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1. Characterizing prostate cancer risk through multi-ancestry genome-wide discovery of 187 novel risk variants.

Author

Wang, Anqi, Shen, Jiayi, Rodriguez, Alex, Saunders, Edward, Chen, Fei, Janivara, Rohini, Darst, Burcu, Sheng, Xin, Xu, Yili, Chou, Alisha, Benlloch, Sara, Dadaev, Tokhir, Brook, Mark, Plym, Anna, Sahimi, Ali, Hoffman, Thomas, Takahashi, Atushi, Matsuda, Koichi, Momozawa, Yukihide, Fujita, Masashi, Laisk, Triin, Figuerêdo, Jéssica, Muir, Kenneth, Ito, Shuji, Liu, Xiaoxi, Uchio, Yuji, Kubo, Michiaki, Kamatani, Yoichiro, Lophatananon, Artitaya, Wan, Peggy, Andrews, Caroline, Lori, Adriana, Choudhury, Parichoy, Schleutker, Johanna, Tammela, Teuvo, Sipeky, Csilla, Auvinen, Anssi, Giles, Graham, Southey, Melissa, MacInnis, Robert, Cybulski, Cezary, Wokolorczyk, Dominika, Lubinski, Jan, Rentsch, Christopher, Cho, Kelly, Mcmahon, Benjamin, Neal, David, Donovan, Jenny, Hamdy, Freddie, Martin, Richard, Nordestgaard, Borge, Nielsen, Sune, Weischer, Maren, Bojesen, Stig, Røder, Andreas, Stroomberg, Hein, Batra, Jyotsna, Chambers, Suzanne, Horvath, Lisa, Clements, Judith, Tilly, Wayne, Risbridger, Gail, Gronberg, Henrik, Aly, Markus, Szulkin, Robert, Eklund, Martin, Nordstrom, Tobias, Pashayan, Nora, Dunning, Alison, Ghoussaini, Maya, Travis, Ruth, Key, Tim, Riboli, Elio, Park, Jong, Sellers, Thomas, Lin, Hui-Yi, Albanes, Demetrius, Weinstein, Stephanie, Cook, Michael, Mucci, Lorelei, Giovannucci, Edward, Lindstrom, Sara, Kraft, Peter, Hunter, David, Penney, Kathryn, Turman, Constance, Tangen, Catherine, Goodman, Phyllis, Thompson, Ian, Hamilton, Robert, Fleshner, Neil, Finelli, Antonio, Parent, Marie-Élise, Stanford, Janet, Ostrander, Elaine, Koutros, Stella, Beane Freeman, Laura, Stampfer, Meir, Wolk, Alicja, and Håkansson, Niclas

Subjects
Humans, Male, Black People, Genetic Predisposition to Disease, Genome-Wide Association Study, Hispanic or Latino, Polymorphism, Single Nucleotide, Prostatic Neoplasms, Risk Factors, White People, Asian People
Abstract

The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups.

Published
2023

2. The efficacy of different biomarkers and endpoints to refine referrals for suspected prostate cancer: the TARGET study (Tiered integrAted tests for eaRly diaGnosis of clinically significant ProstatE Tumours)

Author

Artitaya Lophatananon, Kenneth R. Muir, and Vincent J. Gnanapragasam

Subjects
Prostate cancer, Early detection, PSA, PSA density, Free Total PSA, Polygenic risk scores, Medicine
Abstract

Abstract Background The majority of men referred with a raised PSA for suspected prostate cancer will receive unnecessary tertiary investigations including MRI and biopsy. Here, we compared different types of biomarkers to refine tertiary referrals and when different definitions of clinically significant cancer were used. Methods Data and samples from 798 men referred for a raised PSA (≥ 3 ng/mL) and investigated through an MRI-guided biopsy pathway were accessed for this study. Bloods were acquired pre-biopsy for liquid biomarkers and germline DNA. Variables explored included PSA + Age (base model), free/total PSA (FTPSA), Prostate Health Index (phi), PSA density (PSAd), polygenic risk score (PRS) and MRI (≥ LIKERT 3). Different diagnostic endpoints for significant cancer (≥ grade group 2 [GG2], ≥ GG3, ≥ Cambridge Prognostic Group 2 [CPG2], ≥ CPG3) were tested. The added value of each biomarker to the base model was evaluated using logistic regression models, AUC and decision curve analysis (DCA) plots. Results The median age and PSA was 65 years and 7.13 ng/mL respectively. Depending on definition of clinical significance, ≥ grade group 2 (GG2) was detected in 57.0% (455/798), ≥ GG3 in 27.5% (220/798), ≥ CPG2 in 61.6% (492/798) and ≥ CPG3 in 42.6% (340/798). In the pre-MRI context, the PSA + Age (base model) AUC for prediction of ≥ GG2, ≥ GG3, ≥ CPG2 and ≥ CPG3 was 0.66, 0.68, 0.70 and 0.75 respectively. Adding phi and PSAd to base model improved performance across all diagnostic endpoints but was notably better when the composite CPG prognostic score was used: AUC 0.82, 0.82, 0.83, 0.82 and AUC 0.74, 0.73, 0.79, 0.79 respectively. In contrast, neither FTPSA or PRS scores improved performance especially in detection of ≥ GG3 and ≥ CPG3 disease. Combining biomarkers did not alter results. Models using phi and PSAd post-MRI also improved performances but again benefit varied with diagnostic endpoint. In DCA analysis, models which incorporated PSAd and phi in particular were effective at reducing use of MRI and/or biopsies especially for ≥ CPG3 disease. Conclusion Incorporating phi or PSAd can refine and tier who is referred for tertiary imaging and/or biopsy after a raised PSA test. Incremental value however varied depending on the definition of clinical significance and was particularly useful when composite prognostic endpoints are used.

