Laurent Briollais

DNA methylation of HOXD3 as a marker of prostate cancer progression

DNA methylation in gene promoters causes gene silencing and is a common event in cancer development and progression. The ability of aberrant methylation events to serve as diagnostic and prognostic markers is being appreciated for many cancers, including prostate cancer. Using quantitative MethyLight technology, we evaluated the relationship between HOXD3 methylation and clinicopathological parameters including biochemical recurrence, pathological stage, Gleason score (GS), and Gleason pattern in a series of 232 radical prostatectomies performed between 1998 and 2001. HOXD3 methylation was significantly greater in GS 7 cancers vs GS≤6 cancers (P-value <0.001) as well as pT3/pT4 vs pT2 cancers (P-value <0.001). The proportion of cases with high methylation in GS 7 vs ≤GS 6 and pT3/pT4 vs pT2 were also significantly different (P-values=0.002 and 0.005, respectively). There were also significant increases in methylation from Gleason pattern 2–3 and from pattern 3 to 4/5 (paired t-test P-values=0.01 and <0.001, respectively), whereas methylation from lymph node metastases was decreased when compared with matched tumor tissue (P-value=0.029). HOXD3 methylation was associated with biochemical recurrence in univariate analysis (P-value=0.043) and showed evidence for interaction with pathological stage as a predictor variable in Cox regression analysis (P-value=0.028). The results indicate that HOXD3 methylation distinguishes low-grade prostate cancers from intermediate and high-grade ones and may also have prognostic value when considered together with pathological stage.

Quantitative DNA methylation analysis of genes coding for kallikrein-related peptidases 6 and 10 as biomarkers for prostate cancer

DNA methylation plays an important role in carcinogenesis and is being recognized as a promising diagnostic and prognostic biomarker for a variety of malignancies including Prostate cancer (PCa). The human kallikrein-related peptidases (KLKs) have emerged as an important family of cancer biomarkers, with KLK3, encoding for Prostate Specific Antigen, being most recognized. However, few studies have examined the epigenetic regulation of KLKs and its implications to PCa. To assess the biological effect of DNA methylation on KLK6 and KLK10 expression, we treated PC3 and 22RV1 PCa cells with a demethylating drug, 5-aza-2′deoxycytidine, and observed increased expression of both KLKs, establishing that DNA methylation plays a role in regulating gene expression. Subsequently, we have quantified KLK6 and KLK10 DNA methylation levels in two independent cohorts of PCa patients operated by radical prostatectomy between 2007–2011 (Cohort I, n = 150) and 1998–2001 (Cohort II, n = 124). In Cohort I, DNA methylation levels of both KLKs were significantly higher in cancerous tissue vs. normal. Further, we evaluated the relationship between DNA methylation and clinicopathological parameters. KLK6 DNA methylation was significantly associated with pathological stage only in Cohort I while KLK10 DNA methylation was significantly associated with pathological stage in both cohorts. In Cohort II, low KLK10 DNA methylation was associated with biochemical recurrence in univariate and multivariate analyses. A similar trend for KLK6 DNA methylation was observed. The results suggest that KLK6 and KLK10 DNA methylation distinguishes organ confined from locally invasive PCa and may have prognostic value.

Alcohol metabolism, alcohol intake, and breast cancer risk: a sister-set analysis using the Breast Cancer Family Registry.

Moderate alcohol intake has been consistently associated with a modest (30–50%) increase in breast cancer risk, but it remains unclear if certain individuals have higher susceptibility to the harmful effects of alcohol intake. Individuals differ in their ability to metabolize alcohol through genetic differences in alcohol dehydrogenase (ADH), the enzyme that catalyzes the oxidation of approximately 80% of ethanol to acetaldehyde, a known carcinogen. Using data from the Breast Cancer Family Registry (nxa0=xa0811 sister sets), we examined whether sisters with breast cancer differ with respect to alcohol consumption and alcohol metabolism (measured by polymorphisms in ADH1B and ADH1C) compared to their sisters without breast cancer. Neither alcohol drinking nor alcohol metabolizing ADH1B and ADH1C genotypes were associated with breast cancer risk. However, only 19% and 42% of sisters were discordant by ADH1B and ADH1C, respectively, and even fewer were discordant by both genotype and alcohol intake, making it difficult to detect differences if they existed.

Altered DNA Methylation Landscapes of Polycomb-Repressed Loci Are Associated with Prostate Cancer Progression and ERG Oncogene Expression in Prostate Cancer

Purpose: To assess differentially methylated “landscapes” according to prostate cancer Gleason score (GS) and ERG oncogene expression status, and to determine the extent of polycomb group (PcG) target gene involvement, we sought to assess the genome-wide DNA methylation profile of prostate cancer according to Gleason score and ERG expression. Experimental Design: Genomic DNA from 39 prostate cancer specimens was hybridized to CpG island microarrays through differential methylation hybridization. We compared methylation profiles between Gleason score and ERG expression status as well as Gleason score stratified by ERG expression status. In addition, we compared results from our dataset to publicly available datasets of histone modifications in benign prostate cells. Results: We discovered hundreds of distinct differentially methylated regions (DMR) associated with increasing Gleason score and ERG. Furthermore, the number of DMRs associated with Gleason score was greatly expanded by stratifying samples into ERG-positive versus ERG-negative, with ERG-positive/GS–associated DMRs being primarily hypermethylated as opposed to hypomethylated. Finally, we found that there was a significant overlap between either Gleason score–related or ERG-hypermethylated DMRs and distinct regions in benign epithelial cells that have PcG signatures (H3K27me3, SUZ12) and lack active gene expression signatures (H3K4me3, RNA pol II). Conclusions: This work defines methylation landscapes of prostate cancer according to Gleason score, and suggests that initiating genetic events may influence the prostate cancer epigenome, which is further perturbed as prostate cancer progresses. Moreover, CpG islands with silent chromatin signatures in benign cells are particularly susceptible to prostate cancer–related hypermethylation. Clin Cancer Res; 19(13); 3450–61. ©2013 AACR.

Heterogeneity in IBD Allele Sharing among Covariate-Defined Subgroups: Issues and Findings for Affected Relatives

Objectives: Modelling of variation in identical-by-descent (IBD) allele sharing using covariates can increase power to detect linkage, identify covariate-defined subgroups linked to particular marker regions, and improve the design of subsequent studies to localize genes and characterize their effects. In this report, we highlight issues that arise in studies of families with affected relatives. Methods: Mirea et al. [Genet Epidemiol 2003, in press] extended linear and exponential linkage likelihood models [Kong and Cox, Am J Hum Genet 1997;61: 1179–1188] to model variation in NPL scores among covariate-defined groups of families, and proposed likelihood ratio (LR) and t statistics to detect differences in allele sharing between groups defined by a binary covariate. Here we evaluate factors affecting the power of these tests analytically and by example, as well as effects of constraints, nuisance parameters, and incomplete data on test validity by simulation of locus heterogeneity in families with affected siblings or affected cousins. Results: Provided constraints on the parameters are avoided, these tests are particularly useful when one subgroup has less than expected IBD sharing. The distribution of the LR statistic depends on the extent of linkage, particularly in the presence of constraints. The t statistic may be biased by group differences in information content. Conclusions: We recommend that constraints be applied cautiously, and covariate effects in IBD allele sharing models interpreted with care.