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1. Breast and bowel cancers diagnosed in people 'too young to have cancer': A blueprint for research using family and twin studies

Author

Hopper, JL, Li, S, MacInnis, RJ, Dowty, JG, Nguyen, TL, Bui, M, Dite, GS, Esser, VFC, Ye, Z, Makalic, E, Schmidt, DF, Goudey, B, Alpen, K, Kapuscinski, M, Win, AK, Dugue, P-A, Milne, RL, Jayasekara, H, Brooks, JD, Malta, S, Calais-Ferreira, L, Campbell, AC, Young, JT, Nguyen-Dumont, T, Sung, J, Giles, GG, Buchanan, D, Winship, I, Terry, MB, Southey, MC, Jenkins, MA, Hopper, JL, Li, S, MacInnis, RJ, Dowty, JG, Nguyen, TL, Bui, M, Dite, GS, Esser, VFC, Ye, Z, Makalic, E, Schmidt, DF, Goudey, B, Alpen, K, Kapuscinski, M, Win, AK, Dugue, P-A, Milne, RL, Jayasekara, H, Brooks, JD, Malta, S, Calais-Ferreira, L, Campbell, AC, Young, JT, Nguyen-Dumont, T, Sung, J, Giles, GG, Buchanan, D, Winship, I, Terry, MB, Southey, MC, and Jenkins, MA

Abstract

Young breast and bowel cancers (e.g., those diagnosed before age 40 or 50 years) have far greater morbidity and mortality in terms of years of life lost, and are increasing in incidence, but have been less studied. For breast and bowel cancers, the familial relative risks, and therefore the familial variances in age-specific log(incidence), are much greater at younger ages, but little of these familial variances has been explained. Studies of families and twins can address questions not easily answered by studies of unrelated individuals alone. We describe existing and emerging family and twin data that can provide special opportunities for discovery. We present designs and statistical analyses, including novel ideas such as the VALID (Variance in Age-specific Log Incidence Decomposition) model for causes of variation in risk, the DEPTH (DEPendency of association on the number of Top Hits) and other approaches to analyse genome-wide association study data, and the within-pair, ICE FALCON (Inference about Causation from Examining FAmiliaL CONfounding) and ICE CRISTAL (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLysis) approaches to causation and familial confounding. Example applications to breast and colorectal cancer are presented. Motivated by the availability of the resources of the Breast and Colon Cancer Family Registries, we also present some ideas for future studies that could be applied to, and compared with, cancers diagnosed at older ages and address the challenges posed by young breast and bowel cancers.

Published
2024

2. Genetic and Environmental Causes of Variation in an Automated Breast Cancer Risk Factor Based on Mammographic Textures

Author

Ye, Z, Dite, GS, Nguyen, TL, Macinnis, RJ, Schmidt, DF, Makalic, E, Al-Qershi, OM, Nguyen- Dumont, T, Goudey, B, Stone, J, Dowty, JG, Giles, GG, Southey, MC, Hopper, JL, Li, S, Ye, Z, Dite, GS, Nguyen, TL, Macinnis, RJ, Schmidt, DF, Makalic, E, Al-Qershi, OM, Nguyen- Dumont, T, Goudey, B, Stone, J, Dowty, JG, Giles, GG, Southey, MC, Hopper, JL, and Li, S

Abstract

BACKGROUND: Cirrus is an automated risk predictor for breast cancer that comprises texture-based mammographic features and is mostly independent of mammographic density. We investigated genetic and environmental variance of variation in Cirrus. METHODS: We measured Cirrus for 3,195 breast cancer-free participants, including 527 pairs of monozygotic (MZ) twins, 271 pairs of dizygotic (DZ) twins, and 1,599 siblings of twins. Multivariate normal models were used to estimate the variance and familial correlations of age-adjusted Cirrus as a function of age. The classic twin model was expanded to allow the shared environment effects to differ by zygosity. The SNP-based heritability was estimated for a subset of 2,356 participants. RESULTS: There was no evidence that the variance or familial correlations depended on age. The familial correlations were 0.52 (SE, 0.03) for MZ pairs and 0.16(SE, 0.03) for DZ and non-twin sister pairs combined. Shared environmental factors specific to MZ pairs accounted for 20% of the variance. Additive genetic factors accounted for 32% (SE = 5%) of the variance, consistent with the SNP-based heritability of 36% (SE = 16%). CONCLUSION: Cirrus is substantially familial due to genetic factors and an influence of shared environmental factors that was evident for MZ twin pairs only. The latter could be due to nongenetic factors operating in utero or in early life that are shared by MZ twins. IMPACT: Early-life factors, shared more by MZ pairs than DZ/non-twin sister pairs, could play a role in the variation in Cirrus, consistent with early life being recognized as a critical window of vulnerability to breast carcinogens.

Published
2024

4. Evidence of Gene-Environment Interactions between Common Breast Cancer Susceptibility Loci and Established Environmental Risk Factors

Author

Nickels, S, Truong, T, Hein, R, Stevens, K, Buck, K, Behrens, S, Eilber, U, Schmidt, M, Häberle, L, Vrieling, A, Gaudet, M, Figueroa, J, Schoof, N, Spurdle, AB, Rudolph, A, Fasching, PA, Hopper, JL, Makalic, E, Schmidt, DF, Southey, MC, Beckmann, MW, Ekici, AB, Fletcher, O, Gibson, L, dos Santos Silva, I, Peto, J, Humphreys, MK, Wang, J, Cordina-Duverger, E, Menegaux, F, Nordestgaard, BG, Bojesen, SE, Lanng, C, Anton-Culver, H, Ziogas, A, Bernstein, L, Clarke, CA, Brenner, H, Müller, H, Arndt, V, Stegmaier, C, Brauch, H, Brüning, T, Harth, V, Mannermaa, A, Kataja, V, Kosma, VM, Hartikainen, JM, Lambrechts, D, Smeets, D, Neven, P, Paridaens, R, Flesch-Janys, D, Obi, N, Wang-Gohrke, S, Couch, FJ, Olson, JE, Vachon, CM, Giles, GG, Severi, G, Baglietto, L, Offit, K, John, EM, Miron, A, Andrulis, IL, Knight, JA, Glendon, G, Mulligan, AM, Chanock, SJ, Lissowska, J, Liu, J, Cox, A, Cramp, H, Connley, D, Balasubramanian, S, Dunning, AM, Shah, M, Trentham-Dietz, A, Newcomb, P, Titus, L, Egan, K, Cahoon, EK, and Rajaraman, P

Abstract

Various common genetic susceptibility loci have been identified for breast cancer; however, it is unclear how they combine with lifestyle/environmental risk factors to influence risk. We undertook an international collaborative study to assess gene-environment interaction for risk of breast cancer. Data from 24 studies of the Breast Cancer Association Consortium were pooled. Using up to 34,793 invasive breast cancers and 41,099 controls, we examined whether the relative risks associated with 23 single nucleotide polymorphisms were modified by 10 established environmental risk factors (age at menarche, parity, breastfeeding, body mass index, height, oral contraceptive use, menopausal hormone therapy use, alcohol consumption, cigarette smoking, physical activity) in women of European ancestry. We used logistic regression models stratified by study and adjusted for age and performed likelihood ratio tests to assess gene-environment interactions. All statistical tests were two-sided. We replicated previously reported potential interactions between LSP1-rs3817198 and parity (Pinteraction= 2.4×10-6) and between CASP8-rs17468277 and alcohol consumption (Pinteraction= 3.1×10-4). Overall, the per-allele odds ratio (95% confidence interval) for LSP1-rs3817198 was 1.08 (1.01-1.16) in nulliparous women and ranged from 1.03 (0.96-1.10) in parous women with one birth to 1.26 (1.16-1.37) in women with at least four births. For CASP8-rs17468277, the per-allele OR was 0.91 (0.85-0.98) in those with an alcohol intake of

Published
2013

5. Causal relationships between breast cancer risk factors based on mammographic features

Author

Ye, Z, Nguyen, TL, Dite, GS, Macinnis, RJ, Schmidt, DF, Makalic, E, Al-Qershi, OM, Bui, M, Esser, VFC, Dowty, JG, Trinh, HN, Evans, CF, Tan, M, Sung, J, Jenkins, MA, Giles, GG, Southey, MC, Hopper, JL, Li, S, Ye, Z, Nguyen, TL, Dite, GS, Macinnis, RJ, Schmidt, DF, Makalic, E, Al-Qershi, OM, Bui, M, Esser, VFC, Dowty, JG, Trinh, HN, Evans, CF, Tan, M, Sung, J, Jenkins, MA, Giles, GG, Southey, MC, Hopper, JL, and Li, S

Abstract

BACKGROUND: Mammogram risk scores based on texture and density defined by different brightness thresholds are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. We aimed to investigate causal relationships between these intercorrelated mammogram risk scores to determine their relevance to breast cancer aetiology. METHODS: We used digitised mammograms for 371 monozygotic twin pairs, aged 40-70 years without a prior diagnosis of breast cancer at the time of mammography, from the Australian Mammographic Density Twins and Sisters Study. We generated normalised, age-adjusted, and standardised risk scores based on textures using the Cirrus algorithm and on three spatially independent dense areas defined by increasing brightness threshold: light areas, bright areas, and brightest areas. Causal inference was made using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) method. RESULTS: The mammogram risk scores were correlated within twin pairs and with each other (r = 0.22-0.81; all P < 0.005). We estimated that 28-92% of the associations between the risk scores could be attributed to causal relationships between the scores, with the rest attributed to familial confounders shared by the scores. There was consistent evidence for positive causal effects: of Cirrus, light areas, and bright areas on the brightest areas (accounting for 34%, 55%, and 85% of the associations, respectively); and of light areas and bright areas on Cirrus (accounting for 37% and 28%, respectively). CONCLUSIONS: In a mammogram, the lighter (less dense) areas have a causal effect on the brightest (highly dense) areas, including through a causal pathway via textural features. These causal relationships help us gain insight into the relative aetiological importance of different mammographic features in breast cancer. For example our findings are consistent with the brightest areas being more aetiologically impor

Published
2023

6. Using DEPendency of Association on the Number of Top Hits (DEPTH) as a Complementary Tool to Identify Novel Colorectal Cancer Loci

Author

Lai, J, Wong, CK, Schmidt, DF, Kapuscinski, MK, Alpen, K, Macinnis, RJ, Buchanan, DD, Win, AK, Figueiredo, JC, Chan, AT, Harrison, TA, Hoffmeister, M, White, E, Le Marchand, L, Pai, RK, Peters, U, Hopper, JL, Jenkins, MA, Makalic, E, Lai, J, Wong, CK, Schmidt, DF, Kapuscinski, MK, Alpen, K, Macinnis, RJ, Buchanan, DD, Win, AK, Figueiredo, JC, Chan, AT, Harrison, TA, Hoffmeister, M, White, E, Le Marchand, L, Pai, RK, Peters, U, Hopper, JL, Jenkins, MA, and Makalic, E

Abstract

BACKGROUND: DEPendency of association on the number of Top Hits (DEPTH) is an approach to identify candidate susceptibility regions by considering the risk signals from overlapping groups of sequential variants across the genome. METHODS: We applied a DEPTH analysis using a sliding window of 200 SNPs to colorectal cancer data from the Colon Cancer Family Registry (CCFR; 5,735 cases and 3,688 controls), and Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO; 8,865 cases and 10,285 controls) studies. A DEPTH score > 1 was used to identify candidate susceptibility regions common to both analyses. We compared DEPTH results against those from conventional genome-wide association study (GWAS) analyses of these two studies as well as against 132 published susceptibility regions. RESULTS: Initial DEPTH analysis revealed 2,622 (CCFR) and 3,686 (GECCO) candidate susceptibility regions, of which 569 were common to both studies. Bootstrapping revealed 40 and 49 candidate susceptibility regions in the CCFR and GECCO data sets, respectively. Notably, DEPTH identified at least 82 regions that would not be detected using conventional GWAS methods, nor had they been identified by previous colorectal cancer GWASs. We found four reproducible candidate susceptibility regions (2q22.2, 2q33.1, 6p21.32, 13q14.3). The highest DEPTH scores were in the human leukocyte antigen locus at 6p21 where the strongest associated SNPs were rs762216297, rs149490268, rs114741460, and rs199707618 for the CCFR data, and rs9270761 for the GECCO data. CONCLUSIONS: DEPTH can identify candidate susceptibility regions for colorectal cancer not identified using conventional analyses of larger datasets. IMPACT: DEPTH has potential as a powerful complementary tool to conventional GWAS analyses for discovering susceptibility regions within the genome.

Published
2023

7. Improved Neurophysiological Process Imaging Through Optimization of Kalman Filter Initial Conditions

Author

Zhao, Y, Luong, F, Teshuva, S, Pelentritou, A, Woods, W, Liley, D, Schmidt, DF, Boley, M, Kuhlmann, L, Zhao, Y, Luong, F, Teshuva, S, Pelentritou, A, Woods, W, Liley, D, Schmidt, DF, Boley, M, and Kuhlmann, L

Abstract

Recent work presented a framework for space-time-resolved neurophysiological process imaging that augments existing electromagnetic source imaging techniques. In particular, a nonlinear Analytic Kalman filter (AKF) has been developed to efficiently infer the states and parameters of neural mass models believed to underlie the generation of electromagnetic source currents. Unfortunately, as the initialization determines the performance of the Kalman filter, and the ground truth is typically unavailable for initialization, this framework might produce suboptimal results unless significant effort is spent on tuning the initialization. Notably, the relation between the initialization and overall filter performance is only given implicitly and is expensive to evaluate; implying that conventional optimization techniques, e.g. gradient or sampling based, are inapplicable. To address this problem, a novel efficient framework based on blackbox optimization has been developed to find the optimal initialization by reducing the signal prediction error. Multiple state-of-the-art optimization methods were compared and distinctively, Gaussian process optimization decreased the objective function by 82.1% and parameter estimation error by 62.5% on average with the simulation data compared to no optimization applied. The framework took only 1.6[Formula: see text]h and reduced the objective function by an average of 13.2% on 3.75[Formula: see text]min 4714-source channel magnetoencephalography data. This yields an improved method of neurophysiological process imaging that can be used to uncover complex underpinnings of brain dynamics.

Published
2023

8. Does genetic predisposition modify the effect of lifestyle-related factors on DNA methylation?

Author

Yu, C, Hodge, AM, Wong, EM, Joo, JE, Makalic, E, Schmidt, DF, Buchanan, DD, Severi, G, Hopper, JL, English, DR, Giles, GG, Milne, RL, Southey, MC, Dugue, P-A, Yu, C, Hodge, AM, Wong, EM, Joo, JE, Makalic, E, Schmidt, DF, Buchanan, DD, Severi, G, Hopper, JL, English, DR, Giles, GG, Milne, RL, Southey, MC, and Dugue, P-A

Abstract

Lifestyle-related phenotypes have been shown to be heritable and associated with DNA methylation. We aimed to investigate whether genetic predisposition to tobacco smoking, alcohol consumption, and higher body mass index (BMI) moderates the effect of these phenotypes on blood DNA methylation. We calculated polygenic scores (PGS) to quantify genetic predisposition to these phenotypes using training (N = 7,431) and validation (N = 4,307) samples. Using paired genetic-methylation data (N = 4,307), gene-environment interactions (i.e., PGS × lifestyle) were assessed using linear mixed-effects models with outcomes: 1) methylation at sites found to be strongly associated with smoking (1,061 CpGs), alcohol consumption (459 CpGs), and BMI (85 CpGs) and 2) two epigenetic ageing measures, PhenoAge and GrimAge. In the validation sample, PGS explained ~1.4% (P = 1 × 10-14), ~0.6% (P = 2 × 10-7), and ~8.7% (P = 7 × 10-87) of variance in smoking initiation, alcohol consumption, and BMI, respectively. Nominally significant interaction effects (P < 0.05) were found at 61, 14, and 7 CpGs for smoking, alcohol consumption, and BMI, respectively. There was strong evidence that all lifestyle-related phenotypes were positively associated with PhenoAge and GrimAge, except for alcohol consumption with PhenoAge. There was weak evidence that the association of smoking with GrimAge was attenuated in participants genetically predisposed to smoking (interaction term: -0.022, standard error [SE] = 0.012, P = 0.058) and that the association of alcohol consumption with PhenoAge was attenuated in those genetically predisposed to drink alcohol (interaction term: -0.030, SE = 0.015, P = 0.041). In conclusion, genetic susceptibility to unhealthy lifestyles did not strongly modify the association between observed lifestyle behaviour and blood DNA methylation. Potential associations were observed for epigenetic ageing measures, which should be replicated in additional studies.

Published
2022

9. Minimum Message Length Inference of the Exponential Distribution with Type I Censoring

Author

Makalic, E, Schmidt, DF, Makalic, E, and Schmidt, DF

Abstract

Data with censoring is common in many areas of science and the associated statistical models are generally estimated with the method of maximum likelihood combined with a model selection criterion such as Akaike's information criterion. This manuscript demonstrates how the information theoretic minimum message length principle can be used to estimate statistical models in the presence of type I random and fixed censoring data. The exponential distribution with fixed and random censoring is used as an example to demonstrate the process where we observe that the minimum message length estimate of mean survival time has some advantages over the standard maximum likelihood estimate.

Published
2021

10. Novel mammogram-based measures improve breast cancer risk prediction beyond an established mammographic density measure

Author

Nguyen, TL, Schmidt, DF, Makalic, E, Maskarinec, G, Li, S, Dite, GS, Aung, YK, Evans, CF, Trinh, HN, Baglietto, L, Stone, J, Song, Y-M, Sung, J, MacInnis, RJ, Dugue, P-A, Dowty, JG, Jenkins, MA, Milne, RL, Southey, MC, Giles, GG, Hopper, JL, Nguyen, TL, Schmidt, DF, Makalic, E, Maskarinec, G, Li, S, Dite, GS, Aung, YK, Evans, CF, Trinh, HN, Baglietto, L, Stone, J, Song, Y-M, Sung, J, MacInnis, RJ, Dugue, P-A, Dowty, JG, Jenkins, MA, Milne, RL, Southey, MC, Giles, GG, and Hopper, JL

Abstract

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.

Published
2021

11. Alcohol consumption is associated with widespread changes in blood DNA methylation: Analysis of cross-sectional and longitudinal data

Author

Dugue, P-A, Wilson, R, Lehne, B, Jayasekara, H, Wang, X, Jung, C-H, Joo, JE, Makalic, E, Schmidt, DF, Baglietto, L, Severi, G, Gieger, C, Ladwig, K-H, Peters, A, Kooner, JS, Southey, MC, English, DR, Waldenberger, M, Chambers, JC, Giles, GG, Milne, RL, Dugue, P-A, Wilson, R, Lehne, B, Jayasekara, H, Wang, X, Jung, C-H, Joo, JE, Makalic, E, Schmidt, DF, Baglietto, L, Severi, G, Gieger, C, Ladwig, K-H, Peters, A, Kooner, JS, Southey, MC, English, DR, Waldenberger, M, Chambers, JC, Giles, GG, and Milne, RL

Abstract

DNA methylation may be one of the mechanisms by which alcohol consumption is associated with the risk of disease. We conducted a large-scale, cross-sectional, genome-wide DNA methylation association study of alcohol consumption and a longitudinal analysis of repeated measurements taken several years apart. Using the Illumina HumanMethylation450 BeadChip, DNA methylation was measured in blood samples from 5606 Melbourne Collaborative Cohort Study (MCCS) participants. For 1088 of them, these measures were repeated using blood samples collected a median of 11 years later. Associations between alcohol intake and blood DNA methylation were assessed using linear mixed-effects regression models. Independent data from the London Life Sciences Prospective Population (LOLIPOP) (N = 4042) and Cooperative Health Research in the Augsburg Region (KORA) (N = 1662) cohorts were used to replicate associations discovered in the MCCS. Cross-sectional analyses identified 1414 CpGs associated with alcohol intake at P < 10-7 , 1243 of which had not been reported previously. Of these novel associations, 1078 were replicated (P < .05) using LOLIPOP and KORA data. Using the MCCS data, we also replicated 403 of 518 previously reported associations. Interaction analyses suggested that associations were stronger for women, non-smokers, and participants genetically predisposed to consume less alcohol. Of the 1414 CpGs, 530 were differentially methylated (P < .05) in former compared with current drinkers. Longitudinal associations between the change in alcohol intake and the change in methylation were observed for 513 of the 1414 cross-sectional associations. Our study indicates that alcohol intake is associated with widespread changes in DNA methylation across the genome. Longitudinal analyses showed that the methylation status of alcohol-associated CpGs may change with alcohol consumption changes in adulthood.

Published
2021

12. Biological Aging Measures Based on Blood DNA Methylation and Risk of Cancer: A Prospective Study

Author

Dugue, P-A, Bassett, JK, Wong, EM, Joo, JE, Li, S, Yu, C, Schmidt, DF, Makalic, E, Doo, NW, Buchanan, DD, Hodge, AM, English, DR, Hopper, JL, Giles, GG, Southey, MC, Milne, RL, Dugue, P-A, Bassett, JK, Wong, EM, Joo, JE, Li, S, Yu, C, Schmidt, DF, Makalic, E, Doo, NW, Buchanan, DD, Hodge, AM, English, DR, Hopper, JL, Giles, GG, Southey, MC, and Milne, RL

Abstract

BACKGROUND: We previously investigated the association between 5 "first-generation" measures of epigenetic aging and cancer risk in the Melbourne Collaborative Cohort Study. This study assessed cancer risk associations for 3 recently developed methylation-based biomarkers of aging: PhenoAge, GrimAge, and predicted telomere length. METHODS: We estimated rate ratios (RRs) for the association between these 3 age-adjusted measures and risk of colorectal (N = 813), gastric (N = 165), kidney (N = 139), lung (N = 327), mature B-cell (N = 423), prostate (N = 846), and urothelial (N = 404) cancer using conditional logistic regression models. We also assessed associations by time since blood draw and by cancer subtype, and we investigated potential nonlinearity. RESULTS: We observed relatively strong associations of age-adjusted PhenoAge with risk of colorectal, kidney, lung, mature B-cell, and urothelial cancers (RR per SD was approximately 1.2-1.3). Similar findings were obtained for age-adjusted GrimAge, but the association with lung cancer risk was much larger (RR per SD = 1.82, 95% confidence interval [CI] = 1.44 to 2.30), after adjustment for smoking status, pack-years, starting age, time since quitting, and other cancer risk factors. Most associations appeared linear, larger than for the first-generation measures, and were virtually unchanged after adjustment for a large set of sociodemographic, lifestyle, and anthropometric variables. For cancer overall, the comprehensively adjusted rate ratio per SD was 1.13 (95% CI = 1.07 to 1.19) for PhenoAge and 1.12 (95% CI = 1.05 to 1.20) for GrimAge and appeared larger within 5 years of blood draw (RR = 1.29, 95% CI = 1.15 to 1.44 and 1.19, 95% CI = 1.06 to 1.33, respectively). CONCLUSIONS: The methylation-based measures PhenoAge and GrimAge may provide insights into the relationship between biological aging and cancer and be useful to predict cancer risk, particularly for lung cancer.

