Susan H. Wei

Loss of Estrogen Receptor Signaling Triggers Epigenetic Silencing of Downstream Targets in Breast Cancer

Alterations in histones, chromatin-related proteins, and DNA methylation contribute to transcriptional silencing in cancer, but the sequence of these molecular events is not well understood. Here we demonstrate that on disruption of estrogen receptor (ER) α signaling by small interfering RNA, polycomb repressors and histone deacetylases are recruited to initiate stable repression of the progesterone receptor (PR) gene, a known ERα target, in breast cancer cells. The event is accompanied by acquired DNA methylation of the PR promoter, leaving a stable mark that can be inherited by cancer cell progeny. Reestablishing ERα signaling alone was not sufficient to reactivate the PR gene; reactivation of the PR gene also requires DNA demethylation. Methylation microarray analysis further showed that progressive DNA methylation occurs in multiple ERα targets in breast cancer genomes. The results imply, for the first time, the significance of epigenetic regulation on ERα target genes, providing new direction for research in this classical signaling pathway.

Methylation Target Array for Rapid Analysis of CpG Island Hypermethylation in Multiple Tissue Genomes

Hypermethylation of multiple CpG islands is a common event in cancer. To assess the prognostic values of this epigenetic alteration, we developed Methylation Target Array (MTA), derived from the concept of tissue microarray, for simultaneous analysis of DNA hypermethylation in hundreds of tissue genomes. In MTA, linker-ligated CpG island fragments were digested with methylation-sensitive endonucleases and amplified with flanking primers. A panel of 468 MTA amplicons, which represented the whole repertoire of methylated CpG islands in 93 breast tumors, 20 normal breast tissues, and 4 breast cancer cell lines, were arrayed on nylon membrane for probe hybridization. Positive hybridization signals detected in tumor amplicons, but not in normal amplicons, were indicative of aberrant hypermethylation in tumor samples. This is attributed to aberrant sites that were protected from methylation-sensitive restriction and were amplified by PCR in tumor samples, while the same sites were restricted and could not be amplified in normal samples. Hypermethylation frequencies of the 10 genes tested in breast tumors and cancer cell lines were 60% for GPC3, 58% for RASSF1A, 32% for 3OST3B, 30% for HOXA5, 28% for uPA, 25% for WT1, 23% for BRCA1, 9% for DAPK1, and 0% for KL. Furthermore, hypermethylation of 5 to 7 loci of these genes was significantly correlated with hormone receptor status, clinical stages, and ages at diagnosis of the patients analyzed. This novel approach thus provides an additional avenue for assessing clinicopathological consequences of DNA hypermethylation in breast cancer.

Prognostic DNA Methylation Biomarkers in Ovarian Cancer

Purpose: Aberrant DNA methylation, now recognized as a contributing factor to neoplasia, often shows definitive gene/sequence preferences unique to specific cancer types. Correspondingly, distinct combinations of methylated loci can function as biomarkers for numerous clinical correlates of ovarian and other cancers. Experimental Design: We used a microarray approach to identify methylated loci prognostic for reduced progression-free survival (PFS) in advanced ovarian cancer patients. Two data set classification algorithms, Significance Analysis of Microarray and Prediction Analysis of Microarray, successfully identified 220 candidate PFS-discriminatory methylated loci. Of those, 112 were found capable of predicting PFS with 95% accuracy, by Prediction Analysis of Microarray, using an independent set of 40 advanced ovarian tumors (from 20 short-PFS and 20 long-PFS patients, respectively). Additionally, we showed the use of these predictive loci using two bioinformatics machine-learning algorithms, Support Vector Machine and Multilayer Perceptron. Conclusion: In this report, we show that highly prognostic DNA methylation biomarkers can be successfully identified and characterized, using previously unused, rigorous classifying algorithms. Such ovarian cancer biomarkers represent a promising approach for the assessment and management of this devastating disease.

Applications of CpG Island Microarrays for High-Throughput Analysis of DNA Methylation

Differential methylation hybridization (DMH) is a high-throughput microarray technique designed to identify changes in DNA methylation patterns commonly observed in cancer and other disease states. The DMH methodology comprises three fundamental components: the arraying of CpG island clones on glass slides, the preparation of the sample amplicons under investigation, and the hybridization of amplicon targets onto the CpG island microarray. Herein, we outline the DMH protocol and illustrate its utility and the validation approaches used in analyzing the hypermethylation profile of breast tumor and normal samples.

Hypermethylation of 18S and 28S Ribosomal DNAs Predicts Progression-Free Survival in Patients with Ovarian Cancer

Purpose: Repetitive ribosomal DNA (rDNA) genes are GC-rich clusters in the human genome. The aim of the study was to determine the methylation status of two rDNA subunits, the 18S and 28S genes, in ovarian tumors and to correlate methylation levels with clinicopathologic features in a cohort of ovarian cancer patients. Experimental Design: 18S and 28S rDNA methylation was examined by quantitative methylation-specific PCR in 74 late-stage ovarian cancers, 9 histologically uninvolved, and 11 normal ovarian surface epithelial samples. In addition, methylation and gene expression levels of 18S and 28S rDNAs in two ovarian cancer cell lines were examined by reverse transcription-PCR before and after treatment with the demethylating drug 5′-aza-2′-deoxycytidine. Results: The methylation level (amount of methylated rDNA/β-actin) of 18S and 28S rDNAs was significantly higher (P < 0.05) in tumors than in normal ovarian surface epithelial samples. Methylation of 18S and 28S rDNA was highly correlated (R2 = 0.842). Multivariate analysis by Cox regression found that rDNA hypermethylation [hazard ratio (HR), 0.25; P < 0.01], but not age (HR, 1.29; P = 0.291) and stage (HR, 1.09; P = 0.709), was independently associated with longer progression-free survival. In ovarian cancer cell lines, methylation levels of rDNA correlated with gene down-regulation and 5′-aza-2′-deoxycytidine treatment resulted in a moderate increase in 18S and 28S rDNA gene expressions. Conclusion: This is the first report of rDNA hypermethylation in ovarian tumors. Furthermore, rDNA methylation levels were higher in patients with long progression-free survival versus patients with short survival. Thus, rDNA methylation as a prognostic marker in ovarian cancer warrants further investigation.

Aberrant DNA Methylation in Ovarian Cancer

Abstract: Epigenetic regulation of gene expression has been observed in a variety of tumor types. We have used microarray technology to evaluate the predisposition of drug response by aberrant methylation in ovarian cancer. Results indicate that loss of gene activity due to hypermethylation potentially confers a predisposition in certain cancer types and is an early event in disease progression. Methylation profiles of ovarian cancer might be useful for early cancer detection and prediction of chemotherapy outcome in a clinical context.

Identifying Clinicopathological Association of DNA Hypermethylation in Cancers Using CpG Island Microarrays

Hypermethylation of promoter CpG islands has been associated with gene silencing in cancer. Increasingly, these CpG islands have potential clinical utility as molecular markers for cancer diagnosis. Here we describe a microarray-based technique, called differential methylation hybridization (DMH), for simultaneous screening of methylation alteration across thousands of CpG island loci in one tumor sample at a time. We also describe a second approach, called methylation target array (MTA), for detecting methylation alteration of a single CpG island locus across hundreds of tumor DNA samples. The DMH and MTA assays are complementary to each other in that DMH allows for rapid identification of multiple loci hypermethylated in tumor genomes while MTA can rapidly assess the utility of these loci as markers for clinical diagnosis. Furthermore, the use of clustering algorithms to analyze the array data of multiple CpG island loci can identify an association of DNA hypermethylation with specific clinicopathological features of tumors.