Melbourne School of Population and Global Health - Research Publications

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    Identifying colorectal cancer caused by biallelic MUTYH pathogenic variants using tumor mutational signatures
    Georgeson, P ; Harrison, TA ; Pope, BJ ; Zaidi, SH ; Qu, C ; Steinfelder, RS ; Lin, Y ; Joo, JE ; Mahmood, K ; Clendenning, M ; Walker, R ; Amitay, EL ; Berndt, S ; Brenner, H ; Campbell, PT ; Cao, Y ; Chan, AT ; Chang-Claude, J ; Doheny, KF ; Drew, DA ; Figueiredo, JC ; French, AJ ; Gallinger, S ; Giannakis, M ; Giles, GG ; Gsur, A ; Gunter, MJ ; Hoffmeister, M ; Hsu, L ; Huang, W-Y ; Limburg, P ; Manson, JE ; Moreno, V ; Nassir, R ; Nowak, JA ; Obon-Santacana, M ; Ogino, S ; Phipps, A ; Potter, JD ; Schoen, RE ; Sun, W ; Toland, AE ; Trinh, QM ; Ugai, T ; Macrae, FA ; Rosty, C ; Hudson, TJ ; Jenkins, MA ; Thibodeau, SN ; Winship, IM ; Peters, U ; Buchanan, DD (NATURE PORTFOLIO, 2022-06-06)
    Carriers of germline biallelic pathogenic variants in the MUTYH gene have a high risk of colorectal cancer. We test 5649 colorectal cancers to evaluate the discriminatory potential of a tumor mutational signature specific to MUTYH for identifying biallelic carriers and classifying variants of uncertain clinical significance (VUS). Using a tumor and matched germline targeted multi-gene panel approach, our classifier identifies all biallelic MUTYH carriers and all known non-carriers in an independent test set of 3019 colorectal cancers (accuracy = 100% (95% confidence interval 99.87-100%)). All monoallelic MUTYH carriers are classified with the non-MUTYH carriers. The classifier provides evidence for a pathogenic classification for two VUS and a benign classification for five VUS. Somatic hotspot mutations KRAS p.G12C and PIK3CA p.Q546K are associated with colorectal cancers from biallelic MUTYH carriers compared with non-carriers (p = 2 × 10-23 and p = 6 × 10-11, respectively). Here, we demonstrate the potential application of mutational signatures to tumor sequencing workflows to improve the identification of biallelic MUTYH carriers.
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    Associations between Smoking and Alcohol and Follicular Lymphoma Incidence and Survival: A Family-Based Case-Control Study in Australia
    Odutola, MK ; van Leeuwen, MT ; Turner, J ; Bruinsma, F ; Seymour, JF ; Prince, HM ; Milliken, ST ; Trotman, J ; Verner, E ; Tiley, C ; Roncolato, F ; Underhill, CR ; Opat, SS ; Harvey, M ; Hertzberg, M ; Benke, G ; Giles, GG ; Vajdic, CM (MDPI, 2022-06-01)
    The association between smoking and alcohol consumption and follicular lymphoma (FL) incidence and clinical outcome is uncertain. We conducted a population-based family case-control study (709 cases: 490 controls) in Australia. We assessed lifetime history of smoking and recent alcohol consumption and followed-up cases (median = 83 months). We examined associations with FL risk using unconditional logistic regression and with all-cause and FL-specific mortality of cases using Cox regression. FL risk was associated with ever smoking (OR = 1.38, 95%CI = 1.08-1.74), former smoking (OR = 1.36, 95%CI = 1.05-1.77), smoking initiation before age 17 (OR = 1.47, 95%CI = 1.06-2.05), the highest categories of cigarettes smoked per day (OR = 1.44, 95%CI = 1.04-2.01), smoking duration (OR = 1.53, 95%CI = 1.07-2.18) and pack-years (OR = 1.56, 95%CI = 1.10-2.22). For never smokers, FL risk increased for those exposed indoors to >2 smokers during childhood (OR = 1.84, 95%CI = 1.11-3.04). For cases, current smoking and the highest categories of smoking duration and lifetime cigarette exposure were associated with elevated all-cause mortality. The hazard ratio for current smoking and FL-specific mortality was 2.97 (95%CI = 0.91-9.72). We found no association between recent alcohol consumption and FL risk, all-cause or FL-specific mortality. Our study showed consistent evidence of an association between smoking and increased FL risk and possibly also FL-specific mortality. Strengthening anti-smoking policies and interventions may reduce the population burden of FL.