Published
2021

13. Going Beyond Conventional Mammographic Density to Discover Novel Mammogram-Based Predictors of Breast Cancer Risk.

Author

Hopper, JL, Nguyen, TL, Schmidt, DF, Makalic, E, Song, Y-M, Sung, J, Dite, GS, Dowty, JG, Li, S, Hopper, JL, Nguyen, TL, Schmidt, DF, Makalic, E, Song, Y-M, Sung, J, Dite, GS, Dowty, JG, and Li, S

Abstract

This commentary is about predicting a woman's breast cancer risk from her mammogram, building on the work of Wolfe, Boyd and Yaffe on mammographic density. We summarise our efforts at finding new mammogram-based risk predictors, and how they combine with the conventional mammographic density, in predicting risk for interval cancers and screen-detected breast cancers across different ages at diagnosis and for both Caucasian and Asian women. Using the OPERA (odds ratio per adjusted standard deviation) concept, in which the risk gradient is measured on an appropriate scale that takes into account other factors adjusted for by design or analysis, we show that our new mammogram-based measures are the strongest of all currently known breast cancer risk factors in terms of risk discrimination on a population-basis. We summarise our findings graphically using a path diagram in which conventional mammographic density predicts interval cancer due to its role in masking, while the new mammogram-based risk measures could have a causal effect on both interval and screen-detected breast cancer. We discuss attempts by others to pursue this line of investigation, the measurement challenge that allows different measures to be compared in an open and transparent manner on the same datasets, as well as the biological and public health consequences.

Published
2020

14. Transcriptome-wide association study of breast cancer risk by estrogen-receptor status

Author

Feng, H, Gusev, A, Pasaniuc, B, Wu, L, Long, J, Abu-full, Z, Aittomaki, K, Andrulis, IL, Anton-Culver, H, Antoniou, AC, Arason, A, Arndt, V, Aronson, KJ, Arun, BK, Asseryanis, E, Auer, PL, Azzollini, J, Balmana, J, Barkardottir, RB, Barnes, DR, Barrowdale, D, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Bialkowska, K, Blanco, A, Blomqvist, C, Boeckx, B, Bogdanova, NV, Bojesen, SE, Bolla, MK, Bonanni, B, Borg, A, Brauch, H, Brenner, H, Briceno, I, Broeks, A, Bruening, T, Burwinkel, B, Cai, Q, Caldes, T, Caligo, MA, Campbell, I, Canisius, S, Campa, D, Carter, BD, Carter, J, Castelao, JE, Chang-Claude, J, Chanock, SJ, Christiansen, H, Chung, WK, Claes, KBM, Clarke, CL, Couch, FJ, Cox, A, Cross, SS, Cybulski, C, Czene, K, Daly, MB, de la Hoya, M, De Leeneer, K, Dennis, J, Devilee, P, Diez, O, Domchek, SM, Doerk, T, dos-Santos-Silva, I, Dunning, AM, Dwek, M, Eccles, DM, Ejlertsen, B, Ellberg, C, Engel, C, Eriksson, M, Fasching, PA, Fletcher, O, Flyger, H, Fostira, F, Friedman, E, Fritschi, L, Frost, D, Gabrielson, M, Ganz, PA, Gapstur, SM, Garber, J, Garcia-Closas, M, Garcia-Saenz, JA, Gaudet, MM, Giles, GG, Glendon, G, Godwin, AK, Goldberg, MS, Goldgar, DE, Gonzalez-Neira, A, Greene, MH, Gronwald, J, Guenel, P, Haiman, CA, Hall, P, Hamann, U, Hake, C, He, W, Heyworth, J, Hogervorst, FBL, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Huang, G, Hulick, PJ, Humphreys, K, Imyanitov, EN, Isaacs, C, Jakimovska, M, Jakubowska, A, James, P, Janavicius, R, Jankowitz, RC, John, EM, Johnson, N, Joseph, V, Jung, A, Karlan, BY, Khusnutdinova, E, Kiiski, J, Konstantopoulou, I, Kristensen, VN, Laitman, Y, Lambrechts, D, Lazaro, C, Leroux, D, Leslie, G, Lester, J, Lesueur, F, Lindor, N, Lindstrom, S, Lo, W-Y, Loud, JT, Lubinski, J, Makalic, E, Mannermaa, A, Manoochehri, M, Manoukian, S, Margolin, S, Martens, JWM, Martinez, ME, Matricardi, L, Maurer, T, Mavroudis, D, McGuffog, L, Meindl, A, Menon, U, Michailidou, K, Kapoor, PM, Miller, A, Montagna, M, Moreno, F, Moserle, L, Mulligan, AM, Muranen, TA, Nathanson, KL, Neuhausen, SL, Nevanlinna, H, Nevelsteen, I, Nielsen, FC, Nikitina-Zake, L, Offit, K, Olah, E, Olopade, O, Olsson, H, Osorio, A, Papp, J, Park-Simon, T-W, Parsons, MT, Pedersen, IS, Peixoto, A, Peterlongo, P, Peto, J, Pharoah, PDP, Phillips, K-A, Plaseska-Karanfilska, D, Poppe, B, Pradhan, N, Prajzendanc, K, Presneau, N, Punie, K, Pylkas, K, Radice, P, Rantala, J, Rashid, MU, Rennert, G, Risch, HA, Robson, M, Romero, A, Saloustros, E, Sandler, DP, Santos, C, Sawyer, EJ, Schmidt, MK, Schmidt, DF, Schmutzler, RK, Schoemaker, MJ, Scott, RJ, Sharma, P, Shu, X-O, Simard, J, Singer, CF, Skytte, A-B, Soucy, P, Southey, MC, Spinelli, JJ, Spurdle, AB, Stone, J, Swerdlow, AJ, Tapper, WJ, Taylor, JA, Teixeira, MR, Terry, MB, Teule, A, Thomassen, M, Thoene, K, Thull, DL, Tischkowitz, M, Toland, AE, Tollenaar, RAEM, Torres, D, Truong, T, Tung, N, Vachon, CM, van Asperen, CJ, van den Ouweland, AMW, van Rensburg, EJ, Vega, A, Viel, A, Vieiro-Balo, P, Wang, Q, Wappenschmidt, B, Weinberg, CR, Weitzel, J, Wendt, C, Winqvist, R, Yang, XR, Yannoukakos, D, Ziogas, A, Milne, RL, Easton, DF, Chenevix-Trench, G, Zheng, W, Kraft, P, Jiang, X, Feng, H, Gusev, A, Pasaniuc, B, Wu, L, Long, J, Abu-full, Z, Aittomaki, K, Andrulis, IL, Anton-Culver, H, Antoniou, AC, Arason, A, Arndt, V, Aronson, KJ, Arun, BK, Asseryanis, E, Auer, PL, Azzollini, J, Balmana, J, Barkardottir, RB, Barnes, DR, Barrowdale, D, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Bialkowska, K, Blanco, A, Blomqvist, C, Boeckx, B, Bogdanova, NV, Bojesen, SE, Bolla, MK, Bonanni, B, Borg, A, Brauch, H, Brenner, H, Briceno, I, Broeks, A, Bruening, T, Burwinkel, B, Cai, Q, Caldes, T, Caligo, MA, Campbell, I, Canisius, S, Campa, D, Carter, BD, Carter, J, Castelao, JE, Chang-Claude, J, Chanock, SJ, Christiansen, H, Chung, WK, Claes, KBM, Clarke, CL, Couch, FJ, Cox, A, Cross, SS, Cybulski, C, Czene, K, Daly, MB, de la Hoya, M, De Leeneer, K, Dennis, J, Devilee, P, Diez, O, Domchek, SM, Doerk, T, dos-Santos-Silva, I, Dunning, AM, Dwek, M, Eccles, DM, Ejlertsen, B, Ellberg, C, Engel, C, Eriksson, M, Fasching, PA, Fletcher, O, Flyger, H, Fostira, F, Friedman, E, Fritschi, L, Frost, D, Gabrielson, M, Ganz, PA, Gapstur, SM, Garber, J, Garcia-Closas, M, Garcia-Saenz, JA, Gaudet, MM, Giles, GG, Glendon, G, Godwin, AK, Goldberg, MS, Goldgar, DE, Gonzalez-Neira, A, Greene, MH, Gronwald, J, Guenel, P, Haiman, CA, Hall, P, Hamann, U, Hake, C, He, W, Heyworth, J, Hogervorst, FBL, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Huang, G, Hulick, PJ, Humphreys, K, Imyanitov, EN, Isaacs, C, Jakimovska, M, Jakubowska, A, James, P, Janavicius, R, Jankowitz, RC, John, EM, Johnson, N, Joseph, V, Jung, A, Karlan, BY, Khusnutdinova, E, Kiiski, J, Konstantopoulou, I, Kristensen, VN, Laitman, Y, Lambrechts, D, Lazaro, C, Leroux, D, Leslie, G, Lester, J, Lesueur, F, Lindor, N, Lindstrom, S, Lo, W-Y, Loud, JT, Lubinski, J, Makalic, E, Mannermaa, A, Manoochehri, M, Manoukian, S, Margolin, S, Martens, JWM, Martinez, ME, Matricardi, L, Maurer, T, Mavroudis, D, McGuffog, L, Meindl, A, Menon, U, Michailidou, K, Kapoor, PM, Miller, A, Montagna, M, Moreno, F, Moserle, L, Mulligan, AM, Muranen, TA, Nathanson, KL, Neuhausen, SL, Nevanlinna, H, Nevelsteen, I, Nielsen, FC, Nikitina-Zake, L, Offit, K, Olah, E, Olopade, O, Olsson, H, Osorio, A, Papp, J, Park-Simon, T-W, Parsons, MT, Pedersen, IS, Peixoto, A, Peterlongo, P, Peto, J, Pharoah, PDP, Phillips, K-A, Plaseska-Karanfilska, D, Poppe, B, Pradhan, N, Prajzendanc, K, Presneau, N, Punie, K, Pylkas, K, Radice, P, Rantala, J, Rashid, MU, Rennert, G, Risch, HA, Robson, M, Romero, A, Saloustros, E, Sandler, DP, Santos, C, Sawyer, EJ, Schmidt, MK, Schmidt, DF, Schmutzler, RK, Schoemaker, MJ, Scott, RJ, Sharma, P, Shu, X-O, Simard, J, Singer, CF, Skytte, A-B, Soucy, P, Southey, MC, Spinelli, JJ, Spurdle, AB, Stone, J, Swerdlow, AJ, Tapper, WJ, Taylor, JA, Teixeira, MR, Terry, MB, Teule, A, Thomassen, M, Thoene, K, Thull, DL, Tischkowitz, M, Toland, AE, Tollenaar, RAEM, Torres, D, Truong, T, Tung, N, Vachon, CM, van Asperen, CJ, van den Ouweland, AMW, van Rensburg, EJ, Vega, A, Viel, A, Vieiro-Balo, P, Wang, Q, Wappenschmidt, B, Weinberg, CR, Weitzel, J, Wendt, C, Winqvist, R, Yang, XR, Yannoukakos, D, Ziogas, A, Milne, RL, Easton, DF, Chenevix-Trench, G, Zheng, W, Kraft, P, and Jiang, X

Abstract

Previous transcriptome-wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome-wide association studies (GWAS), but analyses of breast cancer subtype-specific associations have been limited. In this study, we conducted a TWAS using gene expression data from GTEx and summary statistics from the hitherto largest GWAS meta-analysis conducted for breast cancer overall, and by estrogen receptor subtypes (ER+ and ER-). We further compared associations with ER+ and ER- subtypes, using a case-only TWAS approach. We also conducted multigene conditional analyses in regions with multiple TWAS associations. Two genes, STXBP4 and HIST2H2BA, were specifically associated with ER+ but not with ER- breast cancer. We further identified 30 TWAS-significant genes associated with overall breast cancer risk, including four that were not identified in previous studies. Conditional analyses identified single independent breast-cancer gene in three of six regions harboring multiple TWAS-significant genes. Our study provides new information on breast cancer genetics and biology, particularly about genomic differences between ER+ and ER- breast cancer.

Published
2020

15. Ability of known susceptibility SNPs to predict colorectal cancer risk for persons with and without a family history

Author

Jenkins, MA, Win, AK, Dowty, JG, MacInnis, RJ, Makalic, E, Schmidt, DF, Dite, GS, Kapuscinski, M, Clendenning, M, Rosty, C, Winship, IM, Emery, JD, Saya, S, Macrae, FA, Ahnen, DJ, Duggan, D, Figueiredo, JC, Lindor, NM, Haile, RW, Potter, JD, Cotterchio, M, Gallinger, S, Newcomb, PA, Buchanan, DD, Casey, G, Hopper, JL, Jenkins, MA, Win, AK, Dowty, JG, MacInnis, RJ, Makalic, E, Schmidt, DF, Dite, GS, Kapuscinski, M, Clendenning, M, Rosty, C, Winship, IM, Emery, JD, Saya, S, Macrae, FA, Ahnen, DJ, Duggan, D, Figueiredo, JC, Lindor, NM, Haile, RW, Potter, JD, Cotterchio, M, Gallinger, S, Newcomb, PA, Buchanan, DD, Casey, G, and Hopper, JL

Abstract

Before SNP-based risk can be incorporated in colorectal cancer (CRC) screening, the ability of these SNPs to estimate CRC risk for persons with and without a family history of CRC, and the screening implications need to be determined. We estimated the association with CRC of a 45 SNP-based risk using 1181 cases and 999 controls, and its correlation with CRC risk predicted from detailed family history. We estimated the predicted change in the distribution across predefined risk categories, and implications for recommended screening commencement age, from adding SNP-based risk to family history. The inter-quintile risk ratio for colorectal cancer risk of the SNP-based risk was 3.28 (95% CI 2.54-4.22). SNP-based and family history-based risks were not correlated (r = 0.02). For persons with no first-degree relatives with CRC, screening could commence 4 years earlier for women (5 years for men) in the highest quintile of SNP-based risk. For persons with two first-degree relatives with CRC, screening could commence 16 years earlier for men and women in the highest quintile, and 7 years earlier for the lowest quintile. This 45 SNP panel in conjunction with family history, can identify people who could benefit from earlier screening. Risk reclassification by 45 SNPs could inform targeted screening for CRC prevention, particularly in clinical genetics settings when mutations in high-risk genes cannot be identified. Yet to be determined is cost-effectiveness, resources requirements, community, patient and clinician acceptance, and feasibility with potentially ethical, legal and insurance implications.

Published
2019

16. Genome-wide association study of germline variants and breast cancer-specific mortality

Author

Escala-Garcia, M, Guo, Q, Doerk, T, Canisius, S, Keeman, R, Dennis, J, Beesley, J, Lecarpentier, J, Bolla, MK, Wang, Q, Abraham, J, Andrulis, IL, Anton-Culver, H, Arndt, V, Auer, PL, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Bernstein, L, Blomqvist, C, Boeckx, B, Bojesen, SE, Bonanni, B, Borresen-Dale, A-L, Brauch, H, Brenner, H, Brentnall, A, Brinton, L, Broberg, P, Brock, IW, Brucker, SY, Burwinkel, B, Caldas, C, Caldes, T, Campa, D, Canzian, F, Carracedo, A, Carter, BD, Castelao, JE, Chang-Claude, J, Chanock, SJ, Chenevix-Trench, G, Cheng, T-YD, Chin, S-F, Clarke, CL, Cordina-Duverger, E, Couch, FJ, Cox, DG, Cox, A, Cross, SS, Czene, K, Daly, MB, Devilee, P, Dunn, JA, Dunning, AM, Durcan, L, Dwek, M, Earl, HM, Ekici, AB, Eliassen, AH, Ellberg, C, Engel, C, Eriksson, M, Evans, DG, Figueroa, J, Flesch-Janys, D, Flyger, H, Gabrielson, M, Gago-Dominguez, M, Galle, E, Gapstur, SM, Garcia-Closas, M, Garcia-Saenz, JA, Gaudet, MM, George, A, Georgoulias, V, Giles, GG, Glendon, G, Goldgar, DE, Gonzalez-Neira, A, Alnaes, GIG, Grip, M, Guenel, P, Haeberle, L, Hahnen, E, Haiman, CA, Hakansson, N, Hall, P, Hamann, U, Hankinson, S, Harkness, EF, Harrington, PA, Hart, SN, Hartikainen, JM, Hein, A, Hillemanns, P, Hiller, L, Holleczek, B, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Howell, A, Huang, G, Humphreys, K, Hunter, DJ, Janni, W, John, EM, Jones, ME, Jukkola-Vuorinen, A, Jung, A, Kaaks, R, Kabisch, M, Kaczmarek, K, Kerin, MJ, Khan, S, Khusnutdinova, E, Kiiski, J, Kitahara, CM, Knight, JA, Ko, Y-D, Koppert, LB, Kosma, V-M, Kraft, P, Kristensen, VN, Kruger, U, Kuehl, T, Lambrechts, D, Le Marchand, L, Lee, E, Lejbkowicz, F, Li, L, Lindblom, A, Lindstrom, S, Linet, M, Lissowska, J, Lo, W-Y, Loibl, S, Lubinski, J, Lux, MP, MacInnis, RJ, Maierthaler, M, Maishman, T, Makalic, E, Mannermaa, A, Manoochehri, M, Manoukian, S, Margolin, S, Martinez, ME, Mavroudis, D, McLean, C, Meindl, A, Middha, P, Miller, N, Milne, RL, Moreno, F, Mulligan, AM, Mulot, C, Nassir, R, Neuhausen, SL, Newman, WT, Nielsen, SF, Nordestgaard, BG, Norman, A, Olsson, H, Orr, N, Pankratz, VS, Park-Simon, T-W, Perez, JIA, Perez-Barrios, C, Peterlongo, P, Petridis, C, Pinchev, M, Prajzendanc, K, Prentice, R, Presneau, N, Prokofieva, D, Pylkas, K, Rack, B, Radice, P, Ramachandran, D, Rennert, G, Rennert, HS, Rhenius, V, Romero, A, Roylance, R, Saloustros, E, Sawyer, EJ, Schmidt, DF, Schmutzler, RK, Schneeweiss, A, Schoemaker, MJ, Schumacher, F, Schwentner, L, Scott, RJ, Scott, C, Seynaeve, C, Shah, M, Simard, J, Smeets, A, Sohn, C, Southey, MC, Swerdlow, AJ, Talhouk, A, Tamimi, RM, Tapper, WJ, Teixeira, MR, Tengstrom, M, Terry, MB, Thoene, K, Tollenaar, RAEM, Tomlinson, I, Torres, D, Truong, T, Turman, C, Turnbull, C, Ulmer, H-U, Untch, M, Vachon, C, van Asperen, CJ, van den Ouweland, AMW, van Veen, EM, Wendt, C, Whittemore, AS, Willett, W, Winqvist, R, Wolk, A, Yang, XR, Zhang, Y, Easton, DF, Fasching, PA, Nevanlinna, H, Eccles, DM, Pharoah, PDP, Schmidt, MK, Escala-Garcia, M, Guo, Q, Doerk, T, Canisius, S, Keeman, R, Dennis, J, Beesley, J, Lecarpentier, J, Bolla, MK, Wang, Q, Abraham, J, Andrulis, IL, Anton-Culver, H, Arndt, V, Auer, PL, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Bernstein, L, Blomqvist, C, Boeckx, B, Bojesen, SE, Bonanni, B, Borresen-Dale, A-L, Brauch, H, Brenner, H, Brentnall, A, Brinton, L, Broberg, P, Brock, IW, Brucker, SY, Burwinkel, B, Caldas, C, Caldes, T, Campa, D, Canzian, F, Carracedo, A, Carter, BD, Castelao, JE, Chang-Claude, J, Chanock, SJ, Chenevix-Trench, G, Cheng, T-YD, Chin, S-F, Clarke, CL, Cordina-Duverger, E, Couch, FJ, Cox, DG, Cox, A, Cross, SS, Czene, K, Daly, MB, Devilee, P, Dunn, JA, Dunning, AM, Durcan, L, Dwek, M, Earl, HM, Ekici, AB, Eliassen, AH, Ellberg, C, Engel, C, Eriksson, M, Evans, DG, Figueroa, J, Flesch-Janys, D, Flyger, H, Gabrielson, M, Gago-Dominguez, M, Galle, E, Gapstur, SM, Garcia-Closas, M, Garcia-Saenz, JA, Gaudet, MM, George, A, Georgoulias, V, Giles, GG, Glendon, G, Goldgar, DE, Gonzalez-Neira, A, Alnaes, GIG, Grip, M, Guenel, P, Haeberle, L, Hahnen, E, Haiman, CA, Hakansson, N, Hall, P, Hamann, U, Hankinson, S, Harkness, EF, Harrington, PA, Hart, SN, Hartikainen, JM, Hein, A, Hillemanns, P, Hiller, L, Holleczek, B, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Howell, A, Huang, G, Humphreys, K, Hunter, DJ, Janni, W, John, EM, Jones, ME, Jukkola-Vuorinen, A, Jung, A, Kaaks, R, Kabisch, M, Kaczmarek, K, Kerin, MJ, Khan, S, Khusnutdinova, E, Kiiski, J, Kitahara, CM, Knight, JA, Ko, Y-D, Koppert, LB, Kosma, V-M, Kraft, P, Kristensen, VN, Kruger, U, Kuehl, T, Lambrechts, D, Le Marchand, L, Lee, E, Lejbkowicz, F, Li, L, Lindblom, A, Lindstrom, S, Linet, M, Lissowska, J, Lo, W-Y, Loibl, S, Lubinski, J, Lux, MP, MacInnis, RJ, Maierthaler, M, Maishman, T, Makalic, E, Mannermaa, A, Manoochehri, M, Manoukian, S, Margolin, S, Martinez, ME, Mavroudis, D, McLean, C, Meindl, A, Middha, P, Miller, N, Milne, RL, Moreno, F, Mulligan, AM, Mulot, C, Nassir, R, Neuhausen, SL, Newman, WT, Nielsen, SF, Nordestgaard, BG, Norman, A, Olsson, H, Orr, N, Pankratz, VS, Park-Simon, T-W, Perez, JIA, Perez-Barrios, C, Peterlongo, P, Petridis, C, Pinchev, M, Prajzendanc, K, Prentice, R, Presneau, N, Prokofieva, D, Pylkas, K, Rack, B, Radice, P, Ramachandran, D, Rennert, G, Rennert, HS, Rhenius, V, Romero, A, Roylance, R, Saloustros, E, Sawyer, EJ, Schmidt, DF, Schmutzler, RK, Schneeweiss, A, Schoemaker, MJ, Schumacher, F, Schwentner, L, Scott, RJ, Scott, C, Seynaeve, C, Shah, M, Simard, J, Smeets, A, Sohn, C, Southey, MC, Swerdlow, AJ, Talhouk, A, Tamimi, RM, Tapper, WJ, Teixeira, MR, Tengstrom, M, Terry, MB, Thoene, K, Tollenaar, RAEM, Tomlinson, I, Torres, D, Truong, T, Turman, C, Turnbull, C, Ulmer, H-U, Untch, M, Vachon, C, van Asperen, CJ, van den Ouweland, AMW, van Veen, EM, Wendt, C, Whittemore, AS, Willett, W, Winqvist, R, Wolk, A, Yang, XR, Zhang, Y, Easton, DF, Fasching, PA, Nevanlinna, H, Eccles, DM, Pharoah, PDP, and Schmidt, MK

Abstract

BACKGROUND: We examined the associations between germline variants and breast cancer mortality using a large meta-analysis of women of European ancestry. METHODS: Meta-analyses included summary estimates based on Cox models of twelve datasets using ~10.4 million variants for 96,661 women with breast cancer and 7697 events (breast cancer-specific deaths). Oestrogen receptor (ER)-specific analyses were based on 64,171 ER-positive (4116) and 16,172 ER-negative (2125) patients. We evaluated the probability of a signal to be a true positive using the Bayesian false discovery probability (BFDP). RESULTS: We did not find any variant associated with breast cancer-specific mortality at P < 5 × 10-8. For ER-positive disease, the most significantly associated variant was chr7:rs4717568 (BFDP = 7%, P = 1.28 × 10-7, hazard ratio [HR] = 0.88, 95% confidence interval [CI] = 0.84-0.92); the closest gene is AUTS2. For ER-negative disease, the most significant variant was chr7:rs67918676 (BFDP = 11%, P = 1.38 × 10-7, HR = 1.27, 95% CI = 1.16-1.39); located within a long intergenic non-coding RNA gene (AC004009.3), close to the HOXA gene cluster. CONCLUSIONS: We uncovered germline variants on chromosome 7 at BFDP < 15% close to genes for which there is biological evidence related to breast cancer outcome. However, the paucity of variants associated with mortality at genome-wide significance underpins the challenge in providing genetic-based individualised prognostic information for breast cancer patients.

