Correlation of Smoking-Associated DNA Methylation Changes in Buccal Cells With DNA Methylation Changes in Epithelial Cancer.

IMPORTANCE: The utility of buccal cells as an epithelial source tissue for epigenome-wide association studies (EWASs) remains to be demonstrated. Given the direct exposure of buccal cells to potent carcinogens such as smoke, epigenetic changes in these cells may provide insights into the development of smoke-related cancers. OBJECTIVE: To perform an EWAS in buccal and blood cells to assess the relative effect of smoking on the DNA methylation (DNAme) patterns in these cell types and to test whether these DNAme changes are also seen in epithelial cancer. DESIGN, SETTING, AND PARTICIPANTS: In 2013, we measured DNAme at more than 480,000 CpG sites in buccal samples provided in 1999 by 790 women (all aged 53 years in 1999) from the United Kingdom Medical Research Council National Survey of Health and Development. This included matched blood samples from 152 women. We constructed a DNAme-based smoking index and tested its sensitivity and specificity to discriminate normal from cancer tissue in more than 5000 samples. MAIN OUTCOMES AND MEASURES: CpG sites whose DNAme level correlates with smoking pack-years, and construction of an associated sample-specific smoking index, which measures the mean deviation of DNAme at smoking-associated CpG sites from a normal reference. RESULTS: In a discovery set of 400 women, we identified 1501 smoking-associated CpG sites at a genome-wide significance level of P < 10-7, which were validated in an independent set of 390 women. This represented a 40-fold increase of differentially methylated sites in buccal cells compared with matched blood samples. Hypermethylated sites were enriched for bivalently marked genes and binding sites of transcription factors implicated in DNA repair and chromatin architecture (P < 10-10). A smoking index constructed from the DNAme changes in buccal cells was able to discriminate normal tissue from cancer tissue with a mean receiver operating characteristic area under the curve of 0.99 (range, 0.99-1.00) for lung cancers and of 0.91 (range, 0.71-1.00) for 13 other organs. The corresponding area under the curve of a smoking signature derived from blood cells was lower than that derived from buccal cells in 14 of 15 cancer types (Wilcoxon signed rank test, P = .001). CONCLUSIONS AND RELEVANCE: These data point toward buccal cells as being a more appropriate source of tissue than blood to conduct EWASs for smoking-related epithelial cancers.

A BRCA1-mutation associated DNA methylation signature in blood cells predicts sporadic breast cancer incidence and survival.

BACKGROUND: BRCA1 mutation carriers have an 85% risk of developing breast cancer but the risk of developing non-hereditary breast cancer is difficult to assess. Our objective is to test whether a DNA methylation (DNAme) signature derived from BRCA1 mutation carriers is able to predict non-hereditary breast cancer. METHODS: In a case/control setting (72 BRCA1 mutation carriers and 72 BRCA1/2 wild type controls) blood cell DNA samples were profiled on the Illumina 27 k methylation array. Using the Elastic Net classification algorithm, a BRCA1-mutation DNAme signature was derived and tested in two cohorts: (1) The NSHD (19 breast cancers developed within 12 years after sample donation and 77 controls) and (2) the UKCTOCS trial (119 oestrogen receptor positive breast cancers developed within 5 years after sample donation and 122 controls). RESULTS: We found that our blood-based BRCA1-mutation DNAme signature applied to blood cell DNA from women in the NSHD resulted in a receiver operating characteristics (ROC) area under the curve (AUC) of 0.65 (95% CI 0.51 to 0.78, P = 0.02) which did not validate in buccal cells from the same individuals. Applying the signature in blood DNA from UKCTOCS volunteers resulted in AUC of 0.57 (95% CI 0.50 to 0.64; P = 0.03) and is independent of family history or any other known risk factors. Importantly the BRCA1-mutation DNAme signature was able to predict breast cancer mortality (AUC = 0.67; 95% CI 0.51 to 0.83; P = 0.02). We also found that the 1,074 CpGs which are hypermethylated in BRCA1 mutation carriers are significantly enriched for stem cell polycomb group target genes (P <10(-20)). CONCLUSIONS: A DNAme signature derived from BRCA1 carriers is able to predict breast cancer risk and death years in advance of diagnosis. Future studies may need to focus on DNAme profiles in epithelial cells in order to reach the AUC thresholds required of preventative measures or early detection strategies.

