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Scientific Frontline: Extended "At a Glance" Summary: Decoding the Epigenome of Acute Myeloid Leukemia
The Core Concept: Acute myeloid leukemia (AML) is driven not only by gene mutations but also by its epigenome—specifically, the chromatin state that dictates which genes are active. By mapping these accessible genome regions, researchers have established a new framework that classifies AML into sixteen distinct epigenetic subgroups.
Key Distinction/Mechanism: While traditional oncological classifications rely solely on genomic mutations, this approach uses ATAC-seq technology to map the structural accessibility of chromatin across the entire genome. This reveals underlying transcription-factor networks and super-enhancer architectures that dictate disease behavior, revealing unexpected drug sensitivities completely missed by DNA sequencing alone.
Major Frameworks/Components:
- The eCHROMA AML Dataset: The largest ATAC-seq dataset ever compiled for any cancer, containing chromatin profiling from 1,563 patient samples across independent cohorts in Japan and Sweden.
- Epigenomic Subgrouping: The classification of AML into sixteen distinct, chromatin-based subgroups, each featuring unique molecular wiring, differentiation states, gene-expression profiles, and DNA methylation patterns.
- Single-Cell Multi-Omics: The integration of single-cell RNA and ATAC sequencing across more than 280,000 cells to verify that chromatin states remain tightly conserved within specific leukemic cell populations.
- 30-Gene Expression Signature: A compact, targeted diagnostic tool developed by the research team to identify high-risk, chromatin-defined subgroups using standard clinical sequencing workflows.
Branch of Science: Oncology, Epigenomics, Molecular Biology, and Hematology.
Future Application: This epigenomic framework provides the foundation for next-generation precision medicine, allowing clinicians to match AML patients with highly targeted therapies—such as deploying ABL or MEK inhibitors for specific epigenetic subgroups lacking traditional mutation markers. The findings will also drive the development of simplified, lower-cost diagnostic assays for routine clinical environments.
Why It Matters: Incorporating chromatin data into existing genomic risk categories substantially improves prognostic accuracy and uncovers vital, life-saving treatment options for aggressive blood cancers. It proves that leukemia is a complex epigenomic disease, fundamentally changing how oncologists understand, predict, and treat leukemic proliferation.
Glossary
- ATAC-seq: A next-generation sequencing technique that comprehensively profiles open chromatin regions across the genome in a single experiment. By identifying DNA regions accessible to the cell’s transcriptional machinery, it provides a readout of cellular state and gene regulation.
- Chromatin: The complex of DNA and histone proteins inside the cell nucleus. Its state (whether a given region is open or closed) determines which genes are accessible and can be expressed.
- Epigenome: A collective term for mechanisms that regulate gene activity without altering the DNA sequence itself, including DNA methylation and chromatin structure.
- Super-enhancer: A large and highly active regulatory region of the genome that drives the expression of genes important for cellular identity. Cancers often co-opt super-enhancers to lock cells into an abnormal state.
Acute myeloid leukemia (AML) is one of the most aggressive of all blood cancers. Its classification helps determine each patient’s treatment. For decades, this classification has rested on gene mutations identified in leukemic cells, which have driven both clinical decisions and the development of targeted drugs. However, gene mutations are only part of the story. The epigenome, the layer of regulation that determines which genes a cell uses, has long been thought to play an equally important role in AML, but the full picture has remained unclear.
A research team led by Professor Seishi Ogawa (Department of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University; concurrently Principal Investigator at the Institute for the Advanced Study of Human Biology [WPI-ASHBi], Kyoto University), Assistant Professor Yotaro Ochi (same department), and Professor Sören Lehmann (Karolinska Institute) has carried out a large-scale epigenomic analysis of more than 1,500 AML patient samples. The team showed that AML can be classified into 16 subgroups based on its epigenomic features, each with its own molecular wiring, clinical prognosis, and drug sensitivity. The results reveal an additional dimension of AML diversity that cannot be captured by gene mutations alone.
These findings indicate that AML is not simply a “genomic disease”: its epigenomic architecture also determines disease behavior, prognosis, and treatment response, providing a foundation for a new generation of precision medicine that draws on epigenomic information.
Background
Acute myeloid leukemia (AML) is one of the most aggressive blood cancers. It develops when mutations in blood-forming cells disrupt the normal production of mature red blood cells, white blood cells, and platelets from hematopoietic stem cells. Instead, immature blood cells accumulate in the bone marrow and proliferate uncontrollably. Over the past two decades, next-generation sequencing has revealed a wide variety of gene mutations involved in AML, transforming both our understanding of the disease and the development of targeted therapies.
