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The whole-genome landscape of medulloblastoma subtypes.
Nature. 2017 Jul 19;547(7663):311-317. doi: 10.1038/nature22973.

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Next-generation sequencing (NGS) studies have tremendously advanced our understanding of the genes, pathways and molecular processes that underly most commonly diagnosed human cancers. These efforts have identified core sets of ‘driver’ genes that are frequently mutated across a wide spectrum of different cancer entities[1,2].
Although the genetic underpinnings of some cancers were largely resolved during the first ‘wave’ of NGS studies, especially for comparatively simple malignancies driven by deregulation of a single pathway[3,4], others remain enigmatic and require further interrogation with sufficient power to overcome confounding molecular heterogeneity and diversity.

Medulloblastoma (MB) (World Health Organization grade IV) is a highly malignant childhood brain tumour that has been the subject of several NGS studies conducted by the International Cancer Genome Consortium (ICGC) [5–8], the Paediatric Cancer Genome Project (PCGP) [9], and the Medulloblastoma Advanced Genomics Consortium (MAGIC) [10,11]. Consensus molecular subgroups of MB, namely WNT, SHH, Group 3 and Group 4, exhibit distinctive transcriptional and epigenetic signatures that define clinically relevant patient subsets[12,13.]

WNT and SHH subgroup MBs are primarily driven by mutations leading to constitutive activation of the Wingless and Sonic hedgehog signalling pathways, respectively. By contrast, the genetics and biology underlying the Group 3 and Group 4 MB subgroups remain less clear[12].

Targeted therapies for MB are scarce yet desperately needed, warranting intensive investigation into the full range of genetic lesions and molecular heterogeneity that contribute to MB subgroups, especially as it relates to poorly characterized Group 3 and Group 4 disease.

Here we report the genomic landscape across a series of 491 previously untreated MBs.
Our comprehensive and integrative approach to this multilayered dataset provides considerable biological insight into each of the core subgroups, including the identification of new subgroup-specific driver genes, epigenetic subtypes, and candidate targets for therapy.
This dataset provides a rich resource for the cancer genomics community and will serve as the foundation of ongoing and future candidate-driven functional studies focused on resolving MB aetiology.




Our highly integrative genomic analysis of the paediatric brain tumour MB has enabled the discovery of new cancer genes and actionable pathways, effectively assigning candidate drivers to most tumours across molecular subgroups.
The sizable increase in power over previous studies has allowed us to deal more effectively with the intrinsic heterogeneity characteristic of MB, splitting the entity into molecularly distinct consensus subgroups and subtypes within them, summarizing the disease as a collection of several diseases rather than a single entity.

At the level of individual genes, novel candidate drivers were discovered in each of the consensus subgroups.
Hotspot insertions that target KBTBD4 were not featured in previous MB NGS studies, probably owing to inferior cohort sizes and insensitive indel-calling pipelines.
KBTBD4 insertions were highly specific for discrete patient subtypes that were devoid of other obvious oncogenic driver events, suggesting that these mutations are functional.
Likewise, PRDM6—a presumed histone methyltransferase[38] not previously implicated in MB—was identified as the probable target of SNCAIP-associated enhancer hijacking in Group 4, now representing the most prevalent driver alteration in this subgroup. Studies further detailing the normal, physiological cellular functions of KBTBD4 and PRDM6 and how somatic alterations targeting these genes specifically contribute to MB pathogenesis are essential and will be required to determine their potential ‘actionability’ in affected patients.

The relatively recent recognition of consensus MB subgroups has rapidly changed the way MB is studied in the research setting and how it is diagnosed and treated in the clinic[39]. Still, considerable molecular and clinical heterogeneity has been demonstrated[11,40], suggesting that currently defined MB subgroups are likely to be an oversimplification of true molecular substructure.
Methylation analysis of over 1,250 Mbs discovered new tumour subtypes enriched for specific genetic and transcriptional signatures, especially those underlying Group 3 and Group 4.
Definitive de-convolution of these subtypes will enable a better understanding of the developmental origins of MB, creating a path towards the efficient modelling of each individual subtype in the correct cellular context using subtype-relevant genetic perturbations.
Moreover, by redefining molecular substructure as we have described here, new opportunities for improved risk-stratification tailored to treat individual patient subtypes according to their genotype are likely to emerge.


In conclusion, this study embodies an unparalleled resource of high-resolution genetic, epigenetic and transcriptional data for the childhood brain tumour MB.
Our data underscore the heterogeneous, complex nature of disease subgroups and the utility of continued efforts to divulge the full spectrum of molecular mechanisms underlying MB aetiology.
We anticipate that the findings reported here, combined with the future exploration and mining of this large genomics resource, will undoubtedly advance treatments and the outlook for children and families affected by this devastating malignancy.