Abstract
Medulloblastoma, a malignant childhood cerebellar tumour, segregates molecularly into biologically distinct subgroups, suggesting that a personalized approach to therapy would be beneficial1. Mouse modelling and cross-species genomics have provided increasing evidence of discrete, subgroup-specific developmental origins2. However, the anatomical and cellular complexity of developing human tissues3—particularly within the rhombic lip germinal zone, which produces all glutamatergic neuronal lineages before internalization into the cerebellar nodulus—makes it difficult to validate previous inferences that were derived from studies in mice. Here we use multi-omics to resolve the origins of medulloblastoma subgroups in the developing human cerebellum. Molecular signatures encoded within a human rhombic-lip-derived lineage trajectory aligned with photoreceptor and unipolar brush cell expression profiles that are maintained in group 3 and group 4 medulloblastoma, suggesting a convergent basis. A systematic diagnostic-imaging review of a prospective institutional cohort localized the putative anatomical origins of group 3 and group 4 tumours to the nodulus. Our results connect the molecular and phenotypic features of clinically challenging medulloblastoma subgroups to their unified beginnings in the rhombic lip in the early stages of human development.
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Data availability
Previously unpublished scRNA-seq, bulk RNA-seq and array-based DNA methylation datasets described in this study have been deposited in the GEO with the accession code GSE207266. The harmonized mouse single-cell cerebellar atlas expression matrix is accessible in the GEO under the same accession code (GSE207266).
References
Northcott, P. A. et al. Medulloblastoma. Nat. Rev. Dis. Primers 5, 11 (2019).
Hovestadt, V. et al. Medulloblastomics revisited: biological and clinical insights from thousands of patients. Nat. Rev. Cancer 20, 42–56 (2020).
Haldipur, P., Millen, K. J. & Aldinger, K. A. Human cerebellar development and transcriptomics: implications for neurodevelopmental disorders. Annu. Rev. Neurosci. 45, 515–531 (2022).
Jones, D. T. W. et al. Molecular characteristics and therapeutic vulnerabilities across paediatric solid tumours. Nat. Rev. Cancer 19, 420–438 (2019).
Hovestadt, V. et al. Resolving medulloblastoma cellular architecture by single-cell genomics. Nature 572, 74–79 (2019).
Vladoiu, M. C. et al. Childhood cerebellar tumours mirror conserved fetal transcriptional programs. Nature 572, 67–73 (2019).
Haldipur, P. et al. Spatiotemporal expansion of primary progenitor zones in the developing human cerebellum. Science 366, 454–460 (2019).
Aldinger, K. A. et al. Spatial and cell type transcriptional landscape of human cerebellar development. Nat. Neurosci. 24, 1163–1175 (2021).
Jessa, S. et al. Stalled developmental programs at the root of pediatric brain tumors. Nat. Genet. 51, 1702–1713 (2019).
Gibson, P. et al. Subtypes of medulloblastoma have distinct developmental origins. Nature 468, 1095–1099 (2010).
Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018).
Consalez, G. G., Goldowitz, D., Casoni, F. & Hawkes, R. Origins, development, and compartmentation of the granule cells of the cerebellum. Front. Neural Circuits 14, 611841 (2020).
Cao, J. et al. A human cell atlas of fetal gene expression. Science 370, eaba7721 (2020).
Englund, C. et al. Unipolar brush cells of the cerebellum are produced in the rhombic lip and migrate through developing white matter. J. Neurosci. 26, 9184–9195 (2006).
Hagan, N. & Zervas, M. Wnt1 expression temporally allocates upper rhombic lip progenitors and defines their terminal cell fate in the cerebellum. Mol. Cell. Neurosci. 49, 217–229 (2012).
McDonough, A. et al. Unipolar (dendritic) brush cells are morphologically complex and require Tbr2 for differentiation and migration. Front. Neurosci. 14, 598548 (2020).
