Melbourne School of Population and Global Health - Research Publications

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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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    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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    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.
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    Methylation scores for smoking, alcohol consumption and body mass index and risk of seven types of cancer
    Dugue, P-A ; Yu, C ; Hodge, AMM ; Wong, EM ; Joo, JEE ; Jung, C-H ; Schmidt, D ; Makalic, E ; Buchanan, DDD ; Severi, G ; English, DRR ; Hopper, JLL ; Milne, RLL ; Giles, GGG ; Southey, MCC (WILEY, 2023-08-01)
    Methylation marks of exposure to health risk factors may be useful markers of cancer risk as they might better capture current and past exposures than questionnaires, and reflect different individual responses to exposure. We used data from seven case-control studies nested within the Melbourne Collaborative Cohort Study of blood DNA methylation and risk of colorectal, gastric, kidney, lung, prostate and urothelial cancer, and B-cell lymphoma (N cases = 3123). Methylation scores (MS) for smoking, body mass index (BMI), and alcohol consumption were calculated based on published data as weighted averages of methylation values. Rate ratios (RR) and 95% confidence intervals for association with cancer risk were estimated using conditional logistic regression and expressed per SD increase of the MS, with and without adjustment for health-related confounders. The contribution of MS to discriminate cases from controls was evaluated using the area under the curve (AUC). After confounder adjustment, we observed: large associations (RR = 1.5-1.7) with lung cancer risk for smoking MS; moderate associations (RR = 1.2-1.3) with urothelial cancer risk for smoking MS and with mature B-cell neoplasm risk for BMI and alcohol MS; moderate to small associations (RR = 1.1-1.2) for BMI and alcohol MS with several cancer types and cancer overall. Generally small AUC increases were observed after inclusion of several MS in the same model (colorectal, gastric, kidney, urothelial cancers: +3%; lung cancer: +7%; B-cell neoplasms: +8%). Methylation scores for smoking, BMI and alcohol consumption show independent associations with cancer risk, and may provide some improvements in risk prediction.
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    Alcohol and tobacco use and risk of multiple myeloma: A case‐control study
    Cheah, S ; Bassett, JK ; Bruinsma, FJ ; Cozen, W ; Hopper, JL ; Jayasekara, H ; Joshua, D ; MacInnis, RJ ; Prince, HM ; Vajdic, CM ; van Leeuwen, MT ; Doo, NW ; Harrison, SJ ; English, DR ; Giles, GG ; Milne, RL (Wiley, 2022-02)
    Abstract Multiple myeloma (MM) is the second most common hematological cancer and causes significant mortality and morbidity. Knowledge regarding modifiable risk factors for MM remains limited. This analysis of an Australian population‐based case–control family study investigates whether smoking or alcohol consumption is associated with risk of MM and related diseases. Incident cases (n = 789) of MM were recruited via cancer registries in Victoria and New South Wales. Controls (n = 1,113) were either family members of cases (n = 696) or controls recruited for a similarly designed study of renal cancers (n = 417). Adjusted odds ratios (OR) and 95% confidence intervals (CI) were estimated using unconditional multivariable logistic regression. Heavy intake (>20 g ethanol/day) of alcohol had a lower risk of MM compared with nondrinkers (OR = 0.68, 95% CI: 0.50–0.93), and there was an inverse dose–response relationship for average daily alcohol intake (OR per 10 g ethanol per day = 0.92, 95% CI: 0.86–0.99); there was no evidence of an interaction with sex. There was no evidence of an association with MM risk for smoking‐related exposures (p > 0.18). The associations between smoking and alcohol with MM are similar to those with non‐Hodgkin lymphoma. Further research into potential underlying mechanisms is warranted.
