Simon Julia, Liu Zhiwei, Brenner Nicole, Yu Kelly J, Hsu Wan-Lun, Wang Cheng-Ping, Chien Yin-Chu, Coghill Anna E, Chen Chien-Jen, Butt Julia, Proietti Carla, Doolan Denise L, Hildesheim Allan, Waterboer Tim
Infections and Cancer Epidemiology, Infection, Inflammation and Cancer Program, German Cancer Research Center (DKFZ), Heidelberg, Germany
Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.
J Clin Microbiol. 2020 Apr 23;58(5). doi: 10.1128/JCM.00077-20.
Serological testing for nasopharyngeal carcinoma (NPC) has recently been reinvigorated by the implementation of novel Epstein-Barr virus (EBV)-specific IgA and IgG antibodies from a proteome array. Although proteome arrays are well suited for comprehensive antigen selection, they are not applicable for large-scale studies. We adapted a 13-marker EBV antigen signature for NPC risk identified by proteome arrays to multiplex serology to establish an assay for large-scale studies. Taiwanese NPC cases ( = 175) and matched controls ( = 175) were used for assay validation. Spearman's correlation was calculated, and the diagnostic value of all multiplex markers was assessed independently using the area under the receiver operating characteristic curve (AUC). Two refined signatures were identified using stepwise logistic regression and internally validated with 10-fold cross validation. Array and multiplex serology showed strong correlation for each individual EBV marker, as well as for a 13-marker combined model on continuous data. Two refined signatures with either four (LF2 and BGLF2 IgG, LF2 and BMRF1 IgA) or two (LF2 and BGLF2 IgG) antibodies on dichotomous data were identified as the most parsimonious set of serological markers able to distinguish NPC cases from controls with AUCs of 0.992 (95% confidence interval [CI], 0.983 to 1.000) and 0.984 (95% CI, 0.971 to 0.997), respectively. Neither differed significantly from the 13-marker model (AUC, 0.992; 95% CI, 0.982 to 1.000). All models were internally validated. Multiplex serology successfully validated the original EBV proteome microarray data. Two refined signatures of four and two antibodies were capable of detecting NPC with 99.2% and 98.4% accuracy.
最近，通过蛋白质组芯片检测新型爱泼斯坦-巴尔病毒（EBV）特异性IgA和IgG抗体，鼻咽癌（NPC）的血清学检测重新受到关注。虽然蛋白质组芯片非常适合进行全面的抗原筛选，但并不适用于大规模研究。我们采用了蛋白质组芯片鉴定出的用于鼻咽癌风险评估的13种标志物EBV抗原特征，将其应用于多重血清学检测，以建立一种适用于大规模研究的检测方法。台湾地区的鼻咽癌病例（n = 175）和匹配的对照（n = 175）用于检测方法的验证。计算了斯皮尔曼相关性，并使用受试者工作特征曲线下面积（AUC）独立评估所有多重标志物的诊断价值。通过逐步逻辑回归确定了两个优化特征，并通过10倍交叉验证进行内部验证。芯片检测和多重血清学检测显示，对于每个单独的EBV标志物以及连续数据上的13种标志物组合模型，二者具有很强的相关性。在二分数据上，由四种（LF2和BGLF2 IgG、LF2和BMRF1 IgA）或两种（LF2和BGLF2 IgG）抗体组成的两个优化特征被确定为最简约的血清学标志物组合，能够区分鼻咽癌病例和对照，其AUC分别为0.992（95%置信区间[CI]，0.983至1.000）和0.984（95%CI，0.971至0.997）。二者与13种标志物模型（AUC，0.992；95%CI，0.982至1.000）相比均无显著差异。所有模型均进行了内部验证。多重血清学检测成功验证了原始的EBV蛋白质组芯片数据。由四种和两种抗体组成的两个优化特征能够以99.2%和98.4%的准确率检测鼻咽癌。
