Identifying individuals at risk of progression to active tuberculosis

项目摘要 近20亿人感染结核分枝杆菌(Mtb)，这是结核病的病原体 (TB)。其中大约10%的人会在一生中发展为活动性结核病，但有 目前还没有临床测试来区分那些将发展为活动性结核病的人，以及那些不会发展为活动性结核病的人。如果 我们将实现世界卫生组织(WHO)到2035年实现无结核病世界的目标，大规模的 必须用具有成本效益的、合乎道德的疗法来解决结核病感染者的问题， 基于只治疗最有可能进展的患者。一种能够准确预测疾病风险的诊断测试 进展对于治疗这些高危人群和根除结核病至关重要。我们的目标是发展 这样的化验。我们的中心假设是五个独立的宿主免疫生物标记物，结合成一个 单一的多项检测特征将以至少90%的灵敏度预测从潜伏期结核病到活动性结核病的进展情况，并 专一性。我们将检验这一假设，并通过实现以下具体目标来实现我们的目标： 1：汇编未来队列中有高血压风险的个体的生物标记物的综合数据集 进展为活动性肺结核。与摩尔多瓦卫生部的国家结核病计划合作，我们将登记 3685例活动性肺结核病例密切接触者。所有参与者将被跟踪两年，以确定谁是 进展为活动性结核病。我们希望确定≥140的进步者。我们将评估之前建立的三个 基于血液的活动性结核病进展的预测指标，以及两种新的分析方法。我们将验证其性能 以前发表的用于区分进展者和非进展者的生物标志物 利用抗原刺激的PBMC的RNA序列识别新的候选生物标志物和检测结核分枝杆菌多肽 目标2：使用一组发现的样本来开发进展为活跃状态的预测模型 结核病。使用来自目标1的140个进步者和140个非进步者的数据，我们将(1)验证绩效 现有生物标记物，(2)使用交叉验证来识别新的候选生物标记物，以及(3)推导 用Logistic回归和机器学习方法识别最优生物标志物的预测模型 最好地预测12个月内进展为活动性结核病的签名。目标3：验证模型的能力 预测进展为活动性结核病。使用与目标1相同的方法，我们将招收1,340名新学生 与活动性结核病的家庭接触者，并确定至少60名进展者和60名匹配的非进展者 在临床上验证我们的模型和生物签名(目标2)的敏感性/特异性，以预测进展到 活动性疾病。一种联合宿主生物标记物特征可以预测从少量血液中的结核病进展 数量将对世卫组织结束结核病计划产生重大影响。

Project Summary Almost 2 billion people are infected with Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB). Approximately 10% of these individuals will progress to active TB disease over their lifetimes, but there is currently no clinical test to distinguish those that will progress to active TB disease, from those that will not. If we are to realize the World Health Organization's (WHO) goal of a world free of TB by 2035, the massive reservoir of TB infection must be addressed with a cost-effective, ethical therapy for preventing progression, based on treating only those most likely to progress. A diagnostic test that can accurately predict the risk of progression is critical for treating these high-risk individuals and the eradication of TB. Our goal is to develop such an assay. Our central hypothesis is that five independent host immune biomarkers, combined into a single multimetric signature will predict progression from latent to active TB with at least 90% sensitivity and specificity. We will test this hypothesis and achieve our goal by implementing the following specific aims: Aim 1: Compile a comprehensive dataset of biomarkers in a prospective cohort of individuals who are at risk of progressing to active TB. Working with the Moldova Ministry of Health's National TB Program, we will enroll 3,685 close contacts of active TB cases. All participants will be followed for two years to determine who progresses to active TB. We expect to identify ≥ 140 progressors. We will assess three previously established blood-based predictors of active TB progression, and two novel assays. We will verify the performance of previously published biomarkers in this population to discriminate progressors from non-progressors and identify new candidate biomarkers using RNA-Seq of antigen stimulated PBMC and detection of Mtb-peptides by NanoDisk MS. Aim 2: Use a discovery set of samples to develop predictive models of progression to active TB. Using data from 140 progressors and 140 non-progressors from Aim 1 we will (1) Verify the performance of existing biomarkers, (2) Use a cross-validation to identify new candidate biomarkers, and (3) derive predictive models using logistic regression and machine learning methods to identify optimal biomarker signatures that best predict progression to active TB within 12 months. Aim 3: Verify the ability of the model to predict progression to active TB disease. Using the same approach as Aim 1, we will enroll a new set of 1,340 household contacts of active TB and identify at least 60 progressors and 60 matched non-progressors and verify clinically the sensitivity/specificity of our models and biosignatures (Aim 2) to predict progression to active disease. A combined host biomarker signature that can predict TB progression from a small blood volume will have significant impact on the WHO End TB Program.