The oncoprotein LMO2 is expressed in normal germinal-center B cells and in human B-cell lymphomas
The oncoprotein LMO2 is expressed in normal germinal-center B cells and in human B-cell lymphomas
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DOI：
10.1182/blood-2006-08-039024
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发表时间：
2007-02-15
期刊：
影响因子：
20.3
通讯作者：
Levy, Ronald
中科院分区：
文献类型：
--
作者：
Natkunam, Yasodha;Zhao, Shuchun;Levy, Ronald
我们之前开发了一个基于6个基因（LMO 2，BCL 6，FN 1，CCND 2，SCYA 3）的RNA表达的多变量模型。和BCL 2），其预测弥漫性大B细胞淋巴瘤（DLBCL）患者的存活。由于LMO 2是上级结果的最强预测因子，我们产生了单克隆抗LMO 2抗体以研究其组织表达模式。对1200多个正常和肿瘤组织和细胞系的免疫组织学分析显示，LMO 2蛋白在正常生殖中心（GC）B细胞和GC衍生的B细胞系以及GC衍生的B细胞淋巴瘤亚群中表达为核标记物。LMO 2还在红细胞和髓样前体细胞和巨核细胞中表达，还在淋巴母细胞和急性髓性白血病中表达。它很少在成熟T细胞、自然杀伤细胞（NK）和浆细胞肿瘤中表达，并且除了内皮细胞外，在非淋巴组织中不存在。DLBCL免疫组织学数据的分层聚类分析表明，LMO 2蛋白的表达谱与其他GC相关蛋白（HGAL，BCL 6和CD 10）相似，但与非GC蛋白（MUM 1/ARF 4和BCL 2）不同。我们的研究结果证明，在多变量分析中纳入LMO 2，以构建一个临床适用的免疫组织学算法，用于预测DLBCL患者的生存率。
We previously developed a multivariate model based on the RNA expression of 6 genes (LMO2, BCL6, FN1, CCND2, SCYA3. and BCL2) that predicts survival in diffuse large B-cell lymphoma (DLBCL) patients. Since LMO2 emerged as the strongest predictor of superior outcome, we generated a monoclonal anti-LMO2 antibody in order to study its tissue expression pattern. Immumohistologic analysis of over 1200 normal and neoplastic tissue and cell lines showed that LMO2 protein is expressed as a nuclear marker in normal germinal-center (GC) B cells and GC-derived B-cell lines and in a subset of GC-derived B-cell lymphomas. LMO2 was also expressed in erythrold and myeloid precursors and in megakaryocytes and also in lymphoblastic and acute myeloid leukemias. It was rarely expressed in mature T, natural killer (NK), and plasma cell neoplasms and was absent from nonhernatolymphoid tissues except for endothelial cells. Hierarchical cluster analysis of immunolhistologic data in DLBCL demonstrated that the expression profile of the LMO2 protein was similar to that of other GC-associated proteins (HGAL, BCL6, and CD10) but different from that of non-GC proteins (MUM1/ARF4 and BCL2). Our results warrant inclusion of LMO2 in multivariate analyses to construct a clinically applicable immumohistologic algorithm for predicting survival in patients with DLBCL.