To Örebro University

Refractory acute myeloblastic leukemia (AML) is poorly studied. In this study, we characterized primary refractory AML and investigated treatment and outcome in a population-based setting. Based on all AML patients receiving intensive induction therapy at 12 Swedish hospitals from 2011 to 2018 (N = 1221), we identified 306 patients that failed to achieve composite complete remission (CRc) after first-line therapy. Two-hundred-sixteen (71%) of these patients received salvage treatment with intensive chemotherapy (ICT), of which 126 (58%) achieved CRc and 85 (39%) underwent allogeneic stem cell transplantation (HSCT). One- and 3-year overall survival (OS) in patients receiving salvage ICT were 56.8% and 28.9%, respectively. Secondary AML and adverse ELN risk were associated with worse OS after salvage ICT, while fludarabine-based FAIDA versus amsacrine-based ACE salvage and HSCT were associated with better OS. Three-year OS after first or second salvage chemotherapy, followed by HSCT were 55% and 71%, respectively. Refractory patients responding to salvage ICT showed only a nonsignificant trend toward inferior OS compared to patients in CRc after the first cycle. In conclusion, refractory AML patients eligible for further intensive therapy have a reasonable chance of obtaining remission and long-term survival when followed by HSCT. The results can serve as a basis for evaluation of new treatments in refractory AML.

Background: Acute myeloid leukemia (AML) is a clinically and genetically heterogeneous disease. While traditional classification systems such as FAB relied on morphology and immunophenotype, recent classifications (WHO, ICC) emphasize genetic alterations for diagnosis and therapy. However, more directly involved in the phenotypic cellular inheritance, the epigenomic profile may offer an additional dimension for understanding AML heterogeneity. Previously, we performed ATAC-seq of 1,536 AML samples and identified 16 epigenetically defined AML subgroups (subgroups A-P), which were associated with distinct clinical, genetic, and transcriptional features (Ochi et al., ASH 2024). In the present study, we extend these findings by using single-cell RNA and ATAC-seq (scRNA/ATAC-seq) profiling to explore intra- and inter-tumor epigenetic heterogeneity, transcriptional regulation, and hierarchical differentiation trajectories across AML subgroups.

Methods: We performed multi-platform scRNA/ATAC-seq on 36 AML samples including all the 16 epigenomic subgroups and 4 remission samples (as controls), profiling a total of 281,167 mononuclear cells. Computational analyses included cell clustering, projection onto normal hematopoiesis reference maps, pseudotime inference, transcription factor (TF) activity analysis using SCENIC+, and leukemic stem cell (LSC) signature scoring.

Results: scATAC-based clustering showed that leukemic cells from individual AML samples formed distinct clusters largely separated from normal cells, typically co-clustered by subgroup, regardless of their differentiation status, indicating that leukemic cells within each subgroup share a distinct intrinsic epigenetic program. Projection onto a normal hematopoiesis reference map revealed divergent differentiation arrest among subgroups, even within genetically similar cases. For instance, all NPM1-mutant subgroups (D–F) were HOX-related but differed in differentiation states: subgroup E was arrested at the HSC stage, D at the GMP stage, and F contained both progenitors and mature cells. Single-cell analysis also refined differentiation states of subgroups F-H: while bulk ATAC-based deconvolution suggested monocyte enrichment in these subgroups, single-cell analysis revealed mature monocytes predominated in H, whereas F and G were enriched for immature promonocytes.

Combined with LSC signature analysis based on gene expression, pseudotime analysis demonstrated that cells showing high LSC scores consistently mapped to early stages of the differentiation trajectory across all subgroups. These results indicate the presence of a conserved leukemic hierarchy, with stem-like cells positioned at the apex, irrespective of epigenetic subgroup, thereby underpinning AML pathogenesis. We further analyzed TF activity using the SCENIC+ program based on scRNA/ATAC-seq data, which identified key TFs enriched in each subgroup, many of which overlapped with those found in bulk RNA/ATAC-seq analysis. Pseudotime analysis further showed that TFs exhibited subgroup-specific activation dynamics. For example, HOXA9 was activated throughout the myeloid differentiation trajectory in HOX-related subgroups but peaked at different stages according to subgroups. In the RUNX1-enriched subgroup, IRF8 and BCL11A were both active but peaked at mature and immature stages, respectively. These findings indicate that key TFs exhibit subgroup-specific activation patterns at distinct stages along the myeloid differentiation trajectory.

