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.
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.
Showing the most recent 10 out of 64 publications
