DEVELOPMENT AND VALIDATION OF A 2-GENE HOST-VIRAL TRANSCRIPTOMIC CLASSIFIER FOR ENHANCED COVID-19 DIAGNOSIS

DEVELOPMENT AND VALIDATION OF A 2-GENE HOST-VIRAL TRANSCRIPTOMIC CLASSIFIER FOR ENHANCED COVID-19 DIAGNOSIS

Researchers at UCSF and CZ Biohub SF have developed a two gene host signature that detects COVID infection regardless of the causative viral variant.

Though COVID-19 has largely transitioned from pandemic to endemic status, viral variants of SARS-CoV2 continue to emerge and enact devastating human health consequences on vulnerable populations. Throughout the pandemic, diagnostic testing has been key to public health mitigation strategies. Direct nucleic acid detection using nasopharyngeal (NP) swab RT-PCR remains the gold standard in diagnostic testing for SARS-CoV2. However, this test is limited in its accuracy due to both human error as well as the emergence of new viral variants with mutations at diagnostic primer target sites, leading to false-positive and false-negative results. These erroneous results have lasting consequences on both large-scale public health efforts to contain the virus, as well as at an individual level to those who receive false results. In a similar vein, several groups have recently developed classifiers to identify host determinants of SARS-CoV2 infection by performing RNA-seq on NP swabs. However, these approaches rely on a relatively large number of genes. Furthermore, RNA-seq is not widely available in clinical settings, reducing this approach’s practicality in point of care settings.

Stage of Research

The inventors have identified and validated a 2-gene diagnostic classifier for COVID-19. Using a large cohort of over 300 NP swab tests from those with and without SAR-CoV2 infection, the inventors identified IFN6 and GBP5 as a particularly parsimonious pairing that form an optimal classifier for COVID-19. IFN6 is an interferon-stimulated gene that is strongly upregulated in COVID-19 as compared to nonviral conditions, while GBP5 is a second immune response gene that is more strongly induced in other viral infections compared to COVID-19. The inventors found that in an independent RNA-seq cohort of over 550 patients, this 2-gene classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.91. Additionally, this performance did not differ significantly across viral variants of SARS-CoV2, indicating that this method is potentially variant-independent and can be used to overcome false-negative test results due to variants with mutations in key primer target sites. Finally, the inventors demonstrated that this classifier is not significantly influenced by laboratory cross-contamination which presents the potential of this test to overcome false-positive results due to human error.

Applications

Providing more accurate COVID results by reducing the number of false-positive and false-negative results reported to patients.

Advantages

Mitigates false-positive results due to laboratory contamination by providing an additional layer of screening for each qPCR sample.
Reduces false-negative results due to viral variants with mutations at key primer target sites by providing an additional layer of screening for each qPCR sample.

Stage of Development

Research- in vivo

Publications

A 2-Gene Host Signature for Improved Accuracy of COVID-19 Diagnosis Agnostic to Viral Variants. mSystems. 2022 Dec 12; e0067122. Albright J, Mick E, Sanchez-Guerrero E, Kamm J, Mitchell A, Detweiler AM, Neff N, Tsitsiklis A, Hayakawa Serpa P, Ratnasiri K, Havlir D, Kistler A, DeRisi JL, Pisco AO, Langelier CR. PMID: 36507688 (PDF attached).

WO2023/283139