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ItemMethylation-based markers of aging and lifestyle-related factors and risk of breast cancer: a pooled analysis of four prospective studies(BMC, 2022-09-06)BACKGROUND: DNA methylation in blood may reflect adverse exposures accumulated over the lifetime and could therefore provide potential improvements in the prediction of cancer risk. A substantial body of research has shown associations between epigenetic aging and risk of disease, including cancer. Here we aimed to study epigenetic measures of aging and lifestyle-related factors in association with risk of breast cancer. METHODS: Using data from four prospective case-control studies nested in three cohorts of European ancestry participants, including a total of 1,655 breast cancer cases, we calculated three methylation-based measures of lifestyle factors (body mass index [BMI], tobacco smoking and alcohol consumption) and seven measures of epigenetic aging (Horvath-based, Hannum-based, PhenoAge and GrimAge). All measures were regression-adjusted for their respective risk factors and expressed per standard deviation (SD). Odds ratios (OR) and 95% confidence intervals (CI) were calculated using conditional or unconditional logistic regression and pooled using fixed-effects meta-analysis. Subgroup analyses were conducted by age at blood draw, time from blood sample to diagnosis, oestrogen receptor-positivity status and tumour stage. RESULTS: None of the measures of epigenetic aging were associated with risk of breast cancer in the pooled analysis: Horvath 'age acceleration' (AA): OR per SD = 1.02, 95%CI: 0.95-1.10; AA-Hannum: OR = 1.03, 95%CI:0.95-1.12; PhenoAge: OR = 1.01, 95%CI: 0.94-1.09 and GrimAge: OR = 1.03, 95%CI: 0.94-1.12, in models adjusting for white blood cell proportions, body mass index, smoking and alcohol consumption. The BMI-adjusted predictor of BMI was associated with breast cancer risk, OR per SD = 1.09, 95%CI: 1.01-1.17. The results for the alcohol and smoking methylation-based predictors were consistent with a null association. Risk did not appear to substantially vary by age at blood draw, time to diagnosis or tumour characteristics. CONCLUSION: We found no evidence that methylation-based measures of aging, smoking or alcohol consumption were associated with risk of breast cancer. A methylation-based marker of BMI was associated with risk and may provide insights into the underlying associations between BMI and breast cancer.
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ItemAnti-Mullerian hormone and risk of ovarian cancer in nine cohorts(WILEY, 2018-01-15)Animal and experimental data suggest that anti-Müllerian hormone (AMH) serves as a marker of ovarian reserve and inhibits the growth of ovarian tumors. However, few epidemiologic studies have examined the association between AMH and ovarian cancer risk. We conducted a nested case-control study of 302 ovarian cancer cases and 336 matched controls from nine cohorts. Prediagnostic blood samples of premenopausal women were assayed for AMH using a picoAMH enzyme-linked immunosorbent assay. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using multivariable-adjusted conditional logistic regression. AMH concentration was not associated with overall ovarian cancer risk. The multivariable-adjusted OR (95% CI), comparing the highest to the lowest quartile of AMH, was 0.99 (0.59-1.67) (Ptrend : 0.91). The association did not differ by age at blood draw or oral contraceptive use (all Pheterogeneity : ≥0.26). There also was no evidence for heterogeneity of risk for tumors defined by histologic developmental pathway, stage, and grade, and by age at diagnosis and time between blood draw and diagnosis (all Pheterogeneity : ≥0.39). In conclusion, this analysis of mostly late premenopausal women from nine cohorts does not support the hypothesized inverse association between prediagnostic circulating levels of AMH and risk of ovarian cancer.
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ItemThe use and interpretation of anthropometric measures in cancer epidemiology: A perspective from the world cancer research fund international continuous update project.(Wiley, 2016-12-01)Anthropometric measures relating to body size, weight and composition are increasingly being associated with cancer risk and progression. Whilst practical in epidemiologic research, where population-level associations with disease are revealed, it is important to be aware that such measures are imperfect markers of the internal physiological processes that are the actual correlates of cancer development. Body mass index (BMI), the most commonly used marker for adiposity, may mask differences between lean and adipose tissue, or fat distribution, which varies across individuals, ethnicities, and stage in the lifespan. Other measures, such as weight gain in adulthood, waist circumference and waist-to-hip ratio, contribute information on adipose tissue distribution and insulin sensitivity. Single anthropometric measures do not capture maturational events, including the presence of critical windows of susceptibility (i.e., age of menarche and menopause), which presents a challenge in epidemiologic work. Integration of experimental research on underlying dynamic genetic, hormonal, and other non-nutritional mechanisms is necessary for a confident conclusion of the overall evidence in cancer development and progression. This article discusses the challenges confronted in evaluating and interpreting the current evidence linking anthropometric factors and cancer risk as a basis for issuing recommendations for cancer prevention.
