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

BACKGROUNDAccurate 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.METHODSWe 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.RESULTSFatal 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).CONCLUSIONOur 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.FUNDINGNIH: 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.

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Journal
JCI Insight
Published
2025-11-09
DOI
https://doi.org/10.1172/jci.insight.195436
Primary Topic
COVID-19 Clinical Research Studies
Type
article
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article

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

Walter L. Eckalbar, Joann Diray‐Arce, Hanno Steen, Charles Langelier et al.
JCI Insight
COVID-19 Clinical Research Studies
article

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

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

Abstract

BACKGROUNDAccurate 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.METHODSWe 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.RESULTSFatal 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).CONCLUSIONOur 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.FUNDINGNIH: 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.

JCI InsightVol. 10(21)
Boston Children's Hospital (US), La Jolla Institute for Immunology (US), Emory University (US), City College of San Francisco (US), Palo Alto University (US), UCLA Health (US), Yale University (US), Chan Zuckerberg Initiative (United States) (US), National Institute of Allergy and Infectious Diseases (US), University of Washington Applied Physics Laboratory (US), Drexel University (US), Stanford University (US), Icahn School of Medicine at Mount Sinai (US)
Openalex Percentile: Top 57%
COVID-19 Clinical Research Studies
DOIOpen Access PDFOpenAlex
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