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Evolutionary trajectories of IDH-mutant astrocytoma identify molecular grading markers related to cell cycling

Nature Cancer (2025)Cite this article

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Abstract

The evolutionary processes that drive malignant progression of IDH-mutant astrocytomas remain unclear. Here, we performed multiomics on matched initial and recurrent tumor samples from a cohort of 105 patients and overlaid the data with detailed clinical annotation. We identified overlapping features associated with malignant progression that are derived from three molecular mechanisms: cell cycling, tumor cell (de)differentiation and remodeling of the extracellular matrix. Together, they provide a rationale of the underlying biology of tumor malignancy. DNA methylation levels decreased over time, predominantly in tumors with malignant transformation, and co-occurred with poor prognostic genetic events. We identified a DNA methylation-based signature strongly associated with survival, which allows objective, molecular-based grading of IDH-mutant astrocytomas to aid clinical decision making. Our findings were validated on large, independent cohorts of IDH-mutant astrocytoma samples. Lastly, in this retrospective study, we found little effect of radiotherapy or chemotherapy on the molecular features associated with malignant progression.

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Fig. 1: Longitudinal genomic alterations in IDH-mutant astrocytomas.
Fig. 2: Temporal changes in DNA methylation and identification of GMS.
Fig. 3: Validation of the GMS.
Fig. 4: Temporal changes in RNA expression and identification of coexpression clusters.
Fig. 5: scRNA-seq and snRNA-seq validation of expression clusters.
Fig. 6: Effect of chemotherapy and radiotherapy on RNA expression and DNA methylation.

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Data availability

GLASS-NL data are available from the European Genome-Phenome Archive (EGA) under accession codes EGAS00001007546 (DNA methylation), EGAS00001007551 (RNA-seq) and EGAS00001007527 (sWGS and WES) and at the Proteomics Identifications Database (PRIDE) under accession code PXD062328 (proteomics). Access to data deposited to the EGA is controlled to comply with the General Data Protection Regulation and patient consent. To request access, a formal application must be submitted to the Data Access Committee (DAC) that is associated with the respective dataset(s) through the EGA. Further information can be found at https://ega-archive.org. The accession of files is limited to research purposes and regulated with a data use agreement. The timeframe for response to requests is as promptly as possible (expected within 10 working days), pending DAC approval. Access to the proteomics data will be made public through the same PRIDE identifier upon obtaining a PMID or DOI. Source data are provided with this paper.

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Acknowledgements

This project was funded by the Dutch Cancer Society (KWF Kankerbestrijding, project number 11026/2017-1, to W.R.V., K.A.v.G., B.A.W., M.Sm., M.J.v.d.B., P.W. and P.J.F.). We acknowledge the contribution of the PALGA foundation in the Netherlands and FGCZ. The CATNON study was funded by Merck, Sharp and Dohme (formerly Schering Plough) by an educational grant and by the provision of TMZ (to M.J.v.d.B.).

Author information

Author notes

  1. These authors contributed equally: Youri Hoogstrate, Karin A. van Garderen, Levi van Hijfte, Erik van Dijk.

  2. These authors jointly supervised this work: Bart A. Westerman, Pieter Wesseling, Pim J. French.

Authors and Affiliations

  1. Neurology Department Erasmus MC, Rotterdam, The Netherlands

    Wies R. Vallentgoed, Youri Hoogstrate, Levi van Hijfte, C. Mircea S. Tesileanu, Iris de Heer, Maud Diepeveen, Martin J. van den Bent & Pim J. French

