Proteomic Biomarkers of Survival in Idiopathic Pulmonary Fibrosis

Abstract

Rationale: Idiopathic pulmonary fibrosis (IPF) causes progressive lung scarring and high mortality. Reliable and accurate prognostic biomarkers are urgently needed. Objectives: To identify and validate circulating protein biomarkers of IPF survival. Methods: High-throughput proteomic data were generated using prospectively collected plasma samples from patients with IPF from the Pulmonary Fibrosis Foundation Patient Registry (discovery cohort) and the Universities of California, Davis; Chicago; and Virginia (validation cohort). Proteins associated with three-year transplant-free survival (TFS) were identified using multivariable Cox proportional hazards regression. Those associated with TFS after adjustment for false discovery in the discovery cohort were advanced for testing in the validation cohort, with proteins maintaining TFS association with consistent effect direction considered validated. After combining cohorts, functional analyses were performed, and machine learning was used to derive a proteomic signature of TFS. Measurements and Main Results: Of 2,921 proteins tested in the discovery cohort (n = 871), 231 were associated with differential TFS. Of these, 140 maintained TFS association with consistent effect direction in the validation cohort (n = 355). After cohorts were combined, the validated proteins with the strongest TFS association were latent-transforming growth factor β-binding protein 2 (hazard ratio [HR], 2.43; 95% confidence interval [CI] = 2.09-2.82), collagen α-1(XXIV) chain (HR, 2.21; 95% CI = 1.86-2.39), and keratin 19 (HR, 1.60; 95% CI = 1.47-1.74). In decision curve analysis, a proteomic signature of TFS outperformed a similarly derived clinical prediction model. Conclusions: In the largest proteomic investigation of IPF outcomes performed to date, we identified and validated 140 protein biomarkers of TFS. These results shed important light on potential drivers of IPF progression.

Keywords: IPF; interstitial lung disease; proteomics; survival; transplant.

Figure 1.

Volcano plot displaying discovery cohort results. Proteins indicated in blue are associated with differential transplant-free survival at a false discovery rate with FDR P < 0.05.

Figure 2.

(A and B) Canonical pathway analysis (A) and protein interaction network (B) for protein biomarkers associated with increased risk of death or lung transplant. The top 10 overrepresented pathways are shown.

Figure 3.

(A and B) Canonical pathway analysis (A) and protein interaction network (B) for protein biomarkers associated with reduced risk of death or lung transplant. The top 10 overrepresented pathways are shown.

Figure 4.

(A) Decision curve analysis showing similar net utility between a stand-alone proteomic model and combined clinical–proteomic model, both of which outperformed a stand-alone clinical prediction model. (B and C) Kaplan-Meier survival curves for a proteomic prediction model (B) and a clinical prediction model (C) stratified by quartile of model fitted value. Decision curve analysis calculates a “net benefit” across a range of threshold probabilities for one or more prediction models in comparison with default strategies of assuming a state (disease, outcome) or pursuing an intervention in all or no patients.