CYTOGENETIC PROFILING OF ACUTE MYELOID LEUKEMIA IN ADULTS: INTEGRATING ASSOCIATION BETWEEN NEXT-GENERATION FISH, CYTOGENETICS, AND FLOW CYTOMETRY
Main Article Content
Keywords
Acute Myeloid Leukemia, Next-Generation FISH, Cytogenetics, Flow Cytometry, Integrated Diagnostics, Immunophenotype, Genotype-Phenotype Correlation
Abstract
Background: The accurate diagnosis and risk stratification of acute myeloid leukemia (AML) are fundamentally reliant on a comprehensive assessment of its genetic and immunophenotypic landscape. While conventional karyotyping, fluorescence in situ hybridization (FISH), and multiparameter flow cytometry (MFC) are standard, their integration into a unified diagnostic model remains underexplored, particularly with the advent of Next-Generation FISH (NG-FISH) technologies. Aims & Objectives: This study aimed to establish an integrated diagnostic framework for adult AML by evaluating the synergy between conventional cytogenetics, NG-FISH, and MFC. The primary objectives were to determine the concordance and incremental diagnostic yield of NG-FISH compared to karyotyping and to identify significant associations between specific cytogenetic abnormalities and immunophenotypic profiles. Methodology: In this prospective, multicenter study conducted in Lahore (three tertiary care Hospitals) and Lodhran (one tertiary care hospital), 43 newly diagnosed adult AML patients were enrolled over 12 months (January 2025 – January 2026). The sample size was calculated using G*Power software. Diagnostic bone marrow samples underwent simultaneous triple-platform analysis: conventional G-banded karyotyping, targeted NG-FISH using an automated system with an AML-specific probe panel, and comprehensive immunophenotyping by 8-color MFC. Data were integrated and analyzed statistically to assess concordance and genotype-phenotype correlations. Results & Findings: Conventional cytogenetics detected abnormalities in 72.1% of cases. NG-FISH demonstrated an incremental diagnostic yield of 9.3%, identifying cryptic KMT2A and CBFB rearrangements in patients with a normal karyotype, thereby reclassifying their European LeukemiaNet (ELN) 2022 risk category. Strong immunophenotypic associations were identified: t(8;21) was significantly correlated with CD19+/CD56+ expression (p=0.003), and aberrant CD7 expression was a marker of adverse-risk cytogenetics (p=0.009). The integrated approach using all three modalities refined the initial risk stratification for 11.6% of the cohort. Conclusion: The strategic integration of conventional cytogenetics, NG-FISH, and MFC significantly enhances diagnostic precision in adult AML. NG-FISH provides critical detection of occult abnormalities, while MFC reveals phenotypic signatures predictive of genetic risk. This multimodal model mitigates the limitations of single-platform analysis, leading to more accurate risk stratification and informing timely, appropriate therapeutic decision-making.
References
2. Papaemmanuil E, Gerstung M, Bullinger L, Gaidzik VI, Paschka P, Roberts ND, et al. Genomic classification and prognosis in acute myeloid leukemia. N Engl J Med. 2016;374(23):2209-21.
3. Döhner H, Wei AH, Appelbaum FR, Craddock C, DiNardo CD, Dombret H, et al. Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood. 2022;140(12):1345-77.
4. Grimwade D, Hills RK, Moorman AV, Walker H, Chatters S, Goldstone AH, et al. Refinement of cytogenetic classification in acute myeloid leukemia: determination of prognostic significance of rare recurring chromosomal abnormalities among 5876 younger adult patients treated in the United Kingdom Medical Research Council trials. Blood. 2010;116(3):354-65.
5. Wan TS. Cancer cytogenetics: methodology revisited. Ann Lab Med. 2014;34(6):413-25.
6. Zheng Y, Forster VJ, Weatherby SC, Payne K, Canham LJ, Hudson C, et al. Next-generation fluorescence in situ hybridization (FISH): unlocking molecular pathology for precision medicine. J Mol Diagn. 2021;23(9):1065-79.
7. Lacombe F, Campos L, Allou K, Arnoulet C, Bene MC, Bernal E, et al. The role of multiparameter flow cytometry in the diagnosis and monitoring of acute myeloid leukemia: a review by the European LeukemiaNet International Working Group. Cytometry B Clin Cytom. 2021;100(1):25-37.
