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A probabilistic approach for the evaluation of minimal residual disease by multiparameter flow cytometry in leukemic B-cell chronic lymphoproliferative disorders

AutorPedreira, C. E.; Almeida, Julia ; Fernández, Carlos; Quijano, Sandra; Flores-Montero, Juan; Barrena, Susana; Lécrevisse, Quentin; Orfao, Alberto
Fecha de publicación2008
EditorWiley-Blackwell
CitaciónCytometry Part A 73A(12): 1141-1150 (2008)
ResumenMultiparameter flow cytometry has become an essential tool for monitoring response to therapy in hematological malignancies, including B-cell chronic lymphoproliferative disorders (B-CLPD). However, depending on the expertise of the operator minimal residual disease (MRD) can be misidentified, given that data analysis is based on the definition of expert-based bidimensional plots, where an operator selects the subpopulations of interest. Here, we propose and evaluate a probabilistic approach based on pattern classification tools and the Bayes theorem, for automated analysis of flow cytometry data from a group of 50 B-CLPD versus normal peripheral blood B-cells under MRD conditions, with the aim of reducing operator-associated subjectivity. The proposed approach provided a tool for MRD detection in B-CLPD by flow cytometry with a sensitivity of ≤8 × 10-5 (median of ≤2 × 10-7). Furthermore, in 86% of B-CLPD cases tested, no events corresponding to normal B-cells were wrongly identified as belonging to the neoplastic B-cell population at a level of ≤10-7. Thus, this approach based on the search for minimal numbers of neoplastic B-cells similar to those detected at diagnosis could potentially be applied with both a high sensitivity and specificity to investigate for the presence of MRD in virtually all B-CLPD. Further studies evaluating its efficiency in larger series of patients, where reactive conditions and non-neoplastic disorders are also included, are required to confirm these results. © 2008 International Society for Advancement of Cytometry.
URIhttp://hdl.handle.net/10261/59448
DOI10.1002/cyto.a.20638
Identificadoresdoi: 10.1002/cyto.a.20638
issn: 1552-4922
e-issn: 1552-4930
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