Identifying individuals at risk of progression to active tuberculosis

识别有进展为活动性结核病风险的个体

• 批准号: 10092079
• 负责人: ANTONINO CATANZARO
• 金额: $ 72.89万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-02-22 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10092079
• 关键词: Address; Antigens; Biological Assay; Biological Markers; Blood; Blood Volume; Cells; Characteristics; Child; Clinical; Clinical Sensitivity; Data; Data Set; Detection; Diagnostic tests; Disease; Enrollment; Ethics; Event; Filtration; Flow Cytometry; Foundations; Freezing; Frequencies; Gender; Gene Expression; Genes; Genetic Transcription; Goals; Health; Household; Immune; Immune response; Immunologic Markers; Individual; Interferons; Logistic Regressions; Lymphocyte; Modeling; Moldova; Mycobacterium tuberculosis; Mycobacterium tuberculosis antigens; National Health Programs; Organizational Objectives; Outcomes Research; Participant; Patients; Peptide Fragments; Peptides; Performance; Peripheral Blood Mononuclear Cell; Plasma; Population; Procedures; Production; Prospective cohort; Proteins; Publications; Publishing; RNA; Research Personnel; Risk; Sampling; Sensitivity and Specificity; Specificity; T cell response; T-Lymphocyte; Testing; Tuberculosis; Validation; World Health Organization; age group; base; biobank; biomarker performance; biomarker signature; biosignature; blood-based biomarker; candidate marker; classification algorithm; clinical Diagnosis; cohort; cost effective; deep neural network; enzyme linked immunospot assay; falls; follow-up; high risk; indexing; innovation; machine learning method; monocyte; nanodisk; novel; novel diagnostics; predictive modeling; predictive test; prevent; programs; progression marker; random forest; research clinical testing; support vector machine; transcriptome sequencing; transmission process; treatment planning

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.

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）。这些人中约有10%在其一生中将发展为活动性结核病， 目前还没有临床试验来区分那些将发展为活动性结核病的人和那些不会发展为活动性结核病的人。如果 我们要实现世界卫生组织（WHO）到2035年实现世界无结核病的目标， 必须采用具有成本效益的、符合伦理的治疗方法来预防结核病的进展， 基于只治疗那些最有可能进展的人。一种诊断测试，可以准确地预测风险 进展对于治疗这些高危个体和根除结核病至关重要。我们的目标是开发 这样的分析。我们的中心假设是，五个独立的宿主免疫生物标志物，结合成一个 单个多指标特征将以至少90%的灵敏度预测从潜伏性到活动性TB的进展， 的特异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：在一个前瞻性队列中汇编一个全面的生物标志物数据集， 发展为活动性结核病。与摩尔多瓦卫生部的国家结核病项目合作，我们将招募 活动性肺结核病例密切接触者3,685人。所有参与者将被跟踪两年，以确定谁 发展为活动性结核病。我们预计将确定≥ 140例进展者。我们将评估三个先前建立的 基于血液的活动性结核病进展的预测因子，以及两种新的检测方法。我们将验证 先前公布的生物标志物在该人群中区分进展者和非进展者， 使用抗原刺激PBMC的RNA-Seq和Mtb-肽的检测鉴定新的候选生物标志物 目标2：使用一组发现样本来开发进展到活性药物的预测模型 TB.使用来自目标1的140名进步者和140名非进步者的数据，我们将（1）验证性能 （2）使用交叉验证来鉴定新的候选生物标志物，以及（3）推导出 使用逻辑回归和机器学习方法的预测模型来识别最佳生物标志物 这些特征最能预测12个月内进展为活动性结核病。目标3：验证模型的能力， 预测进展为活动性结核病。采用与目标1相同的方法，我们将招募一组新的1，340人 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特征的敏感性/特异性（目标2），以预测进展到 活动性疾病。联合宿主生物标志物特征，可从少量血液中预测TB进展 将对世卫组织终止结核病规划产生重大影响。