Published
2019

17. Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

Author

Mavaddat, N, Michailidou, K, Dennis, J, Lush, M, Fachal, L, Lee, A, Tyrer, JP, Chen, T-H, Wang, Q, Bolla, MK, Yang, X, Adank, MA, Ahearn, T, Aittomaki, K, Allen, J, Andrulis, IL, Anton-Culver, H, Antonenkova, NN, Arndt, V, Aronson, KJ, Auer, PL, Auvinen, P, Barrdahl, M, Freeman, LEB, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Bernstein, L, Blomqvist, C, Bogdanova, N, Bojesen, SE, Bonanni, B, Borresen-Dale, A-L, Brauch, H, Bremer, M, Brenner, H, Brentnall, A, Brock, IW, Brooks-Wilson, A, Brucker, SY, Bruening, T, Burwinkel, B, Campa, D, Carter, BD, Castelao, JE, Chanock, SJ, Chlebowski, R, Christiansen, H, Clarke, CL, Collee, JM, Cordina-Duverger, E, Cornelissen, S, Couch, FJ, Cox, A, Cross, SS, Czene, K, Daly, MB, Devilee, P, Doerk, T, dos-Santos-Silva, I, Dumont, M, Durcan, L, Dwek, M, Eccles, DM, Ekici, AB, Eliassen, AH, Ellberg, C, Engel, C, Eriksson, M, Evans, DG, Fasching, PA, Figueroa, J, Fletcher, O, Flyger, H, Foersti, A, Fritschi, L, Gabrielson, M, Gago-Dominguez, M, Gapstur, SM, Garcia-Saenz, JA, Gaudet, MM, Georgoulias, V, Giles, GG, Gilyazova, IR, Glendon, G, Goldberg, MS, Goldgar, DE, Gonzalez-Neira, A, Alnaes, GIG, Grip, M, Gronwald, J, Grundy, A, Guenel, P, Haeberle, L, Hahnen, E, Haiman, CA, Hakansson, N, Hamann, U, Hankinson, SE, Harkness, EF, Hart, SN, He, W, Hein, A, Heyworth, J, Hillemanns, P, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Howell, A, Huang, G, Humphreys, K, Hunter, DJ, Jakimovska, M, Jakubowska, A, Janni, W, John, EM, Johnson, N, Jones, ME, Jukkola-Vuorinen, A, Jung, A, Kaaks, R, Kaczmarek, K, Kataja, V, Keeman, R, Kerin, MJ, Khusnutdinova, E, Kiiski, J, Knight, JA, Ko, Y-D, Kosma, V-M, Koutros, S, Kristensen, VN, Kruger, U, Kuehl, T, Lambrechts, D, Le Marchand, L, Lee, E, Lejbkowicz, F, Lilyquist, J, Lindblom, A, Lindstrom, S, Lissowska, J, Lo, W-Y, Loibl, S, Long, J, Lubinski, J, Lux, MP, MacInnis, RJ, Maishman, T, Makalic, E, Kostovska, IM, Mannermaa, A, Manoukian, S, Margolin, S, Martens, JWM, Martinez, ME, Mavroudis, D, McLean, C, Meindl, A, Menon, U, Middha, P, Miller, N, Moreno, F, Mulligan, AM, Mulot, C, Munoz-Garzon, VM, Neuhausen, SL, Nevanlinna, H, Neven, P, Newman, WG, Nielsen, SF, Nordestgaard, BG, Norman, A, Offit, K, Olson, JE, Olsson, H, Orr, N, Pankratz, VS, Park-Simon, T-W, Perez, JIA, Perez-Barrios, C, Peterlongo, P, Peto, J, Pinchev, M, Plaseska-Karanfilska, D, Polley, EC, Prentice, R, Presneau, N, Prokofyeva, D, Purrington, K, Pylkas, K, Rack, B, Radice, P, Rau-Murthy, R, Rennert, G, Rennert, HS, Rhenius, V, Robson, M, Romero, A, Ruddy, KJ, Ruebner, M, Saloustros, E, Sandler, DP, Sawyer, EJ, Schmidt, DF, Schmutzler, RK, Schneeweiss, A, Schoemaker, MJ, Schumacher, F, Schuermann, P, Schwentner, L, Scott, C, Scott, RJ, Seynaeve, C, Shah, M, Sherman, ME, Shrubsole, MJ, Shu, X-O, Slager, S, Smeets, A, Sohn, C, Soucy, P, Southey, MC, Spinelli, JJ, Stegmaier, C, Stone, J, Swerdlow, AJ, Tamimi, RM, Tapper, WJ, Taylor, JA, Terry, MB, Thoene, K, Tollenaar, RAEM, Tomlinson, I, Truong, T, Tzardi, M, Ulmer, H-U, Untch, M, Vachon, CM, van Veen, EM, Vijai, J, Weinberg, CR, Wendt, C, Whittemore, AS, Wildiers, H, Willett, W, Winqvist, R, Wolk, A, Yang, XR, Yannoukakos, D, Zhang, Y, Zheng, W, Ziogas, A, Clarke, C, Balleine, R, Baxter, R, Braye, S, Carpenter, J, Dahlstrom, J, Forbes, J, Lee, CS, Marsh, D, Morey, A, Pathmanathan, N, Scott, R, Simpson, P, Spigelman, A, Wilcken, N, Yip, D, Zeps, N, Sexton, A, Dobrovic, A, Christian, A, Trainer, A, Fellows, A, Shelling, A, De Fazio, A, Blackburn, A, Crook, A, Meiser, B, Patterson, B, Saunders, C, Hunt, C, Amor, D, Ortega, DG, Edkins, E, Salisbury, E, Haan, E, Macrea, F, Farshid, G, Lindeman, G, Trench, G, Mann, G, Giles, G, Gill, G, Thorne, H, Campbell, I, Hickie, I, Caldon, L, Winship, I, Cui, J, Flanagan, J, Kollias, J, Visvader, J, Taylor, J, Burke, J, Saunus, J, Forbs, J, Hopper, J, Beesley, J, Kirk, J, French, J, Tucker, K, Wu, K, Phillips, K, Forrest, L, Lipton, L, Andrews, L, Lobb, L, Walker, L, Kentwell, M, Spurdle, M, Cummings, M, Gleeson, M, Harris, M, Jenkins, M, Young, MA, Delatycki, M, Wallis, M, Burgess, M, Brown, M, Southey, M, Bogwitz, M, Field, M, Friedlander, M, Gattas, M, Saleh, M, Aghmesheh, M, Hayward, N, Pachter, N, Cohen, P, Duijf, P, James, P, Fong, P, Butow, P, Williams, R, Kefford, R, Simard, J, Balleine, R-M, Dawson, S-J, Lok, S, O'connell, S, Greening, S, Nightingale, S, Edwards, S, Fox, S, McLachlan, S-A, Lakhani, S, Dudding, T, Antill, Y, Sahlberg, KK, Ottestad, L, Karesen, R, Schlichting, E, Holmen, MM, Sauer, T, Haakensen, V, Engebraten, O, Naume, B, Fossa, A, Kiserud, CE, Reinertsen, K, Helland, A, Riis, M, Geisler, J, Dunning, AM, Thompson, DJ, Chenevix-Trench, G, Chang-Claude, J, Schmidt, MK, Hall, P, Milne, RL, Pharoah, PDP, Antoniou, AC, Chatterjee, N, Kraft, P, Garcia-Closas, M, Easton, DF, Mavaddat, N, Michailidou, K, Dennis, J, Lush, M, Fachal, L, Lee, A, Tyrer, JP, Chen, T-H, Wang, Q, Bolla, MK, Yang, X, Adank, MA, Ahearn, T, Aittomaki, K, Allen, J, Andrulis, IL, Anton-Culver, H, Antonenkova, NN, Arndt, V, Aronson, KJ, Auer, PL, Auvinen, P, Barrdahl, M, Freeman, LEB, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Bernstein, L, Blomqvist, C, Bogdanova, N, Bojesen, SE, Bonanni, B, Borresen-Dale, A-L, Brauch, H, Bremer, M, Brenner, H, Brentnall, A, Brock, IW, Brooks-Wilson, A, Brucker, SY, Bruening, T, Burwinkel, B, Campa, D, Carter, BD, Castelao, JE, Chanock, SJ, Chlebowski, R, Christiansen, H, Clarke, CL, Collee, JM, Cordina-Duverger, E, Cornelissen, S, Couch, FJ, Cox, A, Cross, SS, Czene, K, Daly, MB, Devilee, P, Doerk, T, dos-Santos-Silva, I, Dumont, M, Durcan, L, Dwek, M, Eccles, DM, Ekici, AB, Eliassen, AH, Ellberg, C, Engel, C, Eriksson, M, Evans, DG, Fasching, PA, Figueroa, J, Fletcher, O, Flyger, H, Foersti, A, Fritschi, L, Gabrielson, M, Gago-Dominguez, M, Gapstur, SM, Garcia-Saenz, JA, Gaudet, MM, Georgoulias, V, Giles, GG, Gilyazova, IR, Glendon, G, Goldberg, MS, Goldgar, DE, Gonzalez-Neira, A, Alnaes, GIG, Grip, M, Gronwald, J, Grundy, A, Guenel, P, Haeberle, L, Hahnen, E, Haiman, CA, Hakansson, N, Hamann, U, Hankinson, SE, Harkness, EF, Hart, SN, He, W, Hein, A, Heyworth, J, Hillemanns, P, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Howell, A, Huang, G, Humphreys, K, Hunter, DJ, Jakimovska, M, Jakubowska, A, Janni, W, John, EM, Johnson, N, Jones, ME, Jukkola-Vuorinen, A, Jung, A, Kaaks, R, Kaczmarek, K, Kataja, V, Keeman, R, Kerin, MJ, Khusnutdinova, E, Kiiski, J, Knight, JA, Ko, Y-D, Kosma, V-M, Koutros, S, Kristensen, VN, Kruger, U, Kuehl, T, Lambrechts, D, Le Marchand, L, Lee, E, Lejbkowicz, F, Lilyquist, J, Lindblom, A, Lindstrom, S, Lissowska, J, Lo, W-Y, Loibl, S, Long, J, Lubinski, J, Lux, MP, MacInnis, RJ, Maishman, T, Makalic, E, Kostovska, IM, Mannermaa, A, Manoukian, S, Margolin, S, Martens, JWM, Martinez, ME, Mavroudis, D, McLean, C, Meindl, A, Menon, U, Middha, P, Miller, N, Moreno, F, Mulligan, AM, Mulot, C, Munoz-Garzon, VM, Neuhausen, SL, Nevanlinna, H, Neven, P, Newman, WG, Nielsen, SF, Nordestgaard, BG, Norman, A, Offit, K, Olson, JE, Olsson, H, Orr, N, Pankratz, VS, Park-Simon, T-W, Perez, JIA, Perez-Barrios, C, Peterlongo, P, Peto, J, Pinchev, M, Plaseska-Karanfilska, D, Polley, EC, Prentice, R, Presneau, N, Prokofyeva, D, Purrington, K, Pylkas, K, Rack, B, Radice, P, Rau-Murthy, R, Rennert, G, Rennert, HS, Rhenius, V, Robson, M, Romero, A, Ruddy, KJ, Ruebner, M, Saloustros, E, Sandler, DP, Sawyer, EJ, Schmidt, DF, Schmutzler, RK, Schneeweiss, A, Schoemaker, MJ, Schumacher, F, Schuermann, P, Schwentner, L, Scott, C, Scott, RJ, Seynaeve, C, Shah, M, Sherman, ME, Shrubsole, MJ, Shu, X-O, Slager, S, Smeets, A, Sohn, C, Soucy, P, Southey, MC, Spinelli, JJ, Stegmaier, C, Stone, J, Swerdlow, AJ, Tamimi, RM, Tapper, WJ, Taylor, JA, Terry, MB, Thoene, K, Tollenaar, RAEM, Tomlinson, I, Truong, T, Tzardi, M, Ulmer, H-U, Untch, M, Vachon, CM, van Veen, EM, Vijai, J, Weinberg, CR, Wendt, C, Whittemore, AS, Wildiers, H, Willett, W, Winqvist, R, Wolk, A, Yang, XR, Yannoukakos, D, Zhang, Y, Zheng, W, Ziogas, A, Clarke, C, Balleine, R, Baxter, R, Braye, S, Carpenter, J, Dahlstrom, J, Forbes, J, Lee, CS, Marsh, D, Morey, A, Pathmanathan, N, Scott, R, Simpson, P, Spigelman, A, Wilcken, N, Yip, D, Zeps, N, Sexton, A, Dobrovic, A, Christian, A, Trainer, A, Fellows, A, Shelling, A, De Fazio, A, Blackburn, A, Crook, A, Meiser, B, Patterson, B, Saunders, C, Hunt, C, Amor, D, Ortega, DG, Edkins, E, Salisbury, E, Haan, E, Macrea, F, Farshid, G, Lindeman, G, Trench, G, Mann, G, Giles, G, Gill, G, Thorne, H, Campbell, I, Hickie, I, Caldon, L, Winship, I, Cui, J, Flanagan, J, Kollias, J, Visvader, J, Taylor, J, Burke, J, Saunus, J, Forbs, J, Hopper, J, Beesley, J, Kirk, J, French, J, Tucker, K, Wu, K, Phillips, K, Forrest, L, Lipton, L, Andrews, L, Lobb, L, Walker, L, Kentwell, M, Spurdle, M, Cummings, M, Gleeson, M, Harris, M, Jenkins, M, Young, MA, Delatycki, M, Wallis, M, Burgess, M, Brown, M, Southey, M, Bogwitz, M, Field, M, Friedlander, M, Gattas, M, Saleh, M, Aghmesheh, M, Hayward, N, Pachter, N, Cohen, P, Duijf, P, James, P, Fong, P, Butow, P, Williams, R, Kefford, R, Simard, J, Balleine, R-M, Dawson, S-J, Lok, S, O'connell, S, Greening, S, Nightingale, S, Edwards, S, Fox, S, McLachlan, S-A, Lakhani, S, Dudding, T, Antill, Y, Sahlberg, KK, Ottestad, L, Karesen, R, Schlichting, E, Holmen, MM, Sauer, T, Haakensen, V, Engebraten, O, Naume, B, Fossa, A, Kiserud, CE, Reinertsen, K, Helland, A, Riis, M, Geisler, J, Dunning, AM, Thompson, DJ, Chenevix-Trench, G, Chang-Claude, J, Schmidt, MK, Hall, P, Milne, RL, Pharoah, PDP, Antoniou, AC, Chatterjee, N, Kraft, P, Garcia-Closas, M, and Easton, DF

Abstract

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.

Published
2019

18. Cirrus: An Automated Mammography-Based Measure of Breast Cancer Risk Based on Textural Features

Author

Schmidt, DF, Makalic, E, Goudey, B, Dite, GS, Stone, J, Nguyen, TL, Dowty, JG, Baglietto, L, Southey, MC, Maskarinec, G, Giles, GG, Hopper, JL, Schmidt, DF, Makalic, E, Goudey, B, Dite, GS, Stone, J, Nguyen, TL, Dowty, JG, Baglietto, L, Southey, MC, Maskarinec, G, Giles, GG, and Hopper, JL

Abstract

Background We applied machine learning to find a novel breast cancer predictor based on information in a mammogram. Methods Using image-processing techniques, we automatically processed 46 158 analog mammograms for 1345 cases and 4235 controls from a cohort and case–control study of Australian women, and a cohort study of Japanese American women, extracting 20 textural features not based on pixel brightness threshold. We used Bayesian lasso regression to create individual- and mammogram-specific measures of breast cancer risk, Cirrus. We trained and tested measures across studies. We fitted Cirrus with conventional mammographic density measures using logistic regression, and computed odds ratios (OR) per standard deviation adjusted for age and body mass index. Results Combining studies, almost all textural features were associated with case–control status. The ORs for Cirrus measures trained on one study and tested on another study ranged from 1.56 to 1.78 (all P < 10−6). For the Cirrus measure derived from combining studies, the OR was 1.90 (95% confidence interval [CI] = 1.73 to 2.09), equivalent to a fourfold interquartile risk ratio, and was little attenuated after adjusting for conventional measures. In contrast, the OR for the conventional measure was 1.34 (95% CI = 1.25 to 1.43), and after adjusting for Cirrus it became 1.16 (95% CI = 1.08 to 1.24; P = 4 × 10−5). Conclusions A fully automated personal risk measure created from combining textural image features performs better at predicting breast cancer risk than conventional mammographic density risk measures, capturing half the risk-predicting ability of the latter measures. In terms of differentiating affected and unaffected women on a population basis, Cirrus could be one of the strongest known risk factors for breast cancer.

Published
2018

19. Genetic susceptibility markers for a breast-colorectal cancer phenotype: Exploratory results from genome-wide association studies

Author

Toland, AE, Pande, M, Joon, A, Brewster, AM, Chen, WV, Hoppers, JL, Eng, C, Shete, S, Casey, G, Schumacher, F, Lin, Y, Harrison, TA, White, E, Ahsan, H, Andrulis, IL, Whittemore, AS, John, EM, Win, AK, Makalic, E, Schmidt, DF, Kapuscinski, MK, Ochs-Balcom, HM, Gallinger, S, Jenkins, MA, Newcomb, PA, Lindor, NM, Peters, U, Amos, CI, Lynch, PM, Toland, AE, Pande, M, Joon, A, Brewster, AM, Chen, WV, Hoppers, JL, Eng, C, Shete, S, Casey, G, Schumacher, F, Lin, Y, Harrison, TA, White, E, Ahsan, H, Andrulis, IL, Whittemore, AS, John, EM, Win, AK, Makalic, E, Schmidt, DF, Kapuscinski, MK, Ochs-Balcom, HM, Gallinger, S, Jenkins, MA, Newcomb, PA, Lindor, NM, Peters, U, Amos, CI, and Lynch, PM

Abstract

BACKGROUND: Clustering of breast and colorectal cancer has been observed within some families and cannot be explained by chance or known high-risk mutations in major susceptibility genes. Potential shared genetic susceptibility between breast and colorectal cancer, not explained by high-penetrance genes, has been postulated. We hypothesized that yet undiscovered genetic variants predispose to a breast-colorectal cancer phenotype. METHODS: To identify variants associated with a breast-colorectal cancer phenotype, we analyzed genome-wide association study (GWAS) data from cases and controls that met the following criteria: cases (n = 985) were women with breast cancer who had one or more first- or second-degree relatives with colorectal cancer, men/women with colorectal cancer who had one or more first- or second-degree relatives with breast cancer, and women diagnosed with both breast and colorectal cancer. Controls (n = 1769), were unrelated, breast and colorectal cancer-free, and age- and sex- frequency-matched to cases. After imputation, 6,220,060 variants were analyzed using the discovery set and variants associated with the breast-colorectal cancer phenotype at P<5.0E-04 (n = 549, at 60 loci) were analyzed for replication (n = 293 cases and 2,103 controls). RESULTS: Multiple correlated SNPs in intron 1 of the ROBO1 gene were suggestively associated with the breast-colorectal cancer phenotype in the discovery and replication data (most significant; rs7430339, Pdiscovery = 1.2E-04; rs7429100, Preplication = 2.8E-03). In meta-analysis of the discovery and replication data, the most significant association remained at rs7429100 (P = 1.84E-06). CONCLUSION: The results of this exploratory analysis did not find clear evidence for a susceptibility locus with a pleiotropic effect on hereditary breast and colorectal cancer risk, although the suggestive association of genetic variation in the region of ROBO1, a potential tumor suppressor gene, merits further investigation.

Published
2018

20. Heritable methylation marks associated with breast and prostate cancer risk

Author

Dugue, P-A, Dowty, JG, Joo, JE, Wong, EM, Makalic, E, Schmidt, DF, English, DR, Hopper, JL, Pedersen, J, Severi, G, MacInnis, RJ, Milne, RL, Giles, GG, Southey, MC, Dugue, P-A, Dowty, JG, Joo, JE, Wong, EM, Makalic, E, Schmidt, DF, English, DR, Hopper, JL, Pedersen, J, Severi, G, MacInnis, RJ, Milne, RL, Giles, GG, and Southey, MC

Abstract

BACKGROUND: DNA methylation can mimic the effects of germline mutations in cancer predisposition genes. Recently, we identified twenty-four heritable methylation marks associated with breast cancer risk. As breast and prostate cancer share genetic risk factors, including rare, high-risk mutations (eg, in BRCA2), we hypothesized that some of these heritable methylation marks might also be associated with the risk of prostate cancer. METHODS: We studied 869 incident prostate cancers (430 aggressive and 439 non-aggressive) and 869 matched controls nested within a prospective cohort study. DNA methylation was measured in pre-diagnostic blood samples using the Illumina Infinium HM450K BeadChip. Conditional logistic regression models, adjusted for prostate cancer risk factors and blood cell composition, were used to estimate odds ratios and 95% confidence intervals for the association between the 24 methylation marks and the risk of prostate cancer. RESULTS: Five methylation marks within the VTRNA2-1 promoter region (cg06536614, cg00124993, cg26328633, cg25340688, and cg26896946), and one in the body of CLGN (cg22901919) were associated with the risk of prostate cancer. In stratified analyses, the five VTRNA2-1 marks were associated with the risk of aggressive prostate cancer. CONCLUSIONS: This work highlights a potentially important new area of investigation for prostate cancer susceptibility and adds to our knowledge about shared risk factors for breast and prostate cancer.

