Old and new immunophenotypic markers in multiple myeloma for discrimination of responding and relapsing patients: The importance of "normal" residual plasma cell analysis.

Cytometry B Clin Cytom

D.S.O.U. Specialistic Laboratory Oncology, Hematology and Cell Cultures for Clinical Use, ARNAS Civico, Piazza Nicola Leotta 4, Palermo, 90127, Italy; Department of Pathobiology and Medical and Forensic Biotechnologies, University of Palermo, Corso Tukory 211, Palermo, 90134, Italy.

Published: January 2016

Background: Multiple myeloma is an incurable disease characterized by proliferation of clonal malignant plasma cells (CPCs), which can be immunophenotypically distinguished from polyclonal plasma cells (PPCs) by multiparameter flow cytometry (MFC). The utility of PPCs analysis in detecting prognostic and predictive information is still a matter of debate.

Methods: we tested the ability of 11 MFC markers in detecting differences in the immunophenotype of CPCs and PPCs among patients in various disease stages; we verified if these markers could be associated with disease stage/response to therapy despite the role of clinical parameters.

Results: significant changes in the expression of markers occurred both in CPCs and PPCs. CD58 on PPCs of responding patients was downregulated compared with PPC of relapsing group. Fraction of CD200 expressing PCs was lower in control subjects than in PPCs from MGUS and myeloma groups. CD11a levels of expression on both CPCs and PPCs showed an upregulation in newly diagnosed and relapsing patients versus PCs of controls; CD20 was less expressed on control PCs than on MGUS CPCs and PPCs. CD49d revealed to be advantageous in discrimination of PPCs from CPCs. In our multiple regression model, CD19 and CD49d on CPCs, and CD45, CD58 and CD56 on PPCs maintained their association with groups of patients independently of other prognostic variables.

Conclusions: we provide a feasible start point to put in order ranges of expression on PPCs in healthy and myeloma subjects; we propose a new approach based on PPC analysis to monitor the stages of the disease.

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Source
http://dx.doi.org/10.1002/cyto.b.21218DOI Listing

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