Identification of a Novel, EBV-Based Antibody Risk Stratification Signature for Early Detection of Nasopharyngeal Carcinoma in Taiwan

Background. Epstein–Barr virus (EBV) is necessary for the development of nasopharyngeal carcinoma (NPC). By adulthood, approximately 90% of individuals test EBV-positive, but only a fraction develop cancer. Factors that identify which individuals are most likely to develop disease, including differential antibody response to the virus, could facilitate detection at early stages when treatment is most effective.

Methods. We measured anti-EBV IgG and IgA antibody responses in 607 Taiwanese individuals. Antibodies were measured using a custom protein microarray targeting 199 sequences from 86 EBV proteins. Variation in response patterns between NPC cases and controls was used to develop an antibody-based risk score for predicting NPC. The overall accuracy [area under the curve (AUC)] of this risk score, and its performance relative to currently used biomarkers, was evaluated in two independent Taiwanese cohorts.

Findings. Levels of 60 IgA and 73 IgG anti-EBV antibodies differed between stage I/IIa NPC cases and controls (P < 0.0002). Risk prediction analyses identified antibody targets that best discriminated NPC status—BXLF1, LF2,BZLF1, BRLF1, EAd, BGLF2, BPLF1, BFRF1, and BORF1. When combined with currently used VCA/EBNA1 IgA biomarkers, the resulting risk score predicted NPC with 93% accuracy (95% CI, 87%–98%) in the general Taiwanese population, a significant improvement beyond current biomarkers alone (82%; 95% CI, 75%–90%, P ≤ 0.01). This EBV-based risk score also improved NPC prediction in genetically high-risk families (89%; 95% CI, 82%–96%) compared with current biomarkers (78%; 95% CI, 66%–90%, P ≤ 0.03).

Interpretation. We identified NPC-related differences in 133 anti-EBV antibodies and developed a risk score using this microarray dataset that targeted immune responses against EBV proteins from all stages of the viral life cycle, significantly improving the ability to predict NPC. Clin Cancer Res; 24(6); 1305–14. ©2017 AACR.

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