Published
2024
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4. Prostate cancer risk stratification improvement across multiple ancestries with new polygenic hazard score

Author

Huynh-Le, Minh-Phuong, Karunamuni, Roshan, Fan, Chun Chieh, Asona, Lui, Thompson, Wesley K, Martinez, Maria Elena, Eeles, Rosalind A, Kote-Jarai, Zsofia, Muir, Kenneth R, Lophatananon, Artitaya, Schleutker, Johanna, Pashayan, Nora, Batra, Jyotsna, Grönberg, Henrik, Neal, David E, Nordestgaard, Børge G, Tangen, Catherine M, MacInnis, Robert J, Wolk, Alicja, Albanes, Demetrius, Haiman, Christopher A, Travis, Ruth C, Blot, William J, Stanford, Janet L, Mucci, Lorelei A, West, Catharine ML, Nielsen, Sune F, Kibel, Adam S, Cussenot, Olivier, Berndt, Sonja I, Koutros, Stella, Sørensen, Karina Dalsgaard, Cybulski, Cezary, Grindedal, Eli Marie, Menegaux, Florence, Park, Jong Y, Ingles, Sue A, Maier, Christiane, Hamilton, Robert J, Rosenstein, Barry S, Lu, Yong-Jie, Watya, Stephen, Vega, Ana, Kogevinas, Manolis, Wiklund, Fredrik, Penney, Kathryn L, Huff, Chad D, Teixeira, Manuel R, Multigner, Luc, Leach, Robin J, Brenner, Hermann, John, Esther M, Kaneva, Radka, Logothetis, Christopher J, Neuhausen, Susan L, De Ruyck, Kim, Ost, Piet, Razack, Azad, Newcomb, Lisa F, Fowke, Jay H, Gamulin, Marija, Abraham, Aswin, Claessens, Frank, Castelao, Jose Esteban, Townsend, Paul A, Crawford, Dana C, Petrovics, Gyorgy, van Schaik, Ron HN, Parent, Marie-Élise, Hu, Jennifer J, Zheng, Wei, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, and Seibert, Tyler M

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Aging, Prostate Cancer, Cancer, Urologic Diseases, Prevention, Genetics, Good Health and Well Being, Male, Humans, Prostate-Specific Antigen, Prostatic Neoplasms, Early Detection of Cancer, Polymorphism, Single Nucleotide, Risk Factors, Risk Assessment, Genetic Predisposition to Disease, UKGPCS collaborators, APCB, NC-LA PCaP Investigators, IMPACT Study Steering Committee and Collaborators, Canary PASS Investigators, Profile Study Steering Committee, PRACTICAL Consortium, Urology & Nephrology, Clinical sciences, Oncology and carcinogenesis
Abstract

BackgroundProstate cancer risk stratification using single-nucleotide polymorphisms (SNPs) demonstrates considerable promise in men of European, Asian, and African genetic ancestries, but there is still need for increased accuracy. We evaluated whether including additional SNPs in a prostate cancer polygenic hazard score (PHS) would improve associations with clinically significant prostate cancer in multi-ancestry datasets.MethodsIn total, 299 SNPs previously associated with prostate cancer were evaluated for inclusion in a new PHS, using a LASSO-regularized Cox proportional hazards model in a training dataset of 72,181 men from the PRACTICAL Consortium. The PHS model was evaluated in four testing datasets: African ancestry, Asian ancestry, and two of European Ancestry-the Cohort of Swedish Men (COSM) and the ProtecT study. Hazard ratios (HRs) were estimated to compare men with high versus low PHS for association with clinically significant, with any, and with fatal prostate cancer. The impact of genetic risk stratification on the positive predictive value (PPV) of PSA testing for clinically significant prostate cancer was also measured.ResultsThe final model (PHS290) had 290 SNPs with non-zero coefficients. Comparing, for example, the highest and lowest quintiles of PHS290, the hazard ratios (HRs) for clinically significant prostate cancer were 13.73 [95% CI: 12.43-15.16] in ProtecT, 7.07 [6.58-7.60] in African ancestry, 10.31 [9.58-11.11] in Asian ancestry, and 11.18 [10.34-12.09] in COSM. Similar results were seen for association with any and fatal prostate cancer. Without PHS stratification, the PPV of PSA testing for clinically significant prostate cancer in ProtecT was 0.12 (0.11-0.14). For the top 20% and top 5% of PHS290, the PPV of PSA testing was 0.19 (0.15-0.22) and 0.26 (0.19-0.33), respectively.ConclusionsWe demonstrate better genetic risk stratification for clinically significant prostate cancer than prior versions of PHS in multi-ancestry datasets. This is promising for implementing precision-medicine approaches to prostate cancer screening decisions in diverse populations.

