Timing of pubertal stages and breast cancer risk : the Breakthrough Generations Study

Introduction: Breast development and hormonal changes at puberty might affect breast cancer risk, but epidemiological analyses have focussed largely on age at menarche and not at other pubertal stages. Methods: We investigated associations between the timing of pubertal stages and breast cancer risk using data from a cohort study of 104,931 women (Breakthrough Generations Study, UK, 2003–2013). Pubertal variables were reported retrospectively at baseline. Breast cancer risk was analysed using Cox regression models with breast cancer diagnosis as the outcome of interest, attained age as the underlying time variable, and adjustment for potentially confounding variables. Results: During follow-up (mean = 4.1 years), 1094 breast cancers (including ductal carcinoma in situ) occurred. An increased breast cancer risk was associated with earlier thelarche (age when breast growth begins; HR [95% CI] = 1.23 [1.02, 1.48], 1 [referent] and 0.80 [0.69, 0.93] for ≤10, 11–12 and ≥13 years respectively), menarche (initiation of menses; 1.06 [0.93, 1.21], 1 [referent] and 0.78 [0.62, 0.99] for ≤12, 13–14 and ≥15 years), regular periods (0.99 [0.83, 1.18], 1 [referent] and 0.74 [0.59, 0.92] for ≤12, 13–14 and ≥15 years) and age reached adult height (1.25 [1.03, 1.52], 1 [referent] and 1.07 [0.87, 1.32] for ≤14, 15–16 and ≥17 years), and with increased time between thelarche and menarche (0.87 [0.65, 1.15], 1 [referent], 1.14 [0.96, 1.34] and 1.27 [1.04, 1.55] for <0, 0, 1 and ≥2 years), and shorter time between menarche and regular periods (1 [referent], 0.87 [0.73, 1.04] and 0.66 [0.50, 0.88] for 0, 1 and ≥2 years). These associations were generally similar when considered separately for premenopausal and postmenopausal breast cancer. Conclusions: Breast duct development may be a time of heightened susceptibility to risk of carcinogenesis, and greater attention needs to be given to the relation of breast cancer risk to the different stages of puberty

Mortality and Cancer Incidence in Carriers of Balanced Robertsonian Translocations: A National Cohort Study.

A balanced robertsonian translocation (rob) results from fusion of 2 acrocentric chromosomes. Carriers are phenotypically normal and are often diagnosed because of recurrent miscarriages, infertility, or aneuploid offspring. Mortality and site-specific cancer risks in carriers have not been prospectively investigated. We followed 1,987 carriers diagnosed in Great Britain for deaths and cancer risk, over an average of 24.1 years. Standardized mortality and incidence ratios were calculated comparing the number of observed events against population rates. Overall mortality was higher for carriers diagnosed before age 15 years (standardized mortality ratio (SMR) = 2.00, 95% confidence interval (CI): 1.09, 3.35), similar for those diagnosed aged 15-44 years (SMR = 1.06, 95% CI: 0.86-1.28), and lower for those diagnosed aged 45-84 years (SMR = 0.81, 95% CI: 0.68, 0.95). Cancer incidence was higher for non-Hodgkin lymphoma (standardized incidence ratio (SIR) = 1.90, 95% CI: 1.01, 3.24) and childhood leukemia (SIR = 14.5, 95% CI: 1.75, 52.2), the latter particularly in rob(15;21) carriers (SIR = 447.8, 95% CI: 11.3, 2,495). Rob(13;14) carriers had a higher breast cancer risk (SIR = 1.58, 95% CI: 1.12, 2.15). Mortality risks relative to the population in diagnosed carriers depend on age at cytogenetic diagnosis, possibly reflecting age-specific cytogenetic referral reasons. Carriers might be at greater risk of childhood leukemia and non-Hodgkin lymphoma and those diagnosed with rob(13;14) of breast cancer

Mortality and cancer incidence in carriers of constitutional t(11;22)(q23;q11) translocations: A prospective study.