Published
2018

21. Genome-Wide Measures of Peripheral Blood Dna Methylation and Prostate Cancer Risk in a Prospective Nested Case-Control Study

Author

FitzGerald, LM, Naeem, H, Makalic, E, Schmidt, DF, Dowty, JG, Joo, JE, Jung, C-H, Bassett, JK, Dugue, P-A, Chung, J, Lonie, A, Milne, RL, Wong, EM, Hopper, JL, English, DR, Severi, G, Baglietto, L, Pedersen, J, Giles, GG, Southey, MC, FitzGerald, LM, Naeem, H, Makalic, E, Schmidt, DF, Dowty, JG, Joo, JE, Jung, C-H, Bassett, JK, Dugue, P-A, Chung, J, Lonie, A, Milne, RL, Wong, EM, Hopper, JL, English, DR, Severi, G, Baglietto, L, Pedersen, J, Giles, GG, and Southey, MC

Published
2017

22. Genome-wide measures of DNA methylation in peripheral blood and the risk of urothelial cell carcinoma: a prospective nested case-control study

Author

Dugue, P-A, Brinkman, MT, Milne, RL, Wong, EM, FitzGerald, LM, Bassett, JK, Joo, JE, Jung, C-H, Makalic, E, Schmidt, DF, Park, DJ, Chung, J, Ta, AD, Bolton, DM, Lonie, A, Longano, A, Hopper, JL, Severi, G, Saffery, R, English, DR, Southey, MC, Giles, GG, Dugue, P-A, Brinkman, MT, Milne, RL, Wong, EM, FitzGerald, LM, Bassett, JK, Joo, JE, Jung, C-H, Makalic, E, Schmidt, DF, Park, DJ, Chung, J, Ta, AD, Bolton, DM, Lonie, A, Longano, A, Hopper, JL, Severi, G, Saffery, R, English, DR, Southey, MC, and Giles, GG

Abstract

BACKGROUND: Global DNA methylation has been reported to be associated with urothelial cell carcinoma (UCC) by studies using blood samples collected at diagnosis. Using the Illumina HumanMethylation450 assay, we derived genome-wide measures of blood DNA methylation and assessed them for their prospective association with UCC risk. METHODS: We used 439 case-control pairs from the Melbourne Collaborative Cohort Study matched on age, sex, country of birth, DNA sample type, and collection period. Conditional logistic regression was used to compute odds ratios (OR) of UCC risk per s.d. of each genome-wide measure of DNA methylation and 95% confidence intervals (CIs), adjusted for potential confounders. We also investigated associations by disease subtype, sex, smoking, and time since blood collection. RESULTS: The risk of superficial UCC was decreased for individuals with higher levels of our genome-wide DNA methylation measure (OR=0.71, 95% CI: 0.54-0.94; P=0.02). This association was particularly strong for current smokers at sample collection (OR=0.47, 95% CI: 0.27-0.83). Intermediate levels of our genome-wide measure were associated with decreased risk of invasive UCC. Some variation was observed between UCC subtypes and the location and regulatory function of the CpGs included in the genome-wide measures of methylation. CONCLUSIONS: Higher levels of our genome-wide DNA methylation measure were associated with decreased risk of superficial UCC and intermediate levels were associated with reduced risk of invasive disease. These findings require replication by other prospective studies.

Published
2016

23. Identification of four novel susceptibility loci for oestrogen receptor negative breast cancer

Author

Couch, FJ, Kuchenbaecker, KB, Michailidou, K, Mendoza-Fandino, GA, Nord, S, Lilyquist, J, Olswold, C, Hallberg, E, Agata, S, Ahsan, H, Aittomaeki, K, Ambrosone, C, Andrulis, IL, Anton-Culver, H, Arndt, V, Arun, BK, Arver, B, Barile, M, Barkardottir, RB, Barrowdale, D, Beckmann, L, Beckmann, MW, Benitez, J, Blank, SV, Blomqvist, C, Bogdanova, NV, Bojesen, SE, Bolla, MK, Bonanni, B, Brauch, H, Brenner, H, Burwinkel, B, Buys, SS, Caldes, T, Caligo, MA, Canzian, F, Carpenter, J, Chang-Claude, J, Chanock, SJ, Chung, WK, Claes, KBM, Cox, A, Cross, SS, Cunningham, JM, Czene, K, Daly, MB, Damiola, F, Darabi, H, de la Hoya, M, Devilee, P, Diez, O, Ding, YC, Dolcetti, R, Domchek, SM, Dorfling, CM, dos-Santos-Silva, I, Dumont, M, Dunning, AM, Eccles, DM, Ehrencrona, H, Ekici, AB, Eliassen, H, Ellis, S, Fasching, PA, Figueroa, J, Flesch-Janys, D, Foersti, A, Fostira, F, Foulkes, WD, Friebel, T, Friedman, E, Frost, D, Gabrielson, M, Gammon, MD, Ganz, PA, Gapstur, SM, Garber, J, Gaudet, MM, Gayther, SA, Gerdes, A-M, Ghoussaini, M, Giles, GG, Glendon, G, Godwin, AK, Goldberg, MS, Goldgar, DE, Gonzalez-Neira, A, Greene, MH, Gronwald, J, Guenel, P, Gunter, M, Haeberle, L, Haiman, CA, Hamann, U, Hansen, TVO, Hart, S, Healey, S, Heikkinen, T, Henderson, BE, Herzog, J, Hogervorst, FBL, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Humphreys, K, Hunter, DJ, Huzarski, T, Imyanitov, EN, Isaacs, C, Jakubowska, A, James, P, Janavicius, R, Jensen, UB, John, EM, Jones, M, Kabisch, M, Kar, S, Karlan, BY, Khan, S, Khaw, K-T, Kibriya, MG, Knight, JA, Ko, Y-D, Konstantopoulou, I, Kosma, V-M, Kristensen, V, Kwong, A, Laitman, Y, Lambrechts, D, Lazaro, C, Lee, E, Le Marchand, L, Lester, J, Lindblom, A, Lindor, N, Lindstrom, S, Liu, J, Long, J, Lubinski, J, Mai, PL, Makalic, E, Malone, KE, Mannermaa, A, Manoukian, S, Margolin, S, Marme, F, Martens, JWM, McGuffog, L, Meindl, A, Miller, A, Milne, RL, Miron, P, Montagna, M, Mazoyer, S, Mulligan, AM, Muranen, TA, Nathanson, KL, Neuhausen, SL, Nevanlinna, H, Nordestgaard, BG, Nussbaum, RL, Offit, K, Olah, E, Olopade, OI, Olson, JE, Osorio, A, Park, SK, Peeters, PH, Peissel, B, Peterlongo, P, Peto, J, Phelan, CM, Pilarski, R, Poppe, B, Pylkaes, K, Radice, P, Rahman, N, Rantala, J, Rappaport, C, Rennert, G, Richardson, A, Robson, M, Romieu, I, Rudolph, A, Rutgers, EJ, Sanchez, M-J, Santella, RM, Sawyer, EJ, Schmidt, DF, Schmidt, MK, Schmutzler, RK, Schumacher, F, Scott, R, Senter, L, Sharma, P, Simard, J, Singer, CF, Sinilnikova, OM, Soucy, P, Southey, M, Steinemann, D, Stenmark-Askmalm, M, Stoppa-Lyonnet, D, Swerdlow, A, Szabo, CI, Tamimi, R, Tapper, W, Teixeira, MR, Teo, S-H, Terry, MB, Thomassen, M, Thompson, D, Tihomirova, L, Toland, AE, Tollenaar, RAEM, Tomlinson, I, Truong, T, Tsimiklis, H, Teule, A, Tumino, R, Tung, N, Turnbull, C, Ursin, G, van Deurzen, CHM, van Rensburg, EJ, Varon-Mateeva, R, Wang, Z, Wang-Gohrke, S, Weiderpass, E, Weitzel, JN, Whittemore, A, Wildiers, H, Winqvist, R, Yang, XR, Yannoukakos, D, Yao, S, Zamora, MP, Zheng, W, Hall, P, Kraft, P, Vachon, C, Slager, S, Chenevix-Trench, G, Pharoah, PDP, Monteiro, AAN, Garcia-Closas, M, Easton, DF, Antoniou, AC, Couch, FJ, Kuchenbaecker, KB, Michailidou, K, Mendoza-Fandino, GA, Nord, S, Lilyquist, J, Olswold, C, Hallberg, E, Agata, S, Ahsan, H, Aittomaeki, K, Ambrosone, C, Andrulis, IL, Anton-Culver, H, Arndt, V, Arun, BK, Arver, B, Barile, M, Barkardottir, RB, Barrowdale, D, Beckmann, L, Beckmann, MW, Benitez, J, Blank, SV, Blomqvist, C, Bogdanova, NV, Bojesen, SE, Bolla, MK, Bonanni, B, Brauch, H, Brenner, H, Burwinkel, B, Buys, SS, Caldes, T, Caligo, MA, Canzian, F, Carpenter, J, Chang-Claude, J, Chanock, SJ, Chung, WK, Claes, KBM, Cox, A, Cross, SS, Cunningham, JM, Czene, K, Daly, MB, Damiola, F, Darabi, H, de la Hoya, M, Devilee, P, Diez, O, Ding, YC, Dolcetti, R, Domchek, SM, Dorfling, CM, dos-Santos-Silva, I, Dumont, M, Dunning, AM, Eccles, DM, Ehrencrona, H, Ekici, AB, Eliassen, H, Ellis, S, Fasching, PA, Figueroa, J, Flesch-Janys, D, Foersti, A, Fostira, F, Foulkes, WD, Friebel, T, Friedman, E, Frost, D, Gabrielson, M, Gammon, MD, Ganz, PA, Gapstur, SM, Garber, J, Gaudet, MM, Gayther, SA, Gerdes, A-M, Ghoussaini, M, Giles, GG, Glendon, G, Godwin, AK, Goldberg, MS, Goldgar, DE, Gonzalez-Neira, A, Greene, MH, Gronwald, J, Guenel, P, Gunter, M, Haeberle, L, Haiman, CA, Hamann, U, Hansen, TVO, Hart, S, Healey, S, Heikkinen, T, Henderson, BE, Herzog, J, Hogervorst, FBL, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Humphreys, K, Hunter, DJ, Huzarski, T, Imyanitov, EN, Isaacs, C, Jakubowska, A, James, P, Janavicius, R, Jensen, UB, John, EM, Jones, M, Kabisch, M, Kar, S, Karlan, BY, Khan, S, Khaw, K-T, Kibriya, MG, Knight, JA, Ko, Y-D, Konstantopoulou, I, Kosma, V-M, Kristensen, V, Kwong, A, Laitman, Y, Lambrechts, D, Lazaro, C, Lee, E, Le Marchand, L, Lester, J, Lindblom, A, Lindor, N, Lindstrom, S, Liu, J, Long, J, Lubinski, J, Mai, PL, Makalic, E, Malone, KE, Mannermaa, A, Manoukian, S, Margolin, S, Marme, F, Martens, JWM, McGuffog, L, Meindl, A, Miller, A, Milne, RL, Miron, P, Montagna, M, Mazoyer, S, Mulligan, AM, Muranen, TA, Nathanson, KL, Neuhausen, SL, Nevanlinna, H, Nordestgaard, BG, Nussbaum, RL, Offit, K, Olah, E, Olopade, OI, Olson, JE, Osorio, A, Park, SK, Peeters, PH, Peissel, B, Peterlongo, P, Peto, J, Phelan, CM, Pilarski, R, Poppe, B, Pylkaes, K, Radice, P, Rahman, N, Rantala, J, Rappaport, C, Rennert, G, Richardson, A, Robson, M, Romieu, I, Rudolph, A, Rutgers, EJ, Sanchez, M-J, Santella, RM, Sawyer, EJ, Schmidt, DF, Schmidt, MK, Schmutzler, RK, Schumacher, F, Scott, R, Senter, L, Sharma, P, Simard, J, Singer, CF, Sinilnikova, OM, Soucy, P, Southey, M, Steinemann, D, Stenmark-Askmalm, M, Stoppa-Lyonnet, D, Swerdlow, A, Szabo, CI, Tamimi, R, Tapper, W, Teixeira, MR, Teo, S-H, Terry, MB, Thomassen, M, Thompson, D, Tihomirova, L, Toland, AE, Tollenaar, RAEM, Tomlinson, I, Truong, T, Tsimiklis, H, Teule, A, Tumino, R, Tung, N, Turnbull, C, Ursin, G, van Deurzen, CHM, van Rensburg, EJ, Varon-Mateeva, R, Wang, Z, Wang-Gohrke, S, Weiderpass, E, Weitzel, JN, Whittemore, A, Wildiers, H, Winqvist, R, Yang, XR, Yannoukakos, D, Yao, S, Zamora, MP, Zheng, W, Hall, P, Kraft, P, Vachon, C, Slager, S, Chenevix-Trench, G, Pharoah, PDP, Monteiro, AAN, Garcia-Closas, M, Easton, DF, and Antoniou, AC

Abstract

Common variants in 94 loci have been associated with breast cancer including 15 loci with genome-wide significant associations (P<5 × 10(-8)) with oestrogen receptor (ER)-negative breast cancer and BRCA1-associated breast cancer risk. In this study, to identify new ER-negative susceptibility loci, we performed a meta-analysis of 11 genome-wide association studies (GWAS) consisting of 4,939 ER-negative cases and 14,352 controls, combined with 7,333 ER-negative cases and 42,468 controls and 15,252 BRCA1 mutation carriers genotyped on the iCOGS array. We identify four previously unidentified loci including two loci at 13q22 near KLF5, a 2p23.2 locus near WDR43 and a 2q33 locus near PPIL3 that display genome-wide significant associations with ER-negative breast cancer. In addition, 19 known breast cancer risk loci have genome-wide significant associations and 40 had moderate associations (P<0.05) with ER-negative disease. Using functional and eQTL studies we implicate TRMT61B and WDR43 at 2p23.2 and PPIL3 at 2q33 in ER-negative breast cancer aetiology. All ER-negative loci combined account for ∼11% of familial relative risk for ER-negative disease and may contribute to improved ER-negative and BRCA1 breast cancer risk prediction.

Published
2016

24. Childhood body mass index and adult mammographic density measures that predict breast cancer risk

Author

Hopper, JL, Nguyen, TL, Stone, J, Aujard, K, Matheson, MC, Abramson, MJ, Burgess, JA, Walters, EH, Dite, GS, Bui, M, Evans, C, Makalic, E, Schmidt, DF, Ward, G, Jenkins, MA, Giles, GG, Dharmage, SC, Apicella, C, Hopper, JL, Nguyen, TL, Stone, J, Aujard, K, Matheson, MC, Abramson, MJ, Burgess, JA, Walters, EH, Dite, GS, Bui, M, Evans, C, Makalic, E, Schmidt, DF, Ward, G, Jenkins, MA, Giles, GG, Dharmage, SC, and Apicella, C

Abstract

The aim of the present study is to determine if body mass index (BMI) during childhood is associated with the breast cancer risk factor 'adult mammographic density adjusted for age and BMI'. In 1968, the Tasmanian Longitudinal Health Study studied every Tasmanian school child born in 1961. We obtained measured heights and weights from annual school medical records across ages 7-15 years and imputed missing values. Between 2009 and 2012, we administered to 490 women a questionnaire that asked current height and weight and digitised at least one mammogram per woman. Absolute and percent mammographic densities were measured using the computer-assisted method CUMULUS. We used linear regression and adjusted for age at interview and log current BMI. The mammographic density measures were negatively associated: with log BMI at each age from 7 to 15 years (all p < 0.05); with the average of standardised log BMIs across ages 7-15 years (p < 0.0005); and more strongly with standardised log BMI measures closer to age 15 years (p < 0.03). Childhood BMI measures explained 7 and 10 % of the variance in absolute and percent mammographic densities, respectively, and 25 and 20 % of the association between current BMI and absolute and percent mammographic densities, respectively. Associations were not altered by adjustment for age at menarche. There is a negative association between BMI in late childhood and the adult mammographic density measures that predict breast cancer risk. This could explain, at least in part, why BMI in adolescence is negatively associated with breast cancer risk.

Published
2016

25. Quantifying the utility of single nucleotide polymorphisms to guide colorectal cancer screening

Author

Jenkins, MA, Makalic, E, Dowty, JG, Schmidt, DF, Dite, GS, MacInnis, RJ, Ouakrim, DA, Clendenning, M, Flander, LB, Stanesby, OK, Hopper, JL, Win, AK, Buchanan, DD, Jenkins, MA, Makalic, E, Dowty, JG, Schmidt, DF, Dite, GS, MacInnis, RJ, Ouakrim, DA, Clendenning, M, Flander, LB, Stanesby, OK, Hopper, JL, Win, AK, and Buchanan, DD

Abstract

AIM: To determine whether single nucleotide polymorphisms (SNPs) can be used to identify people who should be screened for colorectal cancer. METHODS: We simulated one million people with and without colorectal cancer based on published SNP allele frequencies and strengths of colorectal cancer association. We estimated 5-year risks of colorectal cancer by number of risk alleles. RESULTS: We identified 45 SNPs with an average 1.14-fold increase colorectal cancer risk per allele (range: 1.05-1.53). The colorectal cancer risk for people in the highest quintile of risk alleles was 1.81-times that for the average person. CONCLUSION: We have quantified the extent to which known susceptibility SNPs can stratify the population into clinically useful colorectal cancer risk categories.

Published
2016

26. Common non-synonymous SNPs associated with breast cancer susceptibility: findings from the Breast Cancer Association Consortium

Author

Milne, RL, Burwinkel, B, Michailidou, K, Arias-Perez, JI, Zamora, MP, Menendez-Rodriguez, P, Hardisson, D, Mendiola, M, Gonzalez-Neira, A, Pita, G, Alonso, MR, Dennis, J, Wang, Q (Qing), Bolla, MK, Swerdlow, A, Ashworth, A, Orr, N, Schoemaker, M, Ko, YD, Brauch, H, Hamann, U, Andrulis, IL, Knight, JA, Glendon, G, Tchatchou, S, Matsuo, K, Ito, H, Iwata, H, Tajima, K, Li, JM, Brand, JS, Brenner, H, Dieffenbach, AK, Arndt, V, Stegmaier, C, Lambrechts, D, Peuteman, G, Christiaens, MR, Smeets, A, Jakubowska, A, Lubinski, J, Jaworska-Bieniek, K, Durda, K, Hartman, M, Hui, M, Lim, WY, Chan, CW, Marme, F, Yang, RX, Bugert, P, Lindblom, A, Margolin, S, Garcia-Closas, M, Chanock, SJ, Lissowska, J, Figueroa, JD, Bojesen, SE, Nordestgaard, BG, Flyger, H, Hooning, Maartje, Kriege, Mieke, van den Ouweland, Ans, Koppert, Linetta, Fletcher, O, Johnson, N, dos-Santos-Silva, I, Peto, J, Zheng, W, Deming-Halverson, S, Shrubsole, MJ, Long, JR, Chang-Claude, J, Rudolph, A, Seibold, P, Flesch-Janys, D, Winqvist, R, Pylkas, K, Jukkola-Vuorinen, A, Grip, M, Cox, A, Cross, SS, Reed, MWR, Schmidt, MK (Marjanka), Broeks, A, Cornelissen, S, Braaf, L, Kang, D, Choi, JY, Park, SK, Noh, DY, Simard, J, Dumont, M, Goldberg, MS, Labreche, F, Fasching, PA, Hein, A, Ekici, AB, Beckmann, MW, Radice, P, Peterlongo, P, Azzollini, J, Barile, M, Sawyer, E, Tomlinson, I, Kerin, M, Miller, N, Hopper, JL, Schmidt, DF, Makalic, E, Southey, MC, Teo, SH, Yip, CH, Sivanandan, K, Tay, WT, Shen, CY, Hsiung, CN, Yu, JC, Hou, MF, Guenel, P, Sanchez, M, Mulot, C, Blot, W, Cai, QY, Nevanlinna, H, Muranen, TA, Aittomaki, K, Blomqvist, C, Wu, AH, Tseng, CC, van den Berg, D, Stram, DO, Bogdanova, N, Dork, T, Muir, K, Lophatananon, A, Stewart-Brown, S, Siriwanarangsan, P, Mannermaa, A, Kataja, V, Kosma, VM, Hartikainen, JM, Shu, XO, Lu, W, Gao, YT, Zhang, B, Couch, FJ, Toland, AE, Yannoukakos, D, Sangrajrang, S, Mckay, J, Wang, XS, Olson, JE, Vachon, C, Purrington, K, Severi, G, Baglietto, L, Haiman, CA, Henderson, BE, Schumacher, F, Le Marchand, L, Devilee, P, Tollenaar, RAEM, Seynaeve, Caroline, Czene, K, Eriksson, M, Humphreys, K, Darabi, H, Ahmed, S (Shahana), Shah, M, Pharoah, PDP, Hall, P, Giles, GG, Benitez, J, Dunning, AM, Chenevix-Trench, G, Easton, DF, Clinical Genetics, Cardiothoracic Surgery, Medical Oncology, and Surgery

Subjects
SDG 3 - Good Health and Well-being
Abstract

Candidate variant association studies have been largely unsuccessful in identifying common breast cancer susceptibility variants, although most studies have been underpowered to detect associations of a realistic magnitude. We assessed 41 common non-synonymous single-nucleotide polymorphisms (nsSNPs) for which evidence of association with breast cancer risk had been previously reported. Case-control data were combined from 38 studies of white European women (46 450 cases and 42 600 controls) and analyzed using unconditional logistic regression. Strong evidence of association was observed for three nsSNPs: ATXN7-K264R at 3p21 [rs1053338, per allele OR = 1.07, 95% confidence interval (CI) = 1.04-1.10, P = 2.9 x 10 26], AKAP9-M463I at 7q21 (rs6964587, OR = 1.05, 95% CI = 1.03-1.07, P = 1.7 x 10(-6)) and NEK10-L513S at 3p24 (rs10510592, OR = 1.10, 95% CI = 1.07-1.12, P = 5.1 x 10(-17)). The first two associations reached genome-wide statistical significance in a combined analysis of available data, including independent data from nine genome-wide association studies (GWASs): for ATXN7-K264R, OR = 1.07 (95% CI = 1.05-1.10, P = 1.0 x 10(-8)); for AKAP9-M463I, OR = 1.05 (95% CI = 1.04-1.07, P = 2.0 x 10(-10)). Further analysis of other common variants in these two regions suggested that intronic SNPs nearby are more strongly associated with disease risk. We have thus identified a novel susceptibility locus at 3p21, and confirmed previous suggestive evidence that rs6964587 at 7q21 is associated with risk. The third locus, rs10510592, is located in an established breast cancer susceptibility region; the association was substantially attenuated after adjustment for the known GWAS hit. Thus, each of the associated nsSNPs is likely to be a marker for another, non-coding, variant causally related to breast cancer risk. Further fine-mapping and functional studies are required to identify the underlying risk-modifying variants and the genes through which they act.

