Gene Expression Risk Scores for COVID-19 Illness Severity

• Published in: The Journal of infectious diseases Vol. 227; no. 3; pp. 322 - 331
• Main Authors: Peterson, Derick R, Baran, Andrea M, Bhattacharya, Soumyaroop, Branche, Angela R, Croft, Daniel P, Corbett, Anthony M, Walsh, Edward E, Falsey, Ann R, Mariani, Thomas J
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 01.02.2023
• Subjects: Adult; COVID-19 - genetics; Gene Expression; Humans; Major; Patient Acuity; Retrospective Studies; Risk Factors; SARS-CoV-2 - genetics; Severity of Illness Index; COVID-19; RNA sequencing; severity; prediction; ICU; molecular markers
• ISSN: 0022-1899; 1537-6613; 1537-6613
• DOI: 10.1093/infdis/jiab568

The correlates of coronavirus disease 2019 (COVID-19) illness severity following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are incompletely understood. We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2 infection clinically adjudicated as having mild, moderate, or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and nonsevere COVID-19. Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus nonsevere illness, we identified >4000 genes differentially expressed (false discovery rate < 0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T-cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated receiver operating characteristic-area under the curve [ROC-AUC] = 0.98), and need for intensive care in an independent cohort (ROC-AUC = 0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort. These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.