Furukawa, T., Morrow, E. M. & Cepko, C. L. Crx, a novel otx-like homeobox gene, shows photoreceptor-specific expression and regulates photoreceptor differentiation. Cell 91, 531–541 (1997).
Kozareva, V. et al. A transcriptomic atlas of mouse cerebellar cortex comprehensively defines cell types. Nature 598, 214–219 (2021).
Nayler, S., Agarwal, D., Curion, F., Bowden, R. & Becker, E. B. E. High-resolution transcriptional landscape of xeno-free human induced pluripotent stem cell-derived cerebellar organoids. Sci. Rep. 11, 12959 (2021).
Cho, Y. J. et al. Integrative genomic analysis of medulloblastoma identifies a molecular subgroup that drives poor clinical outcome. J. Clin. Oncol. 29, 1424–1430 (2011).
Garancher, A. et al. NRL and CRX define photoreceptor identity and reveal subgroup-specific dependencies in medulloblastoma. Cancer Cell 33, 435–449 (2018).
Kool, M. et al. Integrated genomics identifies five medulloblastoma subtypes with distinct genetic profiles, pathway signatures and clinicopathological features. PLoS One 3, e3088 (2008).
Northcott, P. A. et al. Medulloblastoma comprises four distinct molecular variants. J. Clin. Oncol. 29, 1408–1414 (2011).
Northcott, P. A. et al. The whole-genome landscape of medulloblastoma subtypes. Nature 547, 311–317 (2017).
Sharma, T. et al. Second-generation molecular subgrouping of medulloblastoma: an international meta-analysis of Group 3 and Group 4 subtypes. Acta Neuropathol. 138, 309–326 (2019).
Lin, C. Y. et al. Active medulloblastoma enhancers reveal subgroup-specific cellular origins. Nature 530, 57–62 (2016).
Bunt, J. et al. OTX2 directly activates cell cycle genes and inhibits differentiation in medulloblastoma cells. Int. J. Cancer 131, E21–E32 (2012).
Sweet-Cordero, E. A. & Biegel, J. A. The genomic landscape of pediatric cancers: implications for diagnosis and treatment. Science 363, 1170–1175 (2019).
Grobner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018).
Northcott, P. A. et al. Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma. Nature 511, 428–434 (2014).
Goodrich, L. V., Milenkovic, L., Higgins, K. M. & Scott, M. P. Altered neural cell fates and medulloblastoma in mouse patched mutants. Science 277, 1109–1113 (1997).
Kawauchi, D. et al. A mouse model of the most aggressive subgroup of human medulloblastoma. Cancer Cell 21, 168–180 (2012).
Pei, Y. et al. An animal model of MYC-driven medulloblastoma. Cancer Cell 21, 155–167 (2012).
Swartling, F. J. et al. Distinct neural stem cell populations give rise to disparate brain tumors in response to N-MYC. Cancer Cell 21, 601–613 (2012).
Perreault, S. et al. MRI surrogates for molecular subgroups of medulloblastoma. Am. J. Neuroradiol. 35, 1263–1269 (2014).
Wefers, A. K. et al. Subgroup-specific localization of human medulloblastoma based on pre-operative MRI. Acta Neuropathol. 127, 931–933 (2014).
Gajjar, A. et al. Outcomes by clinical and molecular features in children with medulloblastoma treated with risk-adapted therapy: results of an international phase III trial (SJMB03). J. Clin. Oncol. 39, 822–835 (2021).
Robinson, G. W. et al. Risk-adapted therapy for young children with medulloblastoma (SJYC07): therapeutic and molecular outcomes from a multicentre, phase 2 trial. Lancet Oncol. 19, 768–784 (2018).
Patay, Z. et al. MR imaging characteristics of Wingless-type-subgroup pediatric medulloblastoma. Am. J. Neuroradiol. 36, 2386–2393 (2015).
Aldape, K. et al. Challenges to curing primary brain tumours. Nat. Rev. Clin. Oncol. 16, 509–520 (2019).
Gajjar, A. J. & Robinson, G. W. Medulloblastoma—translating discoveries from the bench to the bedside. Nat. Rev. Clin. Oncol. 11, 714–722 (2014).
