Multiparameter flow cytometry in the evaluation of myelodysplasia: Analytical issues: Recommendations from the European LeukemiaNet/International Myelodysplastic Syndrome Flow Cytometry Working Group

Anna Porwit, Marie C. Béné, Carolien Duetz, Sergio Matarraz, Uta Oelschlaegel, Theresia M. Westers, Orianne Wagner-Ballon, Shahram Kordasti, Peter Valent, Frank Preijers, Canan Alhan, Frauke Bellos, Peter Bettelheim, Kate Burbury, Nicolas Chapuis, Eline Cremers, Matteo G. Della Porta, Alan Dunlop, Lisa Eidenschink-Brodersen, Patricia FontMichaela Fontenay, Willemijn Hobo, Robin Ireland, Ulrika Johansson, Michael R. Loken, Kiyoyuki Ogata, Alberto Orfao, Katherina Psarra, Leonie Saft, Dolores Subira, Jeroen te Marvelde, Denise A. Wells, Vincent H. J. van der Velden, Wolfgang Kern, Arjan A. van de Loosdrecht

Research output: Contribution to journalReview articleAcademicpeer-review

11 Citations (Scopus)

Abstract

Multiparameter flow cytometry (MFC) is one of the essential ancillary methods in bone marrow (BM) investigation of patients with cytopenia and suspected myelodysplastic syndrome (MDS). MFC can also be applied in the follow-up of MDS patients undergoing treatment. This document summarizes recommendations from the International/European Leukemia Net Working Group for Flow Cytometry in Myelodysplastic Syndromes (ELN iMDS Flow) on the analytical issues in MFC for the diagnostic work-up of MDS. Recommendations for the analysis of several BM cell subsets such as myeloid precursors, maturing granulocytic and monocytic components and erythropoiesis are given. A core set of 17 markers identified as independently related to a cytomorphologic diagnosis of myelodysplasia is suggested as mandatory for MFC evaluation of BM in a patient with cytopenia. A myeloid precursor cell (CD34+CD19−) count >3% should be considered immunophenotypically indicative of myelodysplasia. However, MFC results should always be evaluated as part of an integrated hematopathology work-up. Looking forward, several machine-learning-based analytical tools of interest should be applied in parallel to conventional analytical methods to investigate their usefulness in integrated diagnostics, risk stratification, and potentially even in the evaluation of response to therapy, based on MFC data. In addition, compiling large uniform datasets is desirable, as most of the machine-learning-based methods tend to perform better with larger numbers of investigated samples, especially in such a heterogeneous disease as MDS.
Original languageEnglish
Pages (from-to)27-50
Number of pages24
JournalCytometry Part B - Clinical Cytometry
Volume104
Issue number1
Early online date2022
DOIs
Publication statusPublished - 15 Jan 2023

Keywords

  • ELN
  • consensus
  • flow cytometry
  • myelodysplastic syndromes
  • standardization

Cite this