Lu Ting-Xun, Gong Qi-Xing, Wang Li, Fan Lei, Zhang Xiao-Yan, Chen Yao-Yu, Wang Zhen, Xu Wei, Zhang Zhi-Hong, Li Jian-Yong
Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital Nanjing 210029, China.
Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital Nanjing 210029, China.
Int J Clin Exp Pathol. 2015 Jan 1;8(1):275-86. eCollection 2015.
Gene expression profiling (GEP), which can divide DLBCL into three groups, is impractical to perform routinely. Although algorithms based on immunohistochemistry (IHC) have been proposed as a surrogate for GEP analysis, the power of them has diminished since rituximab added to the chemotherapy. We assessed the prognostic value of four conventional algorithms and the genes in each and out of algorithm by IHC and fluorescence in situ hybridization in DLBCL patients receiving immunochemotherapy. The results showed that neither single protein within algorithms nor the IHC algorithms themselves had strong prognostic power. Using MYC aberrations (MA) either on the genetic or protein levels, we established a new algorithm called MA that could divide patients into distinct prognostic groups. Patients of MA had much shorter overall survival (OS) and progression-free survival (PFS) than non-MA (2-year OS: 56.9% vs. 98.7%; 2-year PFS: 26.8% vs. 86.9%; P < 0.0001 for both). In conclusions, using additional prognostic markers not associated with cell of origin may accurately predict outcomes of DLBCL. Studies with larger samples should be performed to confirm our algorithm and optimize the prognostic system of DLBCL.
基因表达谱分析(GEP)可将弥漫性大B细胞淋巴瘤(DLBCL)分为三组,但常规开展该分析并不实际。尽管基于免疫组织化学(IHC)的算法已被提议作为GEP分析的替代方法,但自利妥昔单抗加入化疗后,这些算法的效能有所下降。我们通过免疫组织化学和荧光原位杂交评估了四种传统算法以及各算法内外基因在接受免疫化疗的DLBCL患者中的预后价值。结果显示,算法中的单一蛋白以及IHC算法本身均无强大的预后能力。利用基因或蛋白水平的MYC异常(MA),我们建立了一种名为MA的新算法,该算法可将患者分为不同的预后组。MA组患者的总生存期(OS)和无进展生存期(PFS)比非MA组短得多(2年OS:56.9% 对98.7%;2年PFS:26.8% 对86.9%;两者P均<0.0001)。总之,使用与细胞起源无关的额外预后标志物可准确预测DLBCL的预后。应开展更大样本量的研究以证实我们的算法并优化DLBCL的预后系统。