Melbourne School of Population and Global Health - Research Publications

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    Adherence to 2018 WCRF/AICR Cancer Prevention Recommendations and Risk of Cancer: The Melbourne Collaborative Cohort Study.
    Peng, Y ; Bassett, JK ; Hodge, AM ; Melaku, YA ; Afshar, N ; Hopper, JL ; MacInnis, RJ ; Lynch, BM ; Smith-Warner, SA ; Giles, GG ; Milne, RL ; Jayasekara, H (American Association for Cancer Research (AACR), 2024-01-09)
    BACKGROUND: We examined associations between adherence to adaptations of the 2018 World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) cancer prevention recommendations and total, exposure-related and site-specific cancer risk. METHODS: A total of 20,001 participants ages 40 to 69 years at enrollment into the Melbourne Collaborative Cohort Study in 1990 to 1994, who had diet, body size, and lifestyle reassessed in 2003 to 2007 ("baseline"), were followed-up through June 2021. We constructed diet and standardized lifestyle scores based on core WCRF/AICR recommendations on diet, alcohol intake, body size and physical activity, and additional scores incorporating weight change, sedentary behavior, and smoking. Associations with cancer risk were estimated using Cox regression, adjusting for confounders. RESULTS: During follow-up (mean = 16 years), 4,710 incident cancers were diagnosed. For highest quintile ("most adherent") of the standardized lifestyle score, compared with lowest ("least adherent"), a HR of 0.82 [95% confidence interval (CI): 0.74-0.92] was observed for total cancer. This association was stronger with smoking included in the score (HR = 0.74; 95% CI: 0.67-0.81). A higher score was associated with lower breast and prostate cancer risk for the standardized score, and with lung, stomach, rectal, and pancreatic cancer risk when the score included smoking. Our analyses identified alcohol use, waist circumference and smoking as key drivers of associations with total cancer risk. CONCLUSIONS: Adherence to WCRF/AICR cancer prevention recommendations is associated with lower cancer risk. IMPACT: With <0.2% of our sample fully adherent to the recommendations, the study emphasizes the vast potential for preventing cancer through modulation of lifestyle habits.
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    Modifiable lifestyle risk factors and survival after diagnosis with multiple myeloma
    Cheah, S ; Bassett, JK ; Bruinsma, FJ ; Hopper, J ; Jayasekara, H ; Joshua, D ; Macinnis, RJ ; Prince, HM ; Southey, MC ; Vajdic, CM ; van Leeuwen, MT ; Doo, NW ; Harrison, SJ ; English, DR ; Giles, GG ; Milne, RL (TAYLOR & FRANCIS LTD, 2023-10-03)
    BACKGROUND: While remaining incurable, median overall survival for MM now exceeds 5 years. Yet few studies have investigated how modifiable lifestyle factors influence survival. We investigate whether adiposity, diet, alcohol, or smoking are associated with MM-related fatality. RESEARCH DESIGN AND METHODS: We recruited 760 incident cases of MM via cancer registries in two Australian states during 2010-2016. Participants returned questionnaires on health and lifestyle. Follow-up ended in 2020. Flexible parametric survival models were used to estimate hazard ratios (HR) and 95% confidence intervals (CI) for lifestyle exposures and risk of all-cause and MM-specific fatality. RESULTS: Higher pre-diagnosis Alternative Healthy Eating Index (AHEI) scores were associated with reduced MM-specific fatality (per 10-unit score, HR = 0.84, 95%CI = 0.70-0.99). Pre-diagnosis alcohol consumption was inversely associated with MM-specific fatality, compared with nondrinkers (0.1-20 g per day, HR = 0.59, 95%CI = 0.39-0.90; >20 g per day, HR = 0.67, 95%CI = 0.40-1.13). Tobacco smoking was associated with increased all-cause fatality compared with never smoking (former smokers: HR = 1.44, 95%CI = 1.10-1.88; current smokers: HR = 1.30, 95%CI = 0.80-2.10). There was no association between pre-enrollment body mass index (BMI) and MM-specific or all-cause fatality. CONCLUSIONS: Our findings support established recommendations for healthy diets and against smoking. Higher quality diet, as measured by the AHEI, may improve survival post diagnosis with MM.
