Bakar Computational Health Sciences Institute, University of California, San Francisco, United States; Department of Microbiology and Immunology, University of California, San Francisco, United States
Department of Medicine, Stanford University, Stanford, United States; Department of Epidemiology and Population Health, Stanford University, Stanford, United States
Quantitative Sciences Unit, Stanford University, Stanford, United States
Lauren de la Parte
Department of Medicine, Stanford University, Stanford, United States
Kathleen Press
Department of Medicine, Stanford University, Stanford, United States
Maureen Ty
Department of Medicine, Stanford University, Stanford, United States
Gene S Tan
J. Craig Venter Institute, San Diego, United States; Division of Infectious Diseases, Department of Medicine, University of California, San Diego, United States
Catherine Blish
Department of Medicine, Stanford University, Stanford, United States; Chan Zuckerberg Biohub, San Francisco, United States
Saki Takahashi
Department of Medicine, University of California, San Francisco, United States
Department of Medicine, Stanford University, Stanford, United States; Department of Microbiology and Immunology, Stanford University, Stanford, United States
Institute for Immunity, Transplantation, and Infection, Stanford University, Stanford, United States; Department of Microbiology and Immunology, Stanford University, Stanford, United States; Department of Pathology, Stanford University, Stanford, United States
Taia T Wang
Department of Medicine, Stanford University, Stanford, United States; Chan Zuckerberg Biohub, San Francisco, United States; Department of Microbiology and Immunology, Stanford University, Stanford, United States
Background: The great majority of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) infections are mild and uncomplicated, but some individuals with initially mild COVID-19 progressively develop more severe symptoms. Furthermore, there is substantial heterogeneity in SARS-CoV-2-specific memory immune responses following infection. There remains a critical need to identify host immune biomarkers predictive of clinical and immunological outcomes in SARS-CoV-2-infected patients. Methods: Leveraging longitudinal samples and data from a clinical trial (N=108) in SARS-CoV-2-infected outpatients, we used host proteomics and transcriptomics to characterize the trajectory of the immune response in COVID-19 patients. We characterized the association between early immune markers and subsequent disease progression, control of viral shedding, and SARS-CoV-2-specific T cell and antibody responses measured up to 7 months after enrollment. We further compared associations between early immune markers and subsequent T cell and antibody responses following natural infection with those following mRNA vaccination. We developed machine-learning models to predict patient outcomes and validated the predictive model using data from 54 individuals enrolled in an independent clinical trial. Results: We identify early immune signatures, including plasma RIG-I levels, early IFN signaling, and related cytokines (CXCL10, MCP1, MCP-2, and MCP-3) associated with subsequent disease progression, control of viral shedding, and the SARS-CoV-2-specific T cell and antibody response measured up to 7 months after enrollment. We found that several biomarkers for immunological outcomes are shared between individuals receiving BNT162b2 (Pfizer–BioNTech) vaccine and COVID-19 patients. Finally, we demonstrate that machine-learning models using 2–7 plasma protein markers measured early within the course of infection are able to accurately predict disease progression, T cell memory, and the antibody response post-infection in a second, independent dataset. Conclusions: Early immune signatures following infection can accurately predict clinical and immunological outcomes in outpatients with COVID-19 using validated machine-learning models. Funding: Support for the study was provided from National Institute of Health/National Institute of Allergy and Infectious Diseases (NIH/NIAID) (U01 AI150741-01S1 and T32-AI052073), the Stanford’s Innovative Medicines Accelerator, National Institutes of Health/National Institute on Drug Abuse (NIH/NIDA) DP1DA046089, and anonymous donors to Stanford University. Peginterferon lambda provided by Eiger BioPharmaceuticals.
