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The contribution of HGAL|[sol]|GCET2 in immunohistological algorithms: a comparative study in 424 cases of nodal diffuse large B-cell lymphoma

机译:HGAL | [sol] | GCET2在免疫组织学算法中的作用:424例淋巴结弥漫性大B细胞淋巴瘤病例的比较研究

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Diffuse large B-cell lymphoma can be subclassified into at least two molecular subgroups by gene expression profiling: germinal center B-cell like and activated B-cell like diffuse large B-cell lymphoma. Several immunohistological algorithms have been proposed as surrogates to gene expression profiling at the level of protein expression, but their reliability has been an issue of controversy. Furthermore, the proportion of misclassified cases of germinal center B-cell subgroup by immunohistochemistry, in all reported algorithms, is higher compared with germinal center B-cell cases defined by gene expression profiling. We analyzed 424 cases of nodal diffuse large B-cell lymphoma with the panel of markers included in the three previously described algorithms: Hans, Choi, and Tally. To test whether the sensitivity of detecting germinal center B-cell cases could be improved, the germinal center B-cell marker HGAL/GCET2 was also added to all three algorithms. Our results show that the inclusion of HGAL/GCET2 significantly increased the detection of germinal center B-cell cases in all three algorithms (P<0.001). The proportions of germinal center B-cell cases in the original algorithms were 27%, 34%, and 19% for Hans, Choi, and Tally, respectively. In the modified algorithms, with the inclusion of HGAL/GCET2, the frequencies of germinal center B-cell cases were increased to 38%, 48%, and 35%, respectively. Therefore, HGAL/GCET2 protein expression may function as a marker for germinal center B-cell type diffuse large B-cell lymphoma. Consideration should be given to the inclusion of HGAL/GCET2 analysis in algorithms to better predict the cell of origin. These findings bear further validation, from comparison to gene expression profiles and from clinical/therapeutic data.
机译:弥漫性大B细胞淋巴瘤可通过基因表达谱分为至少两个分子亚组:生发中心B细胞样和活化B细胞样弥散性大B细胞淋巴瘤。已经提出了几种免疫组织学算法作为蛋白质表达水平的基因表达谱的替代物,但是它们的可靠性一直是一个有争议的问题。此外,在所有报道的算法中,通过免疫组织化学错误分类的生发中心B细胞亚群的比例高于基因表达谱定义的生发中心B细胞亚群的比例。我们分析了424例淋巴结弥漫性大B细胞淋巴瘤,其标志物包含在先前描述的三种算法中:Hans,Choi和Tally。为了检验是否可以提高检测生发中心B细胞病例的敏感性,还向所有三种算法添加了生发中心B细胞标记HGAL / GCET2。我们的结果表明,在所有三种算法中,HGAL / GCET2的加入均显着提高了生发中心B细胞病例的检测率(P <0.001)。在原始算法中,Hans,Choi和Tally的生发中心B细胞病例的比例分别为27%,34%和19%。在改进的算法中,包括HGAL / GCET2,生发中心B细胞病例的发生频率分别增加到38%,48%和35%。因此,HGAL / GCET2蛋白表达可能充当生发中心B细胞型弥漫性大B细胞淋巴瘤的标志物。应该考虑将HGAL / GCET2分析纳入算法中,以更好地预测起源细胞。从与基因表达谱的比较以及从临床/治疗数据来看,这些发现具有进一步的验证。

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