Flow cytometry evaluation of acute myeloid leukemia minimal residual disease based on an understanding of the normal maturation patterns in the blast compartments

微小残留病 流式细胞术 髓样 造血 祖细胞 髓系白血病 表型 白血病 免疫学 阶段(地层学) 生物 祖细胞 疾病 干细胞 癌症研究 医学 病理 细胞生物学 遗传学 古生物学 基因
作者
Mikhail Roshal,Qi Gao
出处
期刊:American Journal of Clinical Pathology [Oxford University Press]
被引量:2
标识
DOI:10.1093/ajcp/aqae187
摘要

Detection of minimal/measurable disease (MRD) in acute myeloid leukemia (AML) is critical for both clinical decision-making and prognostication, yet remains a challenge. Flow cytometry is a well-established method for MRD detection. Flow cytometric (FC) evaluation of MRD must consider a complex maturational pattern of normal hematopoietic development to separate normal from abnormal progenitors. Here, we offer an example of an interpretive approach based on a thorough understanding of stage- and lineage-specific hematopoietic maturation. We provide a comprehensive overview of blast maturation from early precursors (hematopoietic stem cells) to committed late-stage unilineage progenitors and commonly observed stage-specific abnormalities based on cases we have encountered in practice. We emphasize the importance of stage-specific comparisons for accurate MRD detection by flow cytometry. The AML blasts almost invariably show abnormal phenotypes, and the phenotypes may evolve upon therapy. The detected phenotypes are necessarily confined to the target antigens included in the panel. It is therefore critical to evaluate a range of antigens to establish a specific stage/state of lineage commitment and detect potential common abnormalities. Moreover, enough cells must be acquired to allow for the detection of MRD at desired levels. Significant technical and analytical validation is critical. Flow cytometry offers a powerful single-cell-based platform for MRD detection in AML, and the results have been proven critical for disease management. Leukemia-associated phenotype-informed difference from the normal approach presented in this review presents an analytical framework for sensitive and accurate MRD detection.
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