Deep Learning Integration of Chest CT 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 patterns of systemic inflammation have not been fully described. Objectives To identify novel relationships between lung structural changes measured by chest computed tomography (CT) and systemic inflammation 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. Measurements and Main 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 gene expression data identified two IEAs that capture distinct inflammatory processes associated with emphysema and airway-predominant COPD. Scientific Knowledge on the Subject Chronic obstructive pulmonary disease (COPD) is characterized by lung structural changes and has a prominent systemic inflammatory component, but the links between lung structural changes and patterns of systemic inflammation in COPD have not been fully described. What This Study Adds to the Field We identified novel relationships between lung structural changes and systemic inflammation by simultaneously analyzing CT scans and blood RNA-sequencing gene expression using deep learning models. We identified two distinct Image-Expression Axes (IEAs) that characterize different inflammatory processes associated with emphysema and airway predominant COPD. This article has an online data supplement, which is accessible from this issue’s table of content online at [www.atsjournals.org][1]. ### Competing Interest Statement Peter J. Castaldi has received grant support from Bayer and consulting fees from Novartis and GSK. Craig P. Hersh reports grant support from Bayer, Boehringer-Ingelheim, and Vertex, and consulting fees from AstraZeneca and Takeda. Edwin K. Silverman has received grant support from Bayer and GSK. ### Funding Statement This work was supported by NHLBI K08 HL141601, R01 HL124233, R01 HL126596, R01 HL147326, U01 HL089897, and U01 HL089856. The COPDGene study ([NCT00608764][2]) is also supported by the COPD Foundation through contributions made to an Industry Advisory Committee comprised of AstraZeneca, Bayer Pharmaceuticals, Boehringer-Ingelheim, Genentech, GlaxoSmithKline, Novartis, Pfizer and Sunovion. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Institutional review board (IRB) approval was obtained. IRB Protocol Title: Genetic Epidemiology of COPD. IRB Protocol Number: Brigham and Women's Hospital / 2007P000554. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced are available online at dbGaP [1]: http://www.atsjournals.org [2]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT00608764&atom=%2Fmedrxiv%2Fearly%2F2022%2F10%2F14%2F2022.09.26.22280242.atom