Novel mutations target distinct subgroups of medulloblastoma.
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ABSTRACT:
SUBMITTER: Robinson G
PROVIDER: S-EPMC3412905 | biostudies-literature | 2012 Aug
REPOSITORIES: biostudies-literature
Publications
Novel mutations target distinct subgroups of medulloblastoma.
Robinson Giles G Parker Matthew M Kranenburg Tanya A TA Lu Charles C Chen Xiang X Ding Li L Phoenix Timothy N TN Hedlund Erin E Wei Lei L Zhu Xiaoyan X Chalhoub Nader N Baker Suzanne J SJ Huether Robert R Kriwacki Richard R Curley Natasha N Thiruvenkatam Radhika R Wang Jianmin J Wu Gang G Rusch Michael M Hong Xin X Becksfort Jared J Gupta Pankaj P Ma Jing J Easton John J Vadodaria Bhavin B Onar-Thomas Arzu A Lin Tong T Li Shaoyi S Pounds Stanley S Paugh Steven S Zhao David D Kawauchi Daisuke D Roussel Martine F MF Finkelstein David D Ellison David W DW Lau Ching C CC Bouffet Eric E Hassall Tim T Gururangan Sridharan S Cohn Richard R Fulton Robert S RS Fulton Lucinda L LL Dooling David J DJ Ochoa Kerri K Gajjar Amar A Mardis Elaine R ER Wilson Richard K RK Downing James R JR Zhang Jinghui J Gilbertson Richard J RJ
Nature 20120801 7409
Medulloblastoma is a malignant childhood brain tumour comprising four discrete subgroups. Here, 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 machine ...[more]
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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
| S-ECPF-GEOD-37418 | biostudies-other
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
2012-07-05 | E-GEOD-37418 | biostudies-arrayexpress
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.
2012-07-06 | GSE37418 | GEO
Project description:BACKGROUND AND PURPOSE:Distinct molecular subgroups of pediatric medulloblastoma confer important differences in prognosis and therapy. Currently, tissue sampling is the only method to obtain information for classification. Our goal was to develop and validate radiomic and machine learning approaches for predicting molecular subgroups of pediatric medulloblastoma. MATERIALS AND METHODS:In this multi-institutional retrospective study, we evaluated MR imaging datasets of 109 pediatric patients with medulloblastoma from 3 children's hospitals from January 2001 to January 2014. A computational framework was developed to extract MR imaging-based radiomic features from tumor segmentations, and we tested 2 predictive models: a double 10-fold cross-validation using a combined dataset consisting of all 3 patient cohorts and a 3-dataset cross-validation, in which training was performed on 2 cohorts and testing was performed on the third independent cohort. We used the Wilcoxon rank sum test for feature selection with assessment of area under the receiver operating characteristic curve to evaluate model performance. RESULTS:Of 590 MR imaging-derived radiomic features, including intensity-based histograms, tumor edge-sharpness, Gabor features, and local area integral invariant features, extracted from imaging-derived tumor segmentations, tumor edge-sharpness was most useful for predicting sonic hedgehog and group 4 tumors. Receiver operating characteristic analysis revealed superior performance of the double 10-fold cross-validation model for predicting sonic hedgehog, group 3, and group 4 tumors when using combined T1- and T2-weighted images (area under the curve = 0.79, 0.70, and 0.83, respectively). With the independent 3-dataset cross-validation strategy, select radiomic features were predictive of sonic hedgehog (area under the curve = 0.70-0.73) and group 4 (area under the curve = 0.76-0.80) medulloblastoma. CONCLUSIONS:This study provides proof-of-concept results for the application of radiomic and machine learning approaches to a multi-institutional dataset for the prediction of medulloblastoma subgroups.
| S-EPMC6330121 | biostudies-literature
Project description:Medulloblastoma is the most common malignant pediatric brain tumor. Prior studies have concentrated their efforts studying the four molecular subgroups: SHH, Wnt, group 3, and group 4. SHH and Wnt are driven by their canonical pathways. Groups 3 and 4 are highly metastatic and associated with aberrations in epigenetic regulators. Recent developments in the field have revealed that these subgroups are not as homogenous as previously believed. The objective of this study is to investigate the involvement of somatic driver gene mutations in these medulloblastoma subgroups. We obtained medulloblastoma data from the Catalogue of Somatic Mutations in Cancer (COSMIC), which contains distinct samples that were not previously studied in a large cohort. We identified somatic driver gene mutations and the signaling pathways affected by these driver genes for medulloblastoma subgroups using bioinformatics tools. We have revealed novel infrequent drivers in these subgroups that contribute to our understanding of tumor heterogeneity in medulloblastoma. Normally SHH signaling is activated in the SHH subgroup, however, we determined gain-of-function mutations in ubiquitin ligase (CUL1) that inhibit Gli-mediated transcription. This suggests a potential hindrance in SHH signaling for some patients. For group 3, gain-of-function in the inhibitor of proinflammatory cytokines (HIVEP3) suggests an immunosuppressive phenotype and thus a more hostile tumor microenvironment. Surprisingly, group 4 tumors possess mutations that may prompt the activation of Wnt signaling through gain-of-function mutations in MUC16 and PCDH9. These infrequent mutations detected in this study could be due to subclonal or spatially restricted alterations. The investigation of aberrant driver gene mutations can lead to the identification of new drug targets and a greater understanding of human medulloblastoma heterogeneity.
