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Background Aromatase inhibitors (AI) can exert unfavorable effects on lipid profiles, but previous studies have reported inconsistent results. Given the intricate biological relationship between estrogen and lipid profiles, these mixed results may be explained in part by variation in genes encoding proteins involved in the drug's target and in estrogen metabolism and signaling. The purpose of this study was to investigate associations of single-nucleotide polymorphisms (SNP) in candidate genes with AI-mediated changes in lipid profiles. Methods We completed a prospective multicenter randomized observational open-label study to test the association of SNPs in candidate genes on biomarkers of estrogenic and anti-estrogenic activity in post-menopausal women with early breast cancer who were recommended adjuvant AI therapy. Eligible women were randomly assigned to exemestane or letrozole, and were followed for 2 years. We genotyped 137 SNPs from germ line DNA in the following candidate genes: ARVCF, COMT, CYP19A1, ESR1, ESR2, PGR, EP300, EZH2, NCOA1-3, NCOR1-2, NRIP, and PELP1. Lipid profiles including total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides (TG) were measured at baseline and 3 months after initiating AI. We conducted genetic association data analysis and multivariate linear regressions to analyze the genetic effects using dominant, recessive, and additive models. Multivariate analysis included age, body mass index, prior hormone replacement therapy, and prior tamoxifen. To adjust for multiple comparisons, only SNPs with a p Results We enrolled 502 women in to the study, but for this analysis we excluded women who did not have genetic data (n = 33), had incomplete data (n = 23), discontinued or crossed over AI therapy (n = 48), women not fasting at both time points (n = 89), or those on lipid-lowering medications (n = 162). A total of 200 women were evaluable (letrozole 107, exemestane 93). Lipid profiles in all patients (n = 200) at baseline and 3 months after initiating AI, respectively, were as follows: TC 204.9 and 203.3 (unchanged, p = 0.43); HDL 61.3 and 56.8 (decreased, p = 6.3E-10); LDL 122.2 and 124.6 (unchanged, p = 0.22); and TG 107.1 and 103.6 (unchanged, p = 0.26). Genetic association and multivariate analysis revealed that SNPs in ESR1 and NCOR1 are significantly associated with additional changes in lipid parameters as summarized in Table 1. Table 1.Significant findings of multivariate linear regressions analyzing genetic associations between candidate gene SNPs and lipid profiles of AI-treated women.CohortNumberSNP (gene)Minor Allele FrequencyLipid ParameterModel UsedMean Absolute Change (mg/dL)P-valueAll patients184rs9340958 (ESR1)0.07TCRecessive-2.250.0003Letrozole96rs9340958 (ESR1)0.07TCRecessive5.280.00009 101rs3020368 (ESR1)0.09TCRecessive6.350.00007Exemestane93rs3798758 (ESR1)0.03HDLDominant, additive-7.970.00001 88rs926848 (ESR1)0.03HDLDominant, additive-7.970.00002 93rs61753150 (NCOR1)0.01TGDominant, additive-11.630.00003 Conclusions Variants in genes involved in estrogen metabolism and signaling are associated with changes in lipid profiles in AI-treated women and should be validated in other studies. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P1-08-11.

Background: Change in breast density may predict outcome of women receiving adjuvant hormone therapy for breast cancer. We performed a prospective clinical trial to evaluate the impact of inherited variants in genes involved in oestrogen metabolism and signalling on change in mammographic percent density (MPD) with aromatase inhibitor (AI) therapy. Methods: Postmenopausal women with breast cancer who were initiating adjuvant AI therapy were enrolled onto a multicentre, randomised clinical trial of exemestane vs letrozole, designed to identify associations between AI-induced change in MPD and single-nucleotide polymorphisms in candidate genes. Subjects underwent unilateral craniocaudal mammography before and following 24 months of treatment. Results: Of the 503 enrolled subjects, 259 had both paired mammograms at baseline and following 24 months of treatment and evaluable DNA. We observed a statistically significant decrease in mean MPD from 17.1 to 15.1% (P

X-linked chronic granulomatous disease (X-CGD) is an inherited immunodeficiency with absent phagocyte NADPH-oxidase activity caused by defects in the gene-encoding gp91(phox). Here, we evaluated strategies for less intensive conditioning for gene therapy of genetic blood disorders without selective advantage for gene correction, such as might be used in a human X-CGD protocol. We compared submyeloablative with ablative irradiation as conditioning in murine X-CGD, examining engraftment, oxidase activity and vector integration in mice transplanted with marrow transduced with a gamma-retroviral vector for gp91(phox) expression. The frequency of oxidase-positive neutrophils in the donor population was unexpectedly higher in many 300 cGy-conditioned mice compared with lethally irradiated recipients, as was the fraction of vector-marked donor secondary CFU-S12. Vector integration sites in marrow, spleen and secondary CFU-S12 DNA from primary recipients were enriched for cancer-associated genes, including Evi1, and integrations in or near cancer-associated genes were more frequent in marrow and secondary CFU-S12 from 300 cGy-conditioned mice compared with fully ablated mice. These findings support the concept that vector integration can confer a selection bias, and suggest that the intensity of the conditioning regimen may further influence the effects of vector integration on clonal selection in post-transplant engraftment and hematopoiesis.

