Proteomic biomarkers of progressive fibrosing interstitial lung disease: a multicentre cohort analysis

Willis S. Bowman, Chad A. Newton, Angela L. Linderholm, Megan L. Neely, Janelle Vu Pugashetti, Bhavika Kaul, Vivian Vo, Gabrielle A. Echt, William Leon, Rupal J. Shah, Yong Huang, Christine Kim Garcia, Paul J. Wolters, Justin M. Oldham

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Background: Progressive fibrosing interstitial lung disease (ILD) is characterised by parenchymal scar formation, leading to high morbidity and mortality. The ability to predict this phenotype remains elusive. We conducted a proteomic analysis to identify novel plasma biomarkers of progressive fibrosing ILD and developed a proteomic signature to predict this phenotype. Methods: Relative plasma concentrations for 368 biomarkers were determined with use of a semi-quantitative, targeted proteomic platform in patients with connective tissue disease-associated ILD, chronic hypersensitivity pneumonitis, or unclassifiable ILD who provided research blood draws at the University of California (discovery cohort) and the University of Texas (validation cohort). Univariable logistic regression was used to identify individual biomarkers associated with 1-year ILD progression, defined as death, lung transplant, or 10% or greater relative forced vital capacity (FVC) decline. A proteomic signature of progressive fibrosing ILD was then derived with use of machine learning in the University of California cohort and validated in the University of Texas cohort. Findings: The discovery cohort comprised 385 patients (mean age 63·6 years, 59% female) and the validation cohort comprised 204 patients (mean age 60·7 years, 61% female). 31 biomarkers were associated with progressive fibrosing ILD in the discovery cohort, with 17 maintaining an association in the validation cohort. Validated biomarkers showed a consistent association with progressive fibrosing ILD irrespective of ILD clinical diagnosis. A proteomic signature comprising 12 biomarkers was derived by machine learning and validated in the University of Texas cohort, in which it had a sensitivity of 0·90 and corresponding negative predictive value of 0·91, suggesting that approximately 10% of patients with a low-risk proteomic signature would experience ILD progression in the year after blood draw. Those with a low-risk proteomic signature experienced an FVC change of +85·7 mL (95% CI 6·9 to 164·4) and those with a high-risk signature experienced an FVC change of −227·1 mL (−286·7 to −167·5). A theoretical clinical trial restricted to patients with a high-risk proteomic signature would require 80% fewer patients than one designed without regard to proteomic signature. Interpretation: 17 plasma biomarkers of progressive fibrosing ILD were identified and showed consistent associations across ILD subtypes. A proteomic signature of progressive fibrosing ILD could enrich clinical trial cohorts and avoid the need for antecedent progression when defining progressive fibrosing ILD for clinical trial enrolment. Funding: National Heart Lung and Blood Institute.

Original languageEnglish (US)
JournalThe Lancet Respiratory Medicine
DOIs
StateAccepted/In press - 2022

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine

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