Page 314 - Haematologica Vol. 109 - July 2024

LETTER TO THE EDITOR
represents the proportion of cases classified by the IMPACT panel for a certain subtype in the cases classified by the NCI panel for the same subtype (considered as ground truth in this comparison). (B) Performance of the classification. (C) Cases from the NCI cohort filtered into the IMPACT panel were classified by LymphGen into 6 subclasses (MCD, EZB, BN2, N1, ST2, A53). The cases with more than one assigned subclass are indicated as “Composite”. The cases that cannot be classified into these sub- classes are labeled as “Other”. Regarding categories of the subclassification: tumors with subtype probabilities of >90% or 50-90% were defined as ‘‘Core’’ or ‘‘Extended’’ subtype members, respectively. (D) Correlation of LymphGen classification and cell-of-or- igin by Han’s algorithm. (E) Composition of “Core” and “Extended” group in each subtypes. (F) Confusion matrix of the IMPACT panel compared to the NCI panel for “Core” predicted group only. The target represents the classification by the NCI panel; the prediction represents the classification by the IMPACT panel. The center number shows the number of cases; the percentage represents the proportion of cases classified by IMPACT for a certain subtype in the cases classified by the NCI panel for the same subtype (considered as ground truth in this comparison). (G) Performance of the IMPACT panel compared to the NCI pan- el within the “Core” group. ABC: activated B-cell subtype; GCB: germinal center B-cell subtype.
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Figure 2. LymphGen classification on the Memorial Sloan Kettering cohort. (A) 396 diffuse large B-cell lymphoma cases sequenced by IMPACT were classified by Lymph- Gen into 6 subclasses: MCD, EZB, BN2, N1, ST2, A53. Cases with more than one assigned subclass are indicated as “Composite”. Cases that cannot be classified into these subclasses are labeled as “Other”. (B) Correlation of Lymph- Gen classification and cell-of-or- igin (COO) by Han’s algorithm. (C) Categories of the subclassification: tumors with subtype probabilities of >90% or 50-90% were defined as ‘‘Core’’ or ‘‘Extended’’ subtype members, respectively. (D) Com- position of the subtypes in the cases from the “Core” group (clas- sified based on probability >90%). (E) Correlation of LymphGen clas- sification and COO by Han’s algo- rithm in the cases from the “Core” group.
formation to the algorithm. The overall balanced accuracy was 81%, with 83% sensitivity and 89% specificity overall (Online Supplementary Figure S2A). The A53 subtype was omitted as expected, because the classification of the A53 subtype mainly relied on CNA. Twenty-three cases (4.7%) were misclassified into the “Other” (unclassified) group, suggesting that the lack of CNA reduces the algo- rithm’s ability to classify cases into subclasses (Online Supplementary Figure S2B). These results showed that the optimized CNA information is essential for classifying the A53 subtype and further helps increase the overall
were 86% and 98%, respectively. These results suggested that we could confidently classify cases into subtypes if defined by the “Core” (Figure 1F, G).
Copy number alteration was included in LymphGen input data to use the full algorithm. However, in the clinical setting, the copy number calling accuracy might be com- promised by poor sample quality or low tumor content. To explore the accuracy of classification using the IMPACT gene panel by LymphGen when CNA is not available, we applied only SNV, small INDEL, and fluorescence in situ hybridization (FISH) for BCL2 and BCL6 translocation in-
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