Multisystem inflammatory syndrome in children: a microcosm of challenges and opportunities for translational bioinformatics in pediatric research

Abstract

Purpose of review: Despite significant progress in our understanding and clinical management of multisystem inflammatory syndrome in children (MIS-C), significant challenges remain. Here, we review recently published studies on the clinical diagnosis, risk stratification, and treatment of MIS-C, highlighting key gaps in research progress that are a microcosm for challenges in translational pediatric research. We then discuss potential solutions in the realm of translational bioinformatics.

Recent findings: Current case definitions are inconsistent and do not capture the underlying pathophysiology of MIS-C, which remains poorly understood. Although overall mortality is low, some patients rapidly decompensate, and a test to identify those at risk for severe outcomes remains an unmet need. Treatment consists of various combinations of immunoglobulins, corticosteroids, and biologics, based on extrapolated data and expert opinion, while the benefits remain unclear as we await the completion of clinical trials.

Summary: The small size and heterogeneity of the pediatric population contribute to unmet needs because of financial and logistical constraints of the current research infrastructure focused on eliminating most sources of heterogeneity, leading to ungeneralizable results. Data sharing and meta-analysis of gene expression shows promise to accelerate progress in the field of MIS-C as well as other childhood diseases beyond the current pandemic.

Figure 1:. Guidelines for integrating heterogeneous independent datasets.

Datasets from public repositories such as the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) are divided into ‘discovery’ and ‘validation’ cohorts, where all datasets from one research group are either in the discovery or validation set. By ensuring that the data from the same group are not in both discovery and validation, we ensure that results from analysis of discovery datasets is always verified in completely independent datasets. Ideally, the discovery datasets should include 4-5 datasets that collectively represent the biological, clinical, and technical heterogeneity observed in the real-world patient population. These datasets do not need to have a large sample size, and should have moderate statistical power (e.g., 30-50 samples in each dataset). FDR = false discovery rate.