BERKELEY, CA (UroToday.com) - Prostate cancer (PCa) is the most common malignancy in Western countries, and since the introduction of prostate specific antigen (PSA) testing PCa incidence has increased dramatically. With PSA tests detecting both indolent and aggressive tumors, a proportion of men will be over-treated and unnecessarily undergo radical prostatectomy as their tumors would not likely affect their life span or quality of life. Currently, there are no markers to accurately distinguish between tumors that will or will not progress. There is a clear clinical need for the discovery of novel and complementary biomarkers that will allow for the precise discrimination between indolent and aggressive PCas.
Epigenetic changes, including DNA methylation, have been shown to play a role in controlling gene expression and have been implicated in PCa pathogenesis. CpG gene methylation is involved in cancer by silencing tumor suppressor genes by hypermethylation of their promoter regions. Gene methylation has also been shown to be a valuable diagnostic, prognostic, and predictive marker for cancers including PCa.
We preformed an integrated analysis using PCa-derived cell line models, two independent patient cohorts, and publicly available databases to identify novel methylation biomarkers associated with PCa progression. In order to identify genes that are methylated in PCa, we treated two PCa cell lines, 22Rv1 and DU-145, with the demethylating agent 5-Aza-2’-deoxycytidine (DAC) and performed methylation-sensitive restriction enzyme based differential methylation hybridization strategy, followed by CpG methylation profiling. This approach was distinctive in that we analyzed the hypomethylated fraction to identify regions of gene methylation before DAC treatment. This differs from the majority of other microarray-based epigenetic studies, which analyze the hypermethylated fraction. We found that interrogation of the unmethylated fraction is significantly more informative. Based on restriction enzyme digestion, a higher number of amplicons are generated using enzymes, selecting for the unmethylated fraction rather than methylated CpGs. Also, because we were inducing hypomethylation by DAC treatment, we reasoned it would be best to study the direct effect of this treatment on CpG sites localized to distinct gene regions. Genes that showed increased hypomethylation after treatment with DAC were identified as being methylated in the untreated cells.
In parallel, we performed mRNA expression profiling on these DAC-treated cells, using a custom microarray (GEO platform accession GPL16604) enriched for PCa metastasis-associated genes. This would allow for the identification of genes that are likely associated with aggressive PCa. Gene methylation and expression results were overlaid to identify genes showing decreased methylation and increased expression, post-DAC. These genes represent genes that are potentially regulated by methylation in PCa and also represent potential novel biomarkers.
We correlated these potential candidates with genes that were shown to be significantly hypermethylated among PCa patients with Gleason score 8 (GS8; 4+4) vs GS6 (3+3) tumors by genome-wide methylation profiling. Gene methylation associated with high grade PCa was further verified using the publicly available database, the Cancer Genome Atlas (TCGA), where complete gene methylation, clinical, and survival data were available for 127 PCa patients. Publically available databases are a rich source of genomic, epigenomic, and transcriptomic data that are being more commonly used to verify experimental findings. They provide relatively easy access to large patient cohorts with reliable molecular and pathological data. We correlated gene methylation with Gleason score and found 3 genes were hypermethylated in GS ≥ 8 vs GS ≤ 7 tumors. One gene was previously identified to be associated with aggressive PCa, while the other 2 genes were not previously identified in this manner. We further verified hypermethylation of the two novel genes in a separate, independent patient cohort of 20 patients using the highly sensitive and quantitative MethyLight assay.
This study has a number of unique aspects. Firstly, we used the less common approach of analyzing the hypomethylated DNA fraction in PCa cell lines to identify potential gene methylation markers. We also used a custom gene expression array to help identify genes that would be associated with PCa metastasis. Verification on 3 independent patient cohorts, including the publically available TCGA database, was used to finally choose 3 genes that showed significant hypermethylation in high-grade PCa tumors. This integrated approach not only combines data from different tissue sources, but also across different platforms. We used PCa cell lines, along with both fresh and formalin-fixed paraffin-embedded tissue from PCa tumors in this study. Furthermore, expression data was collected using a custom Agilent array while methylation data was generated using Agilent arrays (PCa cell lines and one patient cohort), the Illumina 450K array (TCGA cohort), and by MethyLight analysis of selected candidate genes. Identification of a methylation marker across all platforms suggests a biomarker that is highly robust. Our integrated analysis has identified a number of potential methylation markers for aggressive PCa. These markers may help elucidate the pathogenesis of PCa and represent potential prognostic markers for PCa patients.
Nicole White-Al Habeeb1 and Bharati Bapat1, 2 as part of Beyond the Abstract on UroToday.com. This initiative offers a method of publishing for the professional urology community. Authors are given an opportunity to expand on the circumstances, limitations etc... of their research by referencing the published abstract.
1Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; Toronto, ON Canada; Department of Laboratory Medicine and Pathobiology; University of Toronto; Toronto, ON Canada
2Department of Pathology; University Health Network; University of Toronto; Toronto, ON Canada