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Intra-tumoral T cells in pediatric brain tumors display clonal expansion and effector properties

Nature Cancer volume 5, pages 791–807 (2024)Cite this article

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Abstract

Brain tumors in children are a devastating disease in a high proportion of patients. Owing to inconsistent results in clinical trials in unstratified patients, the role of immunotherapy remains unclear. We performed an in-depth survey of the single-cell transcriptomes and clonal relationship of intra-tumoral T cells from children with brain tumors. Our results demonstrate that a large fraction of T cells in the tumor tissue are clonally expanded with the potential to recognize tumor antigens. Such clonally expanded T cells display enrichment of transcripts linked to effector function, tissue residency, immune checkpoints and signatures of neoantigen-specific T cells and immunotherapy response. We identify neoantigens in pediatric brain tumors and show that neoantigen-specific T cell gene signatures are linked to better survival outcomes. Notably, among the patients in our cohort, we observe substantial heterogeneity in the degree of clonal expansion and magnitude of T cell response. Our findings suggest that characterization of intra-tumoral T cell responses may enable selection of patients for immunotherapy, an approach that requires prospective validation in clinical trials.

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Fig. 1: T cells within pediatric brain tumors show marked clonal expansion.
Fig. 2: Tumor-infiltrating T cells display neoantigen-specific T cell gene signatures that are associated with improved survival.
Fig. 3: Clonally expanded CD8+ T cells display cell states linked to anti-tumor immunity.
Fig. 4: Clonally expanded CD8+ T cells display effector properties.
Fig. 5: PDCD1-expressing intra-tumoral CD8+ T cells are not dysfunctional.
Fig. 6: Heterogeneity in the expression of immunotherapy targets in CD8+ T cells.
Fig. 7: CD4-CTLs are clonally expanded in pediatric brain tumors.
Fig. 8: PD-1+CD4+ T cells display increased expression of cytotoxic molecules and cytokines.

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Data availability

Processed scRNA-seq and TCR-seq data generated from pediatric brain tumors and NSCLC can be accessed in GEO under accession number GSE221776. Previously published scRNA-seq data that were reanalyzed here are available under accession codes GSE163108 (ref. 40) and GSE123813 (ref. 30). Public pediatric brain tumor datasets used for survival analysis, and expression analysis of MHCI, MHCII, KEGG pathway enrichment and CLEC2D can be accessed from the Gabriella Miller Kids First Data Resource Portal (https://portal.kidsfirstdrc.org/login) through the CAVATICA (https://www.cavatica.org) cloud-based platform, and clinical data can be accessed using PedcBioPortal (https://pedcbioportal.kidsfirstdrc.org). Source data for all main and extended data figures are provided as Source Data files. 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

All codes for bioinformatic analysis were deposited in our GitHub repository (https://github.com/vijaybioinfo/PBT_2023).

References

  1. Pollack, I. F. Brain tumors in children. N. Engl. J. Med. 331, 1500–1507 (1994).

    CAS  PubMed  Google Scholar 

  2. Jones, C. & Baker, S.J. Unique genetic and epigenetic mechanisms driving paediatric diffuse high-grade glioma. Nat. Rev. Cancer 14, 651–661 (2014).

    CAS  Google Scholar 

  3. Chevignard, M., Câmara-Costa, H., Doz, F. & Dellatolas, G. Core deficits and quality of survival after childhood medulloblastoma: a review. Neurooncol. Pract. 4, 82–97 (2017).

    PubMed  Google Scholar 

  4. Makale, M. T., McDonald, C. R., Hattangadi-Gluth, J. A. & Kesari, S. Mechanisms of radiotherapy-associated cognitive disability in patients with brain tumours. Nat. Rev. Neurol. 13, 52–64 (2017).

    CAS  PubMed  Google Scholar 

  5. Hwang, E. I. et al. The current landscape of immunotherapy for pediatric brain tumors. Nat. Cancer 3, 11–24 (2022).

    PubMed  Google Scholar 

  6. Dunkel, I. J. et al. Nivolumab with or without ipilimumab in pediatric patients with high-grade CNS malignancies: safety, efficacy, biomarker, and pharmacokinetics: checkMate 908. Neuro. Oncol. 25, 1530–1545 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Bouffet, E. et al. Immune checkpoint inhibition for hypermutant glioblastoma multiforme resulting from germline biallelic mismatch repair deficiency. J. Clin. Oncol. 34, 2206–2211 (2016).