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    A likelihood ratio approach for utilizing case-control data in the clinical classification of rare sequence variants: Application to BRCA1 and BRCA2
    Zanti, M ; O'Mahony, DG ; Parsons, MT ; Li, H ; Dennis, J ; Aittomäkkiki, K ; Andrulis, IL ; Anton-Culver, H ; Aronson, KJ ; Augustinsson, A ; Becher, H ; Bojesen, SE ; Bolla, MK ; Brenner, H ; Brown, MA ; Buys, SS ; Canzian, F ; Caputo, SM ; Castelao, JE ; Chang-Claude, J ; Czene, K ; Daly, MB ; De Nicolo, A ; Devilee, P ; Dörk, T ; Dunning, AM ; Dwek, M ; Eccles, DM ; Engel, C ; Gareth Evans, D ; Fasching, PA ; Gago-Dominguez, M ; García-Closas, M ; García-Sáenz, JA ; Gentry-Maharaj, A ; Geurts-Giele, WRR ; Giles, GG ; Glendon, G ; Goldberg, MS ; Gómez Garcia, EB ; Göendert, M ; Guénel, P ; Hahnen, E ; Haiman, CA ; Hall, P ; Hamann, U ; Harkness, EF ; Hogervorst, FBL ; Hollestelle, A ; Hoppe, R ; Hopper, JL ; Houdayer, C ; Houlston, RS ; Howell, A ; Jakimovska, M ; Jakubowska, A ; Jernström, H ; John, EM ; Kaaks, R ; Kitahara, CM ; Koutros, S ; Kraft, P ; Kristensen, VN ; Lacey, JV ; Lambrechts, D ; Léoné, M ; Lindblom, A ; Lubiski, J ; Lush, M ; Mannermaa, A ; Manoochehri, M ; Manoukian, S ; Margolin, S ; Martinez, ME ; Menon, U ; Milne, RL ; Monteiro, AN ; Murphy, RA ; Neuhausen, SL ; Nevanlinna, H ; Newman, WG ; Offit, K ; Park, SK ; James, P ; Peterlongo, P ; Peto, J ; Plaseska-Karanfilska, D ; Punie, K ; Radice, P ; Rashid, MU ; Rennert, G ; Romero, A ; Rosenberg, EH ; Saloustros, E ; Sandler, DP ; Schmidt, MK ; Schmutzler, RK ; Shu, XO ; Simard, J ; Southey, MC ; Cutting, G (Hindawi Limited, 2023-01-01)
    A large number of variants identified through clinical genetic testing in disease susceptibility genes are of uncertain significance (VUS). Following the recommendations of the American College of Medical Genetics and Genomics (ACMG) and Association for Molecular Pathology (AMP), the frequency in case-control datasets (PS4 criterion) can inform their interpretation. We present a novel case-control likelihood ratio-based method that incorporates gene-specific age-related penetrance. We demonstrate the utility of this method in the analysis of simulated and real datasets. In the analysis of simulated data, the likelihood ratio method was more powerful compared to other methods. Likelihood ratios were calculated for a case-control dataset of BRCA1 and BRCA2 variants from the Breast Cancer Association Consortium (BCAC) and compared with logistic regression results. A larger number of variants reached evidence in favor of pathogenicity, and a substantial number of variants had evidence against pathogenicity—findings that would not have been reached using other case-control analysis methods. Our novel method provides greater power to classify rare variants compared with classical case-control methods. As an initiative from the ENIGMA Analytical Working Group, we provide user-friendly scripts and preformatted Excel calculators for implementation of the method for rare variants in BRCA1, BRCA2, and other high-risk genes with known penetrance.
