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Patterns of hypermutation shape tumorigenesis and immunotherapy response in mismatch-repair-deficient glioma

Nature Genetics volume 58, pages 132–142 (2026)Cite this article

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Abstract

Primary mismatch-repair-deficient high-grade gliomas (priMMRD-HGG) are lethal tumors characterized by hypermutation, resistance to chemoradiation and variable response to immunotherapy. To investigate the mechanisms governing the emergence of driver mutations and their impact on gliomagenesis and patient outcomes, we analyzed genomic and clinical data from 162 priMMRD-HGG. Here we identified three subgroups defined by secondary driver mutations in replicative DNA polymerases or IDH1. These subgroups converge on glioma drivers through distinct combinations of genomic instability–generating mechanisms, displaying an inverse correlation between point mutations and copy number alterations. MMRD signatures drive the emergence of specific mutations in TP53 and IDH1, notably excluding common pediatric glioma drivers. Global hypomethylation stratifies priMMRD-HGG into a unique methylation cluster. DNA-polymerasemut priMMRD-HGG exhibit ultrahypermutation, an immune-hot microenvironment and immunotherapy responsiveness, whereas IDH1mut priMMRD-HGG are immune-cold and immunotherapy resistant. MMRD-driven gliomagenesis defines the role of nonrandom mutagenesis patterns in cancer development, providing frameworks for targeted and immune-therapeutics.

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Fig. 1: Integrated clinical and genetic analysis of priMMRD-HGG reveals three distinct subgroups.
Fig. 2: priMMRD gliomas have a distinct methylation profile characterized by global hypomethylation.
Fig. 3: Time and MMRD signatures determine the oncogenic driver profile of priMMRD glioma.
Fig. 4: PriMMRD subgroups have distinct immune activation patterns and responses to ICIs.
Fig. 5: PriMMRD subgroups provide a human model of cancer under accelerated mutagenesis.

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Data availability

Methylation array data for this cohort are available through Gene Expression Omnibus (GEO) at GSE307219. Previously published and newly generated WES data for matched tumor and normal pairs are available in the repository in anonymized format (EGAD50000001794). These data are under controlled access to protect the identities of research participants and will be available by application to the Replication Repair Deficiency Data Access Committee. Data access requests take approximately 2–3 weeks to review. NanoString expression data were previously published41 and are available through GEO at GSE227756. The remaining processed clinical and molecular characterization data are available within Supplementary Data 1. Material requests are available from U.T., including a relevant research proposal to be approved by the IRRDC Governance Committee (replicationrepair.ca).

Code availability

No custom code or software packages were developed as part of this study. Use of existing software packages and versions is indicated with their respective methods.

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Acknowledgements

U.T. was supported by the Canadian Institutes for Health Research (CIHR) (PJT-156006), the CIHR Joint Canada-Israel Health Research Program (MOP-137899), a Stand Up to Cancer (SU2C)—Bristol Myers Squibb Catalyst Research (SU2C-AACR-CT07-17) grant and the Department of Defense Rare Cancers Research Program (HT9425-24-1-1096). This research is also generously supported by SickKids Foundation donors Harry and Agnieszka Hall, Meagan’s Hug (MW-2014-10), BRAINchild Canada and The LivWise Foundation. U.T. and G.G. were supported by a Children’s Oncology Group National Cancer Institute Community Oncology Research Program Research Base Administrative Supplement Request (3UG1CA189955-08S2). U.T., C.H., P.B.D. and A.L. were supported by a Terry Fox Research Institute Program Projects Grant at The Hospital for Sick Children (TFRI Project 1156-03). P.B.D. was supported by the Canadian Institutes for Health Research (PJT180605). A.D. was supported by the Cannonball Kids’ Cancer Foundation Young Investigator Grant in Partnership with Kindred Foundation and a Rally Foundation for Childhood Cancer Research award (25CDN08). D.M. is supported in part by the CIBC Children’s Foundation Chair in Child Health Research. Y.E.M. is supported by an ISF grant (2794/21). A.S. is supported by the Garron Family Chair in Childhood Cancer Research and the Canadian Institutes for Health Research (CIHR). G.G. was partially funded by the Paul C. Zamecnik Chair in Oncology at the Mass General Cancer Center. The other authors declare no relevant funding sources.

