Associations between genetic variants and the effect of letrozole and exemestane on bone mass and bone turnover.

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.

Genetic associations with toxicity-related discontinuation of aromatase inhibitor therapy for breast cancer.

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.

The impact of glucocorticoid polymorphisms on markers of neonatal respiratory disease after antenatal betamethasone administration.

OBJECTIVE: 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. STUDY DESIGN: 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. RESULTS: 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). CONCLUSION: 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.

Identifying viral integration sites using SeqMap 2.0.

UNLABELLED: 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/.

The impact of drug metabolizing enzyme polymorphisms on outcomes after antenatal corticosteroid use.

OBJECTIVE: To determine the impact of maternal and fetal single nucleotide polymorphisms in key betamethasone pathways on neonatal outcomes. STUDY DESIGN: 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. RESULTS: 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). CONCLUSION: Maternal and fetal genotypes are independently associated with neonatal respiratory distress syndrome after treatment with betamethasone for preterm labor.

MutDB: update on development of tools for the biochemical analysis of genetic variation.

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.

MutDB services: interactive structural analysis of mutation data.

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/.

Identification of multiple rare variants associated with a disease.

Identifying rare variants that are responsible for complex disease has been promoted by advances in sequencing technologies. However, statistical methods that can handle the vast amount of data generated and that can interpret the complicated relationship between disease and these variants have lagged. We apply a zero-inflated Poisson regression model to take into account the excess of zeros caused by the extremely low frequency of the 24,487 exonic variants in the Genetic Analysis Workshop 17 data. We grouped the 697 subjects in the data set as Europeans, Asians, and Africans based on principal components analysis and found the total number of rare variants per gene for each individual. We then analyzed these collapsed variants based on the assumption that rare variants are enriched in a group of people affected by a disease compared to a group of unaffected people. We also tested the hypothesis with quantitative traits Q1, Q2, and Q4. Analyses performed on the combined 697 individuals and on each ethnic group yielded different results. For the combined population analysis, we found that UGT1A1, which was not part of the simulation model, was associated with disease liability and that FLT1, which was a causal locus in the simulation model, was associated with Q1. Of the causal loci in the simulation models, FLT1 and KDR were associated with Q1 and VNN1 was correlated with Q2. No significant genes were associated with Q4. These results show the feasibility and capability of our new statistical model to detect multiple rare variants influencing disease risk.