| Literature DB >> 30827681 |
Peter van Galen1, Volker Hovestadt1, Marc H Wadsworth Ii2, Travis K Hughes2, Gabriel K Griffin3, Sofia Battaglia1, Julia A Verga1, Jason Stephansky4, Timothy J Pastika4, Jennifer Lombardi Story5, Geraldine S Pinkus6, Olga Pozdnyakova6, Ilene Galinsky7, Richard M Stone7, Timothy A Graubert5, Alex K Shalek2, Jon C Aster8, Andrew A Lane9, Bradley E Bernstein10.
Abstract
Acute myeloid leukemia (AML) is a heterogeneous disease that resides within a complex microenvironment, complicating efforts to understand how different cell types contribute to disease progression. We combined single-cell RNA sequencing and genotyping to profile 38,410 cells from 40 bone marrow aspirates, including 16 AML patients and five healthy donors. We then applied a machine learning classifier to distinguish a spectrum of malignant cell types whose abundances varied between patients and between subclones in the same tumor. Cell type compositions correlated with prototypic genetic lesions, including an association of FLT3-ITD with abundant progenitor-like cells. Primitive AML cells exhibited dysregulated transcriptional programs with co-expression of stemness and myeloid priming genes and had prognostic significance. Differentiated monocyte-like AML cells expressed diverse immunomodulatory genes and suppressed T cell activity in vitro. In conclusion, we provide single-cell technologies and an atlas of AML cell states, regulators, and markers with implications for precision medicine and immune therapies. VIDEO ABSTRACT.Entities:
Keywords: acute myeloid leukemia; cancer genetics; genotyping; immunity; leukemia stem cells; single-cell RNA-sequencing
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Year: 2019 PMID: 30827681 PMCID: PMC6515904 DOI: 10.1016/j.cell.2019.01.031
Source DB: PubMed Journal: Cell ISSN: 0092-8674 Impact factor: 41.582