Patients with acute myeloid leukemia (AML) spend a substantial amount of time in hospital while receiving intensive chemotherapy, affecting their quality of life and healthcare costs. This study evaluated the safety of outpatient management of febrile neutropenia with support from hospital at home (HaH). Two cohorts of intensively treated patients with AML were retrospectively compared, one from Stockholm (n = 116) with access to HaH and another from Gothenburg (n = 49) without HaH support. Primary outcome was incidence of severe complications, defined as death or requirement of higher level of care. During cycle 1 (C1), 109 of 116 patients (94

Acute myeloid leukaemia (AML) is an aggressive blood cancer characterized by the unregulated proliferation of immature myeloblasts. Gene mutations have been shown to have a large effect on pathogenesis, inter-tumour heterogeneity and clinical outcomes in AML1-8; however, the role of epigenetic alterations in these respects has been investigated less extensively. Here we use ATAC-seq (assay for transposase-accessible chromatin with sequencing) in a cohort of 1,563 individuals with a recent diagnosis of AML (the 'eCHROMA' cohort) to show that AML can be classified into 16 subgroups on the basis of chromatin accessibility profiles. Multiomics analyses of gene mutations, the transcriptome, DNA methylation and histone marks show that these ATAC subgroups exhibit distinct driver mutations, differentiation states, gene expression, DNA methylation and super-enhancer profiles, and are also associated with clinical outcomes. These findings were validated in independent cohorts. Single-cell ATAC sequencing reveals that all leukaemic cells in each subgroup share a common chromatin accessibility profile, which suggests that subgroup-specific epigenomic fingerprints underlie the ATAC-based classification. Mechanistically, the subgroups have distinct gene-regulatory networks that are driven by the activities of key transcription factors in haematopoiesis, and in which subgroup-specific super-enhancers have a pivotal role. Multiomics single-cell analysis further reveals deregulated trajectories of differentiation coupled with chromatin accessibility and gene expression. Notably, ATAC subgroups have an independent prognostic effect, compared with genomic classification, and are associated with particular drug sensitivities. In summary, ATAC-based chromatin profiling, combined with multiomics data, provides insights into AML pathogenesis beyond genomics and constitutes a valuable resource for AML research.

Effective strategies to maintain complete remission in adults with acute myeloid leukemia (AML) are critically needed. Early clinical trials aimed at preventing relapse in the postconsolidation phase explored prolonged chemotherapy, single-agent immunotherapy, and hybrid chemo-immunotherapy, but none of these approaches produced practice-changing results. More recent trials have identified efficacious remission maintenance strategies, including (1) midostaurin or quizartinib for patients with FLT3-mutated AML, (2) oral azacitidine for older AML patients, and (3) immunotherapy with histamine dihydrochloride and low-dose interleukin-2 (HDC/IL-2) for younger patients. In this review, we examine key phase III trial and follow-up study results for approved remission maintenance therapies, with a particular focus on HDC/IL-2. We discuss clinical efficacy in relation to patient age and anti-leukemic immunity as well as leukemic cell chemosensitivity, chromosomal integrity, and mutational profiles. Finally, we propose a role for HDC/IL-2 within an evolving landscape of strategies to achieve durable remission in a broader population of AML patients.

Abstract 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.

Hydroxyurea (HU) is frequently used in the early phase of chronic myeloid leukemia (CML) to achieve cytoreduction prior to tyrosine kinase inhibitor therapy. However, its impact on CML stem and progenitor cells (SPC) remains largely unknown. This study utilized targeted proteo-transcriptomic expression data on 596 genes and 51 surface proteins in 60,000 CD14-CD34+ cells from chronic phase CML patients to determine effects of short-term HU treatment (4-19 days) on CML SPC. Peripheral blood and bone marrow samples were obtained from 17 CML patients eligible for short-term HU treatment (3 patients before and after HU, 7 patients before HU and 7 patients after HU) and subjected to single-cell CITE-sequencing and/or flow cytometry analysis. The analysis revealed enhanced frequencies of hemoglobin-expressing (HBA1, HBA2, HBB) erythroid progenitor cells in blood and bone marrow following HU treatment. In addition, there was an accumulation of cell subsets with S/G2/M phase-related gene and protein expression, likely representing cells arrested in, or progressing slowly through, the cell cycle. The increased frequency of cells in S/G2/M phase after HU was observed already among the most immature leukemic stem cells (LSC), and patients with a large fraction of LSC in the S/G2/M phase showed poor responsiveness to tyrosine kinase inhibitor treatment. We conclude that short-term HU treatment entails differentiation of erythroid progenitor cells and alters the characteristics of LSC in CML. The results imply that studies of LSC and progenitor populations in CML should take effects of initial HU therapy into account.

