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Clinical implementation of patient cell-based drug testing is hampered by inconsistencies and irreproducible results. Using linear mixed models and drug response data from four cohorts of patients with acute myeloid leukemia (n = 993 samples), we identified sources of variability in ex vivo drug sensitivity testing across the studies. We identified several experimental factors, including the time until sample was tested, the type of culture media and viability assay, which led to systematic differences in drug sensitivity profiles. We further investigated the effects of sample types (fresh or frozen) and disease status (diagnosis, relapse, or refractory), and observed that peripheral blood samples resulted in higher drug sensitivity levels, compared to those derived from bone marrow. Technical factors, such as the type of plate reader and centrifugation procedure, further contributed to the response variability depending on the chosen drug response metric. These factors need to be standardized for improved consistency in functional precision medicine studies.

Clonal hematopoiesis of indeterminate potential (CHIP), defined by somatic mutations in hematopoietic cells without overt malignancy, is linked to increased risks of hematologic malignancies, cardiovascular disease, and mortality. However, the contribution of environmental exposures and lifestyle factors remains poorly understood.

Our exploratory study characterized early-stage CHIP using a variant allele frequency cut-off > 0.1% in 888 well-phenotyped 70-year-olds leveraging ultra-deep targeted sequencing (mean coverage 8331X). Associations with dietary patterns, specific nutrient intakes, persistent organic pollutants (POPs), per- and polyfluoroalkyl substances (PFAS), heavy metals, and germline variants were evaluated.

The prevalence of early-stage CHIP was 41.0%. Elevated blood cadmium levels were associated with an increased prevalence of non-DTA-CHIP (mutations occurring outside of DNMT3A, TET2, and ASXL1, odds ratio (OR) 2.13, 95% confidence interval (CI) 1.32–3.45), especially TP53-CHIP (OR 3.34, 95% CI 1.65–6.72). PCB-189 levels were positively linked to DNMT3A-CHIP (OR 1.42, 95% CI 1.13–1.79). Healthy dietary patterns were associated with decreased odds of TET2-CHIP (OR 0.88, 95% CI 0.78–0.98) whereas increased odds for TET2-CHIP were observed for higher intakes of vitamin B2 (OR 2.07, 95% CI 1.17–3.69). Higher intakes of vitamins B2 and B6 as well as smoking were associated with an increased prevalence of non-DTA CHIP (OR 2.35, 95% CI 1.03–5.33, OR 2.05, 95% CI 1.03–4.05, and OR 3.17, 95% CI 1.43–6.64, respectively).

Our exploratory study suggests that the exposome may influence clonal selection in the bone marrow, but future studies are required to validate these factors and determine their clinical significance.

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, 2, 3, 4, 5, 6, 7-8; 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.

Circular RNAs (circRNAs) have emerged as important regulators in cancer biology, but their roles in acute myeloid leukemia (AML) remain poorly characterized due to limited sample sizes and technical challenges in RNA sequencing. Here, we analyze RNA-sequencing data from 315 Swedish AML patients to create the most comprehensive circRNA profile in AML to date. We identify 5,711 high-confidence circRNAs across 315 AML samples, including 402 differentially expressed between AML and healthy controls, with host genes enriched in hematopoietic pathways. We further discover two circRNAs including hsa_circ_0024048 (p = 2.16 & times;10(-)(6), FDR = 0.012) and hsa_circ_0084678 (p = 1.33 & times;10(-)(5), FDR = 0.075) whose high expression is associated with significantly improved overall survival, a relationship not observed in their respective host genes. Furthermore, these circRNAs are associated with sensitivities of several drugs, as validated in external datasets (p < 0.05). We identify 451 circRNAs with ELN2022 risk group-specific expression patterns, highlighting circRNA heterogeneity. Subtype analysis further reveals that hsa_circ_0080850 is specifically associated with worse survival (p = 2.13 & times;10(-)(5) and lower remission rates (38.9% vs 74.7%) within the ELN2022 Favorable subgroup. To conclude, this study establishes the most comprehensive circRNA landscape in AML to date and demonstrates their potential as biomarkers and therapeutic targets, suggesting further investigation into circRNA-driven precision medicine in AML.

FLT3-mutated acute myeloid leukemia (AML) remains difficult to treat due to frequent resistance to FLT3 inhibitors like midostaurin. In this study, we observed a progenitor-like CD38+CD45RA+ leukemic cell population that may be associated with midostaurin resistance. Midostaurin-resistant cells display disrupted membrane architecture and a shift in signaling from STAT5 to PI3K/AKT, favoring survival over apoptosis. Functional drug testing was consistent with clinical response to midostaurin, and together with multi-omic profiling, including single-cell and proteomic analyses, indicated the presence and relevance of this resistant phenotype. Drug combination screening revealed that co-targeting with SMAC mimetics restores apoptotic competence and selectively depletes the resistant population when combined with midostaurin. In contrast, venetoclax combinations preferentially affected CD34hi cells, underscoring distinct subpopulation vulnerabilities. These findings may point to a biologically relevant mechanism underlying midostaurin resistance.

As the non-coding genome remains poorly characterized in acute myeloid leukemia (AML), we aimed to identify and functionally characterize novel long non-coding RNAs (lncRNAs) relevant to AML biology and treatment. We first identified lncRNAs overexpressed in AML blasts and, among them, discovered a novel transcript, which we named myeloid and AML-associated intergenic long non-coding RNA (MALNC). MALNC is overexpressed in AML, particularly in cases with the PML-RARA fusion or IDH2^R140/NPM1 co-mutations, and is associated with a distinct gene expression profile. Functional studies showed that MALNC knockout impairs AML cell proliferation and colony formation, enhances ATRA-induced differentiation, and sensitizes cells to arsenic trioxide. Transcriptomic analysis revealed that MALNC loss alters the expression of retinoic acid pathway genes, and chromatin binding studies showed that MALNC binds to genes related to the retinoic acid and Rho GTPase pathways. In conclusion, we have identified MALNC as a novel lncRNA that promotes leukemic cell proliferation, counteracts ATRA-induced differentiation, and modulates drug sensitivity in AML.

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 Drug synergy Interaction Prediction (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 human cell-line 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 model's ability to handle novel combinations. The Spearman's 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.

Consistent handling of samples is crucial for achieving reproducible molecular and functional testing results in translational research. Here, we used 229 acute myeloid leukemia (AML) patient samples to assess the impact of sample handling on high-throughput functional drug testing, mass spectrometry-based proteomics, and flow cytometry. Our data revealed novel and previously described changes in cell phenotype and drug response dependent on sample biobanking. Specifically, myeloid cells with a CD117 (c-KIT) positive phenotype decreased after biobanking, potentially distorting cell population representations and affecting drugs targeting these cells. Additionally, highly granular AML cell numbers decreased after freezing. Secondly, protein expression levels, as well as sensitivity to drugs targeting cell proliferation, metabolism, tyrosine kinases (e.g., JAK, KIT, FLT3), and BH3 mimetics were notably affected by biobanking. Moreover, drug response profiles of paired fresh and frozen samples showed that freezing samples can lead to systematic errors in drug sensitivity scores. While a high correlation between fresh and frozen for the entire drug library was observed, freezing cells had a considerable impact at an individual level, which could influence outcomes in translational studies. Our study highlights conditions where standardization is needed to improve reproducibility, and where validation of data generated from biobanked cohorts may be particularly important.