Impact of long-term lipid-lowering therapy on clinical outcomes in breast cancer.

INTRODUCTION: The use of statins has been associated with improved survival in patients with breast cancer in several studies but results have been mixed. This study evaluates the impact of duration of statin use on breast cancer patient outcomes. METHODS: This is a single-institution, retrospective cohort, examining the impact of statin use on the outcomes of 1523 women diagnosed with operable breast cancer between1995 and 2015. Clinical variables were compared using Student's t test, Fisher's exact and Chi square tests. Overall (OS) and disease-free (DFS) survival were performed using Kaplan-Meier and Cox-Proportional Hazard (Cox-PH) analysis in the statistical software R. RESULTS: Patients were grouped by duration of statin use: never-statin user [N] (n = 1092), short (< 3 years) [S] (n = 115), moderate [M] (3-5 years) (n = 109) and long [L] (> 5 years) (n = 207) term. Over a median follow-up of 70.2 months, 138 women died (84 died of breast cancer) and 125 had disease recurrence. On multivariable Cox-PH analysis adjusting for clinical variables including metabolic comorbidities using the Charlson comorbidity index, OS in the [S] and [M] subgroups did not differ [N], while OS was improved in [L] (adjusted hazard ratio (AHR) 0.38, confidence interval (CI) 0.17-0.85, p < 0.018). DFS was also significantly improved in the [L] subgroup (adjusted HR 0.15, CI 0.05-0.48, p < 0.001). Subanalysis stratified by receptor status showed a trend towards improved DFS in all tumor subtypes including triple-negative breast cancer. CONCLUSIONS: Our retrospective analyses suggest that long-term statin use (> 5 years) was associated with improved OS and DFS in women with breast cancer regardless of receptor subtype, even after adjusting for metabolic comorbidities.

The impact of aspirin use on breast cancer subtype and clinical course.

BACKGROUND: The use of aspirin has been associated with improved survival in patients with breast cancer, but the results have been mixed. We aim to analyze the impact of aspirin use before or after breast cancer diagnosis on breast cancer clinical characteristics and outcomes. MATERIALS AND METHODS: We performed a single-institution, retrospective analysis of 1113 women diagnosed with operable breast cancer between 1995 and 2015. Patients were grouped according to their aspirin use: never (944), before diagnosis (79), and after diagnosis (90). Clinical variables, overall survival (OS), and disease-free survival (DFS) were compared between groups. RESULTS: Women using aspirin before diagnosis were older, more likely to be black, and to have associated medical comorbidities than patients in other groups (all P <0.001). These patients were also more likely to present with hormone receptor-negative cancers, including triple-negative breast cancer (P = 0.002). Aspirin use before diagnosis was associated with a worse OS in univariate and multivariate analyses (both P <0.001), but there were no other differences in OS or DFS related to aspirin use. CONCLUSIONS: Despite a potential impact on tumor subtype in patients using aspirin before their breast cancer diagnosis, aspirin use does not appear to alter breast cancer-related survival.

Breast cancer subtype distribution is different in normal weight, overweight, and obese women.

PURPOSE: Obesity is associated with tumor promoting pathways related to insulin resistance and chronic low-grade inflammation which have been linked to various disease states, including cancer. Many studies have focused on the relationship between obesity and increased estrogen production, which contributes to the pathogenesis of estrogen receptor-positive breast cancers. The link between obesity and other breast cancer subtypes, such as triple-negative breast cancer (TNBC) and Her2/neu+ (Her2+) breast cancer, is less clear. We hypothesize that obesity may be associated with the pathogenesis of specific breast cancer subtypes resulting in a different subtype distribution than normal weight women. METHODS: A single-institution, retrospective analysis of tumor characteristics of 848 patients diagnosed with primary operable breast cancer between 2000 and 2013 was performed to evaluate the association between BMI and clinical outcome. Patients were grouped based on their BMI at time of diagnosis stratified into three subgroups: normal weight (BMI = 18-24.9), overweight (BMI = 25-29.9), and obese (BMI > 30). The distribution of breast cancer subtypes across the three BMI subgroups was compared. RESULTS: Obese and overweight women were more likely to present with TNBC and normal weight women with Her2+ breast cancer (p = 0.008). CONCLUSIONS: We demonstrated, for the first time, that breast cancer subtype distribution varied significantly according to BMI status. Our results suggested that obesity might activate molecular pathways other than the well-known obesity/estrogen circuit in the pathogenesis of breast cancer. Future studies are needed to understand the molecular mechanisms that drive the variation in subtype distribution across BMI subgroups.

Concept and design of a genome-wide association genotyping array tailored for transplantation-specific studies.

BACKGROUND: In addition to HLA genetic incompatibility, non-HLA difference between donor and recipients of transplantation leading to allograft rejection are now becoming evident. We aimed to create a unique genome-wide platform to facilitate genomic research studies in transplant-related studies. We designed a genome-wide genotyping tool based on the most recent human genomic reference datasets, and included customization for known and potentially relevant metabolic and pharmacological loci relevant to transplantation. METHODS: We describe here the design and implementation of a customized genome-wide genotyping array, the 'TxArray', comprising approximately 782,000 markers with tailored content for deeper capture of variants across HLA, KIR, pharmacogenomic, and metabolic loci important in transplantation. To test concordance and genotyping quality, we genotyped 85 HapMap samples on the array, including eight trios. RESULTS: We show low Mendelian error rates and high concordance rates for HapMap samples (average parent-parent-child heritability of 0.997, and concordance of 0.996). We performed genotype imputation across autosomal regions, masking directly genotyped SNPs to assess imputation accuracy and report an accuracy of >0.962 for directly genotyped SNPs. We demonstrate much higher capture of the natural killer cell immunoglobulin-like receptor (KIR) region versus comparable platforms. Overall, we show that the genotyping quality and coverage of the TxArray is very high when compared to reference samples and to other genome-wide genotyping platforms. CONCLUSIONS: We have designed a comprehensive genome-wide genotyping tool which enables accurate association testing and imputation of ungenotyped SNPs, facilitating powerful and cost-effective large-scale genotyping of transplant-related studies.