Age-stratified machine learning identifies divergent prognostic significance of molecular alterations in AML

Abstract

Risk stratification in acute myeloid leukemia (AML) is driven by genetics, yet patient age substantially influences therapeutic decisions. To evaluate how age alters the prognostic impact of genetic mutations, we pooled data from 3062 pediatric and adult AML patients from multiple cohorts. Signaling pathway mutations dominated in younger patients, while mutations in epigenetic regulators, spliceosome genes, and TP53 alterations became more frequent with increasing age. Machine learning models were trained to identify prognostic variables and predict complete remission and 2-year overall survival, achieving area-under-the-curve scores of 0.801 and 0.791, respectively. Using Shapley (SHAP) values, we quantified the contribution of each variable to model decisions and traced their impact across six age groups: infants, children, adolescents/young adults, adults, seniors, and elderly. The highest contributions to model decisions among genetic variables were found for alterations of NPM1, CEBPA, inv(16), and t(8;21) conferring favorable risk and alterations of TP53, RUNX1, ASXL1, del(5q), -7, and -17 conferring adverse risk, while FLT3-ITD had an ambiguous role conferring favorable treatment responses yet poor overall survival. Age significantly modified the prognostic value of genetic alterations, with no single alteration consistently predicting outcomes across all age groups. Specific alterations associated with aging such as TP53, ASXL1, or del(5q) posed a disproportionately higher risk in younger patients. These results challenge uniform risk stratification models and highlight the need for context-sensitive AML treatment strategies.