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    Genetic Aspects of Mammographic Density Measures Associated with Breast Cancer Risk
    Li, S ; Nguyen, TL ; Tu, N-D ; Dowty, JG ; Dite, GS ; Ye, Z ; Trinh, HN ; Evans, CF ; Tan, M ; Sung, J ; Jenkins, MA ; Giles, GG ; Hopper, JL ; Southey, MC (MDPI, 2022-06-01)
    Cumulus, Altocumulus, and Cirrocumulus are measures of mammographic density defined at increasing pixel brightness thresholds, which, when converted to mammogram risk scores (MRSs), predict breast cancer risk. Twin and family studies suggest substantial variance in the MRSs could be explained by genetic factors. For 2559 women aged 30 to 80 years (mean 54 years), we measured the MRSs from digitized film mammograms and estimated the associations of the MRSs with a 313-SNP breast cancer polygenic risk score (PRS) and 202 individual SNPs associated with breast cancer risk. The PRS was weakly positively correlated (correlation coefficients ranged 0.05-0.08; all p < 0.04) with all the MRSs except the Cumulus-white MRS based on the "white but not bright area" (correlation coefficient = 0.04; p = 0.06). After adjusting for its association with the Altocumulus MRS, the PRS was not associated with the Cumulus MRS. There were MRS associations (Bonferroni-adjusted p < 0.04) with one SNP in the ATXN1 gene and nominally with some ESR1 SNPs. Less than 1% of the variance of the MRSs is explained by the genetic markers currently known to be associated with breast cancer risk. Discovering the genetic determinants of the bright, not white, regions of the mammogram could reveal substantial new genetic causes of breast cancer.
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    Breast cancer risks associated with missense variants in breast cancer susceptibility genes.
    Dorling, L ; Carvalho, S ; Allen, J ; Parsons, MT ; Fortuno, C ; González-Neira, A ; Heijl, SM ; Adank, MA ; Ahearn, TU ; Andrulis, IL ; Auvinen, P ; Becher, H ; Beckmann, MW ; Behrens, S ; Bermisheva, M ; Bogdanova, NV ; Bojesen, SE ; Bolla, MK ; Bremer, M ; Briceno, I ; Camp, NJ ; Campbell, A ; Castelao, JE ; Chang-Claude, J ; Chanock, SJ ; Chenevix-Trench, G ; NBCS Collaborators, ; Collée, JM ; Czene, K ; Dennis, J ; Dörk, T ; Eriksson, M ; Evans, DG ; Fasching, PA ; Figueroa, J ; Flyger, H ; Gabrielson, M ; Gago-Dominguez, M ; García-Closas, M ; Giles, GG ; Glendon, G ; Guénel, P ; Gündert, M ; Hadjisavvas, A ; Hahnen, E ; Hall, P ; Hamann, U ; Harkness, EF ; Hartman, M ; Hogervorst, FBL ; Hollestelle, A ; Hoppe, R ; Howell, A ; kConFab Investigators, ; SGBCC Investigators, ; Jakubowska, A ; Jung, A ; Khusnutdinova, E ; Kim, S-W ; Ko, Y-D ; Kristensen, VN ; Lakeman, IMM ; Li, J ; Lindblom, A ; Loizidou, MA ; Lophatananon, A ; Lubiński, J ; Luccarini, C ; Madsen, MJ ; Mannermaa, A ; Manoochehri, M ; Margolin, S ; Mavroudis, D ; Milne, RL ; Mohd Taib, NA ; Muir, K ; Nevanlinna, H ; Newman, WG ; Oosterwijk, JC ; Park, SK ; Peterlongo, P ; Radice, P ; Saloustros, E ; Sawyer, EJ ; Schmutzler, RK ; Shah, M ; Sim, X ; Southey, MC ; Surowy, H ; Suvanto, M ; Tomlinson, I ; Torres, D ; Truong, T ; van Asperen, CJ ; Waltes, R ; Wang, Q ; Yang, XR ; Pharoah, PDP ; Schmidt, MK ; Benitez, J ; Vroling, B ; Dunning, AM ; Teo, SH ; Kvist, A ; de la Hoya, M ; Devilee, P ; Spurdle, AB ; Vreeswijk, MPG ; Easton, DF (Springer Science and Business Media LLC, 2022-05-18)
