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: 10847826
• 负责人: Charles Yen Chiu
• 金额: $ 160.63万
• 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/10847826
• 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.

抽象的 早期准确诊断 COVID-19 相关综合征的新方法 迫切需要评估儿童 COVID-19 疾病的临床严重程度和结果。 该拨款提案的总体目标是开发能够评估和预测的临床检测方法 儿科 COVID-19 疾病的严重程度，从无症状或轻微症状到严重 迄今为止，我们已经收集并收集了多系统炎症综合征（MIS-C）等表现。 来自 3 家学术医院 400 多名患者的生物样本库临床样本，包括 在该项目的第一个 R61 阶段，我们将继续招募约 100 名 MIS-C 患者。 患有儿科 COVID-19 和 MIS-C 的患者进行样本收集和纵向图表审查 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（目标 2） 和血浆中的无细胞 DNA 分析（目标 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、病毒和组学数据来确定结果（目标 4）。 纳入这些样本的理由是它们通常是在医院和诊所获得的，并且 允许简单且非侵入性的收集，无需任何特殊的处理或处理要求， 这将加速基于组学的临床检测的发展。 2年后过渡到R33阶段的里程碑包括：（1）样本的纵向收集 每个确定的表现至少有 120 名患者（轻度症状的门诊患者、 重症监护病房（ICU）和 MIS-C 中的重病）和相当数量的匹配对照，（2） 通过 qPCR 确定生物标志物子集的严重程度和确认的候选小组，(3) 开发 仅使用生物标记物使用机器学习的分类器模型（用于临床分析 开发），以及（4）将这些组学生物标志物与其他临床、病毒和实验室相结合 使用机器学习将生物标志物转化为组合分类器模型。 最低/目标性能要求为 70%/>80% 灵敏度和 80%/>90% 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，用于诊断和治疗 鼻科儿童中 COVID-19 相关综合征（包括 MIS-C）的严重程度预测 拭子和血液（目标 5）并验证这些生物标志物组合作为实验室开发的测试 (LDT) 在 CLIA（临床实验室改进修正案）诊断实验室（目标 6）。 将评估测定的准确性、精密度、重现性、检测限 (LOD)、矩阵 我们将与 RADx-rad 密切合作。 数据协调中心 (DCC) 负责分析开发、测试和验证，以提交给 FDA 紧急使用授权 (EUA) 并及时部署这些检测方法用于临床。