Prior studies combining pegylated interferon-α to Imatinib and Nilotinib in newly diagnosed CML patients have indicated an additive effect with faster and deeper molecular responses (SPIRIT, TIGER, NordCML002, NiloPEG). In this trial we used a bosutinib (BOS) backbone combined with low-dose ropeginterferon alfa-2b (BESREMi, AOP Health), which in contrast to previously tested interferons has a longer half-life and a more favorable tolerability profile. The aim was to investigate efficacy and tolerability of the combination, with the goal of deepening molecular response to eventually allow a higher proportion of patients to achieve treatment free remission. Patients were in 1st chronic phase and TKI-naïve. Inclusion criteria were standard including good organ function without history of liver, autoimmune or psychiatric disease. Based on clinical experience to improve tolerability, BOS was introduced at 200 mg OD and the dose titrated to 400 mg OD if tolerated. At month 3 (M3), patients who tolerated ≥300 mg OD and had transaminase levels grade ≤1 were randomized 1:1 for addition of ropeginterferon 50 μg every second week or not. The primary endpoint was MR4 at M12 in the intention-to-treat (IIT) population (all randomized patients). Other endpoints were safety and other response levels (e.g. MR4.5). Data up to M12 are presented. We also examined the response and tolerability of BOS up to M3 as the ramp-up dosing is not standard although frequently used in the clinic. 163 patients were included from 2019 to 2023 in 18 hospitals in Denmark (6), Finland (1), Norway (4) and Sweden (7). Despite gradual up-titration of BOS, many patients experienced toxicity as described in the BOS prescribing information, including grade 2, 3 and 4 transaminase elevations (11%; 16%; 3%). Dose reduction or interruption were insufficient to manage transaminase elevations, which frequently recurred upon BOS re-exposure. Compared with data from the BOS registration study (BFORE), GI AEs were less frequent with our present schedule. Twenty-seven % of patients could not be randomized: 15% for transaminitis, 4% for GI AEs and 8% for other reasons including inability to tolerate BOS 300mg OD. Non-randomized patients had M3 responses on par with randomized patients, indicating no harm of the dosing strategy despite the lower or intermittent BOS dose. Their responses at M6, M9 and M12 were similar to the standard arm. At M3, 118 patients were randomized, 58 to BOS and 60 to the combination. AEs were distributed evenly between study arms, but hematological toxicity (grades 3-4, 6.7% vs 0%), neuropsychiatric disorders (grade 2, 13% vs 4%) and infections grade 3-4 (13% vs 0%) were more frequent in the combination arm. Of note, transaminase elevations were similarly distributed, but analysis of attributability to the study drugs is ongoing and will be updated Overall, efficacy in the present study compared favorably with previous experience with BOS. The combination arm demonstrated a clear trend for faster and deeper response at all time points: The combination arm compared with the standard arm showed MR4 or better in 22.0% vs 13.8% at M6, 32.8% vs 17.2% at M9, and 38.3% vs 31.0% at M12. Attainment of MR4.5 or better was at M6, M9 and M12, 11% vs 5,2%, 22,4% vs 6,9% and 31,7% vs 20,7%, respectively. Discontinuation of treatment occurred in 22% of patients in the combination arm and 8,3% in the standard arm, of whom 11,5% and 5% for treatment resistance. The primary endpoint in the IIT dataset was not statistically significantly different between treatment arms; note that all patients were included in the ITT analysis even if no interferon was administered or the TKI was switched. Most switched patients received 1st and 2nd generation TKIs, but some were transplanted or received ponatinib. A per protocol analysis and data on drug exposure will be presented at the meeting. The step-up dosing strategy for BOS reduced GI toxicity, but not transaminitis. Efficacy was excellent in comparison with the registration study (BFORE). The combination of ropeginterferon and BOS was safe and efficacious. Responses occurred earlier and were deeper with the combination.

