Philippé, J. (Jan)

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    Quality assessment of a large multi-center flow cytometric dataset of acute myeloid leukemia patients-a EuroFlow study
    (2022) Grigore, G. (Georgiana); Fernández, P. (Paula); Vieira-de-Mello, F. (Fabiana); Orfao, A. (Alberto); Nierkens, S. (Stefan); Bras, A.E. (Anne E.); Matarraz, S. (Sergio); Aanei, C.M. (Carmen Mariana); Burgos, L. (Leire); Dongen, J.J.M. (Jacques J. M.) van; Sluijs-Gelling, A. (Alita) van der; Philippé, J. (Jan); Velden, V.H.J. (Vicent H. J.) van der
    Simple Summary Flow cytometry allows detailed characterization of large numbers of cells and plays an important role in the diagnosis of acute myeloid leukemia. To facilitate analysis of flowcytometric data, reference databases of normal bone marrow samples and samples from acute myeloid leukemia patients, together with new software tools, are required. We here report on the building of a large database of acute myeloid leukemia patients (n = 1142) and 22 normal samples. We report on the quality assessment procedure used and its validation, discuss potential pitfalls, and provide possible solutions for avoiding such flaws in the construction of other databases. Our data show that obtaining and collecting reproducible flow cytometric data over time and across centers is feasible, but also that strict quality assessment remains crucial, even when standardized protocols for staining and instrument settings are being used in a multicenter setting. Flowcytometric analysis allows for detailed identification and characterization of large numbers of cells in blood, bone marrow, and other body fluids and tissue samples and therefore contributes to the diagnostics of hematological malignancies. Novel data analysis tools allow for multidimensional analysis and comparison of patient samples with reference databases of normal, reactive, and/or leukemia/lymphoma patient samples. Building such reference databases requires strict quality assessment (QA) procedures. Here, we compiled a datase
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    Expert-independent classification of mature B-cell neoplasms using standardized flow cytometry: a multicentric study
    (2022) Böttcher, S. (Sebastian); Fluxá, R. (Rafael); Verde, J. (Javier); Montero, J. (Juan); Grigore, G. (Georgiana); Novakova, M. (Michaela); Fernández, P. (Paula); Orfao, A. (Alberto); Kalina, T. (Tomas); Engelmann, R. (Robby); Ritgen, M. (Matthias); Burgos, L. (Leire); Lange, S. (Sandra); Dongen, J.J.M. (Jacques J. M.) van; Caetano, J. (Joana); Lecrevisse, Q. (Quentin); Philippé, J. (Jan); Velden, V.H.J. (Vicent H. J.) van der; Pedreira, C.E. (Carlos E.)
    Reproducible expert-independent flow-cytometric criteria for the differential diagnoses between mature B-cell neoplasms are lacking. We developed an algorithm-driven classification for these lymphomas by flow cytometry and compared it to the WHO gold standard diagnosis. Overall, 662 samples from 662 patients representing 9 disease categories were analyzed at 9 laboratories using the previously published EuroFlow 5-tube-8-color B-cell chronic lymphoproliferative disease antibody panel. Expression levels of all 26 markers from the panel were plotted by B-cell entity to construct a univariate, fully standardized diagnostic reference library. For multivariate data analysis, we subsequently used canonical correlation analysis of 176 training cases to project the multidimensional space of all 26 immunophenotypic parameters into 36 2-dimensional plots for each possible pairwise differential diagnosis. Diagnostic boundaries were fitted according to the distribution of the immunophenotypes of a given differential diagnosis. A diagnostic algorithm based on these projections was developed and subsequently validated using 486 independent cases. Negative predictive values exceeding 92.1% were observed for all disease categories except for follicular lymphoma. Particularly high positive predictive values were returned in chronic lymphocytic leukemia (99.1%), hairy cell leukemia (97.2%), follicular lymphoma (97.2%), and mantle cell lymphoma (95.4%). Burkitt and CD101 diffuse large B-cell lymphomas were difficult to distinguish by the algorithm. A similar ambiguity was observed between marginal zone, lymphoplasmacytic, and CD102 diffuse large B-cell lymphomas. The specificity of the approach exceeded 98% for all entities. The univariate immunophenotypic library and the multivariate expert-independent diagnostic algorithm might contribute to increased reproducibility of future diagnostics in mature B-cell neoplasms.