Background 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. 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 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. Results 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). Conclusion 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.

Objective To determine the impact of maternal and fetal single nucleotide polymorphisms (SNPs) in key betamethasone (BMZ) pathways on neonatal outcomes. Study design DNA was obtained from women given BMZ and their infants. Samples were genotyped for 73 exploratory drug metabolism and glucocorticoid pathway SNPs. 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 (RDS) was performed. Results 109 women delivering 117 babies were analyzed. Sixty-four babies (49%) developed RDS. Multivariable analysis revealed that RDS was associated with maternal SNPs in CYP3A5 (OR 1.63, 95%CI 1.16–2.30) and the glucocorticoid receptor (OR 0.28, 95%CI0.08–0.95) and fetal SNPs 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 RDS after treatment with BMZ for preterm labor.

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 and the web interface is at .

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.eduSupplementary information: Supplementary data are available at Bioinformatics online.