BAL Transcriptomes Characterize Idiopathic Pulmonary Fibrosis Endotypes With Prognostic Impact

Abstract

Background: Given the plethora of pathophysiologic mechanisms described in idiopathic pulmonary fibrosis (IPF), we hypothesize that the mechanisms driving fibrosis in IPF may be different from one patient to another.

Research question: Do IPF endotypes exist and are they associated with outcome?

Study design and methods: Using a publicly available gene expression dataset retrieved from BAL samples of patients with IPF and control participants (GSE70867), we clustered IPF samples based on a dimension reduction algorithm specifically designed for -omics data, called DDR Tree. After clustering, gene set enrichment analysis was performed for functional annotation, associations with clinical variables and prognosis were investigated, and differences in transcriptional regulation were determined using motif enrichment analysis. The findings were validated in three independent publicly available gene expression datasets retrieved from IPF blood samples.

Results: One hundred seventy-six IPF samples from three centers were clustered in six IPF clusters, with distinct functional enrichment. Although clinical characteristics did not differ between the clusters, one cluster conferred worse sex-age-physiology score-corrected survival, whereas another showed a numeric trend toward worse survival (P = .08). The first was enriched for increased epithelial and innate and adaptive immunity signatures, whereas the other showed important telomere and mitochondrial dysfunction, loss of proteostasis, and increased myofibroblast signatures. The existence of these two endotypes, including the impact on survival of the immune endotype, was validated in three independent validation cohorts. Finally, we identified transcription factors regulating the expression of endotype-specific survival-associated genes.

Interpretation: Gene expression-based endotyping in IPF is feasible and can inform clinical evolution. As endotype-specific pathways and survival-associated transcription factors are identified, endotyping may open up the possibility of endotype-tailored therapy.

Keywords: IPF; endotyping; gene expression.

Figure 1

Clustering of IPF samples based on gene expression data and association with survival. IPF samples were clustered based on gene expression data using the DDR Tree algorithm. A, Three-dimensional scatterplot of the three DDR Tree dimensions and clustering based on Euclidean distance. B, Euler diagram showing differentially expressed genes compared with those of control participants. C, Kaplan-Meier curves showing IPF endotypes. IPF = idiopathic pulmonary fibrosis.

Figure 2

Top 100 gene ontologies most differentially expressed between endotypes. Each row represents the expression of a specific gene ontology, each column represents one sample. Functional annotations are manually adjudicated. P values are determined based on differential expression between endotypes. GAP = gender-age-physiology; Go = gene ontology.

Figure 3

Density plots showing z scores for expression of gene ontologies involved in specific functions. For each specific function, density plots stratified per cluster are shown. The density plots include the z scores of gene ontology expression of gene ontologies included in that specific function. EGF = epidermal growth factor; ER = endoplasmic reticulum; FGF = fibroblast growth factor; IPF = idiopathic pulmonary fibrosis; NK: natural killer cells;PDGF = platelet-derived growth factor; TGFβ = transforming growth factor β.

Figure 4

Gene expression of marker genes associated with enriched functions of IPF5 and cell type deconvolution. A, Gene expression of several epithelial and immune archetypical genes. P values were determined vs all other idiopathic pulmonary fibrosis samples. B, Cell subtype deconvolution using xCell based on bulk transcriptome datasets. aDC = activated DC; cDC = conventional DC; DC = dendritic cells; iDC = immature DC; IPF = idiopathic pulmonary fibrosis; ly = lymphatic; mv = microvascular; NKT = natural killer T-cells; pDC = plasmacytoid DC; Tcm = central memory T-cells; Tem = effector memory T-cells; Tgd = gamma-delta T-cells; Th = T-helper cells; Tregs = regulatory T-cells.

Figure 4

Gene expression of marker genes associated with enriched functions of IPF5 and cell type deconvolution. A, Gene expression of several epithelial and immune archetypical genes. P values were determined vs all other idiopathic pulmonary fibrosis samples. B, Cell subtype deconvolution using xCell based on bulk transcriptome datasets. aDC = activated DC; cDC = conventional DC; DC = dendritic cells; iDC = immature DC; IPF = idiopathic pulmonary fibrosis; ly = lymphatic; mv = microvascular; NKT = natural killer T-cells; pDC = plasmacytoid DC; Tcm = central memory T-cells; Tem = effector memory T-cells; Tgd = gamma-delta T-cells; Th = T-helper cells; Tregs = regulatory T-cells.

Figure 5

Gene expression of transcription factors regulating endotype-specific survival genes. P values were determined vs control participants. The y-axis represents normalized expression. IPF = idiopathic pulmonary fibrosis.

Figure 6

IPF endotype validation in separate blood gene expression datasets. A, Heatmap showing gene expression of most differentially expressed genes for IPF5 in three independent gene expression datasets based on blood RNA from patients with IPF. B, Patients with a IPF5-like gene expression in the validation cohorts showing worse survival compared with other patients. IPF = idiopathic pulmonary fibrosis.