    CAS  PubMed  Google Scholar 

  8. Fried, I. et al. Preliminary results of immune modulating antibody MDV9300 (pidilizumab) treatment in children with diffuse intrinsic pontine glioma. J. Neurooncol. 136, 189–195 (2018).

    CAS  PubMed  Google Scholar 

  9. Binnewies, M. et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat. Med. 24, 541–550 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Bruni, D., Angell, H. K. & Galon, J. The immune contexture and Immunoscore in cancer prognosis and therapeutic efficacy. Nat. Rev. Cancer 20, 662–680 (2020).

    CAS  PubMed  Google Scholar 

  11. Tumeh, P. C. et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 515, 568–571 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Patel, R. R., Ramkissoon, S. H., Ross, J. & Weintraub, L. Tumor mutational burden and driver mutations: characterizing the genomic landscape of pediatric brain tumors. Pediatr. Blood Cancer 67, e28338 (2020).

    CAS  PubMed  Google Scholar 

  13. Mok, T. S. K. et al. Pembrolizumab versus chemotherapy for previously untreated, PD-L1-expressing, locally advanced or metastatic non-small-cell lung cancer (KEYNOTE-042): a randomised, open-label, controlled, phase 3 trial. Lancet 393, 1819–1830 (2019).

    CAS  PubMed  Google Scholar 

  14. Schumacher, T. N. & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69–74 (2015).

    CAS  PubMed  Google Scholar 

  15. Gubin, M. M. et al. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature 515, 577–581 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Robbins, P. F. et al. Mining exomic sequencing data to identify mutated antigens recognized by adoptively transferred tumor-reactive T cells. Nat. Med. 19, 747–752 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Tran, E. et al. T-cell transfer therapy targeting mutant KRAS in cancer. N. Engl. J. Med. 375, 2255–2262 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Carreno, B. M. et al. Cancer immunotherapy. A dendritic cell vaccine increases the breadth and diversity of melanoma neoantigen-specific T cells. Science 348, 803–808 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Lowery, F. J. et al. Molecular signatures of antitumor neoantigen-reactive T cells from metastatic human cancers. Science 375, 877–884 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Kvistborg, P. & Yewdell, J. W. Enhancing responses to cancer immunotherapy. Science 359, 516–517 (2018).

    CAS  PubMed  Google Scholar 

  22. Chiou, S. H. et al. Global analysis of shared T cell specificities in human non-small cell lung cancer enables HLA inference and antigen discovery. Immunity 54, 586–602.e8 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Ganesan, A. P. et al. Tissue-resident memory features are linked to the magnitude of cytotoxic T cell responses in human lung cancer. Nat. Immunol. 18, 940–950 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Clarke, J. et al. Single-cell transcriptomic analysis of tissue-resident memory T cells in human lung cancer. J. Exp. Med. 216, 2128–2149 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Savas, P. et al. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat. Med. 24, 986–993 (2018).

    CAS  PubMed  Google Scholar 

  26. Han, J. et al. Resident and circulating memory T cells persist for years in melanoma patients with durable responses to immunotherapy. Nat. Cancer 2, 300–311 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Liu, B. et al. Temporal single-cell tracing reveals clonal revival and expansion of precursor exhausted T cells during anti-PD-1 therapy in lung cancer. Nature Cancer 3, 108–121 (2022).

    CAS  PubMed  Google Scholar 

  28. Li, H. et al. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176, 775–789.e18 (2019).

    CAS  PubMed  Google Scholar 

  29. Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998–1013.e20 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Yost, K. E. et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25, 1251–1259 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Gide, T. N. et al. Distinct immune cell populations define response to anti-PD-1 monotherapy and anti-PD-1/anti-CTLA-4 combined therapy. Cancer Cell 35, 238–255.e6 (2019).

    CAS  PubMed  Google Scholar 

  32. Kurtulus, S. et al. Checkpoint blockade immunotherapy induces dynamic changes in PD-1−CD8+ tumor-Infiltrating T cells. Immunity 50, 181–194.e6 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Siddiqui, I. et al. Intratumoral Tcf1+PD-1+CD8+ T cells with stem-like properties promote tumor control in response to vaccination and checkpoint blockade immunotherapy. Immunity 50, 195–211.e10 (2019).

