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BRCA1-associated breast and ovarian cancer risks can be modified by common genetic variants. To identify further cancer risk-modifying loci, we performed a multi-stage GWAS of 11,705 BRCA1 carriers (of whom 5,920 were diagnosed with breast and 1,839 were diagnosed with ovarian cancer), with a further replication in an additional sample of 2,646 BRCA1 carriers. We identified a novel breast cancer risk modifier locus at 1q32 for BRCA1 carriers (rs2290854, P = 2.7 x 10(-8), HR = 1.14, 95% CI: 1.09-1.20). In addition, we identified two novel ovarian cancer risk modifier loci: 17q21.31 (rs17631303, P = 1.4 x 10(-8), HR = 1.27, 95% CI: 1.17-1.38) and 4q32.3 (rs4691139, P = 3.4 x 10(-8), HR = 1.20, 95% CI: 1.17-1.38). The 4q32.3 locus was not associated with ovarian cancer risk in the general population or BRCA2 carriers, suggesting a BRCA1-specific association. The 17q21.31 locus was also associated with ovarian cancer risk in 8,211 BRCA2 carriers (P = 2 x 10(-4)). These loci may lead to an improved understanding of the etiology of breast and ovarian tumors in BRCA1 carriers. Based on the joint distribution of the known BRCA1 breast cancer risk-modifying loci, we estimated that the breast cancer lifetime risks for the 5% of BRCA1 carriers at lowest risk are 28%-50% compared to 81%-100% for the 5% at highest risk. Similarly, based on the known ovarian cancer risk-modifying loci, the 5% of BRCA1 carriers at lowest risk have an estimated lifetime risk of developing ovarian cancer of 28% or lower, whereas the 5% at highest risk will have a risk of 63% or higher. Such differences in risk may have important implications for risk prediction and clinical management for BRCA1 carriers.
While interplay between BRCA1 and AURKA-RHAMM-TPX2-TUBG1 regulates mammary epithelial polarization, common genetic variation in HMMR (gene product RHAMM) may be associated with risk of breast cancer in BRCA1 mutation carriers. Following on these observations, we further assessed the link between the AURKA-HMMR-TPX2-TUBG1 functional module and risk of breast cancer in BRCA1 or BRCA2 mutation carriers. Forty-one single nucleotide polymorphisms (SNPs) were genotyped in 15,252 BRCA1 and 8,211 BRCA2 mutation carriers and subsequently analyzed using a retrospective likelihood approach. The association of HMMR rs299290 with breast cancer risk in BRCA1 mutation carriers was confirmed: per-allele hazard ratio (HR) = 1.10, 95% confidence interval (CI) 1.04 - 1.15, p = 1.9 x 10\(^{-4}\) (false discovery rate (FDR)-adjusted p = 0.043). Variation in CSTF1, located next to AURKA, was also found to be associated with breast cancer risk in BRCA2 mutation carriers: rs2426618 per-allele HR = 1.10, 95% CI 1.03 - 1.16, p = 0.005 (FDR-adjusted p = 0.045). Assessment of pairwise interactions provided suggestions (FDR-adjusted p\(_{interaction}\) values > 0.05) for deviations from the multiplicative model for rs299290 and CSTF1 rs6064391, and rs299290 and TUBG1 rs11649877 in both BRCA1 and BRCA2 mutation carriers. Following these suggestions, the expression of HMMR and AURKA or TUBG1 in sporadic breast tumors was found to potentially interact, influencing patients' survival. Together, the results of this study support the hypothesis of a causative link between altered function of AURKA-HMMR-TPX2-TUBG1 and breast carcinogenesis in BRCA1/2 mutation carriers.
Genome-wide association studies (GWAS) have identified more than 170 breast cancer susceptibility loci. Here we hypothesize that some risk-associated variants might act in non-breast tissues, specifically adipose tissue and immune cells from blood and spleen. Using expression quantitative trait loci (eQTL) reported in these tissues, we identify 26 previously unreported, likely target genes of overall breast cancer risk variants, and 17 for estrogen receptor (ER)-negative breast cancer, several with a known immune function. We determine the directional effect of gene expression on disease risk measured based on single and multiple eQTL. In addition, using a gene-based test of association that considers eQTL from multiple tissues, we identify seven (and four) regions with variants associated with overall (and ER-negative) breast cancer risk, which were not reported in previous GWAS. Further investigation of the function of the implicated genes in breast and immune cells may provide insights into the etiology of breast cancer.
