Blood-Based Transcriptomic and Proteomic Biomarkers of Emphysema

ABSTRACT Rationale Emphysema is a COPD phenotype with important prognostic implications. Identifying blood-based biomarkers of emphysema will facilitate early diagnosis and development of targeted therapies. Objectives Discover blood omics biomarkers for chest CT-quantified emphysema and develop predictive biomarker panels. Methods Emphysema blood biomarker discovery was performed using differential gene expression, alternative splicing, and protein association analyses in a training set of 2,370 COPDGene participants with available whole blood RNA sequencing, plasma SomaScan proteomics, and clinical data. Validation was conducted in a testing set of 1,016 COPDGene subjects. Since low body mass index (BMI) and emphysema often co-occur, we performed a mediation analysis to quantify the effect of BMI on gene and protein associations with emphysema. Elastic net models were also developed in the training sample sequentially using clinical, complete blood count (CBC) cell proportions, RNA sequencing, and proteomic biomarkers to predict quantitative emphysema. Model accuracy was assessed in the testing sample by the area under the receiver-operator-characteristic-curves (AUROC) for subjects stratified into tertiles of emphysema severity. Measurements and Main Results 4,913 genes, 1,478 isoforms, 386 exons, and 881 proteins were significantly associated with emphysema (FDR 10%) and yielded 109 biological pathways. 75% of the genes and 77% of the proteins associated with emphysema showed evidence of mediation by BMI. The highest-performing predictive model used clinical, CBC, and protein biomarkers, distinguishing the top from the bottom tertile of emphysema with an AUROC of 0.92. Conclusions Blood transcriptome and proteome-wide analyses reveal key biological pathways of emphysema and enhance the prediction of emphysema. AT A GLANCE COMMENTARY Scientific Knowledge on the Subject Differential gene expression and protein analyses have uncovered some of the molecular underpinnings of emphysema. However, no studies have assessed alternative splicing mechanisms and analyzed proteomic data from recently developed high-throughput panels. In addition, although emphysema has been associated with low body mass index (BMI), it is still unclear how BMI affects the transcriptome and proteome of the disease. Finally, the effectiveness of multi-omic biomarkers in determining the severity of emphysema has not yet been investigated. What This Study Adds to the Field We performed whole-blood genome-wide RNA sequencing and plasma SomaScan proteomic analyses in the large and well-phenotyped COPDGene study. In addition to confirming earlier findings, our differential gene expression, alternative splicing, and protein analyses identified novel biomarkers and pathways of chest CT-quantified emphysema. Our mediation analysis detected varying degrees of transcriptomic and proteomic mediation due to BMI. Our supervised machine learning modeling demonstrated the utility of incorporating multi-omics data in enhancing the prediction of emphysema.