Aanei Carmen-Mariana, Veyrat-Masson Richard, Selicean Cristina, Marian Mirela, Rigollet Lauren, Trifa Adrian Pavel, Tomuleasa Ciprian, Serban Adrian, Cherry Mohamad, Flandrin-Gresta Pascale, Tardy Emmanuelle Tavernier, Guyotat Denis, Campos Catafal Lydia
Laboratoire d'Hématologie, Centre Hospitalier Universitaire de Saint-Etienne, Saint-Etienne, France.
Laboratoire d'Hématologie, Centre Hospitalier Universitaire de Clermont-Ferrand, Clermont-Ferrand, France.
Front Oncol. 2021 Nov 5;11:746951. doi: 10.3389/fonc.2021.746951. eCollection 2021.
Acute myeloid leukemias (AMLs) are hematologic malignancies with varied molecular and immunophenotypic profiles, making them difficult to diagnose and classify. High-dimensional analysis algorithms might increase the utility of multicolor flow cytometry for AML diagnosis and follow-up. The objective of the present study was to assess whether a Compass database-guided analysis can be used to achieve rapid and accurate diagnoses. We conducted this study to determine whether this method could be employed to pilote the genetic and molecular tests and to objectively identify different-from-normal (DfN) patterns to improve measurable residual disease follow-up in AML. Three Compass databases were built using Infinicyt 2.0 software, including normal myeloid-committed hematopoietic precursors ( = 20) and AML blasts harboring the most frequent recurrent genetic abnormalities ( = 50). The diagnostic accuracy of the Compass database-guided analysis was evaluated in a prospective validation study (125 suspected AML patients). This method excluded AML associated with the following genetic abnormalities: (8;21), (15;17), inv(16), and translocation, with 92% sensitivity [95% confidence interval (CI): 78.6%-98.3%] and a 98.5% negative predictive value (95% CI: 90.6%-99.8%). Our data showed that the Compass database-guided analysis could identify phenotypic differences between AML groups, representing a useful tool for the identification of DfN patterns.
急性髓系白血病（AML）是具有多种分子和免疫表型特征的血液系统恶性肿瘤，这使得其诊断和分类变得困难。高维分析算法可能会提高多色流式细胞术在AML诊断和随访中的效用。本研究的目的是评估Compass数据库引导分析是否可用于实现快速准确的诊断。我们开展这项研究以确定该方法是否可用于指导基因和分子检测，并客观识别异常模式以改善AML中可测量残留病的随访。使用Infinicyt 2.0软件构建了三个Compass数据库，包括正常髓系定向造血前体细胞（n = 20）和携带最常见复发性基因异常的AML原始细胞（n = 50）。在一项前瞻性验证研究（125例疑似AML患者）中评估了Compass数据库引导分析的诊断准确性。该方法排除了与以下基因异常相关的AML：（8；21）、（15；17）、inv（16）和t（12；21），灵敏度为92%[95%置信区间（CI）：78.6%-98.3%]，阴性预测值为98.5%（95%CI：90.6%-99.8%）。我们的数据表明，Compass数据库引导分析可以识别AML组之间的表型差异，是识别异常模式的有用工具。