    BACKGROUND: Protein truncating variants in ATM, BRCA1, BRCA2, CHEK2, and PALB2 are associated with increased breast cancer risk, but risks associated with missense variants in these genes are uncertain. METHODS: We analyzed data on 59,639 breast cancer cases and 53,165 controls from studies participating in the Breast Cancer Association Consortium BRIDGES project. We sampled training (80%) and validation (20%) sets to analyze rare missense variants in ATM (1146 training variants), BRCA1 (644), BRCA2 (1425), CHEK2 (325), and PALB2 (472). We evaluated breast cancer risks according to five in silico prediction-of-deleteriousness algorithms, functional protein domain, and frequency, using logistic regression models and also mixture models in which a subset of variants was assumed to be risk-associated. RESULTS: The most predictive in silico algorithms were Helix (BRCA1, BRCA2 and CHEK2) and CADD (ATM). Increased risks appeared restricted to functional protein domains for ATM (FAT and PIK domains) and BRCA1 (RING and BRCT domains). For ATM, BRCA1, and BRCA2, data were compatible with small subsets (approximately 7%, 2%, and 0.6%, respectively) of rare missense variants giving similar risk to those of protein truncating variants in the same gene. For CHEK2, data were more consistent with a large fraction (approximately 60%) of rare missense variants giving a lower risk (OR 1.75, 95% CI (1.47-2.08)) than CHEK2 protein truncating variants. There was little evidence for an association with risk for missense variants in PALB2. The best fitting models were well calibrated in the validation set. CONCLUSIONS: These results will inform risk prediction models and the selection of candidate variants for functional assays and could contribute to the clinical reporting of gene panel testing for breast cancer susceptibility.
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    Epigenetic mechanisms of lung carcinogenesis involve differentially methylated CpG sites beyond those associated with smoking
    Petrovic, D ; Bodinier, B ; Dagnino, S ; Whitaker, M ; Karimi, M ; Campanella, G ; Haugdahl Nost, T ; Polidoro, S ; Palli, D ; Krogh, V ; Tumino, R ; Sacerdote, C ; Panico, S ; Lund, E ; Dugue, P-A ; Giles, GG ; Severi, G ; Southey, M ; Vineis, P ; Stringhini, S ; Bochud, M ; Sandanger, TM ; Vermeulen, RCH ; Guida, F ; Chadeau-Hyam, M (SPRINGER, 2022-05-20)
    Smoking-related epigenetic changes have been linked to lung cancer, but the contribution of epigenetic alterations unrelated to smoking remains unclear. We sought for a sparse set of CpG sites predicting lung cancer and explored the role of smoking in these associations. We analysed CpGs in relation to lung cancer in participants from two nested case-control studies, using (LASSO)-penalised regression. We accounted for the effects of smoking using known smoking-related CpGs, and through conditional-independence network. We identified 29 CpGs (8 smoking-related, 21 smoking-unrelated) associated with lung cancer. Models additionally adjusted for Comprehensive Smoking Index-(CSI) selected 1 smoking-related and 49 smoking-unrelated CpGs. Selected CpGs yielded excellent discriminatory performances, outperforming information provided by CSI only. Of the 8 selected smoking-related CpGs, two captured lung cancer-relevant effects of smoking that were missed by CSI. Further, the 50 CpGs identified in the CSI-adjusted model complementarily explained lung cancer risk. These markers may provide further insight into lung cancer carcinogenesis and help improving early identification of high-risk patients.
