Cluster analysis of transcriptomic datasets to identify endotypes of idiopathic pulmonary fibrosis

Abstract

BackgroundConsiderable clinical heterogeneity in idiopathic pulmonary fibrosis (IPF) suggests the existence of multiple disease endotypes. Identifying these endotypes would improve our understanding of the pathogenesis of IPF and could allow for a biomarker-driven personalised medicine approach. We aimed to identify clinically distinct groups of patients with IPF that could represent distinct disease endotypes.MethodsWe 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).FindingsWe identified three clusters of patients with IPF with statistically significant differences in lung function (p=0.009) and mortality (p=0.009) between groups. Gene enrichment analysis implicated 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: HR 4.25, 95% CI 2.14 to 8.46, p=3.7×10−5).InterpretationWe have identified blood gene expression signatures capable of discerning groups of patients with IPF 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.

Topological data analysis identifies molecular phenotypes of idiopathic pulmonary fibrosis

“…Kraven et al 45 have recently taken a similar approach to our study, although there are several limitations that we believe are solved by our present study. Kraven et al combine several datasets to form a larger IPF dataset, but do so combining different tissue types.…”

A Long and Winding Road: Drug Development in Idiopathic Pulmonary Fibrosis

“…One strategy for improving the performance of clinical trials in IPF is to move away from the “one disease, one drug” approach and instead embrace the principle of causative molecular mechanisms, generally referred to as “endotypes.” Identification of endotypes has the potential to explain disease heterogeneity, thus enabling treatment tailored to the individual patient. Recently, Kraven and colleagues, by applying machine learning to multiple gene expression datasets, identified three clusters of patients with IPF with distinct clinical features and survival ( 8 ). Patients in different clusters demonstrated activation of different underlying biological pathways, including immune system response and metabolic changes that could potentially be modulated therapeutically ( 8 ).…”

“…Recently, Kraven and colleagues, by applying machine learning to multiple gene expression datasets, identified three clusters of patients with IPF with distinct clinical features and survival ( 8 ). Patients in different clusters demonstrated activation of different underlying biological pathways, including immune system response and metabolic changes that could potentially be modulated therapeutically ( 8 ). Precision medicine trials will be necessary to ensure that potential beneficial effects of drugs in specific endotypes are not overlooked because of heterogeneous effects in an unselected population.…”

Lung immune signatures define two groups of end-stage IPF patients

“…identified a cilium associated subtype and a fatty acid metabolism one [ 43 ], but the expression of immune related genes or the associated cell types was not reported. Using blood transcriptomics, Kraven et al described three clusters of IPF patients, one of them enriched in immune response genes [ 44 ]. Additionally, Herazo-Maya JD et al identified a 52 gene signature on PBMCs that stratified patients with different disease outcomes [ 45 , 46 ], and an increase of peripheral blood monocytes has been associated with poor prognosis [ 47 ].…”