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A population-based analysis of the molecular landscape of glioma in adolescents and young adults reveals insights into gliomagenesis

Nature Cancer volume 6, pages 1102–1119 (2025)Cite this article

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Abstract

Gliomas are a major cause of cancer-related deaths in adolescents and young adults (AYAs; ages 15–39 years). Different molecular alterations drive gliomas in children and adults, leading to distinct biology and clinical consequences, but the implications of pediatric- versus adult-type alterations in AYAs are unknown. Our population-based analysis of 1,456 clinically and molecularly characterized gliomas in patients aged 0–39 years addresses this gap. Pediatric-type alterations were found in 31% of AYA gliomas and conferred superior outcomes compared to adult-type alterations. AYA low-grade gliomas with specific RAS–MAPK alterations exhibited senescence, tended to arise in different locations and were associated with superior outcomes compared to gliomas in children, suggesting different cellular origins. Hemispheric IDH-mutant, BRAF p.V600E and FGFR-altered gliomas were associated with the risk of malignant transformation, having worse outcomes with increased age. These insights into gliomagenesis may provide a rationale for earlier intervention for certain tumors to disrupt the typical behavior, leading to improved outcomes.

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Fig. 1: Toronto AYA cohort characteristics.
Fig. 2: Molecular findings in the AYA cohort.
Fig. 3: IDH-mutant gliomas from 0 to 40 years of age.
Fig. 4: FGFR-altered gliomas from 0 to 40 years of age.
Fig. 5: BRAF-altered gliomas from 0 to 40 years of age.
Fig. 6: Age-related implications for glioma biology.
Fig. 7: Model of gliomagenesis.

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Data availability

The clinical and molecular (immunohistochemistry, ddPCR) source dataset for the AYA population is available as Supplementary Data. Survival data are not publicly available per CCO policy. Newly generated panel sequencing data and copy number analysis of AYA gliomas are available at the European Genome–phenome Archive (EGA) under study ID EGAD50000000560. Previously generated panel sequencing data of pediatric HGGs are available at the EGA under study ID EGAS50000000221 and dataset ID EGAD50000000326. The data are available under controlled access to comply with data protection regulations and can be accessed by application to the data access committee via C.H. (cynthia.hawkins@sickkids.ca). Previously published RNA and targeted DNA sequencing data for pediatric LGG are available at the EGA (EGAD00001005987) and can be accessed by application to the data access committee via C.H. (cynthia.hawkins@sickkids.ca). Additional clinical data and molecular characterization for the pediatric LGG cohort (using immunohistochemistry, fluorescence in situ hybridization, NanoString gene fusion panels, single-nucleotide polymorphism array) are available as source data in the manuscript website at https://doi.org/10.1016/j.ccell.2020.03.011 (ref. 17). Methylation data discussed in this publication have been deposited in the National Center for Biotechnology Information’s Gene Expression Omnibus (GEO)79 and are accessible through GEO Series accession number GSE290136.

The publicly available PBTA raw data are available through KidsFirstPortal (https://portal.kidsfirstdrc.org/login) accession codes PBTA-CBTN and PBTA-PNOC and Cavatica (https://cavatica.sbgenomics.com/u/cavatica/openpbta) upon request to the Children’s Brain Tumor Network (CBTN), and processed summary files are accessible via GitHub at https://github.com/AlexsLemonade/OpenPBTA-analysis.

Source data are provided with this paper. All other data supporting the findings of this study are available upon request from the corresponding author.

