Plasma Proteomic Profiling Reveals Distinct Signatures of Chest CT Phenotypes in Sarcoidosis

TL;DR

This study identifies unique blood protein patterns linked to different chest CT features in sarcoidosis, shedding light on disease progression.

Contribution

Novel plasma proteomic signatures are identified for distinct sarcoidosis phenotypes using longitudinal chest CT data.

Findings

Distinct proteomic signatures differentiate progressive fibrosis from progressive nodular inflammation in sarcoidosis.

Progressive fibrosis is associated with epithelial–mesenchymal transition pathways.

Progressive nodular disease is linked to mTORC1 and MYC signaling and metabolic activation.

Abstract

Sarcoidosis is a granulomatous disease of unknown cause with a highly variable clinical course. The inability to predict progressive inflammation, fibrosis, or both underscores the limited understanding of the underlying molecular mechanisms. This study aimed to identify novel protein signatures associated with distinct pulmonary phenotypes of sarcoidosis, including progressive inflammation, progressive fibrosis, and disease resolution. We performed SomaScan 11K Assay to measure more than 10,000 unique human plasma proteins and compared protein expression between chest CT-defined phenotypes using principal component analysis, differential expression, correlation analysis, and gene set enrichment analysis. We identified distinct proteomic signatures that differentiated progressive fibrosis from progressive nodular inflammation in sarcoidosis. Enrichment and differential expression analyses revealed that progressive fibrosis was associated with epithelial–mesenchymal transition pathways, while progressive nodular disease was linked to mTORC1 and MYC signaling, as well as metabolic activation. Additionally, expression of 44 proteins correlated moderately to strongly with thoracic lymph node enlargement, suggesting immune activity in enlarged lymph node may be reflected in the circulating proteomic signals. This study leverages a unique longitudinal imaging approach to define extreme pulmonary phenotypes based on serial chest CT scoring, enabling the discovery of proteomic signals linked to distinct trajectories of sarcoidosis progression.

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