Preleukemic single-cell landscapes reveal mutation-specific mechanisms and gene programs predictive of AML patient outcomes

Tomoya Isobe; Iwo Kucinski; Melania Barile; Xiaonan Wang; Rebecca Hannah; Hugo P Bastos; Shirom Chabra; M S Vijayabaskar; Katherine H M Sturgess; Matthew J Williams; George Giotopoulos; Ludovica Marando; Juan Li; Justyna Rak; Malgorzata Gozdecka; Daniel Prins; Mairi S Shepherd; Sam Watcham; Anthony R Green; David G Kent; George S Vassiliou; Brian J P Huntly; Nicola K Wilson; Berthold Göttgens

doi:10.1016/j.xgen.2023.100426

Preleukemic single-cell landscapes reveal mutation-specific mechanisms and gene programs predictive of AML patient outcomes

Cell Genom. 2023 Oct 27;3(12):100426. doi: 10.1016/j.xgen.2023.100426. eCollection 2023 Dec 13.

Authors

Tomoya Isobe¹, Iwo Kucinski¹, Melania Barile¹, Xiaonan Wang¹, Rebecca Hannah¹, Hugo P Bastos¹, Shirom Chabra¹, M S Vijayabaskar¹, Katherine H M Sturgess¹, Matthew J Williams¹, George Giotopoulos¹, Ludovica Marando¹, Juan Li¹, Justyna Rak^{1

2}, Malgorzata Gozdecka^{1

2}, Daniel Prins¹, Mairi S Shepherd¹, Sam Watcham¹, Anthony R Green¹, David G Kent^{1

3}, George S Vassiliou^{1

2}, Brian J P Huntly¹, Nicola K Wilson¹, Berthold Göttgens¹

Affiliations

¹ Wellcome-MRC Cambridge Stem Cell Institute, Department of Hematology, University of Cambridge, Cambridge, UK.
² Hematological Cancer Genetics, Wellcome Trust Sanger Institute, Hinxton, UK.
³ York Biomedical Research Institute, Department of Biology, University of York, York, UK.

Abstract

Acute myeloid leukemia (AML) and myeloid neoplasms develop through acquisition of somatic mutations that confer mutation-specific fitness advantages to hematopoietic stem and progenitor cells. However, our understanding of mutational effects remains limited to the resolution attainable within immunophenotypically and clinically accessible bulk cell populations. To decipher heterogeneous cellular fitness to preleukemic mutational perturbations, we performed single-cell RNA sequencing of eight different mouse models with driver mutations of myeloid malignancies, generating 269,048 single-cell profiles. Our analysis infers mutation-driven perturbations in cell abundance, cellular lineage fate, cellular metabolism, and gene expression at the continuous resolution, pinpointing cell populations with transcriptional alterations associated with differentiation bias. We further develop an 11-gene scoring system (Stem11) on the basis of preleukemic transcriptional signatures that predicts AML patient outcomes. Our results demonstrate that a single-cell-resolution deep characterization of preleukemic biology has the potential to enhance our understanding of AML heterogeneity and inform more effective risk stratification strategies.

Keywords: acute myeloid leukemia; hematopoiesis; myeloid malignancies; preleukemia; single-cell RNA-seq.

Abstract

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