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

摘要 早期准确诊断新冠肺炎相关综合征的新方法 迫切需要评估儿童新冠肺炎病的临床严重性和预后。 这项拨款提案的首要目标是开发能够评估和预测的临床检测方法。 儿童新冠肺炎病的严重程度，从无症状或轻微症状到重度 表现为多系统炎症综合征(MIS-C)。到目前为止，我们已经收集了 来自3家学术医院的400多名患者的生物库临床样本，包括 大约有100名患者患有MISC。在这个项目的第一个R61阶段，我们将继续招生 儿童新冠肺炎和MISC的样本采集和纵向图表回顾 测试(目标1)，利用机器学习来识别诊断和预后的“组学”宿主 基于鼻拭子和全血样本RNA转录组分析的生物标记物(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 通过纳入纵向的临床、实验室、病毒和组学数据来评估结果(目标4)。我们的 包括这些样本的理由是，它们通常是从医院和诊所获得的， 允许在不需要任何特殊处理或处理要求的情况下进行简单且非侵入性的收集， 这将加速以组学为基础的临床检测的发展。我们的去/不去的过渡 两年后过渡到R33阶段的里程碑包括：(1)纵向样品的收集 对于每个已识别的表现，至少有120名患者(轻度症状门诊患者， 重症监护室和管理信息系统-C)和相当数量的匹配对照，(2)产生 通过定量聚合酶链式反应确认生物标志物子集和严重程度候选小组，(3)开发 仅使用生物标记物使用机器学习的分类器模型(用于临床检测 开发)，以及(4)将这些组学生物标记物与其他临床、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。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%的特异度。 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 从鼻腔预测包括MISC在内的儿童新冠肺炎相关综合征的严重性 拭子和血液(目标5)，并将这些生物标志物面板作为实验室开发测试(LDT)进行验证 在CLIA(临床实验室改进修正案)诊断实验室(AIM 6)。这些 将评估分析的准确度、精密度、重现性、检测限(LOD)、基质 效果、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)，负责分析开发、测试和验证，以提交给 FDA的紧急使用授权(EUA)，并及时部署这些检测方法用于临床。

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.