Disentangling the relationships of body mass index and circulating sex hormone concentrations in mammographic density using Mendelian randomization.

PURPOSE: Mammographic density phenotypes, adjusted for age and body mass index (BMI), are strong predictors of breast cancer risk. BMI is associated with mammographic density measures, but the role of circulating sex hormone concentrations is less clear. We investigated the relationship between BMI, circulating sex hormone concentrations, and mammographic density phenotypes using Mendelian randomization (MR). METHODS: We applied two-sample MR approaches to assess the association between genetically predicted circulating concentrations of sex hormones [estradiol, testosterone, sex hormone-binding globulin (SHBG)], BMI, and mammographic density phenotypes (dense and non-dense area). We created instrumental variables from large European ancestry-based genome-wide association studies and applied estimates to mammographic density phenotypes in up to 14,000 women of European ancestry. We performed analyses overall and by menopausal status. RESULTS: Genetically predicted BMI was positively associated with non-dense area (IVW: ß = 1.79; 95% CI = 1.58, 2.00; p = 9.57 × 10-63) and inversely associated with dense area (IVW: ß = - 0.37; 95% CI = - 0.51,- 0.23; p = 4.7 × 10-7). We observed weak evidence for an association of circulating sex hormone concentrations with mammographic density phenotypes, specifically inverse associations between genetically predicted testosterone concentration and dense area (ß = - 0.22; 95% CI = - 0.38, - 0.053; p = 0.009) and between genetically predicted estradiol concentration and non-dense area (ß = - 3.32; 95% CI = - 5.83, - 0.82; p = 0.009), although results were not consistent across a range of MR approaches. CONCLUSION: Our findings support a positive causal association between BMI and mammographic non-dense area and an inverse association between BMI and dense area. Evidence was weaker and inconsistent for a causal effect of circulating sex hormone concentrations on mammographic density phenotypes. Based on our findings, associations between circulating sex hormone concentrations and mammographic density phenotypes are weak at best.

Differences in polygenic score distributions in European ancestry populations: implications for breast cancer risk prediction.

The 313-variant polygenic risk score (PRS313) provides a promising tool for breast cancer risk prediction. However, evaluation of the PRS313 across different European populations which could influence risk estimation has not been performed. Here, we explored the distribution of PRS313 across European populations using genotype data from 94,072 females without breast cancer, of European-ancestry from 21 countries participating in the Breast Cancer Association Consortium (BCAC) and 225,105 female participants from the UK Biobank. The mean PRS313 differed markedly across European countries, being highest in south-eastern Europe and lowest in north-western Europe. Using the overall European PRS313 distribution to categorise individuals leads to overestimation and underestimation of risk in some individuals from south-eastern and north-western countries, respectively. Adjustment for principal components explained most of the observed heterogeneity in mean PRS. Country-specific PRS distributions may be used to calibrate risk categories in individuals from different countries.

A genome-wide gene-environment interaction study of breast cancer risk for women of European ancestry.

BACKGROUND: Genome-wide studies of gene-environment interactions (G×E) may identify variants associated with disease risk in conjunction with lifestyle/environmental exposures. We conducted a genome-wide G×E analysis of ~ 7.6 million common variants and seven lifestyle/environmental risk factors for breast cancer risk overall and for estrogen receptor positive (ER +) breast cancer. METHODS: Analyses were conducted using 72,285 breast cancer cases and 80,354 controls of European ancestry from the Breast Cancer Association Consortium. Gene-environment interactions were evaluated using standard unconditional logistic regression models and likelihood ratio tests for breast cancer risk overall and for ER + breast cancer. Bayesian False Discovery Probability was employed to assess the noteworthiness of each SNP-risk factor pairs. RESULTS: Assuming a 1 × 10-5 prior probability of a true association for each SNP-risk factor pairs and a Bayesian False Discovery Probability < 15%, we identified two independent SNP-risk factor pairs: rs80018847(9p13)-LINGO2 and adult height in association with overall breast cancer risk (ORint = 0.94, 95% CI 0.92-0.96), and rs4770552(13q12)-SPATA13 and age at menarche for ER + breast cancer risk (ORint = 0.91, 95% CI 0.88-0.94). CONCLUSIONS: Overall, the contribution of G×E interactions to the heritability of breast cancer is very small. At the population level, multiplicative G×E interactions do not make an important contribution to risk prediction in breast cancer.

