Introduction: AML is a heterogeneous entity with respect to clinical outcomes and disease biology. Cytogenetic abnormalities, as well as gene mutations in FLT3, NPM1, CEBPA, ASXL1, RUNX1 and TP53 only partly explain this heterogeneity. These formed the basis of European LeukemiaNet (ELN) 17 risk stratification of de-novo adult AML patients treated with intensive chemotherapy. ELN22 risk stratification incorporates recent information from next-generation sequencing (NGS) data and newer entities such as AML with myelodysplasia-related abnormalities. Here, in a single-centre study, we validate this classification in a cohort of de-novo adult AML.

Methods: We studied 506 adult AML patients (³18 years) who received “3+7” induction therapy between 2012 and 2023 from a single centre. AML was diagnosed according to the WHO 2017 criteria. Diagnostic samples were sequenced using a 50-gene myeloid panel (till 2020) based on single molecule molecular inversion probes and subsequently using a 135-gene hybrid capture-based panel. All cases were risk-stratified per ELN22 recommendations. MRD was evaluated at post-induction (PI) time point using 10-colour or 16-colour flow cytometry. (MFC-MRD). We studied associations between ELN22 risk groups and other patient characteristics (WBC counts, age) using Chi-squared test for categorical and Kruskal-Wallis test for continuous variables. The prognostic impact of ELN22 risk stratification on overall survival (OS) and relapse-free survival (RFS) was computed using the Kaplan-Meier method and compared using log-rank test for time-to-event analyses. Multivariable Cox proportional hazard models were used for survival endpoints OS and RFS.

Results: The median age of the cohort was 35.0 years (M:F, 1.6:1) with a median follow-up of 29.5 months. The median OS was 72 months (95% CI: 35.5 - 87.3), and the median RFS was 52.3 months (95% CI: 35.2 -87.9). Majority of patients were stratified as ELN22 favorable (54.5%, n=276) and the rest as intermediate (29.4%, n=149) and adverse risk (15.8%, n=81). ELN22 adverse risk was significantly associated with a lower white blood cell count (WBC) at diagnosis (p=0.004) as compared to other risk groups. Favorable-risk patients were more likely to be younger (p=0.01). ELN22 adverse risk patients had inferior OS [HR 3.2; 95% CI: 2.0 - 5.0; p <0.0001] and RFS [HR 2.7; 95% CI: 1.4 - 5.5; p=0.0002] compared to favorable risk. Similarly, patients classified as ELN22 intermediate risk had inferior OS [HR 1.7; 95% CI: 1.2 - 2.4] and RFS [(HR 1.3; 95% CI: 0.9 - 1.9] as compared to favorable risk. Amongst the newly defined categories, AML-MR accounted for 9.9% of cases. Nearly 45.5% (n=234) of patients were PI MFC-MRD positive. The presence of PI MFC-MRD was associated with inferior OS [HR 1.9; 95% CI: 1.4 to 2.6, p< 0.0001]; however, it did not have an impact on RFS (p=0.32). On multivariate analysis OS, ELN22 adverse risk [HR=2.9; 95% CI: 1.73 - 5.05; p=0.0001], intermediate risk [HR=1.6; 95% CI: 1.06 - 2.5; p=1.6], and PI MFC MRD [HR=1.5; 95% CI: 1.1 - 2.1; p=0.005] were significantly associated with shorter OS. For RFS, only ELN22 adverse risk was significantly associated with shorter RFS (HR=4.2; CI: 1.7 - 10.2; p= 0.0018). Of the patients classified as ELN22 favorable risk category, 10.1% (n=28/276) of cases had MR gene mutations and/or cytogenetic abnormalities. Myelodysplasia-related (MR) gene mutations did not impact OS and RFS among ELN22 favorable risk entities. ELN22 adverse risk patients defined by the presence of MR gene mutations (BCOR, EZH2, SF3B1, SRSF2, STAG2, U2AF1, ZRSR2) had outcomes similar to ELN17 adverse risk groups (OS; p=0.20 and RFS; p=0.96). FLT3-ITDhigh allelic ratio (19.35%, n=24/149) did not impact OS and RFS within the intermediate risk group (OS; p=0.89 and RFS; p=0.59).

Conclusion: We demonstrate that ELN22 risk stratification for AML is applicable to a predominantly young population of AML treated with intensive chemotherapy. This allows for meaningful and precise incorporation of both cytogenetics and genomics-derived data for adult AML and simplifies risk stratification for treating physicians.

Disclosures

Patkar:Illumina Inc: Research Funding.

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2024
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