Medical Affairs and Clinical Development, Adaptive Biotechnologies, Seattle, Washington, USA.
Division of Infectious Diseases and Geographic Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA.
Clin Infect Dis. 2022 Dec 19;75(12):2079-2087. doi: 10.1093/cid/ciac353.
While diagnostic, therapeutic, and vaccine development in the coronavirus disease 2019 (COVID-19) pandemic has proceeded at unprecedented speed, critical gaps in our understanding of the immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remain unaddressed by current diagnostic strategies.
A statistical classifier for identifying prior SARS-CoV-2 infection was trained using >4000 SARS-CoV-2-associated T-cell receptor (TCR) β sequences identified by comparing 784 cases and 2447 controls from 5 independent cohorts. The T-Detect COVID (Adaptive Biotechnologies) 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 with 2 commercial serology assays, and pathogen cross-reactivity was evaluated.
T-Detect COVID demonstrated high PPA in individuals with prior reverse transcription-polymerase chain reaction (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.
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 of protective immunity and long-term sequelae.
在 2019 年冠状病毒病(COVID-19)大流行中,诊断、治疗和疫苗的研发速度前所未有,但目前的诊断策略仍未解决我们对严重急性呼吸综合征冠状病毒 2(SARS-CoV-2)免疫反应的理解中的关键空白。
使用通过比较来自 5 个独立队列的 784 例病例和 2447 例对照中确定的>4000 个 SARS-CoV-2 相关 T 细胞受体(TCR)β序列,训练了用于识别先前 SARS-CoV-2 感染的统计分类器。T-Detect COVID(Adaptive Biotechnologies)检测应用该分类器对从血液样本中测序的 TCR 库进行分析,从而对过去的感染进行二进制评估。为了确定阳性百分比一致性(PPA)和阴性百分比一致性(NPA),在 2 个回顾性队列(n=346;n=69)和 1 个前瞻性队列(n=87)中评估了检测性能。将 PPA 与 2 种商业血清学检测进行比较,并评估病原体交叉反应性。
T-Detect COVID 在经逆转录-聚合酶链反应(RT-PCR)确诊的 SARS-CoV-2 感染患者中具有较高的 PPA(诊断后 15 天以上为 97.1%;症状出现后 15 天以上为 94.5%),在假定或确诊的 SARS-CoV-2 阴性病例中具有较高的 NPA(接近 100%),与 2 种商业血清学检测的 PPA 相当或更高,且无病原体交叉反应的证据。
T-Detect COVID 是一种新型的 T 细胞免疫测序检测,可从血液样本中高度准确地识别近期或先前的 SARS-CoV-2 感染,这对临床管理、风险分层、监测以及对保护性免疫和长期后遗症的理解具有重要意义。