Polymorphisms in the UBC9 and PIAS3 genes of the SUMO-conjugating system and breast cancer risk.

SUMOylation consists in the covalent conjugation of small ubiquitin-related modifiers to target proteins. SUMOylation participates in processes that are tightly linked to tumorigenesis, and genetic variability in the SUMO-conjugating system may influence the development of breast cancer. We recently reported that variation in the UBC9 gene encoding the SUMO-conjugating enzyme may affect the grade of breast tumors. Following comprehensive in silico analyses for detection of putative functional polymorphisms in 14 genes of the SUMO system, we selected one coding SNP in PIAS3 and seven tag SNPs in UBC9 for association analyses. Results were based on 1,021 cases, and 1,015 matched controls from the population-based GENICA study. Odds ratios (OR) and 95% confidence intervals (CI) were estimated by conditional logistic regression. To explore the association with polymorphisms closely linked to the genotyped variants, multiple imputation based on HapMap data was applied. The study revealed associations of four UBC9 polymorphisms with risk of grade 1 tumors. Comparison of genotype and haplotype models indicated that the best representation of risk solely relied on rs7187167 under dominant penetrance. Women carrying the rare allele showed an increased risk of grade 1 tumors compared with common homozygotes (OR 1.87, 95% CI 1.18-2.95). This effect appeared to be stronger in women with a family history of breast or ovarian cancer. Imputation of polymorphisms in a 300-kb region around the genotyped polymorphisms identified no variants with stronger associations. Our findings suggest that genetic variation in UBC9 may affect the risk of grade 1 breast tumors.

Common variants in the UBC9 gene encoding the SUMO-conjugating enzyme are associated with breast tumor grade.

UBC9 encodes a protein that conjugates small ubiquitin-related modifier (SUMO) to target proteins resulting in a change of their localization, activity or stability. Genetic variability may affect expression and activity of UBC9 and may have an impact on breast tumor progression. We investigated associations between UBC9 genotypes and histopathological parameters in 1,021 breast cancer cases of the GENICA collection using a single nucleotide polymorphism (SNP) tagging approach. Genotyping analyses were performed by TaqMan(R) allelic discrimination. Odds ratios (OR) and 95% confidence intervals (CI) were calculated by ordinal logistic regression. Multiple imputation based on HapMap data was applied to boost the power of the study. The study revealed significant associations of three UBC9 SNPs with histological grade (rs7187167, p(trend) = 0.001; rs11248866, p(trend) = 0.009; rs8052688, p(trend) = 0.008). Model selection identified a recessive penetrance model for rs7187167 as the best representation of tumor grade (global p = 0.001). This model did not improve by inclusion of additional SNPs in linkage disequilibrium. Imputation of SNPs in a 300 kb region around the genotyped SNPs supported rs7187167 as a major contributor to tumor grade. Compared with common allele carriers, rare homozygotes presented less frequently with high grade tumors (G3 vs. G1: OR 0.26, 95% CI 0.11-0.62; G3 vs. G2: OR 0.45, 95% CI 0.23-0.86). In addition to tumor size, nodal status and estrogen receptor status, multivariate analyses confirmed an independent role of rs7187167 as predictor of tumor grade (p = 0.0003). The present results underline the value of genetic variation in UBC9 for breast cancer prognosis.

Polymorphic loci of E2F2, CCND1 and CCND3 are associated with HER2 status of breast tumors.

Overexpression of the human epidermal growth factor receptor 2 (HER2) in breast tumors is associated with bad prognosis. Therefore, it is highly relevant to further improve understanding of the regulatory mechanisms of HER2 expression. In addition to gene amplification, transcriptional regulation plays a crucial role in HER2 overexpression. In this study, we analyzed 3 polymorphisms E2F2_-5368_A>G, CCND1_870_A>G and CCND3_-677_C>T located in genes involved in cell cycle regulation in the GENICA population-based and age-matched breast cancer case-control study from Germany. We genotyped 1,021 cases and 1,015 controls by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Statistical analyses were performed by conditional logistic regression. We observed no differences in genotype frequencies between breast cancer cases and controls. Subgroup analysis showed associations between carriers of the E2F2_-5368_G allele (OR: 0.60, 95% CI: 0.42-0.85), carriers of the CCND1_870_G allele (OR: 0.66, 95% CI: 0.45-0.96) and carriers of the CCND3_-677_T allele (OR: 1.72, 95% CI: 1.20-2.49) and HER2 expression in breast tumors. This finding points to an association of an increased expression of these cell cycle regulators with lower expression of HER2. An explanation for this observation might be that low expression of E2F2, CCND1 and CCND3 decrease levels of factors down-regulating HER2. We conclude that the analyzed polymorphisms located in E2F2, CCND1 and CCND3 are potential markers for HER2 status of breast tumors.

CYP2C19*17 is associated with decreased breast cancer risk.

Cytochrome P450 2C19 (CYP2C19) plays an important role in the metabolism of xenobiotics and drugs and contributes to the catabolism of endogenous substrates like estradiol. Genetic variability impacts expression and activity of CYP2C19 and therefore can influence catabolism of estrogens. In the present study we analyzed the association of three polymorphisms of CYP2C19 namely CYP2C19*2 (CYP2C19_681_G>A, rs4244285), CYP2C19*3 (CYP2C19_636_G>A, rs57081121) and CYP2C19*17 (CYP2C19_-806_C>T, rs12248560), with breast cancer susceptibility. We genotyped 1,015 breast cancer cases and 1,021 age-matched, population-based controls of the German GENICA study by matrix assisted laser desorption/ionization time-of-flight mass spectrometry. Risk estimates were calculated by logistic regression. All tests were two-sided. We observed a decreased breast cancer risk for carriers of the CYP2C19*17 allele (OR 0.77, 95% CI: 0.65-0.93; P = 0.005). In subgroup analysis we observed a significant decreased breast cancer risk for women using hormone therapy for ten years or longer who were carriers of the CYP2C19*17 allele (OR 0.57, 95% CI: 0.39-0.83; P = 0.003). Since CYP2C19*17 defines an ultra rapid metabolizer phenotype we suggest that an increased catabolism of estrogens by CYP2C19 may lead to decreased estrogen levels and therefore reduces breast cancer risk. This protective effect seems to be stronger in combination with long-term intake of supplemental estrogens during hormone therapy.

