Stefan Nickels

Obesity and risk of ovarian cancer subtypes: evidence from the Ovarian Cancer Association Consortium

Whilst previous studies have reported that higher BMI increases a womans risk of developing ovarian cancer, associations for the different histological subtypes have not been well defined. As the prevalence of obesity has increased dramatically, and classification of ovarian histology has improved in the last decade, we sought to examine the association in a pooled analysis of recent studies participating in the Ovarian Cancer Association Consortium. We evaluated the association between BMI (recent, maximum and in young adulthood) and ovarian cancer risk using original data from 15 case-control studies (13 548 cases and 17 913 controls). We combined study-specific adjusted odds ratios (ORs) using a random-effects model. We further examined the associations by histological subtype, menopausal status and post-menopausal hormone use. High BMI (all time-points) was associated with increased risk. This was most pronounced for borderline serous (recent BMI: pooled OR=1.24 per 5 kg/m(2); 95% CI 1.18-1.30), invasive endometrioid (1.17; 1.11-1.23) and invasive mucinous (1.19; 1.06-1.32) tumours. There was no association with serous invasive cancer overall (0.98; 0.94-1.02), but increased risks for low-grade serous invasive tumours (1.13, 1.03-1.25) and in pre-menopausal women (1.11; 1.04-1.18). Among post-menopausal women, the associations did not differ between hormone replacement therapy users and non-users. Whilst obesity appears to increase risk of the less common histological subtypes of ovarian cancer, it does not increase risk of high-grade invasive serous cancers, and reducing BMI is therefore unlikely to prevent the majority of ovarian cancer deaths. Other modifiable factors must be identified to control this disease.

The role of genetic breast cancer susceptibility variants as prognostic factors

Recent genome-wide association studies identified 11 single nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk. We investigated these and 62 other SNPs for their prognostic relevance. Confirmed BC risk SNPs rs17468277 (CASP8), rs1982073 (TGFB1), rs2981582 (FGFR2), rs13281615 (8q24), rs3817198 (LSP1), rs889312 (MAP3K1), rs3803662 (TOX3), rs13387042 (2q35), rs4973768 (SLC4A7), rs6504950 (COX11) and rs10941679 (5p12) were genotyped for 25 853 BC patients with the available follow-up; 62 other SNPs, which have been suggested as BC risk SNPs by a GWAS or as candidate SNPs from individual studies, were genotyped for replication purposes in subsets of these patients. Cox proportional hazard models were used to test the association of these SNPs with overall survival (OS) and BC-specific survival (BCS). For the confirmed loci, we performed an accessory analysis of publicly available gene expression data and the prognosis in a different patient group. One of the 11 SNPs, rs3803662 (TOX3) and none of the 62 candidate/GWAS SNPs were associated with OS and/or BCS at P<0.01. The genotypic-specific survival for rs3803662 suggested a recessive mode of action [hazard ratio (HR) of rare homozygous carriers=1.21; 95% CI: 1.09-1.35, P=0.0002 and HR=1.29; 95% CI: 1.12-1.47, P=0.0003 for OS and BCS, respectively]. This association was seen similarly in all analyzed tumor subgroups defined by nodal status, tumor size, grade and estrogen receptor. Breast tumor expression of these genes was not associated with prognosis. With the exception of rs3803662 (TOX3), there was no evidence that any of the SNPs associated with BC susceptibility were associated with the BC survival. Survival may be influenced by a distinct set of germline variants from those influencing susceptibility.

BALL - biochemical algorithms library 1.3

BackgroundThe Biochemical Algorithms Library (BALL) is a comprehensive rapid application development framework for structural bioinformatics. It provides an extensive C++ class library of data structures and algorithms for molecular modeling and structural bioinformatics. Using BALL as a programming toolbox does not only allow to greatly reduce application development times but also helps in ensuring stability and correctness by avoiding the error-prone reimplementation of complex algorithms and replacing them with calls into the library that has been well-tested by a large number of developers. In the ten years since its original publication, BALL has seen a substantial increase in functionality and numerous other improvements.ResultsHere, we discuss BALLs current functionality and highlight the key additions and improvements: support for additional file formats, molecular edit-functionality, new molecular mechanics force fields, novel energy minimization techniques, docking algorithms, and support for cheminformatics.ConclusionsBALL is available for all major operating systems, including Linux, Windows, and MacOS X. It is available free of charge under the Lesser GNU Public License (LPGL). Parts of the code are distributed under the GNU Public License (GPL). BALL is available as source code and binary packages from the project web site at http://www.ball-project.org. Recently, it has been accepted into the debian project; integration into further distributions is currently pursued.