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Acknowledgements

We would like to thank the patients and their families. J.B. was supported by a Garron Family Cancer Centre Research Fellowship and the Aflac Archie Bleyer Young Investigator Award in Adolescent and Young Adult Oncology from the Children’s Oncology Group. A.L.L. was supported by the Brain Tumor Foundation of Canada Richard Motyka Research Fellowship, the SickKids Clinician Scientist Training Program and the Canadian Institutes of Health Research (CIHR) Fellowship. Funding for this project was provided by the CIHR (grant nos. 159805 (C.H.) and 480606 (C.H.)), b.r.a.i.n.child and the Canadian Cancer Society/Brain Canada Sparks grant (SPARK-21, #707089, U.T.). This study was supported by Cancer Care Ontario (CCO), and these data have the following restrictions: parts of this material are based on data and information provided by Ontario Health (CCO) and include data received by Ontario Health (CCO) from the Canadian Institute for Health Information (CIHI). The opinions, reviews, views and conclusions reported in this publication are those of the authors and do not necessarily reflect those of Ontario Health (CCO) and/or the CIHI. No endorsement by Ontario Health (CCO) and/or the CIHI is intended or should be inferred. Ontario Health is prohibited from making the data used in this research publicly accessible if they include potentially identifiable personal health information and/or personal information as defined in Ontario law, specifically the Personal Health Information Protection Act (PHIPA) and the Freedom of Information and Protection of Privacy Act (FIPPA). Due to these legal and ethical restrictions, data will not be made publicly available. However, upon request, data deidentified to a level suitable for public release may be provided. This research was supported in part by the National Cancer Institute Cancer Center Support Grant P30CA008748 to the MSKCC, the Molecular Diagnostics Service in the Department of Pathology and the Marie-Josée and Henry R. Kravis Center for Molecular Oncology. The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Julie Bennett, Adrian B. Levine.

  2. These authors jointly supervised this work: Uri Tabori, Cynthia Hawkins.

Authors and Affiliations

  1. Division of Haematology/Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada

    Julie Bennett, Eric Bouffet & Uri Tabori

  2. Division of Medical Oncology and Hematology, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada

    Julie Bennett

  3. Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada

    Julie Bennett, Adrian B. Levine, Logine Negm, Jiil Chung, Karen Fang, Martin Komosa, Stacey Krumholtz, Nuno Miguel Nunes, Scott Ryall, Javal Sheth, Robert Siddaway, Eric Bouffet, Sunit Das, Peter Dirks, Uri Tabori & Cynthia Hawkins

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

    Adrian B. Levine, Monique Johnson, Mansuba Rana, Scott Ryall, Javal Sheth, Robert Siddaway & Cynthia Hawkins

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

    Adrian B. Levine, Lananh Nguyen, Andrew Gao, Julia Keith, David G. Munoz & Cynthia Hawkins

  6. Division of Hematology/Oncology (iHOPE), Department of Pediatrics, Stollery Children’s Hospital, University of Alberta, Edmonton, Alberta, Canada

    Liana Nobre

  7. Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Tejus A. Bale

  8. Division of Neurosurgery, Unity Health, Toronto, Ontario, Canada

    Michael D. Cusimano & Sunit Das

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

    Michael D. Cusimano & Peter Dirks

  10. Department of Neurosurgery, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada

    Michael D. Cusimano, Paul Kongkham & Gelareh Zadeh

  11. Department of Radiation Oncology, Sunnybrook Health Science Centre, Toronto, Ontario, Canada

    Jay Detsky & Arjun Sahgal

  12. Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Matthias A. Karajannis, Bryan Kincheon Li, Alexandra Miller & Sameer Farouk Sait

  13. Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Alexandra Giantini-Larsen

  14. Division of Pediatric Hematology/Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Bryan Kincheon Li

  15. Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada

    Mary Jane Lim-Fat & James R. Perry

  16. Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Andrew L. Lin & Alexandra Miller

  17. Department of Medicine, Princess Margaret Cancer Centre, University Health Network and University of Toronto, Toronto, Ontario, Canada

