National Clinical Research Center for Infectious Diseases, Guangdong Key Laboratory for Diagnosis & Treatment of Emerging Infectious Diseases, Shenzhen Third People's Hospital, Shenzhen, China.
Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pathogen Biology, Shenzhen University School of Medicine, Shenzhen, China.
Thorax. 2020 Jul;75(7):576-583. doi: 10.1136/thoraxjnl-2018-213021. Epub 2020 Mar 22.
Biomarker-based tests for diagnosing TB currently rely on detecting (Mtb) antigen-specific cellular responses. While this approach can detect Mtb infection, it is not efficient in diagnosing TB, especially for patients who lack aetiological evidence of the disease.
We prospectively enrolled three cohorts for our study for a total of 630 subjects, including 160 individuals to screen protein biomarkers of TB, 368 individuals to establish and test the predictive model and 102 individuals for biomarker validation. Whole blood cultures were stimulated with pooled Mtb-peptides or mitogen, and 640 proteins within the culture supernatant were analysed simultaneously using an antibody-based array. Sixteen candidate biomarkers of TB identified during screening were then developed into a custom multiplexed antibody array for biomarker validation.
A two-round screening strategy identified eight-protein biomarkers of TB: I-TAC, I-309, MIG, Granulysin, FAP, MEP1B, Furin and LYVE-1. The sensitivity and specificity of the eight-protein biosignature in diagnosing TB were determined for the training (n=276), test (n=92) and prediction (n=102) cohorts. The training cohort had a 100% specificity (95% CI 98% to 100%) and 100% sensitivity (95% CI 96% to 100%) using a random forest algorithm approach by cross-validation. In the test cohort, the specificity and sensitivity were 83% (95% CI 71% to 91%) and 76% (95% CI 56% to 90%), respectively. In the prediction cohort, the specificity was 84% (95% CI 74% to 92%) and the sensitivity was 75% (95% CI 57% to 89%).
An eight-protein biosignature to diagnose TB in a high-burden TB clinical setting was identified.
目前基于生物标志物的结核病诊断测试依赖于检测(Mtb)抗原特异性细胞反应。虽然这种方法可以检测 Mtb 感染,但在诊断结核病方面效率不高,特别是对于缺乏疾病病因证据的患者。
我们前瞻性地招募了三个队列进行研究,共有 630 名受试者,包括 160 名筛选结核病蛋白生物标志物的个体、368 名建立和测试预测模型的个体以及 102 名验证生物标志物的个体。全血培养物用 Mtb 肽或有丝分裂原刺激,同时使用基于抗体的阵列分析培养上清液中的 640 种蛋白质。在筛选过程中鉴定出的 16 种结核病候选生物标志物随后被开发成用于生物标志物验证的定制多重抗体阵列。
两轮筛选策略确定了 8 种结核病生物标志物:I-TAC、I-309、MIG、Granulysin、FAP、MEP1B、Furin 和 LYVE-1。在训练(n=276)、测试(n=92)和预测(n=102)队列中,确定了该八蛋白生物标志物诊断结核病的敏感性和特异性。训练队列采用随机森林算法通过交叉验证的方法,特异性为 100%(95%CI 98%至 100%),敏感性为 100%(95%CI 96%至 100%)。在测试队列中,特异性和敏感性分别为 83%(95%CI 71%至 91%)和 76%(95%CI 56%至 90%)。在预测队列中,特异性为 84%(95%CI 74%至 92%),敏感性为 75%(95%CI 57%至 89%)。
在高负担结核病临床环境中,确定了一种用于诊断结核病的八蛋白生物标志物。