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

Area of Science:

• Biomedical diagnostics
• Molecular biology
• Pediatric infectious diseases

Background:

• Kawasaki disease (KD) is a critical pediatric vasculitis impacting children under five.
• Coronary artery abnormalities affect up to 30% of KD patients, emphasizing the need for early diagnosis.
• Current KD diagnosis is challenging, lacking a definitive clinical test.

Purpose of the Study:

• To bridge a microarray-based host response classifier to a clinically applicable quantitative reverse transcription-polymerase chain reaction (qRT-PCR) assay.
• To develop the Kawasaki Disease Gene Expression Profiling (KiDs-GEP) classifier for improved KD diagnosis.
• To create a rapid, cost-efficient diagnostic tool for KD.

Main Methods:

• Designed and optimized a multiplex qRT-PCR assay.
• Applied the assay to a subset of samples from prior classifier discovery.
• Reweighted the original microarray-based classifier to create the KiDs-GEP classifier.

Main Results:

• The KiDs-GEP classifier demonstrated performance comparable to the original microarray classifier (AUC 0.964 vs 0.992).
• Both classifiers effectively distinguished KD patients from those with viral infections.
• The KiDs-GEP assay showed similar diagnostic trends across various conditions.

Conclusions:

• Successfully bridged a microarray classifier to a qRT-PCR based assay (KiDs-GEP).
• The KiDs-GEP classifier offers a more rapid and cost-efficient diagnostic approach for KD.
• This development brings a valuable diagnostic test for KD closer to clinical laboratory application.