Early immune markers of clinical, virological, and immunological outcomes in patients with COVID-19

Background

One complication in treating COVID-19 is that there is substantial heterogeneity in SARS-CoV-2-specific memory immune responses following infection. There also remains a critical need to identify host immune biomarkers predictive of clinical and immunological outcomes in SARS-CoV-2-infected patients. A multiomic COVID-19 study from Stanford University followed a prospective, longitudinal outpatient trial of a type III IFN drug in individuals newly infected with SARS-CoV-2, over the course of 7 months. In this extensive investigation they looked at plasma proteomics with Olink, bulk RNAseq, viral shedding, virus-specific antibody loads and virus-specific T-cell responses in order to characterize early immune responses to infection

Outcome

This approach data identified several early immune signatures, including plasma RIG-I, levels, early IFN signaling (with IFN-responsive cytokine increases in CXCL10, MCP1, MCP-2, and MCP-3) associated with subsequent disease progression, viral shedding, and T-cell responses. Interestingly, several biomarkers for immunological outcomes were also evident both in individuals receiving the Pfizer vaccine and COVID-19 patients. Machine learning models using 2–7 plasma protein markers measured at day zero in the longitudinal analysis could accurately predict disease progression, T cell memory, and the antibody response post-infection. Most strikingly, a 4-protein signature (CXCL10, CXCL11, MCP2 and PRDX3) predicted severe disease progression in patients at the earliest stage of infection with an AUC=0.9.

Hu-et-al-2022

Citation

Hu Z, van der Ploeg K, Chakraborty S, et al. Early immune markers of clinical, virological, and immunological outcomes in patients with COVID-19: a multi-omics study. (2022) eLife  DOI: 10.7554/eLife.77943

Early immune signatures following infection can accurately predict clinical and immunological outcomes in outpatients with COVID-19 using validated machine-learning models

Hu et al. (2022)

Peer-reviewed publications citing the use of Olink panels

Olink’s Proximity Extension Assay (PEA) technology has been used for protein biomarker discovery and analysis across a very broad range of disease areas and applications, providing actionable insights into disease biology and helping to drive future development of new and better therapeutics. There are now well over 1000 publications citing the use of our assays and the list is growing rapidly. Please visit our library of publications to see some of the extraordinary work produced by Olink customers.

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