Published
2022

5. Exploring the Relationships between Lifestyle Patterns and Epigenetic Biological Age Measures in Men

Author

Te-Min Ke, Artitaya Lophatananon, and Kenneth R. Muir

Subjects
epigenetic biological age, biological age clock, DNA methylation, epigenetic markers, lifestyle patterns, healthy lifestyle index (HLI), Biology (General), QH301-705.5
Abstract

DNA methylation, validated as a surrogate for biological age, is a potential tool for predicting future morbidity and mortality outcomes. This study aims to explore how lifestyle patterns are associated with epigenetic changes in British men. Five biological age clocks were utilised to investigate the relationship between these epigenetic markers and lifestyle-related factors in a prospective study involving 221 participants. Spearman’s correlation test, Pearson’s correlation test, and univariate linear regression were employed for analysis. The results indicate that higher consumption of saturated fat and total daily calories, and a higher body mass index (BMI) are associated with accelerated biological aging. Conversely, higher vitamin D intake and a higher healthy lifestyle index (HLI) are linked to decelerated biological aging. These findings highlight the potential impact of specific lifestyle-related factors on biological aging and can serve as a reference for applying healthy lifestyle improvements in future disease prevention studies.

Published
2024
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6. Performance of African-ancestry-specific polygenic hazard score varies according to local ancestry in 8q24

Author

Karunamuni, Roshan A, Huynh-Le, Minh-Phuong, Fan, Chun C, Thompson, Wesley, Lui, Asona, Martinez, Maria Elena, Rose, Brent S, Mahal, Brandon, Eeles, Rosalind A, Kote-Jarai, Zsofia, Muir, Kenneth, Lophatananon, Artitaya, Tangen, Catherine M, Goodman, Phyllis J, Thompson, Ian M, Blot, William J, Zheng, Wei, Kibel, Adam S, Drake, Bettina F, Cussenot, Olivier, Cancel-Tassin, Géraldine, Menegaux, Florence, Truong, Thérèse, Park, Jong Y, Lin, Hui-Yi, Taylor, Jack A, Bensen, Jeannette T, Mohler, James L, Fontham, Elizabeth TH, Multigner, Luc, Blanchet, Pascal, Brureau, Laurent, Romana, Marc, Leach, Robin J, John, Esther M, Fowke, Jay H, Bush, William S, Aldrich, Melinda C, Crawford, Dana C, Cullen, Jennifer, Petrovics, Gyorgy, Parent, Marie-Élise, Hu, Jennifer J, Sanderson, Maureen, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, and Seibert, Tyler M

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Prostate Cancer, Prevention, Genetics, Cancer, Urologic Diseases, Black People, Case-Control Studies, Chromosomes, Human, Pair 8, Genetic Predisposition to Disease, Humans, Male, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Prostatic Neoplasms, Risk Assessment, White People, UKGPCS Collaborators, PRACTICAL Consortium, Urology & Nephrology, Clinical sciences, Oncology and carcinogenesis
Abstract

BackgroundWe previously developed an African-ancestry-specific polygenic hazard score (PHS46+African) that substantially improved prostate cancer risk stratification in men with African ancestry. The model consists of 46 SNPs identified in Europeans and 3 SNPs from 8q24 shown to improve model performance in Africans. Herein, we used principal component (PC) analysis to uncover subpopulations of men with African ancestry for whom the utility of PHS46+African may differ.Materials and methodsGenotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Genetic variation in a window spanning 3 African-specific 8q24 SNPs was estimated using 93 PCs. A Cox proportional hazards framework was used to identify the pair of PCs most strongly associated with the performance of PHS46+African. A calibration factor (CF) was formulated using Cox coefficients to quantify the extent to which the performance of PHS46+African varies with PC.ResultsCF of PHS46+African was strongly associated with the first and twentieth PCs. Predicted CF ranged from 0.41 to 2.94, suggesting that PHS46+African may be up to 7 times more beneficial to some African men than others. The explained relative risk for PHS46+African varied from 3.6% to 9.9% for individuals with low and high CF values, respectively. By cross-referencing our data set with 1000 Genomes, we identified significant associations between continental and calibration groupings.ConclusionWe identified PCs within 8q24 that were strongly associated with the performance of PHS46+African. Further research to improve the clinical utility of polygenic risk scores (or models) is needed to improve health outcomes for men of African ancestry.

