Acute myeloid leukemia (AML) is one of the most aggressive of all blood cancers, and how it is classified helps determine how each patient is treated. For decades, that classification has rested on the gene mutations identified in leukemic cells, which have driven both clinical decisions and the development of targeted drugs. But 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, though the full picture has remained unclear.
A research team led by Professor Seishi Ogawa from Kyoto University carried out a large-scale epigenomic analysis of more than 1,500 AML patient samples. The team found 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 findings, published in Nature, point to an additional dimension of AML diversity that gene mutations alone cannot capture.
AML 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, causing immature blood cells to accumulate in the bone marrow and proliferate uncontrollably. While next-generation sequencing has revealed a wide range of gene mutations involved in AML over the past two decades, cells are also governed by the epigenome, which includes chemical modifications to DNA and the proteins that package it into chromatin.
The team applied ATAC-seq to 1,563 AML patient samples from cohorts in Sweden and Japan, producing a dataset called eCHROMA AML, the largest of its kind for any cancer. Single-cell RNA and ATAC sequencing of more than 280,000 cells from 36 patients confirmed that each of the 16 subgroups carries a distinctive, conserved chromatin state along with its own combination of gene mutations, differentiation states, gene-expression profiles, DNA methylation patterns, and transcriptional regulatory networks. Many subgroups did not align with existing genomic classifications.
Adding chromatin information to existing genomic risk categories improved prognostic accuracy in both cohorts. The analysis also uncovered unexpected drug sensitivities: three subgroups responded to MEK inhibitors despite lacking RAS mutations, and one subgroup with frequent RUNX1 mutations proved highly sensitive to ABL inhibitors, a drug class traditionally used for a different blood cancer.
The team has identified a 30-gene expression signature to help identify high-risk subgroups using standard sequencing workflows, and plans to develop lower-cost diagnostic methods and refine treatment strategies for each subgroup.
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, though the full picture has remained unclear.
A research team led by Professor Seishi Ogawa from Kyoto University carried out a large-scale epigenomic analysis of more than 1,500 AML patient samples. The team found 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 findings, published in Nature, point to an additional dimension of AML diversity that gene mutations alone cannot capture.
AML 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, causing immature blood cells to accumulate in the bone marrow and proliferate uncontrollably. While next-generation sequencing has revealed a wide range of gene mutations involved in AML over the past two decades, cells are also governed by the epigenome, which includes chemical modifications to DNA and the proteins that package it into chromatin.
The team applied ATAC-seq to 1,563 AML patient samples from cohorts in Sweden and Japan, producing a dataset called eCHROMA AML, the largest of its kind for any cancer. Single-cell RNA and ATAC sequencing of more than 280,000 cells from 36 patients confirmed that each of the 16 subgroups carries a distinctive, conserved chromatin state along with its own combination of gene mutations, differentiation states, gene-expression profiles, DNA methylation patterns, and transcriptional regulatory networks. Many subgroups did not align with existing genomic classifications.
Adding chromatin information to existing genomic risk categories improved prognostic accuracy in both cohorts. The analysis also uncovered unexpected drug sensitivities: three subgroups responded to MEK inhibitors despite lacking RAS mutations, and one subgroup with frequent RUNX1 mutations proved highly sensitive to ABL inhibitors, a drug class traditionally used for a different blood cancer.
The team has identified a 30-gene expression signature to help identify high-risk subgroups using standard sequencing workflows, and plans to develop lower-cost diagnostic methods and refine treatment strategies for each subgroup.