Bidimensional integration for pan-omics pan-cancer analysis

项目摘要 我们对癌症病因学的了解越来越多地推动了同时进行研究的需要。 新的泛组学和泛癌。泛组学defi实现了多个高维数据的集成 捕捉不同分子成分的平台，这是必要的，因为细胞功能的变化 可在基因组、转录组、翻译组、蛋白质组、 或表观基因组。泛癌fi是对来自多个组织起源或组织学的数据的综合分析- 临床癌症类型，这是必要的，因为临床上相关的分子变化通常在 癌症类型。最近的统计进展促进了泛组学分析(即垂直整合) 单一平台的单一癌症类型和泛癌症分析(即横向整合)。这些分析是 同时，泛组学和泛癌具有完全和强有力的特征的巨大潜力- 使癌症中的分子异质性，但需要新的统计方法的原则二维 这些数据的集成。我们将开发一个支持要素的二维集成框架 在多个组学平台和癌症类型之间共享的功能，以及 特定的平台或癌症类型。此框架扩展了联合和个体差异解释(Jive)和 用于一维(例如，垂直或水平)积分的相关方法，以允许降维， 泛组学泛癌症数据的可视化和分子表征。我们的激励应用程序是 癌症基因组图谱(TCGA)计划，这是对癌症最全面和最精心策划的研究 来自11,000名患者的6个不同组学平台的基因组数据，代表33种不同的癌症 类型。我们将使用我们的新方法从整体上表征分子癌症的异质性 TCGA数据库，我们将利用这些结果来开发一个全面的患者生存模型，以重新fi 传统的病理诊断。我们的团队有资格承担这一具有挑战性和影响力的项目， 拥有统计数据集成(博士洛克)、大规模计算和生物信息学(博士迈尔斯)的专业知识， 癌症基因组学和TCGA(霍德利博士)我们的方法将对其他(例如，非癌症)研究有用 涉及基因组学数据，我们将在免费、开源和易于访问的软件中实现它们 方便其他研究人员和从业者使用它们。

Project Summary Our understanding of cancer etiology has increasingly motivated the need for investigations that are simulta- neously pan-omics and pan-cancer. Pan-omics defines the integration of data from multiple high-dimensional platforms that capture different molecular components, which is needed because changes to cellular function that affect cancer development can occur at the level of the genome, transcriptome, translatome, proteome, or epigenome. Pan-cancer defines the integrative analysis of data from multiple tissue-of-origin or histolog- ical cancer types, which is needed because clinically relevant molecular alterations are often shared across cancer types. Recent statistical advances have facilitated pan-omics analyses (i.e., vertical integration) of a single cancer type and pan-cancer analyses (i.e., horizontal integration) of a single platform. Analyses that are simultaneously pan-omics and pan-cancer have tremendous potential to completely and powerfully character- ize molecular heterogeneity in cancer, but require new statistical approaches for the principled bi-dimensional integration of such data. We will develop a framework for bi-dimensional integration that allows for features that are shared across multiple omics platforms and cancer types, as well as features that are unique to a particular platform or cancer type. This framework extends Joint and Individual Variation Explained (JIVE) and related methods for unidimensional (e.g., vertical or horizontal) integration, to allow for dimension reduction, visualization, and molecular characterization of pan-omics pan-cancer data. Our motivating application is The Cancer Genome Atlas (TCGA) project, which is the most comprehensive and well-curated study of the cancer genome with data for 6 different omics platforms from 11,000 patients representing 33 different cancer tumor types. We will use our novel methodology to characterize molecular cancer heterogeneity from the entire TCGA database, and we will use these results to develop a comprehensive model for patient survival to refine traditional pathological diagnoses. Our team is qualified to undertake this challenging and impactful project, with expertise in statistical data integration (Dr. Lock), large-scale computing and bioinformatics (Dr. Myers), and cancer genomics and TCGA (Dr. Hoadley). Our methods will be useful for other (e.g., non-cancer) studies involving genomics data, and we will implement them in free, open-source and easily accessible software to facilitate their use by other researchers and practitioners.