Published
2022

9. Aggregation tests identify new gene associations with breast cancer in populations with diverse ancestry

Author

Mueller, Stefanie H., Lai, Alvina G., Valkovskaya, Maria, Michailidou, Kyriaki, Bolla, Manjeet K., Wang, Qin, Dennis, Joe, Lush, Michael, Abu-Ful, Zomoruda, Ahearn, Thomas U., Andrulis, Irene L., Anton-Culver, Hoda, Antonenkova, Natalia N., Arndt, Volker, Aronson, Kristan J., Augustinsson, Annelie, Baert, Thais, Freeman, Laura E. Beane, Beckmann, Matthias W., Behrens, Sabine, Benitez, Javier, Bermisheva, Marina, Blomqvist, Carl, Bogdanova, Natalia V., Bojesen, Stig E., Bonanni, Bernardo, Brenner, Hermann, Brucker, Sara Y., Buys, Saundra S., Castelao, Jose E., Chan, Tsun L., Chang-Claude, Jenny, Chanock, Stephen J., Choi, Ji-Yeob, Chung, Wendy K., Colonna, Sarah V., Cornelissen, Sten, Couch, Fergus J., Czene, Kamila, Daly, Mary B., Devilee, Peter, Dörk, Thilo, Dossus, Laure, Dwek, Miriam, Eccles, Diana M., Ekici, Arif B., Eliassen, A. Heather, Engel, Christoph, Evans, D. Gareth, Fasching, Peter A., Fletcher, Olivia, Flyger, Henrik, Gago-Dominguez, Manuela, Gao, Yu-Tang, García-Closas, Montserrat, García-Sáenz, José A., Genkinger, Jeanine, Gentry-Maharaj, Aleksandra, Grassmann, Felix, Guénel, Pascal, Gündert, Melanie, Haeberle, Lothar, Hahnen, Eric, Haiman, Christopher A., Håkansson, Niclas, Hall, Per, Harkness, Elaine F., Harrington, Patricia A., Hartikainen, Jaana M., Hartman, Mikael, Hein, Alexander, Ho, Weang-Kee, Hooning, Maartje J., Hoppe, Reiner, Hopper, John L., Houlston, Richard S., Howell, Anthony, Hunter, David J., Huo, Dezheng, Ito, Hidemi, Iwasaki, Motoki, Jakubowska, Anna, Janni, Wolfgang, John, Esther M., Jones, Michael E., Jung, Audrey, Kaaks, Rudolf, Kang, Daehee, Khusnutdinova, Elza K., Kim, Sung-Won, Kitahara, Cari M., Koutros, Stella, Kraft, Peter, Kristensen, Vessela N., Kubelka-Sabit, Katerina, Kurian, Allison W., Kwong, Ava, Lacey, James V., Lambrechts, Diether, Le Marchand, Loic, Li, Jingmei, Linet, Martha, Lo, Wing-Yee, Long, Jirong, Lophatananon, Artitaya, Mannermaa, Arto, Manoochehri, Mehdi, Margolin, Sara, Matsuo, Keitaro, Mavroudis, Dimitrios, Menon, Usha, Muir, Kenneth, Murphy, Rachel A., Nevanlinna, Heli, Newman, William G., Niederacher, Dieter, O’Brien, Katie M., Obi, Nadia, Offit, Kenneth, Olopade, Olufunmilayo I., Olshan, Andrew F., Olsson, Håkan, Park, Sue K., Patel, Alpa V., Patel, Achal, Perou, Charles M., Peto, Julian, Pharoah, Paul D. P., Plaseska-Karanfilska, Dijana, Presneau, Nadege, Rack, Brigitte, Radice, Paolo, Ramachandran, Dhanya, Rashid, Muhammad U., Rennert, Gad, Romero, Atocha, Ruddy, Kathryn J., Ruebner, Matthias, Saloustros, Emmanouil, Sandler, Dale P., Sawyer, Elinor J., Schmidt, Marjanka K., Schmutzler, Rita K., Schneider, Michael O., Scott, Christopher, Shah, Mitul, Sharma, Priyanka, Shen, Chen-Yang, Shu, Xiao-Ou, Simard, Jacques, Surowy, Harald, Tamimi, Rulla M., Tapper, William J., Taylor, Jack A., Teo, Soo Hwang, Teras, Lauren R., Toland, Amanda E., Tollenaar, Rob A. E. M., Torres, Diana, Torres-Mejía, Gabriela, Troester, Melissa A., Truong, Thérèse, Vachon, Celine M., Vijai, Joseph, Weinberg, Clarice R., Wendt, Camilla, Winqvist, Robert, Wolk, Alicja, Wu, Anna H., Yamaji, Taiki, Yang, Xiaohong R., Yu, Jyh-Cherng, Zheng, Wei, Ziogas, Argyrios, Ziv, Elad, Dunning, Alison M., Easton, Douglas F., Hemingway, Harry, Hamann, Ute, and Kuchenbaecker, Karoline B.

Published
2023
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10. The association of herpes zoster and influenza vaccinations with the risk of developing dementia: a population-based cohort study within the UK Clinical Practice Research Datalink

Author

Artitaya Lophatananon, Matthew Carr, Brian Mcmillan, Curtis Dobson, Ruth Itzhaki, Rosa Parisi, Darren M. Ashcroft, and Kenneth R. Muir

Subjects
Zoster vaccination, Dementia, Public aspects of medicine, RA1-1270
Abstract

Abstract Background Dementia affects ability to remember, think, or make decisions that interfere with doing everyday activities. There is no cure, therefore any prevention or delay of the onset is of importance. This study aims to investigate the association between zoster and influenza vaccinations and the risk of developing dementia. Methods We conducted a retrospective population-based cohort study using electronic health records from 1469 general practices contributing to the Clinical Practice Research Datalink (CPRD) Aurum database with linked hospital episode statistics (HES) and Office for National Statistics (ONS) mortality records. We built two 'matched cohorts': zoster vaccine (854,745 exposed individuals) matched with 8.8 million comparators without a history of zoster vaccination, and influenza vaccine (742,487 exposed individuals) matched with 7.12 million comparators without a history of vaccination as another comparator group. The cohorts were then followed to assess the association of exposure (vaccine) with outcome (dementia diagnosis). Results Zoster vaccination was associated with a lower risk of dementia diagnosis (adjusted hazard ratio (HR) 0.78 with 95% CI: 0.77–0.79), Alzheimer’s diagnosis (adjusted HR 0.91 with 95% CI: 0.89–0.92 and other types of dementia (adjusted HR 0.71 with 95% CI: 0.69–0.72). Influenza vaccination also was associated with a slightly reduced hazard of dementia risk (adjusted HR 0.96 with 95% CI: 0.94–0.97). Conclusion Both zoster vaccine for prevention of shingles / herpes zoster and influenza vaccine to prevent influenza were associated with diminished risk of dementia, with the zoster association appearing more pronounced.

