Kim Isaac E, Oduor Cliff, Stamp Julian, Luftig Micah A, Moormann Ann M, Crawford Lorin, Bailey Jeffrey A
Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA.
The Warren Alpert Medical School, Brown University, Providence, Rhode Island, USA.
Int J Cancer. 2025 Jun 1;156(11):2188-2199. doi: 10.1002/ijc.35384. Epub 2025 Mar 6.
Although 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 latent membrane protein 1 (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.
尽管爱泼斯坦-巴尔病毒（EBV）在伯基特淋巴瘤（BL）的肿瘤发生中起作用，但尚不清楚EBV基因变异是否会影响临床结果。我们从130例EBV阳性BL患者公开可用的全基因组肿瘤序列中，在Cox比例风险框架内使用最小绝对收缩和选择算子（LASSO）回归及贝叶斯变量选择模型，以选择与患者生存时间相关的顶级EBV变异、推定驱动基因和临床特征。这些特征被纳入生存预测和预后亚组模型。我们的模型产生了22种EBV变异，其中包括潜伏膜蛋白1（LMP1）中的7种，这些变异与患者生存时间最为相关。利用顶级EBV变异、驱动基因和临床特征，我们定义了三个预后亚组，这些亚组显示出不同的生存率，为在未来研究中将LMP1等EBV变异作为预测生物标志物候选纳入奠定了基础。
