Novel Methods for Identifying Genetic Interactions for Cancer Prognosis

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 有着重要的意义通过一系列的研究，我们的团队采取了独特的鲁棒性观点， 并在发展G-E相互作用分析的基础上发挥主导作用， 规范化的统计数据。最近，我们的团队开创了I-E（组织病理学成像-环境）交互 分析和显着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，有力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，更准确地识别G-E/I， E相互作用，以真实地描述癌症生物学和信息指导临床决策。在这 在这个项目中，我们将是第一个开发范式转换SDL（统计学原理深度学习）技术的公司。 专门针对癌症预后的G-E/I-E相互作用分析。所提出的方法将继承优势， 现有的深度学习和回归技术，并上级两者。我们将继续分析数据 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）开发基础SDL技术，为G-E/I-E交互分析量身定制。我们将 首先开发“基准”非稳健损失，然后创新性地推进到对模型稳健的损失 错误规范和长尾分布/污染。一种新的惩罚技术将被应用于 建筑结构，这将适应主G/I效应的独特特性，主E 效果，以及它们以定制方式的交互，筛选出噪声，并尊重“主效果， 互动的层次结构。(Aim 2）通过整合额外信息来提高性能。我们要花- 有效地提高SDL的性能，方法是结合以下方面的附加信息：（a） 预后和G-E/I-E相互作用以及主要G/I效应;（B）G/I变量之间的相互关系。 (Aim 3）扩大分析范围，集成多种G/I测量。由于它们的重叠 而且是独立的预后信息，我们将开发新的SDL方法，并率先整合 相互作用分析中的多种类型的分子和成像测量。(Aim 4）分析耶鲁大学的孢子 以及黑色素瘤和肺癌的TCGA数据。将对多个预后结果进行分析。 人口统计学/临床/环境风险因素，多种类型的分子测量（蛋白质，基因 表达、突变、甲基化和微小RNA）和组织病理学成像特征进行分析。的 将对分析结果进行彻底和严格的评估，并与使用替代品的结果进行广泛的比较， 并以多种方式进行验证。

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.