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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
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Advanced Flow Cytometry Analysis Algorithms for Optimizing the Detection of "Different From Normal" Immunophenotypes
Carmen-Mariana Aanei1, Richard Veyrat-Masson2, Lauren Rigollet1
1Laboratoire d'Hématologie, Centre Hospitalier Universitaire de Saint-Étienne, Saint-Étienne, France.
Frontiers in Cell and Developmental Biology
|October 15, 2021
Summary
High-dimensional analysis effectively distinguishes acute myeloid leukemia (AML) blasts from normal cells using flow cytometry. Specific markers like CD34 and HLA-DR are key for identifying AML and its subtypes, improving residual disease monitoring.
Area of Science:
- Hematology
- Immunophenotyping
- Computational Biology
Background:
- Acute myeloid leukemias (AMLs) are diverse hematologic malignancies with varying molecular and immunophenotypic profiles.
- Distinguishing AML blasts from normal myeloid hematopoietic precursors (myHPCs) is crucial for accurate measurable residual disease (MRD) assessment in AML follow-ups.
Purpose of the Study:
- To evaluate the efficacy of antibody combinations within the EuroFlow AML panel using high-dimensional analysis.
- To identify optimal antibody combinations and analysis algorithms for distinguishing AML blasts from normal myHPCs and differentiating between AML subtypes based on recurrent genetic abnormalities.
Main Methods:
- Applied high-dimensional analysis algorithms from Infinicyt 2.0 and Cytobank software.
- Compared the EuroFlow AML/myelodysplastic syndrome panel's antibody combinations on 39 AML samples and 23 normal bone marrow samples.
- Utilized scoring systems and principal component analysis (PCA) for data evaluation and marker identification.
Main Results:
- Infinicyt Compass database-guided analysis was more user-friendly than Cytobank methods.
- PCA-based algorithms demonstrated superior discrimination between AML blasts and myHPCs, and among different AML groups.
- Key markers for AML vs. myHPC discrimination included CD34, CD36, HLA-DR, CD13, CD105, CD71, and SSC.
- Markers for distinguishing between AML subtypes associated with recurrent genetic abnormalities included HLA-DR, CD34, CD13, CD64, CD33, CD117, CD71, CD36, CD11b, SSC, and FSC.
Conclusions:
- High-dimensional analysis, particularly PCA, effectively differentiates AML blasts from normal precursors and classifies AML subtypes.
- Specific immunophenotypic markers are identified as highly informative for AML diagnosis and subtyping.
- Integrating multiple high-dimensional algorithms offers complementary insights into flow cytometry data for AML research.
