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Implications of Incorporating Myelodysplasia-Related Gene Variant Data on Acute Myeloid Leukemia Reclassification
Yu Jeong Choi1, Jee-Soo Lee2, Saeam Shin3
1Department of Laboratory Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
Background:
In the 5th edition of the WHO Classification of Hematopoietic Neoplasms (WHO-HAEM5) and the International Consensus Classification (ICC), acute myeloid leukemia (AML) diagnostic criteria were revised to incorporate genetic abnormalities, such as variants in myelodysplasia-related (MR) genes. As the implications of these changes on AML reclassification in Korean patients had not yet been fully evaluated, we comprehensively investigated them in this large-scale, multicenter study.
Methods:
We retrospectively analyzed data from 2,668 patients with AML aged ≥ 18 yrs from seven institutions who were originally diagnosed according to WHO-HAEM4R criteria. Clinical, cytogenetic, and targeted next-generation sequencing data (including MR genes and TP53 variants) were collected and analyzed. All cases were reassessed according to both WHO-HAEM5 and ICC criteria.
Results:
Overall, 28.5% of patients harbored at least one MR gene variant. Among the MR genes, variants in RUNX1 and ASXL1 were most frequently detected. Compared with the original diagnoses, 20.3% and 29.9% of patients were reclassified to different categories according to the WHO-HAEM5 and ICC criteria, respectively. Discordance between WHO-HAEM5 and ICC classifications was 13.2%, which is largely attributed to the inclusion of "AMLs with TP53" variants in the ICC. AML-MR was associated with older age, male predominance, and poorer survival compared with AML, not otherwise specified. Patients with recurrent fusion genes largely retained their original classification.
Conclusions:
Incorporating MR gene variant data and TP53-variant status in diagnosis influenced AML classification and risk stratification in our cohort, highlighting the potential importance of comprehensive molecular profiling in AML characterization.
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