Prediction of anti-tuberculosis treatment duration based on a 22-gene transcriptomic model
Prediction of anti-tuberculosis treatment duration based on a 22-gene transcriptomic model
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DOI：
10.1183/13993003.03492-2020
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发表时间：
2021-09-01
影响因子：
24.3
通讯作者：
Lange, Christoph
中科院分区：
文献类型：
--
作者：
Heyckendorf, Jan;Marwitz, Sebastian;Lange, Christoph
背景世界卫生组织建议对结核病（TB）患者进行标准化治疗。我们确定并验证了宿主RNA签名作为个体化治疗持续时间的药物敏感（DS）和多药耐药（MDR）-TB患者的生物标志物。方法将肺结核成人患者前瞻性纳入德国和罗马尼亚的5个独立队列。在整个治疗期间的预定时间点收集临床和微生物学数据以及用于RNA转录组学分析的全血。根据TBnet标准（6个月培养状态/1年随访）确定治疗结局。全血RNA治疗结束模型是在一个涉及机器学习算法的多步骤过程中开发的，以确定假设的个体治疗结束时间点。（分别为DS-GIC和MDR-GIC）;德国验证队列中28例DS-TB患者和32例MDR-TB患者（分别为DS-GVC和MDR-GVC）;和罗马尼亚验证队列（MDR-RVC）中的52例MDR-TB患者。从DS和MDR-GIC数据中推导出定义治愈相关治疗结束时间点的22基因RNA模型（TB 22）。TB 22模型在准确预测DS-GVC患者的临床结局方面上级其他已发表的特征（曲线下面积0.94，95%CI 0.9-0.98），并表明MDR-GIC中的TB患者可以在更短的治疗时间内治愈（平均减少218.0天，34.2%; p
Background The World Health Organization recommends standardised treatment durations for patients with tuberculosis (TB). We identified and validated a host-RNA signature as a biomarker for individualised therapy durations for patients with drug-susceptible (DS)-and multidrug-resistant (MDR)-TB.Methods Adult patients with pulmonary TB were prospectively enrolled into five independent cohorts in Germany and Romania. Clinical and microbiological data and whole blood for RNA transcriptomic analysis were collected at pre-defined time points throughout therapy. Treatment outcomes were ascertained by TBnet criteria (6-month culture status/1-year follow-up). A whole-blood RNA therapy-end model was developed in a multistep process involving a machine-learning algorithm to identify hypothetical individual end-of-treatment time points.Results 50 patients with DS-TB and 30 patients with MDR-TB were recruited in the German identification cohorts (DS-GIC and MDR-GIC, respectively); 28 patients with DS-TB and 32 patients with MDR-TB in the German validation cohorts (DS-GVC and MDR-GVC, respectively); and 52 patients with MDR-TB in the Romanian validation cohort (MDR-RVC). A 22-gene RNA model (TB22) that defined cure-associated end-of-therapy time points was derived from the DS-and MDR-GIC data. The TB22 model was superior to other published signatures to accurately predict clinical outcomes for patients in the DS-GVC (area under the curve 0.94, 95% CI 0.9-0.98) and suggests that cure may be achieved with shorter treatment durations for TB patients in the MDR-GIC (mean reduction 218.0 days, 34.2%; p