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A spatial map of MAPK-activated immunosuppressive myeloid populations in pediatric low-grade glioma

Nature Immunology volume 26, pages 1794–1806 (2025)Cite this article

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Abstract

Pediatric low-grade gliomas (pLGGs) are mitogen-activated protein kinase (MAPK) pathway-activated brain tumors prevalent in children and are associated with morbidity despite favorable survival. Here using imaging mass cytometry, we spatially characterized at the single-cell level the tumor microenvironment (TME) of 120 pLGG cases, considering age, molecular drivers, brain location and tumor subtype. Our analysis identified myeloid cells—including resident microglia and bone marrow-derived macrophages—as the predominant immune population in the TME, particularly in optic pathway tumors. Additionally, we discovered an immune signature predictive of progression-free survival. Spatial analysis identified specific cellular interactions, notably myeloid–myeloid contacts and macrophage-enriched regions harboring MAPK-activated, TIM-3+ myeloid cells, suggesting an immunosuppressive TME. Our study provides a comprehensive resource on the immune landscape of these pLGGs and underscores the immunosuppressive role of diverse myeloid infiltrates. These findings also indicate that combining TIM-3 blockade with MAPK inhibition might be a promising therapeutic strategy to target both the TME and oncogenic MAPK activation in pLGG tumors.

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Fig. 1: IMC profiling the TME of pLGGs.
Fig. 2: A myeloid immunophenotype signature is associated with PFS in pLGGs.
Fig. 3: Exploring the immunogenicity of myeloid cells in pLGGs.
Fig. 4: The spatial immune architecture of pLGGs.
Fig. 5: An immune-rich neighborhood is associated with tumor progression in pLGGs.

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Data availability

Processed genomic data from the Open Pediatric Brain Tumor Atlas dataset are available through the Open Pediatric Brain Tumor Atlas portal (https://github.com/AlexsLemonade/OpenPBTA-analysis/). Previously published scRNA-seq data used in this study are deposited in the European Genome-phenome Archive (EGA) under accession numbers EGAS00001005773, EGAS00001003170 and EGAS00001007510. Expression data and metadata for tumor of origin, cluster and t-SNE coordinates for single cells from the Reitman study can be found on the Broad Institute Single Cell Portal under accession number SCP271. Microarray expression of the ICGC PedBrain pilocytic astrocytoma cohort can be found in the original publication.

Additional high-dimensional data also exist for the samples included in this study, such as 850k DNA methylation, Gene Panel sequencing and RNA-seq (see Supplementary Table 1 for the data type available per individual). The corresponding data can be requested from the LOGGIC Core BioClinical Data Bank (https://www.kitz-heidelberg.de/klinische-studien/loggic-core/). CyTOF data are available in Cytobank (experiment ID: 8408) or at https://yale.cytobank.org/cytobank/experiments/8408/.

IMC and CyTOF data are available on Zenodo via https://doi.org/10.5281/zenodo.16614871 (ref. 67). Source data are provided with this paper.

Code availability

All code used to perform IMC analyses is available at https://github.com/walsh-quail-labs/Pediatric-Low--Grade-Gliomas and https://github.com/walsh-quail-labs/. Code for scRNA-seq cell-type annotation is available at https://github.com/fungenomics/scCoAnnotate/.

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Acknowledgements

This work was supported by the Fondation Charles-Bruneau (to N.J.), Canadian Institutes of Health Research (CIHR) grant FDN-154307 (to N.J.). The German HIT-LOGGIC-Registry was kindly supported by grants from the Deutsche Kinderkrebsstiftung (2019.06, 2021.03 and 2023.08). N.J. is a member of the Penny Cole Laboratory and holds a Canada Research Chair (CRC) Tier 1 in Paediatric Oncology from the CIHR. D.C., F.S., D.T.W.J., O.W. and T.M. were supported by The Everest Centre for Low-grade Paediatric Brain Tumours (GN-000707, The Brain Tumour Charity, UK). We thank the Yuhong Wei, Single Cell and Imaging Mass Cytometry Platform (SCIMAP - Life Sciences Complex at McGill University, McGill Genome Centre) and the Histology core facility from the Segal Cancer Centre Research Pathology Facility (Jewish General Hospital). We thank R. Carvalho for administrative support and project management. Figure 1 and Extended Data Fig. 1 were created with BioRender.com.