Aggregation tests identify new gene associations with breast cancer in populations with diverse ancestry.

BACKGROUND: Low-frequency variants play an important role in breast cancer (BC) susceptibility. Gene-based methods can increase power by combining multiple variants in the same gene and help identify target genes. METHODS: We evaluated the potential of gene-based aggregation in the Breast Cancer Association Consortium cohorts including 83,471 cases and 59,199 controls. Low-frequency variants were aggregated for individual genes' coding and regulatory regions. Association results in European ancestry samples were compared to single-marker association results in the same cohort. Gene-based associations were also combined in meta-analysis across individuals with European, Asian, African, and Latin American and Hispanic ancestry. RESULTS: In European ancestry samples, 14 genes were significantly associated (q < 0.05) with BC. Of those, two genes, FMNL3 (P = 6.11 × 10-6) and AC058822.1 (P = 1.47 × 10-4), represent new associations. High FMNL3 expression has previously been linked to poor prognosis in several other cancers. Meta-analysis of samples with diverse ancestry discovered further associations including established candidate genes ESR1 and CBLB. Furthermore, literature review and database query found further support for a biologically plausible link with cancer for genes CBLB, FMNL3, FGFR2, LSP1, MAP3K1, and SRGAP2C. CONCLUSIONS: Using extended gene-based aggregation tests including coding and regulatory variation, we report identification of plausible target genes for previously identified single-marker associations with BC as well as the discovery of novel genes implicated in BC development. Including multi ancestral cohorts in this study enabled the identification of otherwise missed disease associations as ESR1 (P = 1.31 × 10-5), demonstrating the importance of diversifying study cohorts.

A likelihood ratio approach for utilizing case-control data in the clinical classification of rare sequence variants: application to BRCA1 and BRCA2.

A large number of variants identified through clinical genetic testing in disease susceptibility genes, are of uncertain significance (VUS). Following the recommendations of the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP), the frequency in case-control datasets (PS4 criterion), can inform their interpretation. We present a novel case-control likelihood ratio-based method that incorporates gene-specific age-related penetrance. We demonstrate the utility of this method in the analysis of simulated and real datasets. In the analyses of simulated data, the likelihood ratio method was more powerful compared to other methods. Likelihood ratios were calculated for a case-control dataset of BRCA1 and BRCA2 variants from the Breast Cancer Association Consortium (BCAC), and compared with logistic regression results. A larger number of variants reached evidence in favor of pathogenicity, and a substantial number of variants had evidence against pathogenicity - findings that would not have been reached using other case-control analysis methods. Our novel method provides greater power to classify rare variants compared to classical case-control methods. As an initiative from the ENIGMA Analytical Working Group, we provide user-friendly scripts and pre-formatted excel calculators for implementation of the method for rare variants in BRCA1, BRCA2 and other high-risk genes with known penetrance.

Physical activity, sedentary time and breast cancer risk: a Mendelian randomisation study.

OBJECTIVES: Physical inactivity and sedentary behaviour are associated with higher breast cancer risk in observational studies, but ascribing causality is difficult. Mendelian randomisation (MR) assesses causality by simulating randomised trial groups using genotype. We assessed whether lifelong physical activity or sedentary time, assessed using genotype, may be causally associated with breast cancer risk overall, pre/post-menopause, and by case-groups defined by tumour characteristics. METHODS: We performed two-sample inverse-variance-weighted MR using individual-level Breast Cancer Association Consortium case-control data from 130 957 European-ancestry women (69 838 invasive cases), and published UK Biobank data (n=91 105-377 234). Genetic instruments were single nucleotide polymorphisms (SNPs) associated in UK Biobank with wrist-worn accelerometer-measured overall physical activity (nsnps=5) or sedentary time (nsnps=6), or accelerometer-measured (nsnps=1) or self-reported (nsnps=5) vigorous physical activity. RESULTS: Greater genetically-predicted overall activity was associated with lower breast cancer overall risk (OR=0.59; 95% confidence interval (CI) 0.42 to 0.83 per-standard deviation (SD;~8 milligravities acceleration)) and for most case-groups. Genetically-predicted vigorous activity was associated with lower risk of pre/perimenopausal breast cancer (OR=0.62; 95% CI 0.45 to 0.87,≥3 vs. 0 self-reported days/week), with consistent estimates for most case-groups. Greater genetically-predicted sedentary time was associated with higher hormone-receptor-negative tumour risk (OR=1.77; 95% CI 1.07 to 2.92 per-SD (~7% time spent sedentary)), with elevated estimates for most case-groups. Results were robust to sensitivity analyses examining pleiotropy (including weighted-median-MR, MR-Egger). CONCLUSION: Our study provides strong evidence that greater overall physical activity, greater vigorous activity, and lower sedentary time are likely to reduce breast cancer risk. More widespread adoption of active lifestyles may reduce the burden from the most common cancer in women.

