The 2022 European LeukaemiaNet (ELN) genetic risk classification for acute myeloid leukemia (AML) has been validated and refined in a cohort of 757 patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HCT). The study, published in Nature, demonstrates the enhanced prognostic accuracy of the ELN2022 classification in predicting relapse, relapse-free survival (RFS), and overall survival (OS) post-transplant.
The research involved a training cohort of 757 de novo AML patients (401 males and 356 females) with a median follow-up of 30 months after allo-HCT. The median age at allo-HCT was 40 years. According to the ELN2022 classification, 34% were classified as favorable, 42% as intermediate, and 24% as adverse.
Survival Outcomes
The ELN2017 classification showed 3-year cumulative incidence of relapse (CIR) for the favorable, intermediate, and adverse groups was 13%, 18%, and 40%, respectively (P < 0.001). The corresponding RFS rates were 81%, 75%, and 52%, and OS rates were 85%, 81%, and 59% (P < 0.001). The ELN2022 classification demonstrated 3-year CIR of 11%, 19%, and 40% (P < 0.001); 3-year RFS of 84%, 74%, and 52% (P < 0.001); and 3-year OS of 88%, 79%, and 59% (P < 0.001) across the favorable, intermediate, and adverse groups, respectively.
Prognostic Value
Multivariate analysis confirmed that the ELN2022 risk classification at diagnosis was an independent prognostic factor for CIR, RFS, and OS. Receiver operating characteristic (ROC) analysis showed no statistically significant difference between the ELN2022 and ELN2017 risk systems in predicting relapse (AUCELN2017 = 0.660 vs. AUCELN2022 = 0.668, P = 0.530), RFS (AUCELN2017 = 0.648 vs. AUCELN2022 = 0.658, P = 0.372), and OS (AUCELN2017 = 0.641 vs. AUCELN2022 = 0.653, P = 0.318).
Clinical Implications
These findings support the use of the ELN2022 genetic risk classification in AML patients undergoing allo-HCT, providing a more refined approach to risk stratification and potentially informing personalized treatment strategies. The refined classification allows for better identification of patients at high risk of relapse, enabling clinicians to tailor post-transplant interventions accordingly.
