Naresh Kikkeri N, Ibrahim Hazem A H, Lazzi Stefano, Rince Patricia, Onorati Monica, Ambrosio Maria R, Bilhou-Nabera Chrystèle, Amen Furrat, Reid Alistair, Mawanda Michael, Calbi Valeria, Ogwang Martin, Rogena Emily, Byakika Bessie, Sayed Shahin, Moshi Emma, Mwakigonja Amos, Raphael Martine, Magrath Ian, Leoncini Lorenzo
Department of Histopathology, Hammersmith Hospital Campus, Imperial College, London, UKDepartment of Human Pathology and Oncology, University of Siena, Siena, ItalyUniv Paris-Sud, F-94270, Le Kremlin-Bicêtre; AP-HP, Hôpital Bicêtre, Service d'Hématologie et Immunologie Biologiques, Cytogénétique, F-94270, Le Kremlin Bicêtre, FranceDepartment of Haematology, Hammersmith Hospital Campus, Imperial College, London, UKSaint Mary Hospital, Lacor, Gulu, UgandaUniversity of NairobiNairobi HospitalAga Khan University Hospital, Nairobi, KenyaMuhimbili National HospitalMuhimbili University of Health and Allied Sciences, Dar Es Salam, TanzaniaInternational Network for Cancer Treatment and Research, Brussels, BelgiumDepartment of Histopathology, Faculty of Medicine, Mansoura University, Egypt.
Br J Haematol. 2011 Sep;154(6):770-6. doi: 10.1111/j.1365-2141.2011.08771.x. Epub 2011 Jul 1.
Distinguishing Burkitt lymphoma (BL) from B cell lymphoma, unclassifiable with features intermediate between diffuse large B-cell lymphoma (DLBCL) and BL (DLBCL/BL), and DLBCL is challenging. We propose an immunohistochemistry and fluorescent in situ hybridization (FISH) based scoring system that is employed in three phases - Phase 1 (morphology with CD10 and BCL2 immunostains), Phase 2 (CD38, CD44 and Ki-67 immunostains) and Phase 3 (FISH on paraffin sections for MYC, BCL2, BCL6 and immunoglobulin family genes). The system was evaluated on 252 aggressive B-cell lymphomas from Europe and from sub-Saharan Africa. Using the algorithm, we determined a specific diagnosis of BL or not-BL in 82%, 92% and 95% cases at Phases 1, 2 and 3, respectively. In 3·4% cases, the algorithm was not completely applicable due to technical reasons. Overall, this approach led to a specific diagnosis of BL in 122 cases and to a specific diagnosis of either DLBCL or DLBCL/BL in 94% of cases that were not diagnosed as BL. We also evaluated the scoring system on 27 cases of BL confirmed on gene expression/microRNA expression profiling. Phase 1 of our scoring system led to a diagnosis of BL in 100% of these cases.
区分伯基特淋巴瘤（BL）与具有弥漫性大B细胞淋巴瘤（DLBCL）和BL之间特征的无法分类的B细胞淋巴瘤（DLBCL/BL）以及DLBCL具有挑战性。我们提出了一种基于免疫组织化学和荧光原位杂交（FISH）的评分系统，该系统分三个阶段使用——第1阶段（结合CD10和BCL2免疫染色的形态学检查）、第2阶段（CD38、CD44和Ki-67免疫染色）和第3阶段（对石蜡切片进行MYC、BCL2、BCL6和免疫球蛋白家族基因的FISH检测）。该系统在来自欧洲和撒哈拉以南非洲的252例侵袭性B细胞淋巴瘤中进行了评估。使用该算法，我们在第1、2和3阶段分别对82%、92%和95%的病例确定了BL或非BL的明确诊断。在3.4%的病例中，由于技术原因该算法不完全适用。总体而言，这种方法在122例病例中明确诊断为BL，在未诊断为BL的病例中，94%明确诊断为DLBCL或DLBCL/BL。我们还在27例经基因表达/微小RNA表达谱确认的BL病例中评估了该评分系统。我们评分系统的第1阶段在这些病例中的100%都诊断为BL。
