Current therapeutic options for chronic obstructive pulmonary disease (COPD) are limited, and new drug development in COPD has been hampered by the lack of clinically relevant biomarkers to assess likely drug efficacy. We will identify differentially abundant and lung-specific protein biomarkers of COPD in lung tissue and blood (plasma) using comprehensive proteomic analysis. Genetic determinants influence COPD susceptibility, but previously identified COPD genetic determinants account for only a small percentage of COPD heritability. Genetic analysis of protein biomarkers could reveal the network of regulatory factors influencing COPD susceptibility and COPD heterogeneity. Our overall hypothesis is that functional genetic variants lead to abnormal proteomic states that influence the development and heterogeneity of COPD. We will identify potential COPD biomarkers by studying lung-specific proteins that differ in lung tissue and plasma from 100 COPD cases and 50 smoking control subjects. We will then measure the most promising lung- specific plasma proteins in 1950 non-Hispanic White and African American COPDGene subjects with distinct imaging characteristics (airway-predominant COPD, emphysema-predominant COPD, and resistant smokers) to identify protein biomarkers associated with COPD and COPD subtypes. We will also perform genetic association analysis of lung-specific protein levels using whole genome sequencing data in these COPDGene subjects. Genetic determinants of protein biomarker levels will be tested for association with COPD susceptibility and COPD subtypes in multiple COPD populations. We will then determine whether the integrated analysis of the identified protein biomarkers of COPD susceptibility, several previously reported COPD protein biomarkers (CC16, Surfactant Protein D, sRAGE, and PARC), and the genetic determinants of COPD and COPD protein biomarkers enables prediction of disease progression rates in mild-to-moderate COPD subjects. The identification and characterization of novel COPD protein biomarkers may provide insights into COPD pathogenesis and tools for future clinical trials.

Public Health Relevance

Chronic obstructive pulmonary disease (COPD) is a major public health problem that is strongly influenced by cigarette smoking and genetic predisposition. The primary objectives of this proposal are to identify new plasma protein biomarkers for COPD, to determine the genetic determinants of these protein biomarker levels, and to integrate the protein biomarkers and genetic determinants of both COPD and COPD protein biomarkers to diagnose COPD and COPD subtypes, to predict COPD disease progression, and ultimately to evaluate treatment efficacy. Identification and investigation of novel protein biomarkers may lead to new insights into the biological mechanisms causing COPD, suggest new pathways for treatment, and provide tools to select subjects for future clinical trials of COPD medications.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
1R01HL133135-01A1
Application #
9311233
Study Section
Infectious Diseases, Reproductive Health, Asthma and Pulmonary Conditions Study Section (IRAP)
Program Officer
Postow, Lisa
Project Start
2017-04-12
Project End
2021-03-31
Budget Start
2017-04-12
Budget End
2018-03-31
Support Year
1
Fiscal Year
2017
Total Cost
$923,458
Indirect Cost
$167,030
Name
Brigham and Women's Hospital
Department
Type
Independent Hospitals
DUNS #
030811269
City
Boston
State
MA
Country
United States
Zip Code
02115
Lee, Joon-Yong; Choi, Hyungwon; Colangelo, Christopher M et al. (2018) ABRF Proteome Informatics Research Group (iPRG) 2016 Study: Inferring Proteoforms from Bottom-up Proteomics Data. J Biomol Tech 29:39-45
Hoopmann, Michael R; Winget, Jason M; Mendoza, Luis et al. (2018) StPeter: Seamless Label-Free Quantification with the Trans-Proteomic Pipeline. J Proteome Res 17:1314-1320
Slama, Patrick; Hoopmann, Michael R; Moritz, Robert L et al. (2018) Robust determination of differential abundance in shotgun proteomics using nonparametric statistics. Mol Omics 14:424-436