Genome- and transcriptome-wide association studies of 386,000 Asian and European-ancestry women provide new insights into breast cancer genetics.

By combining data from 160,500 individuals with breast cancer and 226,196 controls of Asian and European ancestry, we conducted genome- and transcriptome-wide association studies of breast cancer. We identified 222 genetic risk loci and 137 genes that were associated with breast cancer risk at a p < 5.0 × 10-8 and a Bonferroni-corrected p < 4.6 × 10-6, respectively. Of them, 32 loci and 15 genes showed a significantly different association between ER-positive and ER-negative breast cancer after Bonferroni correction. Significant ancestral differences in risk variant allele frequencies and their association strengths with breast cancer risk were identified. Of the significant associations identified in this study, 17 loci and 14 genes are located 1Mb away from any of the previously reported breast cancer risk variants. Pathways analyses including 221 putative risk genes identified multiple signaling pathways that may play a significant role in the development of breast cancer. Our study provides a comprehensive understanding of and new biological insights into the genetics of this common malignancy.

A genome-wide gene-based gene-environment interaction study of breast cancer in more than 90,000 women.

Background: Genome-wide association studies (GWAS) have identified more than 200 susceptibility loci for breast cancer, but these variants explain less than a fifth of the disease risk. Although gene-environment interactions have been proposed to account for some of the remaining heritability, few studies have empirically assessed this. Methods: We obtained genotype and risk factor data from 46,060 cases and 47,929 controls of European ancestry from population-based studies within the Breast Cancer Association Consortium (BCAC). We built gene expression prediction models for 4,864 genes with a significant (P<0.01) heritable component using the transcriptome and genotype data from the Genotype-Tissue Expression (GTEx) project. We leveraged predicted gene expression information to investigate the interactions between gene-centric genetic variation and 14 established risk factors in association with breast cancer risk, using a mixed-effects score test. Results: After adjusting for number of tests using Bonferroni correction, no interaction remained statistically significant. The strongest interaction observed was between the predicted expression of the C13orf45 gene and age at first full-term pregnancy (PGXE=4.44×10-6). Conclusion: In this transcriptome-informed genome-wide gene-environment interaction study of breast cancer, we found no strong support for the role of gene expression in modifying the associations between established risk factors and breast cancer risk. Impact: Our study suggests a limited role of gene-environment interactions in breast cancer risk.

Distinct Reproductive Risk Profiles for Intrinsic-Like Breast Cancer Subtypes: Pooled Analysis of Population-Based Studies.

BACKGROUND: Reproductive factors have been shown to be differentially associated with risk of estrogen receptor (ER)-positive and ER-negative breast cancer. However, their associations with intrinsic-like subtypes are less clear. METHODS: Analyses included up to 23 353 cases and 71 072 controls pooled from 31 population-based case-control or cohort studies in the Breast Cancer Association Consortium across 16 countries on 4 continents. Polytomous logistic regression was used to estimate the association between reproductive factors and risk of breast cancer by intrinsic-like subtypes (luminal A-like, luminal B-like, luminal B-HER2-like, HER2-enriched-like, and triple-negative breast cancer) and by invasiveness. All statistical tests were 2-sided. RESULTS: Compared with nulliparous women, parous women had a lower risk of luminal A-like, luminal B-like, luminal B-HER2-like, and HER2-enriched-like disease. This association was apparent only after approximately 10 years since last birth and became stronger with increasing time (odds ratio [OR] = 0.59, 95% confidence interval [CI] = 0.49 to 0.71; and OR = 0.36, 95% CI = 0.28 to 0.46 for multiparous women with luminal A-like tumors 20 to less than 25 years after last birth and 45 to less than 50 years after last birth, respectively). In contrast, parous women had a higher risk of triple-negative breast cancer right after their last birth (for multiparous women: OR = 3.12, 95% CI = 2.02 to 4.83) that was attenuated with time but persisted for decades (OR = 1.03, 95% CI = 0.79 to 1.34, for multiparous women 25 to less than 30 years after last birth). Older age at first birth (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) and breastfeeding (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) were associated with lower risk of triple-negative breast cancer but not with other disease subtypes. Younger age at menarche was associated with higher risk of all subtypes; older age at menopause was associated with higher risk of luminal A-like but not triple-negative breast cancer. Associations for in situ tumors were similar to luminal A-like. CONCLUSIONS: This large and comprehensive study demonstrates a distinct reproductive risk factor profile for triple-negative breast cancer compared with other subtypes, with implications for the understanding of disease etiology and risk prediction.

