Incorporation of Epstein-Barr viral variation implicates significance of LMP1 in survival prediction and prognostic subgrouping in Burkitt lymphoma

Abstract

ABSTRACTWhile Epstein-Barr virus (EBV) plays a role in Burkitt lymphoma (BL) tumorigenesis, it is unclear if EBV genetic variation impacts clinical outcomes. From 130 publicly available whole-genome tumor sequences of EBV-positive BL patients, we used least absolute shrinkage and selection operator (LASSO) regression and Bayesian variable selection models within a Cox proportional hazards framework to select the top EBV variants, putative driver genes, and clinical features associated with patient survival time. These features were incorporated into survival prediction and prognostic subgrouping models. Our model yielded 22 EBV variants including seven in LMP1 as most associated with patient survival time. Using the top EBV variants, driver genes, and clinical features, we defined three prognostic subgroups that demonstrated differential survival rates, laying the foundation for incorporating EBV variants such as those in LMP1 as predictive biomarker candidates in future studies.