Cavalli, F. M. G. et al. Intertumoral heterogeneity within medulloblastoma subgroups. Cancer Cell 31, 737–754 (2017).
Poli, V. et al. MYC-driven epigenetic reprogramming favors the onset of tumorigenesis by inducing a stem cell-like state. Nat. Commun. 9, 1024 (2018).
Friedmann-Morvinski, D. et al. Dedifferentiation of neurons and astrocytes by oncogenes can induce gliomas in mice. Science 338, 1080–1084 (2012).
Morfouace, M. et al. Pemetrexed and gemcitabine as combination therapy for the treatment of Group 3 medulloblastoma. Cancer Cell 25, 516–529 (2014).
Pei, Y. et al. HDAC and PI3K antagonists cooperate to inhibit growth of MYC-driven medulloblastoma. Cancer Cell 29, 311–323 (2016).
Tao, R. et al. MYC drives group 3 medulloblastoma through transformation of Sox2+ astrocyte progenitor cells. Cancer Res. 79, 1967–1980 (2019).
Kawauchi, D. et al. Novel MYC-driven medulloblastoma models from multiple embryonic cerebellar cells. Oncogene 36, 5231–5242 (2017).
Behesti, H., Kocabas, A., Buchholz, D. E., Carroll, T. S. & Hatten, M. E. Altered temporal sequence of transcriptional regulators in the generation of human cerebellar granule cells. eLife 10, e67074 (2021).
Polanski, K. et al. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics 36, 964–965 (2020).
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Wang, X., Park, J., Susztak, K., Zhang, N. R. & Li, M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat. Commun. 10, 380 (2019).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Riemondy, K. A. et al. Neoplastic and immune single cell transcriptomics define subgroup-specific intra-tumoral heterogeneity of childhood medulloblastoma. Neuro-oncology 24, 273–286 (2021).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Hanzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14, 7 (2013).
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
Buenrostro, J. D., Wu, B., Chang, H. Y. & Greenleaf, W. J. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109, 21.29.21–21.29.29 (2015).
Corces, M. R. et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat. Methods 14, 959–962 (2017).
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).
Ramírez, F., Dündar, F., Diehl, S., Grüning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).
Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).
Vo, B. T. et al. Mouse medulloblastoma driven by CRISPR activation of cellular Myc. Sci Rep. 8, 8733 (2018).
Lee, C. et al. Lsd1 as a therapeutic target in Gfi1-activated medulloblastoma. Nat. Commun. 10, 332 (2019).
Acknowledgements
This work was principally supported by the American Lebanese Syrian Associated Charities and St Jude (P.A.N.), The Sontag Foundation (Distinguish Scientist Award; P.A.N.), St. Baldrick’s Foundation (Robert J. Arceci Innovation Award; P.A.N.) and the National Cancer Institute (P.A.N.; P01CA096832-16A1). P.A.N. is a Pew-Stewart Scholar for Cancer Research (Margaret and Alexander Stewart Trust). L.B. was supported by a Future Leaders Award from The Brain Tumour Charity (GN-000518). V.V.C. acknowledges funding from R01NS093009. From St Jude, we thank the Flow Cytometry Core Laboratory (Department of Developmental Neurobiology), the Flow Cytometry and Cell Sorting Shared Resource Facility, the Hartwell Center (supported in part by NCI grant P30 CA021765) and the Center for In Vivo Imaging and Therapeutics (supported in part by NCI grants R50CA211481 and P30 CA021765 (Cancer Center)). We thank G. Campbell for assistance with immunostaining. Images were acquired at the Cell & Tissue Imaging Center, which is supported by St Jude and NCI P30 CA021765. We also thank A. Vasilyeva for coordination of clinical study information, M. Batts for animal care support and B. Stelter for assistance with artwork. Healthy human samples were provided by the Birth Defects Research Laboratory at the University of Washington (supported by NICHD R24 HD000836 to I.A.G.) and the Joint Medical Research Council/Wellcome (MR/R006237/1) Human Developmental Biology Resource. Human tissue used in this study was covered by a material transfer agreement between SCRI and HDBR.