Author information

Authors and Affiliations

  1. Arthur and Sonia Labatt Brain Tumour Research Centre, Toronto, Ontario, Canada

    Nicholas R. Fernandez, Yuan Chang, Nuno M. Nunes, Jose R. Dimayacyac, Adrian Levine, Amit Ringel, Logine Negm, Ayse Bahar Ercan, Caitlin Lee, Lucie Stengs, Vanessa Bianchi, Melissa Edwards, Sheradan Doherty, Jiil Chung, Julie Bennett, Vijay Ramaswamy, Annie Huang, Peter B. Dirks, Cynthia Hawkins, Anirban Das & Uri Tabori

  2. Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada

    Adrian Levine & Cynthia Hawkins

  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Julian M. Hess & Gad Getz

  4. Division of Pediatric Neurooncology, Hopp Children’s Cancer Center (KiTZ), Heidelberg, Germany

    Olfat Ahmad

  5. Division of Pediatric Glioma Research (B360), German Cancer Research Center (DKFZ), Heidelberg, Germany

    Olfat Ahmad & David T. W. Jones

  6. Department of Pediatric Hematology-Oncology, Stollery Children Hospital, University of Alberta, Edmonton, Alberta, Canada

    Liana Nobre

  7. Division of Haematology & Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada

    Julie Bennett, Anita Villani, David Malkin, Vijay Ramaswamy, Annie Huang, Eric Bouffet, Anirban Das & Uri Tabori

  8. Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada

    Julie Bennett

  9. Children’s Hematology/Oncology Centre, Christchurch Hospital, Christchurch, New Zealand

    Andrew J. Dodgshun

  10. Hopp Children’s Cancer Center (KiTZ), Heidelberg, Germany

    David T. W. Jones & Stefan M. Pfister

  11. National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Hospital, Heidelberg, Germany

    David T. W. Jones

  12. Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK), Heidelberg, Germany

    Stefan M. Pfister

  13. Department of Pediatric Hematology and Oncology, Heidelberg University Hospital and NCT, Heidelberg, Germany

    Stefan M. Pfister

  14. Department of Pediatrics, University of Toronto, Toronto, Ontario, Canada

    David Malkin

  15. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada

    Vijay Ramaswamy & Uri Tabori

  16. Zane Cohen Centre for Digestive Diseases, Sinai Health System, Toronto, Ontario, Canada

    Melyssa Aronson

  17. Division of Neurosurgery, The Hospital for Sick Children, Toronto, Ontario, Canada

    Peter B. Dirks

  18. Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada

    Adam Shlien

  19. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada

    Adam Shlien

  20. Krantz Family Center for Cancer Research and Department of Pathology, Massachusetts General Hospital, Boston, MA, USA

    Gad Getz

  21. Department of Pathology, Harvard Medical School, Boston, MA, USA

    Gad Getz

  22. Technion-Israel Institute of Technology, Tel-Aviv, Israel

    Yosef E. Maruvka

  23. Division of Neuroradiology, Department of Diagnostic and Interventional Radiology, The Hospital for Sick Children, Toronto, Ontario, Canada

    Birgit Ertl-Wagner

  24. Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada

    Birgit Ertl-Wagner

  25. Neurosciences and Mental Health Program, SickKids Research Institute, Toronto, Ontario, Canada

    Birgit Ertl-Wagner

Authors
  1. Nicholas R. Fernandez
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  2. Yuan Chang
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  3. Nuno M. Nunes
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  4. Jose R. Dimayacyac
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  5. Adrian Levine
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  7. Logine Negm
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  9. Julian M. Hess
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  10. Olfat Ahmad
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  11. Caitlin Lee
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  13. Vanessa Bianchi
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  14. Melissa Edwards
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  15. Sheradan Doherty
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  17. Liana Nobre
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  18. Julie Bennett
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  19. Andrew J. Dodgshun
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  21. Stefan M. Pfister
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  22. Anita Villani
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  23. David Malkin
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  24. Vijay Ramaswamy
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  25. Annie Huang
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  26. Eric Bouffet
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  27. Melyssa Aronson
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  28. Peter B. Dirks
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  30. Gad Getz
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  33. Cynthia Hawkins
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  34. Anirban Das
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  35. Uri Tabori
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Contributions

N.R.F., A.D. and U.T. conceived of and designed the research. N.R.F., Y.C. and J.R.D. performed the processing of WES data. N.R.F., A.L. and C.L. performed the immunohistochemistry and RNA NanoString analyses. A.R. and B.E.-W. assessed the magnetic resonance imaging. L. Negm, J.C. and Y.E.M. performed the low-pass whole-genome sequencing and MSI analyses. A.B.E., L.S., V.B., M.E., S.D., L. Nobre, J.B., A.V., D.M., V.R., A.H., E.B., M.A., P.B.D., A.S. and C.H. collected the samples and provided tissue annotations and clinicopathological information. N.R.F., O.A., A.J.D., D.T.W.J., S.M.P., A.D. and U.T. performed the methylation array analyses and interpretation. J.M.H. and G.G. provided the allele-specific copy number profiling. N.R.F., N.M.N., A.D. and U.T. interpreted the data. N.R.F., N.M.N., A.D. and U.T. wrote and edited the paper. U.T. supervised the study. All authors approved the final version of the paper.