Introduction: De novo BCR::ABL1+ acute myeloid leukemia (AML) is a distinct and rare entity (0.1%-3% of AML), classified as adverse-risk in the ELN 2022 classification (Döhner H et al., Blood 2022). However, we recently published the favorable outcome of 18 de novo BCR::ABL1+ AML from the French DATAML registry treated with imatinib + intensive chemotherapy (IC) (Gondran C et al., Blood Cancer Journal 2024): CR/CRi, 94.4%; 3-year OS, 77%; that compared favorably to intermediate- and adverse-risk AML. To confirm these results, we performed a multicentric European retrospective data collection of de novo BCR::ABL1+ AML characteristics, treatment data and outcome. Methods: Inclusion criteria for this retrospective study were: adult AML with ≥ 20% bone marrow blasts, BCR::ABL1+or t(9;22)(q34.1;q11.2), with no history of previous chronic myeloid leukemia (even if < 6 months) and no prior exposition to BCR::ABL1 tyrosine kinase inhibitor (TKI), who were treated with IC +/- TKI. Acute leukemia of ambiguous lineage were excluded. Results: We collected the data of 250 patients with de novo BCR::ABL1+ AML diagnosed from March 1999 to December 2024. 212 patients were treated with IC: 89 with IC alone and 123 with IC + TKI. Median age was 54 years and 67 patients (32%) were older than 60. Forty-three patients (28.5%) presented with splenomegaly. The median WBC was 45.109/L (29 in the IC arm vs. 53 in the IC + TKI arm). One hundred and fifteen patients (60%) had additional chromosomal abnormalities, 28 (15%) had a monosomal karyotype and 62 (32%) had complex abnormalities. BCR::ABL1 isotype was p210 in 80% and p190 in 20%. Molecular data were available for the most recent patients: the co-occurring mutations present in more than 10% of cases were: RUNX1 (N=32/100: 32%), BCOR (N=11/82: 13%), ASXL1 (N=10/79: 13%), WT1 (N=9/72: 13%), DNMT3A (N=8/73: 11%), and NPM1 (N=15/147: 10%); a TP53 mutation was found in 7% (N=5/77). IC was a 7+3 including daunorubicin+cytarabine in 107 patients (59%), idarubicin+cytarabine in 59 patients (36%) or another scheme in 14 (8%). In the IC + TKI arm, imatinib was added in 66 patients (54%; 400-800 mg/day), dasatinib in 40 patients (33%; 100-140 mg/day), ponatinib in 11 patients (9%; 15-45 mg/day), and nilotinib in 6 patients (5%; 600-800 mg/day). Response assessment was available in 78 patients in IC arm and 122 patients in IC + TKI arm. Composite complete remission (CR/CRi) was achieved in 52 patients (66.7%) in the IC arm, and 110 patients (90.2%) in the IC + TKI arm (P<0.0001). 28.2% and 9.0% of patients had primary refractory disease in the IC and IC + TKI arm, respectively (p<0.001). 97 patients (59.9%) were transplanted in CR1 (26 patients [50%] in IC arm and 71 patients [64.5%] in IC + TKI arm, P=0.077). The relapse rate was 28% in the IC arm and 18% in the IC + TKI arm (P=0.159). With a median follow-up of 66.7 months, the 3-year and 5-year OS were 42.1% and 38.5% in the IC arm and 70.9% and 62.9% in the IC + TKI arm (P<0.0001). The median OS was 20.5 months in the IC arm and not reached in the IC + TKI arm. The 3-year and 5-year RFS were 52.1% and 49.3% in the IC arm and 73.0% and 67.1% in the IC + TKI arm (P=0.0095). The 3-year and 5-year EFS were 32.1% and 30.4% in the IC arm and 66.7% and 61.4% in the IC + TKI arm (P<0.0001). Of note, in the IC + TKI arm, 26 out of 39 patients (67%) who were not transplanted did not relapse and had a median OS of 65.8 months. In multivariate analyses, the addition of TKI to IC was significantly and independently associated with an improvement in CR/CRi rate (OR: 4.74 [2.17-10.35]; P<0.001), in OS (HR: 0.40 [0.27-0.62]; P<0.001), in EFS (HR 0.37 [0.25-0.55]; P<0.001) and RFS (HR: 0.42 [0.23-0.75]; P=0.004) adjusted for confounding factors including allo-HSCT as time-dependent variable. The other independent poor predictive factors were: age older than 60 years for OS and EFS; and WBC over 50.109/L, IC regimen different than 7+3 and absence of alloSCT for RFS. Conclusions: The addition of a TKI significantly improves the outcome of de novo BCR::ABL1+ AML treated with intensive chemotherapy, and should be a standard of care for this entity. This study should lead to the revision of the current ELN AML genetic risk classification, as this entity no longer deserves to be classified as adverse.

