Bridging a diagnostic Kawasaki disease classifier from a microarray platform to a qRT-PCR assay

Kuiper, Rowan; Wright, Victoria J.; Habgood-Coote, Dominic; Shimizu, Chisato; Huigh, Daphne; Tremoulet, Adriana H.; Keulen, Daniëlle van; Hoggart, Clive; Rodríguez-Manzano, Jesús; Herberg, Jethro; Kaforou, Myrsini; Tempel, Dennie; Burns, Jane C.; Levin, Michael

doi:10.1038/s41390-022-02148-y

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…Using 12 classifier genes and 2 reference genes, the qRT-PCR (KiDs-GEP) classifier performed well compared with the original classifier with a cross-validated area under the ROC curve of 0.964 (95% CI 0.924–1.00) versus 0.992 (95% CI 0.978–1.00), respectively. This is an important step toward a reliable diagnostic test for the early and accurate diagnosis of Kawasaki disease [33 ▪▪ ].…”

Section: Biomarkersmentioning

confidence: 99%

Medium-vessel and large-vessel vasculitis in children

Scott

Stander

Phoya

2023

Current Opinion in Rheumatology

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Purpose of review This article serves as an up-to-date examination of the latest findings in the field of paediatric large-vessel and medium-vessel vasculitis. Recent findings Over the last 2 years and in the wake of SARS-CoV2 pandemic, a multitude of studies have increased our insight into these conditions. Although large-vessel and medium-vessel vasculitis are uncommon amongst children, they are a complex and multisystem with a constantly evolving landscape. Increasing numbers of reports from low-income and middle-income countries are shaping our understanding of the epidemiology of vasculitis in children. The influence of infectious disease and the microbiome are of particular interest in unravelling pathogenetic aspects. Improved understanding of the genetics and immunology offer opportunities for better diagnostic options and biomarkers of disease as well as targeted therapies. Summary In this review, we address recent findings in epidemiology, pathophysiology, clinical findings, bio-markers, imaging and treatment that have the potential to offer better management solutions for these uncommon conditions.

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Section: Biomarkersmentioning

confidence: 99%

Medium-vessel and large-vessel vasculitis in children

Scott

Stander

Phoya

2023

Current Opinion in Rheumatology

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A computational framework to improve cross-platform implementation of transcriptomics signatures

Kreitmann,

D'Souza,

Miglietta

et al. 2024

eBioMedicine

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Integration and validation of host transcript signatures, including a novel 3-transcript tuberculosis signature, to enable one-step multiclass diagnosis of childhood febrile disease

Channon-Wells,

Habgood-Coote,

Vito

et al. 2024

J Transl Med

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Background Whole blood host transcript signatures show great potential for diagnosis of infectious and inflammatory illness, with most published signatures performing binary classification tasks. Barriers to clinical implementation include validation studies, and development of strategies that enable simultaneous, multiclass diagnosis of febrile illness based on gene expression. Methods We validated five distinct diagnostic signatures for paediatric infectious diseases in parallel using a single NanoString nCounter® experiment. We included a novel 3-transcript signature for childhood tuberculosis, and four published signatures which differentiate bacterial infection, viral infection, or Kawasaki disease from other febrile illnesses. Signature performance was assessed using receiver operating characteristic curve statistics. We also explored conceptual frameworks for multiclass diagnostic signatures, including additional transcripts found to be significantly differentially expressed in previous studies. Relaxed, regularised logistic regression models were used to derive two novel multiclass signatures: a mixed One-vs-All model (MOVA), running multiple binomial models in parallel, and a full-multiclass model. In-sample performance of these models was compared using radar-plots and confusion matrix statistics. Results Samples from 91 children were included in the study: 23 bacterial infections (DB), 20 viral infections (DV), 14 Kawasaki disease (KD), 18 tuberculosis disease (TB), and 16 healthy controls. The five signatures tested demonstrated cross-platform performance similar to their primary discovery-validation cohorts. The signatures could differentiate: KD from other diseases with area under ROC curve (AUC) of 0.897 [95% confidence interval: 0.822–0.972]; DB from DV with AUC of 0.825 [0.691–0.959] (signature-1) and 0.867 [0.753–0.982] (signature-2); TB from other diseases with AUC of 0.882 [0.787–0.977] (novel signature); TB from healthy children with AUC of 0.910 [0.808–1.000]. Application of signatures outside of their designed context reduced performance. In-sample error rates for the multiclass models were 13.3% for the MOVA model and 0.0% for the full-multiclass model. The MOVA model misclassified DB cases most frequently (18.7%) and TB cases least (2.7%). Conclusions Our study demonstrates the feasibility of NanoString technology for cross-platform validation of multiple transcriptomic signatures in parallel. This external cohort validated performance of all five signatures, including a novel sparse TB signature. Two exploratory multi-class models showed high potential accuracy across four distinct diagnostic groups.

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Bridging a diagnostic Kawasaki disease classifier from a microarray platform to a qRT-PCR assay

Cited by 3 publications

References 41 publications

Medium-vessel and large-vessel vasculitis in children

Medium-vessel and large-vessel vasculitis in children

A computational framework to improve cross-platform implementation of transcriptomics signatures

Integration and validation of host transcript signatures, including a novel 3-transcript tuberculosis signature, to enable one-step multiclass diagnosis of childhood febrile disease

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