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Chapter 13 - Flow Cytometry in Myelodysplastic Syndromes

Published online by Cambridge University Press:  30 January 2025

By
Theresia M. Westers  and
Arjan A. van de Loosdrecht
Edited by
Anna Porwit  and
Marie Christine Béné
Anna Porwit
Affiliation:
Lunds Universitet, Sweden
Marie Christine Béné
Affiliation:
Université de Nantes, France
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Summary

The diagnosis of cytopenic patients suspected of myelodysplastic syndrome (MDS) can be challenging, particularly when initial laboratory assessments are indecisive. In normal haematopoiesis, the expression of differentiation antigens is tightly regulated. Changes in expression patterns may therefore indicate dysplasia, the hallmark of MDS. Multiparameter flow cytometry (MFC) can identify aberrancies in differentiation antigen expression and maturation patterns not recognized by cytology. MFC performed according to recommendations defined by the International and European LeukemiaNet-associated Working Group focusing on standardisation of MFC in MDS (iMDSFlow) may reveal aberrancies in the myeloid progenitor cells, B-cell progenitors, maturing myelomonocytic cells and erythroid cells. Defined abnormalities can be counted in MFC scoring systems to provide a means to determine the extent of dysregulation of the maturation patterns, i.e. dysplasia according to MFC. Ideally, scores should enable a categorization of MFC results from bone marrow assessments in cytopenic patients as ’normal’, ’low probability of’ or ’high probability of’ MDS. Notably, MFC as a single technique is not sufficient for the diagnosis of MDS, and results should always be evaluated as part of an integrated diagnostic workup.

Keywords

flow cytometryimmunophenotypingbone marrowmyelodysplastic syndromeCD34panelsgranulocytesmonocyteserythropoiesisscores
Type
Chapter
Information
Multiparameter Flow Cytometry in the Diagnosis of Hematologic Malignancies , pp. 203 - 218
Publisher: Cambridge University Press
Print publication year: 2025

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References

Brunner, AM, Leitch, HA, van de Loosdrecht, AA, Bonadies, N. Management of patients with lower-risk myelodysplastic syndromes. Blood Cancer J 2022; 12: 166.CrossRefGoogle ScholarPubMed
Nilsson, L, Astrand-Grundstrom, I, Arvidsson, I, et al. Isolation and characterization of hematopoietic progenitor/stem cells in 5q-deleted myelodysplastic syndromes: evidence for involvement at the hematopoietic stem cell level. Blood 2000; 96: 2012–2021.CrossRefGoogle ScholarPubMed
Nilsson, L, Eden, P, Olsson, E, et al. The molecular signature of MDS stem cells supports a stem-cell origin of 5q myelodysplastic syndromes. Blood 2007; 110: 3005–3014.CrossRefGoogle ScholarPubMed
Will, B, Zhou, L, Vogler, TO, et al. Stem and progenitor cells in myelodysplastic syndromes show aberrant stage-specific expansion and harbor genetic and epigenetic alterations. Blood 2012; 120: 2076–2086.CrossRefGoogle ScholarPubMed
Pang, WW, Pluvinage, JV, Price, EA, et al. Hematopoietic stem cell and progenitor cell mechanisms in myelodysplastic syndromes. Proc Natl Acad Sci USA 2013; 110: 3011–3016.CrossRefGoogle ScholarPubMed
Woll, PS, Kjallquist, U, Chowdhury, O, et al. Myelodysplastic syndromes are propagated by rare and distinct human cancer stem cells in vivo. Cancer Cell 2014; 25: 794–808.CrossRefGoogle ScholarPubMed
White, NJ, Nacheva, E, Asimakopoulos, FA, Bloxham, D, Paul, B, Green, AR. Deletion of chromosome 20q in myelodysplasia can occur in a multipotent precursor of both myeloid cells and B-cells. Blood 1994; 83: 2809–2816.CrossRefGoogle Scholar