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 large cohort 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 as drug 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.

Characterization of the leukemic stem cell (LSC) population in chronic myeloid leukemia (CML) may identify therapeutic targets for sustained elimination of leukemic cells. High-throughput single-cell RNAseq (scRNAseq) has paved the way for detailed transcriptional assessment of CML LSC. Building on this technique, multiomic CITE-seq approaches carry additional benefit in that the paired protein expression information may enable identification of associated cell surface marker profiles, subsequent FACS-based isolation and functional characterization. However, detection of BCR-ABL1 transcripts, which is arguably the only unequivocal marker to distinguish CML LSC from healthy hematopoietic stem cells (HSC), remains challenging using current high-throughput 3' end capture-based scRNAseq methods. For the present study, we performed single-cell CITE-seq analysis of the expression of 597 genes and 51 proteins in >70,000 stem and progenitor cells from 16 chronic phase CML patients and five healthy donors. Furthermore, we developed and employed a strategy allowing parallel detection of BCR-ABL1 transcripts at the single-cell level. In bone marrow samples from CML patients, we observed pronounced expansion of erythroid and myeloid progenitors within the CD14 -CD34 + compartment, and substantial heterogeneity within the traditionally defined CD34 +CD38 -/low LSC compartment. In-depth analysis of the latter identified a group of immature BCR-ABL1 +cells displaying a CD45RA -cKIT -CD26 + TKI resistance phenotype sitting atop a hierarchy of myeloid progenitors. Within this group of potential LSC, expression of both previously reported ( e.g. CD25 and CD26) and unreported gene and protein markers was found to distinguish these cells from their healthy counterparts. Unlike HSC, the immature LSC showed high surface expression of TIM3 and high transcription of the von Willebrand factor gene ( VWF). While overexpression of VWF within the stem cell population may be linked to the aberrant myeloid-biased hematopoiesis characterizing the disease, the cell surface upregulation of TIM3 suggests that its targeting may have merit in CML. In conclusion, we here performed detailed multiomic characterization of CML stem and progenitor cells, paired with BCR-ABL1 detection at the single-cell level. Our findings revealed LSC-specific expression patterns that may have implications for the phenotypic definition of CML LSC. Additionally, the results identify TIM3 as a conceivable target for sustained elimination of immature LSC in CML.

Tyrosine kinase inhibitors (TKI) only rarely eradicate leukemic stem cells (LSC) in chronic myeloid leukemia (CML) which commonly necessitates life-long therapy and monitoring of patients. Understanding details of leukemic hematopoiesis in CML may identify targetable pathways for sustained LSC elimination. This study utilized multiomic single-cell characterization of the CD14-CD34+ hematopoietic stem and progenitor cell (HSPC) compartment in CML. Combined proteo-transcriptomic profiling of 597 genes and 51 proteins (CITE-seq) was performed along with parallel detection of BCR-ABL1 transcripts in 70,000 HSPC from 16 chronic phase patients and five healthy controls. CD14-CD34+ HSPC from diagnosis samples displayed distinct myeloid cell bias with cells mainly annotated as LSC, lympho-myeloid progenitors (LMP)-II, erythrocyte and megakaryocyte progenitors, while few hematopoietic stem cells (HSC), LMP-I, dendritic cell or B cell progenitors were detected. In-depth analysis of the immature CD14-CD34+CD38-/low compartment revealed two distinct populations of BCR-ABL1 -expressing CML LSC (denoted LSC-I and LSC-II), where LSC-I showed features of quiescence and CD45RA-cKIT-CD26+ TKI therapy-resistant phenotype. These subtypes of immature LSC showed high surface expression of TIM3 and transcription of the von Willebrand factor gene ( VWF ). Our findings imply that expression of VWF and TIM3 distinguish LSC from HSC and may be linked to aberrant myeloid-biased hematopoiesis in CML. Additionally, the results identify TIM3 as a conceivable target for sustained elimination of immature LSC in CML.Key points ### Competing Interest StatementThe authors received research funding in form of free reagents from BD Biosciences within the BD Multiomics Alliance, but declare no other competing financial interests.

