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

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), DDX41wildtype/FLT3-ITDneg/TP53wildtype (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.

Genomic landscape of patients with FLT3-mutated acute myeloid leukemia (AML) treated within the CALGB 10603/RATIFY trial.

The aim of this study was to characterize the mutational landscape of patients with FLT3-mutated acute myeloid leukemia (AML) treated within the randomized CALGB 10603/RATIFY trial evaluating intensive chemotherapy plus the multi-kinase inhibitor midostaurin versus placebo. We performed sequencing of 262 genes in 475 patients: mutations occurring concurrently with the FLT3-mutation were most frequent in NPM1 (61%), DNMT3A (39%), WT1 (21%), TET2 (12%), NRAS (11%), RUNX1 (11%), PTPN11 (10%), and ASXL1 (8%) genes. To assess effects of clinical and genetic features and their possible interactions, we fitted random survival forests and interpreted the resulting variable importance. Highest prognostic impact was found for WT1 and NPM1 mutations, followed by white blood cell count, FLT3 mutation type (internal tandem duplications vs. tyrosine kinase domain mutations), treatment (midostaurin vs. placebo), ASXL1 mutation, and ECOG performance status. When evaluating two-fold variable combinations the most striking effects were found for WT1:NPM1 (with NPM1 mutation abrogating the negative effect of WT1 mutation), and for WT1:treatment (with midostaurin exerting a beneficial effect in WT1-mutated AML). This targeted gene sequencing study provides important, novel insights into the genomic background of FLT3-mutated AML including the prognostic impact of co-mutations, specific gene-gene interactions, and possible treatment effects of midostaurin.