Conclusion: Single-cell multi-omics analysis reveals that AML epigenetic subgroups exhibit distinct but hierarchically organized differentiation trajectories, driven by dynamically regulated TF programs. These findings refine our understanding of AML diversity and may inform more precise, differentiation-stage–specific therapeutic strategies.

Background: Acute myeloid leukemia (AML) is a heterogeneous disease and is primarily defined by genetic abnormalities. Although accumulating evidence suggests the role of epigenetics in the pathogenesis of AML, it has not fully been investigated in a largcohort of patients.

Methods: We enrolled 1,563 primary AML cases from Swedish (n=1,040) and Japanese (n=523) cohorts and performed ATAC-seq (n=1,563) as well as multi-omics analysis, including targeted-capture sequencing (n=1,563), RNA-seq (n=1,398), whole-genome sequencing (n=207), ChIP-seq (n=120), drug screening (n=112), and single-cell RNA/ATAC-seq (n=31).

Results: ATAC-seq analysis identified 185K recurrent peaks, most of which were found within intergenic/intronic regions. An unbiased clustering analysis based on ATAC-seq identified 16 unique subgroups with distinct genetic drivers, transcriptome profiles, differentiation states, key transcription factors, and clinical features. Among these, three groups were well-known entities defined by t(8;21), inv(16), and t(15;17). In contrast, the remaining 13 subtypes represent a novel classification framework not defined by single genomic abnormalities.

Patients with HOX-related gene abnormalities, such as NPM1 mutation as well as KMT2A and NUP98 rearrangement, converged into four subgroups (D-G). Associated with global chromatin changes in the HOXA locus from repressive to active state, these ATAC subgroups were characterized in common by an elevated expression of the entire HOXA cluster genes, exhibiting unique clinical and molecular features. For example, subgroup D is characterized by co-occurring NPM1 and TET2/IDH1/IDH2 mutations, older age, and high WBC/blast counts, while another HOX-subgroup (E) had monocytic nature and frequent RAS pathway mutations. By contrast, subgroups I and J were enriched for bi-allelic CEBPA mutations with and without frequent bZIP domain inframe mutations, respectively. GATA2 and WT1 mutations were common in subgroup I, while subgroup J was enriched for myelodysplasia-related mutations. TP53 mutations were largely clustered into subgroups (N, O and P) characterized by erythroid, immature progenitor, and tumor microenvironment cells, respectively. Additional ATAC subgroups included those having frequent RUNX1 (K), IDH1/IDH2 (L), and DDX41 (M) mutations, or showed a CMML-like AML phenotype (H). We next analyzed gene regulatory networks by combining RNA-seq and ATAC-seq, revealing key transcription factors (TFs) in each ATAC-subgroup. HOXA members played central roles in the HOX-related subgroups, while IRF members, including IRF4, 7, 8 and 9, were key TFs in the subgroup enriched for RUNX1 mutations (K), leading to the upregulation of the interferon pathway. ATAC subgroups also impacted patients’ survival and significantly improved the risk prediction of ELN, enabling further stratification of each ELN risk group.

Next, we performed an in vitro drug screening for 112 samples against 250 compounds and obtained a drug sensitivity profile for each ATAC-subgroup. As expected, the subgroup with frequent FLT3-ITD mutations showed a high sensitivity to a FLT3 inhibitor (quizartinib), while other subgroups (C, F, H) with monocytic differentiation and common RAS pathway mutations were sensitive to MEK inhibitors. Furthermore, we noted an unexpected sensitivity of subgroup K samples to multiple ABL inhibitors, even though they had no known ABL-related kinase mutations.

To validate these findings, we predicted ATAC-subgroups for four external AML cohorts based on gene expression and successfully reproduced an equivalent ATAC-subgroups with similar clinical, genetic, and transcriptomic features as well asdrug sensitivities.

Finally, we performed single-cell RNA/ATAC-seq and profiled a total of 233,000 mononuclear cells from 31 patients. We observed that leukemic cells were separately clustered from normal cells, while cells from the same ATAC subgroups were co-clustered, supporting that leukemic cells had their own epigenetic profiles unique to each cluster.

Conclusion: Through a large-scale multi-omics analysis of AML, we revealed a comprehensive landscape of chromatin accessibility of AML, highlighting the role of epigenetic profiling as a powerful tool for deciphering heterogeneity of AML, which could be used for a better stratification of patients and therapeutics.