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: 10331789
• 负责人: ANTONINO CATANZARO
• 金额: $ 74.21万
• 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 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10331789
• 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; Persons; 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; gradient boosting; 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.

项目概要 近 20 亿人感染结核病病原体——结核分枝杆菌 (Mtb) （TB）。这些人中大约 10% 在其一生中会发展为活动性结核病，但也有 目前还没有临床测试可以区分那些将发展为活动性结核病的人与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) 提出的到 2035 年实现世界无结核病的目标， 必须通过具有成本效益、合乎道德的治疗来解决结核感染的储存问题，以防止进展， 基于仅治疗那些最有可能取得进展的人。可以准确预测风险的诊断测试 疾病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 这样的测定。我们的中心假设是五个独立的宿主免疫生物标志物组合成一个 单一多指标特征将预测从潜伏性结核病到活动性结核病的进展，敏感性至少为 90% 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： 目标 1：在有风险的个体的前瞻性队列中编制全面的生物标志物数据集 进展为活动性结核病。与摩尔多瓦卫生部的国家结核病规划合作，我们将招募 活动性结核病病例的密切接触者 3,685 名。所有参与者都将被跟踪两年，以确定谁 进展为活动性结核病。我们预计会识别出 ≥ 140 名进展者。我们将评估之前建立的三个 活动性结核病进展的血液预测因子，以及两种新的检测方法。我们将验证性能 先前在该人群中发表的生物标志物，用于区分进展者和非进展者，以及 使用抗原刺激的 PBMC 的 RNA-Seq 和 Mtb 肽的检测来识别新的候选生物标志物 由 NanoDisk MS 提供。目标 2：使用一组发现样本来开发进展到活动的预测模型 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) 验证表现 现有生物标志物，(2) 使用交叉验证来识别新的候选生物标志物，以及 (3) 得出 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 最能预测 12 个月内进展为活动性结核病的特征。目标 3：验证模型的能力 预测活动性结核病的进展。使用与目标 1 相同的方法，我们将注册一组新的 1,340 名学生 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，以及 临床验证我们的模型和生物特征（目标 2）的敏感性/特异性，以预测进展 活动性疾病。可以通过小血液预测结核病进展的组合宿主生物标志物特征 数量将对世卫组织终止结核病规划产生重大影响。

项目成果

Identification of differentially recognized T cell epitopes in the spectrum of tuberculosis infection.

• DOI: 10.1038/s41467-024-45058-9
• 发表时间: 2024-01-26
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Panda, Sudhasini;Morgan, Jeffrey;Cheng, Catherine;Saito, Mayuko;Gilman, Robert H.;Ciobanu, Nelly;Crudu, Valeriu;Catanzaro, Donald G.;Catanzaro, Antonino;Rodwell, Timothy;Perera, Judy S. B.;Chathuranga, Teshan;Gunasena, Bandu;Desilva, Aruna D.;Peters, Bjoern;Sette, Alessandro;Lindestam Arlehamn, Cecilia S.
• 通讯作者: Lindestam Arlehamn, Cecilia S.