Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes.

Area of Science:

• Pulmonary Medicine
• Genetics
• Bioinformatics

Background:

• Chronic obstructive pulmonary disease (COPD) exhibits significant heterogeneity, complicating treatment and prognosis.
• While genetic variants and gene expression are known COPD risk predictors, their role in defining disease subtypes remains unclear.

Approach:

• Defined high-risk COPD subtypes by integrating polygenic risk scores (PRS) and transcriptional risk scores (TRS) in training and test datasets.
• Assessed clinical and molecular differences between identified subgroups, including lung function, BMI, and proteomic profiles.
• Utilized protein-protein interaction networks and drug repurposing analyses to explore subtype-specific therapeutic targets.

Key Points:

• Two high-risk omics-defined COPD subgroups, 'High activity' (low PRS/high TRS) and 'severe risk' (high PRS/high TRS), were identified.
• Both subgroups exhibited lower BMI, reduced lung function, and altered metabolic, growth, and immune signaling compared to a low-risk reference group.
• 'High activity' subgroup showed accelerated FEV1 decline, with proteomic enrichment related to 5-lipoxygenase and ACE inhibitor pathways.

Conclusions:

• Combined PRS and TRS effectively identified clinical and molecular heterogeneity within high-risk COPD populations.
• Proteomic and drug repurposing analyses revealed subtype-specific therapeutic opportunities, suggesting patient selection is crucial for successful drug repurposing in COPD.