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Clin Infect Dis ; 2022 May 06.
Article in English | MEDLINE | ID: covidwho-1831060


BACKGROUND: While diagnostic, therapeutic, and vaccine development in the COVID-19 pandemic has proceeded at unprecedented speed, critical gaps in our understanding of the immune response to SARS-CoV-2 remain unaddressed by current diagnostic strategies. METHODS: A statistical classifier for identifying prior SARS-CoV-2 infection was trained using >4000 SARS-CoV-2-associated TCRß sequences identified by comparing 784 cases and 2447 controls from 5 independent cohorts. The T-Detect™ COVID assay applies this classifier to TCR repertoires sequenced from blood samples to yield a binary assessment of past infection. Assay performance was assessed in 2 retrospective (n = 346; n = 69) and 1 prospective cohort (n = 87) to determine positive percent agreement (PPA) and negative percent agreement (NPA). PPA was compared to 2 commercial serology assays, and pathogen cross-reactivity was evaluated. RESULTS: T-Detect COVID demonstrated high PPA in individuals with prior RT-PCR-confirmed SARS-CoV-2 infection (97.1% 15 + days from diagnosis; 94.5% 15 + days from symptom onset), high NPA (∼100%) in presumed or confirmed SARS-CoV-2 negative cases, equivalent or higher PPA than 2 commercial serology tests, and no evidence of pathogen cross-reactivity. CONCLUSION: T-Detect COVID is a novel T-cell immunosequencing assay demonstrating high clinical performance for identification of recent or prior SARS-CoV-2 infection from blood samples, with implications for clinical management, risk stratification, surveillance, and understanding protective immunity and long-term sequelae.

JCI Insight ; 7(10)2022 05 23.
Article in English | MEDLINE | ID: covidwho-1794308


BACKGROUNDMeasuring the immune response to SARS-CoV-2 enables assessment of past infection and protective immunity. SARS-CoV-2 infection induces humoral and T cell responses, but these responses vary with disease severity and individual characteristics.METHODSA T cell receptor (TCR) immunosequencing assay was conducted using small-volume blood samples from 302 individuals recovered from COVID-19. Correlations between the magnitude of the T cell response and neutralizing antibody (nAb) titers or indicators of disease severity were evaluated. Sensitivity of T cell testing was assessed and compared with serologic testing.RESULTSSARS-CoV-2-specific T cell responses were significantly correlated with nAb titers and clinical indicators of disease severity, including hospitalization, fever, and difficulty breathing. Despite modest declines in depth and breadth of T cell responses during convalescence, high sensitivity was observed until at least 6 months after infection, with overall sensitivity ~5% greater than serology tests for identifying prior SARS-CoV-2 infection. Improved performance of T cell testing was most apparent in recovered, nonhospitalized individuals sampled > 150 days after initial illness, suggesting greater sensitivity than serology at later time points and in individuals with less severe disease. T cell testing identified SARS-CoV-2 infection in 68% (55 of 81) of samples with undetectable nAb titers (<1:40) and in 37% (13 of 35) of samples classified as negative by 3 antibody assays.CONCLUSIONThese results support TCR-based testing as a scalable, reliable measure of past SARS-CoV-2 infection with clinical value beyond serology.TRIAL REGISTRATIONSpecimens were accrued under trial NCT04338360 accessible at work was funded by Adaptive Biotechnologies, Frederick National Laboratory for Cancer Research, NIAID, Fred Hutchinson Joel Meyers Endowment, Fast Grants, and American Society for Transplantation and Cell Therapy.