| S-EPMC6299398 | biostudies-literature
Project description:While molecular subgrouping has revolutionized medulloblastoma classification, the extent of heterogeneity within subgroups is unknown. Similarity network fusion (SNF) applied to genome-wide DNA methylation and gene expression data across 763 primary samples identifies very homogeneous clusters of patients, supporting the presence of medulloblastoma subtypes. After integration of somatic copy-number alterations, and clinical features specific to each cluster, we identify 12 different subtypes of medulloblastoma. Integrative analysis using SNF further delineates group 3 from group 4 medulloblastoma, which is not as readily apparent through analyses of individual data types. Two clear subtypes of infants with Sonic Hedgehog medulloblastoma with disparate outcomes and biology are identified. Medulloblastoma subtypes identified through integrative clustering have important implications for stratification of future clinical trials.
| S-EPMC6163053 | biostudies-literature
Project description:PurposeMedulloblastoma comprises four distinct molecular subgroups: WNT, SHH, Group 3, and Group 4. Current medulloblastoma protocols stratify patients based on clinical features: patient age, metastatic stage, extent of resection, and histologic variant. Stark prognostic and genetic differences among the four subgroups suggest that subgroup-specific molecular biomarkers could improve patient prognostication.Patients and methodsMolecular biomarkers were identified from a discovery set of 673 medulloblastomas from 43 cities around the world. Combined risk stratification models were designed based on clinical and cytogenetic biomarkers identified by multivariable Cox proportional hazards analyses. Identified biomarkers were tested using fluorescent in situ hybridization (FISH) on a nonoverlapping medulloblastoma tissue microarray (n = 453), with subsequent validation of the risk stratification models.ResultsSubgroup information improves the predictive accuracy of a multivariable survival model compared with clinical biomarkers alone. Most previously published cytogenetic biomarkers are only prognostic within a single medulloblastoma subgroup. Profiling six FISH biomarkers (GLI2, MYC, chromosome 11 [chr11], chr14, 17p, and 17q) on formalin-fixed paraffin-embedded tissues, we can reliably and reproducibly identify very low-risk and very high-risk patients within SHH, Group 3, and Group 4 medulloblastomas.ConclusionCombining subgroup and cytogenetic biomarkers with established clinical biomarkers substantially improves patient prognostication, even in the context of heterogeneous clinical therapies. The prognostic significance of most molecular biomarkers is restricted to a specific subgroup. We have identified a small panel of cytogenetic biomarkers that reliably identifies very high-risk and very low-risk groups of patients, making it an excellent tool for selecting patients for therapy intensification and therapy de-escalation in future clinical trials.
| S-EPMC3948094 | biostudies-literature
Project description:Deregulations in fundamental signaling pathways are key events in pathogenesis of cancer. One intriguing illustration that still holds blind spots is the pediatric brain tumor arising from the developing cerebellum: medulloblastoma (MB). Extensive high-throughput sequencing led to the characterization of four MB subgroups (WNT, SHH, Group 3 and Group 4) delineated with distinct molecular signatures and clinical outcomes. However, up-to-date these analyses have not attained the global comprehension of their dynamic network complexity. Wishing to uncover a comprehensive view of all MB subgroups we employed a proteomic analysis to integrate accurate protein expression and activity that should ultimately give rise to a realistic picture of how MB cancer cells are regulated. In this study we present the first analysis regrouping methylation status, whole-transcriptome sequencing and quantitative proteomics (proteome and phosphoproteome) using a super SILAC / spiked in strategy across 38 flash frozen primary human MBs (5 WNT, 10 SHH, 10 Group 3 and 13 Group 4). First, our data pinpointed that proteomic analysis could reveal MB subgroup identity. Second, analysis of proteome and phosphoproteome highlighted disregulated signaling pathways that have not been predicted by transcriptomic analysis. Altogether, combined multi-scale analyses of MB have allowed us to identify unrevealed pathways involved in human MB genesis and progression.
