Clinical performance of a prefabricated immunofluorescence assay for nasopharyngeal cancer screening.

BACKGROUND: Epstein-Barr virus (EBV) IgA serology for viral capsid antigen (VCA) and early antigen (EA) aids early detection of nasopharyngeal cancer (NPC), resulting in improved survival. We evaluated the diagnostic performance of a prefabricated immunofluorescent assay (IFA) for NPC screening in high-risk individuals. METHODS: Sera from 96 biopsy-proven patients with NPC diagnosed at the outpatient clinic and 96 healthy family members were tested for EBV-VCA IgA and EBV-EA IgA using the prefabricated IFA from EUROIMMUN (EI) and the traditional immunofluorescence method. RESULTS: The AUC of EI EBV-VCA IgA and EBV-EA IgA was 0.907 (95% confidence interval [CI]: 0.894-0.965) and 0.898 (95% CI: 0.848-0.947), respectively. Combined testing with the prefabricated assay at a threshold of VCA ≥1:320 or EA ≥1:10 showed 92.7% sensitivity and 81.2% specificity. Overall, the traditional EBV-EA IgA assay demonstrated the best accuracy (sensitivity 91.7% and specificity 96.9%) at a threshold of ≥1:5. CONCLUSION: While the traditional IFA method was more accurate, the prefabricated IFA test kit can be a useful tool for NPC screening in high-risk populations.

Precision Medicine for Nasopharyngeal Cancer-A Review of Current Prognostic Strategies.

Nasopharyngeal carcinoma (NPC) is an Epstein-Barr virus (EBV) driven malignancy arising from the nasopharyngeal epithelium. Current treatment strategies depend on the clinical stage of the disease, including the extent of the primary tumour, the extent of nodal disease, and the presence of distant metastasis. With the close association of EBV infection with NPC development, EBV biomarkers have shown promise in predicting treatment outcomes. Among the omic technologies, RNA and miRNA signatures have been widely studied, showing promising results in the research setting to predict treatment response. The transformation of radiology images into measurable features has facilitated the use of radiomics to generate predictive models for better prognostication and treatment selection. Nonetheless, much of this work remains in the research realm, and challenges remain in clinical implementation.