8. Kern W, Haferlach C, Haferlach T, Schnittger S. Correlation of cytogenetic and immunophenotypic data in acute myeloid leukemia reveals specific genotype-phenotype associations. Ann Hematol. 2020;99(5):961-70.
9. Khoury JD, Solary E, Abla O, Akkari Y, Alaggio R, Apperley JF, et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Myeloid and Histiocytic/Dendritic Neoplasms. Leukemia. 2022;36(7):1703-19.
10. Patel SS, Kuo FC, Gibson CJ, Steensma DP, Soiffer RJ, Alyea EP, et al. High diagnostic yield of clinical exome sequencing in intermediate-risk acute myeloid leukemia. Blood. 2018;132(5):524-32.
11. Grimwade D, Ivey A, Huntly BJP. Molecular landscape of acute myeloid leukemia in younger adults and its clinical relevance. Blood. 2016;127(1):29-41.
12. Othus M, Gale RP, Hourigan CS, Walter RB. Statistics and measurable residual disease (MRD) testing: uses and abuses in hematopoietic cell transplantation. Bone Marrow Transplant. 2020;55(5):843-50.
13. Arber DA, Orazi A, Hasserjian RP, Borowitz MJ, Calvo KR, Kvasnicka HM, et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: integrating morphologic, clinical, and genomic data. Blood. 2022;140(11):1200-28.
14. Hourigan CS, Gale RP, Gormley NJ, Ossenkoppele GJ, Walter RB. Measurable residual disease testing in acute myeloid leukaemia. Leukemia. 2017;31(7):1482-90.
15. Heuser M, Freeman SD, Ossenkoppele GJ, Buccisano F, Hourigan CS, Ngai LL, et al. 2021 Update on MRD in acute myeloid leukemia: a consensus document from the European LeukemiaNet MRD Working Party. Blood. 2021;138(26):2753-67.
16. Short NJ, Zhou S, Fu C, Berry DA, Walter RB, Freeman SD, et al. Association of measurable residual disease with survival outcomes in patients with acute myeloid leukemia: a systematic review and meta-analysis. JAMA Oncol. 2020;6(12):1890-9.
17. Patkar N, Kakirde C, Shaikh AF, Salve R, Bhanshe P, Joshi S, et al. Utility of targeted next-generation sequencing for the evaluation of karyotypically normal acute myeloid leukemia. Cancer Genet. 2019;239:39-42.
18. Murphy KM, Levis M, Hafez MJ, Geiger T, Cooper LC, Smith BD, et al. Detection of FLT3 internal tandem duplication and D835 mutations by a multiplex polymerase chain reaction and capillary electrophoresis assay. J Mol Diagn. 2003;5(2):96-102.
19. Schuurhuis GJ, Heuser M, Freeman S, Béné MC, Buccisano F, Cloos J, et al. Minimal/measurable residual disease in AML: a consensus document from the European LeukemiaNet MRD Working Party. Blood. 2018;131(12):1275-91.
20. Juliusson G, Lazarevic V, Hörstedt AS, Hagberg O, Höglund M; Swedish Acute Leukemia Registry Group. Acute myeloid leukemia in the real world: why population-based registries are needed. Blood. 2012;119(17):3890-9.
21. Huang Y, Wang J, Jia P, Li X, Pei G, Wang C, et al. A multicenter study of the clinical impact of integrated cytogenetic and next-generation sequencing profiling in acute myeloid leukemia. Blood Cancer J. 2023;13(1):12.
22. Falini B, Brunetti L, Sportoletti P, Martelli MP. NPM1-mutated acute myeloid leukemia: from bench to bedside. Blood. 2020;136(15):1707-21.
23. Grimwade D, Walker H, Oliver F, Wheatley K, Harrison C, Harrison G, et al. The importance of diagnostic cytogenetics on outcome in AML: analysis of 1,612 patients entered into the MRC AML 10 trial. Blood. 1998;92(7):2322-33.
24. Kayser S, Levis MJ. Advances in targeted therapy for acute myeloid leukaemia. Br J Haematol. 2018;180(4):484-500.
25. Medeiros BC, Chan SM, Daver NG, Jonas BA, Pollyea DA. Optimizing survival outcomes with post-remission therapy in acute myeloid leukemia. Am J Hematol. 2019;94(7):803-11.

