Machine learning-driven M2 macrophage signature for precision prediction of survival and therapy response in acute myeloid leukemia.

BACKGROUND: M2-type tumor-associated macrophages (TAMs) play critical roles in the immunosuppressive microenvironment and treatment resistance of acute myeloid leukemia (AML). However, their single-cell characteristics and predictive value for prognosis and therapy response remain unclear. METHODS: Single-cell transcriptomic data from AML patients and healthy donors were integrated to characterize AML tumor-associated macrophages (AML-TAMs). CIBERSORT was used to evaluate M2 macrophage infiltration in the AML cohorts. WGCNA and machine learning were applied to construct an M2 macrophage-derived prognostic signature across 8 independent AML cohorts. The signature's predictive value for chemotherapy and immunotherapy response was assessed computationally, and further experimental validation was performed using clinical peripheral blood samples. RESULTS: Single-cell analysis revealed a distinct AML-TAM subcluster with high CD163 expression enriched in immunosuppressive and stress-adapted pathways. M2 macrophage infiltration was greater in AML patients than in controls and was associated with shorter overall survival. High M2 infiltration correlated with adverse clinical features and was negatively correlated with CD8+ T cells. An 18-gene risk score model showed robust prognostic performance across eight cohorts (log-rank P < 0.001 for each). High-risk patients exhibited chemotherapy resistance, upregulated immune checkpoint genes, and a greater predicted response to anti-PD-1 therapy. Clinical validation confirmed increased M2 abundance and key gene expression changes in AML patients. CONCLUSIONS: This study characterizes M2 macrophages in AML at single-cell resolution and establishes an M2 macrophage-derived signature that enables robust prediction of survival and potential therapeutic response, providing a tool for risk stratification and personalized treatment.