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    Genetic insights into biological mechanisms governing human ovarian ageing
    Ruth, KS ; Day, FR ; Hussain, J ; Martinez-Marchal, A ; Aiken, CE ; Azad, A ; Thompson, DJ ; Knoblochova, L ; Abe, H ; Tarry-Adkins, JL ; Gonzalez, JM ; Fontanillas, P ; Claringbould, A ; Bakker, OB ; Sulem, P ; Walters, RG ; Terao, C ; Turon, S ; Horikoshi, M ; Lin, K ; Onland-Moret, NC ; Sankar, A ; Hertz, EPT ; Timshel, PN ; Shukla, V ; Borup, R ; Olsen, KW ; Aguilera, P ; Ferrer-Roda, M ; Huang, Y ; Stankovic, S ; Timmers, PRHJ ; Ahearn, TU ; Alizadeh, BZ ; Naderi, E ; Andrulis, IL ; Arnold, AM ; Aronson, KJ ; Augustinsson, A ; Bandinelli, S ; Barbieri, CM ; Beaumont, RN ; Becher, H ; Beckmann, MW ; Benonisdottir, S ; Bergmann, S ; Bochud, M ; Boerwinkle, E ; Bojesen, SE ; Bolla, MK ; Boomsma, DI ; Bowker, N ; Brody, JA ; Broer, L ; Buring, JE ; Campbell, A ; Campbell, H ; Castelao, JE ; Catamo, E ; Chanock, SJ ; Chenevix-Trench, G ; Ciullo, M ; Corre, T ; Couch, FJ ; Cox, A ; Crisponi, L ; Cross, SS ; Cucca, F ; Czene, K ; Smith, GD ; de Geus, EJCN ; de Mutsert, R ; De Vivo, I ; Demerath, EW ; Dennis, J ; Dunning, AM ; Dwek, M ; Eriksson, M ; Esko, T ; Fasching, PA ; Faul, JD ; Ferrucci, L ; Franceschini, N ; Frayling, TM ; Gago-Dominguez, M ; Mezzavilla, M ; Garcia-Closas, M ; Gieger, C ; Giles, GG ; Grallert, H ; Gudbjartsson, DF ; Gudnason, V ; Guenel, P ; Haiman, CA ; Hakansson, N ; Hall, P ; Hayward, C ; He, C ; He, W ; Heiss, G ; Hoffding, MK ; Hopper, JL ; Hottenga, JJ ; Hu, F ; Hunter, D ; Ikram, MA ; Jackson, RD ; Joaquim, MDR ; John, EM ; Joshi, PK ; Karasik, D ; Kardia, SLR ; Kartsonaki, C ; Karlsson, R ; Kitahara, CM ; Kolcic, I ; Kooperberg, C ; Kraft, P ; Kurian, AW ; Kutalik, Z ; La Bianca, M ; LaChance, G ; Langenberg, C ; Launer, LJ ; Laven, JSE ; Lawlor, DA ; Le Marchand, L ; Li, J ; Lindblom, A ; Lindstrom, S ; Lindstrom, T ; Linet, M ; Liu, Y ; Liu, S ; Luan, J ; Magi, R ; Magnusson, PKE ; Mangino, M ; Mannermaa, A ; Marco, B ; Marten, J ; Martin, NG ; Mbarek, H ; McKnight, B ; Medland, SE ; Meisinger, C ; Meitinger, T ; Menni, C ; Metspalu, A ; Milani, L ; Milne, RL ; Montgomery, GW ; Mook-Kanamori, DO ; Mulas, A ; Mulligan, AM ; Murray, A ; Nalls, MA ; Newman, A ; Noordam, R ; Nutile, T ; Nyholt, DR ; Olshan, AF ; Olsson, H ; Painter, JN ; Patel, AV ; Pedersen, NL ; Perjakova, N ; Peters, A ; Peters, U ; Pharoah, PDP ; Polasek, O ; Porcu, E ; Psaty, BM ; Rahman, I ; Rennert, G ; Rennert, HS ; Ridker, PM ; Ring, SM ; Robino, A ; Rose, LM ; Rosendaal, FR ; Rossouw, J ; Rudan, I ; Rueedi, R ; Ruggiero, D ; Sala, CF ; Saloustros, E ; Sandler, DP ; Sanna, S ; Sawyer, EJ ; Sarnowski, C ; Schlessinger, D ; Schmidt, MK ; Schoemaker, MJ ; Schraut, KE ; Scott, C ; Shekari, S ; Shrikhande, A ; Smith, AV ; Smith, BH ; Smith, JA ; Sorice, R ; Southey, MC ; Spector, TD ; Spinelli, JJ ; Stampfer, M ; Stoeckl, D ; van Meurs, JBJ ; Strauch, K ; Styrkarsdottir, U ; Swerdlow, AJ ; Tanaka, T ; Teras, LR ; Teumer, A ; thorsteinsdottir, U ; Timpson, NJ ; Toniolo, D ; Traglia, M ; Troester, MA ; Truong, T ; Tyrrell, J ; Uitterlinden, AG ; Ulivi, S ; Vachon, CM ; Vitart, V ; Voelker, U ; Vollenweider, P ; Voelzke, H ; Wang, Q ; Wareham, NJ ; Weinberg, CR ; Weir, DR ; Wilcox, AN ; van Dijk, KW ; Willemsen, G ; Wilson, JF ; Wolffenbuttel, BHR ; Wolk, A ; Wood, AR ; Zhao, W ; Zygmunt, M ; Chen, Z ; Li, L ; Franke, L ; Burgess, S ; Deelen, P ; Pers, TH ; Grondahl, ML ; Andersen, CY ; Pujol, A ; Lopez-Contreras, AJ ; Daniel, JA ; Stefansson, K ; Chang-Claude, J ; van der Schouw, YT ; Lunetta, KL ; Chasman, DI ; Easton, DF ; Visser, JA ; Ozanne, SE ; Namekawa, SH ; Solc, P ; Murabito, JM ; Ong, KK ; Hoffmann, ER ; Murray, A ; Roig, I ; Perry, JRB (NATURE PORTFOLIO, 2021-08-04)