    CAS  PubMed  Google Scholar 

  34. Castellino, F. et al. Chemokines enhance immunity by guiding naive CD8+ T cells to sites of CD4+ T cell-dendritic cell interaction. Nature 440, 890–895 (2006).

    CAS  PubMed  Google Scholar 

  35. Brewitz, A. et al. CD8+ T cells orchestrate pDC-XCR1+ dendritic cell spatial and functional cooperativity to optimize priming. Immunity 46, 205–219 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Mackay, L. K. et al. Hobit and Blimp1 instruct a universal transcriptional program of tissue residency in lymphocytes. Science 352, 459–463 (2016).

    CAS  PubMed  Google Scholar 

  37. Mackay, L. K. et al. The developmental pathway for CD103+CD8+ tissue-resident memory T cells of skin. Nat. Immunol. 14, 1294–1301 (2013).

    CAS  PubMed  Google Scholar 

  38. Lim, M. et al. Phase III trial of chemoradiotherapy with temozolomide plus nivolumab or placebo for newly diagnosed glioblastoma with methylated MGMT promoter. Neuro. Oncol. 24, 1935–1949 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Omuro, A. et al. Radiotherapy combined with nivolumab or temozolomide for newly diagnosed glioblastoma with unmethylated MGMT promoter: an international randomized phase III trial. Neuro. Oncol. 25, 123–134 (2022).

    PubMed Central  Google Scholar 

  40. Mathewson, N. D. et al. Inhibitory CD161 receptor identified in glioma-infiltrating T cells by single-cell analysis. Cell 184, 1281–1298.e26 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Corridoni, D. et al. Single-cell atlas of colonic CD8+ T cells in ulcerative colitis. Nat. Med. 26, 1480–1490 (2020).

    CAS  PubMed  Google Scholar 

  42. Jonsson, A. H. et al. Granzyme K+ CD8 T cells form a core population in inflamed human tissue. Sci. Transl. Med. 14, eabo0686 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Xu, T. et al. Single-cell profiling reveals pathogenic role and differentiation trajectory of granzyme K+CD8+ T cells in primary Sjögren’s syndrome. JCI Insight 8, e167490 (2023).

    PubMed  PubMed Central  Google Scholar 

  44. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Baitsch, L. et al. Exhaustion of tumor-specific CD8+ T cells in metastases from melanoma patients. J. Clin. Invest. 121, 2350–2360 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Gros, A. et al. PD-1 identifies the patient-specific CD8+ tumor-reactive repertoire infiltrating human tumors. J. Clin. Invest. 124, 2246–2259 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Thommen, D. S. et al. A transcriptionally and functionally distinct PD-1+ CD8+ T cell pool with predictive potential in non-small-cell lung cancer treated with PD-1 blockade. Nat. Med. 24, 994–1004 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Chen, R. et al. Antigen presentation machinery signature-derived CALR mediates migration, polarization of macrophages in glioma and predicts immunotherapy response. Front. Immunol. 13, 833792 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Sledzinska, A. et al. Regulatory T cells restrain Interleukin-2- and Blimp-1-dependent acquisition of cytotoxic function by CD4+ T cells. Immunity 52, 151–166.e6 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Eschweiler, S. et al. Intratumoral follicular regulatory T cells curtail anti-PD-1 treatment efficacy. Nat. Immunol. 22, 1052–1063 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Awad, M. M. et al. Personalized neoantigen vaccine NEO-PV-01 with chemotherapy and anti-PD-1 as first-line treatment for non-squamous non-small cell lung cancer. Cancer Cell 40, 1010–1026.e11 (2022).

    CAS  PubMed  Google Scholar 

  52. Ott, P. A. et al. A phase Ib trial of personalized neoantigen therapy plus Anti-PD-1 in patients with advanced melanoma, non-small cell lung cancer, or bladder cancer. Cell 183, 347–362.e24 (2020).

    CAS  PubMed  Google Scholar 

  53. Pittet, M. J. et al. High frequencies of naive Melan-A/MART-1-specific CD8+ T cells in a large proportion of human histocompatibility leukocyte antigen (HLA)-A2 individuals. J. Exp. Med. 190, 705–715 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Schmidt, N., Flecken, T. & Thimme, R. Tumor-associated antigen specific CD8+ T cells in hepatocellular carcinoma—a promising target for immunotherapy. Oncoimmunology 3, e954919 (2014).

