Bras Anne E, Matarraz Sergio, Nierkens Stefan, Fernández Paula, Philippé Jan, Aanei Carmen-Mariana, de Mello Fabiana Vieira, Burgos Leire, van der Sluijs-Gelling Alita J, Grigore Georgiana Emilia, van Dongen Jacques J M, Orfao Alberto, van der Velden Vincent H J
Department of Immunology, Erasmus MC, University Medical Center Rotterdam, Wytemaweg 80, 3015 CN Rotterdam, The Netherlands.
Cancer Research Center (IBMCC-CSIC), Department of Medicine and Cytometry Service (NUCLEUS), University of Salamanca, CIBERONC and Institute of Biomedical Research of Salamanca (IBSAL), Campus Miguel de Unamuno, Paseo de la Universidad de Coimbra s/n, 37007 Salamanca, Spain.
Cancers (Basel). 2022 Apr 15;14(8):2011. doi: 10.3390/cancers14082011.
Flowcytometric analysis allows for detailed identification and characterization of large numbers of cells in blood, bone marrow, and other body fluids and tissue samples and therefore contributes to the diagnostics of hematological malignancies. Novel data analysis tools allow for multidimensional analysis and comparison of patient samples with reference databases of normal, reactive, and/or leukemia/lymphoma patient samples. Building such reference databases requires strict quality assessment (QA) procedures. Here, we compiled a dataset and developed a QA methodology of the EuroFlow Acute Myeloid Leukemia (AML) database, based on the eight-color EuroFlow AML panel consisting of six different antibody combinations, including four backbone markers. In total, 1142 AML cases and 42 normal bone marrow samples were included in this analysis. QA was performed on 803 AML cases using multidimensional analysis of backbone markers, as well as tube-specific markers, and data were compared using classical analysis employing median and peak expression values. Validation of the QA procedure was performed by re-analysis of >300 cases and by running an independent cohort of 339 AML cases. Initial evaluation of the final cohort confirmed specific immunophenotypic patterns in AML subgroups; the dataset therefore can reliably be used for more detailed exploration of the immunophenotypic variability of AML. Our data show the potential pitfalls and provide possible solutions for constructing large flowcytometric databases. In addition, the provided approach may facilitate the building of other databases and thereby support the development of novel tools for (semi)automated QA and subsequent data analysis.
流式细胞术分析能够详细鉴定和表征血液、骨髓以及其他体液和组织样本中的大量细胞，因此有助于血液系统恶性肿瘤的诊断。新型数据分析工具能够对患者样本与正常、反应性和/或白血病/淋巴瘤患者样本的参考数据库进行多维度分析和比较。构建此类参考数据库需要严格的质量评估（QA）程序。在此，我们基于由六种不同抗体组合（包括四种主要标志物）组成的八色欧洲流式细胞术急性髓系白血病（AML）检测板，汇编了一个数据集并开发了欧洲流式细胞术AML数据库的QA方法。本分析共纳入了1142例AML病例和42例正常骨髓样本。使用主要标志物以及特定检测管标志物的多维度分析对803例AML病例进行了QA，并使用采用中位数和峰值表达值的经典分析对数据进行了比较。通过对300多例病例的重新分析以及对339例AML病例的独立队列进行检测，对QA程序进行了验证。对最终队列的初步评估证实了AML亚组中的特定免疫表型模式；因此，该数据集可可靠地用于更详细地探索AML的免疫表型变异性。我们的数据显示了潜在的陷阱，并为构建大型流式细胞术数据库提供了可能的解决方案。此外，所提供的方法可能有助于构建其他数据库，从而支持开发用于（半）自动化QA及后续数据分析的新型工具。