Author information

Author notes

  1. These authors contributed equally: Augusto Faria Andrade, Romain Sigaud, Evan Puligandla.

Authors and Affiliations

  1. Charbonneau Cancer Institute, Cumming School of Medicine, Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada

    Augusto Faria Andrade

  2. Department of Human Genetics, McGill University, Montreal, Quebec, Canada

    Augusto Faria Andrade, Evan Puligandla, Alva Annett, Robert Taylor, Claudia L. Kleinman, Logan Walsh & Nada Jabado

  3. The Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada

    Augusto Faria Andrade & Nada Jabado

  4. University Hospital Jena, Department of Pediatrics and Adolescent Medicine, Friedrich Schiller University Jena, Jena, Germany

    Romain Sigaud & Till Milde

  5. Comprehensive Cancer Center Central Germany (CCCG), Jena, Germany

    Romain Sigaud & Till Milde

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

    Romain Sigaud, Florian Selt, Philipp Sievers, Felix Sahm, Stefan M. Pfister, Olaf Witt, David T. W. Jones & Till Milde

  7. Clinical Cooperation Unit Pediatric Oncology, German Cancer Research Center (DKFZ) and German Consortium for Translational Cancer Research (DKTK), Heidelberg, Germany

    Romain Sigaud, Florian Selt, Olaf Witt & Till Milde

  8. National Center for Tumor Diseases (NCT), Heidelberg, Germany

    Romain Sigaud, Florian Selt, Stefan M. Pfister, Olaf Witt, David T. W. Jones & Till Milde

  9. Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada

    Bridget Liu, Elham Karimi, Morteza Rezanejad & Logan Walsh

  10. Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, Canada

    Wajih Jawhar & Nada Jabado

  11. Department of Pediatrics, Yale School of Medicine, New Haven, CT, USA

    Yi Cao & Liza Konnikova

  12. Department of Neuropathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

    Simone Schmid, Arend Koch & David Capper

  13. German Cancer Consortium (DKTK), Partner Site Berlin, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Simone Schmid & David Capper

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

    Florian Selt, Stefan M. Pfister & Olaf Witt

  15. Charité - Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, German HIT-LOGGIC-Registry for pLGG in children and adolescents, Department of Pediatric Oncology and Hematology, Berlin, Germany

    Pablo Hernáiz Driever & Svea Horn

  16. Department of Neuropathology, Heidelberg University Hospital, Heidelberg, Germany

    Philipp Sievers & Felix Sahm

  17. Clinical Cooperation Unit Neuropathology, German Consortium for Translational Cancer Research (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany

    Philipp Sievers & Felix Sahm

  18. Institute of Neuropathology, University of Freiburg, Freiburg, Germany

    Marco Prinz

  19. Signaling Research Centers BIOSS and CIBSS, University of Freiburg, Freiburg, Germany

    Marco Prinz

  20. Institute of Neuropathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany

    Markus Glatzel

  21. Department of Neuropathology, Otto-von-Guericke University Magdeburg, Magdeburg, Germany

    Christian Mawrin

  22. Department of Neuropathology, Institute of Pathology, Hannover Medical School (MHH), Hannover, Germany

    Christian Hartmann

  23. Institute of Pathology, Department of Neuropathology, University of Wuerzburg, Comprehensive Cancer Center (CCC) Mainfranken Wuerzburg, Wuerzburg, Germany

