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Clinical and Molecular Characteristics of Congenital Glioblastoma Multiforme

ABSTRACT: Congenital glioblastoma multiforme (cGBM) historically has been considered an aggressive tumor of infancy requiring extensive chemotherapy to achieve cure. We report on 4 patients at our institution with cGBMs who were treated with surgery and chemotherapy (carboplatin and etoposide every 21 days for 2-6 cycles). Four of four patients are progression free at a median time of 27.5 months (22-103 months). To characterize the molecular biology of cGBM, we compared the gene expression profiles of 3 cGBMs to 12 pediatric and 6 primary adult glioblastomas collected at our institution. Unsupervised hierarchical clustering showed cGBMs grouped together with other high-grade gliomas. cGBMs demonstrated marked similarity to both pediatric and adult GBMs, with only a total of 31 differentially expressed genes identified (FDR < 0.05). Unique molecular features of congenital GBMs identified included over-expression of multiple genes involved in glucose metabolism and tissue hypoxia pathways. Four tyrosine kinases were also mong the up-regulated genes (RET, RASGRF2, EFNA5, ALK). Thus, at our institution congenital GBMs, while similar both histologically and molecularly to other GBMs, appear to have a good prognosis with surgery in combination with relatively moderate chemotherapy. Further study is needed to determine if the few gene expression differences that were identified may contribute to the better survival seen in these tumors compared to pediatric or adult GBMs. Key Words: glioblastoma; congenital; pediatric; gene expression; microarray Molecular profiling of 18 AT/RT patient tumor samples was performed using Affymetrix U133 Plus2 GeneChips. Data were background corrected and normalized using gcRMA (as implemented in Bioconductor). Unsupervised agglomerative hierarchical clustering was performed to identify subsets of AT/RTs with similar gene expression. Limma (moderated t-tests; Bioconductor) was used to identify signature genes for each cluster. Bioinformatics web tool DAVID was used to identify enriched biological processes for each cluster. Survival was analyzed using Kaplan-Meier curves and Cox Hazard Ratio. Bioinformatics tools Gene Set Enrichment (GSEA) and Ingenuity Pathways Analysis were also used to gain further insight into cluster differences.

ORGANISM(S): Homo sapiens

SUBMITTER: Diane Birks

PROVIDER: E-GEOD-32374 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Molecular profiling of tumors has proven a valuable tool for identification of prognostic and diagnostic subgroups in medulloblastomas, glioblastomas and other cancers. However, the molecular landscape of atypical teratoid / rhabdoid tumors (AT/RTs) remains largely unexplored. To address this issue, we used microarrays to measure the gene expression profiles of 18 AT/RTs, and performed unsupervised hierarchical clustering to determine molecularly similar subgroups. Four major subgroups (clusters) were identified. These did not conform to gender, tumor location, or presence of monosomy 22. Clusters showed distinct gene signatures and differences in enriched biological processes, including elevated expression of choroid plexus genes in Cluster 4. In addition, survival differed significantly by cluster, with shortest survival (mean 4.7 months) in both Clusters 3 and 4 compared to Clusters 1 and 2 (mean 28.1 months). Analysis showed that multiple bone morphogenetic protein (BMP) pathway genes were up-regulated in the short survival clusters, with BMP4 showing the most significant up-regulation (270-fold). Thus, high expression of BMP pathway genes was negatively associated with survival in this dataset. Our study indicates that molecular subgroups exist within AT/RTs, and that molecular profiling of these comparatively rare tumors may be of diagnostic, prognostic and therapeutic value. Key Words: atypical teratoid / rhabdoid tumor; bone morphogenetic protein pathway; BMP4; survival; microarray Molecular profiling of 18 AT/RT patient tumor samples was performed using Affymetrix U133 Plus2 GeneChips. Data were background corrected and normalized using gcRMA (as implemented in Bioconductor). Unsupervised agglomerative hierarchical clustering was performed to identify subsets of AT/RTs with similar gene expression. Limma (moderated t-tests; Bioconductor) was used to identify signature genes for each cluster. Bioinformatics web tool DAVID was used to identify enriched biological processes for each cluster. Survival was analyzed using Kaplan-Meier curves and Cox Hazard Ratio. Bioinformatics tools Gene Set Enrichment (GSEA) and Ingenuity Pathways Analysis were also used to gain further insight into cluster differences.

Project description:Rhabdoid tumors (RTs) are aggressive tumors of early childhood that occur most often in brain (AT/RTs) or kidney (KRTs). Regardless of location, they are characterized by loss of functional SMARCB1 protein, a component of the SWI/SNF chromatin remodeling complex. The aim of this study was to determine genes and biological process dysregulated in common to both AT/RTs and KRTs. Gene expression for AT/RTs was compared to that of other brain tumors and normal brain using microarray data from our lab. Similar analysis was performed for KRTs and other kidney tumors and normal kidney using data from GEO. Dysregulated genes common to both analyses were analyzed for functional significance. Unsupervised hierarchical clustering of RTs identified 3 major subsets: 2 comprised of AT/RTs, and 1 of KRTs. Compared to other tumors, 1187, 663 and 539 genes were dysregulated in each subset, respectively. Only 14 dysregulated genes were common to all 3 subsets. Compared to normal tissue, 5209, 4275 and 2841 genes were dysregulated in each subset, with an overlap of 610 dysregulated genes. Among these genes, processes associated with cell proliferation, MYC activation, and epigenetic dysregulation were common to all 3 RT subsets. The low overlap of dysregulated genes in AT/RTs and KRTs suggests that factors in addition to SMARCB1 loss play a role in determining subsequent gene expression. Drugs which target cell cycle or epigenetic genes may be useful in all RTs. Additionally, targeted therapies tailored to specific RT subset molecular profiles should be considered. Molecular profiling of 20 ATRTs, 42 other pediatric CNS WHO Grade IV tumor samples and 9 pediatric normal brain samples was performed using Affymetrix U133 Plus2 GeneChips. Data were background corrected and normalized using gcRMA (as implemented in Bioconductor). ANOVA was used to identify differentially expressed genes for AT/RTs compared to other brain tumor types. Various analyses, including bioinformatics tools DAVID, Gene Set Enrichment (GSEA), and Ingenuity Pathways Analysis, were used to identify biological processes and genes of particular interest among the differentially expressed genes. Similar analysis of publicly available KRT and pediatric kidney tumor and normal kidney data was also performed by us. The overlaps between the results were examined to determine genes and processes dysregulated in common to both ATRTs and KRTs. This study includes a re-analysis of samples from other studies (AT/RT: GSE28026; glioblastoma: GSE33331).

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Clinical and Molecular Characteristics of Congenital Glioblastoma Multiforme

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