2020-07-23 | PXD006607 | Pride
Project description:Medulloblastomas (MBs) are cerebellar tumors that can be classified into molecularly distinct subgroups that differ in pathology and prognosis. The mechanisms that underlie subgroup specification are largely unknown. While human SHH MBs express MYCN, Group3 (G3) MBs are associated with c-MYC (MYC) overexpression and often show metastasis that confers a poor prognosis. Although MYC proteins are thought to be functionally exchangeable, ectopic expression of Myc or N-myc in Trp53-/-;Cdkn2c-/- cerebellar granule neuron progenitors (GNPs) induces G3 and SHH MBs, respectively, demonstrating that each Myc protein has distinct biological properties. We now show that Myc and N-myc differ in their affinity to Miz1 and that Myc, but not N-myc, effectively recruits Miz1 to its target sites on chromatin. The interaction of Myc with Miz1 is required for the genesis of G3 MB. Myc suppresses ciliogenesis and âreprogramsâ the transcriptome of SHH-dependent GNPs to stem-like cells by repressing genes highly expressed in SHH MB via Miz1. Consistently, target genes of Myc/Miz1 are repressed in human G3 MBs but not in other MB subgroups. Collectively, the data show that the interaction of Myc with Miz1 is a defining hallmark of G3 MB development. In this study, we show that Myc and N-myc differ in their affinity for Miz1, and Myc/Miz1 interaction is required for Group3 medulloblastoma (MB). The gene expression profiles of these tumors were compared to our previously published Group3 MB model as well as SHH model of MB (Kawachi et al., 2012, Cancer Cell). Cerebellar granule neuron progenitors (GNPs) [dka220-222] from postnatal (P) 6 Math1-GFP;Trp53-/-;Cdkn2c-/-. For SHH medulloblastomas, [dka204-206] and [blm015-017 or dka050-dka051], spontaneous medulloblastomas from Cdkn2c-/-;Trp53Fl/Fl;Nestin-Cre (Kawachi et al., 2012, Cancer Cell) and Ptch1+/-;Cdkn2c-/- (Uziel et al., 2005, Genes Dev) were used, respectively. Myc- [dka201-203] and MycV394D (termed MycVD thereafter)- [bvo017-bvo023] tumors were from Trp53-/-;Cdkn2c-/- cerebella of P6-P7 pups. Myc/ÎPOZ tumors [bvo002-bvo006] were obtained from Trp5Fl/Fl;Miz1ÎPOZ/POZ;Nestin-Cre cerebella of P6-P7 pups.
2016-01-21 | E-GEOD-66093 | biostudies-arrayexpress
Project description:PURPOSE:Microarray studies indicate medulloblastoma comprises distinct molecular disease subgroups, which offer potential for improved clinical management. EXPERIMENTAL DESIGN:Minimal mRNA expression signatures diagnostic for the Wnt/Wingless (WNT) and Sonic Hedgehog (SHH) subgroups were developed, validated, and used to assign subgroup affiliation in 173 tumors from four independent cohorts, alongside a systematic investigation of subgroup clinical and molecular characteristics. RESULTS:WNT tumors [12% (21/173)] were diagnosed >5 years of age (peak, 10 years), displayed classic histology, CTNNB1 mutation (19/20), and associated chromosome 6 loss, and have previously been associated with favorable prognosis. SHH cases [24% (42/173)] predominated in infants (<3 years) and showed an age-dependent relationship to desmoplastic/nodular pathology; all infant desmoplastic/nodular cases (previously associated with a good outcome) were SHH-positive, but these relationships broke down in noninfants. PTCH1 mutations were common [34% (11/32)], but PTCH1 exon1c hypermethylation, chromosome 9q and REN (KCTD11) genetic loss were not SHH associated, and SMO or SUFU mutation, PTCH1 exon1a or SUFU hypermethylation did not play a role, indicating novel activating mechanisms in the majority of SHH cases. SHH tumors were associated with an absence of COL1A2 methylation. WNT/SHH-independent medulloblastomas [64% (110/173)] showed all histologies, peaked at 3 and 6 years, and were exclusively associated with chromosome 17p loss. CONCLUSIONS:Medulloblastoma subgroups are characterized by distinct genomic, epigenomic and clinicopathologic features, and clinical outcomes. Validated array-independent gene expression assays for the rapid assessment of subgroup affiliation in small biopsies provide a basis for their routine clinical application, in strategies including molecular disease-risk stratification and delivery of targeted therapeutics.
| S-EPMC3071325 | biostudies-literature