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Comprehensive gene expression profiling and immunohistochemical studies support application of immunophenotypic algorithm for molecular subtype classification in diffuse large B-cell lymphoma: A report from the International DLBCL Rituximab-CHOP Consortium Program Study

机译:综合基因表达分析和免疫组化研究支持免疫型算法在弥漫性大B细胞淋巴瘤中分子亚型分类的应用:International DLBCL rituximab-Chop联盟计划研究的报告

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摘要

Gene expression profiling (GEP) has stratified diffuse large B-cell lymphoma (DLBCL) into molecular subgroups that correspond to different stages of lymphocyte development - namely germinal center B-cell-like and activated B-cell-like. This classification has prognostic significance, but GEP is expensive and not readily applicable into daily practice, which has lead to immunohistochemical algorithms proposed as a surrogate for GEP analysis. We assembled tissue microarrays from 475 de novo DLBCL patients who were treated with rituximab-CHOP chemotherapy. All cases were successfully profiled by GEP on formalin-fixed, paraffin-embedded tissue samples. Sections were stained with antibodies reactive with CD10, GCET1, FOXP1, MUM1, and BCL6 and cases were classified following a rationale of sequential steps of differentiation of B-cells. Cutoffs for each marker were obtained using receiver operating characteristic curves, obviating the need for any arbitrary method. An algorithm based on the expression of CD10, FOXP1, and BCL6 was developed that had a simpler structure than other recently proposed algorithms and 92.6% concordance with GEP. In multivariate analysis, both the International Prognostic Index and our proposed algorithm were significant independent predictors of progression-free and overall survival. In conclusion, this algorithm effectively predicts prognosis of DLBCL patients matching GEP subgroups in the era of rituximab therapy.
机译:基因表达分析(GEP)将分层扩散大B细胞淋巴瘤(DLBCL)分解成对应于淋巴细胞发展的不同阶段的分子亚组 - 即发芽中心B细胞样和活化的B细胞样。该分类具有预后意义,但GEP是昂贵的,并且不容易适用于日常生法,这导致免疫组化算法提出作为GEP分析的替代物。我们组装了475名德国DLBCL患者的组织微阵列,该患者被培育斩波化疗治疗。所有病例均通过GEP对福尔马林固定的石蜡包埋的组织样品成功筛选。将切片用与CD10反应的抗体染色,GCET1,FOXP1,MUM1和BCL6以及案例分类为B细胞的顺序步骤的序贯步骤的理由。使用接收器操作特性曲线获得每个标记的截止,避免了对任何任意方法的需求。开发了一种基于CD10,FOXP1和BCL6表达的算法,其具有比其他最近提出的算法更简单的结构和与GEP的92.6%的一致性。在多变量分析中,国际预后指数和我们所提出的算法都是无成本和整体生存的重要独立预测因素。总之,该算法有效地预测了在利妥昔单抗治疗时代匹配GEB亚组的DLBCL患者的预后。

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