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) ␣ signaling by small interfering RNA, polycomb repressors and histone deacetylases are recruited to initiate stable repression of the progesterone receptor (PR) gene, a known ER␣ 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 ER␣ 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 ER␣ targets in breast cancer genomes. The results imply, for the first time, the significance of epigenetic regulation on ER␣ target genes, providing new direction for research in this classical signaling pathway.

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.

Motivated by the prevalence of high dimensional low sample size datasets in modern statistical applications, we propose a general nonparametric framework, Direction-Projection-Permutation (DiProPerm), for testing high dimensional hypotheses. The method is aimed at rigorous testing of whether lower dimensional visual differences are statistically significant. Theoretical analysis under the non-classical asymptotic regime of dimension going to infinity for fixed sample size reveals that certain natural variations of DiProPerm can have very different behaviors. An empirical power study both confirms the theoretical results and suggests DiProPerm is a powerful test in many settings. Finally DiProPerm is applied to a high dimensional gene expression dataset.

Hypermethylation associated silencing of the CpG islands of tumor suppressor genes is a common hallmark of human cancer. Here we report a functional search for hypermethylated CpG islands using the colorectal cancer cell line HCT-116, in which two major DNA methyltransferases, DNMT1 and DNMT3b, have been genetically disrupted (DKO cells). Using two molecular screenings for differentially methylated loci [differential methylation hybridization (DMH) and amplification of inter-methylated sites (AIMS)], we found that DKO cells, but not the single DNMT1 or DNMT3b knockouts, have a massive loss of hypermethylated CpG islands that induces the re-activation of the contiguous genes. We have characterized a substantial number of these CpG island associated genes with potentially important roles in tumorigenesis, such as the cadherin member FAT, or the homeobox genes LMX-1 and DUX-4. For other genes whose role in transformation has not been characterized, such as the calcium channel alpha1I or the thromboxane A2 receptor, their re-introduction in DKO cells inhibited colony formation. Thus, our results demonstrate the role of DNMT1 and DNMT3b in CpG island methylation associated silencing and the usefulness of genetic disruption strategies in searching for new hypermethylated loci.