Project description:Medulloblastoma is a malignant childhood brain tumour comprising four discrete subgroups. To identify mutations that drive medulloblastoma we sequenced the entire genomes of 37 tumours and matched normal blood. One hundred and thirty-six genes harbouring somatic mutations in this discovery set were sequenced in an additional 56 medulloblastomas. Recurrent mutations were detected in 41 genes not yet implicated in medulloblastoma: several target distinct components of the epigenetic machinery in different disease subgroups, e.g., regulators of H3K27 and H3K4 trimethylation in subgroup-3 and 4 (e.g., KDM6A and ZMYM3), and CTNNB1-associated chromatin remodellers in WNT-subgroup tumours (e.g., SMARCA4 and CREBBP). Modelling of mutations in mouse lower rhombic lip progenitors that generate WNT-subgroup tumours, identified genes that maintain this cell lineage (DDX3X) as well as mutated genes that initiate (CDH1) or cooperate (PIK3CA) in tumourigenesis. These data provide important new insights into the pathogenesis of medulloblastoma subgroups and highlight targets for therapeutic development. A total of 76 pediatric medulloblastoma samples were analyzed, representing 4 expression classes
Project description:Medulloblastoma is a malignant childhood brain tumour comprising four discrete subgroups. To identify mutations that drive medulloblastoma we sequenced the entire genomes of 37 tumours and matched normal blood. One hundred and thirty-six genes harbouring somatic mutations in this discovery set were sequenced in an additional 56 medulloblastomas. Recurrent mutations were detected in 41 genes not yet implicated in medulloblastoma: several target distinct components of the epigenetic machinery in different disease subgroups, e.g., regulators of H3K27 and H3K4 trimethylation in subgroup-3 and 4 (e.g., KDM6A and ZMYM3), and CTNNB1-associated chromatin remodellers in WNT-subgroup tumours (e.g., SMARCA4 and CREBBP). Modelling of mutations in mouse lower rhombic lip progenitors that generate WNT-subgroup tumours, identified genes that maintain this cell lineage (DDX3X) as well as mutated genes that initiate (CDH1) or cooperate (PIK3CA) in tumourigenesis. These data provide important new insights into the pathogenesis of medulloblastoma subgroups and highlight targets for therapeutic development.
Project description:Recent genomic approaches have suggested the existence of multiple distinct subtypes of medulloblastoma. We studied a large cohort of medulloblastomas to determine how many subgroups of the disease exist, how they differ, and the extent of overlap between subgroups. We determined gene expression profiles and DNA copy number aberrations for 103 primary medulloblastomas. Bioinformatic tools were used for class discovery of medulloblastoma subgroups based on the most informative genes in the dataset. Immunohistochemistry for subgroup-specific ‘signature’ genes was used to determine subgroup affiliation for 294 non-overlapping medulloblastomas on two independent tissue microarrays (TMAs). Multiple unsupervised analyses of transcriptional profiles identified four distinct, non-overlapping molecular variants: WNT, SHH, Group C, and Group D. Supervised analysis of these four subgroups revealed significant subgroup-specific demographics, histology, metastatic status, and DNA copy number aberrations. Immunohistochemistry for DKK1 (WNT), SFRP1 (SHH), NPR3 (Group C), and KCNA1 (Group D) could reliably and uniquely classify formalin fixed medulloblastomas in ~98% of cases. Group C patients (NPR3 +ve tumors) exhibited a significantly diminished progression free and overall survival irrespective of their metastatic status. Our integrative genomics approach to a large cohort of medulloblastomas has identified four disparate subgroups with distinct demographics, clinical presentation, transcriptional profiles, genetic abnormalities, and clinical outcome. Medulloblastomas can be reliably assigned to subgroups through immunohistochemistry, thereby making medulloblastoma sub-classification widely available. Future research on medulloblastoma and the development of clinical trials should take into consideration these four distinct types of medulloblastoma. A total of 103 primary medulloblastoma specimens were profiled by Affymetrix exon array and gene-level analysis was performed.
Project description:SPO11-promoted DNA double-strand breaks (DSBs) formation is a crucial step for meiotic recombination, and it is indispensable to detect the broken DNA ends accurately for dissecting the molecular mechanisms behind. Here, we report a novel technique, named DEtail-seq (DNA End tailing followed by sequencing), that can directly and quantitatively capture the meiotic DSB 3’ overhang hotspots at single-nucleotide resolution.
Project description:Identification of novel molecular subgroups Background: International consensus recognises four medulloblastoma molecular subgroups - WNT, SHH, Group 3 and Group 4 - each defined by their characteristic genome-wide transcriptomic and DNA methylomic profiles. Subgroups harbor distinct clinico-pathological and molecular features, underpin current disease sub-classification and initial subgroup-directed therapies are underway in clinical trials (i.e. reduced risk-adapted treatments for favorable-risk WNT patients; SMO inhibitors for SHH patients). However, significant biological heterogeneity and differences in survival are apparent within each subgroup, which remain to be resolved. Methods: We undertook comprehensive molecular profiling and unsupervised class discovery (non-negative matrix factorization, t-SNE) of test and validation cohorts to identify consensus primary molecular subgroups within childhood medulloblastoma (<16.0 years), and characterize their clinical and biological significance. Survival modeling was performed in clinically-annotated centrally-reviewed patients (>3.0 years). Findings: Seven robust and reproducible primary molecular subgroups of childhood medulloblastoma were identified, characterized by distinct biological/clinical features. For instance, SHH comprised two age-dependent subgroups, while Grp3 and Grp4 each split into two subgroups with significantly different survival rates. Survival analysis identified secondary features predictive of outcome. Cross-validated subgroup-dependent models incorporating these novel subgroups along with secondary features and established disease risk-factors, outperformed current disease risk-stratification schemes. These schema stratified patients into four clinical risk-groups - favorable-risk (91% 5-year survival, 25% of patients), standard-risk (81%, 23%), high-risk (42%, 38%) and very high-risk (28%, 13%) - to be considered for treatment reduction, intensification or novel therapies respectively. Interpretation: The discovery of seven novel, clinically-significant, subgroups significantly improves disease risk-stratification and provides a new foundation for future research and clinical investigations.