Automated and simplified identification of normal and abnormal plasma cells in Multiple Myeloma by flow cytometry

BackgroundMultiple myeloma (MM) is an incurable disease characterized by clonal plasma cell (PC) proliferation within the bone marrow (BM). Next-generation flow cytometry has become the reference tool to follow minimal residual disease (MRD). We developed a new simpler and cheaper flow cytometry method to analyze bone marrow samples in patients with MM. MethodsTo identify and characterize abnormal PCs, we designed a simple panel composed of anti-CD38, antikappa, and antilambda light chain antibodies, combined with two antibody pools with the same fluorophore (anti-CD19 and anti-CD27 for the negative pool and anti-CD56, anti-CD117, and anti-CD200 antibodies for the positive pool). We also developed dedicated software for the automated identification of malignant PCs and MRD assessment. We then compared PC identification with our simple antibody panel and with the larger antibody panel routinely used at Montpellier University Hospital Center in 52 patients with MM (M-CHU cohort). ResultsResults for total PC detection (r(2)=0.9965; P<0.001; n=52) and malignant PC detection (r(2)=0.9486; P<0.001; n=38) obtained with the two panels were significantly correlated. Moreover, comparison of the results obtained by automated detection with our software and by manual gating analysis in 80 BM samples (38 from the M-CHU cohort and 42 patients from another MM cohort) showed strong correlation for both total and malignant PC selection (respectively, r(2)=0.936; P<0.001 and r(2)=0.9505; P<0.001). ConclusionsOur simple and automated strategy for MRD assessment in MM could help increasing reproducibility and productivity without compromising sensitivity and specificity, while decreasing the test cost. (c) 2017 International Clinical Cytometry Society