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The distinct clinical features and prognosis of the CD10(+)MUM1(+) and CD10(−)Bcl6(−)MUM1(−) diffuse large B-cell lymphoma

Using an immunohistochemistry (IHC) based method, diffuse large B-cell lymphoma (DLBCL) can be classified into germinal center B-cell (GCB) and non-GCB subtypes. However, the prognostic value of Hans algorithm was contradictory in the literature. Using IHC and fluorescence in situ hybridization, we...

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Detalles Bibliográficos
Autores principales: Lu, Ting-Xun, Miao, Yi, Wu, Jia-Zhu, Gong, Qi-Xing, Liang, Jin-Hua, Wang, Zhen, Wang, Li, Fan, Lei, Hua, Dong, Chen, Yao-Yu, Xu, Wei, Zhang, Zhi-Hong, Li, Jian-Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4746587/
https://www.ncbi.nlm.nih.gov/pubmed/26857366
http://dx.doi.org/10.1038/srep20465
Descripción
Sumario:Using an immunohistochemistry (IHC) based method, diffuse large B-cell lymphoma (DLBCL) can be classified into germinal center B-cell (GCB) and non-GCB subtypes. However, the prognostic value of Hans algorithm was contradictory in the literature. Using IHC and fluorescence in situ hybridization, we analyzed the antibodies applied in Hans algorithm and other genetic factors in 601 DLBCL patients and prognostic value of Hans algorithm in 306 cases who were treated with chemoimmunotherapy. The results showed that patients with GCB subtype have better overall survival (OS) and progression-free survival (PFS) than non-GCB cases. However, to some extent, double positive (CD10(+)MUM1(+), DP) and triple negative (CD10(−)Bcl6(−)MUM(−), TN) showed different clinical characteristics and prognosis to others that were assigned to the same cell-of-origin group. The DP group showed similar OS (median OS: both not reached, P = 0.3650) and PFS (median PFS: 47.0 vs. 32.7 months, P = 0.0878) with the non-GCB group while the TN group showed similar OS (median OS: both not reached, P = 0.9278) and PFS (median PFS: both not reached, P = 0.9420) with the GCB group. In conclusion, Recognition of specific entities in Hans algorithm could help us to accurately predict outcome of the patients and choose the best clinical management for them.