Relevance of the MHC region for breast cancer susceptibility in Asians.

BACKGROUND: Human leukocyte antigen (HLA) genes play critical roles in immune surveillance, an important defence against tumors. Imputing HLA genotypes from existing single-nucleotide polymorphism datasets is low-cost and efficient. We investigate the relevance of the major histocompatibility complex region in breast cancer susceptibility, using imputed class I and II HLA alleles, in 25,484 women of Asian ancestry. METHODS: A total of 12,901 breast cancer cases and 12,583 controls from 12 case-control studies were included in our pooled analysis. HLA imputation was performed using SNP2HLA on 10,886 quality-controlled variants within the 15-55 Mb region on chromosome 6. HLA alleles (n = 175) with info scores greater than 0.8 and frequencies greater than 0.01 were included (resolution at two-digit level: 71; four-digit level: 104). We studied the associations between HLA alleles and breast cancer risk using logistic regression, adjusting for population structure and age. Associations between HLA alleles and the risk of subtypes of breast cancer (ER-positive, ER-negative, HER2-positive, HER2-negative, early-stage, and late-stage) were examined. RESULTS: We did not observe associations between any HLA allele and breast cancer risk at P < 5e-8; the smallest p value was observed for HLA-C*12:03 (OR = 1.29, P = 1.08e-3). Ninety-five percent of the effect sizes (OR) observed were between 0.90 and 1.23. Similar results were observed when different subtypes of breast cancer were studied (95% of ORs were between 0.85 and 1.18). CONCLUSIONS: No imputed HLA allele was associated with breast cancer risk in our large Asian study. Direct measurement of HLA gene expressions may be required to further explore the associations between HLA genes and breast cancer risk.

Investigating the relationship between melatonin patterns and methylation in circadian genes among day shift and night shift workers.

OBJECTIVES: Mechanisms underlying the carcinogenicity of night shift work remain uncertain. One compelling yet understudied cancer mechanism may involve altered DNA methylation in circadian genes due to melatonin secretion patterns. The objective of this study was to explore the relationship between melatonin secretion patterns and circadian gene methylation among day and night shift workers. METHODS: Female healthcare employees (n=38 day workers, n=36 night shift workers) for whom we had urinary 6-sulfatoxymelatonin secretion data from a previous study were recontacted. New blood samples were collected and used to measure methylation levels at 1150 CpG loci across 22 circadian genes using the Illumina Infinium MethylationEPIC beadchip. Linear regression was used to examine the association between melatonin (acrophase and mesor) and M values for each CpG site (false discovery rate, q=0.2), while testing for effect modification by shift work status. RESULTS: Among night shift workers, a higher mesor (24 hours of mean production of melatonin) was associated with increased methylation in the body of RORA (q=0.02) and decreased methylation in the putative promoter region of MTNR1A (q=0.03). Later acrophase (ie, time of peak concentration) was associated with increased methylation in the putative promoter region of MTNR1A (q=0.20) and decreased methylation in the body of PER3 (q=0.20). No associations were identified among day workers. CONCLUSIONS: In conclusion, patterns in melatonin secretion were associated with differential circadian gene methylation among night shift workers. Melatonin and alteration of DNA methylation in circadian genes may be one pathway towards increased cancer risk, although larger-scale studies examining multiple time points are needed.

Genome-wide and transcriptome-wide association studies of mammographic density phenotypes reveal novel loci.

