Blood Transcriptomics Predicts Progression of Pulmonary Fibrosis and Associated Natural Killer Cells

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.

Keywords: cell type composition deconvolution; idiopathic pulmonary fibrosis; longitudinal changes of blood gene expression; multigene predictor for progression; relative decline of FVC.

Figure 1.

Flowchart of development and validation of the FVC predictor for progression. (A) COMET training cohort. Steps of identifying the FVC predictor consisting of 25 genes predictive of future FVC decline status using a short-term longitudinal (0–4 mo) within-patient gene-expression changes (ΔGE_0–4-mo) model are shown. (B) Independent validation cohorts and COMET subset cohorts with different transcriptome assay platforms. (B1) Longitudinal gene-expression changes in the independent validation cohorts were calculated between baseline and the median of immediate follow-up sampling time points. “Cross-GE-platform-gene-matched” step comparing varied transcriptome assay platforms retained 23 and 18 of the 25 genes in the FVC predictor from the Imperial College London and the University of Chicago (UChicago) cohorts, respectively. Scores were determined as continuous values by a least-squares multiple regression model for each matched gene in each cohort and regressed with future FVC decline. (B2) Longitudinal gene-expression changes in three COMET time point subsets (ΔGE_0–8-mo, ΔGE_4–8-mo, and ΔGE_0–12-mo) were calculated and regressed with future FVC decline. ROC/AUC analysis was used to test the prognosis prediction efficiency for both internal and external cohorts. (C) Ingenuity pathway analyses were applied to significant genes between stable and progressors with false discovery rate (FDR) > 0.05 and fold change > 2. AUC = area under the curve; COMET = Correlating Outcomes with Biochemical Markers to Estimate Time-Progression in Idiopathic Pulmonary Fibrosis; ΔGE_0–4-mo = gene-expression changes between the baseline and 4-month visit; ΔGE_0–8-mo = gene-expression changes between the baseline and 8-month visit; ΔGE_0–12-mo = gene-expression changes between the baseline and 12-month visit; ΔGE_4–8-mo = gene-expression changes between the 4-month and 8-month visit; LASSO = Logistic Least Absolute Shrinkage and Selection Operator; PBMC = peripheral blood mononuclear cell; ROC = receiver operating characteristic.

Figure 2.

Coefficient of variation (CoV) analyses and power estimation of COMET (Correlating Outcomes with Biochemical Markers to Estimate Time-Progression in Idiopathic Pulmonary Fibrosis) cohort data. The CoV, defined as the ratio of gene-specific SD to the mean, was computed using baseline gene expression (CoV_GE) for the cross-sectional model and 0- to 4-month gene-expression change (CoV_ΔGE) for the longitudinal model in stable and progressor groups of the COMET training cohort, respectively. (A and B) Minus versus average plots assess the portion of the genes with greater within-group variations, as determined by CoV_GE–CoV_ΔGE > 0. (A) Eighty-nine percent of the genes have CoV_GE–CoV_ΔGE > 0, indicating that variations within the stable group are greater in the cross-sectional model than in the longitudinal change model. (B) Sixty-seven and a half percent of the genes have CoV_GE–CoV_ΔGE > 0, indicating that variations within the progressor group are greater in the cross-sectional model than in the longitudinal changes model. (C) Power estimation on the basis of postulated sample sizes of peripheral blood mononuclear transcriptome. The horizontal dotted line indicates a power of 0.9, at an α of 0.05, whereas the corresponding sample size is 63 for baseline GE and 16 for GE changes (ΔGE). (D) Intrasubject CoV of ΔGE analysis across different peripheral blood mononuclear sampling time in COMET. The black bar represents ΔGE with larger intrasubject CoV in progressor than in stable patients, and the gray bar represents ΔGE with larger intrasubject CoV in stable patients than in progressor patients. The results remained consistent with the progressor group retaining fewer variations (34–40%). GE = gene expression.