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ItemProportion of premenopausal and postmenopausal breast cancers attributable to known risk factors: Estimates from the E3N-EPIC cohort(WILEY, 2016-05-15)Breast cancer is the most frequently diagnosed cancer among women worldwide. Breast cancer risk factors have been widely explored individually; however, little is known about their combined impact. We included 67,634 women from the French E3N prospective cohort, aged 42-72 at baseline. During a 15-year follow-up period, 497 premenopausal and 3,138 postmenopausal invasive breast cancer cases were diagnosed. Population-attributable fractions (PAFs) were used to estimate cases proportions attributable to risk factors under hypothetical scenarios of lowest exposure. We examined overall premenopausal and postmenopausal invasive breast cancers and tumour subtypes (ER status and HER2 expression). Premenopausal breast cancer was not significantly attributable to non-behavioral (61.2%, -15.5 to 91.88%) nor to behavioral (39.9%, -71.0 to 93.9%) factors, contrary to postmenopausal breast cancer (41.9%, 4.5 to 68.7% and 53.5%, 12.8 to 78.7%, respectively). Individually, the highest statistically significant PAFs were obtained in premenopause for birth weight (33.6%, 5.7 to 56.6%) and age at menarche (19.8%, 5.2 to 33.6%) for non-behavioral factors and in postmenopause for history of benign breast diseases (14.9%, 11.6 to 18.0%) and age at menarche (9.7%, 3.9 to 15.5%) for non-behavioral factors and for body shape at menarche (17.1%, 9.7 to 24.3%), use of hormone replacement therapy (14.5%, 9.2 to 19.6%), dietary pattern (10.1%, 2.6 to 17.4%) and alcohol consumption (5.6%, 1.9 to 9.3%) for behavioral factors. These proportions were higher for ER+, HER2- and ER+/HER2- postmenopausal breast cancers. Our data support the hypothesis that in postmenopause, never starting unhealthy behaviors can reduce the number of diagnosed breast cancers.
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ItemGenome-wide association study of peripheral blood DNA methylation and conventional mammographic density measures(WILEY, 2019-10-01)Age- and body mass index (BMI)-adjusted mammographic density is one of the strongest breast cancer risk factors. DNA methylation is a molecular mechanism that could underlie inter-individual variation in mammographic density. We aimed to investigate the association between breast cancer risk-predicting mammographic density measures and blood DNA methylation. For 436 women from the Australian Mammographic Density Twins and Sisters Study and 591 women from the Melbourne Collaborative Cohort Study, mammographic density (dense area, nondense area and percentage dense area) defined by the conventional brightness threshold was measured using the CUMULUS software, and peripheral blood DNA methylation was measured using the HumanMethylation450 (HM450) BeadChip assay. Associations between DNA methylation at >400,000 sites and mammographic density measures adjusted for age and BMI were assessed within each cohort and pooled using fixed-effect meta-analysis. Associations with methylation at genetic loci known to be associated with mammographic density were also examined. We found no genome-wide significant (p < 10-7 ) association for any mammographic density measure from the meta-analysis, or from the cohort-specific analyses. None of the 299 methylation sites located at genetic loci associated with mammographic density was associated with any mammographic density measure after adjusting for multiple testing (all p > 0.05/299 = 1.7 × 10-4 ). In summary, our study did not find evidence for associations between blood DNA methylation, as measured by the HM450 assay, and conventional mammographic density measures that predict breast cancer risk.
