Dissecting the genomic complexity underlying medulloblastoma

Abstract

Summary 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 displays tremendous biological and clinical heterogeneity1. 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 discriminated2,3. WNT tumours, displaying activated wingless pathway signalling, carry a favourable prognosis under current treatment regimens4. SHH tumours show hedgehog pathway activation, and have an intermediate prognosis2. Group 3 & 4 tumours are molecularly less well-characterised, and also present the greatest clinical challenges2,3,5. 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. Tetraploidy was identified as a frequent early event in Group 3 & 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 the first medulloblastoma fusion genes. 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 & 4 patients.

The landscape of genomic alterations across childhood cancers

“…Mutation calling. SNVs were called with the previously described samtools-based DKFZ pipeline adjusted for ICGC Pan-Cancer settings, and short indels were called using Platypus (v.0.7.4) 74,83 . Variants were first identified in the tumour sample and germline or somatic origin was determined based on their presence or absence in the matched control tissue.…”

Nuclear β-Catenin Expression is Frequent in Sinonasal Hemangiopericytoma and Its Mimics

“…CTNNB1 alterations in sinonasal HPC have been consistently reported in exon 3, with missense mutations [4,5]; mutations affecting positions 32-45 of the amino-terminal region disrupt phosphorylation-dependent degradation of b-catenin [6]. Numerous other tumor types harbor CTNNB1 mutations, most frequently desmoid-type fibromatosis [7,8] as well as salivary basal cell adenoma [9], pilomatricoma and pilomatrix carcinoma [10], hepatocellular carcinoma [11], colorectal carcinoma [12], medulloblastoma [13], endometrial adenocarcinoma [14], Wilms tumor [15], and adrenocortical carcinoma [16]. Sinonasal HPC shares the features of a uniform spindle and ovoid cell population and HPC-like vessels with SFT, Fig.…”

Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls

“…As whole human genome and targeted sequencing start to offer the real potential to inform clinical decisions [1][2][3][4] , it is becoming critical to assess the accuracy of variant calls and understand biases and sources of error in sequencing and bioinformatics methods. Recent publications have demonstrated hundreds of thousands of differences between variant calls from different whole human genome sequencing methods or different bioinformatics methods [5][6][7][8][9][10][11] .…”