Corresponding author

Correspondence to Uri Tabori.

Ethics declarations

Competing interests

A.S. is a cofounder of, and holds equity in, NewCode Oncology, which has licensed technology from The Hospital for Sick Children (SickKids). C.H. discloses honorarium from Servier. D.T.W.J. is a founder of and stock shareholder in Heidelberg Epignostix GmbH. E.B. discloses participation in an advisory board for Servier. G.G. receives research funds from IBM, Pharmacyclics/AbbVie, Bayer, Genentech, Calico, Ultima Genomics, Inocras, Google, Kite and Novartis, and is also an inventor on patent applications filed by the Broad Institute related to MSMuTect, MSMutSig, POLYSOLVER, SignatureAnalyzer-GPU, MSEye, MinimuMM-seq and DLBclass. He is a founder, consultant and holds privately held equity in Scorpion Therapeutics; is a founder of, and holds privately held equity in, Predicta Biosciences; and holds privately held equity in Antares Therapeutics. J.B. discloses participation in advisory boards for Servier Canada and Alexion-Canada. J.M.H. is an employee of and holds privately held equity in Predicta Biosciences. S.M.P. discloses honoraria from BioSkryb, waived honoraria from Epignostix GmbH, PMC Advisory Board and University Hospital Essen Westdeutsches Tumorzentrum, unpaid memberships in GPOH, SIOP, DGNN, DKG, AACR and SNO, and is a stock shareholder in Epignostix GmbH. The other authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks David Capper 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 Characteristics of the study cohort.

Flowchart of the study cohort obtained from the International Replication Repair Deficiency Consortium (IRRDC). LOGIC, low-coverage genomic instability characterization.

Extended Data Fig. 2 Characterization of mutational signatures and driver events in priMMRD glioma.

a, Average Cosmic v3.2 SBS signature contributions across priMMRD subgroups. Signatures are colored and grouped by etiology. b, Total microsatellite InDel burden across priMMRD-HGG subgroups. Colored diamonds indicate median value. Two-sided Wilcoxon rank-sum test with Benjamini–Hochberg correction. Colored diamonds represent the median value. c, Polymerase epsilon (POLE) deficiency-specific microsatellite instability signature scores across priMMRD subgroups21. Two-sided Wilcoxon rank-sum test with Benjamini–Hochberg correction. Colored diamonds represent the median value. d, Oncoprint of glioma driver mutations and copy number alterations according to priMMRD-HGG subgroups and previously published cohorts of pediatric and adult MMRP glioma22,23. HGG, high-grade glioma; LGG, low-grade glioma; codel, 1p/19q co-deleted.

Extended Data Fig. 3 Copy number variation profiles of priMMRD glioma.

a, Copy number alteration proportions across priMMRD subgroups. Selected genes commonly affected by copy number variation in glioma are indicated. b, Percentage of genome altered between subgroups given as the percentage of bases affected by copy number gains or losses over the total number of covered bases. Two-sided Wilcoxon rank-sum test with Benjamini–Hochberg correction. Colored diamonds represent the median value.

Extended Data Fig. 4 Mechanisms of mutagenesis define priMMRD subgroups.

a, Frequencies of alteration types in recurrent glioma drivers across priMMRD subgroups. Two-sided Fisher’s exact test with Benjamini–Hochberg correction. b, Allele-specific copy number profiles of near-haploid priMMRD-2 gliomas. c, Somatic point mutations of glioma drivers found in haploid gliomas. d, Age at diagnosis in years across near-haploid (n = 3) and diploid (n = 155) priMMRD-HGG. Two-sided Wilcoxon rank-sum test. e, Tumor mutation burden (TMB); (SNVs per megabase) across near-haploid (n = 3) and diploid (n = 159) priMMRD-HGG. Two-sided Wilcoxon rank-sum test. Colored diamonds represent the median value in d and e.

Extended Data Fig. 5 Classification of priMMRD glioma methylation profiles.

a, Heidelberg Brain Tumor Classifier v12.8 classes of priMMRD-HGG (n = 82) subgroups. No classification indicates samples with a confidence score below 0.9. b, Uniform manifold approximation and projection (UMAP) plot of priMMRD-HGG and reference classes from the Heidelberg Brain Tumor Classifier v12.8 and primary mismatch repair-deficient IDH-mutant astrocytoma (PMMRDIA). PriMMRD-HGG subgroups are indicated by outlined shapes according to the included key. Samples are colored according to their v12.8 class prediction or lack of classification (prediction score <0.9). Samples that failed classification but clustered with PMMRDIA are colored in blue.