Understanding how genetic variation affects the molecular function of gene products is an emergent area of bioinformatic research. Here, we present updates to MutDB (http://www.mutdb.org), a tool aiming to aid bioinformatic studies by integrating publicly available databases of human genetic variation with molecular features and clinical phenotype data. MutDB, first developed in 2002, integrates annotated SNPs in dbSNP and amino acid substitutions in Swiss-Prot with protein structural information, links to scores that predict functional disruption and other useful annotations. Though these functional annotations are mainly focused on nonsynonymous SNPs, some information on other SNP types included in dbSNP is also provided. Additionally, we have developed a new functionality that facilitates KEGG pathway visualization of genes containing SNPs and a SNP query tool for visualizing and exporting sets of SNPs that share selected features based on certain filters.

Adjuvant therapy for hormone receptor (HR) positive postmenopausal breast cancer patients includes aromatase inhibitors (AI). While both the non-steroidal AI letrozole and the steroidal AI exemestane decrease serum estrogen concentrations, there is evidence that exemestane may be less detrimental to bone. We hypothesized that single nucleotide polymorphisms (SNP) predict effects of AIs on bone turnover. Early stage HR-positive breast cancer patients were enrolled in a randomized trial of exemestane versus letrozole. Effects of AI on bone mineral density (BMD) and bone turnover markers (BTM), and associations between SNPs in 24 candidate genes and changes in BMD or BTM were determined. Of the 503 enrolled patients, paired BMD data were available for 123 and 101 patients treated with letrozole and exemestane, respectively, and paired BTM data were available for 175 and 173 patients, respectively. The mean change in lumbar spine BMD was significantly greater for letrozole-treated (-3.2 %) compared to exemestane-treated patients (-1.0 %) (p = 0.0016). Urine N-telopeptide was significantly increased in patients treated with exemestane (p = 0.001) but not letrozole. Two SNPs (rs4870061 and rs9322335) in ESR1 and one SNP (rs10140457) in ESR2 were associated with decreased BMD in letrozole-treated patients. In the exemestane-treated patients, SNPs in ESR1 (Rs2813543) and CYP19A1 (Rs6493497) were associated with decreased bone density. Exemestane had a less negative impact on bone density compared to letrozole, and the effects of AI therapy on bone may be impacted by genetic variants in the ER pathway.

Non-synonymous single nucleotide polymorphisms (SNPs) and mutations have been associated with human phenotypes and disease. As more and more SNPs are mapped to phenotypes, understanding how these variations affect the function and expression of genes and gene products becomes an important endeavor. We have developed a set of tools to aid in the understanding of how amino acid substitutions affect protein structures. To do this, we have annotated SNPs in dbSNP and amino acid substitutions in Swiss-Prot with protein structural information, if available. We then developed a novel web interface to this data that allows for visualization of the location of these substitutions. We have also developed a web service interface to the dataset and developed interactive plugins for UCSF's Chimera structural modeling tool and PyMOL that integrate our annotations with these sophisticated structural visualization and modeling tools. The web services portal and plugins can be downloaded from http://www.lifescienceweb.org/ and the web interface is at http://www.mutdb.org/.

Summary: Retroviral integration has been implicated in several biomedical applications, including identification of cancer-associated genes and malignant transformation in gene therapy clinical trials. We introduce an efficient and scalable method for fast identification of viral vector integration sites from long read high-throughput sequencing. Individual sequence reads are masked to remove non-genomic sequence, aligned to the host genome and assembled into contiguous fragments used to pinpoint the position of integration. Availability and Implementation: The method is implemented in a publicly accessible web server platform, SeqMap 2.0, containing analysis tools and both private and shared lab workspaces that facilitate collaboration among researchers. Available at http://seqmap.compbio.iupui.edu/. Contact: troyhawk@iupui.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Up to 25 % of patients discontinue adjuvant aromatase inhibitor (AI) therapy due to intolerable symptoms. Predictors of which patients will be unable to tolerate these medications have not been defined. We hypothesized that inherited variants in candidate genes are associated with treatment discontinuation because of AI-associated toxicity. We prospectively evaluated reasons for treatment discontinuation in women with hormone receptor-positive breast cancer initiating adjuvant AI through a multicenter, prospective, randomized clinical trial of exemestane versus letrozole. Using multiple genetic models, we evaluated potential associations between discontinuation of AI therapy because of toxicity and 138 variants in 24 candidate genes, selected a priori, primarily with roles in estrogen metabolism and signaling. To account for multiple comparisons, statistical significance was defined as p < 0.00036. Of the 467 enrolled patients with available germline DNA, 152 (33 %) discontinued AI therapy because of toxicity. Using a recessive statistical model, an intronic variant in ESR1 (rs9322336) was associated with increased risk of musculoskeletal toxicity-related exemestane discontinuation [HR 5.0 (95 % CI 2.1-11.8), p < 0.0002]. An inherited variant potentially affecting estrogen signaling may be associated with exemestane-associated toxicity, which could partially account for intra-patient differences in AI tolerability. Validation of this finding is required.