  2. Radiology Department Erasmus MC, Rotterdam, The Netherlands

    Karin A. van Garderen, Anna Lavrova & Marion Smits

  3. Pathology Department AUMC, Amsterdam, The Netherlands

    Erik van Dijk, Paul P. Eijk, Bauke Ylstra, Bart A. Westerman & Pieter Wesseling

  4. Neurology Department AUMC, Amsterdam, The Netherlands

    Mathilde C. M. Kouwenhoven & Johanna M. Niers

  5. Neurology and Neurosurgery Department UMCU, Utrecht, The Netherlands

    Kaspar Draaisma & Pierre A. Robe

  6. Epidemiology and Biostatistics Department AUMC, Amsterdam, The Netherlands

    Ivonne Martin & Mark van de Wiel

  7. Pathology Department UMCU, Utrecht, The Netherlands

    Wendy W. J. de Leng

  8. Neurology Department University Hospital and University of Zurich, Zurich, Switzerland

    Marcel Bühler, Michael Weller & Tobias Weiss

  9. Department of Neurology and Neuro-oncology Program, Heidelberg University Hospital, Heidelberg, Germany

    Wolfgang Wick

  10. Department of Oncology, KU Leuven, Leuven, Belgium

    Paul M. Clement

  11. Hôpitaux Universitaires La Pitié Salpêtrière, Paris, France

    Marc Sanson

  12. IRCCS Istituto Scienze Neurologiche di Bologna, Nervous System Medical Oncology Department, Bologna, Italy

    Enrico Franceschi

  13. EORTC Headquarters, Brussels, Belgium

    Thierry Gorlia & Vassilis Golfinopoulos

  14. Pathology Department Erasmus MC, Rotterdam, The Netherlands

    Johan M. Kros

  15. Neurosurgery Department AUMC, Amsterdam, The Netherlands

    Roel G. W. Verhaak

  16. Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA

    Roel G. W. Verhaak

Authors
  1. Wies R. Vallentgoed
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  2. Youri Hoogstrate
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  3. Karin A. van Garderen
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  4. Levi van Hijfte
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  5. Erik van Dijk
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  10. Wendy W. J. de Leng
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  11. C. Mircea S. Tesileanu
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  17. Wolfgang Wick
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  18. Paul M. Clement
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  19. Marc Sanson
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  20. Enrico Franceschi
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  21. Thierry Gorlia
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  22. Vassilis Golfinopoulos
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  23. Michael Weller
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  24. Tobias Weiss
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  25. Pierre A. Robe
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  26. Johan M. Kros
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  27. Marion Smits
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  28. Mark van de Wiel
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  30. Roel G. W. Verhaak
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  31. Martin J. van den Bent
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  32. Bart A. Westerman
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  33. Pieter Wesseling
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  34. Pim J. French
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Contributions

Study design, P.A.R., M.Sm., M.v.d.W., R.G.W.V., M.J.v.d.B., B.A.W., P.W. and P.J.F. Conceptualization, W.R.V., Y.H., K.A.v.G., J.M.K., M.Sm., M.v.d.W., B.Y., R.G.W.V., M.J.v.d.B., B.A.W., P.W. and P.J.F. Methodology, W.R.V., Y.H., K.A.v.G., L.v.H., E.v.D., B.Y., W.W.J.d.L., M.J.v.d.B. and P.J.F. Formal analysis and investigation, W.R.V., I.d.H., Y.H., K.A.v.G., L.v.H., E.v.D., C.M.S.T., A.L., M.B., M.D., I.M. and P.P.E. Resources, W.R.V., K.A.v.G., L.v.H., M.C.M.K., J.M.N., K.D., W.W.J.d.L., C.M.S.T., A.L., W.W., P.M.C., M.Sm., E.F., T.G., M.W., P.A.R., J.M.K., T.W., V.G., M.Sa., M.J.v.d.B., P.W. and P.J.F. Validation, W.R.V., L.v.H., Y.H., C.M.S.T. Writing—original draft, W.R.V., M.J.v.d.B., P.J.F., B.A.W. and P.W. Writing—review and editing, all authors. Supervision and funding, P.W., M.J.v.d.B. and P.J.F.

Corresponding authors

Correspondence to Wies R. Vallentgoed, Pieter Wesseling or Pim J. French.

Ethics declarations

Competing interests

P.J.F. declares research support from Servier. E.F. declares research support from Incyte Steering committee (uncompensated) and is on the Genenta Science advisory board. M.W. has received research grants from Novartis, Quercis and Versameb and honoraria for lectures or advisory board participation or consulting from Anheart, Bayer, Curevac, Medac, Neurosense, Novartis, Novocure, Orbus, Pfizer, Philogen, Roche and Servier. R.G.W.V. is cofounder of and holds equity in Boundless Bio. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Cancer thanks Jiayi Huang, Itay Tirosh 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 Longitudinal genomic changes.