Published
2014

27. Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer

Author

Michailidou, K, Beesley, J, Lindstrom, S, Canisius, S, Dennis, J, Lush, MJ, Maranian, MJ, Bolla, MK, Wang, Q, Shah, M, Perkins, BJ, Czene, K, Eriksson, M, Darabi, H, Brand, JS, Bojesen, SE, Nordestgaard, BG, Flyger, H, Nielsen, SF, Rahman, N, Turnbull, C, Fletcher, O, Peto, J, Gibson, L, dos-Santos-Silva, I, Chang-Claude, J, Flesch-Janys, D, Rudolph, A, Eilber, U, Behrens, S, Nevanlinna, H, Muranen, TA, Aittomaki, K, Blomqvist, C, Khan, S, Aaltonen, K, Ahsan, H, Kibriya, MG, Whittemore, AS, John, EM, Malone, KE, Gammon, MD, Santella, RM, Ursin, G, Makalic, E, Schmidt, DF, Casey, G, Hunter, DJ, Gapstur, SM, Gaudet, MM, Diver, WR, Haiman, CA, Schumacher, F, Henderson, BE, Le Marchand, L, Berg, CD, Chanock, SJ, Figueroa, J, Hoover, RN, Lambrechts, D, Neven, P, Wildiers, H, van Limbergen, E, Schmidt, MK, Broeks, A, Verhoef, S, Cornelissen, S, Couch, FJ, Olson, JE, Hallberg, E, Vachon, C, Waisfisz, Q, Meijers-Heijboer, H, Adank, MA, van der Luijt, RB, Li, J, Liu, J, Humphreys, K, Kang, D, Choi, J-Y, Park, SK, Yoo, K-Y, Matsuo, K, Ito, H, Iwata, H, Tajima, K, Guenel, P, Truong, T, Mulot, C, Sanchez, M, Burwinkel, B, Marme, F, Surowy, H, Sohn, C, Wu, AH, Tseng, C-C, Van den Berg, D, Stram, DO, Gonzalez-Neira, A, Benitez, J, Zamora, MP, Arias Perez, JI, Shu, X-O, Lu, W, Gao, Y-T, Cai, H, Cox, A, Cross, SS, Reed, MWR, Andrulis, IL, Knight, JA, Glendon, G, Mulligan, AM, Sawyer, EJ, Tomlinson, I, Kerin, MJ, Miller, N, Lindblom, A, Margolin, S, Teo, SH, Yip, CH, Taib, NAM, Tan, G-H, Hooning, MJ, Hollestelle, A, Martens, JWM, Collee, JM, Blot, W, Signorello, LB, Cai, Q, Hopper, JL, Southey, MC, Tsimiklis, H, Apicella, C, Shen, C-Y, Hsiung, C-N, Wu, P-E, Hou, M-F, Kristensen, VN, Nord, S, Alnaes, GIG, Giles, GG, Milne, RL, McLean, C, Canzian, F, Trichopoulos, D, Peeters, P, Lund, E, Sund, M, Khaw, K-T, Gunter, MJ, Palli, D, Mortensen, LM, Dossus, L, Huerta, J-M, Meindl, A, Schmutzler, RK, Sutter, C, Yang, R, Muir, K, Lophatananon, A, Stewart-Brown, S, Siriwanarangsan, P, Hartman, M, Miao, H, Chia, KS, Chan, CW, Fasching, PA, Hein, A, Beckmann, MW, Haeberle, L, Brenner, H, Dieffenbach, AK, Arndt, V, Stegmaier, C, Ashworth, A, Orr, N, Schoemaker, MJ, Swerdlow, AJ, Brinton, L, Garcia-Closas, M, Zheng, W, Halverson, SL, Shrubsole, M, Long, J, Goldberg, MS, Labreche, F, Dumont, M, Winqvist, R, Pylkas, K, Jukkola-Vuorinen, A, Grip, M, Brauch, H, Hamann, U, Bruening, T, Radice, P, Peterlongo, P, Manoukian, S, Bernard, L, Bogdanova, NV, Doerk, T, Mannermaa, A, Kataja, V, Kosma, V-M, Hartikainen, JM, Devilee, P, Tollenaar, RAEM, Seynaeve, C, Van Asperen, CJ, Jakubowska, A, Lubinski, J, Jaworska, K, Huzarski, T, Sangrajrang, S, Gaborieau, V, Brennan, P, Mckay, J, Slager, S, Toland, AE, Ambrosone, CB, Yannoukakos, D, Kabisch, M, Torres, D, Neuhausen, SL, Anton-Culver, H, Luccarini, C, Baynes, C, Ahmed, S, Healey, CS, Tessier, DC, Vincent, D, Bacot, F, Pita, G, Rosario Alonso, M, Alvarez, N, Herrero, D, Simard, J, Pharoah, PPDP, Kraft, P, Dunning, AM, Chenevix-Trench, G, Hall, P, Easton, DF, Michailidou, K, Beesley, J, Lindstrom, S, Canisius, S, Dennis, J, Lush, MJ, Maranian, MJ, Bolla, MK, Wang, Q, Shah, M, Perkins, BJ, Czene, K, Eriksson, M, Darabi, H, Brand, JS, Bojesen, SE, Nordestgaard, BG, Flyger, H, Nielsen, SF, Rahman, N, Turnbull, C, Fletcher, O, Peto, J, Gibson, L, dos-Santos-Silva, I, Chang-Claude, J, Flesch-Janys, D, Rudolph, A, Eilber, U, Behrens, S, Nevanlinna, H, Muranen, TA, Aittomaki, K, Blomqvist, C, Khan, S, Aaltonen, K, Ahsan, H, Kibriya, MG, Whittemore, AS, John, EM, Malone, KE, Gammon, MD, Santella, RM, Ursin, G, Makalic, E, Schmidt, DF, Casey, G, Hunter, DJ, Gapstur, SM, Gaudet, MM, Diver, WR, Haiman, CA, Schumacher, F, Henderson, BE, Le Marchand, L, Berg, CD, Chanock, SJ, Figueroa, J, Hoover, RN, Lambrechts, D, Neven, P, Wildiers, H, van Limbergen, E, Schmidt, MK, Broeks, A, Verhoef, S, Cornelissen, S, Couch, FJ, Olson, JE, Hallberg, E, Vachon, C, Waisfisz, Q, Meijers-Heijboer, H, Adank, MA, van der Luijt, RB, Li, J, Liu, J, Humphreys, K, Kang, D, Choi, J-Y, Park, SK, Yoo, K-Y, Matsuo, K, Ito, H, Iwata, H, Tajima, K, Guenel, P, Truong, T, Mulot, C, Sanchez, M, Burwinkel, B, Marme, F, Surowy, H, Sohn, C, Wu, AH, Tseng, C-C, Van den Berg, D, Stram, DO, Gonzalez-Neira, A, Benitez, J, Zamora, MP, Arias Perez, JI, Shu, X-O, Lu, W, Gao, Y-T, Cai, H, Cox, A, Cross, SS, Reed, MWR, Andrulis, IL, Knight, JA, Glendon, G, Mulligan, AM, Sawyer, EJ, Tomlinson, I, Kerin, MJ, Miller, N, Lindblom, A, Margolin, S, Teo, SH, Yip, CH, Taib, NAM, Tan, G-H, Hooning, MJ, Hollestelle, A, Martens, JWM, Collee, JM, Blot, W, Signorello, LB, Cai, Q, Hopper, JL, Southey, MC, Tsimiklis, H, Apicella, C, Shen, C-Y, Hsiung, C-N, Wu, P-E, Hou, M-F, Kristensen, VN, Nord, S, Alnaes, GIG, Giles, GG, Milne, RL, McLean, C, Canzian, F, Trichopoulos, D, Peeters, P, Lund, E, Sund, M, Khaw, K-T, Gunter, MJ, Palli, D, Mortensen, LM, Dossus, L, Huerta, J-M, Meindl, A, Schmutzler, RK, Sutter, C, Yang, R, Muir, K, Lophatananon, A, Stewart-Brown, S, Siriwanarangsan, P, Hartman, M, Miao, H, Chia, KS, Chan, CW, Fasching, PA, Hein, A, Beckmann, MW, Haeberle, L, Brenner, H, Dieffenbach, AK, Arndt, V, Stegmaier, C, Ashworth, A, Orr, N, Schoemaker, MJ, Swerdlow, AJ, Brinton, L, Garcia-Closas, M, Zheng, W, Halverson, SL, Shrubsole, M, Long, J, Goldberg, MS, Labreche, F, Dumont, M, Winqvist, R, Pylkas, K, Jukkola-Vuorinen, A, Grip, M, Brauch, H, Hamann, U, Bruening, T, Radice, P, Peterlongo, P, Manoukian, S, Bernard, L, Bogdanova, NV, Doerk, T, Mannermaa, A, Kataja, V, Kosma, V-M, Hartikainen, JM, Devilee, P, Tollenaar, RAEM, Seynaeve, C, Van Asperen, CJ, Jakubowska, A, Lubinski, J, Jaworska, K, Huzarski, T, Sangrajrang, S, Gaborieau, V, Brennan, P, Mckay, J, Slager, S, Toland, AE, Ambrosone, CB, Yannoukakos, D, Kabisch, M, Torres, D, Neuhausen, SL, Anton-Culver, H, Luccarini, C, Baynes, C, Ahmed, S, Healey, CS, Tessier, DC, Vincent, D, Bacot, F, Pita, G, Rosario Alonso, M, Alvarez, N, Herrero, D, Simard, J, Pharoah, PPDP, Kraft, P, Dunning, AM, Chenevix-Trench, G, Hall, P, and Easton, DF

Abstract

Genome-wide association studies (GWAS) and large-scale replication studies have identified common variants in 79 loci associated with breast cancer, explaining ∼14% of the familial risk of the disease. To identify new susceptibility loci, we performed a meta-analysis of 11 GWAS, comprising 15,748 breast cancer cases and 18,084 controls together with 46,785 cases and 42,892 controls from 41 studies genotyped on a 211,155-marker custom array (iCOGS). Analyses were restricted to women of European ancestry. We generated genotypes for more than 11 million SNPs by imputation using the 1000 Genomes Project reference panel, and we identified 15 new loci associated with breast cancer at P < 5 × 10(-8). Combining association analysis with ChIP-seq chromatin binding data in mammary cell lines and ChIA-PET chromatin interaction data from ENCODE, we identified likely target genes in two regions: SETBP1 at 18q12.3 and RNF115 and PDZK1 at 1q21.1. One association appears to be driven by an amino acid substitution encoded in EXO1.

Published
2015

28. High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies

Author

Goudey, B, Abedini, M, Hopper, JL, Inouye, M, Makalic, E, Schmidt, DF, Wagner, J, Zhou, Z, Zobel, J, Reumann, M, Goudey, B, Abedini, M, Hopper, JL, Inouye, M, Makalic, E, Schmidt, DF, Wagner, J, Zhou, Z, Zobel, J, and Reumann, M

Abstract

Genome-wide association studies (GWAS) are a common approach for systematic discovery of single nucleotide polymorphisms (SNPs) which are associated with a given disease. Univariate analysis approaches commonly employed may miss important SNP associations that only appear through multivariate analysis in complex diseases. However, multivariate SNP analysis is currently limited by its inherent computational complexity. In this work, we present a computational framework that harnesses supercomputers. Based on our results, we estimate a three-way interaction analysis on 1.1 million SNP GWAS data requiring over 5.8 years on the full "Avoca" IBM Blue Gene/Q installation at the Victorian Life Sciences Computation Initiative. This is hundreds of times faster than estimates for other CPU based methods and four times faster than runtimes estimated for GPU methods, indicating how the improvement in the level of hardware applied to interaction analysis may alter the types of analysis that can be performed. Furthermore, the same analysis would take under 3 months on the currently largest IBM Blue Gene/Q supercomputer "Sequoia" at the Lawrence Livermore National Laboratory assuming linear scaling is maintained as our results suggest. Given that the implementation used in this study can be further optimised, this runtime means it is becoming feasible to carry out exhaustive analysis of higher order interaction studies on large modern GWAS.

Published
2015

29. Quantifying the cumulative effect of low-penetrance genetic variants on breast cancer risk

Author

Smyth, C, Spakulova, I, Cotton-Barratt, O, Rafiq, S, Tapper, W, Upstill-Goddard, R, Hopper, JL, Makalic, E, Schmidt, DF, Kapuscinski, M, Fliege, J, Collins, A, Brodzki, J, Eccles, DM, MacArthur, BD, Smyth, C, Spakulova, I, Cotton-Barratt, O, Rafiq, S, Tapper, W, Upstill-Goddard, R, Hopper, JL, Makalic, E, Schmidt, DF, Kapuscinski, M, Fliege, J, Collins, A, Brodzki, J, Eccles, DM, and MacArthur, BD

Abstract

Many common diseases have a complex genetic basis in which large numbers of genetic variations combine with environmental factors to determine risk. However, quantifying such polygenic effects has been challenging. In order to address these difficulties we developed a global measure of the information content of an individual's genome relative to a reference population, which may be used to assess differences in global genome structure between cases and appropriate controls. Informally this measure, which we call relative genome information (RGI), quantifies the relative "disorder" of an individual's genome. In order to test its ability to predict disease risk we used RGI to compare single-nucleotide polymorphism genotypes from two independent samples of women with early-onset breast cancer with three independent sets of controls. We found that RGI was significantly elevated in both sets of breast cancer cases in comparison with all three sets of controls, with disease risk rising sharply with RGI. Furthermore, these differences are not due to associations with common variants at a small number of disease-associated loci, but rather are due to the combined associations of thousands of markers distributed throughout the genome. Our results indicate that the information content of an individual's genome may be used to measure the risk of a complex disease, and suggest that early-onset breast cancer has a strongly polygenic component.

Published
2015

30. A Genome-wide Association Study of Early-Onset Breast Cancer Identifies PFKM as a Novel Breast Cancer Gene and Supports a Common Genetic Spectrum for Breast Cancer at Any Age

Author

Ahsan, H, Halpern, J, Kibriya, MG, Pierce, BL, Tong, L, Gamazon, E, McGuire, V, Felberg, A, Shi, J, Jasmine, F, Roy, S, Brutus, R, Argos, M, Melkonian, S, Chang-Claude, J, Andrulis, I, Hopper, JL, John, EM, Malone, K, Ursin, G, Gammon, MD, Thomas, DC, Seminara, D, Casey, G, Knight, JA, Southey, MC, Giles, GG, Santella, RM, Lee, E, Conti, D, Duggan, D, Gallinger, S, Haile, R, Jenkins, M, Lindor, NM, Newcomb, P, Michailidou, K, Apicella, C, Park, DJ, Peto, J, Fletcher, O, Silva, IDS, Lathrop, M, Hunter, DJ, Chanock, SJ, Meindl, A, Schmutzler, RK, Mueller-Myhsok, B, Lochmann, M, Beckmann, L, Hein, R, Makalic, E, Schmidt, DF, Quang, MB, Stone, J, Flesch-Janys, D, Dahmen, N, Nevanlinna, H, Aittomaki, K, Blomqvist, C, Hall, P, Czene, K, Irwanto, A, Liu, J, Rahman, N, Turnbull, C, Dunning, AM, Pharoah, P, Waisfisz, Q, Meijers-Heijboer, H, Uitterlinden, AG, Rivadeneira, F, Nicolae, D, Easton, DF, Cox, NJ, Whittemore, AS, Ahsan, H, Halpern, J, Kibriya, MG, Pierce, BL, Tong, L, Gamazon, E, McGuire, V, Felberg, A, Shi, J, Jasmine, F, Roy, S, Brutus, R, Argos, M, Melkonian, S, Chang-Claude, J, Andrulis, I, Hopper, JL, John, EM, Malone, K, Ursin, G, Gammon, MD, Thomas, DC, Seminara, D, Casey, G, Knight, JA, Southey, MC, Giles, GG, Santella, RM, Lee, E, Conti, D, Duggan, D, Gallinger, S, Haile, R, Jenkins, M, Lindor, NM, Newcomb, P, Michailidou, K, Apicella, C, Park, DJ, Peto, J, Fletcher, O, Silva, IDS, Lathrop, M, Hunter, DJ, Chanock, SJ, Meindl, A, Schmutzler, RK, Mueller-Myhsok, B, Lochmann, M, Beckmann, L, Hein, R, Makalic, E, Schmidt, DF, Quang, MB, Stone, J, Flesch-Janys, D, Dahmen, N, Nevanlinna, H, Aittomaki, K, Blomqvist, C, Hall, P, Czene, K, Irwanto, A, Liu, J, Rahman, N, Turnbull, C, Dunning, AM, Pharoah, P, Waisfisz, Q, Meijers-Heijboer, H, Uitterlinden, AG, Rivadeneira, F, Nicolae, D, Easton, DF, Cox, NJ, and Whittemore, AS

Abstract

Early-onset breast cancer (EOBC) causes substantial loss of life and productivity, creating a major burden among women worldwide. We analyzed 1,265,548 Hapmap3 single-nucleotide polymorphisms (SNP) among a discovery set of 3,523 EOBC incident cases and 2,702 population control women ages ≤ 51 years. The SNPs with smallest P values were examined in a replication set of 3,470 EOBC cases and 5,475 control women. We also tested EOBC association with 19,684 genes by annotating each gene with putative functional SNPs, and then combining their P values to obtain a gene-based P value. We examined the gene with smallest P value for replication in 1,145 breast cancer cases and 1,142 control women. The combined discovery and replication sets identified 72 new SNPs associated with EOBC (P < 4 × 10(-8)) located in six genomic regions previously reported to contain SNPs associated largely with later-onset breast cancer (LOBC). SNP rs2229882 and 10 other SNPs on chromosome 5q11.2 remained associated (P < 6 × 10(-4)) after adjustment for the strongest published SNPs in the region. Thirty-two of the 82 currently known LOBC SNPs were associated with EOBC (P < 0.05). Low power is likely responsible for the remaining 50 unassociated known LOBC SNPs. The gene-based analysis identified an association between breast cancer and the phosphofructokinase-muscle (PFKM) gene on chromosome 12q13.11 that met the genome-wide gene-based threshold of 2.5 × 10(-6). In conclusion, EOBC and LOBC seem to have similar genetic etiologies; the 5q11.2 region may contain multiple distinct breast cancer loci; and the PFKM gene region is worthy of further investigation. These findings should enhance our understanding of the etiology of breast cancer.

Published
2014

31. MicroRNA Related Polymorphisms and Breast Cancer Risk

Author

Zhao, Z, Khan, S, Greco, D, Michailidou, K, Milne, RL, Muranen, TA, Heikkinen, T, Aaltonen, K, Dennis, J, Bolla, MK, Liu, J, Hall, P, Irwanto, A, Humphreys, K, Li, J, Czene, K, Chang-Claude, J, Hein, R, Rudolph, A, Seibold, P, Flesch-Janys, D, Fletcher, O, Peto, J, Silva, IDS, Johnson, N, Gibson, L, Aitken, Z, Hopper, JL, Tsimiklis, H, Bui, M, Makalic, E, Schmidt, DF, Southey, MC, Apicella, C, Stone, J, Waisfisz, Q, Meijers-Heijboer, H, Adank, MA, van der Luijt, RB, Meindl, A, Schmutzler, RK, Muller-Myhsok, B, Lichtner, P, Turnbull, C, Rahman, N, Chanock, SJ, Hunter, DJ, Cox, A, Cross, SS, Reed, MWR, Schmidt, MK, Broeks, A, Van't Veer, LJ, Hogervorst, FB, Fasching, PA, Schrauder, MG, Ekici, AB, Beckmann, MW, Bojesen, SE, Nordestgaard, BG, Nielsen, SF, Flyger, H, Benitez, J, Zamora, PM, Perez, JIA, Haiman, CA, Henderson, BE, Schumacher, F, Le Marchand, L, Pharoah, PDP, Dunning, AM, Shah, M, Luben, R, Brown, J, Couch, FJ, Wang, X, Vachon, C, Olson, JE, Lambrechts, D, Moisse, M, Paridaens, R, Christiaens, M-R, Guenel, P, Truong, T, Laurent-Puig, P, Mulot, C, Marme, F, Burwinkel, B, Schneeweiss, A, Sohn, C, Sawyer, EJ, Tomlinson, I, Kerin, MJ, Miller, N, Andrulis, IL, Knight, JA, Tchatchou, S, Mulligan, AM, Dork, T, Bogdanova, NV, Antonenkova, NN, Anton-Culver, H, Darabi, H, Eriksson, M, Garcia-Closas, M, Figueroa, J, Lissowska, J, Brinton, L, Devilee, P, Tollenaar, RAEM, Seynaeve, C, van Asperen, CJ, Kristensen, VN, Slager, S, Toland, AE, Ambrosone, CB, Yannoukakos, D, Lindblom, A, Margolin, S, Radice, P, Peterlongo, P, Barile, M, Mariani, P, Hooning, MJ, Martens, JWM, Collee, JM, Jager, A, Jakubowska, A, Lubinski, J, Jaworska-Bieniek, K, Durda, K, Giles, GG, McLean, C, Brauch, H, Bruning, T, Ko, Y-D, Brenner, H, Dieffenbach, AK, Arndt, V, Stegmaier, C, Swerdlow, A, Ashworth, A, Orr, N, Jones, M, Simard, J, Goldberg, MS, Labreche, F, Dumont, M, Winqvist, R, Pylkas, K, Jukkola-Vuorinen, A, Grip, M, Kataja, V, Kosma, V-M, Hartikainen, JM, Mannermaa, A, Hamann, U, Chenevix-Trench, G, Blomqvist, C, Aittomaki, K, Easton, DF, Nevanlinna, H, Zhao, Z, Khan, S, Greco, D, Michailidou, K, Milne, RL, Muranen, TA, Heikkinen, T, Aaltonen, K, Dennis, J, Bolla, MK, Liu, J, Hall, P, Irwanto, A, Humphreys, K, Li, J, Czene, K, Chang-Claude, J, Hein, R, Rudolph, A, Seibold, P, Flesch-Janys, D, Fletcher, O, Peto, J, Silva, IDS, Johnson, N, Gibson, L, Aitken, Z, Hopper, JL, Tsimiklis, H, Bui, M, Makalic, E, Schmidt, DF, Southey, MC, Apicella, C, Stone, J, Waisfisz, Q, Meijers-Heijboer, H, Adank, MA, van der Luijt, RB, Meindl, A, Schmutzler, RK, Muller-Myhsok, B, Lichtner, P, Turnbull, C, Rahman, N, Chanock, SJ, Hunter, DJ, Cox, A, Cross, SS, Reed, MWR, Schmidt, MK, Broeks, A, Van't Veer, LJ, Hogervorst, FB, Fasching, PA, Schrauder, MG, Ekici, AB, Beckmann, MW, Bojesen, SE, Nordestgaard, BG, Nielsen, SF, Flyger, H, Benitez, J, Zamora, PM, Perez, JIA, Haiman, CA, Henderson, BE, Schumacher, F, Le Marchand, L, Pharoah, PDP, Dunning, AM, Shah, M, Luben, R, Brown, J, Couch, FJ, Wang, X, Vachon, C, Olson, JE, Lambrechts, D, Moisse, M, Paridaens, R, Christiaens, M-R, Guenel, P, Truong, T, Laurent-Puig, P, Mulot, C, Marme, F, Burwinkel, B, Schneeweiss, A, Sohn, C, Sawyer, EJ, Tomlinson, I, Kerin, MJ, Miller, N, Andrulis, IL, Knight, JA, Tchatchou, S, Mulligan, AM, Dork, T, Bogdanova, NV, Antonenkova, NN, Anton-Culver, H, Darabi, H, Eriksson, M, Garcia-Closas, M, Figueroa, J, Lissowska, J, Brinton, L, Devilee, P, Tollenaar, RAEM, Seynaeve, C, van Asperen, CJ, Kristensen, VN, Slager, S, Toland, AE, Ambrosone, CB, Yannoukakos, D, Lindblom, A, Margolin, S, Radice, P, Peterlongo, P, Barile, M, Mariani, P, Hooning, MJ, Martens, JWM, Collee, JM, Jager, A, Jakubowska, A, Lubinski, J, Jaworska-Bieniek, K, Durda, K, Giles, GG, McLean, C, Brauch, H, Bruning, T, Ko, Y-D, Brenner, H, Dieffenbach, AK, Arndt, V, Stegmaier, C, Swerdlow, A, Ashworth, A, Orr, N, Jones, M, Simard, J, Goldberg, MS, Labreche, F, Dumont, M, Winqvist, R, Pylkas, K, Jukkola-Vuorinen, A, Grip, M, Kataja, V, Kosma, V-M, Hartikainen, JM, Mannermaa, A, Hamann, U, Chenevix-Trench, G, Blomqvist, C, Aittomaki, K, Easton, DF, and Nevanlinna, H

