Comprehensive Protocol to Sample and Process Bone Marrow for Measuring Measurable Residual Disease and Leukemic Stem Cells in Acute Myeloid Leukemia
Response criteria in acute myeloid leukemia (AML) has recently been re-established, with morphologic examination utilized to determine whether patients have achieved complete remission (CR). Approximately half of the adult patients who entered CR will relapse within 12 months due to the outgrowth of residual AML cells in the bone marrow. The quantitation of these remaining leukemia cells, known as minimal or measurable residual disease (MRD), can be a robust biomarker for the prediction of these relapses. Moreover, retrospective analysis of several studies has shown that the presence of MRD in the bone marrow of AML patients correlates with poor survival. Not only is the total leukemic population, reflected by cells harboring a leukemia associated immune-phenotype (LAIP), associated with clinical outcome, but so is the immature low frequency subpopulation of leukemia stem cells (LSC), both of which can be monitored through flow cytometry MRD or MRD-like approaches. The availability of sensitive assays that enable detection of residual leukemia (stem) cells on the basis of disease-specific or disease-associated features (abnormal molecular markers or aberrant immunophenotypes) have drastically improved MRD assessment in AML. However, given the inherent heterogeneity and complexity of AML as a disease, methods for sampling bone marrow and performing MRD and LSC analysis should be harmonized when possible. In this manuscript we describe a detailed methodology for adequate bone marrow aspirate sampling, transport, sample processing for optimal multi-color flow cytometry assessment, and gating strategies to assess MRD and LSC to aid in therapeutic decision making for AML patients.
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数据分析:流式数据分析、组化数据分析、多因子数据分析
基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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