素瘤和肺癌，有影响力地推进了他们的翻译研究和临床实践。 我们的总体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，以真正描述癌症生物学并提供信息指导临床决策。在这个 项目，我们将是第一个开发范式转移SDL（统计校长深度学习）技术的人 针对癌症提案的G-E/I-E相互作用分析量身定制。提出的方法将继承从 现有的深度学习和回归技术，优于两者。我们将继续分析数据 关于黑色素瘤和肺癌，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）开发针对G-E/I-E相互作用分析的基础SDL技术。我们将 首先发展“基准”非保证损失，然后创新到损失的损失，以建模 错误的指定和长尾分配/污染。将采用一种新颖的惩罚技术 建筑构建将适应主要g/i效果的独特特征，主要e 效果及其相互作用以定制的方式，筛选出噪音，并尊重“主要效果， 层次结构。（AIM 2）通过合并其他信息来提高性能。我们将成本 - 通过合并有关（a）之间的其他信息来有效提高SDL性能 预后和g-e/i-e相互作用以及主g/i效应，以及（b）g/i变量之间的相互作用。 （AIM 3）扩大分析范围并集成了多种类型的G/I测量值。由他们的重叠动机 但也是预后的独立信息，我们将开发新型的SDL方法，并成为第一个整合的人 相互作用分析中的多种类型的分子和成像测量。 （AIM 4）分析耶鲁孢子 以及有关黑色素瘤和肺癌的TCGA数据。分析将对多种预后结局进行。 人口/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）以及组织病理学成像特征。 与使用替代方案的分析结果将进行彻底和严格的评估，并进行广泛的评估， 并通过多种方式进行验证。