Studies of therapy-related AML (t-AML) are usually performed in selected cohorts and reliable incidence rates are lacking. In this study, we characterized, defined the incidence over time and studied prognostic implications in all t-AML patients diagnosed in Sweden between 1997 and 2015. Data were retrieved from nationwide population-based registries. In total, 6,779 AML patients were included in the study, of whom 686 (10%) had t-AML. The median age for t-AML was 71 years and 392 (57%) patients were females. During the study period, the incidence of t-AML almost doubled with a yearly increase in t-AML of 4.5% (95% confidence interval: 2.8%-6.2%), which contributed significantly to the general increase in AML incidence over the study period. t-AML solidly constituted over 10% of all AML cases during the later period of the study. Primary diagnoses with the largest increase in incidence and decrease in mortality rate during the study period (i.e., breast and prostate cancer) contributed significantly to the increased incidence of t-AML. In multivariable analysis, t-AML was associated with poorer outcome in cytogenetically intermediate- and adverse-risk cases but t-AML had no significant impact on outcome in favorable-risk AML, including core binding leukemias, acute promyelocytic leukemia and AML with mutated NPM1 without FLT3-ITD. We conclude that there is a strong increase in incidence in t-AML over time and that t-AML constitutes a successively larger proportion of the AML cases. Furthermore, we conclude that t-AML confers a poor prognosis in cytogenetically intermediate- and adverse-risk, but not in favorable-risk AML.

Background: AML is mostly a disease of the elderly with a median age of 72 years, but most molecular data originate from clinical studies, usually with age restrictions. Aims: To report population-based data on mutations from the Swedish AML Registry by age and outcome. Methods: Selected gene mutations (FLT3-ITD, NPM1 and CEBPA) became possible to report to the registry in 2007. Extended gene analyses started in 2016 with whole-exome sequencing (WES) in a few research sites, and TruSight Myeloid Panel (54 genes), later exchanged for Twist (195 genes) in clinical practice, but reporting could not start until 2020. Mutation data from clinical routine were therefore retrospectively added to the registry through hospital genetic laboratory databases. Data was extracted from the Swedish AML Registry on January 3, 2023. Results: Among 6525 patients diagnosed since 2007 molecular data was available from 3326 (51%). Between 2016 and 2022 the proportion of patients with panel or WES data increased from 22% to 71%. Currently the registry contains panel/WES data from 73% of patients <65 years, 55% 65-79 years, 27% 80-84 years, and 10% 85 years, i.e., from 1579 patients with a median age of 68 years. The overall frequency of specific mutations was slightly different to other studies, probably due to the real-world age distribution. The mean number of mutations per patient was 3.4. Overall, mutations were less common in younger patients, 2.3/pat <40 yrs, 3.0 in patients 40-64 years, 3.7 in patients 65-74 yrs and 3.8 in patients 75+ years. Mutation rates were skewed by age for many genes (Figure). FLT3, CEBPA and NRAS were more frequently mutated in young patients, whereas NPM1, DNMT3A, IDH1/2, STAG2 and SRSF2 were rarely mutated in young. TET2, ASXL1 and TP53 were most frequently mutated in older. Among those with panel/WES-data, 69% received intensive, 22% hypomethylating agents, and 9% palliation only (median ages 63, 76 and 78 years, respectively). Overall survival according to molecular subtypes are shown in the Table. Summary/Conclusion: Our data show that the overall incidence of mutations increases by age up to 65 years, but thereafter remains stable, whereas certain genes have different age distributions. The strong impact of specific genetic alterations on survival in a large population-based study with real-life age distribution is shown. The clinical impact of combinations of specific mutations and karyotypes is being evaluated.Keywords: Mutation status, Age, Acute myeloid leukemia, Survival

