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Updated: Apr 28, 2026

Flow Cytometry to Estimate Leukemia Stem Cells in Primary Acute Myeloid Leukemia and in Patient-derived-xenografts, at Diagnosis and Follow Up
Published on: March 26, 2018
Immunophenotypic Heterogeneity and Clonal Sweep in Acute Myeloid Leukemia Revealed by Flow Cytometry: A Case Series
Angela Bertolini1, Marisa Gorrese1, Serena Luponio1
1Hematology and Transplant Center, University Hospital "San Giovanni di Dio e Ruggi d'Aragona", 84131 Salerno, Italy.
Abstract:
Background/Objectives: Clonal evolution is mainly defined based on the appearance or expansion of clones harboring specific somatic mutations and/or cytogenetic abnormalities, whereas few studies have investigated immunophenotypic heterogeneity assessed by flow cytometry and its relationship with disease progression. In this study, flow cytometry immunophenotyping of acute myeloid leukemia (AML) was carried out to identify phenotypic subclones based on antigen expression and to investigate clonal sweep. Methods: A total of 24 patients diagnosed with AML followed at the Hematology and Transplant Center of Salerno were included. Bone marrow or peripheral blood specimens were subjected to flow cytometry immunophenotyping and leukemic cell characterization. Phenotypic profiles were also compared to molecular alterations detected by next-generation sequencing. Results: We found that flow cytometry-defined clonal heterogeneity was more complex than molecular heterogeneity at diagnosis and disease relapse. Flow cytometry enabled the identification of small phenotypic subclones that were not detected by molecular profiling and that, in several cases, expanded over time, consistent with a phenotypic clonal sweep. The presence of small clones was associated with shorter progression-free survival and overall survival. Conclusions: Flow cytometric clonal heterogeneity, especially the presence of small clones (defined by antigen expression from 2 to 30%), may serve as an additional prognostic factor in AML. Immunophenotyping integrated with molecular data may improve risk stratification, enhance measurable residual disease assessment, and contribute to a more personalized disease monitoring strategy.
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