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    Prospective Evaluation over 15 Years of Six Breast Cancer Risk Models
    Li, SX ; Milne, RL ; Nguyen-Dumont, T ; English, DR ; Giles, GG ; Southey, MC ; Antoniou, AC ; Lee, A ; Winship, I ; Hopper, JL ; Terry, MB ; MacInnis, RJ (MDPI, 2021-10)
    Prospective validation of risk models is needed to assess their clinical utility, particularly over the longer term. We evaluated the performance of six commonly used breast cancer risk models (IBIS, BOADICEA, BRCAPRO, BRCAPRO-BCRAT, BCRAT, and iCARE-lit). 15-year risk scores were estimated using lifestyle factors and family history measures from 7608 women in the Melbourne Collaborative Cohort Study who were aged 50-65 years and unaffected at commencement of follow-up two (conducted in 2003-2007), of whom 351 subsequently developed breast cancer. Risk discrimination was assessed using the C-statistic and calibration using the expected/observed number of incident cases across the spectrum of risk by age group (50-54, 55-59, 60-65 years) and family history of breast cancer. C-statistics were higher for BOADICEA (0.59, 95% confidence interval (CI) 0.56-0.62) and IBIS (0.57, 95% CI 0.54-0.61) than the other models (p-difference ≤ 0.04). No model except BOADICEA calibrated well across the spectrum of 15-year risk (p-value < 0.03). The performance of BOADICEA and IBIS was similar across age groups and for women with or without a family history. For middle-aged Australian women, BOADICEA and IBIS had the highest discriminatory accuracy of the six risk models, but apart from BOADICEA, no model was well-calibrated across the risk spectrum.
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    DNA Methylation Signatures and the Contribution of Age-Associated Methylomic Drift to Carcinogenesis in Early-Onset Colorectal Cancer
    Joo, JE ; Clendenning, M ; Wong, EM ; Rosty, C ; Mahmood, K ; Georgeson, P ; Winship, IM ; Preston, SG ; Win, AK ; Dugue, P-A ; Jayasekara, H ; English, D ; Macrae, FA ; Hopper, JL ; Jenkins, MA ; Milne, RL ; Giles, GG ; Southey, MC ; Buchanan, DD (MDPI, 2021-06)
    We investigated aberrant DNA methylation (DNAm) changes and the contribution of ageing-associated methylomic drift and age acceleration to early-onset colorectal cancer (EOCRC) carcinogenesis. Genome-wide DNAm profiling using the Infinium HM450K on 97 EOCRC tumour and 54 normal colonic mucosa samples was compared with: (1) intermediate-onset CRC (IOCRC; diagnosed between 50-70 years; 343 tumour and 35 normal); and (2) late-onset CRC (LOCRC; >70 years; 318 tumour and 40 normal). CpGs associated with age-related methylation drift were identified using a public dataset of 231 normal mucosa samples from people without CRC. DNAm-age was estimated using epiTOC2. Common to all three age-of-onset groups, 88,385 (20% of all CpGs) CpGs were differentially methylated between tumour and normal mucosa. We identified 234 differentially methylated genes that were unique to the EOCRC group; 13 of these DMRs/genes were replicated in EOCRC compared with LOCRCs from TCGA. In normal mucosa from people without CRC, we identified 28,154 CpGs that undergo ageing-related DNAm drift, and of those, 65% were aberrantly methylated in EOCRC tumours. Based on the mitotic-based DNAm clock epiTOC2, we identified age acceleration in normal mucosa of people with EOCRC compared with normal mucosa from the IOCRC, LOCRC groups (p = 3.7 × 10-16) and young people without CRC (p = 5.8 × 10-6). EOCRC acquires unique DNAm alterations at 234 loci. CpGs associated with ageing-associated drift were widely affected in EOCRC without needing the decades-long accrual of DNAm drift as commonly seen in intermediate- and late-onset CRCs. Accelerated ageing in normal mucosa from people with EOCRC potentially underlies the earlier age of diagnosis in CRC carcinogenesis.
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    Prospective Evaluation of the Addition of Polygenic Risk Scores to Breast Cancer Risk Models
    Li, SX ; Milne, RL ; Nguyen-Dumont, T ; Wang, X ; English, DR ; Giles, GG ; Southey, MC ; Antoniou, AC ; Lee, A ; Li, S ; Winship, I ; Hopper, JL ; Terry, MB ; MacInnis, RJ (OXFORD UNIV PRESS, 2021-06)
    BACKGROUND: The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm and the International Breast Cancer Intervention Study breast cancer risk models are used to provide advice on screening intervals and chemoprevention. We evaluated the performance of these models, which now incorporate polygenic risk scores (PRSs), using a prospective cohort study. METHODS: We used a case-cohort design, involving women in the Melbourne Collaborative Cohort Study aged 50-75 years when surveyed in 2003-2007, of whom 408 had a first primary breast cancer diagnosed within 10 years (cases), and 2783 were from the subcohort. Ten-year risks were calculated based on lifestyle factors, family history data, and a 313-variant PRS. Discrimination was assessed using a C-statistic compared with 0.50 and calibration using the ratio of expected to observed number of cases (E/O). RESULTS: When the PRS was added to models with lifestyle factors and family history, the C-statistic (95% confidence interval [CI]) increased from 0.57 (0.54 to 0.60) to 0.62 (0.60 to 0.65) using IBIS and from 0.56 (0.53 to 0.59) to 0.62 (0.59 to 0.64) using BOADICEA. IBIS underpredicted risk (E/O = 0.62, 95% CI = 0.48 to 0.80) for women in the lowest risk category (<1.7%) and overpredicted risk (E/O = 1.40, 95% CI = 1.18 to 1.67) in the highest risk category (≥5%), using the Hosmer-Lemeshow test for calibration in quantiles of risk and a 2-sided P value less than  .001. BOADICEA underpredicted risk (E/O = 0.82, 95% CI = 0.67 to 0.99) in the second highest risk category (3.4%-5%); the Hosmer-Lemeshow test and a 2-sided P value was equal to .02. CONCLUSIONS: Although the inclusion of a 313 genetic variant PRS doubles discriminatory accuracy (relative to reference 0.50), models with and without this PRS have relatively modest discrimination and might require recalibration before their clinical and wider use are promoted.