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    Genome-wide Association Study of Bladder Cancer Reveals New Biological and Translational Insights
    Koutros, S ; Kiemeney, LA ; Choudhury, PP ; Milne, RL ; de Maturana, EL ; Ye, Y ; Joseph, V ; Florez-Vargas, O ; Dyrskjot, L ; Figueroa, J ; Dutta, D ; Giles, GG ; Hildebrandt, MAT ; Offit, K ; Kogevinas, M ; Weiderpass, E ; McCullough, ML ; Freedman, ND ; Albanes, D ; Kooperberg, C ; Cortessis, VK ; Karagas, MR ; Johnson, A ; Schwenn, MR ; Baris, D ; Furberg, H ; Bajorin, DF ; Cussenot, O ; Cancel-Tassin, G ; Benhamou, S ; Kraft, P ; Porru, S ; Carta, A ; Bishop, T ; Southey, MC ; Matullo, G ; Fletcher, T ; Kumar, R ; Taylor, JA ; Lamy, P ; Prip, F ; Kalisz, M ; Weinstein, SJ ; Hengstler, JG ; Selinski, S ; Harland, M ; Teo, M ; Kiltie, AE ; Tardon, A ; Serra, C ; Carrato, A ; Garcia-Closas, R ; Lloreta, J ; Schned, A ; Lenz, P ; Riboli, E ; Brennan, P ; Tjonneland, A ; Otto, T ; Ovsiannikov, D ; Volkert, F ; Vermeulen, SH ; Aben, KK ; Galesloot, TE ; Turman, C ; De Vivo, I ; Giovannucci, E ; Hunter, DJ ; Hohensee, C ; Hunt, R ; V. Patel, A ; Huang, W-Y ; Thorleifsson, G ; Gago-Dominguez, M ; Amiano, P ; Golka, K ; Stern, MC ; Yan, W ; Liu, J ; Alfred, S ; Katta, S ; Hutchinson, A ; Hicks, B ; Wheeler, WA ; Purdue, MP ; McGlynn, KA ; Kitahara, CM ; Haiman, CA ; Greene, MH ; Rafnar, T ; Chatterjee, N ; Chanock, SJ ; Wu, X ; Real, FX ; Silverman, DT ; Garcia-Closas, M ; Stefansson, K ; Prokunina-Olsson, L ; Malats, N ; Rothman, N (ELSEVIER, 2023-07)
    BACKGROUND: Genomic regions identified by genome-wide association studies (GWAS) for bladder cancer risk provide new insights into etiology. OBJECTIVE: To identify new susceptibility variants for bladder cancer in a meta-analysis of new and existing genome-wide genotype data. DESIGN, SETTING, AND PARTICIPANTS: Data from 32 studies that includes 13,790 bladder cancer cases and 343,502 controls of European ancestry were used for meta-analysis. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSES: Log-additive associations of genetic variants were assessed using logistic regression models. A fixed-effects model was used for meta-analysis of the results. Stratified analyses were conducted to evaluate effect modification by sex and smoking status. A polygenic risk score (PRS) was generated on the basis of known and novel susceptibility variants and tested for interaction with smoking. RESULTS AND LIMITATIONS: Multiple novel bladder cancer susceptibility loci (6p.22.3, 7q36.3, 8q21.13, 9p21.3, 10q22.1, 19q13.33) as well as improved signals in three known regions (4p16.3, 5p15.33, 11p15.5) were identified, bringing the number of independent markers at genome-wide significance (p < 5 × 10-8) to 24. The 4p16.3 (FGFR3/TACC3) locus was associated with a stronger risk for women than for men (p-interaction = 0.002). Bladder cancer risk was increased by interactions between smoking status and genetic variants at 8p22 (NAT2; multiplicative p value for interaction [pM-I] = 0.004), 8q21.13 (PAG1; pM-I = 0.01), and 9p21.3 (LOC107987026/MTAP/CDKN2A; pM-I = 0.02). The PRS based on the 24 independent GWAS markers (odds ratio per standard deviation increase 1.49, 95% confidence interval 1.44-1.53), which also showed comparable results in two prospective cohorts (UK Biobank, PLCO trial), revealed an approximately fourfold difference in the lifetime risk of bladder cancer according to the PRS (e.g., 1st vs 10th decile) for both smokers and nonsmokers. CONCLUSIONS: We report novel loci associated with risk of bladder cancer that provide clues to its biological underpinnings. Using 24 independent markers, we constructed a PRS to stratify lifetime risk. The PRS combined with smoking history, and other established risk factors, has the potential to inform future screening efforts for bladder cancer. PATIENT SUMMARY: We identified new genetic markers that provide biological insights into the genetic causes of bladder cancer. These genetic risk factors combined with lifestyle risk factors, such as smoking, may inform future preventive and screening strategies for bladder cancer.