COVID-19 , Antibodies, Neutralizing , Antibodies, Viral , Humans , Receptors, Antigen, T-Cell/genetics , SARS-CoV-2 , Severity of Illness Index , United States
Open forum infectious diseases ; 8(Suppl 1):87-87, 2021.
Article in English | EuropePMC | ID: covidwho-1564033


Background Our understanding of the SARS-CoV-2 immune response has critical gaps that are inadequately addressed with available tools. We report the clinical performance of T-Detect COVID, the first T-cell assay to identify prior SARS-CoV-2 infection using T-cell receptor (TCR) sequencing and repertoire profiling from whole blood samples. Methods The T-Detect COVID assay combines high-throughput immunosequencing of the TCRß gene from blood samples with a statistical classifier demonstrating 99.8% specificity for identifying prior SARS-CoV-2 infection. The assay was employed in several retrospective and prospective cohorts to assess primary and secondary Positive Percent Agreement (PPA) with SARS-CoV-2 RT-PCR (N=205;N=77);primary and secondary Negative Percent Agreement (NPA;N=87;N=79);PPA compared to SARS-CoV-2 serology (N=55);and pathogen cross-reactivity (N=38). The real-world performance of the test was also evaluated in a retrospective review of test ordering (N=69) at a single primary care clinic in Park City, Utah. Results In validation studies, T-Detect COVID demonstrated high PPA (97.1% ≥15 days from diagnosis) in subjects with prior PCR-confirmed SARS-CoV-2 infection;high NPA (~100%) in SARS-CoV-2 negative cases;equivalent or higher PPA with RT-PCR compared to two commercial EUA antibody tests;and no evidence of pathogen cross-reactivity. Review of assay use in a single clinic showed 100% PPA with RT-PCR in individuals with past confirmed SARS-CoV-2 vs. 85.7% for antibody testing, 100% agreement with positive antibody results, and positive results in 2/4 convalescent subjects with seroreversion to a negative antibody. In addition, 12/69 (17.3%) individuals with absent or negative RT-PCR tested positive by T-Detect COVID, nearly all of whom had compatible symptoms and/or exposure. TCR positivity was observed up to 12+ months (median 118 days) from the date of positive RT-PCR. Conclusion A T-cell immunosequencing assay shows high clinical performance for identifying past SARS-CoV-2 infection from whole blood samples. This assay can provide additional insights on the SARS-CoV-2 immune response, with practical implications for clinical management, risk stratification, surveillance, assessing vaccine immunity, and understanding long-term sequelae. Disclosures Sudeb C. Dalai, MD, PhD, Adaptive Biotechnologies (Employee, Shareholder) Jennifer N. Dines, MD, Adaptive Biotechnologies (Employee, Shareholder) Thomas M. Snyder, PhD, Adaptive Biotechnologies (Employee, Shareholder) Rachel M. Gittelman, PhD, Adaptive Biotechnologies (Employee, Shareholder) Tera Eerkes, PhD, Adaptive Biotechnologies (Employee, Shareholder) Pashmi Vaney, PhD, Adaptive Biotechnologies (Employee, Shareholder) Sally Howard, PhD, Adaptive Biotechnologies (Employee, Shareholder) Kipp Akers, PhD, Adaptive Biotechnologies (Employee, Shareholder) Lynell Skewis, PhD, Adaptive Biotechnologies (Employee, Shareholder) Anthony Monteforte, PhD, Adaptive Biotechnologies (Employee, Shareholder) Pamela R. Witte, PhD, Adaptive Biotechnologies (Employee, Shareholder) Cristina Wolf, PhD, Adaptive Biotechnologies (Employee, Shareholder) Hans Nesse, PhD, Adaptive Biotechnologies (Employee, Shareholder) Jia Qadeer, PhD, Adaptive Biotechnologies (Employee, Shareholder) Sarah Duffy, PhD, Adaptive Biotechnologies (Employee, Shareholder) Emily Svejnoha, PhD, Adaptive Biotechnologies (Employee, Shareholder) Caroline Taromino, PhD, Adaptive Biotechnologies (Employee, Shareholder) Ian M. Kaplan, PhD, Adaptive Biotechnologies (Employee, Shareholder) John Alsobrook, MD, Adaptive Biotechnologies (Employee, Shareholder) Thomas Manley, MD, Adaptive Biotechnologies (Employee, Shareholder) Lance Baldo, MD, Adaptive Biotechnologies (Employee, Shareholder, Leadership Interest)