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Background: Mediastinal lymph node enlargement is prevalent in patients with idiopathic pulmonary fibrosis (IPF). Studies investigating whether this phenomenon reflects specific immunologic activation are lacking. Methods: Programmed cell death-1 (PD-1)/ programmed cell death ligand-1 (PD-L1) expression in mediastinal lymph nodes and lung tissues was analyzed. PD-1, PD-L1 mRNA expression was measured in tracheobronchial lymph nodes of mice following bleomycin-induced injury on day 14. Finally, the effect of the PD-1 inhibitor, pembrolizumab, in bleomycin-induced pulmonary fibrosis was investigated. Results: We analyzed mediastinal lymph nodes of thirty-three patients (n = 33, IPF: n = 14, lung cancer: n = 10, concomitant IPF and lung cancer: n = 9) and lung tissues of two hundred nineteen patients (n = 219, IPF: 123, controls: 96). PD-1 expression was increased, while PD-L1 expression was decreased, in mediastinal lymph nodes of patients with IPF compared to lung cancer and in IPF lungs compared to control lungs. Tracheobronchial lymph nodes isolated on day 14 from bleomycin-treated mice exhibited increased size and higher PD-1, PD-L1 mRNA levels compared to saline-treated animals. Pembrolizumab blunted bleomycin-induced lung fibrosis, as indicated by reduction in Ashcroft score and improvement in respiratory mechanics. Conclusions: Mediastinal lymph nodes of patients with IPF exhibit differential expression profiles than those of patients with lung cancer indicating distinct immune-mediated pathways regulating fibrogenesis and carcinogenesis. PD-1 expression in mediastinal lymph nodes is in line with lung tissue expression. Lower doses of pembrolizumab might exert antifibrotic effects. Clinical trials aiming to endotype patients based on mediastinal lymph node profiling and accordingly implement targeted therapies such as PD-1 inhibitors are greatly anticipated. [ABSTRACT FROM AUTHOR]

Background: Idiopathic Pulmonary Fibrosis (IPF) is a lethal lung disease of unknown etiology. A major limitation in transcriptomic profiling of lung tissue in IPF has been a dependence on snap-frozen fresh tissues (FF). In this project we sought to determine whether genome scale transcript profiling using RNA Sequencing (RNA-Seq) could be applied to archived Formalin-Fixed Paraffin-Embedded (FFPE) IPF tissues.Results: We isolated total RNA from 7 IPF and 5 control FFPE lung tissues and performed 50 base pair paired-end sequencing on Illumina 2000 HiSeq. TopHat2 was used to map sequencing reads to the human genome. On average ~62 million reads (53.4% of ~116 million reads) were mapped per sample. 4,131 genes were differentially expressed between IPF and controls (1,920 increased and 2,211 decreased (FDR < 0.05). We compared our results to differentially expressed genes calculated from a previously published dataset generated from FF tissues analyzed on Agilent microarrays (GSE47460). The overlap of differentially expressed genes was very high (760 increased and 1,413 decreased, FDR < 0.05). Only 92 differentially expressed genes changed in opposite directions. Pathway enrichment analysis performed using MetaCore confirmed numerous IPF relevant genes and pathways including extracellular remodeling, TGF-beta, and WNT. Gene network analysis of MMP7, a highly differentially expressed gene in both datasets, revealed the same canonical pathways and gene network candidates in RNA-Seq and microarray data. For validation by NanoString nCounter® we selected 35 genes that had a fold change of 2 in at least one dataset (10 discordant, 10 significantly differentially expressed in one dataset only and 15 concordant genes). High concordance of fold change and FDR was observed for each type of the samples (FF vs FFPE) with both microarrays (r = 0.92) and RNA-Seq (r = 0.90) and the number of discordant genes was reduced to four.Conclusions: Our results demonstrate that RNA sequencing of RNA obtained from archived FFPE lung tissues is feasible. The results obtained from FFPE tissue are highly comparable to FF tissues. The ability to perform RNA-Seq on archived FFPE IPF tissues should greatly enhance the availability of tissue biopsies for research in IPF. [ABSTRACT FROM AUTHOR]

Background: The increased multi-omics information on carefully phenotyped patients in studies of complex diseases requires novel methods for data integration. Unlike continuous intensity measurements from most omics data sets, phenome data contain clinical variables that are binary, ordinal and categorical. Results: In this paper we introduce an integrative phenotyping framework (iPF) for disease subtype discovery. A feature topology plot was developed for effective dimension reduction and visualization of multi-omics data. The approach is free of model assumption and robust to data noises or missingness. We developed a workflow to integrate homogeneous patient clustering from different omics data in an agglomerative manner and then visualized heterogeneous clustering of pairwise omics sources. We applied the framework to two batches of lung samples obtained from patients diagnosed with chronic obstructive lung disease (COPD) or interstitial lung disease (ILD) with well-characterized clinical (phenomic) data, mRNA and microRNA expression profiles. Application of iPF to the first training batch identified clusters of patients consisting of homogenous disease phenotypes as well as clusters with intermediate disease characteristics. Analysis of the second batch revealed a similar data structure, confirming the presence of intermediate clusters. Genes in the intermediate clusters were enriched with inflammatory and immune functional annotations, suggesting that they represent mechanistically distinct disease subphenotypes that may response to immunomodulatory therapies. The iPF software package and all source codes are publicly available. Conclusions: Identification of subclusters with distinct clinical and biomolecular characteristics suggests that integration of phenomic and other omics information could lead to identification of novel mechanism-based disease sub-phenotypes. [ABSTRACT FROM AUTHOR]

Background: The increased multi-omics information on carefully phenotyped patients in studies of complex diseases requires novel methods for data integration. Unlike continuous intensity measurements from most omics data sets, phenome data contain clinical variables that are binary, ordinal and categorical., Results: In this paper we introduce an integrative phenotyping framework (iPF) for disease subtype discovery. A feature topology plot was developed for effective dimension reduction and visualization of multi-omics data. The approach is free of model assumption and robust to data noises or missingness. We developed a workflow to integrate homogeneous patient clustering from different omics data in an agglomerative manner and then visualized heterogeneous clustering of pairwise omics sources. We applied the framework to two batches of lung samples obtained from patients diagnosed with chronic obstructive lung disease (COPD) or interstitial lung disease (ILD) with well-characterized clinical (phenomic) data, mRNA and microRNA expression profiles. Application of iPF to the first training batch identified clusters of patients consisting of homogenous disease phenotypes as well as clusters with intermediate disease characteristics. Analysis of the second batch revealed a similar data structure, confirming the presence of intermediate clusters. Genes in the intermediate clusters were enriched with inflammatory and immune functional annotations, suggesting that they represent mechanistically distinct disease subphenotypes that may response to immunomodulatory therapies. The iPF software package and all source codes are publicly available., Conclusions: Identification of subclusters with distinct clinical and biomolecular characteristics suggests that integration of phenomic and other omics information could lead to identification of novel mechanism-based disease sub-phenotypes.