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Clin Cancer Res. 2009 Sep 1;15(17):5494-502. doi: 10.1158/1078-0432.CCR-09-0113. Epub 2009 Aug 25.

A new immunostain algorithm classifies diffuse large B-cell lymphoma into molecular subtypes with high accuracy.

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Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska 68198-3135, USA.



Hans and coworkers previously developed an immunohistochemical algorithm with approximately 80% concordance with the gene expression profiling (GEP) classification of diffuse large B-cell lymphoma (DLBCL) into the germinal center B-cell-like (GCB) and activated B-cell-like (ABC) subtypes. Since then, new antibodies specific to germinal center B-cells have been developed, which might improve the performance of an immunostain algorithm.


We studied 84 cases of cyclophosphamide-doxorubicin-vincristine-prednisone (CHOP)-treated DLBCL (47 GCB, 37 ABC) with GCET1, CD10, BCL6, MUM1, FOXP1, BCL2, MTA3, and cyclin D2 immunostains, and compared different combinations of the immunostaining results with the GEP classification. A perturbation analysis was also applied to eliminate the possible effects of interobserver or intraobserver variations. A separate set of 63 DLBCL cases treated with rituximab plus CHOP (37 GCB, 26 ABC) was used to validate the new algorithm.


A new algorithm using GCET1, CD10, BCL6, MUM1, and FOXP1 was derived that closely approximated the GEP classification with 93% concordance. Perturbation analysis indicated that the algorithm was robust within the range of observer variance. The new algorithm predicted 3-year overall survival of the validation set [GCB (87%) versus ABC (44%); P < 0.001], simulating the predictive power of the GEP classification. For a group of seven primary mediastinal large B-cell lymphoma, the new algorithm is a better prognostic classifier (all "GCB") than the Hans' algorithm (two GCB, five non-GCB).


Our new algorithm is significantly more accurate than the Hans' algorithm and will facilitate risk stratification of DLBCL patients and future DLBCL research using archival materials.

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