Boy, M, Bisio, V, Zhao, LP, et al. Myelodysplastic syndrome associated TET2 mutations affect NK cell function and genome methylation. Nat Commun 2023; 14: 588.CrossRefGoogle ScholarPubMed
Khoury, JD, Solary, E, Abla, O, et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: myeloid and histiocytic/dendritic neoplasms. Leukemia 2022; 36: 1703–1719.CrossRefGoogle ScholarPubMed
Arber, DA, Orazi, A, Hasserjian, RP, et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: integrating morphologic, clinical, and genomic data. Blood 2022; 140: 1200–1228.CrossRefGoogle ScholarPubMed
Arber, DA, Orazi, A, Hasserjian, R, et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood 2016; 127: 2391–2405.CrossRefGoogle Scholar
Malcovati, L, Stevenson, K, Papaemmanuil, E, et al. SF3B1-mutant MDS as a distinct disease subtype: a proposal from the International Working Group for the Prognosis of MDS. Blood 2020; 136: 157–170.CrossRefGoogle Scholar
Papaemmanuil, E, Gerstung, M, Malcovati, L, et al. Clinical and biological implications of driver mutations in myelodysplastic syndromes. Blood 2013; 122: 3616–3627.CrossRefGoogle ScholarPubMed
Haferlach, T, Nagata, Y, Grossmann, V, et al. Landscape of genetic lesions in 944 patients with myelodysplastic syndromes. Leukemia 2014; 28: 241–247.CrossRefGoogle ScholarPubMed
Malcovati, L, Hellstrom-Lindberg, E, Bowen, D, et al. Diagnosis and treatment of primary myelodysplastic syndromes in adults: recommendations from the European LeukemiaNet. Blood 2013; 122: 2943–2964.CrossRefGoogle ScholarPubMed
Wang, W, Khoury, JD. Where diagnosis for myelodysplastic neoplasms (MDS) stands today and where it will go: the role of flow cytometry in evaluation of MDS. Cytometry B Clin Cytom 2023; 104: 12–14.CrossRefGoogle ScholarPubMed
Wells, DA, Benesch, M, Loken, MR, et al. Myeloid and monocytic dyspoiesis as determined by flow cytometric scoring in myelodysplastic syndrome correlates with the IPSS and with outcome after hematopoietic stem cell transplantation. Blood 2003; 102: 394–403.CrossRefGoogle ScholarPubMed
Alhan, C, Westers, TM, Cremers, EMP, et al. High flow cytometric scores identify adverse prognostic subgroups within the revised international prognostic scoring system for myelodysplastic syndromes. British Journal of Haematology 2014; 167: 100–109.CrossRefGoogle ScholarPubMed
van Spronsen, MF, Hanekamp, D, Westers, TM, et al. Immunophenotypic aberrant hematopoietic stem cells in myelodysplastic syndromes: a biomarker for leukemic progression. Leukemia 2023; 37: 680–690.CrossRefGoogle ScholarPubMed
Bernard, E, Tuechler, H, Greenberg, PL. Molecular International Prognostic Scoring System for myelodysplastic syndromes. NEJM Evidence 2022; 1: 1–14.CrossRefGoogle ScholarPubMed
van de Loosdrecht, AA, Alhan, C, Bene, MC, et al. Standardization of flow cytometry in myelodysplastic syndromes: report from the first European LeukemiaNet working conference on flow cytometry in myelodysplastic syndromes. Haematologica 2009; 94: 1124–1134.CrossRefGoogle ScholarPubMed
van der Velden, VHJ, Preijers, F, Johansson, U, et al. Flow cytometric analysis of myelodysplasia: pre-analytical and technical issues – recommendations from the European LeukemiaNet. Cytometry B Clin Cytom 2023; 104: 15–26.CrossRefGoogle ScholarPubMed
Porwit, A, Béné, MC, Duetz, C, et al. Multiparameter flow cytometry in the evaluation of myelodysplasia: analytical issues: recommendations from the European LeukemiaNet/International Myelodysplastic Syndrome Flow Cytometry Working Group. Cytometry B Clin Cytom 2023; 104: 27–50.CrossRefGoogle ScholarPubMed
van de Loosdrecht, AA, Kern, W, Porwit, A, et al. Clinical application of flow cytometry in patients with unexplained cytopenia and suspected myelodysplastic syndrome: a report of the European LeukemiaNet International MDS-Flow Cytometry Working Group. Cytometry B Clin Cytom 2023; 104: 77–86.CrossRefGoogle Scholar