Figure 3.

Ingenuity pathway analyses. A two-group comparison with criteria of FDR < 5% and fold change > 2 identified 167 with ΔGE_0–4-mo higher in progressor than in stable patients and 251 genes with ΔGE_0–4-mo lower in progressor than in stable patients. (A and B) Multiple innate immune pathways, including mTOR signaling, APK/JNK, CD40, IL-8, and IL-23 signaling, were upregulated (A), whereas pattern recognition receptors of bacteria and viruses, TREM1, and IFN signaling were downregulated (B). (C) Network analysis for the downregulated genes with EGR1 at the hub center. ΔGE_0–4-mo = gene-expression changes between the baseline and 4-month visit.

Figure 4.

Classification of COMET (Correlating Outcomes with Biochemical Markers to Estimate Time-Progression in Idiopathic Pulmonary Fibrosis) training cohort using the genes constituting FVC predictor for progression. (A) Hierarchical clustering of the 74 patients with idiopathic pulmonary fibrosis in COMET. All of the 16 FVC progressor (FVC = 1) patients (in blue) were enriched in the bottom cluster, whereas the upper cluster only contained FVC stable (FVC = 0) patients (in green). Red, white, and blue colors indicate gene expression change values above, at, or below the average gene-expression changes of the corresponding gene. (B and C) Principal component analysis of COMET training cohort based on the FVC predictor using R/CRAN package “FactoMineR.” (B) Individual factor map with confidence ellipses around FVC progressor (blue) or stable (green) status. (C) Variables factor map with predictor genes. The projection of the arrowhead of each variable (i.e., gene) onto each dimension represents the component loadings of the corresponding gene. Dimension 1 (Dim 1) and Dimension 2 (Dim 2) represent the amount of the variations contained in the original data set.

Figure 5.

Receiver operating characteristic and area under the curve analysis (AUC) of FVC predictor for progression. AUC values with 95% confidence intervals are displayed in the bottom right of each graph. The dashed red line denotes specificity at 75%. (A) Independent validation cohorts. At anchored specificity of approximately 75%, the sensitivities are 75.0% and 80.0% for UChicago and Imperial cohorts, respectively. (B) Training and subset of the COMET (Correlating Outcomes with Biochemical Markers to Estimate Time-Progression in Idiopathic Pulmonary Fibrosis) cohort (I) with increasing transcriptome sampling durations for determination of ΔGE. At anchored specificity of approximately 75%, sensitivities are 100%, 92.3%, and 78.6% for 0–4, 0–8, and 0–12 months, respectively. FVC-predictor performance modestly diminished moving from sampling intervals of 4 month to 8 month and 4 month to 12 month. (C) Training and subsets of the COMET cohort (II) with 4-month transcriptome sampling but varying baselines for ΔGE determination. At anchored specificity of approximately 75%, sensitivities are 100%, 69.2%, and 36.4% for 0–4, 4–8, and 8–12 months, respectively. Performance diminishes more dramatically and the use of months 8–12 is ineffective. Detailed receiver operating characteristic/AUC analysis results can be found in Tables E3 and E4. ΔGE = genetic-expression changes; Imperial = Imperial College London; UChicago = University of Chicago.

Figure 6.

Correlation of FVC-predictor scores with longitudinal changes in cell type abundance. Deconvolution of University of Chicago longitudinal bulk peripheral blood mononuclear RNA-sequencing data was performed using single-cell RNA-sequencing data derived from peripheral blood mononuclear cells of healthy donors. (A) Heat map of the correlation matrix of each cell type and FVC-predictor scores for progression. The red and green represent correlation and anticorrelation, respectively. (B) Pearson’s correlation of FVC predictor for progression score with each cell type change was displayed in the corresponding box (the coefficient on top and P value in parentheses). The color in each box represents the direction of correlation (red) or anticorrelation (green). The scale bar on the right indicates the degree of correlation. NK = natural killer.