In addition to the genome, cells are governed by another layer of regulation known as the epigenome, which includes chemical modifications to DNA (such as methylation) and to the proteins that package it into chromatin. These epigenetic changes are thought to contribute to AML by influencing how blood cells differentiate and proliferate. However, a comprehensive view of epigenomic abnormalities across AML had not previously been available.
Key Findings
The team applied ATAC-seq, a technique that maps which regions of the genome are open and accessible, to 1,563 AML patient samples from independent cohorts in Sweden and Japan. The resulting dataset, named eCHROMA AML, is the largest of its kind for any cancer. Based on their chromatin states, the AML cases were classified into 16 characteristic subgroups. Single-cell RNA and ATAC sequencing of more than 280,000 cells from 36 patients further confirmed that each subgroup is characterized by a distinctive chromatin state that is conserved across its leukemic cell population. Each of the 16 subgroups carried a distinct combination of gene mutations, differentiation states, gene expression profiles, DNA methylation patterns, and transcriptional regulatory networks. Notably, many of the subgroups did not align perfectly with existing genomic classifications, suggesting that important aspects of AML diversity cannot be captured by genome-based analysis alone.
Integrated analysis of the epigenomic data further showed that each subgroup is organized around a distinctive transcription factor network and super-enhancer architecture, with its own gene regulatory program defining the molecular characteristics of its leukemic cells. Clinically, adding chromatin information to the existing genomic risk categories substantially improved the accuracy of prognostic assessment in both the Swedish and Japanese cohorts. The epigenomic analysis also revealed unexpected drug sensitivities that mutations alone had missed: three subgroups responded to MEK inhibitors even when they lacked the RAS mutations that would normally guide the use of these drugs. Perhaps most surprisingly, one subgroup characterized by frequent RUNX1 mutations and a chromatin profile resembling that of early B-cell precursors proved highly sensitive to ABL inhibitors, a class traditionally used for an entirely different blood cancer.
Looking Ahead
This is the first large-scale study to establish that, alongside gene mutations, the chromatin state is essential for understanding how leukemia behaves and what gives each case its biological identity. These findings could improve diagnosis, prognostic assessment, and treatment selection as part of a new generation of precision medicine. To support clinical adoption, the team has identified a compact 30-gene expression signature that distinguishes the chromatin-defined high-risk subgroups using standard sequencing workflows.
The large-scale multiomics database generated in this study is also expected to serve as a foundational resource for cancer epigenomics, both in AML and beyond, supporting the discovery of new therapeutic targets and disease mechanisms. Moving forward, the team aims to develop simpler and lower-cost diagnostic methods and to refine treatment strategies for each subgroup, bringing this approach closer to routine clinical use.
Published in journal: Nature
Title: Chromatin landscape and epigenetic heterogeneity of acute myeloid leukaemia
Authors: Yotaro Ochi, Markus Liew-Littorin, Yasuhito Nannya, Sofia Bengtzen, Benedicte Piauger, Stefan Deneberg, Martin Jädersten, Vladimir Lazarevic, Jörg Cammenga, Anna Robelius, Lovisa Wennström, Emma Ölander, Senji Kasahara, Nobuhiro Hiramoto, Nobuhiro Kanemura, Nobuo Sezaki, Maki Sakurada, Makoto Iwasaki, Junya Kanda, Yasunori Ueda, Satoshi Yoshihara, Tom Erkers, Nona Struyf, Yu Watanabe, Masanori Motomura, Masahiro M. Nakagawa, Ryunosuke Saiki, Hidehito Fukushima, Koji Okazaki, Suguru Morimoto, Akinori Yoda, Rurika Okuda, Shintaro Komatsu, Guoxiang Xie, Albin Österroos, Ayana Kon, Lanying Zhao, Yuichi Shiraishi, Takayuki Ishikawa, Satoru Miyano, Kotoe Katayama, Seiya Imoto, Shuichi Matsuda, Akifumi Takaori-Kondo, Hiroyuki Aburatani, Hiroshi I. Suzuki, Olli Kallioniemi, Gunnar Juliusson, Martin Höglund, Sören Lehmann, and Seishi Ogawa
Source/Credit: Institute for the Advanced Study of Human Biology | Kyoto University
Edited by: Scientific Frontline
Reference Number: ongy072926_01
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