Department of Medicine, Center for Emerging Pathogens, Rutgers-New Jersey Medical School, Newark, New Jersey, USA.
Department of Medicine, Division of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts, USA.
Clin Infect Dis. 2022 Sep 29;75(6):1022-1030. doi: 10.1093/cid/ciac010.
Blood-based biomarkers for diagnosing active tuberculosis (TB), monitoring treatment response, and predicting risk of progression to TB disease have been reported. However, validation of the biomarkers across multiple independent cohorts is scarce. A robust platform to validate TB biomarkers in different populations with clinical end points is essential to the development of a point-of-care clinical test. NanoString nCounter technology is an amplification-free digital detection platform that directly measures mRNA transcripts with high specificity. Here, we determined whether NanoString could serve as a platform for extensive validation of candidate TB biomarkers.
The NanoString platform was used for performance evaluation of existing TB gene signatures in a cohort in which signatures were previously evaluated on an RNA-seq dataset. A NanoString codeset that probes 107 genes comprising 12 TB signatures and 6 housekeeping genes (NS-TB107) was developed and applied to total RNA derived from whole blood samples of TB patients and individuals with latent TB infection (LTBI) from South India. The TBSignatureProfiler tool was used to score samples for each signature. An ensemble of machine learning algorithms was used to derive a parsimonious biomarker.
Gene signatures present in NS-TB107 had statistically significant discriminative power for segregating TB from LTBI. Further analysis of the data yielded a NanoString 6-gene set (NANO6) that when tested on 10 published datasets was highly diagnostic for active TB.
The NanoString nCounter system provides a robust platform for validating existing TB biomarkers and deriving a parsimonious gene signature with enhanced diagnostic performance.
已有研究报道了用于诊断活动性肺结核（TB）、监测治疗反应和预测 TB 发病风险的基于血液的生物标志物。然而，这些生物标志物在多个独立队列中的验证却很少。建立一个稳健的平台，用于在具有临床终点的不同人群中验证 TB 生物标志物，对于开发即时护理临床检验至关重要。NanoString nCounter 技术是一种无需扩增的数字检测平台，可高度特异性地直接测量 mRNA 转录物。在此，我们确定 NanoString 是否可以作为验证候选 TB 生物标志物的平台。
我们使用 NanoString 平台对先前在 RNA-seq 数据集上进行评估的队列中的现有 TB 基因特征进行性能评估。开发了一个包含 12 个 TB 特征和 6 个管家基因的 NanoString 检测集（NS-TB107），并应用于来自印度南部的 TB 患者和潜伏性 TB 感染（LTBI）个体的全血样本中的总 RNA。使用 TBSignatureProfiler 工具对每个特征进行评分。使用集成机器学习算法来推导一个简约的生物标志物。
NS-TB107 中的基因特征在区分 TB 与 LTBI 方面具有统计学上显著的区分能力。对数据的进一步分析得出了一个 NanoString 6 基因集（NANO6），当在 10 个已发表的数据集上进行测试时，对活动性 TB 具有高度诊断价值。
NanoString nCounter 系统为验证现有的 TB 生物标志物和推导出具有增强诊断性能的简约基因特征提供了一个稳健的平台。
