Validating a Proteomic Signature of Severe COVID-19

Abstract

COVID-19 is a heterogenous disease. Biomarker-based approaches may identify patients at risk for severe disease, who may be more likely to benefit from specific therapies. Our objective was to identify and validate a plasma protein signature for severe COVID-19.

Design: Prospective observational cohort study.

Setting: Two hospitals in the United States.

Patients: One hundred sixty-seven hospitalized adults with COVID-19.

Intervention: None.

Measurements and main results: We measured 713 plasma proteins in 167 hospitalized patients with COVID-19 using a high-throughput platform. We classified patients as nonsevere versus severe COVID-19, defined as the need for high-flow nasal cannula, mechanical ventilation, extracorporeal membrane oxygenation, or death, at study entry and in 7-day intervals thereafter. We compared proteins measured at baseline between these two groups by logistic regression adjusting for age, sex, symptom duration, and comorbidities. We used lead proteins from dysregulated pathways as inputs for elastic net logistic regression to identify a parsimonious signature of severe disease and validated this signature in an external COVID-19 dataset. We tested whether the association between corticosteroid use and mortality varied by protein signature. One hundred ninety-four proteins were associated with severe COVID-19 at the time of hospital admission. Pathway analysis identified multiple pathways associated with inflammatory response and tissue repair programs. Elastic net logistic regression yielded a 14-protein signature that discriminated 90-day mortality in an external cohort with an area under the receiver-operator characteristic curve of 0.92 (95% CI, 0.88-0.95). Classifying patients based on the predicted risk from the signature identified a heterogeneous response to treatment with corticosteroids (p = 0.006).

Conclusions: Inpatients with COVID-19 express heterogeneous patterns of plasma proteins. We propose a 14-protein signature of disease severity that may have value in developing precision medicine approaches for COVID-19 pneumonia.

Keywords: COVID-19; acute hypoxic respiratory failure; adult; corticosteroids; plasma proteomics.

Figure 1.

Volcano plot for all proteins comparing nonsevere versus severe COVID-19 at admission in the discovery population. The proteins composing our final proteomic signature, including differential expression by false discovery rate, membership in a differentially expressed pathway, and selection by elastic net logistic regression model, are labeled on the plot. APOM = apolipoprotein M, CCL7 = C-C motif chemokine 7, CD274 = cluster of differentiation 274, IL1RL1 - interleukin-1 receptor like 1, IL12B = interleukin 12B, IL17C = interleukin 17C, LGALS9 = galectin-9, LTA = lymphotoxin alpha, MMP7 = matrix metalloproteinase 7, MMP9 = matrix metalloproteinase 9, NPX = normalized protein expression, SPP1 = osteopontin, TGFA = transforming growth factor alpha, TNC = tenascin C, TNFSF11 = tumor necrosis factor superfamily 11.

Figure 2.

Average risk of mortality as a function of protein-derived risk stratified by steroid administration and adjusted for demographic factors, comorbidity, and disease severity. The solid line reflects the marginal estimate, with the darker and lighter shaded areas reflecting 1 and 2 sds, respectively. Although the analysis is underpowered, the risk of mortality for patients with low protein derived risk is estimated to be higher for those receiving steroids compared with those not receiving steroids, but the balance of benefit versus harm reverses as the protein score predicted risk increases.