The constitutional t(11;22)(q23;q11) translocation is the only recurrent non-Robertsonian translocation known in humans. Carriers are phenotypically normal and are usually referred for cytogenetic testing because of multiple miscarriages, infertility, or having aneuploidy in offspring. A breast cancer predisposition has been suggested, but previous studies have been small and had methodological shortcomings. We therefore conducted a long-term prospective study of cancer and mortality risk in carriers. We followed 65 male and 101 female carriers of t(11;22)(q23;q11) diagnosed in cytogenetic laboratories in Britain during 1976-2005 for cancer and deaths for an average of 21.4 years per subject. Standardised mortality (SMR) and incidence (SIR) ratios were calculated comparing the numbers of observed events with those expected from national age-, sex-, country- and calendar-period-specific population rates. Cancer incidence was borderline significantly raised for cancer overall (SIR = 1.56, 95% CI: 0.98-2.36, n = 22), and significantly raised for invasive breast cancer (SIR = 2.74, 95% CI: 1.18-5.40, n = 8) and in situ breast cancer (SIR = 13.0, 95% CI: 3.55-33.4, n = 4). Breast cancer risks were particularly increased at ages <50 (SIR = 4.37, 95% CI: 1.42-10.2 for invasive, SIR = 22.8, 95% CI: 2.76-82.5 for in situ). Mortality was borderline significantly raised for breast cancer (SMR = 4.82, 95% CI: 0.99-14.1) but not significantly raised for other cancers or causes. Individuals diagnosed with t(11;22)(q23;q11) appear to be at several-fold increased breast cancer risk, with the greatest risks at premenopausal ages. Further research is required to understand the genetic mechanism involving 11q23 and 22q11 and there may be a need for enhanced breast cancer surveillance among female carriers

Comparative performance of lung cancer risk models to define lung screening eligibility in the United Kingdom

Abstract: Background: The National Health Service England (NHS) classifies individuals as eligible for lung cancer screening using two risk prediction models, PLCOm2012 and Liverpool Lung Project-v2 (LLPv2). However, no study has compared the performance of lung cancer risk models in the UK. Methods: We analysed current and former smokers aged 40–80 years in the UK Biobank (N = 217,199), EPIC-UK (N = 30,813), and Generations Study (N = 25,777). We quantified model calibration (ratio of expected to observed cases, E/O) and discrimination (AUC). Results: Risk discrimination in UK Biobank was best for the Lung Cancer Death Risk Assessment Tool (LCDRAT, AUC = 0.82, 95% CI = 0.81–0.84), followed by the LCRAT (AUC = 0.81, 95% CI = 0.79–0.82) and the Bach model (AUC = 0.80, 95% CI = 0.79–0.81). Results were similar in EPIC-UK and the Generations Study. All models overestimated risk in all cohorts, with E/O in UK Biobank ranging from 1.20 for LLPv3 (95% CI = 1.14–1.27) to 2.16 for LLPv2 (95% CI = 2.05–2.28). Overestimation increased with area-level socioeconomic status. In the combined cohorts, USPSTF 2013 criteria classified 50.7% of future cases as screening eligible. The LCDRAT and LCRAT identified 60.9%, followed by PLCOm2012 (58.3%), Bach (58.0%), LLPv3 (56.6%), and LLPv2 (53.7%). Conclusion: In UK cohorts, the ability of risk prediction models to classify future lung cancer cases as eligible for screening was best for LCDRAT/LCRAT, very good for PLCOm2012, and lowest for LLPv2. Our results highlight the importance of validating prediction tools in specific countries

Comparative validation of the BOADICEA and Tyrer-Cuzick breast cancer risk models incorporating classical risk factors and polygenic risk in a population-based prospective cohort of women of European ancestry

