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

Abstract

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.

Fig. 1.

The new algorithm and the Hans' algorithm. The new IHC algorithm (A) uses five markers with 78 of 84 cases concordant (93%) when compared with the GEP classification, whereas the Hans' algorithm (B) had 72 of 84 cases concordant (86%) in the same training set.

Fig. 2.

Survival analysis of the training set. OS and EFS of the 84 cases in the training set classified by GEP (A and C) and the new algorithm (B and D). Eleven of the 84 cases did not have disease progression data and were excluded for EFS analysis.

Fig. 3.

Survival analysis of the validation set. OS and EFS of the 63 cases in the validation set classified by GEP (A and C) and the new algorithm (B and D). Nine of the 63 cases did not have disease progression data and were excluded for EFS analysis.

Fig. 4.

Survival analysis of the GEP-unclassified cases. OS (A) and EFS (C) of the 19 GEP-unclassified cases when classified by the new algorithm. One of the 19 cases did not have disease progression data and was excluded for EFS analysis. Also shown are the OS (B) and EFS (D) for the combined group of GEP-unclassified and GEP-defined GCB and ABC cases in the training set as classified by the new algorithm. Twelve of the 103 cases in the combined group did not have disease progression data and were excluded for EFS analysis.