Database-Guided Analysis for Immunophenotypic Diagnosis and Follow-Up of Acute Myeloid Leukemia With Recurrent Genetic Abnormalities - 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; et al. (Frontiers in Oncology, 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 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.