Rationale Blood-based biomarkers for diagnosis of 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 endpoints is essential to the development of a point-of-care clinical test. Objectives NanoString nCounter technology is an amplification-free digital detection platform that directly measures mRNA transcripts with high specificity. Here, we investigated whether NanoString could serve as a platform for extensive validation of candidate TB biomarkers. Methods The NanoString platform was employed for performance evaluation of existing TB gene signatures in a cohort in which signatures were previously evaluated on RNA-seq dataset. A NanoString Codeset that probes 107 genes comprising twelve TB signatures and six house-keeping 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. Measurements and Main Results Gene signatures present in NS-TB107 had statistically significant discriminative power for segregating TB from LTBI. Further analysis of the data yielded a six-gene set (NANO6) that when tested on ten published datasets was highly diagnostic for active TB. Conclusions The NanoString nCounter system provides a robust platform to validate existing TB biomarkers and to derive a parsimonious gene signature with enhanced diagnostic performance.

中文翻译：

---

使用数字 NanoString nCounter 平台开发和验证简约的结核病基因签名


基本原理 用于诊断活动性结核病 (TB)、监测治疗反应和预测结核病进展风险的血液生物标志物已有报道。然而，在多个独立队列中对生物标志物的验证很少。一个强大的平台可以通过临床终点验证不同人群中的结核病生物标志物，这对于开发即时临床测试至关重要。目标 NanoString nCounter 技术是一种免扩增数字检测平台，可直接测量高特异性的 mRNA 转录本。在这里，我们研究了 NanoString 是否可以作为广泛验证候选结核生物标志物的平台。方法 NanoString 平台用于对队列中现有结核病基因特征进行性能评估，其中特征先前在 RNA-seq 数据集上进行了评估。开发了一种 NanoString 代码集，可探测 107 个基因，其中包括 12 个结核病特征和 6 个管家基因 (NS-TB107)，并将其应用于来自印度南部结核病患者和潜伏性结核感染 (LTBI) 个体的全血样本的总 RNA。 TBSignatureProfiler 工具用于对每个签名的样本进行评分。使用机器学习算法的集合来导出简约的生物标记物。测量和主要结果 NS-TB107 中存在的基因特征对于区分 TB 和 LTBI 具有统计学上显着的区分能力。对数据的进一步分析产生了六基因组（NANO6），在十个已发布的数据集上进行测试时，该组对活动性结核病具有高度诊断性。结论 NanoString nCounter 系统提供了一个强大的平台来验证现有的结核病生物标志物并获得具有增强诊断性能的简约基因特征。