Abstract

Genetic variations, such as single nucleotide polymorphisms (SNPs) in microRNAs (miRNA) or in the miRNA binding sites may affect the miRNA dependent gene expression regulation, which has been implicated in various cancers, including breast cancer, and may alter individual susceptibility to cancer. We investigated associations between miRNA related SNPs and breast cancer risk. First we evaluated 2,196 SNPs in a case-control study combining nine genome wide association studies (GWAS). Second, we further investigated 42 SNPs with suggestive evidence for association using 41,785 cases and 41,880 controls from 41 studies included in the Breast Cancer Association Consortium (BCAC). Combining the GWAS and BCAC data within a meta-analysis, we estimated main effects on breast cancer risk as well as risks for estrogen receptor (ER) and age defined subgroups. Five miRNA binding site SNPs associated significantly with breast cancer risk: rs1045494 (odds ratio (OR) 0.92; 95% confidence interval (CI): 0.88-0.96), rs1052532 (OR 0.97; 95% CI: 0.95-0.99), rs10719 (OR 0.97; 95% CI: 0.94-0.99), rs4687554 (OR 0.97; 95% CI: 0.95-0.99, and rs3134615 (OR 1.03; 95% CI: 1.01-1.05) located in the 3' UTR of CASP8, HDDC3, DROSHA, MUSTN1, and MYCL1, respectively. DROSHA belongs to miRNA machinery genes and has a central role in initial miRNA processing. The remaining genes are involved in different molecular functions, including apoptosis and gene expression regulation. Further studies are warranted to elucidate whether the miRNA binding site SNPs are the causative variants for the observed risk effects.

Published
2014

32. FGF receptor genes and breast cancer susceptibility: results from the Breast Cancer Association Consortium

Author

Agarwal, D, Pineda, S, Michailidou, K, Herranz, J, Pita, G, Moreno, LT, Alonso, MR, Dennis, J, Wang, Q, Bolla, MK, Meyer, KB, Menendez-Rodriguez, P, Hardisson, D, Mendiola, M, Gonzalez-Neira, A, Lindblom, A, Margolin, S, Swerdlow, A, Ashworth, A, Orr, N, Jones, M, Matsuo, K, Ito, H, Iwata, H, Kondo, N, Hartman, M, Hui, M, Lim, WY, Iau, PT-C, Sawyer, E, Tomlinson, I, Kerin, M, Miller, N, Kang, D, Choi, J-Y, Park, SK, Noh, D-Y, Hopper, JL, Schmidt, DF, Makalic, E, Southey, MC, Teo, SH, Yip, CH, Sivanandan, K, Tay, W-T, Brauch, H, Bruening, T, Hamann, U, Dunning, AM, Shah, M, Andrulis, IL, Knight, JA, Glendon, G, Tchatchou, S, Schmidt, MK, Broeks, A, Rosenberg, EH, van't Veer, LJ, Fasching, PA, Renner, SP, Ekici, AB, Beckmann, MW, Shen, C-Y, Hsiung, C-N, Yu, J-C, Hou, M-F, Blot, W, Cai, Q, Wu, AH, Tseng, C-C, Van Den Berg, D, Stram, DO, Cox, A, Brock, IW, Reed, MWR, Muir, K, Lophatananon, A, Stewart-Brown, S, Siriwanarangsan, P, Zheng, W, Deming-Halverson, S, Shrubsole, MJ, Long, J, Shu, X-O, Lu, W, Gao, Y-T, Zhang, B, Radice, P, Peterlongo, P, Manoukian, S, Mariette, F, Sangrajrang, S, Mckay, J, Couch, FJ, Toland, AE, Yannoukakos, D, Fletcher, O, Johnson, N, dos Santos Silva, I, Peto, J, Marme, F, Burwinkel, B, Guenel, P, Truong, T, Sanchez, M, Mulot, C, Bojesen, SE, Nordestgaard, BG, Flyer, H, Brenner, H, Dieffenbach, AK, Arndt, V, Stegmaier, C, Mannermaa, A, Kataja, V, Kosma, V-M, Hartikainen, JM, Lambrechts, D, Yesilyurt, BT, Floris, G, Leunen, K, Chang-Claude, J, Rudolph, A, Seibold, P, Flesch-Janys, D, Wang, X, Olson, JE, Vachon, C, Purrington, K, Giles, GG, Severi, G, Baglietto, L, Haiman, CA, Henderson, BE, Schumacher, F, Le Marchand, L, Simard, J, Dumont, M, Goldberg, MS, Labreche, F, Winqvist, R, Pylkaes, K, Jukkola-Vuorinen, A, Grip, M, Devilee, P, Tollenaar, RAEM, Seynaeve, C, Garcia-Closas, M, Chanock, SJ, Lissowska, J, Figueroa, JD, Czene, K, Eriksson, M, Humphreys, K, Darabi, H, Hooning, MJ, Kriege, M, Collee, JM, Tilanus-Linthorst, M, Li, J, Jakubowska, A, Lubinski, J, Jaworska-Bieniek, K, Durda, K, Nevanlinna, H, Muranen, TA, Aittomaeki, K, Blomqvist, C, Bogdanova, N, Doerk, T, Hall, P, Chenevix-Trench, G, Easton, DF, Pharoah, PDP, Arias-Perez, JI, Zamora, P, Benitez, J, Milne, RL, Agarwal, D, Pineda, S, Michailidou, K, Herranz, J, Pita, G, Moreno, LT, Alonso, MR, Dennis, J, Wang, Q, Bolla, MK, Meyer, KB, Menendez-Rodriguez, P, Hardisson, D, Mendiola, M, Gonzalez-Neira, A, Lindblom, A, Margolin, S, Swerdlow, A, Ashworth, A, Orr, N, Jones, M, Matsuo, K, Ito, H, Iwata, H, Kondo, N, Hartman, M, Hui, M, Lim, WY, Iau, PT-C, Sawyer, E, Tomlinson, I, Kerin, M, Miller, N, Kang, D, Choi, J-Y, Park, SK, Noh, D-Y, Hopper, JL, Schmidt, DF, Makalic, E, Southey, MC, Teo, SH, Yip, CH, Sivanandan, K, Tay, W-T, Brauch, H, Bruening, T, Hamann, U, Dunning, AM, Shah, M, Andrulis, IL, Knight, JA, Glendon, G, Tchatchou, S, Schmidt, MK, Broeks, A, Rosenberg, EH, van't Veer, LJ, Fasching, PA, Renner, SP, Ekici, AB, Beckmann, MW, Shen, C-Y, Hsiung, C-N, Yu, J-C, Hou, M-F, Blot, W, Cai, Q, Wu, AH, Tseng, C-C, Van Den Berg, D, Stram, DO, Cox, A, Brock, IW, Reed, MWR, Muir, K, Lophatananon, A, Stewart-Brown, S, Siriwanarangsan, P, Zheng, W, Deming-Halverson, S, Shrubsole, MJ, Long, J, Shu, X-O, Lu, W, Gao, Y-T, Zhang, B, Radice, P, Peterlongo, P, Manoukian, S, Mariette, F, Sangrajrang, S, Mckay, J, Couch, FJ, Toland, AE, Yannoukakos, D, Fletcher, O, Johnson, N, dos Santos Silva, I, Peto, J, Marme, F, Burwinkel, B, Guenel, P, Truong, T, Sanchez, M, Mulot, C, Bojesen, SE, Nordestgaard, BG, Flyer, H, Brenner, H, Dieffenbach, AK, Arndt, V, Stegmaier, C, Mannermaa, A, Kataja, V, Kosma, V-M, Hartikainen, JM, Lambrechts, D, Yesilyurt, BT, Floris, G, Leunen, K, Chang-Claude, J, Rudolph, A, Seibold, P, Flesch-Janys, D, Wang, X, Olson, JE, Vachon, C, Purrington, K, Giles, GG, Severi, G, Baglietto, L, Haiman, CA, Henderson, BE, Schumacher, F, Le Marchand, L, Simard, J, Dumont, M, Goldberg, MS, Labreche, F, Winqvist, R, Pylkaes, K, Jukkola-Vuorinen, A, Grip, M, Devilee, P, Tollenaar, RAEM, Seynaeve, C, Garcia-Closas, M, Chanock, SJ, Lissowska, J, Figueroa, JD, Czene, K, Eriksson, M, Humphreys, K, Darabi, H, Hooning, MJ, Kriege, M, Collee, JM, Tilanus-Linthorst, M, Li, J, Jakubowska, A, Lubinski, J, Jaworska-Bieniek, K, Durda, K, Nevanlinna, H, Muranen, TA, Aittomaeki, K, Blomqvist, C, Bogdanova, N, Doerk, T, Hall, P, Chenevix-Trench, G, Easton, DF, Pharoah, PDP, Arias-Perez, JI, Zamora, P, Benitez, J, and Milne, RL

Abstract

BACKGROUND: Breast cancer is one of the most common malignancies in women. Genome-wide association studies have identified FGFR2 as a breast cancer susceptibility gene. Common variation in other fibroblast growth factor (FGF) receptors might also modify risk. We tested this hypothesis by studying genotyped single-nucleotide polymorphisms (SNPs) and imputed SNPs in FGFR1, FGFR3, FGFR4 and FGFRL1 in the Breast Cancer Association Consortium. METHODS: Data were combined from 49 studies, including 53 835 cases and 50 156 controls, of which 89 050 (46 450 cases and 42 600 controls) were of European ancestry, 12 893 (6269 cases and 6624 controls) of Asian and 2048 (1116 cases and 932 controls) of African ancestry. Associations with risk of breast cancer, overall and by disease sub-type, were assessed using unconditional logistic regression. RESULTS: Little evidence of association with breast cancer risk was observed for SNPs in the FGF receptor genes. The strongest evidence in European women was for rs743682 in FGFR3; the estimated per-allele odds ratio was 1.05 (95% confidence interval=1.02-1.09, P=0.0020), which is substantially lower than that observed for SNPs in FGFR2. CONCLUSION: Our results suggest that common variants in the other FGF receptors are not associated with risk of breast cancer to the degree observed for FGFR2.

Published
2014

33. MicroRNA Related Polymorphisms and Breast Cancer Risk

Author

Khan, Salima, Greco, D, Michailidou, K, Milne, RL, Muranen, TA, Heikkinen, T, Aaltonen, K, Dennis, J, Bolla, MK, Liu, J (Jingjing), Hall, P, Irwanto, A, Humphreys, K, Li, J, Czene, K, Chang-Claude, J, Hein, R, Rudolph, A, Seibold, P, Flesch-Janys, D, Fletcher, O, Peto, J, Silva, ID, Johnson, N, Gibson, L, Aitken, Z, Hopper, JL, Tsimiklis, H, Bui, M, Makalic, E, Schmidt, DF, Southey, MC, Apicella, C, Stone, J, Waisfisz, Q, Meijers-Heijboer, H, Adank, MA (Muriel), van der Luijt, RB, Meindl, A, Schmutzler, RK, Muller-Myhsok, B, Lichtner, P, Turnbull, C, Rahman, N, Chanock, SJ, Hunter, DJ, Cox, A, Cross, SS, Reed, MWR, Schmidt, MK (Marjanka), Broeks, A, van 't Veer, LJ (Laura), Hogervorst, FB, Fasching, PA, Schrauder, MG, Ekici, AB, Beckmann, MW, Bojesen, SE, Nordestgaard, BG, Nielsen, SF, Flyger, H, Benitez, J, Zamora, PM, Perez, JIA, Haiman, CA, Henderson, BE, Schumacher, F, Le Marchand, L, Pharoah, PDP, Dunning, AM, Shah, M, Luben, R, Brown, J, Couch, FJ, Wang, X, Vachon, C, Olson, JE, Lambrechts, D, Moisse, M, Paridaens, R, Christiaens, MR, Guenel, P, Truong, T, Laurent-Puig, P, Mulot, C, Marme, F, Burwinkel, B, Schneeweiss, A, Sohn, C, Sawyer, EJ, Tomlinson, I, Kerin, MJ, Miller, N, Andrulis, IL, Knight, JA, Tchatchou, S, Mulligan, AM, Dork, T, Bogdanova, NV, Antonenkova, NN, Anton-Culver, H, Darabi, H, Eriksson, M, Garcia-Closas, M, Figueroa, J, Lissowska, J, Brinton, L, Devilee, P, Tollenaar, RAEM, Seynaeve, Caroline, van Asperen, CJ, Kristensen, VN, Slager, S, Toland, AE, Ambrosone, CB, Yannoukakos, D, Lindblom, A, Margolin, S, Radice, P, Peterlongo, P, Barile, M, Mariani, P, Hooning, Maartje, Martens, John, Collee, Margriet, Jager, Agnes, Jakubowska, A, Lubinski, J, Jaworska-Bieniek, K, Durda, K, Giles, GG, McLean, C, Brauch, H, Bruning, T, Ko, YD, Brenner, H, Dieffenbach, AK, Arndt, V, Stegmaier, C, Swerdlow, A, Ashworth, A, Orr, N, Jones, M, Simard, J, Goldberg, MS, Labreche, F, Dumont, M, Winqvist, R, Pylkas, K, Jukkola-Vuorinen, A, Grip, M, Kataja, V, Kosma, VM, Hartikainen, JM, Mannermaa, A, Hamann, U, Chenevix-Trench, G, Blomqvist, C, Aittomaki, K, Easton, DF, Nevanlinna, H, Khan, Salima, Greco, D, Michailidou, K, Milne, RL, Muranen, TA, Heikkinen, T, Aaltonen, K, Dennis, J, Bolla, MK, Liu, J (Jingjing), Hall, P, Irwanto, A, Humphreys, K, Li, J, Czene, K, Chang-Claude, J, Hein, R, Rudolph, A, Seibold, P, Flesch-Janys, D, Fletcher, O, Peto, J, Silva, ID, Johnson, N, Gibson, L, Aitken, Z, Hopper, JL, Tsimiklis, H, Bui, M, Makalic, E, Schmidt, DF, Southey, MC, Apicella, C, Stone, J, Waisfisz, Q, Meijers-Heijboer, H, Adank, MA (Muriel), van der Luijt, RB, Meindl, A, Schmutzler, RK, Muller-Myhsok, B, Lichtner, P, Turnbull, C, Rahman, N, Chanock, SJ, Hunter, DJ, Cox, A, Cross, SS, Reed, MWR, Schmidt, MK (Marjanka), Broeks, A, van 't Veer, LJ (Laura), Hogervorst, FB, Fasching, PA, Schrauder, MG, Ekici, AB, Beckmann, MW, Bojesen, SE, Nordestgaard, BG, Nielsen, SF, Flyger, H, Benitez, J, Zamora, PM, Perez, JIA, Haiman, CA, Henderson, BE, Schumacher, F, Le Marchand, L, Pharoah, PDP, Dunning, AM, Shah, M, Luben, R, Brown, J, Couch, FJ, Wang, X, Vachon, C, Olson, JE, Lambrechts, D, Moisse, M, Paridaens, R, Christiaens, MR, Guenel, P, Truong, T, Laurent-Puig, P, Mulot, C, Marme, F, Burwinkel, B, Schneeweiss, A, Sohn, C, Sawyer, EJ, Tomlinson, I, Kerin, MJ, Miller, N, Andrulis, IL, Knight, JA, Tchatchou, S, Mulligan, AM, Dork, T, Bogdanova, NV, Antonenkova, NN, Anton-Culver, H, Darabi, H, Eriksson, M, Garcia-Closas, M, Figueroa, J, Lissowska, J, Brinton, L, Devilee, P, Tollenaar, RAEM, Seynaeve, Caroline, van Asperen, CJ, Kristensen, VN, Slager, S, Toland, AE, Ambrosone, CB, Yannoukakos, D, Lindblom, A, Margolin, S, Radice, P, Peterlongo, P, Barile, M, Mariani, P, Hooning, Maartje, Martens, John, Collee, Margriet, Jager, Agnes, Jakubowska, A, Lubinski, J, Jaworska-Bieniek, K, Durda, K, Giles, GG, McLean, C, Brauch, H, Bruning, T, Ko, YD, Brenner, H, Dieffenbach, AK, Arndt, V, Stegmaier, C, Swerdlow, A, Ashworth, A, Orr, N, Jones, M, Simard, J, Goldberg, MS, Labreche, F, Dumont, M, Winqvist, R, Pylkas, K, Jukkola-Vuorinen, A, Grip, M, Kataja, V, Kosma, VM, Hartikainen, JM, Mannermaa, A, Hamann, U, Chenevix-Trench, G, Blomqvist, C, Aittomaki, K, Easton, DF, and Nevanlinna, H

Abstract

Genetic variations, such as single nucleotide polymorphisms (SNPs) in microRNAs (miRNA) or in the miRNA binding sites may affect the miRNA dependent gene expression regulation, which has been implicated in various cancers, including breast cancer, and may alter individual susceptibility to cancer. We investigated associations between miRNA related SNPs and breast cancer risk. First we evaluated 2,196 SNPs in a case-control study combining nine genome wide association studies (GWAS). Second, we further investigated 42 SNPs with suggestive evidence for association using 41,785 cases and 41,880 controls from 41 studies included in the Breast Cancer Association Consortium (BCAC). Combining the GWAS and BCAC data within a meta-analysis, we estimated main effects on breast cancer risk as well as risks for estrogen receptor (ER) and age defined subgroups. Five miRNA binding site SNPs associated significantly with breast cancer risk: rs1045494 (odds ratio (OR) 0.92; 95% confidence interval (CI): 0.88-0.96), rs1052532 (OR 0.97; 95% CI: 0.95-0.99), rs10719 (OR 0.97; 95% CI: 0.94-0.99), rs4687554 (OR 0.97; 95% CI: 0.95-0.99, and rs3134615 (OR 1.03; 95% CI: 1.01-1.05) located in the 3' UTR of CASP8, HDDC3, DROSHA, MUSTN1, and MYCL1, respectively. DROSHA belongs to miRNA machinery genes and has a central role in initial miRNA processing. The remaining genes are involved in different molecular functions, including apoptosis and gene expression regulation. Further studies are warranted to elucidate whether the miRNA binding site SNPs are the causative variants for the observed risk effects.

