Imaging and multi-omics analyses to identify molecular subtypes of distinct emphysema patterns

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PROJECT SUMMARY/ABSTRACT Chronic obstructive pulmonary disease (COPD) is a progressive, debilitating disease in critical need of disease- modifying treatments. Emphysema, progressive lung destruction commonly encountered in subjects with COPD, portends a poor prognosis. This project will leverage two large well-phenotyped, NHLBI-funded studies (the COPDGene and Lung Tissue Research Consortium (LTRC)) and the team’s extensive expertise in modern imaging techniques, multi-omics data analysis, machine learning approaches, and in vitro functional validation. The overall objective of this application is to identify novel multi-omics biomarkers and molecular subtypes of centrilobular, panlobular, and paraseptal emphysema patterns utilizing a systems biology approach to understand relationships between the multiple omics data types. In Aim 1, we will apply the local histogram (LH) chest computed tomography (CT) quantification method to generate imaging phenotypes of centrilobular, panlobular, and paraseptal emphysema in each lung lobe. We will cluster these lobar LH data to identify distinct groups of subjects with similar LH patterns. We will then test for single-omics associations of the identified emphysema clusters with genetic variants, DNA methylation marks, telomere length, gene expression, and proteomics in peripheral blood and lung tissue samples. Aim 2 will develop and evaluate a lung-tissue informed, blood-based multi-omics machine learning model for reliable clinical prediction of emphysema patterns. Timely diagnosis calls for a blood-based predictive model as it may identify emphysema in subjects where CT scans are not clinically indicated. This would also overcome the issues of radiation exposure and false positive findings associated with CT scans. Aim 3 will discover molecularly-informed emphysema subtypes by applying an innovative, interpretable, machine learning algorithm that captures directional feature interactions and provides network representations of the molecular determinants of emphysema subtypes. We will then perform cluster analysis on the Bivariate Shapley network representations to identify distinct subgroups of subjects based on their graph similarity. To confirm the critical regulators of the identified pathways, we will conduct targeted gene silencing and overexpression investigations in airway epithelial cells and lung fibroblasts. Genes will be prioritized for functional validation utilizing existing biological knowledge and network analyses. Through a combination of innovative, cutting-edge data generation, analytic approaches, and functional validation, this project will make a significant contribution by enhancing emphysema phenotyping and multi-omics profiling for a more robust prediction and a better understanding of disease pathobiology. Such knowledge will pave the way for the development of much-needed novel and personalized therapeutic strategies.