Deep Learning Integration of Chest Computed Tomography Imaging and Gene Expression Identifies Novel Aspects of COPD.

RATIONALE: Chronic obstructive pulmonary disease (COPD) is characterized by pathologic changes in the airways, lung parenchyma, and persistent inflammation, but the links between lung structural changes and blood transcriptome patterns have not been fully described.

OBJECTIONS: To identify novel relationships between lung structural changes measured by chest computed tomography (CT) and blood transcriptome patterns measured by blood RNA sequencing.

METHODS: CT scan images and blood RNA-seq gene expression from 1,223 subjects in the COPDGene study were jointly analyzed using deep learning to identify shared aspects of inflammation and lung structural changes that we refer to as Image-Expression Axes (IEAs). We related IEAs to COPD-related measurements and prospective health outcomes through regression and Cox proportional hazards models and tested them for biological pathway enrichment.

RESULTS: We identified two distinct IEAs: IEAemph captures an emphysema-predominant process with a strong positive correlation to CT emphysema and a negative correlation to FEV1 and Body Mass Index (BMI); IEAairway captures an airway-predominant process with a positive correlation to BMI and airway wall thickness and a negative correlation to emphysema. Pathway enrichment analysis identified 29 and 13 pathways significantly associated with IEAemph and IEAairway , respectively (adjusted p<0.001).

CONCLUSIONS: Integration of CT scans and blood RNA-seq data identified two IEAs that capture distinct inflammatory processes associated with emphysema and airway-predominant COPD.