Open AccessBlood Transcriptomics Predicts Progression of Pulmonary Fibrosis and Associated Natural Killer Cells
Author(s) -
Yong Huang,
Justin M. Oldham,
Shwu Fan,
Avraham Unterman,
Shu Liao,
Andrew Barros,
Catherine A. Bonham,
John S. Kim,
Rekha Vij,
Ayodeji Adegunsoye,
Mary E. Strek,
Philip L. Molyneaux,
Toby M. Maher,
Jose D. HerazoMaya,
Naftali Kaminski,
Bethany B. Moore,
Fernando J. Martínez,
Imre Noth
Publication year - 2021
Publication title -
american journal of respiratory and critical care medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.272
H-Index - 374
eISSN - 1535-4970
pISSN - 1073-449X
DOI - 10.1164/rccm.202008-3093oc
Subject(s) - medicine , vital capacity , biomarker , peripheral blood mononuclear cell , transcriptome , gene signature , oncology , cohort , gene expression , immunology , bioinformatics , gene , biology , lung , genetics , diffusing capacity , in vitro , lung function
Rationale: Disease activity in idiopathic pulmonary fibrosis (IPF) remains highly variable, poorly understood, and difficult to predict. Objectives: To identify a predictor using short-term longitudinal changes in gene expression that forecasts future FVC decline and to characterize involved pathways and cell types. Methods: Seventy-four patients from COMET (Correlating Outcomes with Biochemical Markers to Estimate Time-Progression in IPF) cohort were dichotomized as progressors (≥10% FVC decline) or stable. Blood gene-expression changes within individuals were calculated between baseline and 4 months and regressed with future FVC status, allowing determination of expression variations, sample size, and statistical power. Pathway analyses were conducted to predict downstream effects and identify new targets. An FVC predictor for progression was constructed in COMET and validated using independent cohorts. Peripheral blood mononuclear single-cell RNA-sequencing data from healthy control subjects were used as references to characterize cell type compositions from bulk peripheral blood mononuclear RNA-sequencing data that were associated with FVC decline. Measurements and Main Results: The longitudinal model reduced gene-expression variations within stable and progressor groups, resulting in increased statistical power when compared with a cross-sectional model. The FVC predictor for progression anticipated patients with future FVC decline with 78% sensitivity and 86% specificity across independent IPF cohorts. Pattern recognition receptor pathways and mTOR pathways were downregulated and upregulated, respectively. Cellular deconvolution using single-cell RNA-sequencing data identified natural killer cells as significantly correlated with progression. Conclusions: Serial transcriptomic change predicts future FVC decline. An analysis of cell types involved in the progressor signature supports the novel involvement of natural killer cells in IPF progression.