    PubMed  PubMed Central  Google Scholar 

  55. Moustaki, A. et al. Antigen cross-presentation in young tumor-bearing hosts promotes CD8+ T cell terminal differentiation. Sci. Immunol. 7, eabf6136 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Sekine, T. et al. TOX is expressed by exhausted and polyfunctional human effector memory CD8+ T cells. Sci. Immunol. 5, eaba7918 (2020).

    CAS  PubMed  Google Scholar 

  57. Alfei, F. et al. TOX reinforces the phenotype and longevity of exhausted T cells in chronic viral infection. Nature 571, 265–269 (2019).

    CAS  PubMed  Google Scholar 

  58. Alspach, E. et al. MHC-II neoantigens shape tumour immunity and response to immunotherapy. Nature 574, 696–701 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Gorsi, H. S. et al. Nivolumab in the treatment of recurrent or refractory pediatric brain tumors: a single institutional experience. J. Pediatr. Hematol. Oncol. 41, e235–e241 (2019).

    CAS  PubMed  Google Scholar 

  60. Mascarenhas, L. et al. Phase 1 clinical trial of durvalumab in children with solid and central nervous system tumors. J. Clin. Oncol. 40, 10029 (2022).

    Google Scholar 

  61. Meckiff, B. J. et al. Imbalance of regulatory and cytotoxic SARS-CoV-2-reactive CD4+ T cells in COVID-19. Cell 183, 1340–1353.e16 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291.e89 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Tran, H. T. N. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods. 16, 1289–1296 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Denisenko, E. et al. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol. 21, 130 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).

    PubMed  Google Scholar 

  68. Behr, F. M. et al. Blimp-1 rather than Hobit drives the formation of tissue-resident memory CD8+ T cells in the lungs. Front. Immunol. 10, 400 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinf. 14, 7 (2013).

    Google Scholar 

  70. Conway, J. R., Lex, A. & Gehlenborg, N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 33, 2938–2940 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Shugay, M. et al. VDJtools: unifying post-analysis of T cell receptor repertoires. PLoS Comput. Biol. 11, e1004503 (2015).

    PubMed  PubMed Central  Google Scholar 

  72. Csárdi, G. & Nepusz, T. The igraph software package for complex network research. InterJ. Complex Syst. 1695, 1–9 (2006).

    Google Scholar 

  73. McCann, K. et al. Targeting the tumor mutanome for personalized vaccination in a TMB low non-small cell lung cancer. J. Immunother. Cancer 10, e003821 (2022).

    PubMed  PubMed Central  Google Scholar 

  74. Therneau, T. M. & Grambsch, P. M. in Modeling Survival Data: Extending the Cox Model (eds Therneau T. M. & Grambsch P. M.) 39–77 (Springer, 2000).

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Acknowledgements

We thank J. D. Elster and M. Paul (Department of Pediatrics, University of California San Diego and Rady Children’s Hospital) for supporting patient recruitment, and we thank R. Newbury, K. Shayan, J. Mo, N. Ellington, D. Wang and S. Tucker (Department of Pathology, University of California San Diego and Rady Children’s Hospital) for providing fresh brain tumor tissue for research from the material that is surplus beyond clinical testing needs. We thank H. Simon and M. Mondal for support with sequencing. This work was supported by Hyundai Hope On Wheels, Peacock Foundation and Curebound (Pedal The Cause) for research into pediatric brain tumors; the funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Support is also acknowledged from the Whitaker Fund (C.H.O.), William K. Bowes Jr Foundation (P.V.) and National Institutes of Health K08 CA230164 (A.P.G.).

