Bohlin, A. | Search bioRxiv

bioRxiv2026

Transcriptionally defined AML cell states associate with treatment response and microenvironmental remodeling

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.

cancer biology↗

bioRxiv2024

Pathway activation model for personalized prediction of drug synergy

Targeted monotherapies for cancer often fail due to inherent or acquired drug resistance. By aiming at multiple targets simultaneously, drug combinations can produce synergistic interactions that increase drug effectiveness and reduce resistance. Computational models based on the integration of omics data have been used to identify synergistic combinations, but predicting drug synergy remains a challenge. Here, we introduce DIPx, an algorithm for personalized prediction of drug synergy based on biologically motivated tumor- and drug-specific pathway activation scores (PASs). We trained and validated DIPx in the AstraZeneca-Sanger (AZS) DREAM Challenge dataset using two separate test sets: Test Set 1 comprised the combinations already present in the training set, while Test Set 2 contained combinations absent from the training set, thus indicating the models ability to handle novel combinations. The Spearman correlation coefficients between predicted and observed drug synergy were 0.50 (95% CI: 0.47-0.53) in Test Set 1 and 0.26 (95% CI: 0.22-0.30) in Test Set 2, compared to 0.38 (95% CI: 0.34-0.42) and 0.18 (95% CI: 0.16-0.20), respectively, for the best performing method in the Challenge. We show evidence that higher synergy is associated with higher functional interaction between the drug targets, and this functional interaction information is captured by PAS. We illustrate the use of PAS to provide a potential biological explanation in terms of activated pathways that mediate the synergistic effects of combined drugs. In summary, DIPx can be a useful tool for personalized prediction of drug synergy and exploration of activated pathways related to the effects of combined drugs.

bioinformatics↗