Can Flow Cytometry Immunophenotyping Predict Cytogenetic Abnormalities in Acute Myeloid Leukemia? A Focus on Myelodysplasia-Related Cytogenetic Abnormalities
Issued Date
2025-01-01
Resource Type
ISSN
17515521
eISSN
1751553X
Scopus ID
2-s2.0-105014092104
Journal Title
International Journal of Laboratory Hematology
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SCOPUS
Bibliographic Citation
International Journal of Laboratory Hematology (2025)
Suggested Citation
Promsuwicha O., Owattanapanich W., Kankhaw S., Ruchutrakool T., Kungwankiattichai S. Can Flow Cytometry Immunophenotyping Predict Cytogenetic Abnormalities in Acute Myeloid Leukemia? A Focus on Myelodysplasia-Related Cytogenetic Abnormalities. International Journal of Laboratory Hematology (2025). doi:10.1111/ijlh.14546 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/111922
Title
Can Flow Cytometry Immunophenotyping Predict Cytogenetic Abnormalities in Acute Myeloid Leukemia? A Focus on Myelodysplasia-Related Cytogenetic Abnormalities
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Abstract
Introduction: The European LeukemiaNet (ELN) 2022 classification introduced significant modifications to acute myeloid leukemia (AML) categorization, including refined criteria for AML with myelodysplasia-related cytogenetic abnormalities (AML-MRC). While cytogenetic analysis is essential for a definitive diagnosis, the question remains whether flow cytometry can aid in the initial identification of this AML subgroup. This study aimed to characterize the immunophenotypic profiles of AML-MRC and validate previously reported immunophenotypic patterns of AML with t(8;21) and inv(16) using flow cytometry. Methods: This retrospective study analyzed 911 non-acute promyelocytic leukemia (APL) AML cases. Flow cytometric immunophenotyping was performed using a comprehensive panel of 23 markers. Statistical analysis included univariate and multivariate logistic regression to identify discriminatory markers. Results: Among 911 patients, 241 (26.5%) were classified as AML-MRC. AML-MRC patients were significantly older (mean age: 55.9 vs. 47.9 years, p < 0.001) and presented with lower WBC counts (median: 8.9 vs. 24.2 × 10^9/L, p < 0.001) compared to non-MRC cases. AML-MRC demonstrated higher expression of CD34 (75.9% vs. 57.6%, p < 0.001), CD41a (10.8% vs. 4.5%, p = 0.002) and CD235a (5.8% vs. 1.2%, p < 0.001), with CD235a showing the highest discriminatory power (OR 4.458, 95% CI 1.720–11.552). For core-binding factor AML, AML with t(8;21) exhibited characteristic expression of CD19 (46.3% vs. 9.4%, p < 0.001) and CD56 (72.5% vs. 34.5%, p < 0.001), while AML with inv(16) showed distinctive CD34 (88.9% vs. 61.7%, p = 0.004) and CD14 (59.3% vs. 18.1%, p < 0.001) expression patterns. Conclusion: This study identifies markers that distinguish AML-MRC, including CD235a, CD41a, and CD34. This suggests that acute erythroid leukemia and acute megakaryocytic leukemia are subsets within the AML-MRC category. Additionally, the study validates previously reported immunophenotypic characteristics of AML with t(8;21) and inv(16).