Author information

Author notes

  1. These authors contributed equally: Aditi Upadhye, Kevin E. Meza Landeros.

  2. These authors jointly supervised this work: Pandurangan Vijayanand, Anusha-Preethi Ganesan.

Authors and Affiliations

  1. La Jolla Institute for Immunology, La Jolla, CA, USA

    Aditi Upadhye, Kevin E. Meza Landeros, Ciro Ramírez-Suástegui, Benjamin J. Schmiedel, Jason A. Greenbaum, Grégory Seumois, Stephen P. Schoenberger, Hilde Cheroutre, Christian H. Ottensmeier, Pandurangan Vijayanand & Anusha-Preethi Ganesan

  2. Center for Genomic Sciences, National Autonomous University of Mexico, Cuernavaca, Mexico

    Kevin E. Meza Landeros

  3. Southampton University Hospitals NHS Trust, Southampton, UK

    Edwin Woo

  4. Department of Respiratory Medicine, Liverpool Heart and Chest Hospital NHS Foundation Trust, Liverpool, UK

    Serena J. Chee

  5. Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK

    Serena J. Chee, Christian H. Ottensmeier & Pandurangan Vijayanand

  6. Department of Pathology, University of California San Diego, La Jolla, CA, USA

    Denise Malicki

  7. Rady Children’s Hospital, San Diego, CA, USA

    Denise Malicki, Nicole G. Coufal, David Gonda, Michael L. Levy, John Crawford, William D. Roberts & Anusha-Preethi Ganesan

  8. Department of Pediatrics, University of California San Diego, La Jolla, CA, USA

    Nicole G. Coufal, William D. Roberts & Anusha-Preethi Ganesan

  9. Department of Neurological Surgery, University of California San Diego, La Jolla, CA, USA

    David Gonda & Michael L. Levy

  10. Department of Neurosciences, University of California San Diego, La Jolla, CA, USA

    John Crawford

  11. Department of Pediatrics, University of California Irvine, Irvine, CA, USA

    John Crawford

  12. Children’s Hospital Orange County, Irvine, CA, USA

    John Crawford

  13. Clatterbridge Cancer Center NHS Foundation Trust, Liverpool, UK

    Christian H. Ottensmeier

  14. Department of Medicine, University of California San Diego, La Jolla, CA, USA

    Pandurangan Vijayanand

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  1. Aditi Upadhye
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Contributions

A.U. performed the experimental work, along with data generation, analysis and interpretation, and manuscript review. K.E.M.L. performed bioinformatic evaluation and data analysis. C.R.S. performed bioinformatic evaluation, data analysis and manuscript review. B.J.S. conducted experimental work related to NSCLC. E.W. and S.J.C. recruited patients with NSCLC and performed sample collection. A.P.G., D.M., N.G.C., D.G. and M.L.L. recruited patients with pediatric brain tumors and performed sample collection. J.A.G. performed mutanome analysis. G.S. supervised the sequencing work. J.C. and W.D.R. were involved with study development, patient recruitment and manuscript review. S.P.S. performed mutanome analysis and paper review. H.C. interpreted the data and reviewed the manuscript. C.H.O. was involved with study development, patient recruitment and manuscript review. P.V. was involved with study design, data generation and review, and manuscript writing and review. A.P.G. was involved with study design, patient recruitment, data generation and review, and manuscript writing and review. P.V. and A.P.G. conceived, supervised and led the work.

Corresponding authors

Correspondence to Pandurangan Vijayanand or Anusha-Preethi Ganesan.

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Competing interests

The authors declare no competing interests.

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Peer review information

Nature Cancer thanks Vassiliki Boussiotis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Extended Data Fig. 1 Clinical parameters do not correlate with T cell clonal expansion.

Correlation of CD4+ (left panel) or CD8+ (right panel) T cell clonal expansion with clinical and pathological characteristics of PBT patients (CD4+, n = 26; CD8+, n = 32). Patients with <50 CD8+ or CD4+ T cells with TCR data were excluded. Error bars represent mean ± s.e.m.

Source data

Extended Data Fig. 2 Mutanome analysis of pediatric brain tumors.

a, Schema of mutanome analysis and neoantigen profiling in pediatric brain tumors. b, Number of genomic tumor-specific variants in pediatric brain tumors (n = 9) (top); table shows top 10 high-confidence tumor-specific variants detected in two index patients (MBL, medulloblastoma; HGG, high-grade glioma) (bottom). c, Kaplan-Meier survival curve based on T cell gene signature in pediatric high-grade glioma from the Pediatric Brain Tumor Atlas (PBTA); n = 36 per signature group; n.s. denotes P = 0.078 by multivariate Cox regression; HR, hazard ratio; CI, confidence interval.