Kern, W, Westers, TM, Bellos, F, et al. Multicenter prospective evaluation of diagnostic potential of flow cytometric aberrancies in myelodysplastic syndromes by the ELN iMDS flow working group. Cytometry B Clin Cytom 2023; 104: 51–65.CrossRefGoogle ScholarPubMed
van Dongen, JJ, Lhermitte, L, Böttcher, S, et al. EuroFlow antibody panels for standardized n-dimensional flow cytometric immunophenotyping of normal, reactive and malignant leukocytes. Leukemia 2012; 26: 1908–1975.CrossRefGoogle ScholarPubMed
Porwit, A, Rajab, A. Flow cytometry immunophenotyping in integrated diagnostics of patients with newly diagnosed cytopenia: one tube 10-color 14-antibody screening panel and 3-tube extensive panel for detection of MDS-related features. Int J Lab Hematol 2015; 37: 133–143.CrossRefGoogle ScholarPubMed
Alhan, C, Westers, TM, Cremers, EM, Cali, C, Ossenkoppele, GJ, van de Loosdrecht, AA. Application of flow cytometry for myelodysplastic syndromes: pitfalls and technical considerations. Cytometry B Clin Cytom 2016; 90: 358–367.CrossRefGoogle ScholarPubMed
Johansson, U, McIver-Brown, N, Cullen, M, et al. The flow cytometry myeloid progenitor count: a reproducible parameter for diagnosis and prognosis of myelodysplastic syndromes. Cytometry B Clin Cytom 2023; 104: 115–122.CrossRefGoogle Scholar
Ogata, K, Mochimaru, Y, Sei, K, Kawahara, N, Ogata, M, Yamamoto, Y. Myeloblasts transition to megakaryoblastic immunophenotypes over time in some patients with myelodysplastic syndromes. PLoS ONE 2023; 18: e0291662.CrossRefGoogle ScholarPubMed
Wells, DA, Benesch, M, Loken, MR, et al. Myeloid and monocytic dyspoiesis as determined by flow cytometric scoring in myelodysplastic syndrome correlates with the IPSS and with outcome after hematopoietic stem cell transplantation. Blood 2003; 102: 394–403.CrossRefGoogle ScholarPubMed
Westers, TM, van der Velden, VH, Alhan, C, et al. Implementation of flow cytometry in the diagnostic work-up of myelodysplastic syndromes in a multicenter approach: report from the Dutch Working Party on Flow Cytometry in MDS. Leuk Res 2012; 36: 422–430.CrossRefGoogle Scholar
Bardet, V, Wagner-Ballon, O, Guy, J, et al. Multicentric study underlining the interest of adding CD5, CD7 and CD56 expression assessment to the flow cytometric Ogata score in myelodysplastic syndromes and myelodysplastic/myeloproliferative neoplasms. Haematologica 2015; 100: 472–478.CrossRefGoogle Scholar
Matarraz, S, Almeida, J, Flores-Montero, J, et al. Introduction to the diagnosis and classification of monocytic-lineage leukemias by flow cytometry. Cytometry B Clin Cytom 2017; 92: 218–227.CrossRefGoogle Scholar
Selimoglu-Buet, D, Wagner-Ballon, O, Saada, V, et al. Characteristic repartition of monocyte subsets as a diagnostic signature of chronic myelomonocytic leukemia. Blood 2015; 125: 3618–3626.CrossRefGoogle ScholarPubMed
Wagner-Ballon, O, Bettelheim, P, Lauf, J, et al. ELN iMDS flow working group validation of the monocyte assay for chronic myelomonocytic leukemia diagnosis by flow cytometry. Cytometry B Clin Cytom 2023; 104: 66–76.CrossRefGoogle Scholar
Tarfi, S, Badaoui, B, Freynet, N, et al. Disappearance of slan-positive non-classical monocytes for diagnosis of chronic myelomonocytic leukemia with associated inflammatory state. Haematologica 2020; 105; e147.CrossRefGoogle ScholarPubMed
Selimoglu-Buet, D, Badaoui, B, Benayoun, E, et al. Accumulation of classical monocytes defines a subgroup of MDS that frequently evolves into CMML. Blood 2017; 130: 832–835.CrossRefGoogle ScholarPubMed
