Dataset Information

Phenotype-specific CpG island methylation events in a murine model of prostate cancer.

ABSTRACT: Aberrant DNA methylation plays a significant role in nearly all human cancers and may contribute to disease progression to advanced phenotypes. Study of advanced prostate cancer phenotypes in the human disease is hampered by limited availability of tissues. We therefore took advantage of the Transgenic Adenocarcinoma of Mouse Prostate (TRAMP) model to study whether three different phenotypes of TRAMP tumors (PRIM, late-stage primary tumors; AIP, androgen-independent primary tumors; and MET, metastases) displayed specific patterns of CpG island hypermethylation using Restriction Landmark Genomic Scanning. Each tumor phenotype displayed numerous hypermethylation events, with the most homogeneous methylation pattern in AIP and the most heterogeneous pattern in MET. Several loci displayed a phenotype-specific methylation pattern; the most striking pattern being loci methylated at high frequency in PRIM and AIP but rarely in MET. Examination of the mRNA expression of three genes, BC058385, Goosecoid, and Neurexin 2, which exhibited nonpromoter methylation, revealed increased expression associated with downstream methylation. Only methylated samples showed mRNA expression, in which tumor phenotype was a key factor determining the level of expression. The CpG island in the human orthologue of BC058385 was methylated in human AIP but not in primary androgen-stimulated prostate cancer or benign prostate. The clinical data show a proof-of-principle that the TRAMP model can be used to identify targets of aberrant CpG island methylation relevant to human disease. In conclusion, phenotype-specific hypermethylation events were associated with the overexpression of different genes and may provide new markers of prostate tumorigenesis.

SUBMITTER: Camoriano M

PROVIDER: S-EPMC2851167 | biostudies-other | 2008 Jun

REPOSITORIES: biostudies-other

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Phenotype-specific CpG island methylation events in a murine model of prostate cancer.

Camoriano Marta M Kinney Shannon R Morey SR Moser Michael T MT Foster Barbara A BA Mohler James L JL Trump Donald L DL Karpf Adam R AR Smiraglia Dominic J DJ

Cancer research 20080601 11

Aberrant DNA methylation plays a significant role in nearly all human cancers and may contribute to disease progression to advanced phenotypes. Study of advanced prostate cancer phenotypes in the human disease is hampered by limited availability of tissues. We therefore took advantage of the Transgenic Adenocarcinoma of Mouse Prostate (TRAMP) model to study whether three different phenotypes of TRAMP tumors (PRIM, late-stage primary tumors; AIP, androgen-independent primary tumors; and MET, meta ...[more]

PMID: 18519676

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Project description:BACKGROUND:Loss of BIN1 tumor suppressor expression is abundant in human cancer and its frequency exceeds that of genetic alterations, suggesting the role of epigenetic regulators (DNA methylation). BIN1 re-expression in the DU145 prostate cancer cell line after 5-aza-2'-deoxycytidine treatment was recently reported but no methylation of the BIN1 promoter CpG island was found in DU145. METHODS:Methylation-sensitive arbitrarily-primed PCR was used to detect genomic loci abnormally methylated in breast cancer. BIN1 CpG island fragment was identified among the differentially methylated loci as a result of direct sequencing of the methylation-sensitive arbitrarily-primed PCR product and subsequent BLAST alliance. BIN1 CpG island cancer related methylation in breast and prostate cancers was confirmed by bisulphite sequencing and its methylation frequency was evaluated by methylation sensitive PCR. Loss of heterozygosity analysis of the BIN1 region was performed with two introgenic and one closely adjacent extragenic microsatellite markers.BIN1 expression was evaluated by real-time RT-PCR. RESULTS:We have identified a 3'-part of BIN1 promoter CpG island among the genomic loci abnormally methylated in breast cancer. The fragment proved to be methylated in 18/99 (18%) and 4/46 (9%) breast and prostate tumors, correspondingly, as well as in MCF7 and T47D breast cancer cell lines, but was never methylated in normal tissues and lymphocytes as well as in DU145 and LNCaP prostate cancer cell lines. The 5'-part of the CpG island revealed no methylation in all samples tested. BIN1 expression losses were detected in MCF7 and T47D cells and were characteristic of primary breast tumors (10/13; 77%), while loss of heterozygosity was a rare event in tissue samples (2/22 informative cases; 9%) and was ruled out for MCF7. CONCLUSION:BIN1 promoter CpG island is composed of two parts differing drastically in the methylation patterns in cancer. This appears to be a common feature of cancer related genes and demands further functional significance exploration. Although we have found no evidence of the functional role of such a non-core methylation in BIN1 expression regulation, our data do not altogether rule this possibility out.

