Yong Huang1, Justin M Oldham2, Shwu-Fan Ma1, Avraham Unterman3, Shu-Yi Liao4, Andrew J Barros1, Catherine A Bonham1, John S Kim1, Rekha Vij5, Ayodeji Adegunsoye5,6, Mary E Strek5, Philip L Molyneaux7,8, Toby M Maher7,8,9, Jose D Herazo-Maya10, Naftali Kaminski3, Bethany B Moore11, Fernando J Martinez12, Imre Noth1. 1. Division of Pulmonary and Critical Care Medicine, The University of Virginia, Charlottesville, Virginia. 2. Division of Pulmonary, Critical Care, and Sleep Medicine, The University of California at Davis, Sacramento, California. 3. Section of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut. 4. Department of Medicine, National Jewish Health, Denver, Colorado. 5. Section of Pulmonary and Critical Care Medicine and. 6. Department of Human Genetics, Genetics, Genomic and Systems Biology, University of Chicago, Chicago, Illinois. 7. National Heart and Lung Institute, Imperial College, London, United Kingdom. 8. Royal Brompton Hospital, London, United Kingdom. 9. Division of Pulmonary, Critical Care and Sleep Medicine, Hastings Center for Pulmonary Research, Keck School of Medicine, University of Southern California, Los Angeles, California. 10. Division of Pulmonary, Critical Care, and Sleep Medicine, Tampa General Hospital, University of South Florida, Tampa, Florida. 11. Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan; and. 12. Internal Medicine, Weill Cornell Medical College, Cornell University, New York, New York.
Abstract
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.
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.
Entities:
Keywords:
cell type composition deconvolution; idiopathic pulmonary fibrosis; longitudinal changes of blood gene expression; multigene predictor for progression; relative decline of FVC
Authors: Yong Huang; Shwu-Fan Ma; Milena S Espindola; Rekha Vij; Justin M Oldham; Gary B Huffnagle; John R Erb-Downward; Kevin R Flaherty; Beth B Moore; Eric S White; Tong Zhou; Jianrong Li; Yves A Lussier; MeiLan K Han; Naftali Kaminski; Joe G N Garcia; Cory M Hogaboam; Fernando J Martinez; Imre Noth Journal: Am J Respir Crit Care Med Date: 2017-07-15 Impact factor: 21.405
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