Large-scale CpG methylation analysis identifies novel candidate genes and reveals methylation hotspots in acute lymphoblastic leukemia.

This study examined DNA methylation associated with acute lymphoblastic leukemia (ALL) and showed that selected molecular targets can be pharmacologically modulated to reverse gene silencing. A CpG island (CGI) microarray containing more than 3,400 unique clones that span all human chromosomes was used for large-scale discovery experiments and led to 262 unique CGI loci being statistically identified as methylated in ALL lymphoblasts. The methylation status of 10 clones encompassing 11 genes (DCC, DLC-1, DDX51, KCNK2, LRP1B, NKX6-1, NOPE, PCDHGA12, RPIB9, ABCB1, and SLC2A14) identified as differentially methylated between ALL patients and controls was independently verified. Consequently, the methylation status of DDX51 was found to differentiate patients with B- and T-ALL subtypes (P = 0.011, Fisher's exact test). Next, the relationship between methylation and expression of these genes was examined in ALL cell lines (NALM-6 and Jurkat) before and after treatments with 5-aza-2-deoxycytidine and trichostatin A. More than a 10-fold increase in mRNA expression was observed for two previously identified tumor suppressor genes (DLC-1 and DCC) and also for RPIB9 and PCDHGA12. Although the mechanisms that lead to the CGI methylation of these genes are unknown, bisulfite sequencing of the promoter of RPIB9 suggests that expression is inhibited by methylation within SP1 and AP2 transcription factor binding motifs. Finally, specific chromosomal methylation hotspots were found to be associated with ALL. This study sets the stage for acquiring a better biological understanding of ALL and for the identification of epigenetic biomarkers useful for differential diagnosis, therapeutic monitoring, and the detection of leukemic relapse.

A class of genes in the HER2 regulon that is poised for transcription in breast cancer cell lines and expressed in human breast tumors.

HER2-positive breast cancer accounts for 25% of all cases and has a poor prognosis. Although progress has been made in understanding signal transduction, little is known of how HER2 achieves gene regulation. We performed whole genome expression analysis on a HER2⁺ and HER2⁻ breast cancer cell lines and compared these results to expression in 812 primary tumors stratified by their HER2 expression level. Chip-on-chip with anti-RNA polymerase II was compared among breast cancer cell lines to identify genes that are potentially activated by HER2. The expression levels of these HER2-dependent POL II binding genes were determined for the 812 HER2+/- breast cancer tissues. Genes differentially expressed between HER2+/- cell lines were generally regulated in the same direction as in breast cancer tissues. We identified genes that had POLII binding in HER2⁺ cell lines, but without significant gene expression. Of 737 such genes "poised" for expression in cell lines, 113 genes were significantly differentially expressed in breast tumors in a HER2-dependent manner. Pathway analysis of these 113 genes revealed that a large group of genes were associated with stem cell and progenitor cell control as indicated by networks centered on NANOG, SOX2, OCT3/4. HER2 directs POL II binding to a large number of genes in breast cancer cells. A "poised" class of genes in HER2⁺ cell lines with POLII binding and low RNA expression but is differentially expressed in primary tumors, strongly suggests a role of the microenvironment and further suggests a role for stem cells proliferation in HER2-regulated breast cancer tissue.

Overexpression of periostin in stroma positively associated with aggressive prostate cancer.

BACKGROUND: Periostin is an important extracellular matrix protein involved in cell development and adhesion. Previously, we identified periostin to be up-regulated in aggressive prostate cancer (CaP) using quantitative glycoproteomics and mass spectrometry. The expression of periostin was further evaluated in primary radical prostatectomy (RP) prostate tumors and adjacent non-tumorous prostate tissues using immunohistochemistry (IHC). Our IHC results revealed a low background periostin levels in the adjacent non-tumorous prostate tissues, but overexpressed periostin levels in the peritumoral stroma of primary CaP tumors. METHODS: In this study, periostin expression in CaP was further examined on multiple tissue microarrays (TMAs), which were conducted in four laboratories. To achieve consistent staining, all TMAs were stained with same protocol and scored by same image computation tool to determine the total periostin staining intensities. The TMAs were further scored by pathologists to characterize the stromal staining and epithelial staining. RESULTS: The periostin staining was observed mainly in peritumoral stromal cells and in some cases in tumor epithelial cells though the stronger staining was found in peritumoral stromal cells. Both periostin stromal staining and epithelial staining can differentiate BPH from CaP including low grade CaP (Gleason score ≤6), with significant p-value of 2.2e-16 and 0.001, respectively. Periostin epithelial staining differentiated PIN from low grade CaP (Gleason score ≤6) (p=0.001), while periostin stromal staining differentiated low grade Cap (Gleason score ≤6) from high grade Cap (Gleason score ≤6) (p=1.7e-05). In addition, a positive correlation between total periostin staining and Gleason score was observed (r=0.87, p=0.002). CONCLUSIONS: The results showed that periostin staining was positively correlated with increasing Gleason score and the aggressiveness of prostate disease.

The identification of trans-associations between prostate cancer GWAS SNPs and RNA expression differences in tumor-adjacent stroma.

Here we tested the hypothesis that SNPs associated with prostate cancer risk, might differentially affect RNA expression in prostate cancer stroma. The most significant 35 SNP loci were selected from Genome Wide Association (GWA) studies of ~40,000 patients. We also selected 4030 transcripts previously associated with prostate cancer diagnosis and prognosis. eQTL analysis was carried out by a modified BAYES method to analyze the associations between the risk variants and expressed transcripts jointly in a single model. We observed 47 significant associations between eight risk variants and the expression patterns of 46 genes. This is the first study to identify associations between multiple SNPs and multiple in trans gene expression differences in cancer stroma. Potentially, a combination of SNPs and associated expression differences in prostate stroma may increase the power of risk assessment for individuals, and for cancer progression.

Expression changes in the stroma of prostate cancer predict subsequent relapse.

Biomarkers are needed to address overtreatment that occurs for the majority of prostate cancer patients that would not die of the disease but receive radical treatment. A possible barrier to biomarker discovery may be the polyclonal/multifocal nature of prostate tumors as well as cell-type heterogeneity between patient samples. Tumor-adjacent stroma (tumor microenvironment) is less affected by genetic alteration and might therefore yield more consistent biomarkers in response to tumor aggressiveness. To this end we compared Affymetrix gene expression profiles in stroma near tumor and identified a set of 115 probe sets for which the expression levels were significantly correlated with time-to-relapse. We also compared patients that chemically relapsed shortly after prostatectomy (<1 year), and patients that did not relapse in the first four years after prostatectomy. We identified 131 differentially expressed microarray probe sets between these two categories. 19 probe sets (15 genes overlapped between the two gene lists with p<0.0001). We developed a PAM-based classifier by training on samples containing stroma near tumor: 9 rapid relapse patient samples and 9 indolent patient samples. We then tested the classifier on 47 different samples, containing 90% or more stroma. The classifier predicted the risk status of patients with an average accuracy of 87%. This is the first general tumor microenvironment-based prognostic classifier. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for predicting outcomes for patients.