Expert-independent classification of mature B-cell neoplasms using standardized flow cytometry: a multicentric study

Author:

Böttcher Sebastian1ORCID,Engelmann Robby1ORCID,Grigore Georgiana2,Fernandez Paula3,Caetano Joana4ORCID,Flores-Montero Juan567ORCID,van der Velden Vincent H. J.8,Novakova Michaela9,Philippé Jan10ORCID,Ritgen Matthias11,Burgos Leire12ORCID,Lecrevisse Quentin2567ORCID,Lange Sandra1,Kalina Tomas9ORCID,Verde Velasco Javier2,Fluxa Rodriguez Rafael2,van Dongen Jacques J. M.13ORCID,Pedreira Carlos E.14,Orfao, Alberto567

Affiliation:

1. Clinic III (Hematology, Oncology and Palliative Medicine), Special Hematology Laboratory, Rostock University Medical School, Rostock, Germany;

2. Cytognos SL, Salamanca, Spain;

3. FACS/Stem Cell Laboratory, Kantonsspital Aarau AG, Aarau, Switzerland;

4. Secção de Citometria de Fluxo, Instituto Português de Oncologia de Lisboa Francisco Gentil, Lisbon, Portugal;

5. Clinical and Translational Research Program, Cancer Research Center (IBMCC-CSIC/USAL-IBSAL), University of Salamanca, Salamanca, Spain;

6. Department of Medicine and Cytometry Service (NUCLEUS), University of Salamanca, Salamanca, Spain;

7. Centro de Investigación Biomédica en Red de Cáncer (CIBER-ONC) (CB16/12/00400), Instituto de Salud Carlos III, Madrid, Spain;

8. Department of Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands;

9. CLIP - Department of Pediatric Hematology and Oncology, Charles University and University Hospital Motol, Prague, Czech Republic;

10. Department of Diagnostic Sciences, Ghent University, Ghent, Belgium;

11. Department of Internal Medicine II, University of Schleswig-Holstein, Kiel, Germany;

12. Clinica Universidad de Navarra, Centro de Investigacion Medica Aplicada (CIMA), Instituto de Investigacion Sanitaria de Navarra (IDISNA), CIBER-ONC CB16/12/00369, Pamplona, Spain;

13. Department of Immunology, Leiden University Medical Center, Leiden, Netherlands; and

14. Systems and Computing Department, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

Abstract

Abstract 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 CD10+ diffuse large B-cell lymphomas were difficult to distinguish by the algorithm. A similar ambiguity was observed between marginal zone, lymphoplasmacytic, and CD10− 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.

Publisher

American Society of Hematology

Subject

Hematology

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