Talati, C, Zhang, L, Shaheen, G, et al. Monocyte subset analysis accurately distinguishes CMML from MDS and is associated with a favorable MDS prognosis. Blood 2017; 129: 1881–1883.CrossRefGoogle ScholarPubMed
Violidaki, D, Axler, O, Jafari, K, et al. Analysis of erythroid maturation in the nonlysed bone marrow with help of radar plots facilitates detection of flow cytometric aberrations in myelodysplastic syndromes. Cytometry B Clin Cytom 2020; 98: 399–411.CrossRefGoogle ScholarPubMed
Béné, MC, Axler, O, Violidaki, D, Lacombe, F, Ehinger, M, Porwit, A. Definition of erythroid differentiation subsets in normal human bone marrow using FlowSOM unsupervised cluster analysis of flow cytometry data. HemaSphere 2021; 5: e512.CrossRefGoogle ScholarPubMed
Westers, TM, Cremers, EM, Oelschlaegel, U, et al. Immunophenotypic analysis of erythroid dysplasia in myelodysplastic syndromes: a report from the IMDSFlow working group. Haematologica 2017; 102: 308–319.CrossRefGoogle ScholarPubMed
Duetz, C, Van Gassen, S, Westers, TM, et al. Computational flow cytometry as a diagnostic tool in suspected-myelodysplastic syndromes. Cytometry A 2021; 99: 814–824.CrossRefGoogle ScholarPubMed
Johansson, U, Rolf, N, Futhee, N, Stewart, A. Erythroid side scatter: a parameter that improves diagnostic accuracy of flow cytometry myelodysplastic syndrome scoring. Cytometry B Clin Cytom 2023; 104: 151–161.CrossRefGoogle ScholarPubMed
Laranjeira, P, Rodrigues, R, Carvalheiro, T, et al. Expression of CD44 and CD35 during normal and myelodysplastic erythropoiesis. Leuk Res 2015; 39: 361–370.CrossRefGoogle ScholarPubMed
Cremers, EM, Westers, TM, Alhan, C, et al. Implementation of erythroid lineage analysis by flow cytometry in diagnostic models for myelodysplastic syndromes. Haematologica 2017; 102: 320–326.CrossRefGoogle ScholarPubMed
Porwit, A, Violidaki, D, Axler, O, Lacombe, F, Ehinger, M, Béné, MC. Unsupervised cluster analysis and subset characterization of abnormal erythropoiesis using the bioinformatic Flow-Self Organizing Maps algorithm. Cytometry B Clin Cytom 2022; 102: 134–142.CrossRefGoogle ScholarPubMed
Wangen, JR, Eidenschink Brodersen, L, Stolk, TT, Wells, DA, Loken, MR. Assessment of normal erythropoiesis by flow cytometry: important considerations for specimen preparation. International Journal of Laboratory Hematology 2014; 36: 184–196.CrossRefGoogle ScholarPubMed
Mathis, S, Chapuis, N, Debord, C, et al. Flow cytometric detection of dyserythropoiesis: a sensitive and powerful diagnostic tool for myelodysplastic syndromes. Leukemia 2013; 27: 1981–1987.CrossRefGoogle ScholarPubMed
Liu, J, Niu, H, Yue, L, et al. Abnormal platelet phenotypes in patients with myelodysplastic syndromes. International Journal of Laboratory Hematology 2020; 42: 371–379.CrossRefGoogle ScholarPubMed
Wu, YF, Gu, MH, Liu, CZ, Huang, WH, Chu, SC, Wang, TF. Abnormal platelet immunophenotypes and percentage of giant platelets in myelodysplastic syndrome: A pilot study. PLoS ONE 2022; 17: e0278040.CrossRefGoogle ScholarPubMed
Ogata, K, Della Porta, MG, Malcovati, L, et al. Diagnostic utility of flow cytometry in low-grade myelodysplastic syndromes: a prospective validation study. Haematologica 2009; 94: 1066–1074.CrossRefGoogle ScholarPubMed
Della Porta, MG, Picone, C, Pascutto, C, et al. Multicenter validation of a reproducible flow cytometric score for the diagnosis of low-grade myelodysplastic syndromes: results of a European LeukemiaNET study. Haematologica 2012; 97: 1209–1217.CrossRefGoogle ScholarPubMed
Kuranda, K, Vargaftig, J, de la Rochere, P, et al. Age-related changes in human hematopoietic stem/progenitor cells. Aging Cell 2011; 10: 542–546.CrossRefGoogle ScholarPubMed