    Reproductive longevity is essential for fertility and influences healthy ageing in women1,2, but insights into its underlying biological mechanisms and treatments to preserve it are limited. Here we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in about 200,000 women of European ancestry. These common alleles were associated with clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations3. The identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR-associated genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increases fertility and extends reproductive life in mice. Causal inference analyses using the identified genetic variants indicate that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases the risk of hormone-sensitive cancers. These findings provide insight into the mechanisms that govern ovarian ageing, when they act, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease.
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    Genetically Predicted Circulating C-Reactive Protein Concentration and Colorectal Cancer Survival: A Mendelian Randomization Consortium Study
    Hua, X ; Dai, JY ; Lindstrom, S ; Harrison, TA ; Lin, Y ; Alberts, SR ; Alwers, E ; Berndt, S ; Brenner, H ; Buchanan, DD ; Campbell, PT ; Casey, G ; Chang-Claude, J ; Gallinger, S ; Giles, GG ; Goldberg, RM ; Gunter, MJ ; Hoffmeister, M ; Jenkins, MA ; Joshi, AD ; Ma, W ; Milne, RL ; Murphy, N ; Pai, RK ; Sakoda, LC ; Schoen, RE ; Shi, Q ; Slattery, ML ; Song, M ; White, E ; Le Marchand, L ; Chan, AT ; Peters, U ; Newcomb, PA (AMER ASSOC CANCER RESEARCH, 2021-07-01)
    BACKGROUND: A positive association between circulating C-reactive protein (CRP) and colorectal cancer survival was reported in observational studies, which are susceptible to unmeasured confounding and reverse causality. We used a Mendelian randomization approach to evaluate the association between genetically predicted CRP concentrations and colorectal cancer-specific survival. METHODS: We used individual-level data for 16,918 eligible colorectal cancer cases of European ancestry from 15 studies within the International Survival Analysis of Colorectal Cancer Consortium. We calculated a genetic-risk score based on 52 CRP-associated genetic variants identified from genome-wide association studies. Because of the non-collapsibility of hazard ratios from Cox proportional hazards models, we used the additive hazards model to calculate hazard differences (HD) and 95% confidence intervals (CI) for the association between genetically predicted CRP concentrations and colorectal cancer-specific survival, overall and by stage at diagnosis and tumor location. Analyses were adjusted for age at diagnosis, sex, body mass index, genotyping platform, study, and principal components. RESULTS: Of the 5,395 (32%) deaths accrued over up to 10 years of follow-up, 3,808 (23%) were due to colorectal cancer. Genetically predicted CRP concentration was not associated with colorectal cancer-specific survival (HD, -1.15; 95% CI, -2.76 to 0.47 per 100,000 person-years; P = 0.16). Similarly, no associations were observed in subgroup analyses by stage at diagnosis or tumor location. CONCLUSIONS: Despite adequate power to detect moderate associations, our results did not support a causal effect of circulating CRP concentrations on colorectal cancer-specific survival. IMPACT: Future research evaluating genetically determined levels of other circulating inflammatory biomarkers (i.e., IL6) with colorectal cancer survival outcomes is needed.