Project description:BackgroundMethylation-induced silencing of promoter CpG islands in tumor suppressor genes plays an important role in human carcinogenesis. In colorectal cancer, the CpG island methylator phenotype (CIMP) is defined as widespread and elevated levels of DNA methylation and CIMP+ tumors have distinctive clinicopathological and molecular features. In contrast, the existence of a comparable CIMP subtype in gastric cancer (GC) has not been clearly established. To further investigate this issue, in the present study we performed comprehensive DNA methylation profiling of a well-characterised series of primary GC.MethodsThe methylation status of 1,421 autosomal CpG sites located within 768 cancer-related genes was investigated using the Illumina GoldenGate Methylation Panel I assay on DNA extracted from 60 gastric tumors and matched tumor-adjacent gastric tissue pairs. Methylation data was analysed using a recursively partitioned mixture model and investigated for associations with clinicopathological and molecular features including age, Helicobacter pylori status, tumor site, patient survival, microsatellite instability and BRAF and KRAS mutations.ResultsA total of 147 genes were differentially methylated between tumor and matched tumor-adjacent gastric tissue, with HOXA5 and hedgehog signalling being the top-ranked gene and signalling pathway, respectively. Unsupervised clustering of methylation data revealed the existence of 6 subgroups under two main clusters, referred to as L (low methylation; 28% of cases) and H (high methylation; 72%). Female patients were over-represented in the H tumor group compared to L group (36% vs 6%; P = 0.024), however no other significant differences in clinicopathological or molecular features were apparent. CpG sites that were hypermethylated in group H were more frequently located in CpG islands and marked for polycomb occupancy.ConclusionsHigh-throughput methylation analysis implicates genes involved in embryonic development and hedgehog signaling in gastric tumorigenesis. GC is comprised of two major methylation subtypes, with the highly methylated group showing some features consistent with a CpG island methylator phenotype.

Project description:BACKGROUND: The computational prediction of DNA methylation has become an important topic in the recent years due to its role in the epigenetic control of normal and cancer-related processes. While previous prediction approaches focused merely on differences between methylated and unmethylated DNA sequences, recent experimental results have shown the presence of much more complex patterns of methylation across tissues and time in the human genome. These patterns are only partially described by a binary model of DNA methylation. In this work we propose a novel approach, based on profile analysis of tissue-specific methylation that uncovers significant differences in the sequences of CpG islands (CGIs) that predispose them to a tissue- specific methylation pattern. RESULTS: We defined CGI methylation profiles that separate not only between constitutively methylated and unmethylated CGIs, but also identify CGIs showing a differential degree of methylation across tissues and cell-types or a lack of methylation exclusively in sperm. These profiles are clearly distinguished by a number of CGI attributes including their evolutionary conservation, their significance, as well as the evolutionary evidence of prior methylation. Additionally, we assess profile functionality with respect to the different compartments of protein coding genes and their possible use in the prediction of DNA methylation. CONCLUSION: Our approach provides new insights into the biological features that determine if a CGI has a functional role in the epigenetic control of gene expression and the features associated with CGI methylation susceptibility. Moreover, we show that the ability to predict CGI methylation is based primarily on the quality of the biological information used and the relationships uncovered between different sources of knowledge. The strategy presented here is able to predict, besides the constitutively methylated and unmethylated classes, two more tissue specific methylation classes conserving the accuracy provided by leading binary methylation classification methods.

Dataset Information

Phenotype-specific CpG island methylation events in a murine model of prostate cancer.

Publications

Phenotype-specific CpG island methylation events in a murine model of prostate cancer.

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