Published
2014

34. Evidence of Gene-Environment Interactions between Common Breast Cancer Susceptibility Loci and Established Environmental Risk Factors

Author

Horwitz, MS, Nickels, S, Truong, T, Hein, R, Stevens, K, Buck, K, Behrens, S, Eilber, U, Schmidt, M, Haeberle, L, Vrieling, A, Gaudet, M, Figueroa, J, Schoof, N, Spurdle, AB, Rudolph, A, Fasching, PA, Hopper, JL, Makalic, E, Schmidt, DF, Southey, MC, Beckmann, MW, Ekici, AB, Fletcher, O, Gibson, L, Silva, IDS, Peto, J, Humphreys, MK, Wang, J, Cordina-Duverger, E, Menegaux, F, Nordestgaard, BG, Bojesen, SE, Lanng, C, Anton-Culver, H, Ziogas, A, Bernstein, L, Clarke, CA, Brenner, H, Mueller, H, Arndt, V, Stegmaier, C, Brauch, H, Bruening, T, Harth, V, Mannermaa, A, Kataja, V, Kosma, V-M, Hartikainen, JM, Lambrechts, D, Smeets, D, Neven, P, Paridaens, R, Flesch-Janys, D, Obi, N, Wang-Gohrke, S, Couch, FJ, Olson, JE, Vachon, CM, Giles, GG, Severi, G, Baglietto, L, Offit, K, John, EM, Miron, A, Andrulis, IL, Knight, JA, Glendon, G, Mulligan, AM, Chanock, SJ, Lissowska, J, Liu, J, Cox, A, Cramp, H, Connley, D, Balasubramanian, S, Dunning, AM, Shah, M, Trentham-Dietz, A, Newcomb, P, Titus, L, Egan, K, Cahoon, EK, Rajaraman, P, Sigurdson, AJ, Doody, MM, Guenel, P, Pharoah, PDP, Schmidt, MK, Hall, P, Easton, DF, Garcia-Closas, M, Milne, RL, Chang-Claude, J, Horwitz, MS, Nickels, S, Truong, T, Hein, R, Stevens, K, Buck, K, Behrens, S, Eilber, U, Schmidt, M, Haeberle, L, Vrieling, A, Gaudet, M, Figueroa, J, Schoof, N, Spurdle, AB, Rudolph, A, Fasching, PA, Hopper, JL, Makalic, E, Schmidt, DF, Southey, MC, Beckmann, MW, Ekici, AB, Fletcher, O, Gibson, L, Silva, IDS, Peto, J, Humphreys, MK, Wang, J, Cordina-Duverger, E, Menegaux, F, Nordestgaard, BG, Bojesen, SE, Lanng, C, Anton-Culver, H, Ziogas, A, Bernstein, L, Clarke, CA, Brenner, H, Mueller, H, Arndt, V, Stegmaier, C, Brauch, H, Bruening, T, Harth, V, Mannermaa, A, Kataja, V, Kosma, V-M, Hartikainen, JM, Lambrechts, D, Smeets, D, Neven, P, Paridaens, R, Flesch-Janys, D, Obi, N, Wang-Gohrke, S, Couch, FJ, Olson, JE, Vachon, CM, Giles, GG, Severi, G, Baglietto, L, Offit, K, John, EM, Miron, A, Andrulis, IL, Knight, JA, Glendon, G, Mulligan, AM, Chanock, SJ, Lissowska, J, Liu, J, Cox, A, Cramp, H, Connley, D, Balasubramanian, S, Dunning, AM, Shah, M, Trentham-Dietz, A, Newcomb, P, Titus, L, Egan, K, Cahoon, EK, Rajaraman, P, Sigurdson, AJ, Doody, MM, Guenel, P, Pharoah, PDP, Schmidt, MK, Hall, P, Easton, DF, Garcia-Closas, M, Milne, RL, and Chang-Claude, J

Abstract

Various common genetic susceptibility loci have been identified for breast cancer; however, it is unclear how they combine with lifestyle/environmental risk factors to influence risk. We undertook an international collaborative study to assess gene-environment interaction for risk of breast cancer. Data from 24 studies of the Breast Cancer Association Consortium were pooled. Using up to 34,793 invasive breast cancers and 41,099 controls, we examined whether the relative risks associated with 23 single nucleotide polymorphisms were modified by 10 established environmental risk factors (age at menarche, parity, breastfeeding, body mass index, height, oral contraceptive use, menopausal hormone therapy use, alcohol consumption, cigarette smoking, physical activity) in women of European ancestry. We used logistic regression models stratified by study and adjusted for age and performed likelihood ratio tests to assess gene-environment interactions. All statistical tests were two-sided. We replicated previously reported potential interactions between LSP1-rs3817198 and parity (Pinteraction = 2.4 × 10(-6)) and between CASP8-rs17468277 and alcohol consumption (Pinteraction = 3.1 × 10(-4)). Overall, the per-allele odds ratio (95% confidence interval) for LSP1-rs3817198 was 1.08 (1.01-1.16) in nulliparous women and ranged from 1.03 (0.96-1.10) in parous women with one birth to 1.26 (1.16-1.37) in women with at least four births. For CASP8-rs17468277, the per-allele OR was 0.91 (0.85-0.98) in those with an alcohol intake of <20 g/day and 1.45 (1.14-1.85) in those who drank ≥ 20 g/day. Additionally, interaction was found between 1p11.2-rs11249433 and ever being parous (Pinteraction = 5.3 × 10(-5)), with a per-allele OR of 1.14 (1.11-1.17) in parous women and 0.98 (0.92-1.05) in nulliparous women. These data provide first strong evidence that the risk of breast cancer associated with some common genetic variants may vary with environmental risk factors.

Published
2013

35. Tumour morphology of early-onset breast cancers predicts breast cancer risk for first-degree relatives: the Australian Breast Cancer Family Registry

Author

Dite, GS, Makalic, E, Schmidt, DF, Giles, GG, Hopper, JL, Southey, MC, Dite, GS, Makalic, E, Schmidt, DF, Giles, GG, Hopper, JL, and Southey, MC

Abstract

INTRODUCTION: We hypothesised that breast cancer risk for relatives of women with early-onset breast cancer could be predicted by tumour morphological features. METHODS: We studied female first-degree relatives of a population-based sample of 452 index cases with a first primary invasive breast cancer diagnosed before the age of 40 years. For the index cases, a standardised tumour morphology review had been conducted for all; estrogen (ER) and progesterone receptor (PR) status was available for 401 (89%), and 77 (17%) had a high-risk mutation in a breast cancer susceptibility gene or methylation of the BRCA1 promoter region in peripheral blood DNA. We calculated standardised incidence ratios (SIR) by comparing the number of mothers and sisters with breast cancer with the number expected based on Australian incidence rates specific for age and year of birth. RESULTS: Using Cox proportional hazards modelling, absence of extensive sclerosis, extensive intraductal carcinoma, absence of acinar and glandular growth patterns, and the presence of trabecular and lobular growth patterns were independent predictors with between a 1.8- and 3.1-fold increased risk for relatives (all P <0.02). Excluding index cases with known genetic predisposition or BRCA1 promoter methylation, absence of extensive sclerosis, circumscribed growth, extensive intraductal carcinoma and lobular growth pattern were independent predictors with between a 2.0- and 3.3-fold increased risk for relatives (all P <0.02). Relatives of the 128 (34%) index cases with none of these four features were at population risk (SIR = 1.03, 95% CI = 0.57 to 1.85) while relatives of the 37 (10%) index cases with two or more features were at high risk (SIR = 5.18, 95% CI = 3.22 to 8.33). CONCLUSIONS: This wide variation in risks for relatives based on tumour characteristics could be of clinical value, help discover new breast cancer susceptibility genes and be an advance on the current clinical practice of using ER and PR

Published
2012

38. Association analysis identifies 65 new breast cancer risk loci

Author

Michailidou, K, Lindström, S, Dennis, J, Beesley, J, Hui, S, Kar, S, Lemaçon, A, Soucy, P, Glubb, D, Rostamianfar, A, Bolla, MK, Wang, Q, Tyrer, J, Dicks, E, Lee, A, Wang, Z, Allen, J, Keeman, R, Eilber, U, French, JD, Qing Chen, X, Fachal, L, McCue, K, McCart Reed, AE, Ghoussaini, M, Carroll, JS, Jiang, X, Finucane, H, Adams, M, Adank, MA, Ahsan, H, Aittomäki, K, Anton-Culver, H, Antonenkova, NN, Arndt, V, Aronson, KJ, Arun, B, Auer, PL, Bacot, F, Barrdahl, M, Baynes, C, Beckmann, MW, Behrens, S, Benitez, J, Bermisheva, M, Bernstein, L, Blomqvist, C, Bogdanova, NV, Bojesen, SE, Bonanni, B, Børresen-Dale, A-L, Brand, JS, Brauch, H, Brennan, P, Brenner, H, Brinton, L, Broberg, P, Brock, IW, Broeks, A, Brooks-Wilson, A, Brucker, SY, Brüning, T, Burwinkel, B, Butterbach, K, Cai, Q, Cai, H, Caldés, T, Canzian, F, Carracedo, A, Carter, BD, Castelao, JE, Chan, TL, David Cheng, T-Y, Seng Chia, K, Choi, J-Y, Christiansen, H, Clarke, CL, NBCS Collaborators, Collée, M, Conroy, DM, Cordina-Duverger, E, Cornelissen, S, Cox, DG, Cox, A, Cross, SS, Cunningham, JM, Czene, K, Daly, MB, Devilee, P, Doheny, KF, Dörk, T, Dos-Santos-Silva, I, Dumont, M, Durcan, L, Dwek, M, Eccles, DM, Ekici, AB, Eliassen, AH, Ellberg, C, Elvira, M, Engel, C, Eriksson, M, Fasching, PA, Figueroa, J, Flesch-Janys, D, Fletcher, O, Flyger, H, Fritschi, L, Gaborieau, V, Gabrielson, M, Gago-Dominguez, M, Gao, Y-T, Gapstur, SM, García-Sáenz, JA, Gaudet, MM, Georgoulias, V, Giles, GG, Glendon, G, Goldberg, MS, Goldgar, DE, González-Neira, A, Grenaker Alnæs, GI, Grip, M, Gronwald, J, Grundy, A, Guénel, P, Haeberle, L, Hahnen, E, Haiman, CA, Håkansson, N, Hamann, U, Hamel, N, Hankinson, S, Harrington, P, Hart, SN, Hartikainen, JM, Hartman, M, Hein, A, Heyworth, J, Hicks, B, Hillemanns, P, Ho, DN, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Hou, M-F, Hsiung, C-N, Huang, G, Humphreys, K, Ishiguro, J, Ito, H, Iwasaki, M, Iwata, H, Jakubowska, A, Janni, W, John, EM, Johnson, N, Jones, K, Jones, M, Jukkola-Vuorinen, A, Kaaks, R, Kabisch, M, Kaczmarek, K, Kang, D, Kasuga, Y, Kerin, MJ, Khan, S, Khusnutdinova, E, Kiiski, JI, Kim, S-W, Knight, JA, Kosma, V-M, Kristensen, VN, Krüger, U, Kwong, A, Lambrechts, D, Le Marchand, L, Lee, E, Lee, MH, Lee, JW, Neng Lee, C, Lejbkowicz, F, Li, J, Lilyquist, J, Lindblom, A, Lissowska, J, Lo, W-Y, Loibl, S, Long, J, Lophatananon, A, Lubinski, J, Luccarini, C, Lux, MP, Ma, ESK, MacInnis, RJ, Maishman, T, Makalic, E, Malone, KE, Kostovska, IM, Mannermaa, A, Manoukian, S, Manson, JE, Margolin, S, Mariapun, S, Martinez, ME, Matsuo, K, Mavroudis, D, McKay, J, McLean, C, Meijers-Heijboer, H, Meindl, A, Menéndez, P, Menon, U, Meyer, J, Miao, H, Miller, N, Taib, NAM, Muir, K, Mulligan, AM, Mulot, C, Neuhausen, SL, Nevanlinna, H, Neven, P, Nielsen, SF, Noh, D-Y, Nordestgaard, BG, Norman, A, Olopade, OI, Olson, JE, Olsson, H, Olswold, C, Orr, N, Pankratz, VS, Park, SK, Park-Simon, T-W, Lloyd, R, Perez, JIA, Peterlongo, P, Peto, J, Phillips, K-A, Pinchev, M, Plaseska-Karanfilska, D, Prentice, R, Presneau, N, Prokofyeva, D, Pugh, E, Pylkäs, K, Rack, B, Radice, P, Rahman, N, Rennert, G, Rennert, HS, Rhenius, V, Romero, A, Romm, J, Ruddy, KJ, Rüdiger, T, Rudolph, A, Ruebner, M, Rutgers, EJT, Saloustros, E, Sandler, DP, Sangrajrang, S, Sawyer, EJ, Schmidt, DF, Schmutzler, RK, Schneeweiss, A, Schoemaker, MJ, Schumacher, F, Schürmann, P, Scott, RJ, Scott, C, Seal, S, Seynaeve, C, Shah, M, Sharma, P, Shen, C-Y, Sheng, G, Sherman, ME, Shrubsole, MJ, Shu, X-O, Smeets, A, Sohn, C, Southey, MC, Spinelli, JJ, Stegmaier, C, Stewart-Brown, S, Stone, J, Stram, DO, Surowy, H, Swerdlow, A, Tamimi, R, Taylor, JA, Tengström, M, Teo, SH, Beth Terry, M, Tessier, DC, Thanasitthichai, S, Thöne, K, Tollenaar, RAEM, Tomlinson, I, Tong, L, Torres, D, Truong, T, Tseng, C-C, Tsugane, S, Ulmer, H-U, Ursin, G, Untch, M, Vachon, C, Van Asperen, CJ, Van Den Berg, D, Van Den Ouweland, AMW, Van Der Kolk, L, Van Der Luijt, RB, Vincent, D, Vollenweider, J, Waisfisz, Q, Wang-Gohrke, S, Weinberg, CR, Wendt, C, Whittemore, AS, Wildiers, H, Willett, W, Winqvist, R, Wolk, A, Wu, AH, Xia, L, Yamaji, T, Yang, XR, Har Yip, C, Yoo, K-Y, Yu, J-C, Zheng, W, Zheng, Y, Zhu, B, Ziogas, A, Ziv, E, ABCTB Investigators, ConFab/AOCS Investigators, Lakhani, Antoniou, AC, Droit, A, Andrulis, IL, Amos, CI, Couch, FJ, Pharoah, PDP, Chang-Claude, J, Hall, P, Hunter, DJ, Milne, RL, García-Closas, M, Schmidt, MK, Chanock, SJ, Dunning, AM, Edwards, SL, Bader, GD, Chenevix-Trench, G, Simard, J, Kraft, P, and Easton, DF

Subjects
ConFab/AOCS Investigators, ABCTB Investigators, skin and connective tissue diseases, 3. Good health, NBCS Collaborators
Abstract

Breast cancer risk is influenced by rare coding variants in susceptibility genes, such as BRCA1, and many common, mostly non-coding variants. However, much of the genetic contribution to breast cancer risk remains unknown. Here we report the results of a genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry. We identified 65 new loci that are associated with overall breast cancer risk at P < 5 × 10-8. The majority of credible risk single-nucleotide polymorphisms in these loci fall in distal regulatory elements, and by integrating in silico data to predict target genes in breast cells at each locus, we demonstrate a strong overlap between candidate target genes and somatic driver genes in breast tumours. We also find that heritability of breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2-5-fold enriched relative to the genome-wide average, with strong enrichment for particular transcription factor binding sites. These results provide further insight into genetic susceptibility to breast cancer and will improve the use of genetic risk scores for individualized screening and prevention.

39. Identification of four novel susceptibility loci for oestrogen receptor negative breast cancer

Author

Couch, FJ, Kuchenbaecker, KB, Michailidou, K, Mendoza-Fandino, GA, Nord, S, Lilyquist, J, Olswold, C, Hallberg, E, Agata, S, Ahsan, H, Aittomäki, K, Ambrosone, C, Andrulis, IL, Anton-Culver, H, Arndt, V, Arun, BK, Arver, B, Barile, M, Barkardottir, RB, Barrowdale, D, Beckmann, L, Beckmann, MW, Benitez, J, Blank, SV, Blomqvist, C, Bogdanova, NV, Bojesen, SE, Bolla, MK, Bonanni, B, Brauch, H, Brenner, H, Burwinkel, B, Buys, SS, Caldes, T, Caligo, MA, Canzian, F, Carpenter, J, Chang-Claude, J, Chanock, SJ, Chung, WK, Claes, KBM, Cox, A, Cross, SS, Cunningham, JM, Czene, K, Daly, MB, Damiola, F, Darabi, H, De La Hoya, M, Devilee, P, Diez, O, Ding, YC, Dolcetti, R, Domchek, SM, Dorfling, CM, Dos-Santos-Silva, I, Dumont, M, Dunning, AM, Eccles, DM, Ehrencrona, H, Ekici, AB, Eliassen, H, Ellis, S, Fasching, PA, Figueroa, J, Flesch-Janys, D, Försti, A, Fostira, F, Foulkes, WD, Friebel, T, Friedman, E, Frost, D, Gabrielson, M, Gammon, MD, Ganz, PA, Gapstur, SM, Garber, J, Gaudet, MM, Gayther, SA, Gerdes, A-M, Ghoussaini, M, Giles, GG, Glendon, G, Godwin, AK, Goldberg, MS, Goldgar, DE, González-Neira, A, Greene, MH, Gronwald, J, Guénel, P, Gunter, M, Haeberle, L, Haiman, CA, Hamann, U, Hansen, TVO, Hart, S, Healey, S, Heikkinen, T, Henderson, BE, Herzog, J, Hogervorst, FBL, Hollestelle, A, Hooning, MJ, Hoover, RN, Hopper, JL, Humphreys, K, Hunter, DJ, Huzarski, T, Imyanitov, EN, Isaacs, C, Jakubowska, A, James, P, Janavicius, R, Jensen, UB, John, EM, Jones, M, Kabisch, M, Kar, S, Karlan, BY, Khan, S, Khaw, K-T, Kibriya, MG, Knight, JA, Ko, Y-D, Konstantopoulou, I, Kosma, V-M, Kristensen, V, Kwong, A, Laitman, Y, Lambrechts, D, Lazaro, C, Lee, E, Le Marchand, L, Lester, J, Lindblom, A, Lindor, N, Lindstrom, S, Liu, J, Long, J, Lubinski, J, Mai, PL, Makalic, E, Malone, KE, Mannermaa, A, Manoukian, S, Margolin, S, Marme, F, Martens, JWM, McGuffog, L, Meindl, A, Miller, A, Milne, RL, Miron, P, Montagna, M, Mazoyer, S, Mulligan, AM, Muranen, TA, Nathanson, KL, Neuhausen, SL, Nevanlinna, H, Nordestgaard, BG, Nussbaum, RL, Offit, K, Olah, E, Olopade, OI, Olson, JE, Osorio, A, Park, SK, Peeters, PH, Peissel, B, Peterlongo, P, Peto, J, Phelan, CM, Pilarski, R, Poppe, B, Pylkäs, K, Radice, P, Rahman, N, Rantala, J, Rappaport, C, Rennert, G, Richardson, A, Robson, M, Romieu, I, Rudolph, A, Rutgers, EJ, Sanchez, M-J, Santella, RM, Sawyer, EJ, Schmidt, DF, Schmidt, MK, Schmutzler, RK, Schumacher, F, Scott, R, Senter, L, Sharma, P, Simard, J, Singer, CF, Sinilnikova, OM, Soucy, P, Southey, M, Steinemann, D, Stenmark-Askmalm, M, Stoppa-Lyonnet, D, Swerdlow, A, Szabo, CI, Tamimi, R, Tapper, W, Teixeira, MR, Teo, S-H, Terry, MB, Thomassen, M, Thompson, D, Tihomirova, L, Toland, AE, Tollenaar, RAEM, Tomlinson, I, Truong, T, Tsimiklis, H, Teulé, A, Tumino, R, Tung, N, Turnbull, C, Ursin, G, Van Deurzen, CHM, Van Rensburg, EJ, Varon-Mateeva, R, Wang, Z, Wang-Gohrke, S, Weiderpass, E, Weitzel, JN, Whittemore, A, Wildiers, H, Winqvist, R, Yang, XR, Yannoukakos, D, Yao, S, Zamora, MP, Zheng, W, Hall, P, Kraft, P, Vachon, C, Slager, S, Chenevix-Trench, G, Pharoah, PDP, Monteiro, AAN, García-Closas, M, Easton, DF, and Antoniou, AC

Subjects
Heterozygote, tRNA Methyltransferases, Genotype, BRCA1 Protein, Breast Neoplasms, Polymorphism, Single Nucleotide, 3. Good health, Cyclophilins, Receptors, Estrogen, Risk Factors, Chromosomes, Human, Pair 2, Mutation, Humans, Female, Genetic Predisposition to Disease, Genome-Wide Association Study
Abstract

This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ncomms11375, Common variants in 94 loci have been associated with breast cancer including 15 loci with genome-wide significant associations (P

40. Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer

Author

Pontificia Universidad Javeriana. Facultad de Medicina. Instituto de Genética Humana, Michailidou, K, Beesley, J, Lindstrom, S, Canisius, S, Dennis, J, Lush, MJ, Maranian, MJ, Bolla, MK, Wang, Q, Shah, M, Perkins, BJ, Czene, K, Eriksson, M, Darabi, H, Brand, JS, Bojesen, SE, Nordestgaard, BG, Flyger, H, Nielsen, SF, Rahman, N, Turnbull, C, BOCS, Fletcher, O, Peto, J, Gibson, L, dos-Santos-Silva, I, Chang-Claude, J, Flesch-Janys, D, Rudolph, A, Eilber, U, Behrens, S, Nevanlinna, H, Muranen, TA, Aittomäki, K, Blomqvist, C, Khan, S, Aaltonen, K, Ahsan, H, Kibriya, MG, Whittemore, AS, John, EM, Malone, KE, Gammon, MD, Santella, RM, Ursin, G, Makalic, E, Schmidt, DF, Casey, G, Hunter, DJ, Gapstur, SM, Gaudet, MM, Diver, WR, Haiman, CA, Schumacher, F, Henderson, BE, Le Marchand, L, Berg, CD, Chanock, SJ, Figueroa, J, Hoover, RN, Lambrechts, D, Neven, P, Wildiers, H, van Limbergen, E, Schmidt, MK, Broeks, A, Verhoef, S, Cornelissen, S, Couch, FJ, Olson, JE, Hallberg, E, Vachon, C, Waisfisz, Q, Meijers-Heijboer, H, Adank, MA, van der Luijt, RB, Li, J, Liu, J, Humphreys, K, Kang, D, Choi, JY, Park, SK, Yoo, KY, Matsuo, K, Ito, H, Iwata, H, Tajima, K, Guénel, P, Truong, T, Mulot, C, Sanchez, M, Burwinkel, B, Marme, F, Surowy, H, Sohn, C, Wu, AH, Tseng, CC, Van Den Berg, D, Stram, DO, González-Neira, A, Benitez, J, Zamora, MP, Perez, JI, Shu, XO, Lu, W, Gao, YT, Cai, H, Cox, A, Cross, SS, Reed, MW, Andrulis, IL, Knight, JA, Glendon, G, Mulligan, AM, Sawyer, EJ, Tomlinson, I, Kerin, MJ, Miller, N, ConFab Investigators, AOCS Group, Lindblom, A, Margolin, S, Teo, SH, Yip, CH, Taib, NA, Tan, GH, Hooning, MJ, Hollestelle, A, Martens, JW, Collée, JM, Blot, W, Signorello, LB, Cai, Q, Hopper, JL, Southey, MC, Tsimiklis, H, Apicella, C, Shen, CY, Hsiung, CN, Wu, PE, Hou, MF, Kristensen, VN, Nord, S, Alnaes, GI, NBCS, Giles, GG, Milne, RL, McLean, C, Canzian, F, Trichopoulos, D, Peeters, P, Lund, E, Sund, M, Khaw, KT, Gunter, MJ, Palli, D, Mortensen, LM, Dossus, L, Huerta, JM, Meindl, A, Schmutzler, RK, Sutter, C, Yang, R, Muir, K, Lophatananon, A, Stewart-Brown, S, Siriwanarangsan, P, Hartman, M, Miao, H, Chia, KS, Chan, CW, Fasching, PA, Hein, A, Beckmann, MW, Haeberle, L, Brenner, H, Dieffenbach, AK, Arndt, V, Stegmaier, C, Ashworth, A, Orr, N, Schoemaker, MJ, Swerdlow, AJ, Brinton, L, Garcia-Closas, M, Zheng, W, Halverson, SL, Shrubsole, M, Long, J, Goldberg, MS, Labrèche, F, Dumont, M, Winqvist, R, Pylkäs, K, Jukkola-Vuorinen, A, Grip, M, Brauch, H, Hamann, U, Brüning, T, GENICA Network, Radice, P, Peterlongo, P, Manoukian, S, Bernard, L, Bogdanova, NV, Dörk, T, Mannermaa, A, Kataja, V, Kosma, VM, Hartikainen, JM, Devilee, P, Tollenaar, RA, Seynaeve, C, Van Asperen, CJ, Jakubowska, A, Lubinski, J, Jaworska, K, Huzarski, T, Sangrajrang, S, Gaborieau, V, Brennan, P, McKay, J, Slager, S, Toland, AE, Ambrosone, CB, Yannoukakos, D, Kabisch, M, Torres, D, Neuhausen, SL, Anton-Culver, H, Luccarini, C, Baynes, C, Ahmed, S, Healey, CS, Tessier, DC, Vincent, D, Bacot, F, Pita, G, Alonso, MR, Álvarez, N, Herrero, D, Simard, J, Pharoah, PP, Kraft, P, Dunning, AM, Chenevix-Trench, G, Hall, P, Easton, DF, Pontificia Universidad Javeriana. Facultad de Medicina. Instituto de Genética Humana, Michailidou, K, Beesley, J, Lindstrom, S, Canisius, S, Dennis, J, Lush, MJ, Maranian, MJ, Bolla, MK, Wang, Q, Shah, M, Perkins, BJ, Czene, K, Eriksson, M, Darabi, H, Brand, JS, Bojesen, SE, Nordestgaard, BG, Flyger, H, Nielsen, SF, Rahman, N, Turnbull, C, BOCS, Fletcher, O, Peto, J, Gibson, L, dos-Santos-Silva, I, Chang-Claude, J, Flesch-Janys, D, Rudolph, A, Eilber, U, Behrens, S, Nevanlinna, H, Muranen, TA, Aittomäki, K, Blomqvist, C, Khan, S, Aaltonen, K, Ahsan, H, Kibriya, MG, Whittemore, AS, John, EM, Malone, KE, Gammon, MD, Santella, RM, Ursin, G, Makalic, E, Schmidt, DF, Casey, G, Hunter, DJ, Gapstur, SM, Gaudet, MM, Diver, WR, Haiman, CA, Schumacher, F, Henderson, BE, Le Marchand, L, Berg, CD, Chanock, SJ, Figueroa, J, Hoover, RN, Lambrechts, D, Neven, P, Wildiers, H, van Limbergen, E, Schmidt, MK, Broeks, A, Verhoef, S, Cornelissen, S, Couch, FJ, Olson, JE, Hallberg, E, Vachon, C, Waisfisz, Q, Meijers-Heijboer, H, Adank, MA, van der Luijt, RB, Li, J, Liu, J, Humphreys, K, Kang, D, Choi, JY, Park, SK, Yoo, KY, Matsuo, K, Ito, H, Iwata, H, Tajima, K, Guénel, P, Truong, T, Mulot, C, Sanchez, M, Burwinkel, B, Marme, F, Surowy, H, Sohn, C, Wu, AH, Tseng, CC, Van Den Berg, D, Stram, DO, González-Neira, A, Benitez, J, Zamora, MP, Perez, JI, Shu, XO, Lu, W, Gao, YT, Cai, H, Cox, A, Cross, SS, Reed, MW, Andrulis, IL, Knight, JA, Glendon, G, Mulligan, AM, Sawyer, EJ, Tomlinson, I, Kerin, MJ, Miller, N, ConFab Investigators, AOCS Group, Lindblom, A, Margolin, S, Teo, SH, Yip, CH, Taib, NA, Tan, GH, Hooning, MJ, Hollestelle, A, Martens, JW, Collée, JM, Blot, W, Signorello, LB, Cai, Q, Hopper, JL, Southey, MC, Tsimiklis, H, Apicella, C, Shen, CY, Hsiung, CN, Wu, PE, Hou, MF, Kristensen, VN, Nord, S, Alnaes, GI, NBCS, Giles, GG, Milne, RL, McLean, C, Canzian, F, Trichopoulos, D, Peeters, P, Lund, E, Sund, M, Khaw, KT, Gunter, MJ, Palli, D, Mortensen, LM, Dossus, L, Huerta, JM, Meindl, A, Schmutzler, RK, Sutter, C, Yang, R, Muir, K, Lophatananon, A, Stewart-Brown, S, Siriwanarangsan, P, Hartman, M, Miao, H, Chia, KS, Chan, CW, Fasching, PA, Hein, A, Beckmann, MW, Haeberle, L, Brenner, H, Dieffenbach, AK, Arndt, V, Stegmaier, C, Ashworth, A, Orr, N, Schoemaker, MJ, Swerdlow, AJ, Brinton, L, Garcia-Closas, M, Zheng, W, Halverson, SL, Shrubsole, M, Long, J, Goldberg, MS, Labrèche, F, Dumont, M, Winqvist, R, Pylkäs, K, Jukkola-Vuorinen, A, Grip, M, Brauch, H, Hamann, U, Brüning, T, GENICA Network, Radice, P, Peterlongo, P, Manoukian, S, Bernard, L, Bogdanova, NV, Dörk, T, Mannermaa, A, Kataja, V, Kosma, VM, Hartikainen, JM, Devilee, P, Tollenaar, RA, Seynaeve, C, Van Asperen, CJ, Jakubowska, A, Lubinski, J, Jaworska, K, Huzarski, T, Sangrajrang, S, Gaborieau, V, Brennan, P, McKay, J, Slager, S, Toland, AE, Ambrosone, CB, Yannoukakos, D, Kabisch, M, Torres, D, Neuhausen, SL, Anton-Culver, H, Luccarini, C, Baynes, C, Ahmed, S, Healey, CS, Tessier, DC, Vincent, D, Bacot, F, Pita, G, Alonso, MR, Álvarez, N, Herrero, D, Simard, J, Pharoah, PP, Kraft, P, Dunning, AM, Chenevix-Trench, G, Hall, P, and Easton, DF

41. Region-Based Analyses of Existing Genome-Wide Association Studies Identifies Novel Potential Genetic Susceptibility Regions for Glioma.

Author

Alpen K, Maclnnis RJ, Vajdic CM, Lai J, Dowty JG, Koh ES, Hovey E, Harrup R, Nguyen TL, Li S, Joseph D, Benke G, Dugué PA, Southey MC, Giles GG, Nowak AK, Drummond KJ, Schmidt DF, Hopper JL, Kapuscinski MK, and Makalic E

Subjects
Humans, Male, Female, Brain Neoplasms genetics, Polymorphism, Single Nucleotide, Glioma genetics, Genetic Predisposition to Disease, Genome-Wide Association Study
Abstract

Significance: Further investigation of the potential susceptibility regions identified in our study may lead to a better understanding of glioma genetic risk and the underlying biological etiology of glioma. Our study suggests sex may play a role in genetic susceptibility and highlights the importance of sex-specific analysis in future glioma research., (©2024 The Authors; Published by the American Association for Cancer Research.)

Published
2024
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42. Using kraft lignin nanoparticles for the stabilization of nano/micro wax carriers.

Author

El Bouhali A, Cambier S, Grysan P, Chuzeville L, Schmidt DF, and Thomann JS

Subjects
Drug Carriers chemistry, Indocyanine Green chemistry, Particle Size, Lipids chemistry, Lignin chemistry, Nanoparticles chemistry, Waxes chemistry, Emulsions chemistry
Abstract

Due to its amphiphilic structure, lignin has the potential to stabilize emulsions via adsorption at the oil/water interface. By converting lignin into nanoparticles, we can significantly enhance its emulsion-stabilizing capabilities through a Pickering-type stabilization mechanism. Two essential elements may be modified to fine-tune emulsion stability: the size of the lignin nanoparticles (LNPs) and the physicochemical nature of the lipid phase. In this context, we highlight the behavior and utility of unmodified LNPs in the preparation of Pickering emulsions made up of water and a complex bio-based pharmaceutical-grade wax that can be used for the formulation of lipid carriers. As a proof-of-concept, we employ the developed Pickering emulsions to encapsulate indocyanine green (ICG), an FDA-approved dye commonly used in medical imaging applications. We demonstrate that ultra-small LNPs are well-suited for the colloidal stabilization of pharmaceutical wax ester micro beads. This stabilization does not require any lignin modification. Additionally, we present evidence that our new lipid/lignin hybrid carrier has potential as a new drug delivery system., Competing Interests: Declaration of competing interest The authors declare no competing financial interest., (Copyright © 2024. Published by Elsevier B.V.)

Published
2024
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43. Dynamic Ion Gels from the Complex Coacervation of Oppositely Charged Poly(ionic liquid)s.

Author

Alaa Eddine M, Nosov DR, Lepre LF, Serghei A, Schmidt DF, Montarnal D, Shaplov AS, and Drockenmuller E

Abstract

A cationic poly(ionic liquid) (PIL) with pendent butyl imidazolium cations and free bis(trifluoromethylsulfonyl)imide (TFSI) anions and an anionic PIL with pendent TFSI anions and free 1-butyl-3-methylimidazolium cations are synthesized by postpolymerization chemical modification and reversible addition-fragmentation chain-transfer radical copolymerization, respectively. Upon mixing solutions of these two PILs in acetone with stoichiometric amounts of ion pairs, ionic exchanges induce coacervation and, after solvent evaporation, lead to the formation of a dynamic ion gel (DIG) and the concomitant release of free [1-methyl-3-butyl imidazolium]TFSI ionic liquid (IL). A comparison of thermal ( T g ), ion conducting (σ DC ), and viscoelastic (elastic moduli ( G ')) properties for DIGs and their parent polyelectrolytes, as well as extracted and IL-doped DIGs, demonstrates the formation of ionic cross-links and the ability to easily produce polymer electrolytes with enhanced ionic conductivity (σ DC up to 4.5 × 10 -5 S cm -1 at 30 °C) and higher elastic moduli ( G ' up to 4 kPa at 25 °C and 1 rad s -1 ), making them highly desirable in many electrochemical applications, including supercapacitors, soft robotics, electrochromic devices, sensors, and solar cells.

Published
2024
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44. Molecular Simulation of Covalent Adaptable Networks and Vitrimers: A Review.

Author

Karatrantos AV, Couture O, Hesse C, and Schmidt DF

Abstract

Covalent adaptable networks and vitrimers are novel polymers with dynamic reversible bond exchange reactions for crosslinks, enabling them to modulate their properties between those of thermoplastics and thermosets. They have been gathering interest as materials for their recycling and self-healing properties. In this review, we discuss different molecular simulation efforts that have been used over the last decade to investigate and understand the nanoscale and molecular behaviors of covalent adaptable networks and vitrimers. In particular, molecular dynamics, Monte Carlo, and a hybrid of molecular dynamics and Monte Carlo approaches have been used to model the dynamic bond exchange reaction, which is the main mechanism of interest since it controls both the mechanical and rheological behaviors. The molecular simulation techniques presented yield sufficient results to investigate the structure and dynamics as well as the mechanical and rheological responses of such dynamic networks. The benefits of each method have been highlighted. The use of other tools such as theoretical models and machine learning has been included. We noticed, amongst the most prominent results, that stress relaxes as the bond exchange reaction happens, and that at temperatures higher than the glass transition temperature, the self-healing properties are better since more bond BERs are observed. The lifetime of dynamic covalent crosslinks follows, at moderate to high temperatures, an Arrhenius-like temperature dependence. We note the modeling of certain properties like the melt viscosity with glass transition temperature and the topology freezing transition temperature according to a behavior ruled by either the Williams-Landel-Ferry equation or the Arrhenius equation. Discrepancies between the behavior in dissociative and associative covalent adaptable networks are discussed. We conclude by stating which material parameters and atomistic factors, at the nanoscale, have not yet been taken into account and are lacking in the current literature.

Published
2024
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45. Breast and bowel cancers diagnosed in people 'too young to have cancer': A blueprint for research using family and twin studies.

Author

Hopper JL, Li S, MacInnis RJ, Dowty JG, Nguyen TL, Bui M, Dite GS, Esser VFC, Ye Z, Makalic E, Schmidt DF, Goudey B, Alpen K, Kapuscinski M, Win AK, Dugué PA, Milne RL, Jayasekara H, Brooks JD, Malta S, Calais-Ferreira L, Campbell AC, Young JT, Nguyen-Dumont T, Sung J, Giles GG, Buchanan D, Winship I, Terry MB, Southey MC, and Jenkins MA

Abstract

Young breast and bowel cancers (e.g., those diagnosed before age 40 or 50 years) have far greater morbidity and mortality in terms of years of life lost, and are increasing in incidence, but have been less studied. For breast and bowel cancers, the familial relative risks, and therefore the familial variances in age-specific log(incidence), are much greater at younger ages, but little of these familial variances has been explained. Studies of families and twins can address questions not easily answered by studies of unrelated individuals alone. We describe existing and emerging family and twin data that can provide special opportunities for discovery. We present designs and statistical analyses, including novel ideas such as the VALID (Variance in Age-specific Log Incidence Decomposition) model for causes of variation in risk, the DEPTH (DEPendency of association on the number of Top Hits) and other approaches to analyse genome-wide association study data, and the within-pair, ICE FALCON (Inference about Causation from Examining FAmiliaL CONfounding) and ICE CRISTAL (Inference about Causation from Examining Changes in Regression coefficients and Innovative STatistical AnaLysis) approaches to causation and familial confounding. Example applications to breast and colorectal cancer are presented. Motivated by the availability of the resources of the Breast and Colon Cancer Family Registries, we also present some ideas for future studies that could be applied to, and compared with, cancers diagnosed at older ages and address the challenges posed by young breast and bowel cancers., (© 2024 The Authors. Genetic Epidemiology published by Wiley Periodicals LLC.)

Published
2024
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46. Unique Method for Facile Postsynthetic Modification of Nonisocyanate Polyurethanes.

Author

Zubkevich SV, Makarov M, Dieden R, Puchot L, Berthé V, Westermann S, Shaplov AS, and Schmidt DF

Abstract

Nonisocyanate polyurethanes (NIPUs) are broadly investigated as a potential replacement for conventional polyurethanes (PUs) to eliminate the use of toxic isocyanates and reduce occupational hazards. One of the most popular approaches to NIPU synthesis is the polyaddition of cyclic bis(carbonate)s and diamines to form poly(hydroxyurethane)s (PHUs). However, such PHUs are highly hydrophilic due to the presence of two hydroxyl groups per repeat unit, and the resulting moisture absorption significantly degrades their thermomechanical performance and physical stability upon exposure to humidity, thus limiting their utility. Here, we introduce a simple and scalable approach for the modification of PHUs to increase hydrophobicity and adjust their properties. The proposed reaction between aldehydes and appropriately spaced hydroxyl groups in the polymer backbone resulted in high degrees of modification (up to 84%) and up to 3-fold reductions in water uptake at 85% RH. Furthermore, the use of aromatic aldehydes in particular enabled the retention of mechanical properties over a wide range of humidity levels, resulting in performance comparable to conventional PUs. Finally, we note that this approach is not limited to reducing moisture sensitivity alone and provides ample opportunities for imparting a broad range of novel properties to PHUs through an appropriate selection of functional aldehydes., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)

Published
2024
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47. Genetic and Environmental Causes of Variation in an Automated Breast Cancer Risk Factor Based on Mammographic Textures.

Author

Ye Z, Dite GS, Nguyen TL, MacInnis RJ, Schmidt DF, Makalic E, Al-Qershi OM, Nguyen-Dumont T, Goudey B, Stone J, Dowty JG, Giles GG, Southey MC, Hopper JL, and Li S

Subjects
Female, Humans, Breast, Mammography, Risk Factors, Twins, Dizygotic genetics, Twins, Monozygotic genetics, Breast Neoplasms diagnostic imaging, Breast Neoplasms genetics
Abstract

Background: Cirrus is an automated risk predictor for breast cancer that comprises texture-based mammographic features and is mostly independent of mammographic density. We investigated genetic and environmental variance of variation in Cirrus., Methods: We measured Cirrus for 3,195 breast cancer-free participants, including 527 pairs of monozygotic (MZ) twins, 271 pairs of dizygotic (DZ) twins, and 1,599 siblings of twins. Multivariate normal models were used to estimate the variance and familial correlations of age-adjusted Cirrus as a function of age. The classic twin model was expanded to allow the shared environment effects to differ by zygosity. The SNP-based heritability was estimated for a subset of 2,356 participants., Results: There was no evidence that the variance or familial correlations depended on age. The familial correlations were 0.52 (SE, 0.03) for MZ pairs and 0.16(SE, 0.03) for DZ and non-twin sister pairs combined. Shared environmental factors specific to MZ pairs accounted for 20% of the variance. Additive genetic factors accounted for 32% (SE = 5%) of the variance, consistent with the SNP-based heritability of 36% (SE = 16%)., Conclusion: Cirrus is substantially familial due to genetic factors and an influence of shared environmental factors that was evident for MZ twin pairs only. The latter could be due to nongenetic factors operating in utero or in early life that are shared by MZ twins., Impact: Early-life factors, shared more by MZ pairs than DZ/non-twin sister pairs, could play a role in the variation in Cirrus, consistent with early life being recognized as a critical window of vulnerability to breast carcinogens., (©2023 American Association for Cancer Research.)

Published
2024
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48. Does the 11-year solar cycle affect lake and river ice phenology?

Author

Schmidt DF, Grise KM, and Pace ML

Subjects
Seasons, El Nino-Southern Oscillation, Ice Cover, Lakes, Rivers
Abstract

Records of ice-on and ice-off dates are available for lakes and rivers across the Northern Hemisphere spanning decades and in some cases centuries. This data provides an opportunity to investigate the climatic processes that may control ice phenology. Previous studies have reported a trend toward shorter ice-covered seasons with global warming, as well as links between ice phenology and several modes of natural climate variability such as the North Atlantic Oscillation, the Pacific-North American Pattern, the El Niño-Southern Oscillation, the Pacific Decadal Oscillation, and the Atlantic Multidecadal Oscillation. The 11-year sunspot cycle has also been proposed as a driver of ice phenology, which is somewhat surprising given that this cycle's strongest impacts are in the stratosphere. In this study, we use a large data set of lakes and rivers across the Northern Hemisphere to test this potential link. We find little or no connection between the sunspot cycle and either ice-on or ice-off dates. We conclude that while many well-known climate cycles do impact ice phenology, we are able to rule out any strong impact of the solar cycle., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Schmidt et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Published
2023
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49. Causal relationships between breast cancer risk factors based on mammographic features.

Author

Ye Z, Nguyen TL, Dite GS, MacInnis RJ, Schmidt DF, Makalic E, Al-Qershi OM, Bui M, Esser VFC, Dowty JG, Trinh HN, Evans CF, Tan M, Sung J, Jenkins MA, Giles GG, Southey MC, Hopper JL, and Li S

Subjects
Female, Humans, Australia epidemiology, Breast diagnostic imaging, Breast Density, Mammography methods, Risk Factors, Adult, Middle Aged, Aged, Breast Neoplasms diagnostic imaging, Breast Neoplasms epidemiology, Breast Neoplasms etiology
Abstract

Background: Mammogram risk scores based on texture and density defined by different brightness thresholds are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. We aimed to investigate causal relationships between these intercorrelated mammogram risk scores to determine their relevance to breast cancer aetiology., Methods: We used digitised mammograms for 371 monozygotic twin pairs, aged 40-70 years without a prior diagnosis of breast cancer at the time of mammography, from the Australian Mammographic Density Twins and Sisters Study. We generated normalised, age-adjusted, and standardised risk scores based on textures using the Cirrus algorithm and on three spatially independent dense areas defined by increasing brightness threshold: light areas, bright areas, and brightest areas. Causal inference was made using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) method., Results: The mammogram risk scores were correlated within twin pairs and with each other (r = 0.22-0.81; all P < 0.005). We estimated that 28-92% of the associations between the risk scores could be attributed to causal relationships between the scores, with the rest attributed to familial confounders shared by the scores. There was consistent evidence for positive causal effects: of Cirrus, light areas, and bright areas on the brightest areas (accounting for 34%, 55%, and 85% of the associations, respectively); and of light areas and bright areas on Cirrus (accounting for 37% and 28%, respectively)., Conclusions: In a mammogram, the lighter (less dense) areas have a causal effect on the brightest (highly dense) areas, including through a causal pathway via textural features. These causal relationships help us gain insight into the relative aetiological importance of different mammographic features in breast cancer. For example our findings are consistent with the brightest areas being more aetiologically important than lighter areas for screen-detected breast cancer; conversely, light areas being more aetiologically important for interval breast cancer. Additionally, specific textural features capture aetiologically independent breast cancer risk information from dense areas. These findings highlight the utility of ICE FALCON and family data in decomposing the associations between intercorrelated disease biomarkers into distinct biological pathways., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Published
2023
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50. Variance of age-specific log incidence decomposition (VALID): a unifying model of measured and unmeasured genetic and non-genetic risks.

Author

Hopper JL, Dowty JG, Nguyen TL, Li S, Dite GS, MacInnis RJ, Makalic E, Schmidt DF, Bui M, Stone J, Sung J, Jenkins MA, Giles GG, Southey MC, and Mathews JD

Subjects
Female, Humans, Age Factors, Incidence, Risk Factors, Breast Neoplasms epidemiology, Breast Neoplasms genetics, Genetic Predisposition to Disease
Abstract

Background: The extent to which known and unknown factors explain how much people of the same age differ in disease risk is fundamental to epidemiology. Risk factors can be correlated in relatives, so familial aspects of risk (genetic and non-genetic) must be considered., Development: We present a unifying model (VALID) for variance in risk, with risk defined as log(incidence) or logit(cumulative incidence). Consider a normally distributed risk score with incidence increasing exponentially as the risk increases. VALID's building block is variance in risk, Δ2, where Δ = log(OPERA) is the difference in mean between cases and controls and OPERA is the odds ratio per standard deviation. A risk score correlated r between a pair of relatives generates a familial odds ratio of exp(rΔ2). Familial risk ratios, therefore, can be converted into variance components of risk, extending Fisher's classic decomposition of familial variation to binary traits. Under VALID, there is a natural upper limit to variance in risk caused by genetic factors, determined by the familial odds ratio for genetically identical twin pairs, but not to variation caused by non-genetic factors., Application: For female breast cancer, VALID quantified how much variance in risk is explained-at different ages-by known and unknown major genes and polygenes, non-genomic risk factors correlated in relatives, and known individual-specific factors., Conclusion: VALID has shown that, while substantial genetic risk factors have been discovered, much is unknown about genetic and familial aspects of breast cancer risk especially for young women, and little is known about individual-specific variance in risk., (© The Author(s) 2023; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Published
2023
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