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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要/摘要现代多平台基因组数据集包含大量可能对发现有用的分子信息使用基于网络的跨多平台数据和跨基因的整合。模型是提取这些数据中嵌入的机械分子信息的关键。在本提案中，我们开发了模型。基于网络的综合方法填补了现有文献中的空白它们将被用来确定关键路径。特定疾病及其亚型，找到这些途径的关键上游调节因子，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特征，并确定调节途径的因素虽然我们的方法适用于任何疾病环境，但我们最初的重点是使用多平台。基因组数据集提供四种最近发现的共有分子亚型的深入分子表征 (CMS) 结直肠癌 (CRC) 为我们的生物医学和临床合作者提供设计和测试知识针对这些亚型的新的精准治疗策略为此，我们提出以下目标：具体目标 1：我们将设计一种新的模型公式，对上游遗传和表观遗传的通路评分进行回归我们将确定每个 CMS 的特征路径，并确定一组稀疏的潜在路径驱动因素。对于每条途径，我们的生物医学合作者将通过 CRISPR 和功能验证潜在的驱动因素我们还将开发新的贝叶斯分层链接回归模型。（BLINK）将确定哪些癌症具有共同的途径驱动因素，因此是具有共同共同途径的候选者靶向治疗，同时增强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来发现假定的因果网络边缘我们将使用这些方法来更深入地表征多平台基因组数据的多层图。关键 CMS 特征途径的网络，并确定哪些潜在的途径驱动因素因果关系，以及哪些中介因素可以预测对治疗的反应，我们将设计方法来预测治疗反应。构建整合多平台分子信息的通路特征，以提供单一数字、患者通路活性的具体总结对于精准治疗的患者选择可能有用。具体目标 3：我们将为无向和多层网络开发新颖的贝叶斯网络回归方法识别跨患者特定协变量线性或非线性变化的异质网络结构。将把这些方法应用于为 CRC 数据识别的关键网络，以发现这些网络在不同环境下的差异协变量，包括亚型 (CMS)、生物因素（过滤中的免疫）和临床反应。这项工作的成功完成将产生一套广泛的严格工具，用于集成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 特定途径的简短列表和通过基于 CMS 的精准疗法进行功能验证和临床转化的驱动力。将包括用于我们的方法的软件和用于探索 CRC 生物学基础的 Shiny 应用程序。