BACKGROUND: Mammographic density (MD) phenotypes, including percent density (PMD), area of dense tissue (DA), and area of non-dense tissue (NDA), are associated with breast cancer risk. Twin studies suggest that MD phenotypes are highly heritable. However, only a small proportion of their variance is explained by identified genetic variants. METHODS: We conducted a genome-wide association study, as well as a transcriptome-wide association study (TWAS), of age- and BMI-adjusted DA, NDA, and PMD in up to 27,900 European-ancestry women from the MODE/BCAC consortia. RESULTS: We identified 28 genome-wide significant loci for MD phenotypes, including nine novel signals (5q11.2, 5q14.1, 5q31.1, 5q33.3, 5q35.1, 7p11.2, 8q24.13, 12p11.2, 16q12.2). Further, 45% of all known breast cancer SNPs were associated with at least one MD phenotype at p < 0.05. TWAS further identified two novel genes (SHOX2 and CRISPLD2) whose genetically predicted expression was significantly associated with MD phenotypes. CONCLUSIONS: Our findings provided novel insight into the genetic background of MD phenotypes, and further demonstrated their shared genetic basis with breast cancer.

Genome-wide interaction analysis of menopausal hormone therapy use and breast cancer risk among 62,370 women.

Use of menopausal hormone therapy (MHT) is associated with increased risk for breast cancer. However, the relevant mechanisms and its interaction with genetic variants are not fully understood. We conducted a genome-wide interaction analysis between MHT use and genetic variants for breast cancer risk in 27,585 cases and 34,785 controls from 26 observational studies. All women were post-menopausal and of European ancestry. Multivariable logistic regression models were used to test for multiplicative interactions between genetic variants and current MHT use. We considered interaction p-values < 5 × 10-8 as genome-wide significant, and p-values < 1 × 10-5 as suggestive. Linkage disequilibrium (LD)-based clumping was performed to identify independent candidate variants. None of the 9.7 million genetic variants tested for interactions with MHT use reached genome-wide significance. Only 213 variants, representing 18 independent loci, had p-values < 1 × 105. The strongest evidence was found for rs4674019 (p-value = 2.27 × 10-7), which showed genome-wide significant interaction (p-value = 3.8 × 10-8) with current MHT use when analysis was restricted to population-based studies only. Limiting the analyses to combined estrogen-progesterone MHT use only or to estrogen receptor (ER) positive cases did not identify any genome-wide significant evidence of interactions. In this large genome-wide SNP-MHT interaction study of breast cancer, we found no strong support for common genetic variants modifying the effect of MHT on breast cancer risk. These results suggest that common genetic variation has limited impact on the observed MHT-breast cancer risk association.

Modeling the relationship between shift work and cardiometabolic risk through circadian disruption, sleep and stress pathways.

The purpose of this study is to elucidate the multiple pathways linking shift work exposure to cardiometabolic risk (CMR) through the intermediates of circadian disruption, sleep disturbances, and stress. A cross-sectional study was conducted at Kingston Health Sciences Center that included female hospital workers, 160 who worked a day-only schedule and 168 who worked rotating days and nights. Participants completed questionnaires, a clinical exam, and wore accelerometers to collect sleep data for 8 days. Participants also collected urine samples at each void during a 24-h collection period, on the day shift for day-only workers and the night shift for rotating shift workers, for cortisol and melatonin measures. We adapted and tested a conceptual model proposed by Knutsson and Boggild for circadian disruption, sleep, and stress mechanistic pathways linking shift work to CMR using structural equation modeling techniques. Status as a rotating shift worker was associated with increased circadian disruption of cortisol and melatonin production compared to day-only workers (P < .001). Increased circadian disruption was associated with an increased CMR (P = .01). Rotating shift work was associated with sleep disturbances (P = .002) and increased job stress (P < .001), but neither was associated with CMR. We conclude that rotating shift work is associated indirectly with increased CMR. This association is mediated by circadian disruption as indicated by attenuated melatonin and cortisol, and flatter cortisol curves.

DNA methylation of circadian genes and markers of cardiometabolic risk in female hospital workers: An exploratory study.

