AUTHOR=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 

TITLE=Database-Guided Analysis for Immunophenotypic Diagnosis and Follow-Up of Acute Myeloid Leukemia With Recurrent Genetic Abnormalities

JOURNAL=Frontiers in Oncology

VOLUME=11

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.746951

DOI=10.3389/fonc.2021.746951

ISSN=2234-943X

ABSTRACT=<p>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 (<italic>n</italic> = 20) and AML blasts harboring the most frequent recurrent genetic abnormalities (<italic>n</italic> = 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: <italic>t</italic>(8;21), <italic>t</italic>(15;17), inv(16), and <italic>KMT2A</italic> 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.</p>