Citations

Purpose We performed a case-control genome-wide association study (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with musculoskeletal adverse events (MS-AEs) in women treated with aromatase inhibitors (AIs) for early breast cancer. Patients and Methods A nested case-control design was used to select patients enrolled onto the MA.27 phase III trial comparing anastrozole with exemestane. Cases were matched to two controls and were defined as patients with grade 3 or 4 MS-AEs (according to the National Cancer Institute's Common Terminology Criteria for Adverse Events v3.0) or those who discontinued treatment for any grade of MS-AE within the first 2 years. Genotyping was performed with the Illumina Human610-Quad BeadChip. Results The GWAS included 293 cases and 585 controls. A total of 551,358 SNPs were analyzed, followed by imputation and fine mapping of a region of interest on chromosome 14. Four SNPs on chromosome 14 had the lowest P values (2.23E-06 to 6.67E-07). T-cell leukemia 1A (TCL1A) was the gene closest (926-7000 bp) to the four SNPs. Functional genomic studies revealed that one of these SNPs (rs11849538) created an estrogen response element and that TCL1A expression was estrogen dependent, was associated with the variant SNP genotypes in estradiol-treated lymphoblastoid cells transfected with estrogen receptor alpha and was directly related to interleukin 17 receptor A (IL17RA) expression. Conclusion This GWAS identified SNPs associated with MS-AEs in women treated with AIs and with a gene (TCL1A) which, in turn, was related to a cytokine (IL17). These findings provide a focus for further research to identify patients at risk for MS-AEs and to explore the mechanisms for these adverse events.

Abstract The Division of Lung Diseases of the National Heart, Lung, and Blood Institute, with the Office of Rare Diseases Research, held a workshop to identify priority areas and strategic goals to enhance and accelerate research that will result in improved understanding of the lung vasculature, translational research needs, and ultimately the care of patients with pulmonary vascular diseases. Multidisciplinary experts with diverse experience in laboratory, translational, and clinical studies identified seven priority areas and discussed limitations in our current knowledge, technologies, and approaches. The focus for future research efforts include the following: (1) better characterizing vascular genotype–phenotype relationships and incorporating systems biology approaches when appropriate; (2) advancing our understanding of pulmonary vascular metabolic regulatory signaling in health and disease; (3) expanding our knowledge of the biologic relationships between the lung circulation and circulating elements, systemic vascular function, and right heart function and disease; (4) improving translational research for identifying disease-modifying therapies for the pulmonary hypertensive diseases; (5) establishing an appropriate and effective platform for advancing translational findings into clinical studies testing; and (6) developing the specific technologies and tools that will be enabling for these goals, such as question-guided imaging techniques and lung vascular investigator training programs. Recommendations from this workshop will be used within the Lung Vascular Biology and Disease Extramural Research Program for planning and strategic implementation purposes.

Purpose Accurate breast cancer risk assessment is vital to personalize screening and risk reduction strategies. Women with atypical hyperplasia have a four-fold higher risk of breast cancer. We evaluated the performance of the Tyrer-Cuzick model, which was designed to predict 10-year risk of breast cancer development, in a well-defined cohort of women with atypia. Patients and Methods The Mayo Benign Breast Disease cohort includes 9,376 women who had a benign breast biopsy between 1967 and 1991. Among those, 331 women with atypia were identified by our study pathologists. Risk factor data for the Tyrer-Cuzick model were collated for each woman and used to predict individual risk of developing invasive breast cancer within 10 years. Results Over a median follow-up of 14.6 years, 64 (19%) of the 331 women developed invasive breast cancer. In the first 10 years after biopsy, 31 women developed invasive breast cancer whereas the Tyrer-Cuzick model predicted 58.9. The observed-to-predicted ratio was 0.53 (95% CI, 0.37 to 0.75). The concordance statistic was 0.540, revealing that the Tyrer-Cuzick model did not accurately distinguish, on an individual level, between women who developed invasive breast cancer and those who did not. Conclusion The Tyrer-Cuzick model significantly overestimated risk of breast cancer for women with atypia, and individual risk estimates showed poor concordance between predicted risk and invasive breast cancer development. Thus, we cannot recommend the use of the Tyrer-Cuzick model to predict 10-year breast cancer risk in women with atypical hyperplasia.

Purpose Accurate, individualized risk prediction for breast cancer is lacking. Tissue-based features may help to stratify women into different risk levels. Breast lobules are the anatomic sites of origin of breast cancer. As women age, these lobular structures should regress, which results in reduced breast cancer risk. However, this does not occur in all women. Methods We have quantified the extent of lobule regression on a benign breast biopsy in 85 patients who developed breast cancer and 142 age-matched controls from the Mayo Benign Breast Disease Cohort, by determining number of acini per lobule and lobular area. We also calculated Gail model 5-year predicted risks for these women. Results There is a step-wise increase in breast cancer risk with increasing numbers of acini per lobule (P = .0004). Adjusting for Gail model score, parity, histology, and family history did not attenuate this association. Lobular area was similarly associated with risk. The Gail model estimates were associated with risk of breast cancer (P = .03). We examined the individual accuracy of these measures using the concordance (c) statistic. The Gail model c statistic was 0.60 (95% CI, 0.50 to 0.70); the acinar count c statistic was 0.65 (95% CI, 0.54 to 0.75). Combining acinar count and lobular area, the c statistic was 0.68 (95% CI, 0.58 to 0.78). Adding the Gail model to these measures did not improve the c statistic. Conclusion Novel, tissue-based features that reflect the status of a woman's normal breast lobules are associated with breast cancer risk. These features may offer a novel strategy for risk prediction.