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Evolving cell states and oncogenic drivers during the progression of IDH-mutant gliomas

Nature Cancer volume 6, pages 145–157 (2025)Cite this article

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Abstract

Isocitrate dehydrogenase (IDH) mutants define a class of gliomas that are initially slow-growing but inevitably progress to fatal disease. To characterize their malignant cell hierarchy, we profiled chromatin accessibility and gene expression across single cells from low-grade and high-grade IDH-mutant gliomas and ascertained their developmental states through a comparison to normal brain cells. We provide evidence that these tumors are initially fueled by slow-cycling oligodendrocyte progenitor cell-like cells. During progression, a more proliferative neural progenitor cell-like population expands, potentially through partial reprogramming of ‘permissive’ chromatin in progenitors. This transition is accompanied by a switch from methylation-based drivers to genetic ones. In low-grade IDH-mutant tumors or organoids, DNA hypermethylation appears to suppress interferon (IFN) signaling, which is induced by IDH or DNA methyltransferase 1 inhibitors. High-grade tumors frequently lose this hypermethylation and instead acquire genetic alterations that disrupt IFN and other tumor-suppressive programs. Our findings explain how these slow-growing tumors may progress to lethal malignancies and have implications for therapies that target their epigenetic underpinnings.

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Fig. 1: scRNA expression and chromatin accessibility in IDH-mutant gliomas.
Fig. 2: Malignant cell types in IDH-mutant gliomas lack chromatin specification.
Fig. 3: Cell states and genetic alterations associated with glioma progression.
Fig. 4: Suppression of IFN responses in IDH-mutant gliomas.
Fig. 5: Malignant cell states are associated with different IFN levels and patient outcomes.

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Data availability

Processed scATAC-seq and scRNA-seq data are available through the Gene Expression Omnibus under accession number GSE241745. Raw data are available through the dbGaP under accession number phs003697. The human glioma bulk RNA-seq and methylation data were obtained from TCGA Research Network (http://cancergenome.nih.gov/). All other data supporting the findings of this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

Original code for the analyses performed in this study was deposited on GitHub (https://github.com/BernsteinLab/IDH_mutant_gliomas_progression_2024).

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Acknowledgements

We thank G. Rahme, C. Couturier, J. Verga, M. Thompsom and all members of the Bernstein laboratory for their discussions. We thank S. Ma, V. Kartha, C. Lareau and J. Buenrostro for guiding the scATAC-seq experiment and analysis. We thank S. Gritsch and D. Silverbush for providing help with the preparation and analysis of glioma samples. This work was supported by funds from the National Cancr Institute (NCI) and National Institutes of Health (NIH) Director’s Fund (DP1CA216873 to B.E.B.) and the Ludwig Center at Harvard. J.W. was supported by a Damon Runyon postdoctoral fellowship award and King Trust fellowship award. L.N.G.C was supported by NIH award K12CA090354 and a Harold Amos faculty development award from the Robert Wood Johnson Foundation. B.E.B. is the Richard and Nancy Lubin Family Endowed Chair at the Dana-Farber Cancer Institute and an American Cancer Society research professor. This work was supported by grants to M.L.S. from the Mark Foundation (emerging leader award), the Sontag Foundation (distinguished scientist award), the MGH Research Scholars and NCI R37CA245523 and R01CA258763.

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Author notes

  1. These authors contributed equally: Jingyi Wu, L. Nicolas Gonzalez Castro.

Authors and Affiliations

  1. Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA

    Jingyi Wu, Sofia Battaglia, Chadi A. El Farran, Joshua P. D’Antonio, Tyler E. Miller & Bradley E. Bernstein

  2. Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Jingyi Wu, L. Nicolas Gonzalez Castro, Sofia Battaglia, Chadi A. El Farran, Joshua P. D’Antonio, Tyler E. Miller, Mario L. Suvà & Bradley E. Bernstein

  3. Departments of Cell Biology and Pathology, Harvard Medical School, Boston, MA, USA

    Jingyi Wu, Sofia Battaglia, Chadi A. El Farran, Joshua P. D’Antonio, Tyler E. Miller & Bradley E. Bernstein

  4. Department of Pathology and Krantz Family Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    L. Nicolas Gonzalez Castro, Tyler E. Miller & Mario L. Suvà

  5. Department of Neurology, Brigham and Women’s Hospital, Boston, MA, USA

    L. Nicolas Gonzalez Castro

  6. Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    L. Nicolas Gonzalez Castro

  7. The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Bradley E. Bernstein

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Contributions

Conceptualization and experimental design, J.W., L.N.G.C., M.L.S. and B.E.B. Methodology and data acquisition, J.W., L.N.G.C., S.B., J.D.A. and T.E.M. Analysis and interpretation of data, J.W., L.N.G.C., C.A.E.F., M.L.S. and B.E.B. Manuscript writing and revision, J.W., L.N.G.C., M.L.S. and B.E.B.