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    Pre-diagnostic cigarette smoking and risk of second primary cancer: The Melbourne Collaborative Cohort Study
    Phua, ZJ ; MacInnis, RJ ; Hodge, AM ; Lynch, BM ; Hopper, JL ; Smith-Warner, SA ; Giles, GG ; Milne, RL ; Jayasekara, H (ELSEVIER SCI LTD, 2023-08)
    Enhanced survival following a diagnosis of cancer has led to a steep rise in the number of individuals diagnosed with a second primary cancer. We examined the association between pre-cancer cigarette smoking and risk of second cancer in 9785 participants diagnosed with first invasive cancer after enrolment in the Melbourne Collaborative Cohort Study. Follow-up was from date of first invasive cancer until diagnosis of second primary invasive cancer, death, or 31 July 2019, whichever came first. Data on cigarette smoking was collected at enrolment (1990-94) along with information on other lifestyle factors including body size, alcohol intake and diet. We estimated hazard ratios (HR) and 95 % confidence intervals (CI) for incident second cancer with several smoking measures, adjusted for potential confounders. After a mean follow-up of 7.3 years, 1658 second cancers were identified. All measures of smoking were associated with increased risk of second cancer. We observed a 44 % higher risk of second cancer for smokers of ≥ 20 cigarettes/day (HR=1.44, 95 % CI: 1.18-1.76), compared with never smokers. We also observed dose-dependent associations with number of cigarettes smoked (HR=1.05 per 10 cigarettes/day, 95 % CI: 1.01-1.09) and duration of smoking (HR=1.07 per 10 years, 95 % CI: 1.03-1.10). The risk of second cancer increased by 4 % per 10 pack-years of smoking (HR=1.04, 95 % CI: 1.02-1.06; p < 0.001). There was suggestive evidence of stronger associations with number of cigarettes smoked and pack-years of smoking for women (pinteraction<0.05), particularly for the highest risk categories of both variables. These associations with pre-diagnostic smoking were markedly stronger for second cancers known to be smoking-related than for others (phomogeneity<0.001). Our findings for pre-diagnostic cigarette smoking indicated increased risk of second primary cancer for cancer sites considered smoking-related, highlighting the importance of assessing smoking habits in cancer survivors.