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K.S.S., L.B., B.L.G., K.J.M. and P.A.N. designed the study. K.S.S., B.L.G., Q.G. and Y.L. performed computational analyses. L.B. performed mouse lineage-tracing experiments, scRNA-seq and ATAC-seq. R.T. generated the developmental mouse scRNA-seq dataset and performed OTX2 functional studies in MB cells. I.Y.I. performed microdissection of the mouse RL. P.H., J.M., A.H.S. and J.G. performed mouse and human ISH experiments and immunofluorescence imaging of human fetal cerebellum. M.D. performed LMD and DNA extraction from human fetal cerebellum. H.S. and B.A.O. performed immunofluorescence and immunohistochemistry in MB and human adult cerebellum. Z.P. reviewed MRI evaluations, A.O.-T. and Z.P. analysed tumour measurements and locations and Z.P., M.S., S.Z. and A.E. generated the heat maps of MB location. K.A.A., V.V.C., I.A.G., L.M.O., J.H., K.M., A.G., G.W.R., V.H. and Z.P. supported the project. K.S.S., L.B., B.L.G., V.H., K.J.M. and P.A.N. wrote the manuscript with contributions from all authors. K.J.M. and P.A.N supervised and funded the study.
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Extended data figures and tables
Extended Data Fig. 1 Transcriptional atlas of the human fetal cerebellum.
(a) Schematic summary of the molecular datasets used to interconnect mouse and human cerebellar profiles with MB. Single-cell and bulk sample counts for each data source are indicated. (b, c) UMAP plots showing published cell-type annotation (b) and predicted cell-cycle phase (c) of the human fetal cerebellar atlas8. (d) Correlation heat map of human cerebellar cell types. (e) Violin plots showing expression of cerebellar lineage markers in the human fetal cerebellar atlas. (f) Inferred cerebellar cell-type proportions in deconvoluted bulk MB transcriptomes. (g) Proportion of cerebellar lineage-associated genes upregulated in MB subgroups. (h) MB subgroup-specific gene set enrichment in cerebellar cell types.
Extended Data Fig. 2 Reciprocal classification of human fetal cerebellar and MB datasets using developmental and tumorigenic signatures.
(a) Predicted pseudotime of MB single cells according to underlying cellular states. (b) RL–GlutaCN/UBC cell state metagenes projected onto group 3/4-MB single cells. MB single cells are positioned according to the previously reported cellular hierarchy5. (c) Predicted pseudotime of bulk MB expression profiles according to underlying developmental cell states. Molecular subgroup, subtype, and driver gene alteration status is annotated for each individual tumour. Inferred cellular proportions of GluCN/UBC cell states are annotated for each tumour profile. (d) UMAP plot of MB Smart-seq2 training dataset, annotated with published subgroup assignments5. (e) UMAP plot of MB 10x scRNA-seq test dataset, annotated with published subgroup assignments57. (f) UMAP plot of MB 10x scRNA-seq test dataset, labelled with predicted subgroup assignments based on the neural-net classifier. (g) Confusion matrix summarizing performance of the neural-net classifier. (h) (Left panel) Pseudotime diffusion map of glutamatergic lineages extracted from the human cerebellar atlas. (Right panel) Classification of glutamatergic single cells according to MB subgroup based on the neural-net classifier. (i) Heat map showing unsupervised clustering of human RL (n = 7), EGL (n = 7), and MB (n=559) DNA methylation profiles. (j) DNA methylation-based classification of microdissected human fetal EGL (n = 7) and RL (n = 7) samples. Reference entities represent an aggregated set of CNS tumour and non-neoplastic methylation classes. Error bars represent standard error of the mean.
Extended Data Fig. 3 Enrichment of photoreceptor and UBC gene signatures in the RLSVZ.