Published
2023
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12. Common genetic and clinical risk factors: association with fatal prostate cancer in the Cohort of Swedish Men

Author

Huynh-Le, Minh-Phuong, Karunamuni, Roshan, Fan, Chun Chieh, Thompson, Wesley K, Muir, Kenneth, Lophatananon, Artitaya, Tye, Karen, Wolk, Alicja, Håkansson, Niclas, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, and Seibert, Tyler M

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Urologic Diseases, Cancer, Prevention, Aging, Clinical Research, Prostate Cancer, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Aged, Aged, 80 and over, Biomarkers, Tumor, Case-Control Studies, Follow-Up Studies, Genetic Predisposition to Disease, Humans, Longitudinal Studies, Male, Prognosis, Prostatic Neoplasms, Survival Rate, Sweden, PRACTICAL Consortium, Urology & Nephrology, Clinical sciences, Oncology and carcinogenesis
Abstract

BackgroundClinical variables-age, family history, genetics-are used for prostate cancer risk stratification. Recently, polygenic hazard scores (PHS46, PHS166) were validated as associated with age at prostate cancer diagnosis. While polygenic scores are associated with all prostate cancer (not specific for fatal cancers), PHS46 was also associated with age at prostate cancer death. We evaluated if adding PHS to clinical variables improves associations with prostate cancer death.MethodsGenotype/phenotype data were obtained from a nested case-control Cohort of Swedish Men (n = 3279; 2163 with prostate cancer, 278 prostate cancer deaths). PHS and clinical variables (family history, alcohol intake, smoking, heart disease, hypertension, diabetes, body mass index) were tested via univariable Cox proportional hazards models for association with age at prostate cancer death. Multivariable Cox models with/without PHS were compared with log-likelihood tests.ResultsMedian age at last follow-up/prostate cancer death was 78.0 (IQR: 72.3-84.1) and 81.4 (75.4-86.3) years, respectively. On univariable analysis, PHS46 (HR 3.41 [95% CI 2.78-4.17]), family history (HR 1.72 [1.46-2.03]), alcohol (HR 1.74 [1.40-2.15]), diabetes (HR 0.53 [0.37-0.75]) were each associated with prostate cancer death. On multivariable analysis, PHS46 (HR 2.45 [1.99-2.97]), family history (HR 1.73 [1.48-2.03]), alcohol (HR 1.45 [1.19-1.76]), diabetes (HR 0.62 [0.42-0.90]) all remained associated with fatal disease. Including PHS46 or PHS166 improved multivariable models for fatal prostate cancer (p

Published
2021

13. Performance of African-ancestry-specific polygenic hazard score varies according to local ancestry in 8q24.

Author

Karunamuni, Roshan A, Huynh-Le, Minh-Phuong, Fan, Chun C, Thompson, Wesley, Lui, Asona, Martinez, Maria Elena, Rose, Brent S, Mahal, Brandon, Eeles, Rosalind A, Kote-Jarai, Zsofia, Muir, Kenneth, Lophatananon, Artitaya, UKGPCS Collaborators, Tangen, Catherine M, Goodman, Phyllis J, Thompson, Ian M, Blot, William J, Zheng, Wei, Kibel, Adam S, Drake, Bettina F, Cussenot, Olivier, Cancel-Tassin, Géraldine, Menegaux, Florence, Truong, Thérèse, Park, Jong Y, Lin, Hui-Yi, Taylor, Jack A, Bensen, Jeannette T, Mohler, James L, Fontham, Elizabeth TH, Multigner, Luc, Blanchet, Pascal, Brureau, Laurent, Romana, Marc, Leach, Robin J, John, Esther M, Fowke, Jay H, Bush, William S, Aldrich, Melinda C, Crawford, Dana C, Cullen, Jennifer, Petrovics, Gyorgy, Parent, Marie-Élise, Hu, Jennifer J, Sanderson, Maureen, PRACTICAL Consortium, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, and Seibert, Tyler M

Subjects
UKGPCS Collaborators, PRACTICAL Consortium, Urologic Diseases, Prevention, Genetics, Prostate Cancer, Cancer, Urology & Nephrology, Oncology and Carcinogenesis
Abstract

BackgroundWe previously developed an African-ancestry-specific polygenic hazard score (PHS46+African) that substantially improved prostate cancer risk stratification in men with African ancestry. The model consists of 46 SNPs identified in Europeans and 3 SNPs from 8q24 shown to improve model performance in Africans. Herein, we used principal component (PC) analysis to uncover subpopulations of men with African ancestry for whom the utility of PHS46+African may differ.Materials and methodsGenotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Genetic variation in a window spanning 3 African-specific 8q24 SNPs was estimated using 93 PCs. A Cox proportional hazards framework was used to identify the pair of PCs most strongly associated with the performance of PHS46+African. A calibration factor (CF) was formulated using Cox coefficients to quantify the extent to which the performance of PHS46+African varies with PC.ResultsCF of PHS46+African was strongly associated with the first and twentieth PCs. Predicted CF ranged from 0.41 to 2.94, suggesting that PHS46+African may be up to 7 times more beneficial to some African men than others. The explained relative risk for PHS46+African varied from 3.6% to 9.9% for individuals with low and high CF values, respectively. By cross-referencing our data set with 1000 Genomes, we identified significant associations between continental and calibration groupings.ConclusionWe identified PCs within 8q24 that were strongly associated with the performance of PHS46+African. Further research to improve the clinical utility of polygenic risk scores (or models) is needed to improve health outcomes for men of African ancestry.

