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=Volume 11 - 2021

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=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 piloted genetic and molecular tests to determine whether this method could be employed 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 (n = 20) and AML blasts harboring the most frequent recurrent genetic abnormalities (n = 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: t(8;21), t(15;17), inv(16), and KMT2A 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.