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Blood transcriptomic predicts progression of pulmonary fibrosis and associates natural killer cells.

  • Huang, Y
  • Oldham, JM
  • Ma, S-F
  • Unterman, A
  • Liao, S-Y
  • Barros, AJ
  • Bonham, CA
  • Kim, JS
  • Vij, R
  • Adegunsoye, A
  • Strek, ME
  • Molyneaux, PL
  • Maher, TM
  • Herazo-Maya, JD
  • Kaminski, N
  • Moore, BB
  • Martinez, FJ
  • Noth, I
Publication Date
Mar 08, 2021
UPCommons. Portal del coneixement obert de la UPC


Rationale: Disease activity in idiopathic pulmonary fibrosis (IPF) remains highly variable, poorly understood, and difficult to predict. Objective: To identify a predictor using short-term longitudinal changes in gene-expression that forecasts future forced vital capacity (FVC) decline and to characterize involved pathways and cell types. Methods: Seventy-four patients from Correlating Outcomes with biochemical Markers to Estimate Time-progression in IPF (COMET) 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-seq (PBMC scRNA-seq) data from healthy controls were used as references to characterize cell type compositions from bulk PBMC RNA-seq data that were associated with FVC decline. Results: The longitudinal model reduced gene-expression variations within stable and progressor groups, resulting in increased statistical power when compared to 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 down- and up-regulated, respectively. Cellular deconvolution using scRNA-seq data identified natural killer (NK) cells as significantly correlated with progression. Conclusions: Serial transcriptomic change predicts future FVC decline. Analysis of cell types involved in the progressor signature supports the novel involvement of NK cells in IPF progression.

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