Mature B, T and NK-cell, plasma cell and histiocytic/dendritic cell neoplasms: classification according to the World Health Organization and International Consensus Classification.

In 2022, two updated classification systems for lymphoid neoplasms were published by the World Health Organization (WHO Classification of Haematolymphoid Tumours, 5th edition, referred to hereafter as WHO-HAEM5) and the International Consensus Conference (ICC) (Alaggio et al. in Leukemia 36(7):1720-1748, 2022; Campo et al. in Blood 140(11):1229-1253, 2022). Both classifications were conceived by both pathologists and clinicians with expertise in the field. The reasons for this have been reviewed previously (Arber et al. in Virchows Arch 482(1):1-9, 2023; Cree in Leukemia 36(7):1701-1702, 2022, Leukemia 36(11):2750, 2022). Given that both groups were using data-driven processes and consensus and used the revised 4th edition of the WHO Classification of Haematolymphoid Tumours (WHO-HAEM4R) as a starting point, it is not entirely surprising that the resulting classifications are quite similar. However, they are not identical and reflect preferences or approaches for certain unsettled areas as well as preferred terminology. In this review, we will compare nomenclature of the WHO-HAEM5 and ICC classifications, focusing on lymphoid neoplasms and lymphoproliferative disorders (LPDs).

Is a 500-Cell Count Necessary for Bone Marrow Differentials?: A Proposed Analytical Method for Validating a Lower Cutoff.

OBJECTIVES: By convention, 500 cells are counted for bone marrow aspirate differentials. Evidence supporting such a cutoff is lacking. We hypothesized that 300-cell counts could be sufficient. METHODS: Cell count results from 165 cases, for which values were recorded at 300 and 500 cells, were analyzed. We tested for statistical differences and changes in diagnostic classification between the two cutoffs. RESULTS: Three hundred cell counts did not produce diagnostically different results, particularly for myeloblasts and plasma cells, where cell percentages are critical for disease classification. Method comparison analysis did not reach statistical significance for any cell type when comparing the two methods. Bias plots showed narrow, even spread about the mean bias. Contingency table analysis yielded no significant diagnostic discrepancies. CONCLUSIONS: Performing differential counts on 300 cells would produce clinically and statistically similar results to 500 cells. Reducing the cell number counted has potential cost/labor reductions without affecting quality of care.

MicroRNA expression in multiple myeloma is associated with genetic subtype, isotype and survival.

BACKGROUND: MicroRNAs are small RNA species that regulate gene expression post-transcriptionally and are aberrantly expressed in many cancers including hematological malignancies. However, the role of microRNAs in the pathogenesis of multiple myeloma (MM) is only poorly understood. We therefore used microarray analysis to elucidate the complete miRNome (miRBase version 13.0) of purified tumor (CD138+) cells from 33 patients with MM, 5 patients with monoclonal gammopathy of undetermined significance (MGUS) and 9 controls. RESULTS: Unsupervised cluster analysis revealed that MM and MGUS samples have a distinct microRNA expression profile from control CD138+ cells. The majority of microRNAs aberrantly expressed in MM (109/129) were up-regulated. A comparison of these microRNAs with those aberrantly expressed in other B-cell and T-cell malignancies revealed a surprising degree of similarity (~40%) suggesting the existence of a common lymphoma microRNA signature. We identified 39 microRNAs associated with the pre-malignant condition MGUS. Twenty-three (59%) of these were also aberrantly expressed in MM suggesting common microRNA expression events in MM progression. MM is characterized by multiple chromosomal abnormalities of varying prognostic significance. We identified specific microRNA signatures associated with the most common IgH translocations (t(4;14) and t(11;14)) and del(13q). Expression levels of these microRNAs were distinct between the genetic subtypes (by cluster analysis) and correctly predicted these abnormalities in > 85% of cases using the support vector machine algorithm. Additionally, we identified microRNAs associated with light chain only myeloma, as well as IgG and IgA-type MM. Finally, we identified 32 microRNAs associated with event-free survival (EFS) in MM, ten of which were significant by univariate (logrank) survival analysis. CONCLUSIONS: In summary, this work has identified aberrantly expressed microRNAs associated with the diagnosis, pathogenesis and prognosis of MM, data which will prove an invaluable resource for understanding the role of microRNAs in this devastating disease.

Genomic profiling of plasmablastic lymphoma using array comparative genomic hybridization (aCGH): revealing significant overlapping genomic lesions with diffuse large B-cell lymphoma.

BACKGROUND: Plasmablastic lymphoma (PL) is a subtype of diffuse large B-cell lymphoma (DLBCL). Studies have suggested that tumors with PL morphology represent a group of neoplasms with clinopathologic characteristics corresponding to different entities including extramedullary plasmablastic tumors associated with plasma cell myeloma (PCM). The goal of the current study was to evaluate the genetic similarities and differences among PL, DLBCL (AIDS-related and non AIDS-related) and PCM using array-based comparative genomic hybridization. RESULTS: Examination of genomic data in PL revealed that the most frequent segmental gain (> 40%) include: 1p36.11-1p36.33, 1p34.1-1p36.13, 1q21.1-1q23.1, 7q11.2-7q11.23, 11q12-11q13.2 and 22q12.2-22q13.3. This correlated with segmental gains occurring in high frequency in DLBCL (AIDS-related and non AIDS-related) cases. There were some segmental gains and some segmental loss that occurred in PL but not in the other types of lymphoma suggesting that these foci may contain genes responsible for the differentiation of this lymphoma. Additionally, some segmental gains and some segmental loss occurred only in PL and AIDS associated DLBCL suggesting that these foci may be associated with HIV infection. Furthermore, some segmental gains and some segmental loss occurred only in PL and PCM suggesting that these lesions may be related to plasmacytic differentiation. CONCLUSION: To the best of our knowledge, the current study represents the first genomic exploration of PL. The genomic aberration pattern of PL appears to be more similar to that of DLBCL (AIDS-related or non AIDS-related) than to PCM. Our findings suggest that PL may remain best classified as a subtype of DLBCL at least at the genome level.