Proteomic biomarkers of progressive fibrosing interstitial lung disease: a multicentre cohort analysis
Selected publication · The Lancet Respiratory Medicine, 2022
Bowman W., Newton C., Linderholm A., Neely M., Pugashetti J., Kaul B., Vo V., Echt G., Leon W., Shah R., Huang Y., Garcia C., Wolters P., Oldham J.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Respiratory Diseases | Patient Stratification | Plasma |  Olink Explore 3072/384 |
Editor's note
Using the Olink Explore 384 Inflammation panel, Bowman and colleagues were able to derive a predictive 12-protein signature to identity patients with interstitial lung disease (ILD) who are likely to progress to the devastating progressive fibrosing variant of the condition. The authors discussed that with a negative predictive value of 0.91, this signature could be used to pre-stratify future preventive clinical trials, greatly reducing the number of ILD patients needed (and hence cost) by 80%. This study exemplifies the potential to reduce drug development costs using protein biomarkers for pre-stratification.
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.