% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Eckardt:300833,
      author       = {J.-N. Eckardt and W. Hahn and R. E. Ries and S. D. Chrost
                      and S. Winter and S. Stasik and C. Röllig and U.
                      Platzbecker and C. Müller-Tidow and H. Serve and C. D.
                      Baldus and C. Schliemann and K. Schäfer-Eckart and M.
                      Hanoun and M. Kaufmann and A. Burchert and J. Schetelig and
                      M. Bornhäuser$^*$ and M. Wolfien and S. Meshinchi and C.
                      Thiede and J. M. Middeke},
      title        = {{A}ge-stratified machine learning identifies divergent
                      prognostic significance of molecular alterations in {AML}.},
      journal      = {HemaSphere},
      volume       = {9},
      number       = {5},
      issn         = {2572-9241},
      address      = {Hoboken},
      publisher    = {John Wiley $\&$ Sons Ltd.},
      reportid     = {DKFZ-2025-00944},
      pages        = {e70132},
      year         = {2025},
      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.},
      cin          = {DD01},
      ddc          = {610},
      cid          = {I:(DE-He78)DD01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40337301},
      pmc          = {pmc:PMC12056602},
      doi          = {10.1002/hem3.70132},
      url          = {https://inrepo02.dkfz.de/record/300833},
}