bioRxivCancer BiologyPreprint
Transcriptionally defined AML cell states associate with treatment response and microenvironmental remodeling
Nona StruyfiD, Leonard HartmanisiD, Lucia Rico PizarroiD, Albin ÖsterroosiD, Anna Bohlin, Sofia Bengtzén, Sören LehmanniD, Olli KallioniemiiD, Tom ErkersiD
Abstract
While therapy resistance in acute myeloid leukemia (AML) is often attributed to leukemic stem cells (LSCs), their functional properties are not fully captured by their well-established genetic landscape and cell lineage transcriptional programs. Here, we explore AML cell states and their associations to drug response and systemic immune context. We performed integrated single-cell transcriptomics and immunophenotyping on diagnostic AML samples (n=6) to define transcriptional cell state gene signatures. These were projected onto bulk RNA-seq data from 448 AML patients to assess associations with drug sensitivity, plasma proteomics, clinical features, and established prognostic scores. Longitudinal single-cell data from external cohorts and cell-cell communication analyses were used to examine treatment dynamics and microenvironmental signaling. We defined nine AML cell states, including progenitor-like, stromal-like, antigen-presenting, and monocytic programs. Stemness features were distributed across multiple states, with lymphoid-primed and stress-adapted progenitors showing the strongest alignment with established stemness scores. Distinct drug sensitivities emerged, including cell cycle checkpoint inhibitor sensitivity in stress-adapted progenitors and kinase inhibitor sensitivity in cycling progenitors, alongside shared resistance to BH3 mimetics in monocytic states. Stress-adapted progenitors were associated with adverse clinical features and expanded following venetoclax-based therapy. Monocytic states acted as immunosuppressive hubs via TIGIT signaling, while stromal-associated states received niche-derived survival signals. Overall, we define a framework that associates AML cell states with stemness, drug response, and microenvironmental interactions. These findings highlight distributed stemness, state-specific vulnerabilities, and niche-driven resistance mechanisms, informing more precise therapeutic strategies in AML.
- organism: human
- assay: scRNA-seq
- assay: bulk RNA-seq
- assay: proteomics
- research focus: cell type annotation
- research focus: drug response / drug discovery
- research focus: cell-cell communication
- approach: deep learning
- approach: statistical model
- Citations
- 0
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