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    Smoking Modifies Pancreatic Cancer Risk Loci on 2q21.3
    Mocci, E ; Kundu, P ; Wheeler, W ; Arslan, AA ; Beane-Freeman, LE ; Bracci, PM ; Brennan, P ; Canzian, F ; Du, M ; Gallinger, S ; Giles, GG ; Goodman, PJ ; Kooperberg, C ; Le Marchand, L ; Neale, RE ; Shu, X-O ; Visvanathan, K ; White, E ; Zheng, W ; Albanes, D ; Andreotti, G ; Babic, A ; Bamlet, WR ; Berndt, S ; Blackford, AL ; Bueno-de-Mesquita, B ; Buring, JE ; Campa, D ; Chanock, SJ ; Childs, EJ ; Duell, EJ ; Fuchs, CS ; Gaziano, JM ; Giovannucci, EL ; Goggins, MG ; Hartge, P ; Hassan, MM ; Holly, EA ; Hoover, RN ; Hung, RJ ; Kurtz, RC ; Lee, I-M ; Malats, N ; Milne, RL ; Ng, K ; Oberg, AL ; Panico, S ; Peters, U ; Porta, M ; Rabe, KG ; Riboli, E ; Rothman, N ; Scelo, G ; Sesso, HD ; Silverman, DT ; Stevens, VL ; Strobel, O ; Thompson, IM ; Tjonneland, A ; Trichopoulou, A ; Van den Eeden, SK ; Wactawski-Wende, J ; Wentzensen, N ; Wilkens, LR ; Yu, H ; Yuan, F ; Zeleniuch-Jacquotte, A ; Amundadottir, LT ; Li, D ; Jacobs, EJ ; Petersen, GM ; Wolpin, BM ; Risch, HA ; Kraft, P ; Chatterjee, N ; Klein, AP ; Stolzenberg-Solomon, R (AMER ASSOC CANCER RESEARCH, 2021-06-01)
    Germline variation and smoking are independently associated with pancreatic ductal adenocarcinoma (PDAC). We conducted genome-wide smoking interaction analysis of PDAC using genotype data from four previous genome-wide association studies in individuals of European ancestry (7,937 cases and 11,774 controls). Examination of expression quantitative trait loci data from the Genotype-Tissue Expression Project followed by colocalization analysis was conducted to determine whether there was support for common SNP(s) underlying the observed associations. Statistical tests were two sided and P < 5 × 10-8 was considered statistically significant. Genome-wide significant evidence of qualitative interaction was identified on chr2q21.3 in intron 5 of the transmembrane protein 163 (TMEM163) and upstream of the cyclin T2 (CCNT2). The most significant SNP using the Empirical Bayes method, in this region that included 45 significantly associated SNPs, was rs1818613 [per allele OR in never smokers 0.87, 95% confidence interval (CI), 0.82-0.93; former smokers 1.00, 95% CI, 0.91-1.07; current smokers 1.25, 95% CI 1.12-1.40, P interaction = 3.08 × 10-9). Examination of the Genotype-Tissue Expression Project data demonstrated an expression quantitative trait locus in this region for TMEM163 and CCNT2 in several tissue types. Colocalization analysis supported a shared SNP, rs842357, in high linkage disequilibrium with rs1818613 (r 2 = 0. 94) driving both the observed interaction and the expression quantitative trait loci signals. Future studies are needed to confirm and understand the differential biologic mechanisms by smoking status that contribute to our PDAC findings. SIGNIFICANCE: This large genome-wide interaction study identifies a susceptibility locus on 2q21.3 that significantly modified PDAC risk by smoking status, providing insight into smoking-associated PDAC, with implications for prevention.
