Flow cytometric aberrancies in plasma cell myeloma and MGUS - correlation with laboratory parameters

SFTSV infection is associated with transient overproliferation of monoclonal lambda-type plasma cells

The impairment of antibody-mediated immunity is a major factor associated with fatal cases of severe fever with thrombocytopenia syndrome (SFTS). By collating the clinical diagnosis reports of 30 SFTS cases, we discovered the overproliferation of monoclonal plasma cells (MCP cells, CD38⁺cLambda⁺cKappa^-) in bone marrow, which has only been reported previously in multiple myeloma. The ratio of CD38⁺cLambda⁺ versus CD38⁺cKappa⁺ in SFTS cases with MCP cells was significantly higher than that in normal cases. MCP cells presented transient expression in the bone marrow, which was distinctly different from multiple myeloma. Moreover, the SFTS patients with MCP cells had higher clinical severity. Further, the overproliferation of MCP cells was also observed in SFTS virus (SFTSV)-infected mice with lethal infectious doses. Together, SFTSV infection induces transient overproliferation of monoclonal lambda-type plasma cells, which have important implications for the study of SFTSV pathogenesis, prognosis, and the rational development of therapeutics.

Multiple Myeloma Immunophenotype Related to Chromosomal Abnormalities Used in Risk Assessment

(1) Background: At diagnosis, multiplemyeloma risk estimation includes disease burden, end-organ damage, and biomarkers, with increasing emphasis on genetic abnormalities. Multicolor flow cytometry (MFC) is not always considered in risk estimation. We demonstrate associations found between genetic abnormalities and antigen expression of plasma cells measured by MFC. (2) Methods: Single nucleotide polymorphism microarray (SNP-A) karyotyping as well as MFC using standardized next-generation flow (NGF) panels and instrument settings were performed from bone marrow aspirates at the time of diagnosis. (3) Results: We uncovered specific immunophenotype features related to different genetic risk factors. Specifically, we found higher malignant/normal plasma cell ratio and lower expression of CD27, CD38, CD45, CD56, CD117 and CD138 in higher-risk genetic groups or risk categories.

Accurate classification of plasma cell dyscrasias is achieved by combining artificial intelligence and flow cytometry

Monoclonal gammopathy of unknown significance (MGUS), smouldering multiple myeloma (SMM), and multiple myeloma (MM) are very common neoplasms. However, it is often difficult to distinguish between these entities. In the present study, we aimed to classify the most powerful markers that could improve diagnosis by multiparametric flow cytometry (MFC). The present study included 348 patients based on two independent cohorts. We first assessed how representative the data were in the discovery cohort (123 MM, 97 MGUS) and then analysed their respective plasma cell (PC) phenotype in order to obtain a set of correlations with a hypersphere visualisation. Cluster of differentiation (CD)27 and CD38 were differentially expressed in MGUS and MM (P < 0·001). We found by a gradient boosting machine method that the percentage of abnormal PCs and the ratio PC/CD117 positive precursors were the most influential parameters at diagnosis to distinguish MGUS and MM. Finally, we designed a decisional algorithm allowing a predictive classification ≥95% when PC dyscrasias were suspected, without any misclassification between MGUS and SMM. We validated this algorithm in an independent cohort of PC dyscrasias (n = 87 MM, n = 41 MGUS). This artificial intelligence model is freely available online as a diagnostic tool application website for all MFC centers worldwide (https://aihematology.shinyapps.io/PCdyscrasiasToolDg/).