Long-term effects of migalastat therapy in clinical practice are currently unknown. We evaluated migalastat efficacy and biomarker changes in a prospective, single-center study on 14 patients with Fabry disease (55 ± 14 years; 11 men). After 1 year of open-label migalastat therapy, patients showed significant changes in alpha-galactosidase-A activity (0.06–0.2 nmol/minute/mg protein; P = 0.001), left ventricular myocardial mass index (137–130 g/m2; P = 0.037), and serum creatinine (0.94–1.0 mg/dL; P = 0.021), accounting for deterioration in estimated glomerular filtration rate (87–78 mL/minute/1.73 m2; P = 0.012). The enzymatic increase correlated with myocardial mass reduction (r = −0.546; P = 0.044) but not with renal function (r = −0.086; P = 0.770). Plasma globotriaosylsphingosine was reduced in therapy-naive patients (10.9–6.0 ng/mL; P = 0.021) and stable (9.6–12.1 ng/mL; P = 0.607) in patients switched from prior enzyme-replacement therapy. These first real-world data show that migalastat substantially increases alpha-galactosidase-A activity, stabilizes related serum biomarkers, and improves cardiac integrity in male and female patients with amenable Fabry disease mutations.
The 7th International Symposium on Neuroprotection and Neurorepair was held from May 2nd to May 5th, 2012 in Potsdam, Germany. The symposium, which directly continues the successful Magdeburg meeting series, attracted over 330 colleagues from 29 countries to discuss recent findings and advances in the field. The focus of the 2012 symposium was widened from stroke and traumatic brain injury to neurodegenerative diseases, notably dementia, and more generally the ageing brain. Thereby, emphasis was given on neurovascular aspects of neurodegeneration and stroke including the blood–brain barrier, recent findings regarding the pathomechanism of Alzheimer’s disease, and brain imaging approaches. In addition, neurobiochemical aspects of neuroprotection, the role of astrogliosis, the clinical progress of cell-based approaches as well as translational hurdles and opportunities were discussed in-depth. This review summarizes some of the most stimulating discussions and reports from the meeting.
Tuberculosis (TB) is one of the leading causes of death by an infectious disease. It remains a major health burden worldwide, in part due to misdiagnosis. Therefore, improved diagnostic tests allowing the faster and more reliable diagnosis of patients with active TB are urgently needed. This prospective study examined the performance of the new molecular whole-blood test T-Track\(^®\) TB, which relies on the combined evaluation of IFNG and CXCL10 mRNA levels, and compared it to that of the QuantiFERON\(^®\)-TB Gold Plus (QFT-Plus) enzyme-linked immunosorbent assay (ELISA). Diagnostic accuracy and agreement analyses were conducted on the whole blood of 181 active TB patients and 163 non-TB controls. T-Track\(^®\) TB presented sensitivity of 94.9% and specificity of 93.8% for the detection of active TB vs. non-TB controls. In comparison, the QFT-Plus ELISA showed sensitivity of 84.3%. The sensitivity of T-Track\(^®\) TB was significantly higher (p < 0.001) than that of QFT-Plus. The overall agreement of T-Track\(^®\) TB with QFT-Plus to diagnose active TB was 87.9%. Out of 21 samples with discordant results, 19 were correctly classified by T-Track\(^®\) TB while misclassified by QFT-Plus (T-Track\(^®\) TB-positive/QFT-Plus-negative), and two samples were misclassified by T-Track\(^®\) TB while correctly classified by QFT-Plus (T-Track\(^®\) TB-negative/QFT-Plus-positive). Our results demonstrate the excellent performance of the T-Track\(^®\) TB molecular assay and its suitability to accurately detect TB infection and discriminate active TB patients from non-infected controls.