Published
2021

14. Additional SNPs improve risk stratification of a polygenic hazard score for prostate cancer.

Author

Karunamuni, Roshan A, Huynh-Le, Minh-Phuong, Fan, Chun C, Thompson, Wesley, Eeles, Rosalind A, Kote-Jarai, Zsofia, Muir, Kenneth, Lophatananon, Artitaya, UKGPCS collaborators, Schleutker, Johanna, Pashayan, Nora, Batra, Jyotsna, APCB BioResource (Australian Prostate Cancer BioResource), Grönberg, Henrik, Walsh, Eleanor I, Turner, Emma L, Lane, Athene, Martin, Richard M, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Nordestgaard, Børge G, Tangen, Catherine M, MacInnis, Robert J, Wolk, Alicja, Albanes, Demetrius, Haiman, Christopher A, Travis, Ruth C, Stanford, Janet L, Mucci, Lorelei A, West, Catharine ML, Nielsen, Sune F, Kibel, Adam S, Wiklund, Fredrik, Cussenot, Olivier, Berndt, Sonja I, Koutros, Stella, Sørensen, Karina Dalsgaard, Cybulski, Cezary, Grindedal, Eli Marie, Park, Jong Y, Ingles, Sue A, Maier, Christiane, Hamilton, Robert J, Rosenstein, Barry S, Vega, Ana, IMPACT Study Steering Committee and Collaborators, Kogevinas, Manolis, Penney, Kathryn L, Teixeira, Manuel R, Brenner, Hermann, John, Esther M, Kaneva, Radka, Logothetis, Christopher J, Neuhausen, Susan L, Razack, Azad, Newcomb, Lisa F, Canary PASS Investigators, Gamulin, Marija, Usmani, Nawaid, Claessens, Frank, Gago-Dominguez, Manuela, Townsend, Paul A, Roobol, Monique J, Zheng, Wei, Profile Study Steering Committee, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, Seibert, Tyler M, and PRACTICAL Consortium

Subjects
UKGPCS collaborators, APCB BioResource, IMPACT Study Steering Committee and Collaborators, Canary PASS Investigators, Profile Study Steering Committee, PRACTICAL Consortium, Prevention, Urologic Diseases, Cancer, Prostate Cancer, Aging, Urology & Nephrology, Oncology and Carcinogenesis
Abstract

BackgroundPolygenic hazard scores (PHS) can identify individuals with increased risk of prostate cancer. We estimated the benefit of additional SNPs on performance of a previously validated PHS (PHS46).Materials and method180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.Results166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.ConclusionsIncorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.

Published
2021

15. Exploring the causal role of the immune response to varicella-zoster virus on multiple traits: a phenome-wide Mendelian randomization study

Author

Xinzhu Yu, Artitaya Lophatananon, Krisztina Mekli, Kenneth R. Muir, and Hui Guo

Subjects
Varicella- zoster virus, Anti-VZV IgG, Mendelian randomization, MR-PheWAS, MHC, Medicine
Abstract

Abstract Background The immune response to infections could be largely driven by the individual’s genes, especially in the major histocompatibility complex (MHC) region. Varicella-zoster virus (VZV) is a highly communicable pathogen. In addition to infection, the reactivations of VZV can be a potential causal factor for multiple traits. Identification of VZV immune response-related health conditions can therefore help elucidate the aetiology of certain diseases. Methods A phenome-wide Mendelian randomization (MR) study of anti-VZV immunoglobulin G (IgG) levels with 1370 traits was conducted to explore the potential causal role of VZV-specific immune response on multiple traits using the UK Biobank cohort. For the robustness of the results, we performed MR analyses using five different methods. To investigate the impact of the MHC region on MR results, the analyses were conducted using instrumental variables (IVs) inside (IVmhc) and outside (IVno.mhc) the MHC region or all together (IVfull). Results Forty-nine single nucleotide polymorphisms (IVfull) were associated with anti-VZV IgG levels, of which five (IVmhc) were located in the MHC region and 44 (IVno.mhc) were not. Statistical evidence (false discovery rate

Published
2023
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17. Gene-Environment Interactions Relevant to Estrogen and Risk of Breast Cancer: Can Gene-Environment Interactions Be Detected Only among Candidate SNPs from Genome-Wide Association Studies?

Author

Park, JooYong, Choi, Ji-Yeob, Choi, Jaesung, Chung, Seokang, Song, Nan, Park, Sue K, Han, Wonshik, Noh, Dong-Young, Ahn, Sei-Hyun, Lee, Jong Won, Kim, Mi Kyung, Jee, Sun Ha, Wen, Wanqing, Bolla, Manjeet K, Wang, Qin, Dennis, Joe, Michailidou, Kyriaki, Shah, Mitul, Conroy, Don M, Harrington, Patricia A, Mayes, Rebecca, Czene, Kamila, Hall, Per, Teras, Lauren R, Patel, Alpa V, Couch, Fergus J, Olson, Janet E, Sawyer, Elinor J, Roylance, Rebecca, Bojesen, Stig E, Flyger, Henrik, Lambrechts, Diether, Baten, Adinda, Matsuo, Keitaro, Ito, Hidemi, Guénel, Pascal, Truong, Thérèse, Keeman, Renske, Schmidt, Marjanka K, Wu, Anna H, Tseng, Chiu-Chen, Cox, Angela, Cross, Simon S, kConFab Investigators, Andrulis, Irene L, Hopper, John L, Southey, Melissa C, Wu, Pei-Ei, Shen, Chen-Yang, Fasching, Peter A, Ekici, Arif B, Muir, Kenneth, Lophatananon, Artitaya, Brenner, Hermann, Arndt, Volker, Jones, Michael E, Swerdlow, Anthony J, Hoppe, Reiner, Ko, Yon-Dschun, Hartman, Mikael, Li, Jingmei, Mannermaa, Arto, Hartikainen, Jaana M, Benitez, Javier, González-Neira, Anna, Haiman, Christopher A, Dörk, Thilo, Bogdanova, Natalia V, Teo, Soo Hwang, Mohd Taib, Nur Aishah, Fletcher, Olivia, Johnson, Nichola, Grip, Mervi, Winqvist, Robert, Blomqvist, Carl, Nevanlinna, Heli, Lindblom, Annika, Wendt, Camilla, Kristensen, Vessela N, Nbcs Collaborators, Tollenaar, Rob AEM, Heemskerk-Gerritsen, Bernadette AM, Radice, Paolo, Bonanni, Bernardo, Hamann, Ute, Manoochehri, Mehdi, Lacey, James V, Martinez, Maria Elena, Dunning, Alison M, Pharoah, Paul DP, Easton, Douglas F, Yoo, Keun-Young, and Kang, Daehee

