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

Brief Bioinform

November 2024

State Key Laboratory of Cellular Stress Biology, Xiang'an Hospital, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, No. 4221, Xiang'an South Road, Xiamen, Fujian 361102, China.

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.

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Lower risk (LR) myelodysplastic syndromes (MDS) are heterogeneous hematopoietic stem and progenitor disorders caused by the accumulation of somatic mutations in various genes including epigenetic regulators that may produce convergent DNA methylation patterns driving specific gene expression profiles. The integration of genomic, epigenomic, and transcriptomic profiling has the potential to spotlight distinct LR-MDS categories on the basis of pathophysiological mechanisms. We performed a comprehensive study of somatic mutations and DNA methylation in a large and clinically well-annotated cohort of treatment-naive patients with LR-MDS at diagnosis from the EUMDS registry (ClinicalTrials.

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Acute myeloid leukaemia (AML) is a haematologic malignancy with high relapse rates in both adults and children. Leukaemic stem cells (LSCs) are central to leukaemopoiesis, treatment response and relapse and frequently associated with measurable residual disease (MRD). However, the dynamics of LSCs within the AML microenvironment is not fully understood.

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Chimeric antigen receptor T cells (CART) targeting CD19 through CD28.ζ signaling induce rapid lysis of leukemic blasts, contrasting with persistent tumor control exhibited by 4-1BB.ζ-CART.

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Cell cycle-dependent gene expression analysis is particularly important as numerous genes show tightly regulated expression patterns at different phases of the cell cycle. For cancer cells, analysis of cell cycle-related events is of paramount significance since tumorigenesis is characteristically coupled to cell cycle perturbations. RT-qPCR is a highly sensitive technique to investigate cell cycle-dependent transcriptional regulation.

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