Parmentier, S, Schetelig, J, Lorenz, K, et al. Assessment of dysplastic hematopoiesis: lessons from healthy bone marrow donors. Haematologica 2012; 97: 723–730.CrossRefGoogle ScholarPubMed
Della Porta, MG, Travaglino, E, Boveri, E, et al. Minimal morphological criteria for defining bone marrow dysplasia: a basis for clinical implementation of WHO classification of myelodysplastic syndromes. Leukemia 2015; 29: 66–75.CrossRefGoogle ScholarPubMed
Aalbers, AM, van den Heuvel-Eibrink, MM, de Haas, V, et al. Applicability of a reproducible flow cytometry scoring system in the diagnosis of refractory cytopenia of childhood. Leukemia 2013; 27: 1923–1925.CrossRefGoogle ScholarPubMed
Aalbers, AM, van den Heuvel-Eibrink, MM, Baumann, I, et al. Bone marrow immunophenotyping by flow cytometry in refractory cytopenia of childhood. Haematologica 2015; 100: 315–323.CrossRefGoogle ScholarPubMed
Cremers, EM, Westers, TM, Alhan, C, et al. Implementation of erythroid lineage analysis by flow cytometry in diagnostic models for myelodysplastic syndromes. Haematologica 2017; 102: 320–326.CrossRefGoogle ScholarPubMed
Oelschlaegel, U, Oelschlaeger, L, von Bonin, M, et al. Comparison of five diagnostic flow cytometry scores in patients with myelodysplastic syndromes: diagnostic power and prognostic impact. Cytometry B Clin Cytom 2023; 104: 141–150.CrossRefGoogle ScholarPubMed
Fenaux, P, Haase, D, Santini, V, Sanz, GF, Platzbecker, U, Mey, U. Myelodysplastic syndromes: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol 2021; 32: 142–156.CrossRefGoogle ScholarPubMed
Cutler, JA, Wells, DA, van de Loosdrecht, AA, et al. Phenotypic abnormalities strongly reflect genotype in patients with unexplained cytopenias. Cytometry B Clin Cytom 2011; 80: 150–157.CrossRefGoogle ScholarPubMed
Oelschlaegel, U, Westers, TM, Mohr, B, et al. Myelodysplastic syndromes with a deletion 5q display a characteristic immunophenotypic profile suitable for diagnostics and response monitoring. Haematologica 2015; 100: e93–e96.CrossRefGoogle ScholarPubMed
Chen, X, Wood, BL, Cherian, S. Immunophenotypic features of myeloid neoplasms associated with chromosome 7 abnormalities. Cytometry B Clin Cytom 2019; 96: 300–309.CrossRefGoogle ScholarPubMed
Duetz, C, Westers, TM, In‘t Hout, FEM, et al. Distinct bone marrow immunophenotypic features define the splicing factor 3B subunit 1 (SF3B1)-mutant myelodysplastic syndromes subtype. Br J Haematol 2021; 193(4): 798–803.CrossRefGoogle ScholarPubMed
Weiß, E, Walter, W, Meggendorfer, M, et al. Identification of a specific immunophenotype associated with a consistent pattern of genetic mutations including SRFS2 and gene expression profile in MDS. Cytometry B Clin Cytom 2023; 104: 173–182.CrossRefGoogle ScholarPubMed
Alhan, C, Westers, TM, van der Helm, LH, et al. Absence of aberrant myeloid progenitors by flow cytometry is associated with favorable response to azacitidine in higher risk myelodysplastic syndromes. Cytometry Part B-Clinical Cytometry 2014; 86: 207–215.CrossRefGoogle ScholarPubMed
van de Loosdrecht, AA, Cremers, EMP, Alhan, C, et al. Determinants of lenalidomide response with or without erythropoiesis-stimulating agents in myelodysplastic syndromes: the HOVON89 trial. Leukemia 2024; 38: 840–850.CrossRefGoogle Scholar
Heuser, M, Freeman, SD, Ossenkoppele, GJ, et al. 2021 update on MRD in acute myeloid leukemia: a consensus document from the European LeukemiaNet MRD Working Party. Blood 2021; 138: 2753–2767.CrossRefGoogle ScholarPubMed
Peterlin, P, Le Bris, Y, Turlure, P, et al. CPX-351 in higher risk myelodysplastic syndrome and chronic myelomonocytic leukaemia: a multicentre, single-arm, phase 2 study. Lancet Haematol 2023; 10: e521–e529.CrossRefGoogle ScholarPubMed

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