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

Authors :
Bowman WS
Newton CA
Linderholm AL
Neely ML
Pugashetti JV
Kaul B
Vo V
Echt GA
Leon W
Shah RJ
Huang Y
Garcia CK
Wolters PJ
Oldham JM
Source :
The Lancet. Respiratory medicine [Lancet Respir Med] 2022 Jun; Vol. 10 (6), pp. 593-602. Date of Electronic Publication: 2022 Jan 18.
Publication Year :
2022

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.<br />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.<br />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.<br />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.<br />Funding: National Heart Lung and Blood Institute.<br />Competing Interests: Declaration of interests CAN reports personal fees from Boehringer Ingelheim, outside of the submitted work. CKG reports a grant from Boehringer Ingelheim and previous advisory board service for Pliant Therapeutics, both unrelated to the submitted work. PJW reports grants from Sanofi, grants and personal fees from Boehringer Ingelheim and Roche/Genentech, and personal fees from Gossamer Bio, Blade Therapeutics, and Pliant, unrelated to the submitted work. JMO reports an unrelated grant from Boehringer Ingelheim and personal fees from Genentech, United Therapeutics, Gatehouse Bio, and AmMax Bio, unrelated to the submitted work. All other authors declare no competing interests.<br /> (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Details

Language :
English
ISSN :
2213-2619
Volume :
10
Issue :
6
Database :
MEDLINE
Journal :
The Lancet. Respiratory medicine
Publication Type :
Academic Journal
Accession number :
35063079
Full Text :
https://doi.org/10.1016/S2213-2600(21)00503-8