Imperial College London
Alternate

Professor Toby Maher

Faculty of Medicine, National Heart & Lung Institute

Professor of Interstitial Lung Disease
 
 
 
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Contact

 

+44 (0)20 7594 2151t.maher

 
 
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Assistant

 

Ms Georgina Moss +44 (0)20 7594 2151

 
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Location

 

364Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Huang:2021:10.1164/rccm.202008-3093OC,
author = {Huang, Y and Oldham, JM and Ma, S-F and Unterman, A and Liao, S-Y and Barros, AJ and Bonham, CA and Kim, JS and Vij, R and Adegunsoye, A and Strek, ME and Molyneaux, PL and Maher, TM and Herazo-Maya, JD and Kaminski, N and Moore, BB and Martinez, FJ and Noth, I},
doi = {10.1164/rccm.202008-3093OC},
journal = {American Journal of Respiratory and Critical Care Medicine},
pages = {197--208},
title = {Blood transcriptomic predicts progression of pulmonary fibrosis and associates natural killer cells.},
url = {http://dx.doi.org/10.1164/rccm.202008-3093OC},
volume = {204},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Huang,Y
AU  - Oldham,JM
AU  - Ma,S-F
AU  - Unterman,A
AU  - Liao,S-Y
AU  - Barros,AJ
AU  - Bonham,CA
AU  - Kim,JS
AU  - Vij,R
AU  - Adegunsoye,A
AU  - Strek,ME
AU  - Molyneaux,PL
AU  - Maher,TM
AU  - Herazo-Maya,JD
AU  - Kaminski,N
AU  - Moore,BB
AU  - Martinez,FJ
AU  - Noth,I
DO  - 10.1164/rccm.202008-3093OC
EP  - 208
PY  - 2021///
SN  - 1073-449X
SP  - 197
TI  - Blood transcriptomic predicts progression of pulmonary fibrosis and associates natural killer cells.
T2  - American Journal of Respiratory and Critical Care Medicine
UR  - http://dx.doi.org/10.1164/rccm.202008-3093OC
UR  - https://www.ncbi.nlm.nih.gov/pubmed/33689671
UR  - https://www.atsjournals.org/doi/10.1164/rccm.202008-3093OC
UR  - http://hdl.handle.net/10044/1/87429
VL  - 204
ER  -
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