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ItemA comprehensive gene-environment interaction analysis in Ovarian Cancer using genome-wide significant common variants(WILEY, 2019-05-01)As a follow-up to genome-wide association analysis of common variants associated with ovarian carcinoma (cancer), our study considers seven well-known ovarian cancer risk factors and their interactions with 28 genome-wide significant common genetic variants. The interaction analyses were based on data from 9971 ovarian cancer cases and 15,566 controls from 17 case-control studies. Likelihood ratio and Wald tests for multiplicative interaction and for relative excess risk due to additive interaction were used. The top multiplicative interaction was noted between oral contraceptive pill (OCP) use (ever vs. never) and rs13255292 (p value = 3.48 × 10-4 ). Among women with the TT genotype for this variant, the odds ratio for OCP use was 0.53 (95% CI = 0.46-0.60) compared to 0.71 (95%CI = 0.66-0.77) for women with the CC genotype. When stratified by duration of OCP use, women with 1-5 years of OCP use exhibited differential protective benefit across genotypes. However, no interaction on either the multiplicative or additive scale was found to be statistically significant after multiple testing correction. The results suggest that OCP use may offer increased benefit for women who are carriers of the T allele in rs13255292. On the other hand, for women carrying the C allele in this variant, longer (5+ years) use of OCP may reduce the impact of carrying the risk allele of this SNP. Replication of this finding is needed. The study presents a comprehensive analytic framework for conducting gene-environment analysis in ovarian cancer.
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ItemNovel mammogram-based measures improve breast cancer risk prediction beyond an established mammographic density measure(WILEY, 2021-05-01)Mammograms contain information that predicts breast cancer risk. We developed two novel mammogram-based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus). Their risk prediction when fitted together, and with an established measure of conventional mammographic density (Cumulus), is not known. We used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen-detected cases and 1197 matched controls; and 354 younger-diagnosis cases and 944 controls frequency-matched for age at mammogram. We conducted conditional and unconditional logistic regression analyses of individually- and frequency-matched studies, respectively. We estimated measure-specific risk gradients as the change in odds per standard deviation of controls after adjusting for age and body mass index (OPERA) and calculated the area under the receiver operating characteristic curve (AUC). For interval, screen-detected and younger-diagnosis cancer risks, the best fitting models (OPERAs [95% confidence intervals]) involved: Cumulus (1.81 [1.41-2.31]) and Cirrus (1.72 [1.38-2.14]); Cirrus (1.49 [1.32-1.67]) and Cirrocumulus (1.16 [1.03 to 1.31]); and Cirrus (1.70 [1.48 to 1.94]) and Cirrocumulus (1.46 [1.27-1.68]), respectively. The AUCs were: 0.73 [0.68-0.77], 0.63 [0.60-0.66], and 0.72 [0.69-0.75], respectively. Combined, our new mammogram-based measures have twice the risk gradient for screen-detected and younger-diagnosis breast cancer (P ≤ 10-12 ), have at least the same discriminatory power as the current polygenic risk score, and are more correlated with causal factors than conventional mammographic density. Discovering more information about breast cancer risk from mammograms could help enable risk-based personalised breast screening.
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ItemEpigenome-wide association study reveals decreased average methylation levels years before breast cancer diagnosis.(Springer Science and Business Media LLC, 2015)BACKGROUND: Interest in the potential of DNA methylation in peripheral blood as a biomarker of cancer risk is increasing. We aimed to assess whether epigenome-wide DNA methylation measured in peripheral blood samples obtained before onset of the disease is associated with increased risk of breast cancer. We report on three independent prospective nested case-control studies from the European Prospective Investigation into Cancer and Nutrition (EPIC-Italy; n = 162 matched case-control pairs), the Norwegian Women and Cancer study (NOWAC; n = 168 matched pairs), and the Breakthrough Generations Study (BGS; n = 548 matched pairs). We used the Illumina 450k array to measure methylation in the EPIC and NOWAC cohorts. Whole-genome bisulphite sequencing (WGBS) was performed on the BGS cohort using pooled DNA samples, combined to reach 50× coverage across ~16 million CpG sites in the genome including 450k array CpG sites. Mean β values over all probes were calculated as a measurement for epigenome-wide methylation. RESULTS: In EPIC, we found that high epigenome-wide methylation was associated with lower risk of breast cancer (odds ratio (OR) per 1 SD = 0.61, 95 % confidence interval (CI) 0.47-0.80; -0.2 % average difference in epigenome-wide methylation for cases and controls). Specifically, this was observed in gene bodies (OR = 0.51, 95 % CI 0.38-0.69) but not in gene promoters (OR = 0.92, 95 % CI 0.64-1.32). The association was not replicated in NOWAC (OR = 1.03 95 % CI 0.81-1.30). The reasons for heterogeneity across studies are unclear. However, data from the BGS cohort was consistent with epigenome-wide hypomethylation in breast cancer cases across the overlapping 450k probe sites (difference in average epigenome-wide methylation in case and control DNA pools = -0.2 %). CONCLUSIONS: We conclude that epigenome-wide hypomethylation of DNA from pre-diagnostic blood samples may be predictive of breast cancer risk and may thus be useful as a clinical biomarker.