Proteomic subtypes enrich current acute myeloid leukemia nomenclature and reflect intrinsic pathogenesis alongside aging.

Acute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy that increasingly affects the older population, with its posttranscriptional landscape remaining largely elusive. Establishing a stable proteomics-based classification system and systematically screening age-related proteins and regulatory networks are crucial for understanding the pathogenesis and outcomes of AML. In this study, we leveraged a multiomics cohort of 374 patients newly diagnosed with AML, integrating proteome, phosphoproteome, genome, transcriptome, and drug screening data. Through similarity network fusion clustering, we established 8 proteomic subtypes with distinct clinical and molecular properties, including S1 (CEBPA mutations), S3 (myelodysplasia-related AML), S4 (PML::RARA), S5 (NPM1 mutations), S6 (PML::RARA and RUNX1::RUNX1T1), S8 (CBFB::MYH11), S2 and S7 (mixed), aligning well with and adding actionable value to the latest World Health Organization nomenclature of AML. Hematopoietic lineage profiling of proteins indicated that megakaryocyte/platelet- and immune-related networks characterized distinct aging patterns in AML, which were consistent with our recent findings at the RNA level. Phosphosites also demonstrated distinct age-related features. The high protein abundance of megakaryocytic signatures was observed in S2, S3, and S7 subtypes, which were associated with advanced age and dismal prognosis of patients. A hematopoietic aging score with an independent prognostic value was established based on proteomic data, where higher scores correlated with myelodysplasia-related AML, NPM1 mutations, and clonal hematopoiesis-related gene mutations. Collectively, this study provides an overview of the molecular circuits and regulatory networks of AML during the aging process, advancing current classification systems and offering a comprehensive perspective on the disease.