Lung Genomics Research Consortium

肺基因组学研究联盟

• 批准号: 8305295
• 负责人: MARK W GERACI
• 金额: $ 165.48万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8305295
• 关键词: Lung; Genomics; Research; Consortium

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.

描述（由申请人提供）：慢性肺部疾病是一种广谱慢性纤维化/炎症性肺部疾病，大多数情况下对治疗反应不良，通常是致命的。COPD/肺气肿是美国第四大死亡原因，肺纤维化的发病率和死亡率每年都在增加。虽然在解释慢性肺部疾病的临床、放射学和病理学特征方面取得了进展，并且在确定病理生物学方面也取得了进展，但其病因、生物学机制和治疗选择仍然不清楚。此外，目前还不可能预测个人或人群是否有患上这些复杂疾病的风险。为了应对这一挑战，我们计划利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实的进展。与定量临床表型相结合的复合遗传、基因组和表观遗传标记具有表征复杂疾病的动态生物学状态并补充依赖于疾病的传统临床测量的现有诊断方法的潜力。在拟议的项目中，我们计划通过创建肺基因组学研究联盟（LGRC）来扩大NHLBI肺组织研究联盟（LTRC）生物储存库的范围和影响，这是一个综合的遗传，分子和定量临床表型仓库。我们的总体假设是，一个基因、分子和定量临床表型数据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发现，完善诊断标准，并在个性化医疗方面取得真实的进展。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文库，以补充LTRC中现有的临床数据库。具体目标二：使用现有的LTRC数据开发定量临床表型分析平台，以及包括新型定量CT和组织学成像分析的增强数据集。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中现有的临床数据整合在一起，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）识别有患慢性肺部疾病风险的个体; 2）早期诊断这些疾病; 3）识别导致这些疾病的新机制; 4）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，而不考虑传统疾病类别; 5）开发个性化治疗方法。 公共卫生相关性：慢性肺部疾病影响很大一部分人群，COPD/肺气肿和特发性间质性肺炎的发病率每年都在增加，COPD是美国第四大死亡原因（www.cdc.gov）。尽管在过去的二十年里，对肺部研究进行了重大投资，但这些疾病仍然是主要的公共卫生问题，其流行率、发病率和严重程度却在矛盾地增加。为了应对这一挑战，我们计划利用生物信息学工具创建一个遗传、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发现，完善诊断标准，并在个性化医疗方面取得真实的进展。这些发现将使临床医生能够：1）识别有患慢性肺部疾病风险的个体; 2）在早期诊断这些疾病; 3）识别导致慢性肺部疾病的新机制; 4）最终制定个性化的干预治疗策略。