BackgroundThe concept of borderline lymphoproliferative disorder (LPD) has not been clearly defined.MethodsThis study aimed to classify patients with leukemic LPD (n = 597, excluding hairy cell leukemia, mantle cell lymphomas, and CD10‐positive LPDs) into CLL or non‐CLL applying three diagnostic strategies (the D'Arena and CLLflow scores and CD43 expression) and to better characterize unclassified patients.ResultsPatients with concurring CLL‐like (n = 441) or non‐CLL like (n = 99) results with the three diagnostic strategies were determined to have CLL and non‐CLL, respectively. Patients with discordant results (n = 57) were analyzed taking into consideration each individual cytometric marker and cytogenetic data: 41 were classified (11 CLL, 30 non‐CLL) and 16 (2.7% of the entire series) could not and were considered borderline LPD. Excluding borderline LPD, the CLLflow score had the highest accuracy of the three strategies. With the addition of CD43 no patient was misclassified. With the aid of hierarchical clustering, 12 of the 16 borderline patients seemed to fall into two well‐defined antigenic groups. None of the diagnostic strategies could reliably pick out borderline LPD.ConclusionThe combination of the CLLflow score and CD43 generally has a high diagnostic accuracy for leukemic LPD but it is not reliable to identify or diagnose borderline LPD. This latter group needs further study to determine its underlying biology. © 2018 International Clinical Cytometry Society

Background Within the hematopoietic compartment, fibromodulin (FMOD) is almost exclusively expressed in chronic lymphocytic leukemia (CLL) lymphocytes. We set out to determine whether FMOD could be of help in diagnosing borderline lymphoproliferative disorders (LPD). Methods We established 3 flow cytometry‐defined groups (CLL [n = 65], borderline LPD [n = 28], broadly defined as those with CLLflow score between 35 and −20 or discordant CD43 and CLLflow, and non‐CLL LPD [n = 40]). FMOD expression levels were determined by standard RT‐PCR in whole‐blood samples. Patients were included regardless of lymphocyte count but with tumor burden ≥40%. Results FMOD expression levels distinguished between CLL (median 98.5, interquartile range [IQR] 37.8–195.1) and non‐CLL LPD (median 0.012, IQR 0.003–0.033) with a sensitivity and specificity of 1. Most borderline LPDs were CD5/CD23/CD200‐positive with no loss of B‐cell antigens and negative or partial expression of CD43. 16/22 patients with available cytogenetic analysis showed trisomy 12. In 25/28 (89%) of these patients, FMOD expression levels fell between CLL and non‐CLL (median 3.58, IQR 1.06–6.21). Discussion This study could suggest that borderline LPDs may constitute a distinct group laying in the biological spectrum of chronic leukemic LPDs. Future studies will have to confirm these results with other biological data. Quantification of FMOD can potentially be of help in the diagnosis of phenotypically complex LPDs.

Plasmacytoid dendritic cells (pDCs) are part of the innate immune system and perform essential functions, such as antigen presenta-

BackgroundThe presence of >94% classical monocytes (MO1, CD14++/CD16‐) in peripheral blood (PB) has an excellent performance for the diagnosis of chronic myelomonocytic leukemia (CMML). However, the monocyte gating strategy is not well defined. The objective of the study was to compare monocyte gating strategies and propose an optimal one.MethodsThis is a prospective, single center study assessing monocyte subsets in PB. First, we compared monocyte subsets using 13 monocyte gating strategies in 10 samples. Then we developed our own 10 color tube and tested it on 124 patients (normal white blood cell counts, reactive monocytosis, CMML and a spectrum of other myeloid malignancies). Both conventional and computational (FlowSOM) analyses were used.ResultsComparing different monocyte gating strategies, small but significant differences in %MO1 and percentually large differences in %MO3 (nonclassical monocytes) were found, suggesting that the monocyte gating strategy can impact monocyte subset quantification. Then, we designed a 10‐color tube for this purpose (CD45/CD33/CD14/CD16/CD64/CD86/CD300/CD2/CD66c/CD56) and applied it to 124 patients. This tube allowed proper monocyte gating even in highly abnormal PB. Computational analysis found a higher %MO1 and lower %MO3 compared to conventional analysis. However, differences between conventional and computational analysis in both MO1 and MO3 were globally consistent and only minimal differences were observed when comparing the ranking of patients according to %MO1 or %MO3 obtained with the conventional versus the computational approach.ConclusionsThe choice of monocyte gating strategy appears relevant for the monocyte subset distribution test. Our 10‐color proposal allowed satisfactory monocyte gating even in highly abnormal PB. Computational analysis seems promising to increase reproducibility in monocyte subset quantification.