    Warren P. Mason

  18. Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada

    Derek S. Tsang & Normand Laperriere

  19. Department of Laboratory Medicine, Unity Health, Toronto, Ontario, Canada

    Lananh Nguyen & David G. Munoz

  20. Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada

    Andrew Gao

  21. Laboratory Medicine and Molecular Diagnostics, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada

    Julia Keith

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Contributions

This project was conceptualized by J.B., U.T. and C.H. Clinical data were collected by J.B. Resources including patient samples were provided by C.H., D.G.M., J.K., N.L., L. Nguyen and A.G. Investigations were carried out by J.B., J.S., K.F., L. Nobre, L. Negm, J.C., M.K., M.J., M.R., R.S., A.B.L., N.M.N. and S.R. Additional data were provided by S.F.S., M.A.K., T.A.B., A.M., A.G.-L., B.K.L., A.L.L., E.B., M.D.C., S.D., J.D., P.D., P.K., M.J.L.-F., W.P.M., J.R.P., A.S., D.S.T., N.L. and G.Z. Funding acquisition was done by U.T. and C.H. Analysis, visualization and manuscript draft preparation was done by J.B., A.B.L. and S.K. All authors participated in manuscript review and editing.

Corresponding author

Correspondence to Julie Bennett.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Cancer thanks Adam Green, Quinn Ostrom and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Approach to molecular testing and CONSORT diagram.

(A) The tiered approach that was taken to molecular testing of the AYA cohort. (B) A CONSORT diagram outlining the population of study.

Extended Data Fig. 2 Characteristics of Toronto 0-40 cohort.

(A) Age at diagnosis divided by sex in 0-40 Toronto cohort (n = 1456 patients). P-value calculated using unpaired t-test (two-tailed). (B) Tumor location in the pediatric ( < 15 years) Toronto cohort (n = 583 patients). (C) Tumor location of IDH-WT tumors in 0-40 Toronto cohort (n = 738 patients). P-value calculated using Tukey’s multiple comparison test. (D) PFS and OS of PLGG in treated vs untreated AYAs (n = 202 patients). P-value calculated using Log-rank test. (E) 2021 WHO diagnosis of tumors in the Toronto pediatric cohort (n = 530 tumors).

Source data

Extended Data Fig. 3 Additional findings of IDH-mutant glioma in ages 0-40.

(A) Age of diagnosis in IDH-mutant astrocytoma (n = 241 patients). P-value calculated using Tukey’s multiple comparison test. (B) Age of diagnosis in IDH-mutant ODG (n = 101 patients). P-value calculated using unpaired t-test (two-tailed). (C) Age of diagnosis of IDH1 p.R132H vs non-canonical IDH mutant tumors in the Toronto cohort (n = 368 patients). P-value calculated using unpaired t-test (two tailed). (D) OS of IDH-mutant astrocytoma based on age of diagnosis in the Toronto cohort (n = 230 patients). P-value calculated using Log-rank test. (E) OS of IDH-mutant astrocytoma with based on ATRX and TP53 status (including MSKCC cohort for those with incomplete phenotype, n = 75 patients). P-value calculated using Log-rank test. (F) OS of IDH-mutant ODG based on age of diagnosis in the Toronto cohort (n = 101 patients). P-value calculated using Log-rank test. (G) OS of IDH-mutant ODG with and without co-occuring TERT promoter mutation (including MSKCC cohort for those without TERT promoter mutation, n = 80 patients). P-value calculated using Log-rank test. (H) Non-canonical IDH mutation and tumor type within the Toronto cohort (n = 48 tumors). (I) OS of IDH-mutant astrocytomas stratified by mutation type and grade (MSKCC cohort included for non-canonical IDH-mutant tumors, n = 253 patients). P-value calculated using Log-rank test. (J) OS of ODGs stratified by mutation type and grade (MSKCC cohort included for non-canonical IDH-mutant tumors, n = 111 patients). P-value calculated using Log-rank test. (K) Multivariate analysis of factors affecting OS in IDH-mutant astrocytoma. P-value calculated using Cox regression, error bars show 95% confidence intervals. (L) Multivariate analysis of factors affecting OS in ODG. P-value calculated using Cox regression, error bars show 95% confidence intervals.

