Identification of a DMBT1 polymorphism associated with increased breast cancer risk and decreased promoter activity

According to present estimations, the unfavorable combination of alleles with low penetrance but high prevalence in the population might account for the major part of hereditary breast cancer risk. Deleted in Malignant Brain Tumors 1 (DMBT1) has been proposed as a tumor suppressor for breast cancer and other cancer types. Genomewide mapping in mice further identified Dmbt1 as a potential modulator of breast cancer risk. Here, we report the association of two frequent and linked single-nucleotide polymorphisms (SNPs) with increased breast cancer risk in women above the age of 60 years: DMBT1 c.-93C>T, rs2981745, located in the DMBT1 promoter; and DMBT1 c.124A>C, p.Thr42Pro, rs11523871(odds ratio [OR]=1.66, 95% confidence interval [CI]=1.21-2.29, P=0.0017; and OR=1.66; 95% CI=1.21-2.28, P=0.0016, respectively), based on 1,195 BRCA1/2 mutation-negative German breast cancer families and 1,466 unrelated German controls. Promoter studies in breast cancer cells demonstrate that the risk-increasing DMBT1 -93T allele displays significantly decreased promoter activity compared to the DMBT1 -93C allele, resulting in a loss of promoter activity. The data suggest that DMBT1 polymorphisms in the 5'-region are associated with increased breast cancer risk. In accordance with previous results, these data link decreased DMBT1 levels to breast cancer risk

Predicting industrial-scale cell culture seed trains – a bayesian framework for model fitting and parameter estimation, dealing with uncertainty in measurements and model parameters, applied to a nonlinear kinetic cell culture model, using an MCMC method

Biotechnology and Bioengineering Published by Wiley Periodicals, Inc. For production of biopharmaceuticals in suspension cell culture, seed trains are required to increase cell number from cell thawing up to production scale. Because cultivation conditions during the seed train have a significant impact on cell performance in production scale, seed train design, monitoring, and development of optimization strategies is important. This can be facilitated by model-assisted prediction methods, whereby the performance depends on the prediction accuracy, which can be improved by inclusion of prior process knowledge, especially when only few high-quality data is available, and description of inference uncertainty, providing, apart from a “best fit”-prediction, information about the probable deviation in form of a prediction interval. This contribution illustrates the application of Bayesian parameter estimation and Bayesian updating for seed train prediction to an industrial Chinese hamster ovarian cell culture process, coppled with a mechanistic model. It is shown in which way prior knowledge as well as input uncertainty (e.g., concerning measurements) can be included and be propagated to predictive uncertainty. The impact of available information on prediction accuracy was investigated. It has been shown that through integration of new data by the Bayesian updating method, process variability (i.e., batch-to-batch) could be considered. The implementation was realized using a Markov chain Monte Carlo method

Identification of a DMBT1 Polymorphism Associated with Increased Breast Cancer Risk and Decreased Promoter Activity

According to present estimations, the unfavorable combination of alleles with low penetrance but high prevalence in the population might account for the major part of hereditary breast cancer risk. Deleted in Malignant Brain Tumors 1 (DMBT1) has been proposed as a tumor suppressor for breast cancer and other cancer types. Genomewide mapping in mice further identified Dmbt1 as a potential modulator of breast cancer risk. Here, we report the association of two frequent and linked single-nucleotide polymorphisms (SNPs) with increased breast cancer risk in women above the age of 60 years: DMBT1 c.-93C>T, rs2981745, located in the DMBT1 promoter; and DMBT1 c.124A>C, p.Thr42Pro, rs11523871(odds ratio [OR] = 1.66, 95% confidence interval [CI] = 1.21-2.29, P = 0.0017; and OR = 1.66; 95% CI = 1.21-2.28, P = 0.0016, respectively), based on 1,195 BRCA1/2 mutation-negative German breast cancer families and 1,466 unrelated German controls. Promoter studies in breast cancer cells demonstrate that the riskincreasing DMBT1 -93T allele displays significantly decreased promoter activity compared to the DMBT1 -93C allele, resulting in a loss of promoter activity. The data suggest that DMBT1 polymorphisms in the 50′-region are associated with increased breast cancer risk. In accordance with previous results, these data link decreased DMBT1 levels to breast cancer risk

Associations of genetic variants in the estrogen receptor coactivators PPARGC1A, PPARGC1B and EP300 with familial breast cancer

The mitogen effect of the ovarian steroid estrogen is a strong risk factor for breast cancer development. This effect is mainly mediated by the estrogen receptor alpha, a hormone inducible transcription factor, which activates gene expression through recruiting multiple coactivators, such as PPARGC1A, PPARGC1B and EP300. We tested the hypothesis that non-conservative, putative functional amino acid exchanges in PPARGC1A, PPARGC1B and EP300 act as low-penetrance familial breast cancer risk factors. The analysis of 816 BRCA1/2 mutation-negative familial breast cancer patients and 1012 controls revealed an association of the PPARGC1A Thr612Met polymorphism with familial breast cancer (OR = 1.35, 95% CI 1.00-1.81, P = 0.049), high-risk familial breast cancer (OR = 1.51, 95% CI 1.08-2.12, P = 0.017) and bilateral familial breast cancer (OR = 2.30, 95% CI 1.24-4.28, P = 0.009). Logistic regression analyses of the PPARGC1B Ala203Pro variant showed an increased familial breast cancer risk of heterozygous and homozygous variant allele carriers (OR = 1.48, 95% CI 1.15-1.91, P = 0.002). The genotype-combination analysis of the associated PPARGC1A Thr612Met variant and the associated PPARGC1B Ala203Pro variant suggests an allele dose-dependent breast cancer risk (P(trend) = 0.0004). Our results indicate for the first time the importance of inherited variants in the estrogen receptor coactivator genes PPARGC1A and PPARGC1B for familial breast cancer susceptibility. Owing to their impact on estrogen signaling, these polymorphisms might also influence adjuvant anti-estrogen therapy, using agents such as tamoxifen and raloxifen, and outcome of breast cancer patients