Minimalistic transcriptomic signatures permit accurate early prediction of COVID-19 mortality

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Abstract

BACKGROUND Accurate prognostic assays for COVID-19 represent an unmet clinical need. We sought to identify and validate early parsimonious transcriptomic signatures that accurately predict fatal outcomes.METHODS We studied 894 patients enrolled in the prospective, multicenter Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) with peripheral blood mononuclear cells (PBMC) and nasal swabs collected within 48 hours of admission. Host gene expression was measured with RNA-Seq. We trained parsimonious prognostic classifiers incorporating host gene expression, age, and SARS-CoV-2 viral load to predict 28-day mortality in 70% of the cohort. Classifier performance was determined in the remaining 30% and externally validated in a contemporary COVID-19 cohort (n = 137) with vaccinated patients.RESULTS Fatal COVID-19 was characterized by 4,189 differentially expressed genes in the peripheral blood. A COVID-specific 3-gene peripheral blood classifier (CD83, ATP1B2, DAAM2) combined with age and SARS-CoV-2 viral load achieved an area under the receiver operating characteristic curve (AUC) of 0.88 (95% CI, 0.82–0.94). A 3-gene nasal classifier (SLC5A5, CD200R1, FCER1A), in comparison, yielded an AUC of 0.74 (95% CI, 0.64–0.83). Notably, OLAH, the most strongly upregulated gene in both PBMC and nasal swab and recently implicated in severe viral infection pathogenesis, yielded AUCs of 0.86 (0.79–0.93) and 0.78 (95% CI, 0.69–0.86), respectively. Both peripheral blood classifiers demonstrated comparable performance in an independent contemporary cohort of vaccinated patients (AUCs 0.74–0.80).CONCLUSION Our parsimonious blood- and nasal-based classifiers accurately predicted COVID-19 mortality and merit further study as accessible prognostic tools to guide triage, resource allocation, and early therapeutic interventions.FUNDING NIH: 5R01AI135803-03, R35HL140026, 5U19AI118608-04, 5U19AI128910-04, 4U19AI090023-11, 4U19AI118610-06, R01AI145835-01A1S1, 5U19AI062629-17, 5U19AI057229-17, 5U19AI125357-05, 5U19AI128913-03, 3U19AI077439-13, 5U54AI142766-03, 5R01AI104870-07, 3U19AI089992-09, 3U19AI128913-03, 5T32DA018926-18, and K0826161611. National Institute of Allergy and Infectious Diseases, NIH: 3U19AI1289130, U19AI128913-04S1, and R01AI122220. National Center for Advancing Translational Sciences, NIH: UM1TR004528. The National Science Foundation: DMS2310836. The Chan Zuckerberg Biohub San Francisco.

Authors

Rithwik Narendra, Emily C. Lydon, Hoang Van Phan, Natasha Spottiswoode, Lucile P. Neyton, Joann Diray-Arce, IMPACC Network, COMET Consortium, EARLI Consortium, Patrice M. Becker, Seunghee Kim-Schulze, Annmarie Hoch, Harry Pickering, Patrick van Zalm, Charles B. Cairns, Matthew C. Altman, Alison D. Augustine, Steve Bosinger, Walter Eckalbar, Leying Guan, Naresh Doni Jayavelu, Steven H. Kleinstein, Florian Krammer, Holden T. Maecker, Al Ozonoff, Bjoern Peters, Nadine Rouphael, Ruth R. Montgomery, Elaine Reed, Joanna Schaenman, Hanno Steen, Ofer Levy, Sidney C. Haller, David Erle, Carolyn M. Hendrickson, Matthew F. Krummel, Michael A. Matthay, Prescott Woodruff, Elias K. Haddad, Carolyn S. Calfee, Charles R. Langelier