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

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 <i>TP53</i> 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 <i>NPM1</i>, <i>CEBPA</i>, inv(16), and t(8;21) conferring favorable risk and alterations of <i>TP53, RUNX1, ASXL1</i>, del(5q), -7, and -17 conferring adverse risk, while <i>FLT3</i>-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 <i>TP53</i>, <i>ASXL1</i>, 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.

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Publication Details

Journal
HemaSphere
Published
2025-05-01
DOI
https://doi.org/10.1002/hem3.70132
Citations
4
Primary Topic
Acute Myeloid Leukemia Research
Type
article
Field-Weighted Citation Impact
7.08
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article

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

Johannes Schetelig, Christian Thiede, Sebastian Stasik, Rhonda E. Ries et al.
4 citations
HemaSphere
Acute Myeloid Leukemia Research
7.08
article

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

Johannes Schetelig, Christian Thiede, Sebastian Stasik, Rhonda E. Ries, Christoph Röllig, Martin Bornhäuser, Jan‐Niklas Eckardt, Carsten Müller‐Tidow, Claudia Dorothea Baldus, Jan Moritz Middeke, Soheil Meshinchi, Markus Wolfien, Martin Kaufmann, Christoph Schliemann, Susann Winter, Andreas Burchert, Maher Hanoun, Uwe Platzbecker, Hubert Serve, Waldemar Hahn, Kerstin Schäfer‐Eckart, Szymon Dariusz Chrost
article en
4 citations

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 <i>TP53</i> 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 <i>NPM1</i>, <i>CEBPA</i>, inv(16), and t(8;21) conferring favorable risk and alterations of <i>TP53, RUNX1, ASXL1</i>, del(5q), -7, and -17 conferring adverse risk, while <i>FLT3</i>-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 <i>TP53</i>, <i>ASXL1</i>, 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.

HemaSphereVol. 9(5)
Goethe University Frankfurt (DE), Seattle Children's Hospital (US), Philipps University of Marburg (DE), German Cancer Research Center (DE), Heidelberg University (DE), Helmholtz-Zentrum Dresden-Rossendorf (DE), University Hospital Heidelberg (DE), Robert Bosch Hospital (DE), Fred Hutch Cancer Center (US), University Hospital Münster (DE), Fresenius (Germany) (DE), National Center for Tumor Diseases (DE), University Hospital Schleswig-Holstein (DE), Paracelsus Medizinische Privatuniversität (DE), Essen University Hospital (DE), University Hospital Carl Gustav Carus (DE), Technische Universität Dresden (DE), University of Lübeck (DE)
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Openalex Percentile: Top 3%
Acute Myeloid Leukemia Research
7.08
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