Abstract: Background: The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and the Tyrer-Cuzick breast cancer risk prediction models are commonly used in clinical practice and have recently been extended to include polygenic risk scores (PRS). In addition, BOADICEA has also been extended to include reproductive and lifestyle factors, which were already part of Tyrer-Cuzick model. We conducted a comparative prospective validation of these models after incorporating the recently developed 313-variant PRS. Methods: Calibration and discrimination of 5-year absolute risk was assessed in a nested case-control sample of 1337 women of European ancestry (619 incident breast cancer cases) aged 23–75 years from the Generations Study. Results: The extended BOADICEA model with reproductive/lifestyle factors and PRS was well calibrated across risk deciles; expected-to-observed ratio (E/O) at the highest risk decile :0.97 (95 % CI 0.51 − 1.86) for women younger than 50 years and 1.09 (0.66 − 1.80) for women 50 years or older. Adding reproductive/lifestyle factors and PRS to the BOADICEA model improved discrimination modestly in younger women (area under the curve (AUC) 69.7 % vs. 69.1%) and substantially in older women (AUC 64.6 % vs. 56.8%). The Tyrer-Cuzick model with PRS showed evidence of overestimation at the highest risk decile: E/O = 1.54(0.81 − 2.92) for younger and 1.73 (1.03 − 2.90) for older women. Conclusion: The extended BOADICEA model identified women in a European-ancestry population at elevated breast cancer risk more accurately than the Tyrer-Cuzick model with PRS. With the increasing availability of PRS, these analyses can inform choice of risk models incorporating PRS for risk stratified breast cancer prevention among women of European ancestry

Cancer incidence and mortality in 23 000 patients with type 1 diabetes in the UK : long‐term follow‐up

Type 2 diabetes is associated with raised risk of several cancers, but for type 1 diabetes risk data are fewer and inconsistent We assembled a cohort of 23 473 UK patients with insulin‐treated diabetes diagnosed at ages <30, almost all of whom will have had type 1 diabetes, and for comparison 5058 diagnosed at ages 30 to 49, of whom we estimate two‐thirds will have had type 2, and followed them for an average of 30 years for cancer incidence and mortality compared with general population rates. Patients aged <30 at diabetes diagnosis had significantly raised risks only for ovarian (standardised incidence ratio = 1.58; 95% confidence interval 1.16‐2.11; P < .01) and vulval (3.55; 1.94‐5.96; P < .001) cancers, with greatest risk when diabetes was diagnosed at ages 10‐14. Risks of cancer overall (0.89; 0.84‐0.95; P < .001) and sites including lung and larynx were significantly diminished. Patients diagnosed with diabetes at ages 30 to 49 had significantly raised risks of liver (1.76;1.08‐2.72) and kidney (1.46;1.03‐2.00) cancers, and reduced risk of cancer overall (0.89; 0.84‐0.95). The raised ovarian and vulval cancer risks in patients with type 1 diabetes, especially with diabetes diagnosed around pubertal ages, suggest possible susceptibility of these organs at puberty to metabolic disruption at diabetes onset. Reduced risk of cancer overall, particularly smoking and alcohol‐related sites, might reflect adoption of a healthy lifestyle

Genome-wide association study meta-analysis identifies three novel loci for circulating anti-Müllerian hormone levels in women