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    Lifetime alcohol intake, drinking patterns over time and risk of stomach cancer: A pooled analysis of data from two prospective cohort studies
    Jayasekara, H ; MacInnis, RJ ; Lujan-Barroso, L ; Mayen-Chacon, A-L ; Cross, AJ ; Wallner, B ; Palli, D ; Ricceri, F ; Pala, V ; Panico, S ; Tumino, R ; Kuehn, T ; Kaaks, R ; Tsilidis, K ; Sanchez, M-J ; Amiano, P ; Ardanaz, E ; Chirlaque Lopez, MD ; Merino, S ; Rothwell, JA ; Boutron-Ruault, M-C ; Severi, G ; Sternby, H ; Sonestedt, E ; Bueno-de-Mesquita, B ; Boeing, H ; Travis, R ; Sandanger, TM ; Trichopoulou, A ; Karakatsani, A ; Peppa, E ; Tjonneland, A ; Yang, Y ; Hodge, AM ; Mitchell, H ; Haydon, A ; Room, R ; Hopper, JL ; Weiderpass, E ; Gunter, MJ ; Riboli, E ; Giles, GG ; Milne, RL ; Agudo, A ; English, DR ; Ferrari, P (WILEY, 2021-06-01)
    Alcohol consumption is causally linked to several cancers but the evidence for stomach cancer is inconclusive. In our study, the association between long-term alcohol intake and risk of stomach cancer and its subtypes was evaluated. We performed a pooled analysis of data collected at baseline from 491 714 participants in the European Prospective Investigation into Cancer and Nutrition and the Melbourne Collaborative Cohort Study. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated for incident stomach cancer in relation to lifetime alcohol intake and group-based life course intake trajectories, adjusted for potential confounders including Helicobacter pylori infection. In all, 1225 incident stomach cancers (78% noncardia) were diagnosed over 7 094 637 person-years; 984 in 382 957 study participants with lifetime alcohol intake data (5 455 507 person-years). Although lifetime alcohol intake was not associated with overall stomach cancer risk, we observed a weak positive association with noncardia cancer (HR = 1.03, 95% CI: 1.00-1.06 per 10 g/d increment), with a HR of 1.50 (95% CI: 1.08-2.09) for ≥60 g/d compared to 0.1 to 4.9 g/d. A weak inverse association with cardia cancer (HR = 0.93, 95% CI: 0.87-1.00) was also observed. HRs of 1.48 (95% CI: 1.10-1.99) for noncardia and 0.51 (95% CI: 0.26-1.03) for cardia cancer were observed for a life course trajectory characterized by heavy decreasing intake compared to light stable intake (Phomogeneity = .02). These associations did not differ appreciably by smoking or H pylori infection status. Limiting alcohol use during lifetime, particularly avoiding heavy use during early adulthood, might help prevent noncardia stomach cancer. Heterogeneous associations observed for cardia and noncardia cancers may indicate etiologic differences.