Functional Polymorphisms in the TERT Promoter Are Associated with Risk of Serous Epithelial Ovarian and Breast Cancers

Genetic variation at the TERT-CLPTM1L locus at 5p15.33 is associated with susceptibility to several cancers, including epithelial ovarian cancer (EOC). We have carried out fine-mapping of this region in EOC which implicates an association with a single nucleotide polymorphism (SNP) within the TERT promoter. We demonstrate that the minor alleles at rs2736109, and at an additional TERT promoter SNP, rs2736108, are associated with decreased breast cancer risk, and that the combination of both SNPs substantially reduces TERT promoter activity.

Mortality and recurrence risk in relation to the use of lipid-lowering drugs in a prospective breast cancer patient cohort.

Lipid-lowering drugs are used for the prevention of cardiovascular diseases. Statins are the most commonly used lipid-lowering drugs. Evidence from preclinical and observational studies suggests that statins might improve the prognosis of breast cancer patients. We analyzed data from the German MARIEplus study, a large prospective population-based cohort of patients aged 50 and older, who were diagnosed with breast cancer between 2001 and 2005. For overall mortality, breast-cancer specific mortality, and non-breast-cancer mortality, we included 3189 patients with invasive breast cancer stage I–IV, and for recurrence risk 3024 patients with breast cancer stage I–III. We used Cox proportional hazards models to assess the association with self-reported lipid-lowering drug use at recruitment. We stratified by study region, tumor grade, and estrogen/progesterone receptor status, and adjusted for age, tumor size, nodal status, metastases (stage I–IV only), menopausal hormone treatment, mode of detection, radiotherapy, and smoking. Mortality analyses were additionally adjusted for cardiovascular disease, diabetes mellitus and body-mass index. During a median follow-up of 5.3 years, 404 of 3189 stage I–IV patients died, and 286 deaths were attributed to breast cancer. Self-reported use of lipid-lowering drugs was non-significantly associated with increased non-breast cancer mortality (Hazard ratio (HR) 1.49, 95% confidence interval (CI) 0.88–2.52) and increased overall mortality (HR 1.21, 95% CI 0.87–1.69) whereas no association with breast cancer-specific mortality was found (HR 1.04, 0.67–1.60). Restricted to stage I–III breast cancer patients, 387 recurrences occurred during a median follow-up of 5.4 years. We found lipid-lowering drug use to be non-significantly associated with a reduced risk of recurrence (HR 0.83, 95% CI 0.54–1.24) and of breast cancer-specific mortality (HR 0.89, 95% CI 0.52–1.49). Although compatible with previous findings of an improved prognosis associated with statin use, our results do not provide clear supportive evidence for an association with lipid-lowering drug use due to imprecise estimates.

Combined and Interactive Effects of Environmental and GWAS-Identified Risk Factors in Ovarian Cancer

Background: There are several well-established environmental risk factors for ovarian cancer, and recent genome-wide association studies have also identified six variants that influence disease risk. However, the interplay between such risk factors and susceptibility loci has not been studied. Methods: Data from 14 ovarian cancer case–control studies were pooled, and stratified analyses by each environmental risk factor with tests for heterogeneity were conducted to determine the presence of interactions for all histologic subtypes. A genetic “risk score” was created to consider the effects of all six variants simultaneously. A multivariate model was fit to examine the association between all environmental risk factors and genetic risk score on ovarian cancer risk. Results: Among 7,374 controls and 5,566 cases, there was no statistical evidence of interaction between the six SNPs or genetic risk score and the environmental risk factors on ovarian cancer risk. In a main effects model, women in the highest genetic risk score quartile had a 65% increased risk of ovarian cancer compared with women in the lowest [95% confidence interval (CI), 1.48–1.84]. Analyses by histologic subtype yielded risk differences across subtype for endometriosis (Phet < 0.001), parity (Phet < 0.01), and tubal ligation (Phet = 0.041). Conclusions: The lack of interactions suggests that a multiplicative model is the best fit for these data. Under such a model, we provide a robust estimate of the effect of each risk factor that sets the stage for absolute risk prediction modeling that considers both environmental and genetic risk factors. Further research into the observed differences in risk across histologic subtype is warranted. Cancer Epidemiol Biomarkers Prev; 22(5); 880–90. ©2013 AACR.

