Resolving medulloblastoma cellular architecture by single-cell genomics.
Journal
Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462
Informations de publication
Date de publication:
08 2019
08 2019
Historique:
received:
13
09
2018
accepted:
21
06
2019
pubmed:
26
7
2019
medline:
20
11
2019
entrez:
26
7
2019
Statut:
ppublish
Résumé
Medulloblastoma is a malignant childhood cerebellar tumour type that comprises distinct molecular subgroups. Whereas genomic characteristics of these subgroups are well defined, the extent to which cellular diversity underlies their divergent biology and clinical behaviour remains largely unexplored. Here we used single-cell transcriptomics to investigate intra- and intertumoral heterogeneity in 25 medulloblastomas spanning all molecular subgroups. WNT, SHH and Group 3 tumours comprised subgroup-specific undifferentiated and differentiated neuronal-like malignant populations, whereas Group 4 tumours consisted exclusively of differentiated neuronal-like neoplastic cells. SHH tumours closely resembled granule neurons of varying differentiation states that correlated with patient age. Group 3 and Group 4 tumours exhibited a developmental trajectory from primitive progenitor-like to more mature neuronal-like cells, the relative proportions of which distinguished these subgroups. Cross-species transcriptomics defined distinct glutamatergic populations as putative cells-of-origin for SHH and Group 4 subtypes. Collectively, these data provide insights into the cellular and developmental states underlying subtype-specific medulloblastoma biology.
Identifiants
pubmed: 31341285
doi: 10.1038/s41586-019-1434-6
pii: 10.1038/s41586-019-1434-6
pmc: PMC6754173
mid: NIHMS1532632
doi:
Substances chimiques
Glutamic Acid
3KX376GY7L
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
74-79Subventions
Organisme : NCI NIH HHS
ID : DP2 CA239145
Pays : United States
Organisme : NCI NIH HHS
ID : DP1 CA216873
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA232143
Pays : United States
Organisme : NCI NIH HHS
ID : K12 CA090354
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA006516
Pays : United States
Références
Gajjar, A. J. & Robinson, G. W. Medulloblastoma-translating discoveries from the bench to the bedside. Nat. Rev. Clin. Oncol. 11, 714–722 (2014).
doi: 10.1038/nrclinonc.2014.181
Taylor, M. D. et al. Molecular subgroups of medulloblastoma: the current consensus. Acta Neuropathol. 123, 465–472 (2012).
doi: 10.1007/s00401-011-0922-z
Northcott, P. A. et al. The whole-genome landscape of medulloblastoma subtypes. Nature 547, 311–317 (2017).
doi: 10.1038/nature22973
Lin, C. Y. et al. Active medulloblastoma enhancers reveal subgroup-specific cellular origins. Nature 530, 57–62 (2016).
doi: 10.1038/nature16546
Hovestadt, V. et al. Decoding the regulatory landscape of medulloblastoma using DNA methylation sequencing. Nature 510, 537–541 (2014).
doi: 10.1038/nature13268
Northcott, P. A. et al. Subgroup-specific structural variation across 1,000 medulloblastoma genomes. Nature 488, 49–56 (2012).
doi: 10.1038/nature11327
Northcott, P. A. et al. Medulloblastomics: the end of the beginning. Nat. Rev. Cancer 12, 818–834 (2012).
doi: 10.1038/nrc3410
Gibson, P. et al. Subtypes of medulloblastoma have distinct developmental origins. Nature 468, 1095–1099 (2010).
doi: 10.1038/nature09587
Yang, Z. J. et al. Medulloblastoma can be initiated by deletion of Patched in lineage-restricted progenitors or stem cells. Cancer Cell 14, 135–145 (2008).
doi: 10.1016/j.ccr.2008.07.003
Oliver, T. G. et al. Loss of patched and disruption of granule cell development in a pre-neoplastic stage of medulloblastoma. Development 132, 2425–2439 (2005).
doi: 10.1242/dev.01793
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).
doi: 10.1200/JCO.2010.28.5148
Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017).
doi: 10.1038/nature21350
Tirosh, I. et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309–313 (2016).
doi: 10.1038/nature20123
Filbin, M. G. et al. Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq. Science 360, 331–335 (2018).
doi: 10.1126/science.aao4750
Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
doi: 10.1126/science.1254257
Venteicher, A. S. et al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science 355, eaai8478 (2017).
doi: 10.1126/science.aai8478
Hovestadt, V. et al. Robust molecular subgrouping and copy-number profiling of medulloblastoma from small amounts of archival tumour material using high-density DNA methylation arrays. Acta Neuropathol. 125, 913–916 (2013).
doi: 10.1007/s00401-013-1126-5
Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protocols 9, 171–181 (2014).
