Flow cytometric expression of CD71, CD81, CD44 and CD39 in B cell lymphoma.

Flow cytometry is a useful ancillary tool for the diagnosis of nodal B cell lymphomas. Well-established antigens have diagnostic limitations. This study aimed to assess the expression of CD71, CD81, CD44 and CD39 by flow cytometry in B cell lymphomas. Expression of these 4 antigens was queried in 185 samples with a diagnosis of a B cell lymphoma according to a histological examination of the lymph node and the World Health Organization (WHO) classification (follicular lymphoma [FL, n = 96], diffuse large B cell lymphoma/High grade B cell lymphoma [DLBCL/HGBH, n = 48], marginal zone lymphoma/lymphoplasmacytic lymphoma [MZL/LPL, n = 14], chronic lymphocytic leukemia/small lymphocytic lymphoma [CLL, n = 10], mantle cell lymphoma [MCL, n = 11], Burkitt lymphoma [BL, n = 4] and other [n = 2]). CD81 was bright and CD44 was dim in germinal center-derived malignancies, particularly aggressive lymphomas (BL and CD10-positive DLBCL/HGBL). CD81 was very dim in CLL. CD71 was bright in aggressive lymphomas (DLBCL/HGBL and BL). CD39 was bright in CD10-negative DLBCL. CD71 appeared valuable in the differential diagnosis between indolent and aggressive lymphomas, CD39 between CD10-negative DLBCL and MZL/LPL and CD81 between MCL and CLL. To conclude, we report the expression of CD71, CD81, CD44 and CD39 by FC in B cell lymphomas. Further studies will have to determine the value they add to specific FC panels.

Optimization of monocyte gating to quantify monocyte subsets for the diagnosis of chronic myelomonocytic leukemia.

BACKGROUND: The 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. METHODS: This 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. RESULTS: Comparing 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. CONCLUSIONS: The 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.

Flow cytometry to detect bone marrow involvement by follicular lymphoma.

BACKGROUND: High-quality data on bone marrow involvement (BMI) assessed by flow cytometry (FC) in follicular lymphoma (FL) is lacking. AIMS: We set up a prospective protocol with a 10-color tube and acquisition of 500.000 leukocytes on a Nav flow cytometer for evaluation of BMI in FL by FC. MATERIALS AND METHODS: FC was compared with a combination of histopathology and IGH gene rearrangement, which were considered the gold standard. We also compared BMI by FC with PET. RESULTS: Fifty-two patients were included (median 67 years, 54% female). BMI by FC was seen in 35 (67%), with a median involvement of 1.2% (interquartile range: 0.3%-7%) of leukocytes. Comparison with the gold standard revealed two false negatives and two false positives (potentially true involvement undetected by the gold standard). BMI by PET was seen in 14/46 (30%). Immunophenotype of FL in the bone marrow was highly heterogeneous. The most common phenotypic abnormality was dim expression of CD19 (>0.5 log loss in 30% of patients). CD10 was negative in 13 (37%) and incompletely positive (overlap with the negative population) in a further 8 (28%) while entirely positive only in 14 (48%). Other abnormalities (loss of CD20, gain or loss of CD79b, expression of CD43, and substantial loss of CD45) were rare. Computational analysis by means of FlowSOM confirmed the heterogeneous phenotype, with FL from different patients clustering in unrelated metaclusters. CONCLUSION: BMI by FL was frequent and immunophenotype was heterogeneous. However, this protocol enabled detection of FL in bone marrow in the vast majority of patients with bone marrow involvement by the gold standard.

Concordance between flow cytometry CLL scores.

INTRODUCTION: Multiple flow cytometry scores/diagnostic systems for the classification of leukemic lymphoproliferative disorders (LPD) have been published but few have been compared between them. PATIENTS AND METHODS: We classified a cohort of leukemic LPD based on eleven published flow cytometry scores/diagnostic systems and compared their classification as chronic lymphocytic leukemia (CLL) or non-CLL LPD. RESULTS: 329 patients were included. Patients classified as CLL ranged from 46% to 73%, depending on the score/diagnostic system used. All eleven scores/diagnostic systems agreed in 184/324 (57%) of patients while in 58/324 (18%) at least two scores/diagnostic systems classified the patient differently (from the majority). Fleiss kappa was 0.74, but pairwise agreement was variable (Cohen's kappa: 0.48 to 0.87). CONCLUSION: This study found a suboptimal agreement between published flow cytometry scores/diagnostic systems for the classification of LPD.

Diagnostic performance of the ClearLLab 10C B cell tube.

INTRODUCTION: Pre-analytical and analytical errors can threaten the reliability of flow cytometry (FC) results. A potential solution to some of these is the use of dry, pre-mixed antibodies, such as the ClearLLab 10C system. The purpose of the present study was to compare the diagnostic performance of the ClearLLab 10C B cell tube with that of our standard laboratory practice. METHODS: We compared the diagnoses made with the ClearLLab 10C B cell tube (experimental strategy) with those made with standard laboratory practice (standard strategy). Samples were selected aiming for representation of the full spectrum of B cell disorders, with an emphasis on mature B cell malignancies, as well as healthy controls. RESULTS: We included 116 samples (34 normal controls, 4 acute lymphoblastic leukemias, 54 mature lymphoproliferative disorders in peripheral blood and bone marrow, 3 myelomas, 6 bone marrow samples with involvement by lymphoma and 1 with elevated hematogone count, 14 lymph node samples, 1 cerebrospinal fluid, and 1 pleural effusion). There were two diagnostic errors (1.7%). The agreement between the two strategies in the percentage of CD19 cells and fluorescence intensity of CD5, CD19, CD20, CD200, and CD10 was very good. CONCLUSIONS: In this study, the ClearLLab 10C B cell tube performed similarly to our standard laboratory practice to diagnose and classify mature B cell malignancies.

Flow Cytometry in the Differential Diagnosis of CD10-Positive Nodal Lymphomas.

BACKGROUND: Differences between follicular lymphoma (FL) and diffuse large B-cell lymphoma/high-grade B-cell lymphoma (DLBCL/HGBL) by flow cytometry are underexplored. METHODS: We retrospectively assessed flow cytometry results from 191 consecutive lymph node biopsies diagnosed with FL or DLBCL/HGBL. RESULTS: The only parameters that differed between the 2 groups in the derivation cohort were forward scatter and side scatter (P < 10-6; area under the curve [AUC], 0.75-0.8) and %CD23 (P = .004; area under the receiver characteristic operating curve, 0.64). However, since light scatter characteristics did not distinguish between grade 3 FL and DLBCL/HGBL, we set out to develop a model with high sensitivity for the exclusion of the latter. Several models, including FS and %CD23, were tested, and 2 models showed a sensitivity of >0.90, with negative predictive values of ≥0.95, albeit with low specificity (0.45 to 0.57). CONCLUSION: Two simple models enable the exclusion of DLBCL/HGBL with a high degree of confidence.

FMOD expression in whole blood aids in distinguishing between chronic lymphocytic leukemia and other leukemic lymphoproliferative disorders. A pilot study.

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.

Refining the Limits of Borderline Lymphoproliferative Disorders.

BACKGROUND: The concept of borderline lymphoproliferative disorder (LPD) has not been clearly defined. METHODS: This 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. RESULTS: Patients 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. CONCLUSION: The 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.