Bayesian Network-Based Integrative Genomics Methods for Precision Medicine

基于贝叶斯网络的精准医学综合基因组学方法

• 批准号: 10577871
• 负责人: Veerabhadran Baladandayuthapani
• 金额: $ 43.36万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10577871
• 关键词: Acceleration; Area; Bayesian Network; Biological; Biological Factors; Biological Process; Cancer Patient; Cancer cell line; Cell Line; Characteristics; Clinical; Clinical Research; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Colorectal Cancer; Communities; Complex; Computer software; Consensus; DNA Sequence Alteration; Data; Data Set; Disease; Drug Targeting; Encyclopedias; Epigenetic Process; Follow-Up Studies; Formulation; Free Will; Genes; Genetic; Genomics; Graph; Heterogeneity; Immune; Individual; Knowledge; Letters; Link; Literature; Malignant Neoplasms; Mediation; Mediator; Messenger RNA; Methods; Modeling; Modernization; Molecular; Mutation; Network-based; Oncogenic; Pathway interactions; Patient Selection; Patients; Pharmaceutical Preparations; Phenotype; Precision therapeutics; Prediction of Response to Therapy; Process; Research; Research Personnel; Resource Sharing; Running; Statistical Methods; Structure; Techniques; Testing; The Cancer Genome Atlas; Therapeutic; Training; University of Texas M D Anderson Cancer Center; Validation; Work; active method; analytical method; arm; cancer subtypes; clinical translation; cohort; colon cancer patients; data sharing; genomic data; genomic platform; improved outcome; individual patient; large datasets; molecular subtypes; network models; new therapeutic target; novel; personalized medicine; precision medicine; preclinical study; rare cancer; response; statistical learning; success; targeted treatment; therapeutic target; tool; tumor; usability; web portal

Project Summary/Abstract Modern multi-platform genomic data sets contain substantial molecular information potentially useful for discovering new precision therapeutic strategies. Integration across multi-platform data and across genes using network-based models is a key to extracting mechanistic molecular information embedded in these data. In this proposal, we develop integrative network-based methods that ll gaps in existing literature. They will be used to identify key pathways for a given disease and its subtypes, nd key upstream regulators of these pathways and determine which appear to be causal, construct pathway signatures potentially usable for patient selection, and identify factors modulating pathway associations. While our methods will be applicable to any disease setting, our initial focus will be to use multi-platform genomic data sets to provide a deep molecular characterization of four recently discovered consensus molecular subtypes (CMS) of colorectal cancer (CRC) to arm our biomedical and clinical collaborators with knowledge to devise and test new precision therapeutic strategies targeting these subtypes. For these purposes, we propose the following aims: Speci c Aim 1: We will devise a novel model formulation regressing pathway scores on upstream genetic and epigenetic factors to identify a sparse set of potential pathway drivers. We will identify characteristic pathways for each CMS and for each pathway identify potential drivers that our biomedical collaborators will functionally validate via CRISPR and identify potential matching drug targets. We will also develop novel Bayesian hierarchically linked regression models (BLINK) that will determine which cancers share common pathway drivers and thus are candidates for sharing a common targeted therapy, while increasing power for discovery of pathway drivers for rare cancers. Speci c Aim 2: We will develop network mediation analysis approaches to discover putative causal network edges in multi-layered graphs of multi-platform genomic data. We will use these methods to more deeply characterize the networks underlying key CMS-characteristic pathways and determine which potential pathway drivers appear to be causal, and which mediators are predictive of response to therapy. From these networks, we will devise methods to construct pathway signatures integrating multi-platform molecular information to provide a single-number, patient- speci c summary of pathway activity potentially useful for patient selection for precision therapeutics. Speci c Aim 3: We will develop novel Bayesian network regression methods for undirected and multi-layer networks that identify heterogeneous network structure varying linearly or nonlinearly across patient-speci c covariates. We will apply these methods to key networks identi ed for CRC data to discover how these networks vary across various covariates, including subtypes (CMS), biological factors (immune in ltration), and clinical response. Successful completion of this work will produce a broad set of rigorous tools for integrative and network modeling of multi-platform genomic data, and will provide our CRC collaborators with a short list of key CMS-speci c pathways and drivers for functional validation and clinical translation via CMS-based precision therapeutics. Our dissemination efforts will include software for our methods and Shiny apps for exploring biological underpinnings of CRC.

项目总结/摘要 现代多平台基因组数据集包含大量的分子信息，可能有助于发现 新的精准治疗策略使用基于网络的跨平台数据和跨基因的集成 模型是提取嵌入在这些数据中的机械分子信息的关键。在本提案中，我们开发 综合网络为基础的方法，填补了现有文献的空白。它们将被用来确定关键途径， 一个给定的疾病及其亚型，和这些途径的关键上游调控，并确定哪些似乎是 因果关系，构建可能用于患者选择的途径签名，并确定调节途径的因素 协会.虽然我们的方法将适用于任何疾病的设置，我们最初的重点将是使用多平台 基因组数据集，为最近发现的四种共有分子亚型提供深入的分子表征 (CMS)结直肠癌（CRC）的研究，以武装我们的生物医学和临床合作者， 针对这些亚型的新的精确治疗策略。为此，我们提出以下目标： 具体目标1：我们将设计一个新的模型公式回归上游遗传和表观遗传的途径得分 因素来识别一组稀疏的潜在途径驱动因素。我们将确定每个CMS的特征途径， 对于每种途径，确定我们的生物医学合作者将通过CRISPR功能验证的潜在驱动因素， 确定潜在的匹配药物靶点。我们还将开发新的贝叶斯分层关联回归模型 （BLINK），这将确定哪些癌症具有共同的途径驱动因素，因此是共享共同的 靶向治疗，同时增加发现罕见癌症途径驱动因素的能力。 具体目标2：我们将开发网络中介分析方法，以发现假定的因果网络边缘 在多平台基因组数据的多层图中。我们将使用这些方法来更深入地描述 网络的关键CMS特征的途径，并确定哪些潜在的途径驱动程序似乎是 因果关系，以及哪些介质可预测对治疗的反应。从这些网络中，我们将设计方法， 构建整合多平台分子信息的途径签名，以提供单一数字，患者- 对精确治疗的患者选择潜在有用的途径活性的具体概述。 具体目标3：我们将开发新的贝叶斯网络回归方法，用于无向和多层网络 其识别跨患者特异性协变量线性或非线性变化的异构网络结构。我们 我将这些方法应用于CRC数据艾德的关键网络，以发现这些网络如何在不同的网络中变化。 协变量，包括亚型（CMS）、生物学因素（免疫滤过）和临床应答。 这项工作的成功完成将产生一套广泛的严格的工具，用于综合和网络建模， 多平台基因组数据，并将为我们的CRC合作者提供关键CMS特异性途径的简短列表， 通过基于CMS的精确疗法进行功能验证和临床转化的驱动因素。我们的传播网络 将包括我们的方法软件和探索CRC生物学基础的Shiny应用程序。