doi: 10.1038/nprot.2014.006
Northcott, P. A. et al. Rapid, reliable, and reproducible molecular sub-grouping of clinical medulloblastoma samples. Acta Neuropathol. 123, 615–626 (2012).
doi: 10.1007/s00401-011-0899-7
Carter, S. L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).
doi: 10.1038/nbt.2203
Carter, R. A. et al. A single-cell transcriptional atlas of the developing murine cerebellum. Curr. Biol. 28, 2910–2920 (2018).
doi: 10.1016/j.cub.2018.07.062
Kool, M. et al. Molecular subgroups of medulloblastoma: an international meta-analysis of transcriptome, genetic aberrations, and clinical data of WNT, SHH, Group 3, and Group 4 medulloblastomas. Acta Neuropathol. 123, 473–484 (2012).
doi: 10.1007/s00401-012-0958-8
Waszak, S. M. et al. Spectrum and prevalence of genetic predisposition in medulloblastoma: a retrospective genetic study and prospective validation in a clinical trial cohort. Lancet Oncol. 19, 785–798 (2018).
doi: 10.1016/S1470-2045(18)30242-0
Shih, D. J. et al. Cytogenetic prognostication within medulloblastoma subgroups. J. Clin. Oncol. 32, 886–896 (2014).
doi: 10.1200/JCO.2013.50.9539
Northcott, P. A. et al. Pediatric and adult sonic hedgehog medulloblastomas are clinically and molecularly distinct. Acta Neuropathol. 122, 231–240 (2011).
doi: 10.1007/s00401-011-0846-7
Kool, M. et al. Genome sequencing of SHH medulloblastoma predicts genotype-related response to smoothened inhibition. Cancer Cell 25, 393–405 (2014).
doi: 10.1016/j.ccr.2014.02.004
Merk, D. J. et al. Opposing effects of CREBBP mutations govern the phenotype of Rubinstein–Taybi syndrome and adult SHH medulloblastoma. Dev. Cell 44, 709–724 (2018).
doi: 10.1016/j.devcel.2018.02.012
Northcott, P. A. et al. Medulloblastoma comprises four distinct molecular variants. J. Clin. Oncol. 29, 1408–1414 (2011).
doi: 10.1200/JCO.2009.27.4324
Jones, D. T. et al. Dissecting the genomic complexity underlying medulloblastoma. Nature 488, 100–105 (2012).
doi: 10.1038/nature11284
Cavalli, F. M. G. et al. Intertumoral heterogeneity within medulloblastoma subgroups Cancer Cell 31, 737–754 (2017).
doi: 10.1016/j.ccell.2017.05.005
Schwalbe, E. C. et al. Novel molecular subgroups for clinical classification and outcome prediction in childhood medulloblastoma: a cohort study. Lancet Oncol. 18, 958–971 (2017).
doi: 10.1016/S1470-2045(17)30243-7
Sharma, T. et al. Second-generation molecular subgrouping of medulloblastoma: an international meta-analysis of Group 3 and Group 4 subtypes. Acta Neuropathol. https://doi.org/10.1007/s00401-019-02020-0 (2019).
doi: 10.1007/s00401-019-02020-0
Chizhikov, V. V. et al. Lmx1a regulates fates and location of cells originating from the cerebellar rhombic lip and telencephalic cortical hem. Proc. Natl Acad. Sci. USA 107, 10725–10730 (2010).
doi: 10.1073/pnas.0910786107
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).
doi: 10.1523/JNEUROSCI.1610-06.2006
Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018).
doi: 10.1038/nature26000
Rusch, M. et al. Clinical cancer genomic profiling by three-platform sequencing of whole genome, whole exome and transcriptome. Nat. Commun. 9, 3962 (2018).
doi: 10.1038/s41467-018-06485-7
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome BMC Bioinformatics 12, 323 (2011).
doi: 10.1186/1471-2105-12-323
Gaujoux, R. & Seoighe, C. A flexible R package for nonnegative matrix factorization. BMC Bioinformatics 11, 367 (2010).
doi: 10.1186/1471-2105-11-367
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
doi: 10.1186/s13059-017-1382-0
Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).
doi: 10.1038/nbt.4096
Pöschl, J. et al. Genomic and transcriptomic analyses match medulloblastoma mouse models to their human counterparts. Acta Neuropathol. 128, 123–136 (2014).
doi: 10.1007/s00401-014-1297-8
Tamayo, P. et al. Metagene projection for cross-platform, cross-species characterization of global transcriptional states. Proc. Natl Acad. Sci. USA 104, 5959–5964 (2007).
doi: 10.1073/pnas.0701068104