Lung Genomics Research Consortium

描述（由申请人提供）：慢性肺部疾病代表了广泛的慢性纤维化/炎症性肺部疾病，这些疾病在很大程度上对治疗反应不佳并且通常是致命的。慢性阻塞性肺病/肺气肿是美国第四大死因，肺纤维化的发病率和死亡率每年都在增加。尽管在解释慢性肺部疾病的临床、放射学和病理学特征方面已经取得了进展，并且在确定病理学方面也在继续取得进展，但其原因、生物学机制和治疗选择仍然不清楚。此外，目前还不可能预测有患这些复杂疾病风险的个人或人群。为了应对这一挑战，我们计划使用生物信息学工具创建一个遗传、分子和定量临床表型数据仓库，使研究人员能够在疾病发病机制方面做出根本性发现，完善诊断标准，并在个性化医疗方面取得真正的成果。复合遗传、基因组和表观遗传特征与定量临床表型相结合，有可能描述复杂疾病的动态生物学状态，并补充依赖于传统疾病临床测量的现有诊断方法。在拟议的项目中，我们计划通过创建肺基因组研究联盟 (LGRC) 这一综合性遗传、分子和定量临床表型分析仓库，扩大 NHLBI 肺组织研究联盟 (LTRC) 生物样本库的范围和影响。我们的总体假设是，遗传、分子和定量临床表型仓库与丰富的临床数据库相结合，将使肺部研究界能够在疾病发病机制方面取得根本性的发现，完善诊断标准，并在个性化医疗方面取得真正的成果。我们计划通过以下目标来实现这一假设。具体目标 1：建立遗传、基因组和表观遗传分子库，以补充 LTRC 现有的临床数据库。具体目标 2：使用现有 LTRC 数据以及包括新型定量 CT 和组织学成像分析的增强数据集开发定量临床表型平台。具体目标 3：建立一个可公开访问的数据库，将遗传、分子和定量表型数据与 LTRC 中现有的临床数据相整合，并提供肺部研究界易于访问的查询和数据探索工具。这些发现将使临床医生能够：1）识别有患慢性肺病风险的个体； 2) 及早诊断这些病症； 3）确定导致这些疾病的新机制； 4）无论传统疾病类别如何，将疾病实体重新分类为更能代表分子和细胞致病机制的类别； 5) 制定个性化的治疗方法。 公共卫生相关性：慢性肺病影响很大一部分人口，COPD/肺气肿和特发性间质性肺炎的发病率逐年增加，COPD 是美国第四大死因 (www.cdc.gov)。尽管过去二十年来在肺部研究方面进行了大量投资，但这些疾病仍然是主要的公共卫生问题，其患病率、发病率和严重程度却在不断增加。为了应对这一挑战，我们计划创建一个带有生物信息学工具的遗传、分子和定量表型数据仓库，使研究人员能够在疾病发病机制方面做出根本性的发现，完善诊断标准，并在个性化医疗方面取得真正的成果。这些发现将使临床医生能够：1）识别有患慢性肺病风险的个体； 2) 早期诊断这些病症； 3）确定导致慢性肺病的新机制； 4）最终制定个性化的干预治疗策略。

DESCRIPTION (provided by applicant): Chronic lung diseases represent a broad spectrum of chronic fibrosing/inflammatory lung conditions that are for the most part poorly responsive to treatment and often fatal. COPD/emphysema is the fourth leading cause of death in the United States and the incidence and rate of death from pulmonary fibrosis is increasing each year. Although progress has been made in interpretation of the clinical, radiological, and pathological features of chronic lung disorders, and progress in determining the pathobiology continues, the causes, biologic mechanisms, and therapeutic options remain obscure. Moreover, predicting individuals or populations at risk for developing any of these complex diseases, at present, is not possible. To address this challenge, we plan to create a genetic, molecular, and quantitative clinical phenotyping data warehouse with bioinformatic tools that will empower investigators to make fundamental discoveries in disease pathogenesis, refine diagnostic criteria, and lead to real gains in personalized medicine. The composite genetic, genomic, and epigenetic signature combined with quantitative clinical phenotypes has the potential to characterize the dynamic biological state of a complex disease and complement existing diagnostic approaches that are reliant on traditional clinical measures of disease. In the proposed project, we plan to extend the scope and impact of the NHLBI Lung Tissue Research Consortium (LTRC) biorepository by creating the Lung Genomics Research Consortium (LGRC), a comprehensive genetic, molecular, and quantitative clinical phenotyping warehouse. Our overall hypothesis is that a genetic, molecular, and quantitative clinical phenotyping warehouse combined with a rich clinical database will enable the lung research community to make fundamental discoveries in disease pathogenesis, refine diagnostic criteria, and lead to real gains in personalized medicine. We plan to pursue this hypothesis through the following aims. Specific Aim 1: Establish a genetic, genomic, and epigenetic molecular library to complement the existing clinical database in the LTRC. Specific Aim 2: Develop a quantitative clinical phenotyping platform using existing LTRC data, as well as an enhanced data set including novel quantitative CT and histology imaging analyses. Specific Aim 3: Establish a publicly-accessible database that would integrate the genetic, molecular, and quantitative phenotyping data with the existing clinical data in the LTRC and provide query and data exploration tools that are easily accessible to the lung research community. These discoveries will enable clinicians to: 1) identify individuals at risk of developing chronic lung diseases; 2) diagnose these conditions earlier; 3) identify novel mechanisms that cause these diseases; 4) reclassify disease entities into categories more representative of molecular and cellular pathogenic mechanisms regardless of traditional disease categories; and 5) develop personalized approaches to treatment. PUBLIC HEALTH RELEVANCE: Chronic lung diseases affect a significant portion of the population, the incidence of COPD/emphysema and idiopathic interstitial pneumonia are increasing annually, and COPD is the fourth leading cause of death in the U.S. (www.cdc.gov). Despite major investments that have been made in lung research over the past two decades, these disease remains major public health problems that paradoxically are increasing in prevalence, incidence, and severity. To address this challenge, we plan to create a genetic, molecular, and quantitative phenotyping data warehouse with bioinformatic tools that will empower investigators to make fundamental discoveries in disease pathogenesis, refine diagnostic criteria, and lead to real gains in personalized medicine. These discoveries will enable clinicians to: 1) identify individuals at risk of developing chronic lung diseases; 2) diagnose these conditions at an earlier stage; 3) identify novel mechanisms that cause chronic lung disease; and 4) eventually develop personalized therapeutic strategies for intervention.