    Camelia-Maria Monoranu

  24. Department of Obstetrics, Gynecology and Reproductive Sciences, Yale School of Medicine, New Haven, CT, USA

    Liza Konnikova

  25. Human and Translational Immunology Program, Yale School of Medicine, New Haven, CT, USA

    Liza Konnikova

  26. Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA

    Liza Konnikova

  27. Center for Systems & Engineering Immunology, Yale School of Medicine, New Haven, CT, USA

    Liza Konnikova

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

    Stefan M. Pfister

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

    David T. W. Jones

  30. Lady Davis Research Institute, Jewish General Hospital, Montreal, Quebec, Canada

    Claudia L. Kleinman

  31. Department of Pediatrics, McGill University, Montreal, Quebec, Canada

    Nada Jabado

Authors
  1. Augusto Faria Andrade
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  2. Romain Sigaud
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  3. Evan Puligandla
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  4. Bridget Liu
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  5. Elham Karimi
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  6. Alva Annett
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  7. Morteza Rezanejad
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  8. Wajih Jawhar
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  9. Robert Taylor
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  10. Yi Cao
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  11. Simone Schmid
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  13. Pablo Hernáiz Driever
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  14. Svea Horn
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  15. Philipp Sievers
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  16. Marco Prinz
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  17. Markus Glatzel
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  18. Christian Mawrin
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  19. Christian Hartmann
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  20. Camelia-Maria Monoranu
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  21. Liza Konnikova
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  22. Felix Sahm
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  23. Stefan M. Pfister
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  24. Olaf Witt
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  29. Logan Walsh
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  30. Nada Jabado
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  31. Till Milde
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Contributions

A.F.A., R.S. and E.P. designed and performed the experiments, interpreted the results and wrote the paper. B.L., E.K. and M.R. performed IMC data analysis/interpretation. R.S. and A.A. performed scRNA-seq data analysis/interpretation. W.J. and R.T. performed experiments and provided imaging analyses. Y.C. performed CyTOF experiments and data analysis. S.S. provided samples and/or clinico-molecular data, scientific input and revised the manuscript. F. Selt provided scientific input and revised the manuscript. P.H.D., S.H., P.S., M.P., M.G., C.M., C.H., C.-M.M. and A.K. provided samples and/or clinico-molecular data, and revised the manuscript. F. Sahm provided samples and/or clinico-molecular data, scientific input and revised the manuscript. L.K., S.M.P., O.W., D.T.W.J. and C.L.K. provided scientific input, supervised data analysis and revised the manuscript. D.C. provided samples and/or clinico-molecular data, scientific input and revised the manuscript. L.W. designed the study, performed the IMC data analysis and wrote and revised the paper. N.J. and T.M. designed the study and wrote and revised the paper.

Corresponding authors

Correspondence to Logan Walsh, Nada Jabado or Till Milde.

Ethics declarations

Competing interests

O.W. and T.M. declare research grant support from Biomed Valley Discoveries and Day One Biopharmaceuticals. The other authors declare no competing interests.

Peer review

Peer review information

Nature Immunology thanks Sean Bendall, Gregor Hutter, John Maris and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Nick Bernard, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 Imaging Mass Cytometry to profile the tumor microenvironment of pLGG, as Related to Fig. 1.

a. Illustration of the data acquisition workflow and overview of the data analysis pipeline used to characterize human pediatric low-grade glioma tumors (PA = 113 and GG = 7) by IMC. b. Antibodies used to classify the different cell types in the tumor microenvironment cell lineage assignment tree. c. Heat map showing relative average expression of markers across cell populations identified by IMC. d. Immunofluorescence validation of target detection of antibodies that underwent metal-conjugated in-house and lacked pre-validation from literature. Human tonsil (CD206) or brain tumor tissues (GFAP, SOX2, MAP2, SOX10 and Olig2) were used. e. Representative IMC images of human tonsil tissue used as a control. Macrophages (CD68+), monocytes (CD14 + CD16+), B cells (CD20+), T cells (CD45 + CD3 + CD4 + CD8+), dendritic cells (HLA-DR+CD11c+), and endothelial cells (CD31+) are represented. Antibodies that did not show consistent staining by IMC were excluded from the analysis.