a, Difference in tumor purity between initial and recurrent tumor samples, calculated by using two-sided Wilcoxon rank sum tests for the ACE estimated cellularity based on sWGS data (left, P = 0.33, 96 initial vs 113 recurrent tumor samples) and the RFpurify Absolute estimated purity based on DNA-methylation array data (right, P = 0.79, 103 initial vs 128 recurrent tumor samples). b, Differential CNA analysis between matched initial and recurrent tumor pairs (n = 93). Each panel represents a chromosome, with on the x-axis the chromosomal position of each segment breakpoint and on the y-axis the log2(read counts), centromere is allocated with a vertical grey line. Segments mean with error bars (SE = SD/sqrt(n)) are separately visualized for initial (orange) and recurrent tumor samples (purple). Significant differential segments between initial and recurrent tumors (in red) were identified using a paired two-sided Wilcoxon signed-rank test (BH-corrected, FDR < 0.05). Chromosomal regions chr1p12-q23.3, chr3p24.3-p11.1, chr9p24.3-p21.2, chr10q21.3-q26.3, chr14q11.2-q32.33, and chr22q11.21-q13.33 had significantly lower log2 read counts in recurrent tumors compared to initial tumors. c, Kaplan-Meier curves for PRS when stratifying patients on the CNA status (HD < -0.4 and LOH < -0.2 mean log2(read counts)) of differential regions (identified in Extended Data Fig. 1b) of the recurrent tumor. Compared to cases without an alteration, loss of chr3p24.3.p11.1 (LOH (n = 16), P = 7.74×10−4, HR = 2.900 (1.559-5.395); HD (n = 4), P = 0.075, HR = 2.564 (0.9101-7.223)), chr9p24.3-p21.2 (LOH (n = 25), P = 0.024, HR = 2.035 (1.098-3.770); HD (n = 18), P = 6.57×10−4, HR = 3.106 (1.618-5.963)) and chr10q21.3-q26.3 (LOH (n = 26), P = 8.62×10−5, HR = 2.968 (1.724-5.109); HD (n = 2), P = 0.004, HR = 8.888 (2.048-38.582), two-sided Wald test) were negatively associated with PRS. d, Hypermutant cut-off value for the GLASS-NL cohort. Upper panel shows the log scale of the TMB (muts/Mb) of each tumor sample, ordered based on the log(TMB). The elbow of the curve is located at 6 mutations per Mb and marked with a horizontal red dashed line. Samples located on the left side of the vertical red dashed line are considered to be hypermutated. Lower panel depicts the TMB per sample with the cut-off of 6 muts/Mb marked with a red dashed line. Hypermutant samples are colored in green. e, Regression coefficients plot of linear regression model testing the effect and pair-wise interaction of TMZ treatment and MMR/POLE mutations to the TMB (TMB ~ TMZ*MMR), with MMR/POLE wildtype tumor samples (15 TMZ treated vs 68 without CT) in the left, and MMR/POLE mutated tumor samples (6 TMZ treated vs 4 without CT) in the right panel. Estimates, 95% CI, and residuals are plotted and coefficients table is included (p-values are obtained using a two-sided Wald test).

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Extended Data Fig. 2 Glioma Methylation Signature identification.

a, DMRs on chromosomal region chr6p22.2-21.33 and overlapping genes of top DMRs (P < 1×10−40, n = 37). b, DMRs on chromosomal region chr11q12.2-13.5 and overlapping genes of top DMRs (P < 1×10−21, n = 41). c, GO enrichment analysis of genes overlapping with on chr11q12.2-13.5. Top 10 pathways are shown, the -log10(Pvalues) for overrepresentation of the gene-set are obtained using Wallenius’ noncentral hypergeometric distribution and FDR adjusted using BH correction. Xaxis depicts the percentage of genes from the gene-set that were differentially methylated, GO subontologies are indicated at the end of the bar for each gene-set. d, Regression coefficients plot of multiple linear regression models testing the effect and pair-wise interaction of known molecular markers of poor prognosis to the GMS-score (GMS ~ (markers)^2) in matched initial (circles) and recurrent (squares) tumor samples (n = 103). Estimates and 95% CI are plotted, the n for each molecular marker is indicated, p-values were calculated using a two-sided Wald test. e, Histogram depicting the distribution of GMS-scores of all GLASS-NL DNA-methylation samples. Bars are colored based on the assigned GMS-class (‘hyper’: >1 M; ‘intermediate’: 0 – 1 M; ‘hypo’: <0 M). f, Kaplan-Meier curve for OS when stratifying patients on the GMS-class of the initial tumor (‘hypo’ vs ‘hyper’, P = 0.699, HR = 1.326 (0.3162-5.564); ‘intermediate’ vs ‘hyper’: P = 0.105, HR = 1.929 (0.8720-4.266), two-sided Wald test). g,h, Multivariate CoxPH models on TTR and PRS with patient and tumor sample characteristics related to initial and recurrent surgery, respectively, including the M-value of all CpGs (DNAm Score) (g) or the GMS-score (h) as a continuous variable. Hazard ratios and 95% CI are plotted, P values are indicated. i, Kaplan-Meier curve for PRS when stratifying patients on the GMS-class and WHO grade of the recurrent tumor. WHO grade 2 and 3, and GMS-class hyper and intermediate are grouped together. Compared to the ‘WHO23/GMShigh’ group, all other groups of recurrent tumors were negatively associated with PRS (‘WHO23/GMSlow’, P = 0.004, HR = 6.306 (1.804-22.042); ‘WHO4/GMShigh’, P = 5.6×10−4, HR = 3.462 (1.711-7.005); ‘WHO4/GMSlow’: P = 1.35×10−10, HR = 8.031 (4.252-15.167), two-sided Wald test). j, Volcano plot depicting the CpG-level temporal changes in DNA-methylation of patients with CNS WHO grade 4 initial and recurrent tumors (n = 10). Differentially methylated CpGs, from a top 10.000 CpG selection, were identified using a paired two-sided Wilcoxon signed-rank test (BH-corrected, FDR < 0.05 & ΔM > 1).