Source data

Extended Data Fig. 4 Additional findings in FGFR-altered glioma in ages 0-40.

(A) Location of FGFR mutant tumors by age of diagnosis (n = 44 patients). P-value calculated using unpaired t-test (two-tailed). (B) Tumor grade (LGG/HGG) of FGFR mutant tumors by age of diagnosis (n = 45 patients). P-value calculated using ANOVA. (C) Location of FGFR fused tumors by age of diagnosis (n = 39 patients). P-value calculated using unpaired t-test (two-tailed). (D) Tumor grade (LGG/HGG) of FGFR fused tumors by age of diagnosis (n = 42 patients). P-value calculated using unpaired t-test (two-tailed).

Extended Data Fig. 5 Additional findings in BRAF-mutant glioma in ages 0-40.

(A) OS of HGG with different driver mutations (n = 97 patients). P-value calculated using Log-rank test. (B) PFS and OS of BRAF-mutant tumors in AYA Toronto cohort stratified by histology (n = 71 patients). P-value calculated using Log-rank test. (C) PFS of BRAF mutant LGG in AYA based on extent of resection (n = 57 patients). P-value calculated using Log-rank test. (D) PFS of BRAF mutant LGG based on location (n = 57 patients). P-value calculated using Log-rank test. (E) Multivariate analysis of factors influencing PFS in BRAF p.V600E mutant LGG. P-value calculated using Cox regression, error bars show 95% confidence intervals. (F) Multivariate analysis of factors influencing OS in BRAF p.V600E mutant HGG. P-value calculated using Cox regression, error bars show 95% confidence intervals.

Source data

Extended Data Fig. 6 Additional findings in BRAF-fused glioma in ages 0-40.

(A) Tumor location of BRAF fused glioma based on age of diagnosis in Toronto cohort (n = 207 patients). P-value calculated using Tukey’s multiple comparison test. (B) Age of diagnosis for KIAA1549-BRAF fusion vs non-canonical BRAF fusion partners in the Toronto cohort (n = 176 patients). P-value calculated using unpaired t-test (two-tailed). (C) Gene constructs of non-canonical BRAF fusions from AYA cohort. (D) BRAF fusion breakpoint/binding partner based on age of diagnosis in the Toronto cohort (n = 167 tumors). P-value calculated using one-way ANOVA, using Tukey’s multiple comparison test. (E) PFS and OS of BRAF fused glioma in the AYA cohort (including MSKCC cohort, n = 33 patients). P-value calculated using Log-rank test. (F) Multivariate analysis of factors influencing PFS in BRAF fused LGG. P-value calculated using Cox regression, error bars show 95% confidence intervals.

Extended Data Fig. 7 BRAF/FGFR Mutant Tumors in AYA.

(A) Bar graph showing proportion of LGG vs HGG in children vs AYA (n = 156 tumors). (B) Regression analysis showing probability of diagnosis of HGG if patient presents with BRAF or FGFR mutant tumor based on age (n = 362 tumors). (C) PFS of LGG harboring BRAF/FGFR SNV in children and AYA (n = 156 patients). P-value calculated using Log-rank test. (D) Regression analysis showing probability of progression of LGG based on age of diagnosis (n = 362 tumors).

Supplementary information

Supplementary Information

Supplementary Tables 1–4.

Reporting Summary

Supplementary Data

Clinical data for the pediatric and AYA cohorts from Toronto and the MSKCC.

Source data

Source Data Figs. 1, 3, 4 and 6 and Extended Data Figs. 2, 3 and 5

Data for OS curves, methylation data and analysis using the PBTA dataset.

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Bennett, J., Levine, A.B., Nobre, L. et al. A population-based analysis of the molecular landscape of glioma in adolescents and young adults reveals insights into gliomagenesis. Nat Cancer 6, 1102–1119 (2025). https://doi.org/10.1038/s43018-025-00962-x

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