Utility of peripheral protein biomarkers for the prediction of incident interstitial features: a multicentre retrospective cohort study.

Introduction/rationale: Protein biomarkers may help enable the prediction of incident interstitial features on chest CT.
Methods: We identified which protein biomarkers in a cohort of smokers (COPDGene) differed between those with and without objectively measured interstitial features at baseline using a univariate screen (t-test false discovery rate, FDR p<0.001), and which of those were associated with interstitial features longitudinally (multivariable mixed effects model FDR p<0.05). To predict incident interstitial features, we trained four random forest classifiers in a two-thirds random subset of COPDGene: (1) imaging and demographic information, (2) univariate screen biomarkers, (3) multivariable confirmation biomarkers and (4) multivariable confirmation biomarkers available in a separate testing cohort (Pittsburgh Lung Screening Study (PLuSS)). We evaluated classifier performance in the remaining one-third of COPDGene, and, for the final model, also in PLuSS.
Results: In COPDGene, 1305 biomarkers were available and 20 differed between those with and without interstitial features at baseline. Of these, 11 were associated with feature progression over a mean of 5.5 years of follow-up, and of these 4 were available in PLuSS, (angiopoietin-2, matrix metalloproteinase 7, macrophage inflammatory protein 1 alpha) over a mean of 8.8 years of follow-up. The area under the curve (AUC) of classifiers using demographics and imaging features in COPDGene and PLuSS were 0.69 and 0.59, respectively. In COPDGene, the AUC of the univariate screen classifier was 0.78 and of the multivariable confirmation classifier was 0.76. The AUC of the final classifier in COPDGene was 0.75 and in PLuSS was 0.76. The outcome for all of the models was the development of incident interstitial features.
Conclusions: Multiple novel and previously identified proteomic biomarkers are associated with interstitial features on chest CT and may enable the prediction of incident interstitial diseases such as idiopathic pulmonary fibrosis.
Competing Interests: Competing interests: SA reports equity/dividends from Quantitative Imaging Solutions and consulting for Vertex Pharmaceuticals, Verona Pharmaceuticals and Triangulate Knowledge, all unrelated to the current work. TJD has received grant support from Bristol Myers Squibb, consulting fees from Boehringer Ingelheim and L.E.K. consulting, and has been part of a clinical trial funded by Genentech, unrelated to the current work. BC reports consulting fees from Quantitative Imaging Solutions, unrelated to the current work. RuSJE reports consulting fees from Quantitative Imaging Solutions, unrelated to the current work. VC reports no competing interests. NE reports no competing interests. RK reports grants and personal fees from AstraZeneca, personal fees from CVS Caremark, personal fees from Aptus Health, grants and personal fees from GlaxoSmithKline, personal fees from Boston Scientific, personal fees from Boston Consulting Group, all outside the submitted work. GL reports no competing interests. RB reports no competing interests. DOW reports advisory board membership and shareholder of Online Disruptive Technologies, unrelated to the current work. RaSJE reports equity/dividends from Quantitative Imaging Solutions, unrelated to the current work. IOR reports no competing interests. GRW reports grants from Boehringer Ingelheim, BTG Interventional Medicine and Janssen Pharmaceuticals; consultancies/advisory board participation for Boehringer Ingelheim, Janssen Pharmaceuticals, Pulmonx, Novartis, Philips, CSL Behring and Vertex; and equity/dividends from Quantitative Imaging Solutions, unrelated to the current work, all outside the submitted work. GRW’s wife works for Biogen.
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