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Extended Data Fig. 3 Glioma Methylation Signature validation.

a, Histogram depicting the distribution of GMS-scores of CATNON DNA-methylation samples. Bars are colored based on the assigned GMS-class (‘hyper’: >1 M; ‘intermediate’: 0 – 1 M; ‘hypo’: <0 M). b, Kaplan-Meier curve for OS when stratifying patients of the TCGA cohort on the GMS-class. The hypo GMS-class was negatively associated with OS (‘hypo’, P < 0.001, HR = 4.0 (2.21-7.39); ‘intermediate’, P = 0.21, HR = 1.4 (0.81-2.51)). c-d, Kaplan-Meier curves for OS when stratifying based on the GMS-class of WHO grade 2 and grade 3 patients of the TCGA cohort, respectively. As compared to the hyper GMS-class, the hypo GMS-class was negatively associated with OS in WHO grade 2 tumors (c) (‘hypo’, P = 0.002, HR = 3.7495 (1.6344-8.601); ‘intermediate’, P = 0.316, HR = 1.4868 (0.6843-3.230)), and WHO grade 3 tumors (d) (‘hypo’, P = 0.004, HR = 3.9954 (1.55631-10.257); ‘intermediate’, P = 0.765, HR = 1.1407 (0.4812-2.704), two-sided Wald test). e, Output of multivariate CoxPH model on OS using data of the TCGA cohort, with the hyper GMS-class and WHO grade 2 as reference. f, Histogram depicting the distribution of GMS-scores of 5-hmc DNA-methylation samples of Glowaka et al.20. Cut-point for high and low GMS-scores was set at the median (red dotted line). g, Volcanoplot depicting the differential hydroxy-methylation analysis between GMS high (n = 21) and low (n = 21) samples of Glowaka et al. None exceeded the significance threshold of <0.05.

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Extended Data Fig. 4 snRNA validation of gene expression clusters.

a, The integrated snRNA dataset of all snRNA samples (n = 6) from three IDH-mutant astrocytoma patients from the GLASS-NL cohort. b, UMAP projection of single nuclei of integrated dataset, colored by the enrichment score of the cell type clusters. c, Density plots of expression of gene expression cluster C2 (left) and C4 (right) by single nuclei. Dashed line indicates cutoff for cells to be marked as ‘positive’ cluster expression. d, Distributions of CNS WHO grades within the expression cluster value classes (‘high’: PC1 > 0; ‘low’: PC1 < 0) of C1 and C3 (graphs of C2 and C4 are found in Fig. 5d).

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Supplementary information

Reporting Summary

Supplementary Tables 1–5

Supplementary Table 1: Identified DMRs between initial and recurrent tumor samples (n = 44,623), with their chromosomal location, number of CpGs and the overlapping genes. DMRs on the chromosome 6p locus are indicated. Supplementary Table 2. Output of differential methylation analysis between initial and recurrent tumor samples. The table includes all probes used for the analysis (n = 656,517), with their chromosomal position and relation to CpG island. Change (up and down) is indicated for all CpGs with FDR < 0.05, CpGs with FDR < 10−9 and absolute logFC > 1, which are included in the GMS. Supplementary Table 3. Output of differential expression analysis between initial and recurrent tumor samples. The table includes all genes used for the analysis (n = 22,346). Genes with FDR < 0.01 and absolute log2FC > 0.75 are differentially expressed (n = 604). The gene expression cluster (C1–C4) to which DEGs belong is indicated. Coefficients of covariates from multivariate analysis are also included for each gene. Supplementary Table 4. Output of differential protein expression analysis. The table includes all proteins used for the analysis (n = 3216). Change (up and down) is indicated for all DEPs with FDR < 0.05 and absolute logFC > 0.75 (n = 80). Supplementary Table 5. Output of mixed-effects model on differential methylation between treated and untreated patients. The table includes all probes used for the analysis (n = 656,517), with their chromosomal position and relation to CpG island. Change (up and down) is indicated for all CpGs with FDR < 0.05 (n = 13).

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Vallentgoed, W.R., Hoogstrate, Y., van Garderen, K.A. et al. Evolutionary trajectories of IDH-mutant astrocytoma identify molecular grading markers related to cell cycling. Nat Cancer (2025). https://doi.org/10.1038/s43018-025-01023-z

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