Page 315 - Haematologica Vol. 109 - July 2024

LETTER TO THE EDITOR
accuracy of the classification method.
We then applied LymphGen to the MSK DLBCL clinical co- hort of 396 cases (320 cases of the cohort have both BCL2 and BCL6 clinical cytogenetic results available) sequenced by IMPACT (data available at https://www.cbioportal.org/ study/summary?id=mbn_msk_2024). When the LymphGen algorithm was applied to the MSK DLBCL cohort, 55% of the cases were assigned into one of the subtypes (MCD: 10%, EZB: 22%, BN2: 10%, N1: 3%, ST2: 7%, A53: 2%, composite: 2%) (Figure 2A). Within these cases, about two-thirds were in the “Core” group, and one-third were in the “Extended” group (Figure 2C). The correlation of genetic subtypes and COO based on Hans’ algorithm was similar to the NCI co- hort. Most EZB subtypes were GCB type, while most MCD and N1 subtypes were non-GCB type; BN2, ST2, and A53 subtypes carry a mixture of GCB and non-GCB types (Fig- ure 3B). We further investigated the composition of each subclass and its relationship with COO classification in the “Core” group; 148 (37.4% of total cases) cases classified as “Core” group were assigned into one of the 6 classes (MCD: 18.2%, EZB: 44%, BN2: 22%, N1: 7%, ST2: 7%, A53: 2%). The correlation of genetics subtypes and COO was similar to that observed in all cases. (Figure 2D, E). Essential genes in the IMPACT panel which significantly distinguish each subtype were identified by Benjamini-Hochberg procedure and χ2 test (Online Supplementary Figure S3A).
In the MSK DLBCL cohort, cytogenetics and immunophe- notypes observed in each genetic subtype were similar to those identified in the NCI cohort. As expected, BCL2 trans- locations were enriched in the EZB subtype (54 out of 67 cases, 81%), and BCL6 translocations were enriched in the BN2 subtype (24 out of 31 cases, 77%). MYC translocations were seen more frequently (6 out of 39 cases, 15.3%) in BN2 subtypes when co-existing with BCL6 translocation. MYC translocations were also seen in a small proportion of EZB
Figure 3. Heatmap of immunophenotype and cytogenetic features in subtypes. All cases classified into one of the 6 classes are clus- tered by the subclass. The immunohisto- chemistry status of each maker is shown in a color block (red: positive; yellow: negative; white: not available / not performed). Flu- orescence in situ hybridization results for BCL2, BCL6, and MYC translocations are shown in the color block as indicated here. Cell-of-origin (COO) and classification type (“Core” vs. “Extended”) are also annotated by color block.
(7 out of 86 cases, 8.1%), MCD (2 out of 40 cases, 5.0%), and ST2 (2 out of 27 cases, 7.4%) subtypes. Immunophenotypes were evaluated by immunohistochemical stains in all cases as part of the diagnostic work-up. Consistent with the ger- minal center phenotype, CD10, CD23, and LMO2 were more frequently found expressed in the EZB and ST2 subtypes, while MUM1 was seen more frequently in the BN2, MCD, N1, and A53 subtypes. Interestingly, 50% (4 out of 8 cases) of N1 subtypes expressed CD5 and LEF1 by immunohistochemistry, indicating that some of the N1 subtype cases might represent Richter’s transformation of chronic lymphocytic leukemia. Only 2 cases with BCL2 translocation fell in the unclassified group, suggesting that BCL2 translocation is a robust clas- sifier for the EZB subtype (Figure 3A). Furthermore, survival analyses within the MSK cohort have demonstrated trends consistent with those observed in other studies. However, statistical significance was not achieved, likely due to the inherent heterogeneity of the cohort and the relatively short duration of follow-up (data not shown).
In conclusion, in this study, we validated genetic classifi- cation for DLBCL by LymphGen on our clinically validated 400-gene targeted NGS panel, IMPACT, with high sensitivity and specificity and showed that the classification can be applied to routine clinical practice. To note, in our practice, over 99% of DLBCL cases were successfully sequenced, thanks to detailed pre-analytical checks that identified and excluded inadequate samples. Success rates, how- ever, may differ with other workflows. The findings also indicate the accuracy of the classification can be further enhanced if detection of CNA can be improved. Our study supports the view that genomic classifications of DLBCL using high complexity testing such as LymphGen can be readily translated into routine clinical care using well-de- signed smaller targeted NGS panels such as IMPACT. Such panels offer opportunities for clinical decision-making
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