STUDY QUESTION: Can additional genetic variants for circulating anti-Müllerian hormone (AMH) levels be identified through a genome-wide association study (GWAS) meta-analysis including a large sample of premenopausal women? SUMMARY ANSWER: We identified four loci associated with AMH levels at P < 5 × 10(−8): the previously reported MCM8 locus and three novel signals in or near AMH, TEX41 and CDCA7. WHAT IS KNOWN ALREADY: AMH is expressed by antral stage ovarian follicles in women, and variation in age-specific circulating AMH levels has been associated with disease outcomes. However, the physiological mechanisms underlying these AMH-disease associations are largely unknown. STUDY DESIGN, SIZE, DURATION: We performed a GWAS meta-analysis in which we combined summary statistics of a previous AMH GWAS with GWAS data from 3705 additional women from three different cohorts. PARTICIPANTS/MATERIALS, SETTING, METHODS: In total, we included data from 7049 premenopausal female participants of European ancestry. The median age of study participants ranged from 15.3 to 48 years across cohorts. Circulating AMH levels were measured in either serum or plasma samples using different ELISA assays. Study-specific analyses were adjusted for age at blood collection and population stratification, and summary statistics were meta-analysed using a standard error-weighted approach. Subsequently, we functionally annotated GWAS variants that reached genome-wide significance (P < 5 × 10(−8)). We also performed a gene-based GWAS, pathway analysis and linkage disequilibrium score regression and Mendelian randomization (MR) analyses. MAIN RESULTS AND THE ROLE OF CHANCE: We identified four loci associated with AMH levels at P < 5 × 10(−8): the previously reported MCM8 locus and three novel signals in or near AMH, TEX41 and CDCA7. The strongest signal was a missense variant in the AMH gene (rs10417628). Most prioritized genes at the other three identified loci were involved in cell cycle regulation. Genetic correlation analyses indicated a strong positive correlation among single nucleotide polymorphisms for AMH levels and for age at menopause (r(g) = 0.82, FDR = 0.003). Exploratory two-sample MR analyses did not support causal effects of AMH on breast cancer or polycystic ovary syndrome risk, but should be interpreted with caution as they may be underpowered and the validity of genetic instruments could not be extensively explored. LARGE SCALE DATA: The full AMH GWAS summary statistics will made available after publication through the GWAS catalog (https://www.ebi.ac.uk/gwas/). LIMITATIONS, REASONS FOR CAUTION: Whilst this study doubled the sample size of the most recent GWAS, the statistical power is still relatively low. As a result, we may still lack power to identify more genetic variants for AMH and to determine causal effects of AMH on, for example, breast cancer. Also, follow-up studies are needed to investigate whether the signal for the AMH gene is caused by reduced AMH detection by certain assays instead of actual lower circulating AMH levels. WIDER IMPLICATIONS OF THE FINDINGS: Genes mapped to the MCM8, TEX41 and CDCA7 loci are involved in the cell cycle and processes such as DNA replication and apoptosis. The mechanism underlying their associations with AMH may affect the size of the ovarian follicle pool. Altogether, our results provide more insight into the biology of AMH and, accordingly, the biological processes involved in ovarian ageing. STUDY FUNDING/COMPETING INTEREST(S): Nurses’ Health Study and Nurses’ Health Study II were supported by research grants from the National Institutes of Health (CA172726, CA186107, CA50385, CA87969, CA49449, CA67262, CA178949). The UK Medical Research Council and Wellcome (217065/Z/19/Z) and the University of Bristol provide core support for ALSPAC. This publication is the work of the listed authors, who will serve as guarantors for the contents of this article. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). Funding for the collection of genotype and phenotype data used here was provided by the British Heart Foundation (SP/07/008/24066), Wellcome (WT092830M and WT08806) and UK Medical Research Council (G1001357). M.C.B., A.L.G.S. and D.A.L. work in a unit that is funded by the University of Bristol and UK Medical Research Council (MC_UU_00011/6). M.C.B.’s contribution to this work was funded by a UK Medical Research Council Skills Development Fellowship (MR/P014054/1) and D.A.L. is a National Institute of Health Research Senior Investigator (NF-0616-10102). A.L.G.S. was supported by the study of Dynamic longitudinal exposome trajectories in cardiovascular and metabolic non-communicable diseases (H2020-SC1-2019-Single-Stage-RTD, project ID 874739). The Doetinchem Cohort Study was financially supported by the Ministry of Health, Welfare and Sports of the Netherlands. The funder had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Ansh Labs performed the AMH measurements for the Doetinchem Cohort Study free of charge. Ansh Labs was not involved in the data analysis, interpretation or reporting, nor was it financially involved in any aspect of the study. R.M.G.V. was funded by the Honours Track of MSc Epidemiology, University Medical Center Utrecht with a grant from the Netherlands Organization for Scientific Research (NWO) (022.005.021). The Study of Women's Health Across the Nation (SWAN) has grant support from the National Institutes of Health (NIH), DHHS, through the National Institute on Aging (NIA), the National Institute of Nursing Research (NINR) and the NIH Office of Research on Women’s Health (ORWH) (U01NR004061; U01AG012505, U01AG012535, U01AG012531, U01AG012539, U01AG012546, U01AG012553, U01AG012554, U01AG012495). The SWAN Genomic Analyses and SWAN Legacy have grant support from the NIA (U01AG017719). The Generations Study was funded by Breast Cancer Now and the Institute of Cancer Research (ICR). The ICR acknowledges NHS funding to the NIHR Biomedical Research Centre. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent official views of the funders. The Sister Study was funded by the Intramural Research Program of the National Institutes of Health (NIH), National Institute of Environmental Health Sciences (Z01-ES044005 to D.P.S.); the AMH assays were supported by the Avon Foundation (02-2012-065 to H.B. Nichols and D.P.S.). The breast cancer genome-wide association analyses were supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the ‘Ministère de l’Économie, de la Science et de l’Innovation du Québec’ through Genome Québec and grant PSR-SIIRI-701, The National Institutes of Health (U19 CA148065, X01HG007492), Cancer Research UK (C1287/A10118, C1287/A16563, C1287/A10710) and The European Union (HEALTH-F2-2009-223175 and H2020 633784 and 634935). All studies and funders are listed in Michailidou et al. (Nature, 2017). F.J.M.B. has received fees and grant support from Merck Serono and Ferring BV. D.A.L. has received financial support from several national and international government and charitable funders as well as from Medtronic Ltd and Roche Diagnostics for research that is unrelated to this study. N.S. is scientific consultant for Ansh Laboratories. The other authors declare no competing interests

