Prognostic value of Epstein-Barr virus biomarkers for nasopharyngeal carcinoma staging and post-treatment surveillance

Abstract

: The relationship between Epstein-Barr virus (EBV) infection and endemic nasopharyngeal carcinoma (NPC) has facilitated more than 40 years of biomarker-inspired translational research. Serologic and nucleic acid EBV biomarkers span the spectrum of this disease from population-level early detection, pre-treatment prognostication, response-adapted therapy, and long-term surveillance. Plasma EBV DNA remains the cornerstone of biomarker prognostication and surveillance for NPC, and there is increasing high-quality evidence that it merits inclusion in future staging systems. Recently-completed and ongoing biomarker-adapted clinical trials will determine whether biomarker-adapted management will become the standard of care. The HKNPCSG-0502 randomized trial demonstrated that post-treatment EBV DNA is prognostic but not predictive for response to adjuvant chemotherapy (AC), while the ongoing NRG-HN001 randomized trial may ultimately support the omission of AC in most patients. The next generation of biomarker-informed clinical trials may integrate early response to induction chemotherapy and/or immunotherapy. In this review, we discuss the clinical role and prognostic performance of EBV-based biomarkers for pre-treatment staging and post-treatment surveillance. In particular, we synthesize the available evidence which suggests that biomarker-informed staging systems might improve upon anatomic staging, but highlight the challenges in inter-laboratory reproducibility inherent to diagnostic assays without international standardization. Thereafter, we review a breadth of evidence which supports that undetectable post-treatment EBV biomarkers are highly specific for long-term cure. Finally, we contextualize emerging biomarkers that may further improve prognostication. Although these novel biomarkers have yet to supersede plasma EBV DNA in clinical performance, they may complement EBV DNA and identify the subset of patients at highest risk for clinical relapse. 12