A new immunostain algorithm classifies diffuse large B-cell lymphoma into molecular subtypes with high accuracy.
Choi WW., Weisenburger DD., Greiner TC., Piris MA., Banham AH., Delabie J., Braziel RM., Geng H., Iqbal J., Lenz G., Vose JM., Hans CP., Fu K., Smith LM., Li M., Liu Z., Gascoyne RD., Rosenwald A., Ott G., Rimsza LM., Campo E., Jaffe ES., Jaye DL., Staudt LM., Chan WC.
PURPOSE: 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. EXPERIMENTAL DESIGN: 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. RESULTS: 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). CONCLUSION: 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.