Discovery and clinical validation of host biomarkers of disease severity and multi-system inflammatory syndrome in children (MIS-C) with Covid-19

Covid-19 儿童疾病严重程度和多系统炎症综合征 (MIS-C) 宿主生物标志物的发现和临床验证

• 批准号: 10733698
• 负责人: Charles Yen Chiu
• 金额: $ 161.95万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10733698
• 关键词: Acceleration; Acute; Age; Applications Grants; BLase; Biological Assay; Biological Markers; Blood; Blood specimen; COVID-19; COVID-19 assay; COVID-19 diagnosis; COVID-19 patient; COVID-19 severity; COVID-19 treatment; California; Cells; Characteristics; Child; Childhood; Classification; Clinical; Clinical Laboratory Improvement Amendments; Clinics and Hospitals; Collaborations; Collection; Critical Illness; DNA; DNA Sequence; DNA analysis; Data; Data Collection; Data Coordinating Center; Detection; Development; Diabetes Mellitus; Diagnosis; Diagnostic; Disease; Early Diagnosis; Enrollment; Ethnic Origin; Evaluation; FDA Emergency Use Authorization; Gender; Generations; Genes; Genetic Fingerprintings; Goals; Hospitals; Hour; Household; Immune response; Immunocompromised Host; Infection; Inflammatory; Laboratories; Licensing; Machine Learning; Medical center; Methylation; Modeling; Monitor; Multisystem Inflammatory Syndrome in Children; Myocardial dysfunction; Obesity; Organ; Outcome; Outpatients; Patients; Pediatric cohort; Performance; Phase; Plasma; Pneumonia; Population; RADx; RADx Radical; RNA; Race; Reproducibility; SARS-CoV-2 genome; SARS-CoV-2 infection; Sampling; San Francisco; Severities; Severity of illness; Specificity; Symptoms; Syndrome; System; Technology; Testing; Time; Tissues; Universities; Validation; Viral; Viral Load result; Whole Blood; Work; accurate diagnosis; assay development; biobank; biomarker identification; biomarker panel; biomarker validation; cell free DNA; clinical assay development; clinical predictive model; clinical predictors; cohort; detection limit; diagnostic value; digital; genetic pedigree; machine learning classification; nasal swab; novel strategies; participant enrollment; pathogenic virus; point of care; portability; predict clinical outcome; predictive modeling; prognostic; prognostic value; research clinical testing; respiratory; response; sample collection; tool; transcriptome sequencing; transcriptomic profiling; transcriptomics

ABSTRACT Novel approaches for early and accurate diagnosis of COVID-19 associated syndromes and evaluation of clinical severity and outcomes of COVID-19 disease in children are urgently needed. The overarching goal of this grant proposal is to develop clinical assays that can evaluate and predict severity of pediatric COVID-19 disease, ranging from asymptomatic or mildly symptomatic to severe manifestations such as multisystem inflammatory syndrome (MIS-C). To date, we have collected and biobanked clinical samples from more than 400 patients across 3 academic hospitals, including approximately 100 patients with MIS-C. In the first R61 phase of this project, we will continue to enroll patients with pediatric COVID-19 and MIS-C for sample collection and longitudinal chart review and testing (Aim 1), leverage machine learning to identify diagnostic and prognostic “omics” host biomarkers based on RNA transcriptome profiling from nasal swab and whole blood samples (Aim 2) and cell-free DNA analysis from plasma (Aim 3), and generate predictive models of clinical severity and outcomes by incorporating longitudinal clinical, laboratory, viral, and omics data (Aim 4). Our rationale for including these samples is that they are routinely obtained in hospitals and clinics and permit easy and noninvasive collection without any special processing or handling requirements, which will accelerate the development of omics-based clinical assays. Our Go/No-Go transition milestones for transition to the R33 phase after 2 years include: (1) collection of longitudinal samples from a minimum of 120 patients for each identified presentation (mildly symptomatic outpatient, severely ill in the ICU, and MIS-C) and a comparable number of matched controls, (2) generation of panels of candidate of severity and confirmation of a subset of biomarkers by qPCR, (3) development of classifier models using machine learning using the biomarkers alone (for clinical assay development), and (4) combining these omics biomarkers with additional clinical, viral, and laboratory biomarkers into combined classifier models using machine learning. For the classifier models, the minimum/goal performance requirements would be 70%/>80% sensitivity and 80%/>90% specificity. In the second R33 phase, we propose to develop host-based clinical assays for diagnosis and severity prediction of COVID-19-associated syndromes, including MIS-C, in children from nasal swabs and blood (Aim 5) and validate these biomarker panels as a Laboratory Developed Test (LDT) in a CLIA (Clinical Laboratory Improvement Amendments) diagnostic laboratory (Aim 6). These assays will be evaluated for accuracy, precision, reproducibility, limits of detection (LOD), matrix effect, interference, among other performance characteristics. We will work closely with the RADx-rad Data Coordination Center (DCC) on assay development, testing, and validation for submission to the FDA for Emergency Use Authorization (EUA) and timely deployment of these assays for clinical use.

摘要 用于早期和准确诊断COVID-19相关综合征的新方法， 因此，迫切需要评估COVID-19疾病在儿童中的临床严重程度和结果。 这项拨款提案的总体目标是开发临床检测， 儿童COVID-19疾病的严重程度，从无症状或轻度症状到重度 多系统炎症综合征（MIS-C）。到目前为止，我们已经收集和 来自3家学术医院的400多名患者的生物库临床样本，包括 大约100名MIS-C患者。在本项目的第一个R61阶段，我们将继续招募 儿童COVID-19和MIS-C患者进行样本采集和纵向病历审查， 测试（目标1），利用机器学习来识别诊断和预后“组学”宿主 基于鼻拭子和全血样本RNA转录组分析的生物标志物（目的2） 和血浆中的无细胞DNA分析（目的3），并生成临床严重程度的预测模型 通过整合纵向临床，实验室，病毒和组学数据（目标4）。我们 纳入这些样本的理由是，它们是在医院和诊所常规获得的， 允许容易和非侵入性的收集，而没有任何特殊的处理或处理要求， 这将加速基于组学的临床分析的发展。我们的Go/No-Go过渡 2年后过渡到R33阶段的里程碑包括：（1）纵向样本的收集 从每种确定的表现的最少120名患者（轻度症状的门诊病人， 重症监护室和MIS-C）和相当数量的匹配对照，（2）产生 严重性候选物组和通过qPCR确认生物标志物子集，（3）开发 使用单独使用生物标志物的机器学习的分类器模型（用于临床测定 开发），以及（4）将这些组学生物标志物与其他临床、病毒和实验室 使用机器学习将生物标志物转化为组合的分类器模型。对于分类器模型， 最低/目标性能要求是70%/>80%的灵敏度和80%/>90%的特异性。 在第二个R33阶段，我们建议开发基于宿主的临床检测方法，用于诊断和 从鼻内镜检查中预测儿童COVID-19相关综合征（包括MIS-C）的严重程度 拭子和血液（目标5），并将这些生物标志物组作为实验室开发检测（LDT）进行验证 CLIA（临床实验室改进修正案）诊断实验室（目标6）。这些 将评价检测试剂盒的准确度、精密度、重现性、检测限（LOD）、基质 影响、干扰以及其他性能特征。我们将与RADx-rad密切合作， 数据协调中心（DCC）负责检测试剂盒开发、检测和验证，以提交给 FDA的紧急使用授权（EUA），并及时部署这些检测试剂盒用于临床。