Corresponding author

Correspondence to Bradley E. Bernstein.

Ethics declarations

Competing interests

L.N.G.C. has received research funding from Merck & Co. (to the Dana-Farber Cancer Institute) and has received consulting fees from Elsevier, Servier Laboratories, BMJ Best Practice, Prime Education and Oakstone Publishing. B.E.B. discloses financial interests in HiFiBio, Arsenal Biosciences, Chroma Medicine, Cell Signaling Technologies and Design Pharmaceuticals. M.L.S. discloses financial interests in Immunitas Therapeutics. T.E.M. discloses financial interest in Reify Health, Care Access Research and Telomere Diagnostics. The remaining authors declare no competing interests.

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Nature Cancer thanks Shi-Yuan Cheng, Roel Verhaak and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Identification of malignant cells and their developmental states from scRNA-seq and scATAC-seq data.

a. Genome tracks show aggregated (pseudo-bulk) scATAC-seq data for IDH-mutant gliomas (n=10) over a representative neural locus (ASCL1). b. UMAP visualization of IDH-mutant glioma cells profiled by scRNA-seq and scATAC-seq. The leftmost plots indicate annotated malignant cells, while the others depict expression or promoter and gene body accessibility (red) of cell type-specific genes. c. Genome tracks show aggregated scRNA-seq and scATAC-seq data over representative cell type-specific genes. d. CNAs inferred from scATAC-seq data for malignant cells from IDH-mutant cohorts used in this study (see Methods). CNAs inferred directly from the scATAC-seq data (right) were consistent with CNAs derived by applying inferCNV to imputed gene activity scores (left). e. UMAP visualizations of integrated scRNA-seq and scATAC-seq data from normal fetal and adult brain cells. Cells are colored by annotated cell types (left) and donor types (right). f. Pie charts depict the distributions of cell state annotations nominated by scRNA-seq (left) or scATAC-seq (right). g. Gene programs enriched in OPC-like cells from IDH-mutant oligodendrogliomas (IDH-O) or astrocytomas (IDH-A) by NMF analysis. h. Genome tracks show aggregated scATAC-seq data for each cell state over oligodendrocyte- (APOD) and astrocyte-specific (APOE) genes.

Source data

Extended Data Fig. 2 Marker genes and TF motifs associated with glioma cell states.

a. Heatmaps show the expression of variable genes (rows) across individual brain or malignant cells (columns). Cells are grouped by their assigned states. b. Heatmaps depict TF motif enrichments (rows) in scATAC-seq profiles for individual brain or malignant cells (columns). c. Genome tracks show aggregate accessibility over portions of the MYC and PDGFRA loci in normal brain and malignant glioma cell types. Shaded intervals correspond to an OPC-specific enhancer in the MYC locus that harbors a genetic variant associated with glioma risk (left) and an OPC-specific enhancer implicated in PDGFRA induction. d. Heatmaps show pairwise correlations of scRNA-seq (top) or scATAC-seq (bottom) data for normal brain cells or malignant glioma cells grouped by their cell type classifications, as in Fig. 2c, but with data from three high-grade IDH-mutant gliomas with matched scRNA-seq and scATAC-seq data.