To determine the impact of maternal and fetal single nucleotide polymorphisms in key betamethasone pathways on neonatal outcomes.DNA was obtained from women given betamethasone and their infants. Samples were genotyped for 73 exploratory drug metabolism and glucocorticoid pathway single nucleotide polymorphisms. Clinical variables and neonatal outcomes were obtained. Logistic regression analysis using relevant clinical variables and genotypes to model for associations with neonatal respiratory distress syndrome was performed.One hundred nine women delivering 117 infants were analyzed. Sixty-four infants (49%) developed respiratory distress syndrome. Multivariable analysis revealed that respiratory distress syndrome was associated with maternal single nucleotide polymorphisms in CYP3A5 (odds ratio [OR], 1.63; 95% confidence interval [CI], 1.16-2.30) and the glucocorticoid resistance (OR, 0.28; 95% CI, 0.08-0.95) and fetal single nucleotide polymorphisms in ADCY9 (OR, 0.17; 95% CI, 0.03-0.80) and CYP3A7*1E (rs28451617; OR, 23.68; 95% CI, 1.33-420.6).Maternal and fetal genotypes are independently associated with neonatal respiratory distress syndrome after treatment with betamethasone for preterm labor.

Research in gene therapy involving genome-integrating vectors now often includes analysis of vector integration sites across the genome using methods such as ligation-mediated PCR (LM-PCR) or linear amplification-mediated PCR (LAM-PCR). To help researchers analyze these sites and the functions of nearby genes, we have developed SeqMap (http://seqmap.compbio.iupui.edu/) a secure, web-based comprehensive vector integration site management tool that automatically analyzes and annotates large numbers of vector integration sites derived from LM-PCR experiments in human and model organisms upon a common genome database. We believe the use of this resource will enable better reproducibility and understanding of this important data.

We previously demonstrated that maternal and fetal genotypes are associated independently with neonatal respiratory distress syndrome. The objective of the current study was to determine the impact of maternal and fetal single-nucleotide polymorphisms (SNPs) in key betamethasone pathways on respiratory outcomes that serve as markers for severity of disease.DNA was obtained from women who were given betamethasone and from their infants. Samples were genotyped for 73 exploratory drug metabolism and glucocorticoid pathway SNPs. Clinical variables and neonatal outcomes were obtained. Logistic regression analysis that controlled for relevant clinical variables to determine SNP impact on bronchopulmonary dysplasia (BPD), the need for respiratory support, and surfactant therapy use was performed.Data from 109 women who delivered 117 infants were analyzed: 14.5% of the infants experienced BPD; 70.8% of the infants needed some respiratory support after birth, and 27.5% of the infants needed surfactant therapy. In a multivariable regression analysis, gestational age at delivery was associated with most neonatal respiratory outcomes (P ≤ .01), and chorioamnionitis was associated with BPD (P.03). The following genotypes were associated with respiratory severity outcomes: BPD-fetal Importin 13 gene (IPO13; rs4448553; odds ratio [OR], 0.01; 95% confidence interval [CI], 0.00-0.92); surfactant use-maternal IPO13 (rs2428953 and 2486014; OR, 13.8; 95% CI, 1.80-105.5; and OR, 35.5; 95% CI, 1.71-736.6, respectively).Several discrete maternal and fetal SNPs in the IPO13 family may be associated with neonatal respiratory outcomes after maternal antenatal corticosteroid treatment for anticipated preterm birth.

525 Background: Discontinuation of adjuvant AI therapy due to intolerable symptoms occurs in up to 30% of patients. Predictors of which patients will be unable to tolerate these medications have not been defined. We hypothesized that inherited variants in candidate genes are associated with treatment discontinuation because of AI-associated toxicity. Methods: We prospectively evaluated reasons for treatment discontinuation in women with hormone receptor positive breast cancer initiating adjuvant AI through a multicenter, prospective, randomized clinical trial of exemestane (exe) versus letrozole (let). Using multiple genetic models, we evaluated potential associations between 136 single nucleotide polymorphisms (SNP) in 24 candidate genes, selected a priori, primarily with roles in estrogen metabolism and signaling, and discontinuation of AI therapy because of toxicity. To account for multiple comparisons, statistical significance was defined as p

4645 Background: PCa risk is multi-factorial with both genetic and environmental influences. Candidate SNPs identifying risk of lethal PCa have been poorly characterized. This case control study wa...