An Integrative Radiogenomic Approach to Design Genetically-Informed Image Biomarker for Characterizing COPD

设计用于表征 COPD 的遗传信息图像生物标志物的综合放射基因组方法

• 批准号: 9499218
• 负责人: Kayhan Batmanghelich
• 金额: $ 57.09万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9499218
• 关键词: Acute; Address; Affect; Airway Disease; Alzheimer' s Disease; Anatomy; Area; Back; Biological; Biological Markers; Biological Neural Networks; Caring; Cause of Death; Chronic Bronchitis; Chronic Obstructive Airway Disease; Clinical; Complex; Computers; Data; Data Set; Deformity; Descriptor; Dimensions; Disease; Disease Progression; Disease Vectors; Drug Targeting; Economic Burden; Environmental Risk Factor; Etiology; Event; Family; Feedback; Genes; Genetic; Genetic Markers; Genetic Risk; Genome; Genotype; Goals; Heritability; High Resolution Computed Tomography; Image; Image Analysis; Individual; Inflammatory Response; Joints; Link; Lung; Lung CAT Scan; Machine Learning; Mathematics; Measurement; Measures; Methods; Modeling; Monitor; Obstruction; Pathogenicity; Pathologic; Pathway interactions; Patients; Pattern; Phenotype; Physiological; Pollution; Population; Predisposition; Process; Pulmonary Emphysema; Pulmonary Function Test/Forced Expiratory Volume 1; Radiogenomics; Reporting; Resolution; Respiratory physiology; Risk Factors; Severity of illness; Smoking; Spirometry; Statistical Methods; Statistical Models; Time; Treatment Efficacy; Twin Multiple Birth; Variant; X-Ray Computed Tomography; airway remodeling; alveolar destruction; attenuation; base; clinical Diagnosis; deep learning; design; disorder risk; disorder subtype; drug development; economic cost; endophenotype; follow-up; genotyped patients; high dimensionality; high risk; imaging biomarker; imaging genetics; lung lobe; non-invasive imaging; novel; risk variant; small airways disease; socioeconomics; tomography; vector

! Abstract Chronic Obstructive Pulmonary Disease (COPD) is one of the leading causes of death worldwide with a devastating socio-economic burden impacting more than three million individuals per year in the US. The primary environmental risk factor in the susceptible population is smoking, which causes an exaggerated inflammatory response. However, many factors including several genetic risk variants substantially influence the susceptibility. Twin-based studies show that families with emphysema have a higher risk for the disease. The two different major phenotypes of COPD are small airway remodeling (airway disease) and alveolar destruction (emphysema). Although these two major phenotypes result in a similar deficiency in global lung function, the relationship between them is complicated and likely involves feedback mechanisms. Developing an objective method to characterize lung phenotypes is critical since treatment candidates vary based on phenotype. Measurements from High-Resolution Computed Tomography (HRCT) images are increasingly used to describe COPD since they can quantitatively describe the contribution of the phenotypes. To discover the genetic risk variants, Genome-Association Studies (GWAS) have focused on either the physiological lung function or a simple threshold-based measurement from lung CT, neither of which fully characterizes phenotypic subtypes or the distribution pattern of disease. The proposed studies will take advantage of the rich image and genetic data jointly to build a genetically-informed imaging biomarker to characterize each patient. For each patient, our method summarizes the CT image to a vector representation that accurately describes the severity of the disease. Also, a method to link the representation back to the genetic risk variants will be developed. If successful, these methods can be used to monitor the efficacy of treatment or progression of the disease using imaging data. Successful execution of the second aim will result in better understanding of the etiology of different disease subtypes and discovery of novel genetic pathways that could be used as potential drug targets. Furthermore, the patient representation enables the use of image data to construct a more powerful model to predict the so-called acute exacerbation event. Predicting the exacerbations is clinically important since they cause further damage to the lung. In Aim 1, we develop and implement a novel image biomarker that is mutually informed by imaging and genetic data from each patient. Our statistical method in Aim 2 elucidates the underlying genetic pathways behind the abnormal anatomical variations explained by the biomarker. We validate our method on data from 10,300 patients in the COPDGene dataset. !

！ 抽象的 慢性阻塞性肺疾病（COPD）是全球范围内导致死亡的主要原因之一 毁灭性的社会经济负担每年影响美国超过三百万人。这 易感人群的主要环境危险因素是吸烟，这会导致过度吸烟 炎症反应。然而，包括几种遗传风险变异在内的许多因素都会严重影响 的敏感性。基于双胞胎的研究表明，患有肺气肿的家庭患这种疾病的风险更高。 COPD 的两种不同的主要表型是小气道重塑（气道疾病）和肺泡 破坏（肺气肿）。尽管这两种主要表型导致了全球肺的类似缺陷 功能，它们之间的关系很复杂，并且可能涉及反馈机制。发展 表征肺表型的客观方法至关重要，因为候选治疗方案因不同因素而异 表型。高分辨率计算机断层扫描 (HRCT) 图像的测量越来越多 用于描述 COPD，因为它们可以定量描述表型的贡献。去发现 对于遗传风险变异，基因组关联研究 (GWAS) 重点关注生理性肺 函数或肺部 CT 的基于阈值的简单测量，两者都不能完全表征 表型亚型或疾病的分布模式。拟议的研究将利用丰富的 图像和遗传数据共同构建遗传信息成像生物标志物来表征每个患者。 对于每位患者，我们的方法将 CT 图像总结为准确描述的矢量表示 疾病的严重程度。此外，一种将表征与遗传风险变异联系起来的方法将是 发达。如果成功，这些方法可用于监测治疗效果或病情进展 使用影像数据的疾病。成功执行第二个目标将导致更好地理解 不同疾病亚型的病因学以及可用作潜在用途的新遗传途径的发现 药物靶点。此外，患者表示能够使用图像数据来构建更多 预测所谓急性加重事件的强大模型。临床上可预测病情加重 很重要，因为它们会对肺部造成进一步的损害。 在目标 1 中，我们开发并实现了一种新颖的图像生物标志物，该标志物通过成像和 每个患者的基因数据。我们在目标 2 中的统计方法阐明了潜在的遗传途径 生物标志物解释的异常解剖变异背后。我们根据以下数据验证我们的方法 COPDGene 数据集中有 10,300 名患者。 ！