Cluster analysis of transcriptomic datasets to identify endotypes of Idiopathic Pulmonary Fibrosis

Rationale: Considerable clinical heterogeneity in Idiopathic Pulmonary Fibrosis (IPF) suggests the existence of multiple disease endotypes. Identifying these endotypes could allow for a biomarker-driven personalised medicine approach in IPF. Objectives: To improve our understanding of the pathogenesis of IPF by identifying clinically distinct groups of patients with IPF that could represent distinct disease endotypes. Methods: We co-normalised, pooled and clustered three publicly available blood transcriptomic datasets (total 220 IPF cases). We compared clinical traits across clusters and used gene enrichment analysis to identify biological pathways and processes that were over-represented among the genes that were differentially expressed across clusters. A gene-based classifier was developed and validated using three additional independent datasets (total 194 IPF cases). Measurements and main results: We identified three clusters of IPF patients with statistically significant differences in lung function (P=0.009) and mortality (P=0.009) between groups. Gene enrichment analysis implicated dysregulation of mitochondrial homeostasis, apoptosis, cell cycle and innate and adaptive immunity in the pathogenesis underlying these groups. We developed and validated a 13-gene cluster classifier that predicted mortality in IPF (high-risk clusters vs low-risk cluster: hazard ratio= 4.25, 95% confidence interval= [2.14, 8.46], P=3.7×10-5). Conclusions: We have identified blood gene expression signatures capable of discerning groups of IPF patients with significant differences in survival. These clusters could be representative of distinct pathophysiological states, which would support the theory of multiple endotypes of IPF. Although more work must be done to confirm the existence of these endotypes, our classifier could be a useful tool in patient stratification and outcome prediction in IPF.