Overexpression of TEAD4 in atypical teratoid/rhabdoid tumor: New insight to the pathophysiology of an aggressive brain tumor

BackgroundAtypical teratoid/rhabdoid tumor (AT/RT) is a highly malignant embryonal brain tumor that occurs mainly in early childhood. Although most of the tumors are characterized by inactivating mutations of the tumor suppressor gene, SMARCB1, the biological basis of its tumorigenesis and aggressiveness is still unknown.ProcedureWe performed high‐throughput copy number variation analysis of primary cell lines generated from primary and relapsed tumors from one of our patients to identify new genes involved in AT/RT biology. The expression of the identified gene was validated in 29 AT/RT samples by gene expression profiling, quantitative real‐time polymerase chain reaction, and immunohistochemistry (IHC). Furthermore, we investigated the function of this gene by mutating it in rhabdoid tumor cells.ResultsTEAD4 amplification was detected in the primary cell lines and its overexpression was confirmed at mRNA and protein levels in an independent cohort of AT/RT samples. TEAD4’s co‐activator, YAP1, and the downstream targets, MYC and CCND1, were also found to be upregulated in AT/RT when compared to medulloblastoma. IHC showed TEAD4 and YAP1 overexpression in all samples. Cell proliferation and migration were significantly reduced in TEAD4‐mutated cells.ConclusionsWe report the overexpression of TEAD4 in AT/RT, which is a key component of Hippo pathway. Recent reports revealed that dysregulation of the Hippo pathway is implicated in tumorigenesis and poor prognosis of several human cancers. Our results suggest that TEAD4 plays a role in the pathophysiology of AT/RT, which represents a new insight into the biology of this aggressive tumor.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137309/1/pbc26398_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137309/2/pbc26398.pd

BTECH: A Platform to Integrate Genomic, Transcriptomic and Epigenomic Alterations in Brain Tumors

The identification of molecular signatures predictive of clinical behavior and outcome in brain tumors has been the focus of many studies in the recent years. Despite the wealth of data that are available in the public domain on alterations in the genome, epigenome and transcriptome of brain tumors, the underlying molecular mechanisms leading to tumor initiation and progression remain largely unknown. Unfortunately, most of these data are scattered in multiple databases and supplementary materials of publications, thus making their retrieval, evaluation, comparison and visualization a rather arduous task. Here we report the development and implementation of an open access database (BTECH), a community resource for the deposition of a wide range of molecular data derived from brain tumor studies. This comprehensive database integrates multiple datasets, including transcript profiles, epigenomic CpG methylation data, DNA copy number alterations and structural chromosomal rearrangements, tumor-associated gene lists, SNPs, genomic features concerning Alu repeats and general genomic annotations. A genome browser has also been developed that allows for the simultaneous visualization of the different datasets and the various annotated features. Besides enabling an integrative view of diverse datasets through the genome browser, we also provide links to the original references for users to have a more accurate understanding of each specific dataset. This integrated platform will facilitate uncovering interactions among genetic and epigenetic factors associated with brain tumor development. BTECH is freely available at http://cmbteg.childrensmemorial.org/. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12021-010-9091-9) contains supplementary material, which is available to authorized users

Targeting CD133 improves chemotherapeutic efficacy of recurrent pediatric pilocytic astrocytoma following prolonged chemotherapy

Quantification of western blot results showing overexpression of pAKT(S473, T308) and pNF-ƙB/p65 in recurrent PAs with chemotherapy, compared to matched primary tumors. (TIF 52 kb

Identification of MicroRNAs as Potential Prognostic Markers in Ependymoma

INTRODUCTION: We have examined expression of microRNAs (miRNAs) in ependymomas to identify molecular markers of value for clinical management. miRNAs are non-coding RNAs that can block mRNA translation and affect mRNA stability. Changes in the expression of miRNAs have been correlated with many human cancers. MATERIALS AND METHODS: We have utilized TaqMan Low Density Arrays to evaluate the expression of 365 miRNAs in ependymomas and normal brain tissue. We first demonstrated the similarity of expression profiles of paired frozen tissue (FT) and paraffin-embedded specimens (FFPE). We compared the miRNA expression profiles of 34 FFPE ependymoma samples with 8 microdissected normal brain tissue specimens enriched for ependymal cells. miRNA expression profiles were then correlated with tumor location, histology and other clinicopathological features. RESULTS: We have identified miRNAs that are over-expressed in ependymomas, such as miR-135a and miR-17-5p, and down-regulated, such as miR-383 and miR-485-5p. We have also uncovered associations between expression of specific miRNAs which portend a worse prognosis. For example, we have identified a cluster of miRNAs on human chromosome 14q32 that is associated with time to relapse. We also found that miR-203 is an independent marker for relapse compared to the parameters that are currently used. Additionally, we have identified three miRNAs (let-7d, miR-596 and miR-367) that strongly correlate to overall survival. CONCLUSION: We have identified miRNAs that are differentially expressed in ependymomas compared with normal ependymal tissue. We have also uncovered significant associations of miRNAs with clinical behavior. This is the first report of clinically relevant miRNAs in ependymomas

High-throughput sequence-based epigenomic analysis of Alu repeats in human cerebellum

DNA methylation, the only known covalent modification of mammalian DNA, occurs primarily in CpG dinucleotides. 51% of CpGs in the human genome reside within repeats, and 25% within Alu elements. Despite that, no method has been reported for large-scale ascertainment of CpG methylation in repeats. Here we describe a sequencing-based strategy for parallel determination of the CpG-methylation status of thousands of Alu repeats, and a computation algorithm to design primers that enable their specific amplification from bisulfite converted genomic DNA. Using a single primer pair, we generated amplicons of high sequence complexity, and derived CpG-methylation data from 31 178 Alu elements and their 5′ flanking sequences, altogether representing over 4 Mb of a human cerebellum epigenome. The analysis of the Alu methylome revealed that the methylation level of Alu elements is high in the intronic and intergenic regions, but low in the regions close to transcription start sites. Several hypomethylated Alu elements were identified and their hypomethylated status verified by pyrosequencing. Interestingly, some Alu elements exhibited a strikingly tissue-specific pattern of methylation. We anticipate the amplicons herein described to prove invaluable as epigenome representations, to monitor epigenomic alterations during normal development, in aging and in diseases such as cancer

Genome-wide quantitative assessment of variation in DNA methylation patterns

Genomic DNA methylation contributes substantively to transcriptional regulations that underlie mammalian development and cellular differentiation. Much effort has been made to decipher the molecular mechanisms governing the establishment and maintenance of DNA methylation patterns. However, little is known about genome-wide variation of DNA methylation patterns. In this study, we introduced the concept of methylation entropy, a measure of the randomness of DNA methylation patterns in a cell population, and exploited it to assess the variability in DNA methylation patterns of Alu repeats and promoters. A few interesting observations were made: (i) within a cell population, methylation entropy varies among genomic loci; (ii) among cell populations, the methylation entropies of most genomic loci remain constant; (iii) compared to normal tissue controls, some tumors exhibit greater methylation entropies; (iv) Alu elements with high methylation entropy are associated with high GC content but depletion of CpG dinucleotides and (v) Alu elements in the intronic regions or far from CpG islands are associated with low methylation entropy. We further identified 12 putative allelic-specific methylated genomic loci, including four Alu elements and eight promoters. Lastly, using subcloned normal fibroblast cells, we demonstrated the highly variable methylation patterns are resulted from low fidelity of DNA methylation inheritance