Evolving Risk Classifications in AML in a Real-Life Scenario: After Changes upon Changes, Is It More and More Adverse?

SIMPLE SUMMARY: Risk classifications in AML models modify definition criteria over time according to the advances in the knowledge of the molecular pathology of the disease. These evolving criteria impact the therapeutic strategy for individual patients. In this study, we aimed to analyze the evolutionary behavior of risk-classification models in a consecutive cohort of unbiased patients. ABSTRACT: Acute myeloid leukemia (AML) is a heterogeneous disease classified into three risk categories (favorable, intermediate and adverse) with significant differences in outcomes. Definitions of risk categories evolve overtime, incorporating advances in molecular knowledge of AML. In this study, we analyzed the impacts of evolving risk classifications in 130 consecutive AML patients in a single-center real-life experience. Complete cytogenetic and molecular data were collected using conventional qPCR and targeted Next Generation Sequencing (NGS). Five-year OS probabilities were consistent among all classification models (roughly 50–72%, 26–32% and 16–20% for favorable, intermediate and adverse risk groups, respectively). In the same way, the medians of survival months and prediction power were similar in all models. In each update, around 20% of patients were re-classified. The adverse category consistently increased over time (31% in MRC, 34% in ELN2010, 50% in ELN2017), reaching up to 56% in the recent ELN2022. Noteworthily, in multivariate models, only age and the presence of TP53 mutations remained statistically significant. With updates in risk-classification models, the percentage of patients assigned to the adverse group is increasing, and so will the indications for allogeneic stem cell transplantation.