Identification of Leukemia-Associated Immunophenotypes by Databaseguided Flow Cytometry Provides a Highly Sensitive and Reproducible Strategy for the Study of Measurable Residual Disease in Acute Myeloblastic Leukemia.

Abstract

Simple SummaryThe complete immunophenotypic characterization of acute myeloid leukemia is essential for an accurate diagnosis and follow-up, which is determinant in the course of the disease. In many cases, the only option for the evaluation of minimal residual disease is flow cytometry, so the aim of this study is to develop an automatized multidimensional strategy to identify and characterize LAIPs as well as to detect new emerging aberrances in AML patients during the follow-up. The integrated DFN/LAIP strategy that we propose allows the identification of the most useful markers for minimal residual disease monitoring, improving the sensitivity and specificity of these studies. Furthermore, the use of databases and the automation of the analysis provide the basis for the generation of objective conclusions in minimal residual disease evaluations.Background: Multiparametric Flow Cytometry (MFC) is an essential tool to study the involved cell lineages, the aberrant differentiation/maturation patterns and the expression of aberrant antigens in acute myeloid leukemia (AML). The characterization of leukemia-associated immunophenotypes (LAIPs) at the moment of diagnosis is critical to establish reproducible strategies for the study of measurable residual disease using MFC (MFC-MRD). Methods: In this study, we identify and characterize LAIPs by comparing the leukemic populations of 145 AML patients, using the EuroFlow AML/ MDS MFC panel, with six databases of normal myeloid progenitors (MPCs). Principal component analysis was used to identify and characterize the LAIPs, which were then used to generate individual profiles for MFC-MRD monitoring. Furthermore, we investigated the relationship between the expression patterns of LAIPs and the different subtypes of AML. The MFC-MRD study was performed by identifying residual AML populations that matched with the LAIPs at diagnosis. To further validate this approach, the presence of MRD was also assessed by qPCR (qPCR-MRD). Finally, we studied the association between MFC-MRD and progression-free survival (PFS). Results: The strategy used in this study allowed us to describe more than 300 different LAIPs and facilitated the association of specific phenotypes with certain subtypes of AML. The MFC-MRD monitoring based on LAIPs with good/strong specificity was applicable to virtually all patients and showed a good correlation with qPCR-MRD and PFS. Conclusions: The described methodology provides an objective method to identify and characterize LAIPs. Furthermore, it provides a theoretical basis to develop highly sensitive MFC-MRD strategies.