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    Physical activity, sedentary time and breast cancer risk: a Mendelian randomisation study
    Dixon-Suen, SC ; Lewis, SJ ; Martin, RM ; English, DR ; Boyle, T ; Giles, GG ; Michailidou, K ; Bolla, MK ; Wang, Q ; Dennis, J ; Lush, M ; Ahearn, TU ; Ambrosone, CB ; Andrulis, IL ; Anton-Culver, H ; Arndt, V ; Aronson, KJ ; Augustinsson, A ; Auvinen, P ; Beane Freeman, LE ; Becher, H ; Beckmann, MW ; Behrens, S ; Bermisheva, M ; Blomqvist, C ; Bogdanova, N ; Bojesen, SE ; Bonanni, B ; Brenner, H ; Bruening, T ; Buys, SS ; Camp, NJ ; Campa, D ; Canzian, F ; Castelao, JE ; Cessna, MH ; Chang-Claude, J ; Chanock, SJ ; Clarke, CL ; Conroy, DM ; Couch, FJ ; Cox, A ; Cross, SS ; Czene, K ; Daly, MB ; Devilee, P ; Doerk, T ; Dwek, M ; Eccles, DM ; Eliassen, AH ; Engel, C ; Eriksson, M ; Evans, DG ; Fasching, PA ; Fletcher, O ; Flyger, H ; Fritschi, L ; Gabrielson, M ; Gago-Dominguez, M ; Garcia-Closas, M ; Garcia-Saenz, JA ; Goldberg, MS ; Guenel, P ; Guendert, M ; Hahnen, E ; Haiman, CA ; Haeberle, L ; Hakansson, N ; Hall, P ; Hamann, U ; Hart, SN ; Harvie, M ; Hillemanns, P ; Hollestelle, A ; Hooning, MJ ; Hoppe, R ; Hopper, J ; Howell, A ; Hunter, DJ ; Jakubowska, A ; Janni, W ; John, EM ; Jung, A ; Kaaks, R ; Keeman, R ; Kitahara, CM ; Koutros, S ; Kraft, P ; Kristensen, VN ; Kubelka-Sabit, K ; Kurian, AW ; Lacey, J ; Lambrechts, D ; Le Marchand, L ; Lindblom, A ; Loibl, S ; Lubinski, J ; Mannermaa, A ; Manoochehri, M ; Margolin, S ; Martinez, ME ; Mavroudis, D ; Menon, U ; Mulligan, AM ; Murphy, RA ; Nevanlinna, H ; Nevelsteen, I ; Newman, WG ; Offit, K ; Olshan, AF ; Olsson, H ; Orr, N ; Patel, A ; Peto, J ; Plaseska-Karanfilska, D ; Presneau, N ; Rack, B ; Radice, P ; Rees-Punia, E ; Rennert, G ; Rennert, HS ; Romero, A ; Saloustros, E ; Sandler, DP ; Schmidt, MK ; Schmutzler, RK ; Schwentner, L ; Scott, C ; Shah, M ; Shu, X-O ; Simard, J ; Southey, MC ; Stone, J ; Surowy, H ; Swerdlow, AJ ; Tamimi, RM ; Tapper, WJ ; Taylor, JA ; Terry, MB ; Tollenaar, RAEM ; Troester, MA ; Truong, T ; Untch, M ; Vachon, CM ; Joseph, V ; Wappenschmidt, B ; Weinberg, CR ; Wolk, A ; Yannoukakos, D ; Zheng, W ; Ziogas, A ; Dunning, AM ; Pharoah, PDP ; Easton, DF ; Milne, RL ; Lynch, BM (BMJ PUBLISHING GROUP, 2022-10)
    OBJECTIVES: Physical inactivity and sedentary behaviour are associated with higher breast cancer risk in observational studies, but ascribing causality is difficult. Mendelian randomisation (MR) assesses causality by simulating randomised trial groups using genotype. We assessed whether lifelong physical activity or sedentary time, assessed using genotype, may be causally associated with breast cancer risk overall, pre/post-menopause, and by case-groups defined by tumour characteristics. METHODS: We performed two-sample inverse-variance-weighted MR using individual-level Breast Cancer Association Consortium case-control data from 130 957 European-ancestry women (69 838 invasive cases), and published UK Biobank data (n=91 105-377 234). Genetic instruments were single nucleotide polymorphisms (SNPs) associated in UK Biobank with wrist-worn accelerometer-measured overall physical activity (nsnps=5) or sedentary time (nsnps=6), or accelerometer-measured (nsnps=1) or self-reported (nsnps=5) vigorous physical activity. RESULTS: Greater genetically-predicted overall activity was associated with lower breast cancer overall risk (OR=0.59; 95% confidence interval (CI) 0.42 to 0.83 per-standard deviation (SD;~8 milligravities acceleration)) and for most case-groups. Genetically-predicted vigorous activity was associated with lower risk of pre/perimenopausal breast cancer (OR=0.62; 95% CI 0.45 to 0.87,≥3 vs. 0 self-reported days/week), with consistent estimates for most case-groups. Greater genetically-predicted sedentary time was associated with higher hormone-receptor-negative tumour risk (OR=1.77; 95% CI 1.07 to 2.92 per-SD (~7% time spent sedentary)), with elevated estimates for most case-groups. Results were robust to sensitivity analyses examining pleiotropy (including weighted-median-MR, MR-Egger). CONCLUSION: Our study provides strong evidence that greater overall physical activity, greater vigorous activity, and lower sedentary time are likely to reduce breast cancer risk. More widespread adoption of active lifestyles may reduce the burden from the most common cancer in women.