Protein kinase Cε-calcineurin cosignaling downstream of toll-like receptor 4 downregulates fibrosis and induces wound healing gene expression in cardiac myofibroblasts.

The pathways which regulate resolution of inflammation and contribute to positive remodeling of the myocardium following injury are poorly understood. Here we show that protein kinase C epsilon (PKCε) cooperates with the phosphatase calcineurin (CN) to potentiate induction of cardioprotective gene expression while suppressing expression of fibrosis markers. This was achieved by detailed analysis of the regulation of cyclooxygenase 2 (COX-2) expression as a marker gene and by using gene expression profiling to identify genes regulated by coexpression of CN-Aα/PKCε in adult rat cardiac myofibroblasts (ARVFs) on a larger scale. GeneChip analysis of CN-Aα/PKCε-coexpressing ARVFs showed that COX-2 provides a signature for wound healing and is associated with downregulation of fibrosis markers, including connective tissue growth factor (CTGF), fibronectin, and collagens Col1a1, Col3a1, Col6a3, Col11a1, Col12a1, and Col14a1, with concomitant upregulation of cardioprotection markers, including COX-2 itself, lipocalin 2 (LCN2), tissue inhibitor of metalloproteinase 1 (TIMP-1), interleukin-6 (IL-6), and inducible nitric oxide synthase (iNOS). In primary rat cardiomyocyte cultures Toll-like receptor 4 (TLR4) agonist- or PKCε/CN-dependent COX-2 induction occurred in coresident fibroblasts and was blocked by selective inhibition of CN or PKC α/ε or elimination of fibroblasts. Furthermore, ectopic expression of PKCε and CN in ARVFs showed that the effects on COX-2 expression are mediated by specific NFAT sites within the COX-2 promoter as confirmed by site-directed mutagenesis and chromatin immunoprecipitation (ChIP). Therefore, PKCε may negatively regulate adverse myocardial remodeling by cooperating with CN to downregulate fibrosis and induce transcription of cardioprotective wound healing genes, including COX-2.

A boosting approach to structure learning of graphs with and without prior knowledge.

MOTIVATION: Identifying the network structure through which genes and their products interact can help to elucidate normal cell physiology as well as the genetic architecture of pathological phenotypes. Recently, a number of gene network inference tools have appeared based on Gaussian graphical model representations. Following this, we introduce a novel Boosting approach to learn the structure of a high-dimensional Gaussian graphical model motivated by the applications in genomics. A particular emphasis is paid to the inclusion of partial prior knowledge on the structure of the graph. With the increasing availability of pathway information and large-scale gene expression datasets, we believe that conditioning on prior knowledge will be an important aspect in raising the statistical power of structural learning algorithms to infer true conditional dependencies. RESULTS: Our Boosting approach, termed BoostiGraph, is conceptually and algorithmically simple. It complements recent work on the network inference problem based on Lasso-type approaches. BoostiGraph is computationally cheap and is applicable to very high-dimensional graphs. For example, on graphs of order 5000 nodes, it is able to map out paths for the conditional independence structure in few minutes. Using computer simulations, we investigate the ability of our method with and without prior information to infer Gaussian graphical models from artificial as well as actual microarray datasets. The experimental results demonstrate that, using our method, it is possible to recover the true network topology with relatively high accuracy. AVAILABILITY: This method and all other associated files are freely available from http://www.stats.ox.ac.uk/~anjum/.