Multiparameter flow cytometry in the evaluation of myelodysplasia: Analytical issues

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
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 Font, Michaela 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
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引用次数: 4

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.

Abstract Image

多参数流式细胞术评价骨髓发育不良:分析问题
多参数流式细胞术(MFC)是骨髓细胞减少和疑似骨髓增生异常综合征(MDS)患者骨髓(BM)研究的重要辅助方法之一。MFC也可以应用于MDS患者接受治疗的随访。本文件总结了骨髓增生异常综合征流式细胞术国际/欧洲白血病网络工作组(ELN iMDS Flow)关于MDS诊断检查MFC中分析问题的建议。对骨髓前体、成熟粒细胞和单核细胞成分以及红细胞生成等几种骨髓细胞亚群的分析提出了建议。一组由17个标记物组成的核心标记物被鉴定为与骨髓发育不良的细胞形态学诊断独立相关,被认为是细胞减少患者骨髓MFC评估的强制性标记物。骨髓前体细胞(CD34+CD19-)计数>;3%应被视为骨髓发育不良的免疫表型。然而,MFC结果应始终作为综合血液病理学检查的一部分进行评估。展望未来,应将几种感兴趣的基于机器学习的分析工具与传统分析方法并行应用,以研究它们在综合诊断、风险分层中的有用性,甚至可能在基于MFC数据的治疗反应评估中的有用。此外,编译大型统一数据集是可取的,因为大多数基于机器学习的方法往往在大量研究样本的情况下表现更好,尤其是在MDS这样的异质性疾病中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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