Natural killer cells are important effector cells in the immune response against myeloid malignancies. Previous studies show that the expression of activating NK cell receptors is pivotal for efficient recognition of blasts from patients with acute myeloid leukemia (AML) and that high expression levels impact favorably on patient survival. This study investigated the potential impact of activating receptor gene variants on NK cell receptor expression and survival in a cohort of AML patients receiving relapse-preventive immunotherapy with histamine dihydrochloride and low-dose IL-2 (HDC/IL-2). Patients harboring the G allele of rs1049174 in the KLRK1 gene encoding NKG2D showed high expression of NKG2D by CD56 bright NK cells and a favorable clinical outcome in terms of overall survival. For DNAM-1, high therapy-induced receptor expression entailed improved survival, while patients with high DNAM-1 expression before immunotherapy associated with unfavorable clinical outcome. The previously reported SNPs in NCR3 encoding NKp30, which purportedly influence mRNA splicing into isoforms with discrete functions, did not affect outcome in this study. Our results imply that variations in genes encoding activating NK cell receptors determine receptor expression and clinical outcome in AML immunotherapy.

With increasingly effective treatments, early death (ED) has become the predominant reason for therapeutic failure in patients with acute promyelocytic leukemia (APL). To better prevent ED, patients with high-risk of ED must be identified. Our aim was to develop a score that predicts the risk of ED in a real-life setting. We used APL patients in the populationbased Swedish AML Registry (n=301) and a Portuguese hospital-based registry (n=129) as training and validation cohorts, respectively. The cohorts were comparable with respect to age (median, 54 and 53 years) and ED rate (19.6% and 18.6%). The score was developed by logistic regression analyses, risk-per-quantile assessment and scoring based on ridge regression coefficients from multivariable penalized logistic regression analysis. White blood cell count, platelet count and age were selected by this approach as the most significant variables for predicting ED. The score identified low-, high- and very high-risk patients with ED risks of 4.8%, 20.2% and 50.9% respectively in the training cohort and with 6.7%, 25.0% and 36.0% as corresponding values for the validation cohort. The score identified an increased risk of ED already at sub-normal and normal white blood cell counts and, consequently, it was better at predicting ED risk than the Sanz score (AUROC 0.77 vs. 0.64). In summary, we here present an externally validated and population-based risk score to predict ED risk in a real-world setting, identifying patients with the most urgent need of aggressive ED prevention. The results also suggest that increased vigilance for ED is already necessary at sub-normal/normal white blood cell counts.

Abstract The Swedish national guidelines for treatment of acute myeloid leukemia (AML) recommend analysis of measurable residual disease (MRD) by multiparameter flow cytometry (MFC) in bone marrow in the routine clinical setting. The Swedish AML registry contains such MRD data in AML patients diagnosed 2011–2019. Of 327 patients with AML (non-APL) with MRD-results reported in complete remission after two courses of intensive chemotherapy 229 were MRD-negative (70%), as defined by <0.1% cells with leukemia-associated immunophenotype in the bone marrow. MRD-results were reported to clinicians in real time. Multivariate statistical analysis adjusted for known established risk factors did not indicate an association between MFC-MRD and overall survival (HR: 1.00 [95% CI 0.61, 1.63]) with a median follow-up of 2.7 years. Knowledge of the importance of MRD status by clinicians and individualized decisions could have ameliorated the effects of MRD as an independent prognostic factor of overall survival.

SummaryClinical trials show that tyrosine kinase inhibitor (TKI) treatment can be discontinued in selected patients with chronic myeloid leukaemia (CML). Although updated CML guidelines support such procedure in clinical routine, data on TKI stopping outside clinical trials are limited. In this retrospective study utilising the Swedish CML registry, we examined TKI discontinuation in a population‐based setting. Out of 584 patients diagnosed with chronic‐phase CML (CML‐CP) in 2007–2012, 548 had evaluable information on TKI discontinuation. With a median follow‐up of nine years from diagnosis, 128 (23%) discontinued TKI therapy (≥1 month) due to achieving a DMR (deep molecular response) and 107 (20%) due to other causes (adverse events, allogeneic stem cell transplant, pregnancy, etc). Among those stopping in DMR, 49% re‐initiated TKI treatment (median time to restart 4·8 months). In all, 38 patients stopped TKI within a clinical study and 90 outside a study. After 24 months 41·1% of patients discontinuing outside a study had re‐initiated TKI treatment. TKI treatment duration pre‐stop was longer and proportion treated with second‐generation TKI slightly higher outside studies, conceivably affecting the clinical outcome. In summary we show that TKI discontinuation in CML in clinical practice is common and feasible and may be just as successful as when performed within a clinical trial.

