Integrative multi-omics analysis reveals novel idiopathic pulmonary fibrosis endotypes associated with disease progression

• Published in: Respiratory research Vol. 24; no. 1; pp. 141 - 12
• Main Authors: Ruan, Peifeng, Todd, Jamie L, Zhao, Hongyu, Liu, Yi, Vinisko, Richard, Soellner, Julia F., Schmid, Ramona, Kaner, Robert J., Luckhardt, Tracy R., Neely, Megan L., Noth, Imre, Porteous, Mary, Raj, Rishi, Safdar, Zeenat, Strek, Mary E, Hesslinger, Christian, Palmer, Scott M., Leonard, Thomas B., Salisbury, Margaret L.
• Format: Journal Article
• Language: English
• Published: London BioMed Central 31.05.2023; BioMed Central Ltd; BMC
• Subjects: Analysis; Biomarkers; Care and treatment; Cluster analysis; Computational biology; Development and progression; Disease Progression; Endothelial growth factors; Female; Gene expression; Genetic aspects; Humans; Idiopathic Pulmonary Fibrosis - diagnosis; Idiopathic Pulmonary Fibrosis - genetics; Immune response; Lung; Lung fibrosis; Male; Medicine; Medicine & Public Health; MicroRNAs; Multiomics; Platelet-derived growth factor; Pneumology/Respiratory System; Prevention; Proteomics; Pulmonary fibrosis; Respiratory insufficiency; Risk factors; RNA; United States; Cluster analysis; Biomarkers; RNA; Lung fibrosis; Proteomics; Computational biology
• ISSN: 1465-993X; 1465-9921; 1465-993X
• DOI: 10.1186/s12931-023-02435-0

Background Idiopathic pulmonary fibrosis (IPF) is characterized by the accumulation of extracellular matrix in the pulmonary interstitium and progressive functional decline. We hypothesized that integration of multi-omics data would identify clinically meaningful molecular endotypes of IPF. Methods The IPF-PRO Registry is a prospective registry of patients with IPF. Proteomic and transcriptomic (including total RNA [toRNA] and microRNA [miRNA]) analyses were performed using blood collected at enrollment. Molecular data were integrated using Similarity Network Fusion, followed by unsupervised spectral clustering to identify molecular subtypes. Cox proportional hazards models tested the relationship between these subtypes and progression-free and transplant-free survival. The molecular subtypes were compared to risk groups based on a previously described 52-gene (toRNA expression) signature. Biological characteristics of the molecular subtypes were evaluated via linear regression differential expression and canonical pathways (Ingenuity Pathway Analysis [IPA]) over-representation analyses. Results Among 232 subjects, two molecular subtypes were identified. Subtype 1 (n = 105, 45.3%) and Subtype 2 (n = 127, 54.7%) had similar distributions of age (70.1 +/- 8.1 vs. 69.3 +/- 7.6 years; p = 0.31) and sex (79.1% vs. 70.1% males, p = 0.16). Subtype 1 had more severe disease based on composite physiologic index (CPI) (55.8 vs. 51.2; p = 0.002). After adjusting for CPI and antifibrotic treatment at enrollment, subtype 1 experienced shorter progression-free survival (HR 1.79, 95% CI 1.28,2.56; p = 0.0008) and similar transplant-free survival (HR 1.30, 95% CI 0.87,1.96; p = 0.20) as subtype 2. There was little agreement in the distribution of subjects to the molecular subtypes and the risk groups based on 52-gene signature (kappa = 0.04, 95% CI= -0.08, 0.17), and the 52-gene signature risk groups were associated with differences in transplant-free but not progression-free survival. Based on heatmaps and differential expression analyses, proteins and miRNAs (but not toRNA) contributed to classification of subjects to the molecular subtypes. The IPA showed enrichment in pulmonary fibrosis-relevant pathways, including mTOR, VEGF, PDGF, and B-cell receptor signaling. Conclusions Integration of transcriptomic and proteomic data from blood enabled identification of clinically meaningful molecular endotypes of IPF. If validated, these endotypes could facilitate identification of individuals likely to experience disease progression and enrichment of clinical trials. Trial registration NCT01915511