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Acute myeloid leukemia (AML) remains highly lethal, with treatment failure and relapse driven by dynamic cellular states and evolving interactions within the leukemic ecosystem. In this study, we built a comprehensive atlas of leukemia and its microenvironment (CALM) by integrating single-cell profiling from 73 AML samples and 9 healthy donors, bulk RNA sequencing (RNA-seq) from 87 newly diagnosed patients, and 4 paired bone marrow and peripheral blood single-cell data. All datasets were linked to clinical, cytogenetic, molecular, and treatment-response annotations. Immune analysis revealed diverse T cell distribution across disease status, where patients with persistent disease exhibited greater clonal expansion. Machine learning modeling on conventional T cells identified a T-cell signature pertinent to AML and outcomes, which were validated in two cohorts. CALM is a publicly accessible and interactive portal (calm.mdanderson.org) for exploring cellular compartments, cell-cell interactions, and longitudinal transcriptional and clonal dynamics to enable hypothesis generation, biomarker discovery, and target validation.
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Wang et al. (2026) studied this question.
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