Acute myeloid leukemia (AML) with t(9;22)(q34;q11), also known as AML with BCR-ABL1, is a rare, provisional entity in the WHO 2016 classification and is considered a high-risk disease according to the European LeukemiaNet 2017 risk stratification. We here present a retrospective, population-based study of this disease entity from the Swedish Acute Leukemia Registry. By strict clinical inclusion criteria we aimed to identify genetic markers further distinguishing AML with t(9;22) as a separate entity. Twenty-five patients were identified and next-generation sequencing using a 54-gene panel was performed in 21 cases. Interestingly, no mutations were found in NPM1, FLT3, or DNMT3A, three frequently mutated genes in AML. Instead, RUNX1 was the most commonly mutated gene, with aberrations present in 38% of the cases compared to around 10% in de novo AML. Additional mutations were identified in genes involved in RNA splicing (SRSF2, SF3B1) and chromatin regulation (ASXL1, STAG2, BCOR, BCORL1). Less frequently, mutations were found in IDH2, NRAS, TET2, and TP53. The mutational landscape exhibited a similar pattern as recently described in patients with chronic myeloid leukemia (CML) in myeloid blast crisis (BC). Despite the concomitant presence of BCR-ABL1 and RUNX1 mutations in our cohort, both features of high-risk AML, the RUNX1-mutated cases showed a superior overall survival compared to RUNX1 wildtype cases. Our results suggest that the molecular characteristics of AML with t(9;22)/BCR-ABL1 and CML in myeloid BC are similar and do not support a distinction of the two disease entities based on their underlying molecular alterations.

