Complex cross-talk between EZH2 and miRNAs confers hallmark characteristics and shapes the tumor microenvironment.

Cancer epigenetic mechanisms support the acquisition of hallmark characteristics during oncogenesis. EZH2 - an important histone methyltransferase that writes histone H3 lysine 27 trimethylation marks - is known to be dysregulated in cancer cells. However, the interactions between EZH2 and miRNAs that form a complex network of cross-talk and reciprocal regulation that enable cancer cells to acquire hallmark characteristics have been relatively poorly appreciated. The specific functions of EZH2 appear to be regulated by a vast array of miRNAs, which direct EZH2 toward regulation over the development of specific hallmark characteristics. This review discusses recent advances in the understanding of EZH2, focusing on its collaboration with miRNAs to orchestrate oncogenesis. These epigenetic processes promote the evasion of apoptosis/cell cycle arrest, cellular dedifferentiation and the establishment of a tumor microenvironment that facilitates local cancer cell invasion, anti-cancer drug resistance and evasion of the immune response.

Cancer epigenetics involves cellular processes that ensure that gene expression changes that enable cancer cells to outcompete their neighboring normal tissues are passed on from one cancer cell to the next. One key epigenetic player is called EZH2, which is an enzyme that transfers methyl (CH₃) groups from donor molecules to the histone proteins around which DNA is coiled. The transfer of methyl groups to histones ­ a process called histone methylation ­ silences the production of proteins from the genes coded in DNA. To achieve this goal, EZH2 is directed to affect specific genes through interactions with other molecules consisting of short sequences of RNA (a molecule similar in structure to DNA). The specific RNA molecules in question are called miRNAs or non-coding RNAs. These EZH2/miRNA interactions form a complex web of circuits that enables cancer cells to gain characteristics that give them a competitive advantage over their normal tissue neighbors. This review discusses recent advances in the understanding of the role of EZH2 in cancer formation, focusing on the interactions it has with miRNAs (and other non-coding RNAs) to organize many different processes linked with cancer formation. These processes include the avoidance of cell death or cell growth arrest, the development of stem cell properties and the formation of an environment around tumor cells that allows them to invade adjacent normal tissue (i.e., metastasize) and avoid being killed by anti-cancer drugs and the immune system.

Large-scale genotyping identifies a new locus at 22q13.2 associated with female breast size.

BACKGROUND: Individual differences in breast size are a conspicuous feature of variation in human females and have been associated with fecundity and advantage in selection of mates. To identify common variants that are associated with breast size, we conducted a large-scale genotyping association meta-analysis in 7169 women of European descent across three independent sample collections with digital or screen film mammograms. METHODS: The samples consisted of the Swedish KARMA, LIBRO-1 and SASBAC studies genotyped on iCOGS, a custom illumina iSelect genotyping array comprising of 211 155 single nucleotide polymorphisms (SNPs) designed for replication and fine mapping of common and rare variants with relevance to breast, ovary and prostate cancer. Breast size of each subject was ascertained by measuring total breast area (mm(2)) on a mammogram. RESULTS: We confirm genome-wide significant associations at 8p11.23 (rs10086016, p=1.3×10(-14)) and report a new locus at 22q13 (rs5995871, p=3.2×10(-8)). The latter region contains the MKL1 gene, which has been shown to impact endogenous oestrogen receptor α transcriptional activity and is recruited on oestradiol sensitive genes. We also replicated previous genome-wide association study findings for breast size at four other loci. CONCLUSIONS: A new locus at 22q13 may be associated with female breast size.

The heritability of mammographic breast density and circulating sex-hormone levels: two independent breast cancer risk factors.

BACKGROUND: Mammographic breast density and endogenous sex-hormone levels are both strong risk factors for breast cancer. This study investigated whether there is evidence for a shared genetic basis between these risk factors. METHODS: Using data on 1,286 women from 617 families, we estimated the heritabilities of serum estradiol, testosterone, and sex-hormone binding globulin (SHBG) levels and of three measures of breast density (dense area, nondense area, and percentage density). We tested for associations between hormone levels and density measures and estimated the genetic and environmental correlations between pairs of traits using variance and covariance components models and pedigree-based maximum likelihood methods. RESULTS: We found no significant associations between estradiol, testosterone, or SHBG levels and any of the three density measures, after adjusting for body mass index (BMI). The estimated heritabilities were 63%, 66%, and 65% for square root-transformed adjusted percentage density, dense area, and nondense area, respectively, and 40%, 25%, and 58% for log-transformed-adjusted estradiol, testosterone, and SHBG. We found no evidence of a shared genetic basis between any hormone levels and any measure of density, after adjusting for BMI. The negative genetic correlation between dense and nondense areas remained significant even after adjustment for BMI and other covariates (ρ = -0.34; SE = 0.08; P = 0.0005). CONCLUSIONS: Breast density and sex hormones can be considered as independent sets of traits. IMPACT: Breast density and sex hormones can be used as intermediate phenotypes in the search for breast cancer susceptibility loci.