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    Breast Cancer Risk Factors and Survival by Tumor Subtype: Pooled Analyses from the Breast Cancer Association Consortium
    Morra, A ; Jung, AY ; Behrens, S ; Keeman, R ; Ahearn, TU ; Anton-Culver, H ; Arndt, V ; Augustinsson, A ; Auvinen, PK ; Freeman, LEB ; Becher, H ; Beckmann, MW ; Blomqvist, C ; Bojesen, SE ; Bolla, MK ; Brenner, H ; Briceno, I ; Brucker, SY ; Camp, NJ ; Campa, D ; Canzian, F ; Castelao, JE ; Chanock, SJ ; Choi, J-Y ; Clarke, CL ; Couch, FJ ; Cox, A ; Cross, SS ; Czene, K ; Dork, T ; Dunning, AM ; Dwek, M ; Easton, DF ; Eccles, DM ; Egan, KM ; Evans, DG ; Fasching, PA ; Flyger, H ; Gago-Dominguez, M ; Gapstur, SM ; Garcia-Saenz, JA ; Gaudet, MM ; Giles, GG ; Grip, M ; Guenel, P ; Haiman, CA ; Hakansson, N ; Hall, P ; Hamann, U ; Han, SN ; Hart, SN ; Hartman, M ; Heyworth, JS ; Hoppe, R ; Hopper, JL ; Hunter, DJ ; Ito, H ; Jager, A ; Jakimovska, M ; Jakubowska, A ; Janni, W ; Kaaks, R ; Kang, D ; Kapoor, PM ; Kitahara, CM ; Koutros, S ; Kraft, P ; Kristensen, VN ; Lacey, J ; Lambrechts, D ; Le Marchand, L ; Li, J ; Lindblom, A ; Lush, M ; Mannermaa, A ; Manoochehri, M ; Margolin, S ; Mariapun, S ; Matsuo, K ; Mavroudis, D ; Milne, RL ; Muranen, TA ; Newman, WG ; Noh, D-Y ; Nordestgaard, BG ; Obi, N ; Olshan, AF ; Olsson, H ; Park-Simon, T-W ; Petridis, C ; Pharoah, PDP ; Plaseska-Karanfilska, D ; Presneau, N ; Rashid, MU ; Rennert, G ; Rennert, HS ; Rhenius, V ; Romero, A ; Saloustros, E ; Sawyer, EJ ; Schneeweiss, A ; Schwentner, L ; Scott, C ; Shah, M ; Shen, C-Y ; Shu, X-O ; Southey, MC ; Stram, DO ; Tamimi, RM ; Tapper, W ; Tollenaar, RAEM ; Tomlinson, I ; Torres, D ; Troester, MA ; Truong, T ; Vachon, CM ; Wang, Q ; Wang, SS ; Williams, JA ; Winqvist, R ; Wolk, A ; Wu, AH ; Yoo, K-Y ; Yu, J-C ; Zheng, W ; Ziogas, A ; Yang, XR ; Eliassen, AH ; Holmes, MD ; Garcia-Closas, M ; Teo, SH ; Schmidt, MK ; Chang-Claude, J (AMER ASSOC CANCER RESEARCH, 2021-04-01)
    BACKGROUND: It is not known whether modifiable lifestyle factors that predict survival after invasive breast cancer differ by subtype. METHODS: We analyzed data for 121,435 women diagnosed with breast cancer from 67 studies in the Breast Cancer Association Consortium with 16,890 deaths (8,554 breast cancer specific) over 10 years. Cox regression was used to estimate associations between risk factors and 10-year all-cause mortality and breast cancer-specific mortality overall, by estrogen receptor (ER) status, and by intrinsic-like subtype. RESULTS: There was no evidence of heterogeneous associations between risk factors and mortality by subtype (P adj > 0.30). The strongest associations were between all-cause mortality and BMI ≥30 versus 18.5-25 kg/m2 [HR (95% confidence interval (CI), 1.19 (1.06-1.34)]; current versus never smoking [1.37 (1.27-1.47)], high versus low physical activity [0.43 (0.21-0.86)], age ≥30 years versus <20 years at first pregnancy [0.79 (0.72-0.86)]; >0-<5 years versus ≥10 years since last full-term birth [1.31 (1.11-1.55)]; ever versus never use of oral contraceptives [0.91 (0.87-0.96)]; ever versus never use of menopausal hormone therapy, including current estrogen-progestin therapy [0.61 (0.54-0.69)]. Similar associations with breast cancer mortality were weaker; for example, 1.11 (1.02-1.21) for current versus never smoking. CONCLUSIONS: We confirm associations between modifiable lifestyle factors and 10-year all-cause mortality. There was no strong evidence that associations differed by ER status or intrinsic-like subtype. IMPACT: Given the large dataset and lack of evidence that associations between modifiable risk factors and 10-year mortality differed by subtype, these associations could be cautiously used in prognostication models to inform patient-centered care.
