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

ABSTRACTNovel approaches for early and accurate diagnosis of COVID-19 associated syndromes andevaluation 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 predictseverity of pediatric COVID-19 disease, ranging from asymptomatic or mildly symptomatic to severemanifestations such as multisystem inflammatory syndrome (MIS-C). To date, we have collected andbiobanked clinical samples from more than 400 patients across 3 academic hospitals, includingapproximately 100 patients with MIS-C. In the first R61 phase of this project, we will continue to enrollpatients with pediatric COVID-19 and MIS-C for sample collection and longitudinal chart review andtesting (Aim 1), leverage machine learning to identify diagnostic and prognostic "omics" hostbiomarkers 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 severityand outcomes by incorporating longitudinal clinical, laboratory, viral, and omics data (Aim 4). Ourrationale for including these samples is that they are routinely obtained in hospitals and clinics andpermit 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 transitionmilestones for transition to the R33 phase after 2 years include: (1) collection of longitudinal samplesfrom 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 ofpanels of candidate of severity and confirmation of a subset of biomarkers by qPCR, (3) developmentof classifier models using machine learning using the biomarkers alone (for clinical assaydevelopment), and (4) combining these omics biomarkers with additional clinical, viral, and laboratorybiomarkers into combined classifier models using machine learning. For the classifier models, theminimum/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 andseverity prediction of COVID-19-associated syndromes, including MIS-C, in children from nasalswabs 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). Theseassays will be evaluated for accuracy, precision, reproducibility, limits of detection (LOD), matrixeffect, interference, among other performance characteristics. We will work closely with the RADx-radData Coordination Center (DCC) on assay development, testing, and validation for submission to theFDA for Emergency Use Authorization (EUA) and timely deployment of these assays for clinical use.