A groundbreaking study led by researchers from Kyoto University and Karolinska Institute has unveiled a new epigenomic dimension in classifying acute myeloid leukemia (AML), promising to reshape diagnostic and therapeutic strategies for this aggressive blood cancer. Acutely aware that gene mutations have long dominated the landscape of AML classification, this expansive research now highlights the pivotal role of chromatin architecture and transcriptional regulation in tumor heterogeneity and treatment response.
The team conducted an unprecedented epigenomic analysis on over 1,500 AML patient samples, utilizing ATAC-seq technology to map chromatin accessibility across the genome. This method revealed 16 distinct AML subgroups characterized by unique patterns of open chromatin regions, transcription-factor networks, and super-enhancer landscapes. These epigenetic signatures delineate molecular wiring underlying disease progression, clinical outcomes, and drug sensitivity, independently contributing information beyond what gene mutation profiles alone can provide.
Single-cell RNA and ATAC sequencing validated that these epigenomic subgroups maintain consistent chromatin states throughout leukemic cell populations, underscoring their biological relevance. Intriguingly, many groups did not correlate fully with existing genomic classifications, exposing latent AML diversity invisible to mutation-centric analyses. This discovery broadens our understanding of AML as a disease shaped not only by genetic alterations but also by the epigenomic context that governs gene expression programs.
Clinically, integrating epigenomic data substantially improved risk stratification models traditionally based on somatic mutations. The study identified unexpected vulnerabilities: for example, subsets lacking canonical RAS mutations showed sensitivity to MEK inhibitors, while a subgroup with RUNX1 mutations exhibited susceptibility to ABL inhibitors typically reserved for distinct leukemias. This insight paves the way for re-purposing targeted therapies guided by epigenomic biomarkers, potentially enhancing precision medicine approaches.
The researchers further distilled their findings into a 30-gene expression signature capable of pinpointing high-risk epigenomic subgroups using standard sequencing platforms. This compact biomarker panel aims to facilitate clinical adoption, supporting refined prognosis and tailored treatment selection in routine practice. Beyond AML, the comprehensive multi-omics database generated forms a valuable foundation for cancer epigenomics research, catalyzing discoveries of novel mechanisms and therapeutic targets.
Overall, this study marks a paradigm shift by positioning chromatin state and epigenetic heterogeneity as central determinants of AML biology, prognosis, and therapy response. Moving forward, the team plans to develop cost-efficient diagnostic tools and therapeutic algorithms stratified by epigenomic subgroup, working to embed these advances into everyday clinical use. This landmark research, published in Nature, signals a new era in leukemia precision medicine where genome and epigenome jointly inform patient care.
Subject of Research: People
Article Title: Chromatin landscape and epigenetic heterogeneity of acute myeloid leukaemia
News Publication Date: 8-Jul-2026
Web References: https://www.nature.com/articles/s41586-026-10703-4
References: Ochi, Y., Liew-Littorin, M., Nannya, Y., et al. Chromatin landscape and epigenetic heterogeneity of acute myeloid leukaemia. Nature (2026). DOI: 10.1038/s41586-026-10703-4
Image Credits: ASHBi/Kyoto University
Keywords: Acute myeloid leukemia, epigenomics, chromatin, ATAC-seq, transcription-factor networks, super-enhancers, precision medicine
Tags: AML classificationATAC-seq technology in hematologychromatin accessibility in cancerepigenetic signatures and treatment responseepigenomic profiling in leukemiagene mutation vs epigenetic influences in AMLleukemia diagnostics and therapeutic strategiesmolecular subgroups of leukemiasingle-cell RNA and ATAC sequencing in AMLsuper-enhancer landscapes in AMLtranscription factor networks in leukemiatumor heterogeneity in leukemia