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    Genetic architectures of proximal and distal colorectal cancer are partly distinct
    Huyghe, JR ; Harrison, TA ; Bien, SA ; Hampel, H ; Figueiredo, JC ; Schmit, SL ; Conti, D ; Chen, S ; Qu, C ; Lin, Y ; Barfield, R ; Baron, JA ; Cross, AJ ; Diergaarde, B ; Duggan, D ; Harlid, S ; Imaz, L ; Kang, HM ; Levine, DM ; Perduca, V ; Perez-Cornago, A ; Sakoda, LC ; Schumacher, FR ; Slattery, ML ; Toland, AE ; van Duijnhoven, FJB ; Van Guelpen, B ; Agudo, A ; Albanes, D ; Alonso, MH ; Anderson, K ; Arnau-Collell, C ; Arndt, V ; Banbury, BL ; Bassik, MC ; Berndt, S ; Bezieau, S ; Bishop, DT ; Boehm, J ; Boeing, H ; Boutron-Ruault, M-C ; Brenner, H ; Brezina, S ; Buch, S ; Buchanan, DD ; Burnett-Hartman, A ; Caan, BJ ; Campbell, PT ; Carr, PR ; Castells, A ; Castellvi-Bel, S ; Chan, AT ; Chang-Claude, J ; Chanock, SJ ; Curtis, KR ; de la Chapelle, A ; Easton, DF ; English, DR ; Feskens, EJM ; Gala, M ; Gallinger, SJ ; Gauderman, WJ ; Giles, GG ; Goodman, PJ ; Grady, WM ; Grove, JS ; Gsur, A ; Gunter, MJ ; Haile, RW ; Hampe, J ; Hoffmeister, M ; Hopper, JL ; Hsu, W-L ; Huang, W-Y ; Hudson, TJ ; Jenab, M ; Jenkins, MA ; Joshi, AD ; Keku, TO ; Kooperberg, C ; Kuhn, T ; Kury, S ; Le Marchand, L ; Lejbkowicz, F ; Li, C ; Li, L ; Lieb, W ; Lindblom, A ; Lindor, NM ; Mannisto, S ; Markowitz, SD ; Milne, RL ; Moreno, L ; Murphy, N ; Nassir, R ; Offit, K ; Ogino, S ; Panico, S ; Parfrey, PS ; Pearlman, R ; Pharoah, PDP ; Phipps, A ; Platz, EA ; Potter, JD ; Prentice, RL ; Qi, L ; Raskin, L ; Rennert, G ; Rennert, HS ; Riboli, E ; Schafmayer, C ; Schoen, RE ; Seminara, D ; Song, M ; Su, Y-R ; Tangen, CM ; Thibodeau, SN ; Thomas, DC ; Trichopoulou, A ; Ulrich, CM ; Visvanathan, K ; Vodicka, P ; Vodickova, L ; Vymetalkova, V ; Weigl, K ; Weinstein, SJ ; White, E ; Wolk, A ; Woods, MO ; Wu, AH ; Abecasis, GR ; Nickerson, DA ; Scacheri, PC ; Kundaje, A ; Casey, G ; Gruber, SB ; Hsu, L ; Moreno, V ; Hayes, RB ; Newcomb, PA ; Peters, U (BMJ PUBLISHING GROUP, 2021-07)
    OBJECTIVE: An understanding of the etiologic heterogeneity of colorectal cancer (CRC) is critical for improving precision prevention, including individualized screening recommendations and the discovery of novel drug targets and repurposable drug candidates for chemoprevention. Known differences in molecular characteristics and environmental risk factors among tumors arising in different locations of the colorectum suggest partly distinct mechanisms of carcinogenesis. The extent to which the contribution of inherited genetic risk factors for CRC differs by anatomical subsite of the primary tumor has not been examined. DESIGN: To identify new anatomical subsite-specific risk loci, we performed genome-wide association study (GWAS) meta-analyses including data of 48 214 CRC cases and 64 159 controls of European ancestry. We characterised effect heterogeneity at CRC risk loci using multinomial modelling. RESULTS: We identified 13 loci that reached genome-wide significance (p<5×10-8) and that were not reported by previous GWASs for overall CRC risk. Multiple lines of evidence support candidate genes at several of these loci. We detected substantial heterogeneity between anatomical subsites. Just over half (61) of 109 known and new risk variants showed no evidence for heterogeneity. In contrast, 22 variants showed association with distal CRC (including rectal cancer), but no evidence for association or an attenuated association with proximal CRC. For two loci, there was strong evidence for effects confined to proximal colon cancer. CONCLUSION: Genetic architectures of proximal and distal CRC are partly distinct. Studies of risk factors and mechanisms of carcinogenesis, and precision prevention strategies should take into consideration the anatomical subsite of the tumour.