Summary

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.

Overall design

overall_design too long too uplode

Contributors

Peter van Galen 1, Volker Hovestadt 1, Marc H Wadsworth Ii 2, Travis K Hughes 2, Gabriel K Griffin 3, Sofia Battaglia 1, Julia A Verga 1, Jason Stephansky 4, Timothy J Pastika 4, Jennifer Lombardi Story 5, Geraldine S Pinkus 6, Olga Pozdnyakova 6, Ilene Galinsky 7, Richard M Stone 7, Timothy A Graubert 5, Alex K Shalek 2, Jon C Aster 8, Andrew A Lane 9, Bradley E Bernstein 10

Contact

bernstein.bradley@mgh.harvard.edu.(Bradley E Bernstein)