Identification of a novel percent mammographic density locus at 12q24.

Percent mammographic density adjusted for age and body mass index (BMI) is one of the strongest risk factors for breast cancer and has a heritable component that remains largely unidentified. We performed a three-stage genome-wide association study (GWAS) of percent mammographic density to identify novel genetic loci associated with this trait. In stage 1, we combined three GWASs of percent density comprised of 1241 women from studies at the Mayo Clinic and identified the top 48 loci (99 single nucleotide polymorphisms). We attempted replication of these loci in 7018 women from seven additional studies (stage 2). The meta-analysis of stage 1 and 2 data identified a novel locus, rs1265507 on 12q24, associated with percent density, adjusting for age and BMI (P = 4.43 × 10(-8)). We refined the 12q24 locus with 459 additional variants (stage 3) in a combined analysis of all three stages (n = 10 377) and confirmed that rs1265507 has the strongest association in the 12q24 region (P = 1.03 × 10(-8)). Rs1265507 is located between the genes TBX5 and TBX3, which are members of the phylogenetically conserved T-box gene family and encode transcription factors involved in developmental regulation. Understanding the mechanism underlying this association will provide insight into the genetics of breast tissue composition.

Common breast cancer susceptibility variants in LSP1 and RAD51L1 are associated with mammographic density measures that predict breast cancer risk.

BACKGROUND: Mammographic density adjusted for age and body mass index (BMI) is a heritable marker of breast cancer susceptibility. Little is known about the biologic mechanisms underlying the association between mammographic density and breast cancer risk. We examined whether common low-penetrance breast cancer susceptibility variants contribute to interindividual differences in mammographic density measures. METHODS: We established an international consortium (DENSNP) of 19 studies from 10 countries, comprising 16,895 Caucasian women, to conduct a pooled cross-sectional analysis of common breast cancer susceptibility variants in 14 independent loci and mammographic density measures. Dense and nondense areas, and percent density, were measured using interactive-thresholding techniques. Mixed linear models were used to assess the association between genetic variants and the square roots of mammographic density measures adjusted for study, age, case status, BMI, and menopausal status. RESULTS: Consistent with their breast cancer associations, the C-allele of rs3817198 in LSP1 was positively associated with both adjusted dense area (P = 0.00005) and adjusted percent density (P = 0.001), whereas the A-allele of rs10483813 in RAD51L1 was inversely associated with adjusted percent density (P = 0.003), but not with adjusted dense area (P = 0.07). CONCLUSION: We identified two common breast cancer susceptibility variants associated with mammographic measures of radiodense tissue in the breast gland. IMPACT: We examined the association of 14 established breast cancer susceptibility loci with mammographic density phenotypes within a large genetic consortium and identified two breast cancer susceptibility variants, LSP1-rs3817198 and RAD51L1-rs10483813, associated with mammographic measures and in the same direction as the breast cancer association.

Mammographic breast density and breast cancer: evidence of a shared genetic basis.

Percent mammographic breast density (PMD) is a strong heritable risk factor for breast cancer. However, the pathways through which this risk is mediated are still unclear. To explore whether PMD and breast cancer have a shared genetic basis, we identified genetic variants most strongly associated with PMD in a published meta-analysis of five genome-wide association studies (GWAS) and used these to construct risk scores for 3,628 breast cancer cases and 5,190 controls from the UK2 GWAS of breast cancer. The signed per-allele effect estimates of single-nucleotide polymorphisms (SNP) were multiplied with the respective allele counts in the individual and summed over all SNPs to derive the risk score for an individual. These scores were included as the exposure variable in a logistic regression model with breast cancer case-control status as the outcome. This analysis was repeated using 10 different cutoff points for the most significant density SNPs (1%-10% representing 5,222-50,899 SNPs). Permutation analysis was also conducted across all 10 cutoff points. The association between risk score and breast cancer was significant for all cutoff points from 3% to 10% of top density SNPs, being most significant for the 6% (2-sided P = 0.002) to 10% (P = 0.001) cutoff points (overall permutation P = 0.003). Women in the top 10% of the risk score distribution had a 31% increased risk of breast cancer [OR = 1.31; 95% confidence interval (CI), 1.08-1.59] compared with women in the bottom 10%. Together, our results show that PMD and breast cancer have a shared genetic basis that is mediated through a large number of common variants.