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Women's Health, Clinical Research, Cancer, Estrogen, Breast Cancer, Human Genome, Genetics, Prevention, Aging, 2.1 Biological and endogenous factors, breast cancer, estrogen, gene-environment interaction, Oncology and carcinogenesis
Abstract

In this study we aim to examine gene-environment interactions (GxEs) between genes involved with estrogen metabolism and environmental factors related to estrogen exposure. GxE analyses were conducted with 1970 Korean breast cancer cases and 2052 controls in the case-control study, the Seoul Breast Cancer Study (SEBCS). A total of 11,555 SNPs from the 137 candidate genes were included in the GxE analyses with eight established environmental factors. A replication test was conducted by using an independent population from the Breast Cancer Association Consortium (BCAC), with 62,485 Europeans and 9047 Asians. The GxE tests were performed by using two-step methods in GxEScan software. Two interactions were found in the SEBCS. The first interaction was shown between rs13035764 of NCOA1 and age at menarche in the GE|2df model (p-2df = 1.2 × 10-3). The age at menarche before 14 years old was associated with the high risk of breast cancer, and the risk was higher when subjects had homozygous minor allele G. The second GxE was shown between rs851998 near ESR1 and height in the GE|2df model (p-2df = 1.1 × 10-4). Height taller than 160 cm was associated with a high risk of breast cancer, and the risk increased when the minor allele was added. The findings were not replicated in the BCAC. These results would suggest specificity in Koreans for breast cancer risk.

Published
2021

18. African-specific improvement of a polygenic hazard score for age at diagnosis of prostate cancer.

Author

Karunamuni, Roshan A, Huynh-Le, Minh-Phuong, Fan, Chun C, Thompson, Wesley, Eeles, Rosalind A, Kote-Jarai, Zsofia, Muir, Kenneth, UKGPCS Collaborators, Lophatananon, Artitaya, Tangen, Catherine M, Goodman, Phyllis J, Thompson, Ian M, Blot, William J, Zheng, Wei, Kibel, Adam S, Drake, Bettina F, Cussenot, Olivier, Cancel-Tassin, Géraldine, Menegaux, Florence, Truong, Thérèse, Park, Jong Y, Lin, Hui-Yi, Bensen, Jeannette T, Fontham, Elizabeth TH, Mohler, James L, Taylor, Jack A, Multigner, Luc, Blanchet, Pascal, Brureau, Laurent, Romana, Marc, Leach, Robin J, John, Esther M, Fowke, Jay, Bush, William S, Aldrich, Melinda, Crawford, Dana C, Srivastava, Shiv, Cullen, Jennifer C, Petrovics, Gyorgy, Parent, Marie-Élise, Hu, Jennifer J, Sanderson, Maureen, Mills, Ian G, Andreassen, Ole A, Dale, Anders M, Seibert, Tyler M, and PRACTICAL Consortium

Subjects
UKGPCS Collaborators, PRACTICAL Consortium, Humans, Prostatic Neoplasms, Genetic Predisposition to Disease, Proportional Hazards Models, Case-Control Studies, Age Factors, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Models, Genetic, Middle Aged, African Continental Ancestry Group, Male, Genotyping Techniques, African, genome wide association study, genomics, genotypic ancestry, health disparities, polygenic risk, prostate cancer, Aging, Genetics, Urologic Diseases, Cancer, Prostate Cancer, Prevention, Oncology & Carcinogenesis, Oncology and Carcinogenesis
Abstract

Polygenic hazard score (PHS) models are associated with age at diagnosis of prostate cancer. Our model developed in Europeans (PHS46) showed reduced performance in men with African genetic ancestry. We used a cross-validated search to identify single nucleotide polymorphisms (SNPs) that might improve performance in this population. Anonymized genotypic data were obtained from the PRACTICAL consortium for 6253 men with African genetic ancestry. Ten iterations of a 10-fold cross-validation search were conducted to select SNPs that would be included in the final PHS46+African model. The coefficients of PHS46+African were estimated in a Cox proportional hazards framework using age at diagnosis as the dependent variable and PHS46, and selected SNPs as predictors. The performance of PHS46 and PHS46+African was compared using the same cross-validated approach. Three SNPs (rs76229939, rs74421890 and rs5013678) were selected for inclusion in PHS46+African. All three SNPs are located on chromosome 8q24. PHS46+African showed substantial improvements in all performance metrics measured, including a 75% increase in the relative hazard of those in the upper 20% compared to the bottom 20% (2.47-4.34) and a 20% reduction in the relative hazard of those in the bottom 20% compared to the middle 40% (0.65-0.53). In conclusion, we identified three SNPs that substantially improved the association of PHS46 with age at diagnosis of prostate cancer in men with African genetic ancestry to levels comparable to Europeans.