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    Distinct Reproductive Risk Profiles for Intrinsic-Like Breast Cancer Subtypes: Pooled Analysis of Population-Based Studies
    Jung, AY ; Ahearn, TU ; Behrens, S ; Middha, P ; Bolla, MK ; Wang, Q ; Arndt, V ; Aronson, KJ ; Augustinsson, A ; Freeman, LEB ; Becher, H ; Brenner, H ; Canzian, F ; Carey, LA ; Consortium, C ; Czene, K ; Eliassen, AH ; Eriksson, M ; Evans, DG ; Figueroa, JD ; Fritschi, L ; Gabrielson, M ; Giles, GG ; Guenel, P ; Hadjisavvas, A ; Haiman, CA ; Hakansson, N ; Hall, P ; Hamann, U ; Hoppe, R ; Hopper, JL ; Howell, A ; Hunter, DJ ; Huesing, A ; Kaaks, R ; Kosma, V-M ; Koutros, S ; Kraft, P ; Lacey, J ; Le Marchand, L ; Lissowska, J ; Loizidou, MA ; Mannermaa, A ; Maurer, T ; Murphy, RA ; Olshan, AF ; Olsson, H ; Patel, A ; Perou, CM ; Rennert, G ; Shibli, R ; Shu, X-O ; Southey, MC ; Stone, J ; Tamimi, RM ; Teras, LR ; Troester, MA ; Truong, T ; Vachon, CM ; Wang, SS ; Wolk, A ; Wu, AH ; Yang, XR ; Zheng, W ; Dunning, AM ; Pharoah, PDP ; Easton, DF ; Milne, RL ; Chatterjee, N ; Schmidt, MK ; Garcia-Closas, M ; Chang-Claude, J (OXFORD UNIV PRESS INC, 2022-12)
    BACKGROUND: Reproductive factors have been shown to be differentially associated with risk of estrogen receptor (ER)-positive and ER-negative breast cancer. However, their associations with intrinsic-like subtypes are less clear. METHODS: Analyses included up to 23 353 cases and 71 072 controls pooled from 31 population-based case-control or cohort studies in the Breast Cancer Association Consortium across 16 countries on 4 continents. Polytomous logistic regression was used to estimate the association between reproductive factors and risk of breast cancer by intrinsic-like subtypes (luminal A-like, luminal B-like, luminal B-HER2-like, HER2-enriched-like, and triple-negative breast cancer) and by invasiveness. All statistical tests were 2-sided. RESULTS: Compared with nulliparous women, parous women had a lower risk of luminal A-like, luminal B-like, luminal B-HER2-like, and HER2-enriched-like disease. This association was apparent only after approximately 10 years since last birth and became stronger with increasing time (odds ratio [OR] = 0.59, 95% confidence interval [CI] = 0.49 to 0.71; and OR = 0.36, 95% CI = 0.28 to 0.46 for multiparous women with luminal A-like tumors 20 to less than 25 years after last birth and 45 to less than 50 years after last birth, respectively). In contrast, parous women had a higher risk of triple-negative breast cancer right after their last birth (for multiparous women: OR = 3.12, 95% CI = 2.02 to 4.83) that was attenuated with time but persisted for decades (OR = 1.03, 95% CI = 0.79 to 1.34, for multiparous women 25 to less than 30 years after last birth). Older age at first birth (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) and breastfeeding (Pheterogeneity < .001 for triple-negative compared with luminal A-like breast cancer) were associated with lower risk of triple-negative breast cancer but not with other disease subtypes. Younger age at menarche was associated with higher risk of all subtypes; older age at menopause was associated with higher risk of luminal A-like but not triple-negative breast cancer. Associations for in situ tumors were similar to luminal A-like. CONCLUSIONS: This large and comprehensive study demonstrates a distinct reproductive risk factor profile for triple-negative breast cancer compared with other subtypes, with implications for the understanding of disease etiology and risk prediction.
