Abstract
BackgroundThe accurate diagnosis of idiopathic pulmonary fibrosis (IPF) is a major clinical challenge. We developed a model to diagnose IPF by applying Bayesian probit regression (BPR) modelling to gene expression profiles of whole lung tissue.MethodsWhole lung tissue was obtained from patients with idiopathic pulmonary fibrosis (IPF) undergoing surgical lung biopsy or lung transplantation. Controls were obtained from normal organ donors. We performed cluster analyses to explore differences in our dataset. No significant difference was found between samples obtained from different lobes of the same patient. A significant difference was found between samples obtained at biopsy versus explant. Following preliminary analysis of the complete dataset, we selected three subsets for the development of diagnostic gene signatures: the first signature was developed from all IPF samples (as compared to controls); the second signature was developed from the subset of IPF samples obtained at biopsy; the third signature was developed from IPF explants. To assess the validity of each signature, we used an independent cohort of IPF and normal samples. Each signature was used to predict phenotype (IPF versus normal) in samples from the validation cohort. We compared the models' predictions to the true phenotype of each validation sample, and then calculated sensitivity, specificity and accuracy.ResultsSurprisingly, we found that all three signatures were reasonably valid predictors of diagnosis, with small differences in test sensitivity, specificity and overall accuracy.ConclusionsThis study represents the first use of BPR on whole lung tissue; previously, BPR was primarily used to develop predictive models for cancer. This also represents the first report of an independently validated IPF gene expression signature. In summary, BPR is a promising tool for the development of gene expression signatures from non-neoplastic lung tissue. In the future, BPR might be used to develop definitive diagnostic gene signatures for IPF, prognostic gene signatures for IPF or gene signatures for other non-neoplastic lung disorders such as bronchiolitis obliterans.
Highlights
The accurate diagnosis of idiopathic pulmonary fibrosis (IPF) is a major clinical challenge
Examining the hierarchical dendrogram (Figure 1B), we found a natural separation between IPF samples and normal lung samples, with the exception of one outlier, a sample of normal lung (Normal_C) which falls among the IPF samples
This study shows that IPF gene signatures can be derived from whole lung tissue, given appropriate biospecimen selection and acquisition
Summary
The accurate diagnosis of idiopathic pulmonary fibrosis (IPF) is a major clinical challenge. Pulmonary fibrosis is a significant cause of morbidity and mortality worldwide [1,2]. The multiple subtypes of pulmonary fibrosis carry different prognoses. Nonspecific interstitial pneumonia (NSIP) is another subtype of pulmonary fibrosis that has much better rates of survival and treatment response [2,6]. There are perhaps 200 subtypes of pulmonary fibrosis [7]. The American Thoracic Society and European Respiratory Society published a classification scheme that describes the major subtypes of pulmonary fibrosis [2]. Other authors describe complex algorithms for making an accurate diagnosis of pulmonary fibrosis [7,8,9]
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