Night shift work has been linked to increased risk of cardiovascular disease (CVD); however, the underlying mechanisms remain unclear. A compelling yet understudied mechanism involves differential DNA methylation of circadian genes. To investigate the relevance of this mechanism, we conducted an exploratory cross-sectional study of 74 female hospital personnel (38 day workers, 36 night shift workers). Sociodemographic, lifestyle, and health characteristics as well as shift work status and history were determined through self-report. Fasting blood samples were collected to measure markers of cardiometabolic risk and DNA was extracted to measure DNA methylation of 1150 cytosine-guanine (CpG) sites across 22 circadian genes. Associations between methylation levels at individual CpG sites (ß-values) and markers of cardiometabolic risk were analyzed while considering effect modification by shift work status. The false discovery rate was applied to account for multiple comparisons (q ≤ 0.20). Two CpG sites [cg06758649 (CRY1) and cg06899802 (CSNK1A1)] were differentially associated with waist circumference and body mass index by shift work status, and eight CpG sites [cg26103512 (CSNK1D), cg03941313 (CSNK1E), cg18217763 (CSNK1E), cg16682686 (DEC1), cg12061096 (RORA), cg10133825 (RORA), cg19652148 (RORA), and cg22904654 (RORA)] were differentially associated with LDL cholesterol concentration by shift work status (all q ≤ 0.20). Our findings suggest that the relationship between DNA methylation of circadian genes and cardiometabolic risk differs by day and night shift worker status, which may contribute to mechanisms of increased risk of CVD observed among night shift workers.

Common variants in breast cancer risk loci predispose to distinct tumor subtypes.

BACKGROUND: Genome-wide association studies (GWAS) have identified multiple common breast cancer susceptibility variants. Many of these variants have differential associations by estrogen receptor (ER) status, but how these variants relate with other tumor features and intrinsic molecular subtypes is unclear. METHODS: Among 106,571 invasive breast cancer cases and 95,762 controls of European ancestry with data on 173 breast cancer variants identified in previous GWAS, we used novel two-stage polytomous logistic regression models to evaluate variants in relation to multiple tumor features (ER, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and grade) adjusting for each other, and to intrinsic-like subtypes. RESULTS: Eighty-five of 173 variants were associated with at least one tumor feature (false discovery rate < 5%), most commonly ER and grade, followed by PR and HER2. Models for intrinsic-like subtypes found nearly all of these variants (83 of 85) associated at p < 0.05 with risk for at least one luminal-like subtype, and approximately half (41 of 85) of the variants were associated with risk of at least one non-luminal subtype, including 32 variants associated with triple-negative (TN) disease. Ten variants were associated with risk of all subtypes in different magnitude. Five variants were associated with risk of luminal A-like and TN subtypes in opposite directions. CONCLUSION: This report demonstrates a high level of complexity in the etiology heterogeneity of breast cancer susceptibility variants and can inform investigations of subtype-specific risk prediction.

Rare germline copy number variants (CNVs) and breast cancer risk.

Germline copy number variants (CNVs) are pervasive in the human genome but potential disease associations with rare CNVs have not been comprehensively assessed in large datasets. We analysed rare CNVs in genes and non-coding regions for 86,788 breast cancer cases and 76,122 controls of European ancestry with genome-wide array data. Gene burden tests detected the strongest association for deletions in BRCA1 (P = 3.7E-18). Nine other genes were associated with a p-value < 0.01 including known susceptibility genes CHEK2 (P = 0.0008), ATM (P = 0.002) and BRCA2 (P = 0.008). Outside the known genes we detected associations with p-values < 0.001 for either overall or subtype-specific breast cancer at nine deletion regions and four duplication regions. Three of the deletion regions were in established common susceptibility loci. To the best of our knowledge, this is the first genome-wide analysis of rare CNVs in a large breast cancer case-control dataset. We detected associations with exonic deletions in established breast cancer susceptibility genes. We also detected suggestive associations with non-coding CNVs in known and novel loci with large effects sizes. Larger sample sizes will be required to reach robust levels of statistical significance.

Interactions between exposure to polycyclic aromatic hydrocarbons and xenobiotic metabolism genes, and risk of breast cancer.