Source data

Extended Data Fig. 3 CD8+ T cell subsets within pediatric brain tumors.

a, Violin plots show the per-cell distribution of unique genes, unique molecular identifiers (UMI), and percentage of UMI mapped to mitochondrial genome in 26,332 single CD8+ T cells across clusters in PBT (n = 38). Box plots extend from the 25th to 75th percentile and the center line represents the median. Whiskers are bounded by 25th percentile - 1.5*interquartile range or 75th percentile + 1.5*interquartile range. b, UMAP shows Seurat clustering of 26,332 CD8+ T cell transcriptomes in PBT (n = 38). c, Heatmap shows top 50 differentially-expressed genes using MAST across CD8+ T cell clusters. d, GSEA plot shows enrichment of the indicated gene signatures in the indicated CD8+ T cell clusters. e, Gene set enrichment analysis (GSEA) plot showing enrichment of the ICB response signature31 in clonally-expanded versus non-expanded T cells from PBT patients (CD8+, n = 38; CD4+, n = 35). In d and e, FDR-adjusted P value (q) and normalized enrichment score (NES) determined using fgsea package on R. f, Pie charts show TRAV, TRAJ and TRBV gene usage by T cells in MAIT cell clusters (below, key).

Source data

Extended Data Fig. 4 Composition and phenotype of tumor-infiltrating CD8+ T cells.

a, Subset composition of tumor-infiltrating CD8+ T cells (stacked to 100%) across PBT patients (n = 38); numbers above bars represent total number of CD8+ T cells per patient and when <50, numbers are highlighted in red. b, Proportion of CD8+ T cell subsets among total CD8+ T cells in newly diagnosed (n = 34) versus recurrent (n = 4) tumors (above, key); all comparisons are non-significant by nonparametric two-tailed Mann-Whitney test. Error bars represent mean ± s.e.m. c, Analysis of canonical pathways from the Ingenuity Pathway Analysis database (horizontal axis; bars in plot) for which clonally-expanded CD8+ T cells from PBT show enrichment, presented as the frequency of differentially-expressed genes encoding components of each pathway that are upregulated or downregulated (key) in clonally-expanded CD8+ T cells relative to their expression in non-expanded cells (left vertical axis), and adjusted P values (right vertical axis; line; Fisher’s exact test); numbers above bars indicate total genes in each pathway. d, Crater plot displays genes differentially-expressed between clonally-expanded (clone size > 1) versus non-expanded (clone size = 1) CD8+ T cells from PBT (n = 38) (X axis) or pre-ICB tumors from ICB responders30 (n = 6) (Y axis). Top right quadrant displays genes upregulated in clonally-expanded CD8+ T cells that are shared between PBT and tumors from ICB responders. Size of dots represents significance (Benjamini-Hochberg FDR-corrected P-value < 0.05 and log2 fold change > 0.35 or < −0.35 using MAST) and color of dots represents mean expression of displayed genes.

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Extended Data Fig. 5 T cell responses in pediatric brain tumors versus adult brain tumors.

a, UMAP (left) and violin (right) displays TCF7 expression across subsets in tumor-infiltrating CD8+ T cells from PBT (n = 38). Inset (above left) shows proportion of TCF7-expressing cells per subset. b, UMAP (left) and violin (right) displays KLRB1 expression across subsets in tumor-infiltrating CD8+ T cells from PBT (n = 38). Inset (above left) shows proportion of KLRB1-expressing cells per subset. c, Expression of CLEC2D transcripts in adult glioblastoma (GBM, n = 96), pediatric high-grade glioma (pHGG, n = 25) or pediatric low-grade glioma (pLGG, n = 93) from PedcBioPortal datasets. In a-c, box plots extend from the 25th to 75th percentile and the center line represents the median. Whiskers represent minimum and maximum values. d, Single-cell trajectory analysis showing relationship between cells in different CD8+ T cell subsets (line) in pediatric brain tumors, constructed using Monocle 3.