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    Two-stage Study of Familial Prostate Cancer by Whole-exome Sequencing and Custom Capture Identifies 10 Novel Genes Associated with the Risk of Prostate Cancer
    Schaid, DJ ; McDonnell, SK ; FitzGerald, LM ; DeRycke, L ; Fogarty, Z ; Giles, GG ; MacInnis, RJ ; Southey, MC ; Nguyen-Dumont, T ; Cancel-Tassin, G ; Cussenot, O ; Whittemore, AS ; Sieh, W ; Ioannidis, NM ; Hsieh, C-L ; Stanford, JL ; Schleutker, J ; Cropp, CD ; Carpten, J ; Hoegel, J ; Eeles, R ; Kote-Jarai, Z ; Ackerman, MJ ; Klein, CJ ; Mandal, D ; Cooney, KA ; Bailey-Wilson, JE ; Helfand, B ; Catalona, WJ ; Wiklund, F ; Riska, S ; Bahetti, S ; Larson, MC ; Albright, LC ; Teerlink, C ; Xu, J ; Isaacs, W ; Ostrander, EA ; Thibodeau, SN (ELSEVIER, 2021-02-11)
    BACKGROUND: Family history of prostate cancer (PCa) is a well-known risk factor, and both common and rare genetic variants are associated with the disease. OBJECTIVE: To detect new genetic variants associated with PCa, capitalizing on the role of family history and more aggressive PCa. DESIGN, SETTING, AND PARTICIPANTS: A two-stage design was used. In stage one, whole-exome sequencing was used to identify potential risk alleles among affected men with a strong family history of disease or with more aggressive disease (491 cases and 429 controls). Aggressive disease was based on a sum of scores for Gleason score, node status, metastasis, tumor stage, prostate-specific antigen at diagnosis, systemic recurrence, and time to PCa death. Genes identified in stage one were screened in stage two using a custom-capture design in an independent set of 2917 cases and 1899 controls. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Frequencies of genetic variants (singly or jointly in a gene) were compared between cases and controls. RESULTS AND LIMITATIONS: Eleven genes previously reported to be associated with PCa were detected (ATM, BRCA2, HOXB13, FAM111A, EMSY, HNF1B, KLK3, MSMB, PCAT1, PRSS3, and TERT), as well as an additional 10 novel genes (PABPC1, QK1, FAM114A1, MUC6, MYCBP2, RAPGEF4, RNASEH2B, ULK4, XPO7, and THAP3). Of these 10 novel genes, all but PABPC1 and ULK4 were primarily associated with the risk of aggressive PCa. CONCLUSIONS: Our approach demonstrates the advantage of gene sequencing in the search for genetic variants associated with PCa and the benefits of sampling patients with a strong family history of disease or an aggressive form of disease. PATIENT SUMMARY: Multiple genes are associated with prostate cancer (PCa) among men with a strong family history of this disease or among men with an aggressive form of PCa.