Dissecting the genomic complexity underlying medulloblastoma

By David T. W. Jones, N. Jäger, M. Kool, T. Zichner, B. Hutter, M. Sultan, Yoon-Jae Cho, Trevor J Pugh, V. Hovestadt, A. Stütz and 80 more. Nature, 2012

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Medulloblastoma is an aggressively growing tumour, arising in the cerebellum or medulla/brain stem. It is the most common malignant brain tumour in children, and shows tremendous biological and clinical heterogeneity. Despite recent treatment advances, approximately 40% of children experience tumour recurrence, and 30% will die from their disease. Those who survive often have a significantly reduced quality of life. Four tumour subgroups with distinct clinical, biological and genetic profiles are currently identified. WNT tumours, showing activated wingless pathway signalling, carry a favourable prognosis under current treatment regimens. SHH tumours show hedgehog pathway activation, and have an intermediate prognosis. Group 3 and 4 tumours are molecularly less well characterized, and also present the greatest clinical challenges. The full repertoire of genetic events driving this distinction, however, remains unclear. Here we describe an integrative deep-sequencing analysis of 125 tumour–normal pairs, conducted as part of the International Cancer Genome Consortium (ICGC) PedBrain Tumor Project. Tetraploidy was identified as a frequent early event in Group 3 and 4 tumours, and a positive correlation between patient age and mutation rate was observed. Several recurrent mutations were identified, both in known medulloblastoma-related genes (CTNNB1, PTCH1, MLL2, SMARCA4) and in genes not previously linked to this tumour (DDX3X, CTDNEP1, KDM6A, TBR1), often in subgroup-specific patterns. RNA sequencing confirmed these alterations, and revealed the expression of what are, to our knowledge, the first medulloblastoma fusion genes identified. Chromatin modifiers were frequently altered across all subgroups. These findings enhance our understanding of the genomic complexity and heterogeneity underlying medulloblastoma, and provide several potential targets for new therapeutics, especially for Group 3 and 4 patients.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d55f25fcb8cc64ac42cf129c1ebdf66c9a27c724

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Citations in the corpus

The whole-genome landscape of medulloblastoma subtypes (2017, neutral): “CTDNEP1 (5) KMT2D (7) KBTBD4 (8) SMARCA4 (12) MYC (20) Group 3 coverage: 34.”
Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls (2013, neutral): “(3) Any FN variant calls in the truth dataset could result in an assessed FP rate higher than the true FP rate if the assessed calls are correct, or in an assessed FN rate lower than the true ”
Enhancer hijacking activates GFI1 family oncogenes in medulloblastoma (2014, neutral): “Observations extracted from the current study revealed that (1) a considerable proportion of SVs leading to GFI1 and GFI1B activation do not actually include the target gene itself; and (2) multiple distinct classes of SV including duplication, deletion, inversion and other complex rearrangements ca”
Genome Sequencing of SHH Medulloblastoma Predicts Genotype-Related Response to Smoothened Inhibition (2014, methodology): “23 10 (15)), than previously reported across all MB subgroups (Figures 2A and 2B; Jones et al., 2012).”
Integrative and comparative genomic analysis of HPV-positive and HPV-negative head and neck squamous cell carcinomas (2014, background): “MLL genes encode histone lysine methyltransferases that are involved in chromatin remodeling and recurrently mutated in several cancer types (43-46).”
Recurrent somatic alterations of FGFR1 and NTRK2 in pilocytic astrocytoma (2013, neutral): “The somatic mutation rate in our series was markedly lower than those recently reported for the malignant pediatric brain tumor Recurrent somatic alterations of FGFR1 and NTRK2 in pilocytic astrocytoma medulloblastoma 10–12 and for several other pediatric solid tumors 13 .”
New Brain Tumor Entities Emerge from Molecular Classification of CNS-PNETs (2016, neutral): “Next-generation DNA and RNA sequencing was performed using Illumina technologies as previously described (Jones et al., 2012).”