Hypomethylating agents (HMAs) are increasingly used in patients with acute myeloid leukaemia (AML). Still, the benefit of HMAs compared to conventional therapy is debated and results differ between studies (Kantarjian et al., 2012; Quintas-Cardama et al., 2012; Bories et al., 2014; Dombret et al., 2015; Almeida et al., 2017; Maurillo et al., 2018; Talati et al., 2020). In the present study, we report data on the use and benefit of HMAs from a large population-based cohort covering all elderly patients in Sweden during a 10-year period. In total, 3135 patients aged ≥60 years diagnosed with non-acute promyelocytic leukaemia (APL) AML between January 2008 and December 2018 reported to the Swedish AML Registry (>98% coverage (Juliusson et al., 2012)) were studied. In all, 302 patients (9.6%) had been given HMAs (azacytidine or decitabine) as first-line treatment, 1529 (49%) intensive chemotherapy (IC) and 1304 (42%) palliative care (PC; with or without low-dose cytarabine, hydroxyurea or other low-intensive chemotherapy). The use of upfront HMAs increased from 0.4% to 3.6% of patients 2008–2011 to 8% of patients diagnosed in 2012 and 21% of patients diagnosed in 2018 (Fig 1A). The increase in HMA treatment was paralleled by a decrease in PC. HMA was most commonly used in patients aged 75–84 years (Fig 1B) and the increased HMA usage was seen regardless of cytogenetic risk group (Fig 1C, Table S1). Characteristics of treatment groups are shown in Table S2. Patients treated with HMA were significantly older compared to IC patients (78 vs. 70 years, P < 0·001), but younger compared to PC patients (78 vs. 82 years, P < 0.001). PC patients had poorer World Health Organization Performance Status (WHO PS 2–4 in 75%) compared to HMA and IC patients (WHO PS 0–1 in 76% and 82%, respectively) and cytogenetically high-risk patients were equally frequent between treatment regimens. The 1-year survival in HMA patients was 34%, the 3-year survival was 5.9% and the median overall survival (OS) was 8.3 months (95% confidence interval [CI] 7.0–9.8); corresponding figures for PC patients were 6.4%, 0.8% and 1.6 months (95% CI 1.4–1.8) and for IC patients 50%, 22% and 12.0 months (95% CI 11.4–12.9), respectively (Fig 2A, Table S2). The proportion of early deaths (≤30 days), was lower in HMA-treated patients (4.0% compared to 39.2% and 8.9% in PC- and IC-treated patients, respectively). Multivariate analysis for survival in all 3135 patients showed age, WHO PS, cytogenetic risk and AML aetiology to be independent risk factors (all P < 0.001) (Table S3). Regarding treatment modality, IC versus HMA showed no significant difference (hazard ratio [HR] 0.88, 95% CI 0.75–1.03), while patients with PC did significantly worse compared to HMA (HR 2.72, 95% CI 2.35–3.15; P < 0.001). Prognostic factors differed between patients treated with HMA and IC (Table S4). In HMA-treated patients, high haemoglobin was associated with better OS (P = 0·001), whereas older age and higher lactate dehydrogenase conferred poorer OS (P = 0·011 and P < 0·001, respectively). Importantly, cytogenetic risk, WHO PS and AML aetiology had no impact on OS in HMA patients. In contrast, in IC patients, adverse cytogenetics, antecedent haematological disorder (AHD)-AML, higher leucocyte counts, lower albumin and worse WHO PS were independently associated with worse OS (Table S4). To reduce bias between treatment groups, propensity score matching (PSM) was used to compare HMA to IC and PC. We matched 275 HMA patients to IC and PC patients at a ratio of 1:2 (see Table S5 for details). Despite matching, the median age remained higher in HMA compared to IC patients (78 vs. 74 years, P < 0·001). Still, there was no statistically significant difference in median OS between HMA and IC patients (8·3 vs. 9·3 months, P = 0·08) (Fig 2B). To investigate if better matching on age would change outcome, a 1:1 matching of 193 HMA patients aged 60–80 years was also performed (Fig S1), but still without a statistically significant difference between HMA and IC (P = 0·1). In addition, cytogenetic risk did not impact on the degree of benefit of HMA treatment (Fig 2C). In matched HMA versus PC patients, HMA patients showed a clear benefit with a median OS of 8·3 months compared to 2·7 months with PC (P < 0·0001) (Fig 2B). These differences were seen regardless of cytogenetic risk group (Fig 2D). However, it should be noted that co-morbidities, being associated with increased mortality in patients with AML (Storey et al., 2017), and likely to be over-represented in PC patients, could not be matched for due to insufficient data in the Registry. Recently, Talati et al. reported that patients aged ≥70 years treated with HMA had superior OS compared to both intensive and palliative treatment regimens (Talati et al., 2020). In order to investigate if our present results would show similar results in patients aged ≥70 years, we performed a PSM analysis in patients aged ≥70 years, but found the same results with no significant differences between HMA and IC (data not shown). The reason for the difference compared to the Talati et al. study is unclear, but our present results are more in line with other previous studies outside of clinical trials (Quintas-Cardama et al., 2012; Maurillo et al., 2018). The strengths of our present study are its size, as well as the nationwide and truly population-based nature of the cohort, likely to reflect the real-world setting. Still, limitations include lack of information on type of HMA administered and the number of completed cycles. Concerning the type of HMA, azacitidine is dominantly used in Sweden and in a study of health records, 19 of 20 HMA-treated patients received azacitidine. In our present study, as well as most other HMA studies, molecular testing deciphering the full mutational landscape of the patients, including tumour protein p53 (TP53), runt-related transcription factor 1 (RUNX1) and additional sex combs like-1 (ASXL1) status, was lacking. In future studies, such molecular profiling will be important for improving risk stratification for HMA treatment. In summary, we show a steady increase in the usage of HMAs in a population-based setting. HMA treatment was similar to IC in terms of OS in multivariate analysis, as well as in a PSM analysis, while there was a clear benefit of HMA compared to palliative treatment. In addition, prognostic factors differed between patients treated with HMA compared to IC. The authors declare no conflicts of interest. Table S1. Cytogenetic risk stratification*. Table S2. Clinical characteristics and comparisons between treatment groups. Table S3. Cox regression analyses for overall survival of patients aged ≥60 years, n = 3135. Table S4. Cox regression analyses of overall survival for patients aged ≥60 years treated with either HMA or intensive chemotherapy upfront. HMA, n = 302. Table S5. Propensity score matched comparisons between patients treated upfront with hypomethylating agent (HMA) or intensive chemotherapy (IC)*. Figure S1. Propensity score matching (PSM) with optimal age matching. Comparison of overall survival in PSM groups between patients receiving hypomethylating agents or intensive chemotherapy. Analyses were restricted to patients aged 60–80 years for optimal age matching. Variables used for PSM were age, gender, WHO Performance Status, cytogenetic risk, previous myelodysplastic syndrome and previous myeloproliferative disorder. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.