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A 2-Gene Host Signature for Improved Accuracy of COVID-19 Diagnosis Agnostic to Viral Variants

ABSTRACT: The continued emergence of SARS-CoV-2 variants is one of several factors that may cause false negative viral PCR test results. Such tests are also susceptible to false positive results due to trace contamination from high viral titer samples. Host immune response markers provide an orthogonal indication of infection that can mitigate these concerns when combined with direct viral detection. Here, we leverage nasopharyngeal swab RNA-seq data from patients with COVID-19, other viral acute respiratory illnesses and non-viral conditions (n=318) to develop support vector machine classifiers that rely on a parsimonious 2-gene host signature to diagnose COVID-19. We find that optimal classifiers include an interferon-stimulated gene that is strongly induced in COVID-19 compared with non-viral conditions, such as IFI6, and a second immune-response gene that is more strongly induced in other viral infections, such as GBP5. The IFI6+GBP5 classifier achieves an area under the receiver operating characteristic curve (AUC) greater than 0.9 when evaluated on an independent RNA-seq cohort (n=553). We further provide proof-of-concept demonstration that the classifier can be implemented in a clinically relevant RT-qPCR assay. Finally, we show that its performance is robust across common SARS-CoV-2 variants and is unaffected by cross-contamination, demonstrating its utility for improving accuracy of COVID-19 diagnostics.

ORGANISM(S): Homo sapiens

PROVIDER: GSE188678 | GEO | 2021/11/15

REPOSITORIES: GEO

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