Source data

Extended Data Fig. 3 Glioma progression associated with increasing proportions of NPC-like cells.

a. Box plots depict proportions of OPC-like and NPC-like cells in IDH-mutant gliomas, stratified by grade and subtype (n=7 for IDHO grade 2, n=2 for IDHO grade 3, n=5 for IDHA grade 2, n=11 for IDHA grade 3, n=5 for IDHA grade 4). Boxes depict 25th, 50th and 75th percentiles, and whiskers depict extreme values. One-tailed t-test P-value: 0.013 for NPC-like proportions. b. Box plots depict relative proportions of NPC-like versus OPC-like cells, inferred from bulk expression data for IDH-mutant gliomas (TCGA), stratified by grade and subtype (n=81 for IDHO grade 2, n=70 for IDHO grade 3, n=114 for IDHA grade 2, n=104 for IDHA grade 3, n=7 for IDHA grade 4). One-tailed t-test P-value: 0.039; *** defines p<0.001. c. Barplot depicts the ratio of NPC-like to OPC-like cells in scRNA-seq data for six matched pairs of primary and recurrent tumors. Data are presented as mean values +/- SEM. d. UMAP visualization of malignant cells projected onto normal brain cells, as in Figs. 1b and 3a, with heat depicting the proliferation scores of individual malignant cells. e. Trajectory analysis was performed on combined scRNA-seq data for malignant cells and normal OPCs, using the Monocle package. The pseudotime coloring and best-fit trajectory are consistent with progression from normal OPC to OPC-like and then NPC-like malignant cells. f. Plot depicts CNAs for loci subject to CNAs (rows) across single cells (columns) from a second IDH-mutant glioma (OPK438), as in Fig. 3g. Malignant cells are grouped into subclones based on CNAs, and compared to normal cells from the same resection (left). Malignant cell state assignments indicated.

Source data

Extended Data Fig. 4 Validation of IFN signature in malignant cells and glioma organoids.

a. Boxplot depicts IFN signature scores (RNA-seq) for grade 2 IDH mutant and WT gliomas (n=35 for IDH mutant and n=5 for IDH WT) with high purity estimates (>80%). Boxes depict 25th, 50th and 75th percentiles, and whiskers depict extreme values. One-tailed t-test P-values: *** defines p<0.001. b. Heatmap shows DNA methylation levels, proliferation scores, and IFN scores (rows) in grade 2 IDH-mutant and grade 2 IDH WT TCGA gliomas (columns) based on the 2016 WHO classification. c. Heatmap depicts the expression of IFN pathway genes (columns) clustered by their expression across malignant cells, immune cell lineages, and oligodendrocytes (rows). This analysis distinguished malignant cell-specific IFN-related genes. d. UMAP visualization of scRNA-seq data compares cells from a glioma organoid (red) and the primary tumor from which it was derived (blue). e. Heatmaps depict the expression of variable genes associated with different cell states (rows) in single cells (columns) grouped by nominal cell identity. The glioma organoids recapitulate cell types and transcriptional programs as seen in the primary tumors.

Source data

Extended Data Fig. 5 IFN genes are upregulated by DNMT1 and IDH inhibitors.

a. Box plots depict module scores (Seurat) for gene correlates of DNA methylation loss in IDH-mutant glioma organoids treated with DNMT1 inhibitor or control (n=3 technical replicates). Boxes depict 25th, 50th and 75th percentiles, and whiskers depict extreme values. b. Running Enrichment Score (GSEA) for IFNa genes regulated by DNMT1 inhibitor. c. Box plots depict module scores (Seurat) for gene correlates of methylation loss in IDH-mutant gliomas resected from patients treated with IDH inhibitors. Left: paired pre- and post-treatment samples from a single patient. Right: unpaired samples from 6 untreated and 2 treated patients. Two-tailed t-test P values: ***p<0.001. d. Running Enrichment Score (GSEA) for IFNa genes in IDH-mutant gliomas resected from patients treated with IDH inhibitor (as in c). e. Multivariate survival analysis for high-grade IDH-mutant gliomas (grade 3 and 4) stratified by NPC enrichment score and tumor grade. Hazard ratios (HR) and P-value associated with NPC-enriched tumors are indicated.

Source data

Supplementary information

Supplementary Information

Supplementary Fig. 1.

Reporting Summary

Supplementary Table

Supplementary Table 1: The quality control information for ATAC-seq samples. Supplementary Table 2: The P value between NPC ratio and proliferation scores and between genetic lesions and DNA methylation. Supplementary Table 3: The percentage of codeletion. Supplementary Table 4: The subtype and grade information of TCGA cohorts. Supplementary Table 5: Genes upregulated upon DNA demethylation in glioma cell line.

Source data

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Wu, J., Gonzalez Castro, L.N., Battaglia, S. et al. Evolving cell states and oncogenic drivers during the progression of IDH-mutant gliomas. Nat Cancer 6, 145–157 (2025). https://doi.org/10.1038/s43018-024-00865-3

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