A robust data-driven genomic signature for idiopathic pulmonary fibrosis with applications for translational model selection.

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  • Additional Information
    • Source:
      Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
    • Publication Information:
      Original Publication: San Francisco, CA : Public Library of Science
    • Subject Terms:
      Gene Expression Profiling* ; Gene Expression Regulation* ; Models, Biological* ; Transcriptome*; Idiopathic Pulmonary Fibrosis/*metabolism; Animals ; Biomarkers/metabolism ; Bleomycin/adverse effects ; Bleomycin/pharmacology ; Disease Models, Animal ; Female ; Humans ; Idiopathic Pulmonary Fibrosis/chemically induced ; Idiopathic Pulmonary Fibrosis/genetics ; Idiopathic Pulmonary Fibrosis/pathology ; Male ; Rats
    • Abstract:
      Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung disease affecting ~5 million people globally. We have constructed an accurate model of IPF disease status using elastic net regularized regression on clinical gene expression data. Leveraging whole transcriptome microarray data from 230 IPF and 89 control samples from Yang et al. (2013), sourced from the Lung Tissue Research Consortium (LTRC) and National Jewish Health (NJH) cohorts, we identify an IPF gene expression signature. We performed optimal feature selection to reduce the number of transcripts required by our model to a parsimonious set of 15. This signature enables our model to accurately separate IPF patients from controls. Our model outperforms existing published models when tested with multiple independent clinical cohorts. Our study underscores the utility of elastic nets for gene signature/panel selection which can be used for the construction of a multianalyte biomarker of disease. We also filter the gene sets used for model input to construct a model reliant on secreted proteins. Using this approach, we identify the preclinical bleomycin rat model that is most congruent with human disease at day 21 post-bleomycin administration, contrasting with earlier timepoints suggested by other studies.
      Competing Interests: All authors are employed by Bristol Myers Squibb Co. This commercial affiliation does not alter our adherence to PLOS ONE policies on sharing data and materials.
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    • Accession Number:
      0 (Biomarkers)
      11056-06-7 (Bleomycin)
    • Publication Date:
      Date Created: 20190419 Date Completed: 20200107 Latest Revision: 20200309
    • Publication Date:
      20240628
    • Accession Number:
      PMC6472794
    • Accession Number:
      10.1371/journal.pone.0215565
    • Accession Number:
      30998768