Deciphering cell states and the cellular ecosystem to improve risk stratification in acute myeloid leukemia.

Abstract

Acute myeloid leukemia (AML) demonstrates significant cellular heterogeneity in both leukemic and immune cells, providing valuable insights into clinical outcomes. Here, we constructed an AML single-cell transcriptome atlas and proposed sciNMF workflow to systematically dissect underlying cellular heterogeneity. Notably, sciNMF identified 26 leukemic and immune cell states that linked to clinical variables, mutations, and prognosis. By examining the co-existence patterns among these cell states, we highlighted a unique AML cellular ecosystem (ACE) that signifies aberrant tumor milieu and poor survival, which is confirmed by public RNA-seq cohorts. We further developed the ACE signature (ACEsig), comprising 12 genes, which accurately predicts AML prognosis, and outperforms existing signatures. When applied to cytogenetically normal AML or intensively treated patients, the ACEsig continues to demonstrate strong performance. Our results demonstrate that large-scale systematic characterization of cellular heterogeneity has the potential to enhance our understanding of AML heterogeneity and contribute to more precise risk stratification strategy.