Imaging and multi-omics analyses to identify molecular subtypes of distinct emphysema patterns

影像学和多组学分析可识别不同肺气肿模式的分子亚型

• 批准号: 10736162
• 负责人: Adel El Boueiz
• 金额: $ 88.73万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10736162
• 关键词: Address; Affect; Algorithms; Area; Benchmarking; Biological; Biological Markers; Biology; Blood; Blood specimen; Chronic Obstructive Pulmonary Disease; Clinical; Cluster Analysis; DNA Methylation; Data; Data Analyses; Development; Diagnosis; Disease; Epithelial Cells; Equilibrium; Fibroblasts; Funding; Gene Expression; Gene Silencing; Generations; Genes; Graph; Health; Image; Imaging Techniques; In Vitro; Individual; Investigation; Knowledge; Length; Lobar; Lung; Machine Learning; Measurement; Measures; Methodology; Methods; Modality; Modeling; Modernization; Molecular; Morbidity - disease rate; Multiomic Data; National Heart, Lung, and Blood Institute; Pathologic; Pathway Analysis; Pathway interactions; Pattern; Performance; Phenotype; Process; Prognosis; Proteomics; Pulmonary Emphysema; Radiation exposure; Reporting; Reproducibility; Research; Resources; Scanning; Severities; Smoker; Structure of parenchyma of lung; Subgroup; Systems Biology; Testing; Tissue Sample; Validation; X-Ray Computed Tomography; airway epithelium; alpha 1-Antitrypsin; alpha 1-Antitrypsin Deficiency; alveolar destruction; attenuation; biomarker identification; chest computed tomography; clinical predictors; cohort; deep neural network; disorder subtype; early onset; feature selection; functional disability; genetic variant; genome sequencing; graph neural network; improved; innovation; insight; lung injury; lung lobe; machine learning algorithm; machine learning method; machine learning model; molecular subtypes; mortality; multidisciplinary; multiple omics; novel; novel therapeutics; overexpression; peripheral blood; personalized medicine; personalized therapeutic; predictive marker; predictive modeling; support vector machine; telomere; transcriptome sequencing; whole genome

PROJECT SUMMARY/ABSTRACT Chronic obstructive pulmonary disease (COPD) is a progressive, debilitating disease in critical need of disease- modifying treatments. Emphysema, progressive lung destruction commonly encountered in subjects with COPD, portends a poor prognosis. This project will leverage two large well-phenotyped, NHLBI-funded studies (the COPDGene and Lung Tissue Research Consortium (LTRC)) and the team’s extensive expertise in modern imaging techniques, multi-omics data analysis, machine learning approaches, and in vitro functional validation. The overall objective of this application is to identify novel multi-omics biomarkers and molecular subtypes of centrilobular, panlobular, and paraseptal emphysema patterns utilizing a systems biology approach to understand relationships between the multiple omics data types. In Aim 1, we will apply the local histogram (LH) chest computed tomography (CT) quantification method to generate imaging phenotypes of centrilobular, panlobular, and paraseptal emphysema in each lung lobe. We will cluster these lobar LH data to identify distinct groups of subjects with similar LH patterns. We will then test for single-omics associations of the identified emphysema clusters with genetic variants, DNA methylation marks, telomere length, gene expression, and proteomics in peripheral blood and lung tissue samples. Aim 2 will develop and evaluate a lung-tissue informed, blood-based multi-omics machine learning model for reliable clinical prediction of emphysema patterns. Timely diagnosis calls for a blood-based predictive model as it may identify emphysema in subjects where CT scans are not clinically indicated. This would also overcome the issues of radiation exposure and false positive findings associated with CT scans. Aim 3 will discover molecularly-informed emphysema subtypes by applying an innovative, interpretable, machine learning algorithm that captures directional feature interactions and provides network representations of the molecular determinants of emphysema subtypes. We will then perform cluster analysis on the Bivariate Shapley network representations to identify distinct subgroups of subjects based on their graph similarity. To confirm the critical regulators of the identified pathways, we will conduct targeted gene silencing and overexpression investigations in airway epithelial cells and lung fibroblasts. Genes will be prioritized for functional validation utilizing existing biological knowledge and network analyses. Through a combination of innovative, cutting-edge data generation, analytic approaches, and functional validation, this project will make a significant contribution by enhancing emphysema phenotyping and multi-omics profiling for a more robust prediction and a better understanding of disease pathobiology. Such knowledge will pave the way for the development of much-needed novel and personalized therapeutic strategies.

项目概要/摘要 慢性阻塞性肺疾病（COPD）是一种进行性、使人衰弱的疾病，迫切需要疾病治疗。 修改治疗方法，肺气肿、慢性阻塞性肺病患者常见的进行性肺损伤， 该项目将利用两项由 NHLBI 资助的大型表型研究（ COPDGene 和肺组织研究联盟 (LTRC)) 以及该团队在现代 成像技术、多组学数据分析、机器学习方法和体外功能验证。 该应用的总体目标是识别新型多组学生物标志物和分子亚型 利用系统生物学方法研究小叶中心型、全小叶型和间隔旁型肺气肿模式 了解多种组学数据类型之间的关系在目标 1 中，我们将应用局部直方图 (LH)。 胸部计算机断层扫描（CT）量化方法产生小叶中心的成像表型， 每个肺叶的全小叶肺气肿和间隔旁肺气肿 我们将对这些肺叶 LH 数据进行聚类，以识别不同的肺叶 LH 数据。 然后，我们将测试已识别的单组学关联。 具有遗传变异、DNA 甲基化标记、端粒长度、基因表达和 目标 2 将开发和评估外周血和肺组织样本中的蛋白质组学。 基于血液的多组学机器学习模型，可及时可靠地进行肺气肿模式的临床预测。 诊断需要基于血液的预测模型，因为它可以通过 CT 扫描识别受试者的肺气肿 这也将克服辐射暴露和假阳性结果的问题。 与 CT 扫描相关的目标 3 将通过应用分子信息来发现肺气肿亚型。 创新的、可解释的机器学习算法，捕获定向特征交互并提供 然后我们将进行聚类分析肺气肿亚型的分子决定因素的网络表示。 对双变量 Shapley 网络表示进行分析，以根据以下内容识别不同的受试者亚组 为了确认已识别途径的关键调节因子，我们将进行靶向基因分析。 气道上皮细胞和肺成纤维细胞基因的沉默和过度表达研究将是。 优先利用现有的生物学知识和网络分析进行功能验证。 创新、尖端的数据生成、分析方法和功能验证的结合，这 该项目将通过增强肺气肿表型分析和多组学分析做出重大贡献 更可靠的预测和对疾病病理学的更好理解将为我们铺平道路。 开发急需的新颖和个性化治疗策略。