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    Genome-Wide Interaction Analysis of Genetic Variants With Menopausal Hormone Therapy for Colorectal Cancer Risk
    Tian, Y ; Kim, AE ; Bien, SA ; Lin, Y ; Qu, C ; Harrison, TA ; Carreras-Torres, R ; Diez-Obrero, V ; Dimou, N ; Drew, DA ; Hidaka, A ; Huyghe, JR ; Jordahl, KM ; Morrison, J ; Murphy, N ; Obon-Santacana, M ; Ulrich, CM ; Ose, J ; Peoples, AR ; Ruiz-Narvaez, EA ; Shcherbina, A ; Stern, MC ; Su, Y-R ; van Duijnhoven, FJB ; Arndt, V ; Baurley, JW ; Berndt, S ; Bishop, DT ; Brenner, H ; Buchanan, DD ; Chan, AT ; Figueiredo, JC ; Gallinger, S ; Gruber, SB ; Harlid, S ; Hoffmeister, M ; Jenkins, MA ; Joshi, AD ; Keku, TO ; Larsson, SC ; Le Marchand, L ; Li, L ; Giles, GG ; Milne, RL ; Nan, H ; Nassir, R ; Ogino, S ; Budiarto, A ; Platz, EA ; Potter, JD ; Prentice, RL ; Rennert, G ; Sakoda, LC ; Schoen, RE ; Slattery, ML ; Thibodeau, SN ; Van Guelpen, B ; Visvanathan, K ; White, E ; Wolk, A ; Woods, MO ; Wu, AH ; Campbell, PT ; Casey, G ; Conti, D ; Gunter, MJ ; Kundaje, A ; Lewinger, JP ; Moreno, V ; Newcomb, PA ; Pardamean, B ; Thomas, DC ; Tsilidis, KK ; Peters, U ; Gauderman, WJ ; Hsu, L ; Chang-Claude, J (OXFORD UNIV PRESS INC, 2022-08-08)
    BACKGROUND: The use of menopausal hormone therapy (MHT) may interact with genetic variants to influence colorectal cancer (CRC) risk. METHODS: We conducted a genome-wide, gene-environment interaction between single nucleotide polymorphisms and the use of any MHT, estrogen only, and combined estrogen-progestogen therapy with CRC risk, among 28 486 postmenopausal women (11 519 CRC patients and 16 967 participants without CRC) from 38 studies, using logistic regression, 2-step method, and 2- or 3-degree-of-freedom joint test. A set-based score test was applied for rare genetic variants. RESULTS: The use of any MHT, estrogen only and estrogen-progestogen were associated with a reduced CRC risk (odds ratio [OR] = 0.71, 95% confidence interval [CI] = 0.64 to 0.78; OR = 0.65, 95% CI = 0.53 to 0.79; and OR = 0.73, 95% CI = 0.59 to 0.90, respectively). The 2-step method identified a statistically significant interaction between a GRIN2B variant rs117868593 and MHT use, whereby MHT-associated CRC risk was statistically significantly reduced in women with the GG genotype (OR = 0.68, 95% CI = 0.64 to 0.72) but not within strata of GC or CC genotypes. A statistically significant interaction between a DCBLD1 intronic variant at 6q22.1 (rs10782186) and MHT use was identified by the 2-degree-of-freedom joint test. The MHT-associated CRC risk was reduced with increasing number of rs10782186-C alleles, showing odds ratios of 0.78 (95% CI = 0.70 to 0.87) for TT, 0.68 (95% CI = 0.63 to 0.73) for TC, and 0.66 (95% CI = 0.60 to 0.74) for CC genotypes. In addition, 5 genes in rare variant analysis showed suggestive interactions with MHT (2-sided P < 1.2 × 10-4). CONCLUSION: Genetic variants that modify the association between MHT and CRC risk were identified, offering new insights into pathways of CRC carcinogenesis and potential mechanisms involved.