Comparative validation of the BOADICEA and Tyrer-Cuzick breast cancer risk models incorporating classical risk factors and polygenic risk in a population-based prospective cohort of women of European ancestry

Abstract: Background: The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and the Tyrer-Cuzick breast cancer risk prediction models are commonly used in clinical practice and have recently been extended to include polygenic risk scores (PRS). In addition, BOADICEA has also been extended to include reproductive and lifestyle factors, which were already part of Tyrer-Cuzick model. We conducted a comparative prospective validation of these models after incorporating the recently developed 313-variant PRS. Methods: Calibration and discrimination of 5-year absolute risk was assessed in a nested case-control sample of 1337 women of European ancestry (619 incident breast cancer cases) aged 23–75 years from the Generations Study. Results: The extended BOADICEA model with reproductive/lifestyle factors and PRS was well calibrated across risk deciles; expected-to-observed ratio (E/O) at the highest risk decile :0.97 (95 % CI 0.51 − 1.86) for women younger than 50 years and 1.09 (0.66 − 1.80) for women 50 years or older. Adding reproductive/lifestyle factors and PRS to the BOADICEA model improved discrimination modestly in younger women (area under the curve (AUC) 69.7 % vs. 69.1%) and substantially in older women (AUC 64.6 % vs. 56.8%). The Tyrer-Cuzick model with PRS showed evidence of overestimation at the highest risk decile: E/O = 1.54(0.81 − 2.92) for younger and 1.73 (1.03 − 2.90) for older women. Conclusion: The extended BOADICEA model identified women in a European-ancestry population at elevated breast cancer risk more accurately than the Tyrer-Cuzick model with PRS. With the increasing availability of PRS, these analyses can inform choice of risk models incorporating PRS for risk stratified breast cancer prevention among women of European ancestry