BloodCancer BiologyPeer-reviewed journal article
Genotype-immunophenotype relationships in NPM1-mutant AML clonal evolution uncovered by single cell multiomic analysis
Morgan DruckeriD, Darren LeeiD, M. B. Bowman, Xuan ZhangiD, Bailee Nicole KainiD, Deedra NicoletiD, Roopsha BandopadhyayiD, Varsha SinghiD, Zhe WangiD, Richard Maury StoneiD, Caner SayginiD, Krzysztof MrózekiD, Andrew J. CarrolliD, Daniel T. StarczynowskiiD, Ross L. LevineiD, John C. ByrdiD, Nathan SalomonisiD, Sarah J. SkuliiD, H. Leighton GrimesiD, Ann‐Kathrin EisfeldiD, Robert L. BowmaniD, Linde A. MilesiD
Abstract
Acute myeloid leukemia (AML) is a multi-clonal disease, existing as a milieu of clones with unique but related genotypes as initiating clones acquire subsequent mutations. However, bulk sequencing cannot fully capture AML clonal architecture or the clonal evolution that occurs as patients undergo therapy. To interrogate clonal evolution, we performed simultaneous single cell molecular profiling and immunophenotyping on 43 samples from 32 NPM1-mutant AML patients at different stages of disease. Here we show that diagnosis and relapsed AML samples display similar clonal architecture patterns, but signaling mutations can drive increased clonal diversity specifically at relapse. We uncovered unique genotype-immunophenotype relationships regardless of disease state, suggesting leukemic lineage trajectories can be hard-wired by the mutations present. Analysis of longitudinal samples from patients on therapy identified dynamic clonal, transcriptomic, and immunophenotypic changes. Our studies provide resolved understanding of leukemic clonal evolution and the relationships between genotype and cell state in leukemia biology.
- organism: human
- assay: scRNA-seq
- assay: CITE-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: trajectory inference
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
- Citations
- 5
- FWCI
- 16.63
- Field percentile
- top 1%
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