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Extended Data Fig. 2 A comprehensive assessment of the immune landscape in pLGG, as Related to Fig. 1.

a. Distribution of all cell populations as a percentage of all cells identified in the TME, ranked by brain/tumor type (n = 120). NK: Natural killer cells; DN T: Double negative T cells; Mono: monocytes; DCs: Dendritic cells; MG: Microglia; BMDM: Bone marrow-derived macrophages. b. Proportion of brain/tumor cells positive for the various marks (MAP2, SOX10, SOX2, GFAP, Olig2, Ki67) (n = 120 samples). Data are presented as mean, all points plotted. c, d. The myeloid and lymphoid compartments, presented as a percentage of all cells in the TME, were distributed based on tumor subtype (A) and sex (B) (n = 118). Data are presented as mean, with all samples plotted. PA: Pilocytic astrocytoma, GG: Ganglioglioma, DC: Dendritic cells, Mo: Monocyte, Bc: B cells, DN T: Double-negative T cells, NK: Natural killer cells. e. Endothelial cell populations as a percentage of all cells in the TME by tumor subtype (n = 120). Data is presented as mean, with samples plotted. p = 0.0728. f. Samples were split in groups based on poor (below average) or rich (above average) infiltration of T-helper cells (Th) in the TME (n = 120). Subsequently, myeloid populations were plotted based on T cell infiltration. Data are presented as the mean, all samples are plotted, statistical analysis by two-sided Mann-Whitney test. * p values = 0.004764; 0.009136; 0.00187 (left to right). g. Individual cell populations as a percentage of all cells in the TME by genomic alteration. BRAF-fusion n = 95, BRAF-mutation n = 10, Other mutations n = 10. Data are represented as mean. DC: Dendritic cells, Mo: Monocyte, Bc: B cells, DN T: Double-negative T cells, NK: Natural killer cells, Cl Mo: Classical monocyte, Non-Cl Mo: Non-classical monocyte, Int Mo: Intermediate monocyte. h. Individual cell populations as a percentage of all cells in the TME by age groups. 0-3 y n = 27, 4-12 n = 73, 13+ n = 20. Data are represented as mean.

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Extended Data Fig. 3 A myeloid immunophenotype signature is significantly associated with PFS in pLGG, as Related to Fig. 2.

a. Cell populations and their association with progression-free survival (uncorrected log-rank test) in the IMC cohort (n = 92 samples). High = percent of cell population higher than the median, Low = percent of cell population lower than the median. b. Kaplan-Meier curve of IMC samples (n = 92) grouped based on the patients’ Cl/Atl ratio (uncorrected log-rank test). c. Kaplan-Meier curve of IMC samples (n = 85) grouped based on the patients’ resection status. Is also indicated the adjusted pvalue (BH test) for all pairwise comparisons using the Log-Rank test. d. Forest plot depicting the hazard ratio (HR) calculated in a univariate Cox proportional hazards model evaluating which clinico-molecular features are significantly associated with PFS in our pLGG cohort (n = 120). Is also depicted the upper and lower 95% confidence intervals for the hazard ratio. e-f. Kaplan-Meier curve of IMC samples from patients at risk of progression, either without gross-total resection (GTR) (E – n = 70 samples), or based on location and resection (F – n = 31) and grouped based on their immunophenotype defined by the cell populations significantly associated with PFS. Is also indicated the adjusted pvalue (BH test) for all pairwise comparisons using Log-Rank test. g. Kaplan-Meier curve of IMC samples (n = 92) grouped based on their immunophenotype defined by the cell populations significantly associated with PFS (5 groups). h. Heatmap summarizing the adjusted p-values (BH correction) of all pairwise comparison using the log-rank test. i. Connected dot plot depicting all combinations of one, two, three, or four cell populations to calculate the immunophenotype signature and their associated PFS log-rank test p-value in the survival analysis. j. Table summarizing the p-value (log-rank test) and adj-p-val (Bonferonni correction) from the Kaplan-Meier analysis looking at the association of the genes associated with the immune phenotype and PFS in our cohort.

