Identification of candidate epigenetic biomarkers for ovarian cancer detection.

Ovarian cancer ranks the most lethal among gynecologic neoplasms in women. To develop potential biomarkers for diagnosis, we have identified five novel genes (CYP39A1, GTF2A1, FOXD4L4, EBP, and HAAO) that are hypermethylated in ovarian tumors, compared with the non-malignant normal ovarian surface epithelia, using the quantitative methylation-specific polymerase chain reactions. Interestingly enough, multivariate Cox regression analysis has identified hypermethylation of CYP39A1 correlated with an increase rate of relapsing (P=0.

An integrative ChIP-chip and gene expression profiling to model SMAD regulatory modules.

Background: The TGF-beta/SMAD pathway is part of a broader signaling network in which crosstalk between pathways occurs. While the molecular mechanisms of TGF-beta/SMAD signaling pathway have been studied in detail, the global networks downstream of SMAD remain largely unknown. The regulatory effect of SMAD complex likely depends on transcriptional modules, in which the SMAD binding elements and partner transcription factor binding sites (SMAD modules) are present in specific context.

Methylation analysis by microarray.

Differential methylation hybridization (DMH) is a high-throughput DNA methylation screening tool that utilizes methylation-sensitive restriction enzymes to profile methylated fragments by hybridizing them to a CpG island microarray. This array contains probes spanning all the 27,800 islands annotated in the UCSC Genome Browser. Herein we describe a DMH protocol with clearly identified quality control points.

Breast cancer-associated fibroblasts confer AKT1-mediated epigenetic silencing of Cystatin M in epithelial cells.

The interplay between histone modifications and promoter hypermethylation provides a causative explanation for epigenetic gene silencing in cancer. Less is known about the upstream initiators that direct this process. Here, we report that the Cystatin M (CST6) tumor suppressor gene is concurrently down-regulated with other loci in breast epithelial cells cocultured with cancer-associated fibroblasts (CAF).

Differential methylation hybridization: profiling DNA methylation with a high-density CpG island microarray.

Differential methylation hybridization (DMH) is a high-throughput DNA methylation screening tool that utilizes methylation-sensitive restriction enzymes to profile methylated fragments by hybridizing them to a CpG island microarray. This array contains probes spanning all the 27,800 islands annotated in the UCSC Genome Browser. Herein we describe a revised DMH protocol with clearly identified quality control points.

Probe signal correction for differential methylation hybridization experiments.

Background: Non-biological signal (or noise) has been the bane of microarray analysis. Hybridization effects related to probe-sequence composition and DNA dye-probe interactions have been observed in differential methylation hybridization (DMH) microarray experiments as well as other effects inherent to the DMH protocol.

Results: We suggest two models to correct for non-biologically relevant probe signal with an overarching focus on probe-sequence composition.

Epigenetic repression of the estrogen-regulated Homeobox B13 gene in breast cancer.

Several studies have reported that a high expression ratio of HOXB13 to IL17BR predicts tumor recurrence in node-negative, estrogen receptor (ER) alpha-positive breast cancer patients treated with tamoxifen. The molecular mechanisms underlying this dysregulation of gene expression remain to be explored. Our epigenetic analysis has found that increased promoter methylation of one of these genes, HOXB13, correlate with the decreased expression of its transcript in breast cancer cell lines (P < 0.

An application of a service-oriented system to support array annotation in custom chip design for epigenomic analysis.

We present the implementation of an application using caGrid, which is the service-oriented Grid software infrastructure of the NCI cancer Biomedical Informatics Grid (caBIG), to support design and analysis of custom microarray experiments in the study of epigenetic alterations in cancer. The design and execution of these experiments requires synthesis of information from multiple data types and datasets. In our implementation, each data source is implemented as a caGrid Data Service, and analytical resources are wrapped as caGrid Analytical Services.

Diverse histone modifications on histone 3 lysine 9 and their relation to DNA methylation in specifying gene silencing.

Background: Previous studies of individual genes have shown that in a self-enforcing way, dimethylation at histone 3 lysine 9 (dimethyl-H3K9) and DNA methylation cooperate to maintain a repressive mode of inactive genes. Less clear is whether this cooperation is generalized in mammalian genomes, such as mouse genome. Here we use epigenomic tools to simultaneously interrogate chromatin modifications and DNA methylation in a mouse leukemia cell line, L1210.

Heritable clustering and pathway discovery in breast cancer integrating epigenetic and phenotypic data.

Background: In order to recapitulate tumor progression pathways using epigenetic data, we developed novel clustering and pathway reconstruction algorithms, collectively referred to as heritable clustering. This approach generates a progression model of altered DNA methylation from tumor tissues diagnosed at different developmental stages. The samples act as surrogates for natural progression in breast cancer and allow the algorithm to uncover distinct epigenotypes that describe the molecular events underlying this process.

Gamma-Normal-Gamma mixture model for detecting differentially methylated loci in three breast cancer cell lines.

With state-of-the-art microarray technologies now available for whole genome CpG island (CGI) methylation profiling, there is a need to develop statistical models that are specifically geared toward the analysis of such data. In this article, we propose a Gamma-Normal-Gamma (GNG) mixture model for describing three groups of CGI loci: hypomethylated, undifferentiated, and hypermethylated, from a single methylation microarray. This model was applied to study the methylation signatures of three breast cancer cell lines: MCF7, T47D, and MDAMB361.