The CYP1B1_1358_GG genotype is associated with estrogen receptor-negative breast cancer.

Cytochrome P450 1B1 (CYP1B1) is a major enzyme in the initial catabolic step of estradiol (E2) metabolism and belongs to the multitude of genes regulated by the estrogen receptor alpha (ERalpha). The common non-synonymous polymorphisms CYP1B1_1358_A>G and CYP1B1_1294_C>G increase CYP1B1 enzymatic activity. Given a relationship between CYP1B1 and breast tumor E2 level as well as E2 level and breast tumor ERalpha expression it is of interest to know whether CYP1B1 polymorphisms have an impact on the ERalpha status of breast cancer. We genotyped the GENICA population-based breast cancer case-control collection (1,021 cases, 1,015 controls) by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and investigated in cases the association between genotypes and tumor ERalpha status (739 ERalpha positive cases; 212 ERalpha negative cases) by logistic regression. We observed a significant association between the homozygous variant CYP1B1_1358_GG genotype and negative ERalpha status (P = 0.005; OR 2.82, 95% CI: 1.37-5.82) with a highly significant Ptrend for CYP1B1_1358_A>G and negative ERalpha status (P = 0.003). We also observed an association of CYP1B1_1358_GG and negative PR status (P = 0.015; OR 2.36, 95% CI: 1.18-4.70) and a Ptrend of 0.111 for CYP1B1_1358_A>G and negative progesterone receptor (PR) status. We conclude that the CYP1B1_1358_A>G polymorphism has an impact on ERalpha status in breast cancer in that the CYP1B1_1358_GG genotype known to encode higher CYP1B1 activity is associated with ERalpha negativity.

Breast cancer: a candidate gene approach across the estrogen metabolic pathway.

Polymorphisms within the estrogen metabolic pathway are prime candidates for a possible association with breast cancer risk. We investigated 11 genes encoding key proteins of this pathway for their potential contribution to breast cancer risk. Of these CYP17A1, CYP19A1, EPHX1, HSD17B1, SRD5A2, and PPARG2 participate in biosynthesis, CYP1A1, CYP1B1, COMT, GSTP1, and SOD2 in catabolism and detoxification. We performed a population-based case-control study with 688 incident breast cancer cases and 724 controls from Germany and genotyped 18 polymorphisms by matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), PCR based RFLP (restriction fragment length polymorphism), and TaqMan allelic discrimination. Genotype frequencies were compared between cases and controls and odds ratios were calculated by conditional logistic regression. Further statistical analyses were based on cluster analysis, multifactor dimensionality reduction, logic regression, and global testing. Single factor analyses pointed to CYP1B1_1294_GG as a possible breast cancer risk modulator (OR = 2.57; 95% CI: 1.34-4.93) and two way stratification suggested associations between BMI > or = 30 kg/m(2) and COMT_472_GG (P = 0.0076 and P = 0.0026), BMI < 20 kg/m(2) and HSD17B1_937_GG (P = 0.0082) as well as CYP17A1_-34_CC and HRT use > or =10 years (P = 0.0063). Following correction for multiple testing none of these associations remained significant. No significant association between breast cancer risk and genetic polymorphisms was observed in multifactor analyses. The tested polymorphisms of the estrogen metabolic pathway may not play a direct role in breast cancer risk. Therefore, future association studies should be extended to other polymorphisms and other regulatory pathways.

German populations with infrequent CHEK2*1100delC and minor associations with early-onset and familial breast cancer.

CHEK2*1100delC is associated with a twofold increased breast cancer risk. This was shown in a collaborative analysis of European populations, but not in other populations from Europe and the US. Accordingly, there is a need to clarify the role of CHEK2*1100delC in breast cancer. We established its prevalence in two German populations GENICA (Northrhine-Westphalia, n = 724) and KORA (Bavaria, n = 600) and in women with breast cancer. The latter included cases (n = 688) from the GENICA breast cancer case-control study, patients with early-onset breast cancer (n = 86) and patients with familial breast cancer (n = 71). The latter patient groups were previously investigated for BRCA1/2-mutations and tested negative. Mutation analysis was performed by combined PCR/DHPLC methodology. CHEK2*1100delC was found in 0.9% of GENICA controls and was absent in the KORA controls indicating a significant difference between the two populations (P= 0.03). The frequency of CHEK2*1100delC in age-matched cases of the GENICA collection was 0.8% and thus not different from controls (OR 0.88, 95% CI 0.21-3.50). In patients with early-onset disease CHEK2*1100delC was found at a frequency of 2.3% referring to an increased breast cancer risk of 2.56 (95% CI 0.25-14.58). In patients with familial disease the frequency was 1.4% referring to an increased risk of 1.53 (95% CI 0.03-12.93). Our data showed variations in CHEK2*1100delC prevalence within German populations suggesting possible inaccuracies in breast cancer risk assessments from non population-based studies. In patients with a high-risk profile however, CHEK2*1100delC was indicative for this risk and highest for early-onset breast cancer.