TY - JOUR
T1 - Multiparameter flow cytometry in the evaluation of myelodysplasia
T2 - Analytical issues: Recommendations from the European LeukemiaNet/International Myelodysplastic Syndrome Flow Cytometry Working Group
AU - Porwit, Anna
AU - Béné, Marie C.
AU - Duetz, Carolien
AU - Matarraz, Sergio
AU - Oelschlaegel, Uta
AU - Westers, Theresia M.
AU - Wagner-Ballon, Orianne
AU - Kordasti, Shahram
AU - Valent, Peter
AU - Preijers, Frank
AU - Alhan, Canan
AU - Bellos, Frauke
AU - Bettelheim, Peter
AU - Burbury, Kate
AU - Chapuis, Nicolas
AU - Cremers, Eline
AU - Della Porta, Matteo G.
AU - Dunlop, Alan
AU - Eidenschink-Brodersen, Lisa
AU - Font, Patricia
AU - Fontenay, Michaela
AU - Hobo, Willemijn
AU - Ireland, Robin
AU - Johansson, Ulrika
AU - Loken, Michael R.
AU - Ogata, Kiyoyuki
AU - Orfao, Alberto
AU - Psarra, Katherina
AU - Saft, Leonie
AU - Subira, Dolores
AU - te Marvelde, Jeroen
AU - Wells, Denise A.
AU - van der Velden, Vincent H. J.
AU - Kern, Wolfgang
AU - van de Loosdrecht, Arjan A.
N1 - Funding Information: The authors would like to thank all co-workers at their respective laboratories for their dedicated work. The authors would also like to thank all physicians for providing samples and caring for patients as well as collecting data. Publisher Copyright: © 2022 The Authors. Cytometry Part B: Clinical Cytometry published by Wiley Periodicals LLC on behalf of International Clinical Cytometry Society.
PY - 2023/1/15
Y1 - 2023/1/15
N2 - 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.
AB - 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.
KW - ELN
KW - consensus
KW - flow cytometry
KW - myelodysplastic syndromes
KW - standardization
UR - http://www.scopus.com/inward/record.url?scp=85144283609&partnerID=8YFLogxK
U2 - https://doi.org/10.1002/cyto.b.22108
DO - https://doi.org/10.1002/cyto.b.22108
M3 - Review article
C2 - 36537621
SN - 1552-4949
VL - 104
SP - 27
EP - 50
JO - Cytometry Part B - Clinical Cytometry
JF - Cytometry Part B - Clinical Cytometry
IS - 1
ER -