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Extended Data Fig. 4 pLGG show immunosuppressive myeloid populations, as related to Fig. 3.

a. Proportion of BMDM and MG subtypes positive for the activation marks CD206 (n = 120). Samples devoid of these populations were excluded from this analysis. Data are presented as mean, all points plotted. b. Expression of HAVCR2, LGALS9, and PCD1 using the Open Pediatric Brain Tumor Atlas (Boxplots depict the median, first and third quartiles. Whiskers extend from the hinge to the largest/smallest value no further than 1.5 * interquartile range from the hinge). c. Percentage of HAVCR2 (TIM-3) positive myeloid and lymphoid cells, in pLGG and H3K27M-mutant tumors (Andrade et al). d. Percentage of HAVCR2 (TIM-3) positive cells in pLGG immune populations (Andrade et al). e. A representative immunofluorescence image of co-stain using antibodies for PD1 or TIM3 and CD68. Merge depicted in a human tonsil sample used as positive control for both markers. Magnification: 20x. f. Kaplan-Meier curve depicting the association between TIM-3 (HAVCR2) gene expression and PFS in the whole cohort with survival and TIM3 data (n = 63) or in patients at risk of progression (that is non-GTR patients only – n = 51); p-values based on Log-Rank (non-corrected for multiple testing). g. Circle plots for the corresponding interacting pair modules where the HAVCR2-Gal9 pair was enriched. Cell types are shown in the outer circle, while interacting pairs are displayed in the inner one. Directional links originate from ligands and are pointing towards receptors; they are colored by a scaled (−2,2) interaction score to highlight relative differences in expression. h. Expression of C3 and C3AR1 in immune populations by scRNAseq, in H3K27M-mutant tumors (Andrade et al). i. Differentially expressed genes between TIM-3 positive and negative microglia. Genes with an adjusted p value < 0.05 and log2 fold change > 0.5 are shown in red/blue (two-sided Wilcoxon ranked sum test).

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Extended Data Fig. 5 Spatial immune landscape of pediatric LGG, as Related to Fig. 4.

a. Heatmap of percentages of cell types represented across 10 CNs discovered in pLGG cores (n = 120). b. Percentage of CNs comparing the different tumor types (n = 120), locations (n = 117), patient age (n = 120) and sex (n = 118), and genomic alterations (n = 115). CN1 was significantly different between PA and GG, between BRAF-fusion and both other mutation statuses, and between all locations. c. The percentage of all immune cells in the TME was plotted and distributed based on tumor location (n = 117). Data are presented as mean, with all samples plotted. d. Total brain or tumor and immune, presented as a proportion of all cells in the TME, distributed by tumor location in the brain. Data are presented as the mean, with all samples plotted, statistical analysis conducted using the two-way ANOVA. Cerebellum n = 72, Cortex n = 18, Midline n = 16, Optic pathway n = 11. ****p < 0.001, **p < 0.01, *p < 0.05. e. Samples were sorted as described in Fig. 4i and depicted individually by location. The upper and lower quartiles are depicted by a red or blue box, respectively. f. Heat map indicates IMC spatial analysis among cell phenotypes and their patterns of cell-cell interactions or avoidance (blue) in pLGG tumors, determined by pairwise scores. The determination is based on pairwise scores, and only interactions/avoidances exceeding 50% (random chance) are displayed. Interpretation should be done row-to-column, where rows indicate the relationship of a specific cell type, and columns represent the relationship of that cell type with other surrounding cell types. g. Cell-cell interaction network depicting the interaction degree of a given cell population with all other cell populations in in the cerebellum, cortex, or midline vs optic pathway glioma (OPG). The dot size represents the proportion of each cell population in the entire cohort, while the edge thickness represents the degree of interaction/avoidance (two-sided test).

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Supplementary information

Reporting Summary

Supplementary Table 1

Cohort summary and inventory of all human tumors, clinical information and genomic alterations used for immune profiling analysis.

Supplementary Table 2

IMC and CyTOF antibody panel.

Supplementary Table 3

Ligand–receptor interactions in microglia.

Supplementary Table 4

Clusters of interactions related to cytokines and immune response.

Supplementary Table 5

Differentially expressed genes between TIM3+ and TIM3− microglia.

Source data

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Andrade, A.F., Sigaud, R., Puligandla, E. et al. A spatial map of MAPK-activated immunosuppressive myeloid populations in pediatric low-grade glioma. Nat Immunol 26, 1794–1806 (2025). https://doi.org/10.1038/s41590-025-02268-7

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