BAL Transcriptomes Characterize Idiopathic Pulmonary Fibrosis Endotypes With Prognostic Impact

Area of Science:

• Pulmonary Medicine
• Genomics
• Bioinformatics

Background:

• Idiopathic pulmonary fibrosis (IPF) exhibits diverse pathophysiologic mechanisms.
• The existence of distinct IPF patient subgroups (endotypes) driven by different mechanisms is hypothesized.

Purpose of the Study:

• To determine if IPF endotypes exist.
• To investigate the association of these endotypes with clinical outcomes and survival.

Main Methods:

• Clustering of IPF patient gene expression data from bronchoalveolar lavage (BAL) samples using the DDR Tree algorithm.
• Functional annotation via gene set enrichment analysis.
• Validation in independent IPF blood sample gene expression datasets.

Main Results:

• Six distinct IPF clusters were identified, with two showing significant survival associations.
• One cluster demonstrated enrichment for immune signatures and worse survival.
• Another cluster showed mitochondrial dysfunction and myofibroblast signatures, with a trend toward worse survival.

Conclusions:

• Gene expression-based endotyping of IPF is feasible and clinically relevant.
• Identified endotypes and associated pathways can inform prognosis.
• Endotyping may facilitate the development of targeted therapies for IPF.