(a) Heat maps showing expression of RLSVZ-enriched photoreceptor (upper panel) and UBC (lower panel) marker genes in human cerebellar subcompartments. (b) UMAP plots showing mean expression of the photoreceptor (upper panel) and UBC (lower panel) gene sets across 77 human cell types13. (c) Heat maps showing mean expression of RLSVZ-enriched UBC (left panel) and photoreceptor (right panel) marker genes across 77 human cell types. (d, e) Quantification of RLSVZ-enriched photoreceptor (d) and UBC (e) gene sets in human cerebellar glutamatergic lineages.
Extended Data Fig. 4 Status of RLSVZ photoreceptor and RLSVZ UBC gene signatures in adult human cerebellum and cerebellar organoids.
(a) IHC of CRX (photoreceptor marker) and EOMES (UBC marker) in the adult human cerebellum. The precise localization of EOMES positivity was unconfirmed. Scale bars, 40 μm. (b) UMAP summarizing snRNA-seq data derived from >150,000 nuclei isolated from the adult human cerebellum according to published cell types18. (c) Quantification of the RLSVZ, photoreceptor, and UBC gene expression signatures in the adult human cerebellar atlas by cell type. Proportion of excitatory neurons positive for each expression signature is indicated. P-values were calculated as described in Fig. 4d. (d) Expression of select marker genes in the adult human cerebellar atlas. (e) UMAP summary of cell types annotated in published human cerebellar organoids19. (f) Violin plots quantifying enrichment of the RLSVZ, photoreceptor, and UBC gene sets in cell types annotated in human cerebellar organoids. P-values were calculated as described in Fig. 4d. (g) Photoreceptor and UBC marker gene expression in cell types annotated in published human cerebellar organoids.
Extended Data Fig. 5 Expression of RLSVZ marker genes across cerebellar subcompartments and MB subgroups.
(a, b) Heat maps of RLSVZ-photoreceptor (a) and RLSVZ-UBC (b) marker gene expression in group 3/4-MB. (c, d) Box plots of select RLSVZ-photoreceptor (c) and RLSVZ-UBC (d) marker genes summarizing expression across human cerebellar subcompartments and MB subgroups. All box plots were created as described in Fig. 2c.
Extended Data Fig. 6 Correlation of OTX2 expression and transcription factor activity with the RLSVZ-photoreceptor signature.
(a, b) Correlation of OTX2 and RLSVZ-photoreceptor gene set expression across human cerebellar subcompartments (a) and group 3/4-MB (b). P-values were calculated using a two-sided Pearson’s correlation test. (c) iRegulon analysis of the RLSVZ-photoreceptor gene set. (d) Enrichment of the RLSVZ-derived photoreceptor gene set in the RL–GlutaCN/UBC lineage trajectory. (e) OTX2 TF activity in the RL–GlutaCN/UBC lineage trajectory. (f) Correlation of OTX2 TF activity and expression of the RLSVZ-photoreceptor gene set in single cells derived from the RL–GlutaCN/UBC lineage. P-values were calculated using a two-sided Pearson’s correlation test.
Extended Data Fig. 7 OTX2 is required for maintenance of the photoreceptor signature in group 3 MB.
(a) Experimental approach for inactivating OTX2 in group 3-MB cells. (b) Western blot showing efficient reduction of OTX2 protein expression at day 7 in OTX2-edited D283 MB cells. (c) Volcano plot highlighting differentially expressed genes in OTX2-edited versus non-targeted D283 cells. P-values were calculated using a two-sided Wald test and were adjusted for multiple comparisons using FDR correction. (d) GSEA results showing the top upregulated and downregulated gene sets in OTX2-edited D283 cells compared to non-targeted controls. (e) Significant upregulation of the RLSVZ-derived UBC gene set in OTX2-edited D283 cells. (f) Significant downregulation of the RLSVZ-photoreceptor gene set in OTX2-edited D283 cells. P-values for all gene set enrichment analyses were calculated using the Monte Carlo method. (g) Pairwise analysis of recurrent genetic alterations in group 3/4-MB. P-values were calculated using a linear regression model and adjusted for multiple comparisons using the FDR correction. (h) Smoothed expression of DDX31, BARHL1, and OTX2 in the human fetal GlutaCN/UBC lineage sorted by pseudotime.
