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

好了！ 摘要 慢性阻塞性肺疾病(COPD)是世界范围内主要的死亡原因之一， 毁灭性的社会经济负担每年影响着美国300多万人。这个 易感人群的主要环境危险因素是吸烟，这会导致夸大 炎症反应。然而，包括几个遗传风险变量在内的许多因素都会对 敏感度。基于双胞胎的研究表明，患有肺气肿的家庭患肺气肿的风险更高。 COPD的两个不同的主要表型是小气道重塑(呼吸道疾病)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 它们之间的关系是复杂的，很可能涉及反馈机制。发展中 一种客观的方法来表征肺的表型是至关重要的，因为治疗方案根据不同的 表型。高分辨率计算机断层成像(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 遗传风险变异，基因组关联研究(GWAS)关注的是生理性肺 功能或简单的基于阈值的肺部CT测量，这两者都不能完全描述 表型亚型或疾病的分布模式。拟议中的研究将利用富人的优势 图像和遗传数据共同建立一个遗传信息成像生物标记物，以确定每个患者的特征。 对于每个患者，我们的方法将CT图像汇总为准确描述 疾病的严重性。此外，将表示链接回遗传风险变体的方法将是 发展起来的。如果成功，这些方法可以用来监测治疗的疗效或进展 使用成像数据诊断疾病。成功实现第二个目标将有助于更好地理解 不同疾病亚型的病因学和可用作潜在用途的新遗传途径的发现 毒品目标。此外，患者表示使得能够使用图像数据来构建更多 强大的模型来预测所谓的急性加重事件。预测病情恶化是有临床意义的 这很重要，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的图像生物标记物，该标记物通过成像和 每个病人的基因数据。我们在目标2中的统计方法阐明了潜在的遗传途径 在生物标记物解释的异常解剖变异背后。我们在数据上验证了我们的方法 COPDgene数据库中的10,300名患者。 好了！

! 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. !