Extended Data Fig. 6 Characteristics of priMMRD glioma epigenetic outliers.

a, Table of priMMRD-HGG that fall outside of the main priMMRD methylation cluster on t-SNE with a proposed reason for their clustering. b, Representative H3K27me3 immunohistochemistry (IHC) staining of the two priMMRD-HGG that clustered with the DMG_K27 methylation class (n = 1, respectively). Only tumor 4 had confirmed H3-3A K27M and H2K27me3 loss, suggesting that tumor 5 may have noncanonical mechanisms of chromatic landscape alteration. c, Estimated cumulative distribution of average β values plotted individually by priMMRD-HGG. The three priMMRD-HGG that clustered with DMG_K27 or DHG_G34 are indicated. d, Average β values of the three priMMRD-HGG that clustered with DMG_K27 or DHG_G34 versus those that did not. Colored diamonds represent the median value. e, Histogram of variant allele frequency (VAF) distributions in tumor 4, VAF of H3-3A p.K27M is indicated, demonstrating a clonal mutation. f, Histogram of variant allele frequency (VAF) distributions in tumor 6, VAF of H3-3A p.G34R is indicated, demonstrating a clonal mutation. g, Copy number profile of tumor 6 with copy gain of chromosome 1q, which includes H3-3A. h, Copy number profile of tumor 7 with MYCN amplification. CRTL_CORPCAL, control tissue corpus callosum; DHG_G34, diffuse hemispheric glioma H3.3 G34 mutant; DMG_K27, diffuse midline glioma H3 K27M; GBM_MYCN, glioblastoma subclass MYCN; HGG_E, high-grade glioma subtype E; IHG, infantile hemispheric glioma; PA_INF, pilocytic astrocytoma subclass infratentorial; pedHGG_MYCN, pediatric high-grade glioma subclass MYCN; pedHGG_RTK1A, pediatric high-grade glioma subclass RTK1A.

Extended Data Fig. 7 priMMRD glioma harbors a distinct spectrum of TP53 hotspot mutations.

a, Mutational spectrum of TP53 hotspot mutations in priMMRD-HGG subgroups as well as previously published IDH-mutant and IDH-wildtype glioma cohorts10,22. b, Recurrently mutated codons in TP53 across priMMRD and MMRP glioma subtypes. For codons with multiple protein changes, they are listed in order of frequency. c, Average mutational spectra of priMMRD-HGG and hotspot mutations in ATRX, NF1 and RB1 driven by MMRD signatures.

Extended Data Fig. 8 SBS96D has high similarity to time signatures and MMRd-A.

From top to bottom, mutational spectra of the MutSα-specific de novo mutational signature SBS96D, mutational spectra of SBS96D reconstructed from COSMIC v3.2 SBS signature loadings. Cosine similarity between SBS96D and the reconstructed profile is given, mutational spectra of the COSMIC v.3.2 SBS signatures that form the components of SBS96D and mutational spectra of previously published MutSα-specific mutational signature MMRd-A36,39.

Extended Data Fig. 9 Extended survival analyses of priMMRD glioma.

a, Overall survival of priMMRD-HGG by subgroup, which did not receive immune checkpoint inhibitor therapy. b, Forest plot of the Cox-proportional hazards model of priMMRD-1 glioma treated with either anti-PD1 monotherapy or chemoirradiation. c, Forest plot of Cox-proportional hazards model of priMMRD-2 glioma treated with either anti-PD1 monotherapy or chemoirradiation. d, Forest plot of the Cox-proportional hazards model of priMMRD-3 glioma treated with either anti-PD1 monotherapy or chemoirradiation. e, Forest plot of Cox-proportional hazards model of priMMRD-HGG treated with anti-PD1 monotherapy, adjusted for subgroup, MMRD syndrome, age and sex. Error bars indicate a 95% confidence interval for b through e.

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Supplementary Data 1

Clinical and methylation classification data of the priMMRD glioma cohort. Clinical, methylation, expression and genomic data and metadata of the priMMRD glioma cohort.

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Fernandez, N.R., Chang, Y., Nunes, N.M. et al. Patterns of hypermutation shape tumorigenesis and immunotherapy response in mismatch-repair-deficient glioma. Nat Genet 58, 132–142 (2026). https://doi.org/10.1038/s41588-025-02420-x

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