Single-cell profiling reveals reprogrammed hierarchy and disrupted immune-stromal ecosystem in TP53-mutated AML

Abstract Background TP53-mutated acute myeloid leukemia (AML) represents one of the most adverse-risk subtypes of AML, yet the mechanisms underlying its resistance and relapse remain poorly defined. Methods We performed single-cell RNA sequencing on bone marrow samples from 30 de novo AML patients (11 TP53-mutated, 19 TP53-wild-type) and systematically analyzed leukemic, immune, and stromal compartments to delineate differentiation trajectories, transcriptional heterogeneity, and microenvironmental remodeling. We also performed in vitro assays to validate ferroptosis resistance, leukemia-T cell dysfunction, and stromal remodeling suggested by the single-cell data. Results TP53-mutated AML exhibited a differentiation bias toward granulocyte-monocyte and late myeloid progenitors rather than arrest at the stem cell stage, with enhanced anti-apoptotic and inflammatory programs and a transcriptionally and functionally supported ferroptosis resistance phenotype as a novel hallmark linked to poor prognosis. Functionally, CD8⁺ T cells were predominantly exhausted with an enrichment of dysfunctional subsets and a concomitant reduction of NK cells. B cells showed impaired activation with skewed plasma cell composition, and myeloid cells acquired immunosuppressive features. In the stromal compartment, mesenchymal cells lost hematopoietic and immune-supportive functions and shifted toward osteogenic programs, further reinforcing leukemic survival. We also established an integrated ecosystem score that, together with TP53 mutation burden and mono- versus multi-hit status, captured prognostic heterogeneity and enabled clinical stratification. Conclusions This study provides the first single-cell landscape of de novo TP53-mutated AML, highlighting its reprogrammed leukemic hierarchy and disrupted immune-stromal ecosystem, and offering mechanistic insights and potential therapeutic targets for this high-risk subtype. Graphical Abstract