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Extended Data Fig. 6 Tumor-infiltrating PD-1 + CD8+ T cells display clonal expansion and cytokine production.

a, Proportion of clonally-expanded CD8+ T cells in PDCD1-non-expressing versus PDCD1-expressing CD8+ T cells (n = 32) (top); ****P = 1.6×10−5. Proportion of PDCD1-expressing CD8+ T cells in non-expanded versus clonally-expanded CD8+ T cells in PBT (n = 32) (bottom); ****P = 1.8×10−5. P value determined by nonparametric two-tailed Wilcoxon matched-pairs signed rank test in both analyses. Patients with <50 CD8+ T cells with TCR data were excluded. b, Representative flow-cytometric gating strategy for the assessment of CD103, cytotoxic molecule, and cytokine expression in total CD8+ T cells and in PD-1negCD8+ T cells versus PD-1+CD8+ T cells from PBT (also related to Fig. 4b,c). c, Proportion (left plot) of cells expressing IL2 in PDCD1-non-expressing versus PDCD1-expressing CD8+ T cells in PBT (n = 38). Flow-cytometric analysis (right) of the expression and proportion of IL-2+ cells in PD-1negCD8+ T cells versus PD-1+CD8+ T cells from PBT patients (n = 7); n.s. denotes P = 0.16 by non-parametric two-tailed Wilcoxon matched-pairs signed rank test. d, Bar chart shows polyfunctionality based on the production of multiple cytokines in PD-1negCD8+ T cells versus PD-1+CD8+ T cells from PBT (n = 7). e, GSEA plot shows enrichment of cell cycle gene signature in PDCD1-expressing versus PDCD1-non-expressing CD8+ T cells in PBT (n = 38). P value and NES as in Fig. 2a.

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Extended Data Fig. 7 Expression of transcripts encoding HLA molecules and features of LAG3-expressing CD8+ T cells in pediatric brain tumors.

Expression of (a) HLA-A, HLA-B, HLA-C, HLA-DRB1, HLA-DPB1,and HLA-DQB1 transcripts and (b) antigen processing and presentation signature genes across six diagnoses in the PBTA; error bars represent mean ± s.e.m. CPP, choroid plexus papilloma (n = 16); CrPh, craniopharyngioma (n = 36); LGG, low-grade glioma (n = 302); HGG, high-grade glioma (n = 148); MBL, medulloblastoma (n = 119); AE, anaplastic ependymoma (n = 93). c, Volcano plot shows differentially-expressed genes between LAG3-non-expressing versus LAG3-expressing CD8+ T cells from PBT patients with low expression of PDCD1 in CD8+ T cells (n = 10) (Benjamini-Hochberg FDR-corrected P value < 0.05, log2 fold change > 0.35 or < −0.35 using MAST); dot size and color as in Fig. 6b.

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Extended Data Fig. 8 CD4+ T cell subsets within pediatric brain tumors.

a, Violin plots show the per-cell distribution of unique genes, unique molecular identifiers (UMI), and percentage of UMI mapped to mitochondrial genome in 14,994 single CD4+ T cells across clusters in PBT (n = 35). Box plots extend from the 25th to 75th percentile and the center line represents the median. Whiskers are bounded by 25th percentile - 1.5*interquartile range or 75th percentile + 1.5*interquartile range. b, UMAP shows Seurat clustering of 14,994 CD4+ T cell transcriptomes in PBT (n = 35). c, Heatmap shows top 50 differentially-expressed genes using MAST across CD4+ T cell clusters. d, GSEA plot shows enrichment of the indicated gene signatures in the indicated CD4+ T cell clusters. e, GSEA plot shows enrichment of CD4-CTL gene signature in clonally-expanded versus non-expanded non-TREG CD4+ T cells. In d and e, P value and NES determined as in Fig. 2a. f, Proportion of clonally-expanded CD4+ T cells in PDCD1-non-expressing versus PDCD1-expressing non-TREG CD4+ T cells (n = 24); Patients with <50 CD4+ T cells with TCR data or patients with 0 PDCD1+ CD4+ T cells were excluded; **P = 0.0018 by nonparametric two-tailed Wilcoxon matched-pairs signed rank test. g, Flow-cytometric analysis of IL-2 expression and proportion of IL-2+ cells in PD-1negCD4+ T cells versus PD-1+CD4+ T cells from PBT patients (n = 10); n.s. denotes P = 0.37 by non-parametric two-tailed Wilcoxon matched-pairs signed rank test.

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Upadhye, A., Meza Landeros, K.E., Ramírez-Suástegui, C. et al. Intra-tumoral T cells in pediatric brain tumors display clonal expansion and effector properties. Nat Cancer 5, 791–807 (2024). https://doi.org/10.1038/s43018-023-00706-9

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