PURPOSE: Polycyclic aromatic hydrocarbons (PAHs) are a group of environmental pollutants associated with multiple cancers, including female breast cancer. Several xenobiotic metabolism genes (XMGs), including the CYP450 family, play an important role in activating and detoxifying PAHs, and variations in the activity of the enzymes they encode can impact this process. This study aims to examine the association between XMGs and breast cancer, and to assess whether these variants modify the effects of PAH exposure on breast cancer risk. METHODS: In a case-control study in Vancouver, British Columbia, and Kingston, Ontario, 1037 breast cancer cases and 1046 controls had DNA extracted from blood or saliva and genotyped for 138 single nucleotide polymorphisms (SNPs) and tagSNPs in 27 candidate XMGs. Occupational PAH exposure was assessed using a measurement-based job-exposure matrix. RESULTS: An association between genetic variants and breast cancer was observed among six XMGs, including increased risk among the minor allele carriers of AKR1C3 variant rs12387 (OR 2.71, 95% CI 1.42-5.19) and AKR1C4 variant rs381267 (OR 2.50, 95% CI 1.23-5.07). Heterogeneous effects of occupational PAH exposure were observed among carriers of AKR1C3/4 variants, as well as the PTGS2 variant rs5275. CONCLUSION: Our findings support an association between SNPs of XMGs and female breast cancer, including novel genetic variants that modify the toxicity of PAH exposure. These results highlight the interplay between genetic and environmental factors, which can be helpful in understanding the modifiable risks of breast cancer and its complex etiology.

Exploring the impact of night shift work on methylation of circadian genes.

Night shift work is associated with increased breast cancer risk, but the molecular mechanisms are not well-understood. The objective of this study was to explore the relationship between night shift work parameters (current status, duration/years, and intensity) and methylation in circadian genes as a potential mechanism underlying the carcinogenic effects of night shift work. A cross-sectional study was conducted among 74 female healthcare employees (n = 38 day workers, n = 36 night shift workers). The Illumina Infinium MethylationEPIC beadchip was applied to DNA extracted from blood samples to measure methylation using a candidate gene approach at 1150 CpG loci across 22 circadian genes. Linear regression models were used to examine the association between night shift work parameters and continuous methylation measurements (ß-values) for each CpG site. The false-discovery rate (q = 0.2) was used to account for multiple comparisons. Compared to day workers, current night shift workers demonstrated hypermethylation in the 5'UTR region of CSNK1E (q = 0.15). Individuals that worked night shifts for ≥10 years exhibited hypomethylation in the gene body of NR1D1 (q = 0.08) compared to those that worked <10 years. Hypermethylation in the gene body of ARNTL was also apparent in those who worked ≥3 consecutive night shifts a week (q = 0.18). These findings suggest that night shift work is associated with differential methylation in core circadian genes, including CSNK1E, NR1D1 and ARNTL. Future, larger-scale studies with long-term follow-up and detailed night shift work assessment are needed to confirm and expand on these findings.

Lifetime caffeine intake and the risk of epithelial ovarian cancer.

BACKGROUND: Caffeine intake has been inconsistently associated with the risk of ovarian cancer in previous studies. The measure of caffeine in these studies has not always distinguished between caffeinated and decaffeinated sources, and the time for which intake was assessed was often for late adulthood and thus may have excluded the etiologic window. We investigated lifetime caffeine intake from caffeinated coffee, black tea, green tea and cola sodas in relation to ovarian cancer risk. METHODS: Among 497 cases and 904 controls in a population-based case-control study in Montreal, Canada, lifetime intake of caffeinated coffee, black tea, green tea and cola sodas was assessed and used to calculate lifetime total intake of caffeine. Unconditional multivariable logistic regression was used to estimate odds ratios (ORs) with 95% confidence intervals (CIs) for the association between caffeine intake and ovarian cancer risk overall, as well as by menopausal status. Multivariable polytomous logistic regression was used to estimate the associations for invasive and borderline ovarian cancers separately. RESULTS: Almost all participants (98.4% of cases and 97.5% of controls) had consumed caffeine in their lifetime. The mean (standard deviation) daily consumption of caffeine over the lifetime was of 117 (89) mg/day among cases and 120 (118) mg/day among controls. The OR (95% CI) of ovarian cancer for the highest versus lowest quartile of lifetime caffeine intake was 1.17 (0.83-1.64). According to menopausal status, the OR (95% CI) was 1.56 (0.85-2.86) for premenopausal women and 0.94 (0.66-1.34) for postmenopausal women, comparing the highest to lowest tertiles of intake. Associations for invasive and borderline ovarian cancers separately were similar to that observed for ovarian cancer overall. CONCLUSION: Lifetime caffeine intake was not strongly associated with ovarian cancer risk. A difference in relationship by menopausal status is possible.