Background
X-chromosomal inheritance patterns and generally rare occurrence of Fabry disease (FD) account for mono-mutational hemizygous male and heterozygous female patients. Female mutation carriers are usually clinically much less severely affected, which has been explained by a suggested mosaicism in cell phenotype due to random allele shutdown. However, clinical evidence is scarce and potential additional effects in female gene carriers, which might account for specific clinical characteristics such as less severe chronic kidney disease, are yet unknown.
Case presentation
This article reports on a 45 year old female patient carrying the two alpha-galactosidase A gene mutations c.416A > G, p.N139S in exon 3 and c.708G > C, p.W236C in exon 5, but still showing only mild organ manifestations.
Conclusion
This current case highlights the importance of careful clinical characterization in patients with Fabry disease, who may show additional rare constellations and, therefore, are in need of personalized medicine. The impact of potential additional protective effects exceeding the presence of a non-pathogenic GLA allele in female gene carriers requires further investigation.
The analysis of real data by means of statistical methods with the aid of a software package common in industry and administration usually is not an integral part of mathematics studies, but it will certainly be part of a future professional work. The present book links up elements from time series analysis with a selection of statistical procedures used in general practice including the statistical software package SAS. Consequently this book addresses students of statistics as well as students of other branches such as economics, demography and engineering, where lectures on statistics belong to their academic training. But it is also intended for the practician who, beyond the use of statistical tools, is interested in their mathematical background. Numerous problems illustrate the applicability of the presented statistical procedures, where SAS gives the solutions. The programs used are explicitly listed and explained. No previous experience is expected neither in SAS nor in a special computer system so that a short training period is guaranteed. This book is meant for a two semester course (lecture, seminar or practical training) where the first three chapters can be dealt within the first semester. They provide the principal components of the analysis of a time series in the time domain. Chapters 4, 5 and 6 deal with its analysis in the frequency domain and can be worked through in the second term. In order to understand the mathematical background some terms are useful such as convergence in distribution, stochastic convergence, maximum likelihood estimator as well as a basic knowledge of the test theory, so that work on the book can start after an introductory lecture on stochastics. Each chapter includes exercises. An exhaustive treatment is recommended. Chapter 7 (case study) deals with a practical case and demonstrates the presented methods. It is possible to use this chapter independent in a seminar or practical training course, if the concepts of time series analysis are already well understood. This book is consecutively subdivided in a statistical part and an SAS-specific part. For better clearness the SAS-specific parts are highlighted. This book is an open source project under the GNU Free Documentation License.
The analysis of real data by means of statistical methods with the aid of a software package common in industry and administration usually is not an integral part of mathematics studies, but it will certainly be part of a future professional work. The present book links up elements from time series analysis with a selection of statistical procedures used in general practice including the statistical software package SAS. Consequently this book addresses students of statistics as well as students of other branches such as economics, demography and engineering, where lectures on statistics belong to their academic training. But it is also intended for the practician who, beyond the use of statistical tools, is interested in their mathematical background. Numerous problems illustrate the applicability of the presented statistical procedures, where SAS gives the solutions. The programs used are explicitly listed and explained. No previous experience is expected neither in SAS nor in a special computer system so that a short training period is guaranteed. This book is meant for a two semester course (lecture, seminar or practical training) where the first three chapters can be dealt within the first semester. They provide the principal components of the analysis of a time series in the time domain. Chapters 4, 5 and 6 deal with its analysis in the frequency domain and can be worked through in the second term. In order to understand the mathematical background some terms are useful such as convergence in distribution, stochastic convergence, maximum likelihood estimator as well as a basic knowledge of the test theory, so that work on the book can start after an introductory lecture on stochastics. Each chapter includes exercises. An exhaustive treatment is recommended. Chapter 7 (case study) deals with a practical case and demonstrates the presented methods. It is possible to use this chapter independent in a seminar or practical training course, if the concepts of time series analysis are already well understood. This book is consecutively subdivided in a statistical part and an SAS-specific part. For better clearness the SAS-specific parts are highlighted. This book is an open source project under the GNU Free Documentation License.