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    Lifetime ovulatory years and risk of epithelial ovarian cancer: a multinational pooled analysis
    Fu, Z ; Brooks, MM ; Irvin, S ; Jordan, S ; Aben, KKH ; Anton-Culver, H ; Bandera, E ; Beckmann, MW ; Berchuck, A ; Brooks-Wilson, A ; Chang-Claude, J ; Cook, LS ; Cramer, DW ; Cushing-Haugen, KL ; Doherty, JA ; Ekici, AB ; Fasching, PA ; Fortner, RT ; Gayther, SA ; Gentry-Maharaj, A ; Giles, GG ; Goode, EL ; Goodman, MT ; Harris, HR ; Hein, A ; Kaaks, R ; Kiemeney, LA ; Koebel, M ; Kotsopoulos, J ; Le, ND ; Lee, AW ; Matsuo, K ; McGuire, V ; McLaughlin, JR ; Menon, U ; Milne, RL ; Moysich, KB ; Pearce, CL ; Pike, MC ; Qin, B ; Ramus, SJ ; Riggan, MJ ; Rothstein, JH ; Schildkraut, JM ; Sieh, W ; Sutphen, R ; Terry, KL ; Thompson, PJ ; Titus, L ; van Altena, AM ; White, E ; Whittemore, AS ; Wu, AH ; Zheng, W ; Ziogas, A ; Taylor, SE ; Tang, L ; Songer, T ; Wentzensen, N ; Webb, PM ; Risch, HA ; Modugno, F (OXFORD UNIV PRESS INC, 2023-05-08)
    BACKGROUND: The role of ovulation in epithelial ovarian cancer (EOC) is supported by the consistent protective effects of parity and oral contraceptive use. Whether these factors protect through anovulation alone remains unclear. We explored the association between lifetime ovulatory years (LOY) and EOC. METHODS: LOY was calculated using 12 algorithms. Odds ratios (ORs) and 95% confidence intervals (CIs) estimated the association between LOY or LOY components and EOC among 26 204 control participants and 21 267 case patients from 25 studies. To assess whether LOY components act through ovulation suppression alone, we compared beta coefficients obtained from regression models with expected estimates assuming 1 year of ovulation suppression has the same effect regardless of source. RESULTS: LOY was associated with increased EOC risk (OR per year increase = 1.014, 95% CI = 1.009 to 1.020 to OR per year increase = 1.044, 95% CI = 1.041 to 1.048). Individual LOY components, except age at menarche, also associated with EOC. The estimated model coefficient for oral contraceptive use and pregnancies were 4.45 times and 12- to 15-fold greater than expected, respectively. LOY was associated with high-grade serous, low-grade serous, endometrioid, and clear cell histotypes (ORs per year increase = 1.054, 1.040, 1.065, and 1.098, respectively) but not mucinous tumors. Estimated coefficients of LOY components were close to expected estimates for high-grade serous but larger than expected for low-grade serous, endometrioid, and clear cell histotypes. CONCLUSIONS: LOY is positively associated with nonmucinous EOC. Differences between estimated and expected model coefficients for LOY components suggest factors beyond ovulation underlie the associations between LOY components and EOC in general and for non-HGSOC.
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    Does genetic predisposition modify the effect of lifestyle-related factors on DNA methylation?
    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 (TAYLOR & FRANCIS INC, 2022-12-02)
    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.