282. A Data-Driven Multiomic Approach Identifies Signatures on the Same Spectrum for MIS-C and Kawasaki Disease
Creators: name: Tsoukas1, Paul
name: Sun*2, Sophie
name: Lu2, Henry
name: Duong2, Trang
name: Tal1, Tala El
Publisher: name: Zenodo
Year: 2022
Subjects: Vasculitis
ANCA
Abstracts
Giant Cell Arthritis
MPA
GPA
EGPA
Takayasu
IgA vasculitis

description: <strong>B</strong><strong>ackground</strong><strong>:</strong> Multisystem inflammatory syndrome in children (MIS-C) has clinical similarities to Kawasaki Disease (KD). Despite multiple studies highlighting the clinical characteristics of MIS-C there remains limited data describing the pathobiology driving MIS-C, and direct comparisons to KD. We aim to characterize children with KD and MIS-C with unsupervised machine-learning approaches.<br> <strong>Methods:</strong> Study design: A single center prospective cohort study of consecutive children with MIS-C diagnosis over a 16 month period. Core clinical data/outcomes with standardized set of biospecimens pre-treatment and at serial time-points after discharge were collected. Data from patients with pre-pandemic KD (pKD) were also collected from a partner study. In total, 26 MIS-C patients and 35 pKD patients where included. Analytical procedures/framework: Using plasma samples 71 cytokines/chemokines and soluble receptors were measured via Luminex-based multiplex assays. RNA was extracted, with gene expression focused on interferon (IFN) response genes. Data was processed and used for subsequent dimensionality reduction by cross-validated probabilistic principal component analysis (PPCA). Patient scores on sparsified composite signatures produced by PPCA were then used for clustering by Gaussian Mixture Model clustering (GMMs). <br> <strong>Results:</strong> Five composite variables/principal components (PC) describe meaningful clinical and biologic patterns: PC1 (Proinflammatory cytokines), PC2 (IRG and cytopenias), PC3 (endothelial injury and platelet activation), PC4 (viral response) and PC5 (vascular remodeling and low chemotaxins). Five distinct patient clusters identified by GMMs. These clusters had unique genomic signatures and clinical phenotypes and were given the following cluster names: A) Classic KD; B) Interferon (IFN) mediated KD; C) Compensated KD; D) Mild KD and E) KD shock. Cluster A (Classic KD) consists predominantly of typical KD patients. Cluster B (IFN mediated KD) is composed almost evenly of MIS-C and pKD patients. The remaining three clusters were composed of smaller patient numbers. Stratifying patients accordingly to comprehensive clinical and multi-omic data not only correlated with clinical severity but also with response to treatment. In rank order of disease severity, the clusters were: E (KD shock), C (Compensated KD), B (IFN KD), A (Classic KD), and D (Mild KD). Those treated with IVIG alone, treatment response rate matching exactly with the reverse order of disease severity. <strong>Conclusions:</strong> Unbiased machine learning approaches using system-wide data, identified unique patient subgroups with distinct genomic and phenotypic signatures in children with post-infectious hyperinflammation. These patient groupings are clinically and biologically meaningful with clear associations with disease activity, treatment response and clinical outcomes. This contrasts with data using classic disease definitions showing shared and overlapping genomic and clinical features between patients with MIS-C and KD, suggesting that KD and MIS-C both fit on the same disease spectrum.<br> <strong>Disclosures</strong><strong>:</strong> None