New insights into the genetics of primary open-angle glaucoma based on meta-analyses of intraocular pressure and optic disc characteristics

Primary open-angle glaucoma (POAG), the most common optic neuropathy, is a heritable disease. Siblings of POAG cases have a ten-fold increased risk of developing the disease. Intraocular pressure (IOP) and optic nerve head characteristics are used clinically to predict POAG risk. We conducted a genome-wide association meta-analysis of IOP and optic disc parameters and validated our findings in multiple sets of POAG cases and controls. Using imputation to the 1000 genomes (1000G) reference set, we identified 9 new genomic regions associated with vertical cup-disc ratio (VCDR) and 1 new region associated with IOP. Additionally, we found 5 novel loci for optic nerve cup area and 6 for disc area. Previously it was assumed that genetic variation influenced POAG either through IOP or via changes to the optic nerve head; here we present evidence that some genomic regions affect both IOP and the disc parameters. We characterized the effect of the novel loci through pathway analysis and found that pathways involved are not entirely distinct as assumed so far. Further, we identified a novel association between CDKN1A and POAG. Using a zebrafish model we show that six6b (associated with POAG and optic nerve head variation) alters the expression of cdkn1a. In summary, we have identified several novel genes influencing the major clinical risk predictors of POAG and showed that genetic variation in CDKN1A is important in POAG risk.

Measuring properties of molecular surfaces using ray casting

Molecular geometric properties, such as volume, exposed surface area, and occurrence of internal cavities, are important inputs to many applications in molecular modeling. In this work we describe a very general and highly efficient approach for the accurate computation of such properties, which is applicable to arbitrary molecular surface models. The technique relies on a high performance ray casting framework that can be easily adapted to the computation of further quantities of interest at interactive speed, even for huge models.

ProteinScanAR - An Augmented Reality Web Application for High School Education in Biomolecular Life Sciences

Understanding protein structures is a crucial step in creating molecular insight for researchers as well as students and pupils. The enormous scaling gap between an atomic point of view and objects in daily life hampers developing an intuitive relation between them. Especially for high school students, it can be difficult to understand the spatial relations of a protein structure. Due to lack of direct imaging techniques, molecules can only be explored by studying abstract molecular models. Here, the use of Augmented reality (AR) techniques has proven to strongly improve structural perception. In this work we present ProteinScanAR, an augmented reality framework for biomolecular education that allows connecting virtual and real worlds intuitively, and thus enables focusing on the scientific or educational content. Special attention was taken to guarantee implementational and technical requirements as general and simple as possible to alleviate application in nonexpert computer settings. The ProteinScanAR framework is freely available under the GNU Public License (GPL).

Genome-Wide Association Study for Ovarian Cancer Susceptibility Using Pooled DNA

Recent Genome-Wide Association Studies (GWAS) have identified four low-penetrance ovarian cancer susceptibility loci. We hypothesized that further moderate- or low-penetrance variants exist among the subset of single-nucleotide polymorphisms (SNPs) not well tagged by the genotyping arrays used in the previous studies, which would account for some of the remaining risk. We therefore conducted a time- and cost-effective stage 1 GWAS on 342 invasive serous cases and 643 controls genotyped on pooled DNA using the high-density Illumina 1M-Duo array. We followed up 20 of the most significantly associated SNPs, which are not well tagged by the lower density arrays used by the published GWAS, and genotyping them on individual DNA. Most of the top 20 SNPs were clearly validated by individually genotyping the samples used in the pools. However, none of the 20 SNPs replicated when tested for association in a much larger stage 2 set of 4,651 cases and 6,966 controls from the Ovarian Cancer Association Consortium. Given that most of the top 20 SNPs from pooling were validated in the same samples by individual genotyping, the lack of replication is likely to be due to the relatively small sample size in our stage 1 GWAS rather than due to problems with the pooling approach. We conclude that there are unlikely to be any moderate or large effects on ovarian cancer risk untagged by less dense arrays. However, our study lacked power to make clear statements on the existence of hitherto untagged small-effect variants.