Clinical impact of the genomic landscape and leukemogenic trajectories in non-intensively treated elderly acute myeloid leukemia patients

• Published in: Leukemia 2023-11, Vol.37 (11), p.2187-2196
• Main Authors: Jahn, Ekaterina, Saadati, Maral, Fenaux, Pierre, Gobbi, Marco, Roboz, Gail J., Bullinger, Lars, Lutsik, Pavlo, Riedel, Anna, Plass, Christoph, Jahn, Nikolaus, Walter, Claudia, Holzmann, Karlheinz, Hao, Yong, Naim, Sue, Schreck, Nicholas, Krzykalla, Julia, Benner, Axel, Keer, Harold N., Azab, Mohammad, Döhner, Konstanze, Döhner, Hartmut
• Format: Article
• Language: English
• Subjects: 45/22; 45/47; 45/77; 631/208/2489/144/68; 631/208/2489/1512; 631/67/1990/283/1897; 692/308/2056; 692/308/575; Acute myeloid leukemia; Aged; Algorithms; Branches; Cancer Research; Cluster analysis; Clustering; Critical Care Medicine; DNA sequencing; fms-Like Tyrosine Kinase 3 - genetics; fms-Like Tyrosine Kinase 3 - therapeutic use; Genes; Genetics; Genomics; Hematology; Hematopoiesis; Hemopoiesis; Humans; Intensive; Internal Medicine; Leukemia; Leukemia, Myeloid, Acute - drug therapy; Leukemia, Myeloid, Acute - therapy; Medicine; Medicine & Public Health; Modelling; Mutation; Nucleophosmin; Oncology; p53 Protein; Prognosis; Prospective Studies; Regression analysis; Regression models; Risk; Risk groups; Transcription Factors - genetics
• ISSN: 0887-6924; 1476-5551; 1476-5551
• DOI: 10.1038/s41375-023-01999-6

To characterize the genomic landscape and leukemogenic pathways of older, newly diagnosed, non-intensively treated patients with AML and to study the clinical implications, comprehensive genetics analyses were performed including targeted DNA sequencing of 263 genes in 604 patients treated in a prospective Phase III clinical trial. Leukemic trajectories were delineated using oncogenetic tree modeling and hierarchical clustering, and prognostic groups were derived from multivariable Cox regression models. Clonal hematopoiesis-related genes ( ASXL1 , TET2 , SRSF2 , DNMT3A ) were most frequently mutated. The oncogenetic modeling algorithm produced a tree with five branches with ASXL1 , DDX41 , DNMT3A , TET2 , and TP53 emanating from the root suggesting leukemia-initiating events which gave rise to further subbranches with distinct subclones. Unsupervised clustering mirrored the genetic groups identified by the tree model. Multivariable analysis identified FLT3 internal tandem duplications (ITD), SRSF2 , and TP53 mutations as poor prognostic factors, while DDX41 mutations exerted an exceptionally favorable effect. Subsequent backwards elimination based on the Akaike information criterion delineated three genetic risk groups: DDX41 mutations (favorable-risk), DDX41 wildtype / FLT3 -ITD neg / TP53 wildtype (intermediate-risk), and FLT3 -ITD or TP53 mutations (high-risk). Our data identified distinct trajectories of leukemia development in older AML patients and provide a basis for a clinically meaningful genetic outcome stratification for patients receiving less intensive therapies.