Extended Data Fig. 8 Single-cell profiles of the developing mouse cerebellum and lineage-enriched subpopulations.
(a, b) UMAP plots of the harmonized mouse cerebellar atlas summarized by data origin (a) and developmental stage (b). (c) UMAP plot of the harmonized mouse cerebellar atlas indicating consensus cell-type annotations. (d) Heat map showing relative enrichment of mouse cerebellar lineage gene sets in human fetal cerebellar cell types. (e,f) UMAP plots showing enrichment of the human RLVZ (e) and RLSVZ (f) gene signatures in the mouse cerebellar atlas. (g) Violin plots quantifying enrichment of RLVZ (upper panel) and RLSVZ (lower panel) gene sets in mouse cerebellar cell types. Cell types with enrichment in >25% of cells are indicated; adjusted p < 0.05. (h) Enrichment of the mouse RL gene signature in the human cerebellar atlas. (i) Violin plots quantifying enrichment of the mouse RL gene signature in human cerebellar cell types. (j) Violin plots quantifying enrichment of the human RLSVZ-UBC gene signature across mouse (upper panel) and human (lower panel) cell types. P-values for all violin plots were calculated as described in Fig. 4d. Additional details regarding the harmonization of the mouse cerebellar atlas are summarized in the Supplementary Information.
Extended Data Fig. 9 Quantification of photoreceptor and UBC gene sets in mouse MB models.
(a) Enrichment strategy for isolating the mouse RL–UBC lineage. (b) UMAP plots of the enriched mouse RL–UBC trajectory, showing experimental source and select marker gene expression. (c) (Left panel) Latent time analysis of RL–UBC lineage-enriched single cells. (Right panels) Enrichment quantification of the RLSVZ-photoreceptor and -UBC gene sets in the mouse RL–UBC lineage trajectory. (d) ISH of photoreceptor marker genes in mouse cerebellum. Scale bars, 200μm. (e, f) Box plots summarizing enrichment of the RLSVZ-photoreceptor (e) and UBC (f) gene sets across published mouse MB transcriptional datasets (n = 14). Human MB and normal P7 cerebellum datasets were included as a reference. All box plots were created as described in Fig. 2c.
Extended Data Fig. 10 Anatomical mapping of MB diagnosis in the cerebellum.
(a–d) Select pre- and post-surgical MRIs of ‘small’ MBs according to subgroup. Group 3 (a) and group 4 (b) tumours indicate a common anatomical point of origin in the nodulus. Additional details for each ‘small’ MB are summarized in the Supplementary Information.
Supplementary information
Supplementary Information
This file contains Supplementary Figs. 1–4 and Supplementary Notes.
Supplementary Table 1
Summary of human and mouse molecular datasets.
Supplementary Table 2
Human cerebellar atlas annotations by developmental stage.
Supplementary Table 3
Enrichment of MB subgroup-specific gene sets in human cerebellar lineages.
Supplementary Table 4
DNA methylation classification of human cerebellar subcompartments.
Supplementary Table 5
Human cerebellar compartment-specific signatures and associated gene sets.
Supplementary Table 6
Enrichment of Descartes CNS cell-type gene sets in human cerebellar subcompartments.
Supplementary Table 7
Mouse cerebellar atlas annotations by developmental stage.
Supplementary Table 8
Summary of MB tumour size and location for institutional MRI cohort.
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Smith, K.S., Bihannic, L., Gudenas, B.L. et al. Unified rhombic lip origins of group 3 and group 4 medulloblastoma. Nature 609, 1012–1020 (2022). https://doi.org/10.1038/s41586-022-05208-9
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DOI: https://doi.org/10.1038/s41586-022-05208-9
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