Published
2021

19. Strengthening the Evidence for a Causal Link between Type 2 Diabetes Mellitus and Pancreatic Cancer: Insights from Two-Sample and Multivariable Mendelian Randomization

Author

Te-Min Ke, Artitaya Lophatananon, and Kenneth R. Muir

Subjects
two-sample Mendelian randomization, type 2 diabetes mellitus, pancreatic cancer, FinnGen, UK Biobank, Biology (General), QH301-705.5, Chemistry, QD1-999
Abstract

This two-sample Mendelian randomization (MR) study was conducted to investigate the causal associations between type 2 diabetes mellitus (T2DM) and the risk of pancreatic cancer (PaCa), as this causal relationship remains inconclusive in existing MR studies. The selection of instrumental variables for T2DM was based on two genome-wide association study (GWAS) meta-analyses from European cohorts. Summary-level data for PaCa were extracted from the FinnGen and UK Biobank databases. Inverse variance weighted (IVW) and four other robust methods were employed in our MR analysis. Various sensitivity analyses and multivariable MR approaches were also performed to enhance the robustness of our findings. In the IVW and Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) analyses, the odds ratios (ORs) for each 1-unit increase in genetically predicted log odds of T2DM were approximately 1.13 for PaCa. The sensitivity tests and multivariable MR supported the causal link between T2DM and PaCa without pleiotropic effects. Therefore, our analyses suggest a causal relationship between T2DM and PaCa, shedding light on the potential pathophysiological mechanisms of T2DM’s impact on PaCa. This finding underscores the importance of T2DM prevention as a strategy to reduce the risk of PaCa.

Published
2024
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20. Germline HOXB13 mutations p.G84E and p.R217C do not confer an increased breast cancer risk.

Author

Liu, Jingjing, Prager-van der Smissen, Wendy JC, Collée, J Margriet, Bolla, Manjeet K, Wang, Qin, Michailidou, Kyriaki, Dennis, Joe, Ahearn, Thomas U, Aittomäki, Kristiina, Ambrosone, Christine B, Andrulis, Irene L, Anton-Culver, Hoda, Antonenkova, Natalia N, Arndt, Volker, Arnold, Norbert, Aronson, Kristan J, Augustinsson, Annelie, Auvinen, Päivi, Becher, Heiko, Beckmann, Matthias W, Behrens, Sabine, Bermisheva, Marina, Bernstein, Leslie, Bogdanova, Natalia V, Bogdanova-Markov, Nadja, Bojesen, Stig E, Brauch, Hiltrud, Brenner, Hermann, Briceno, Ignacio, Brucker, Sara Y, Brüning, Thomas, Burwinkel, Barbara, Cai, Qiuyin, Cai, Hui, Campa, Daniele, Canzian, Federico, Castelao, Jose E, Chang-Claude, Jenny, Chanock, Stephen J, Choi, Ji-Yeob, Christiaens, Melissa, Clarke, Christine L, NBCS Collaborators, Couch, Fergus J, Czene, Kamila, Daly, Mary B, Devilee, Peter, Dos-Santos-Silva, Isabel, Dwek, Miriam, Eccles, Diana M, Eliassen, A Heather, Fasching, Peter A, Figueroa, Jonine, Flyger, Henrik, Fritschi, Lin, Gago-Dominguez, Manuela, Gapstur, Susan M, García-Closas, Montserrat, García-Sáenz, José A, Gaudet, Mia M, Giles, Graham G, Goldberg, Mark S, Goldgar, David E, Guénel, Pascal, Haiman, Christopher A, Håkansson, Niclas, Hall, Per, Harrington, Patricia A, Hart, Steven N, Hartman, Mikael, Hillemanns, Peter, Hopper, John L, Hou, Ming-Feng, Hunter, David J, Huo, Dezheng, ABCTB Investigators, Ito, Hidemi, Iwasaki, Motoki, Jakimovska, Milena, Jakubowska, Anna, John, Esther M, Kaaks, Rudolf, Kang, Daehee, Keeman, Renske, Khusnutdinova, Elza, Kim, Sung-Won, Kraft, Peter, Kristensen, Vessela N, Kurian, Allison W, Le Marchand, Loic, Li, Jingmei, Lindblom, Annika, Lophatananon, Artitaya, Luben, Robert N, Lubiński, Jan, Mannermaa, Arto, Manoochehri, Mehdi, Manoukian, Siranoush, Margolin, Sara, and Mariapun, Shivaani

Subjects
NBCS Collaborators, ABCTB Investigators
Abstract

In breast cancer, high levels of homeobox protein Hox-B13 (HOXB13) have been associated with disease progression of ER-positive breast cancer patients and resistance to tamoxifen treatment. Since HOXB13 p.G84E is a prostate cancer risk allele, we evaluated the association between HOXB13 germline mutations and breast cancer risk in a previous study consisting of 3,270 familial non-BRCA1/2 breast cancer cases and 2,327 controls from the Netherlands. Although both recurrent HOXB13 mutations p.G84E and p.R217C were not associated with breast cancer risk, the risk estimation for p.R217C was not very precise. To provide more conclusive evidence regarding the role of HOXB13 in breast cancer susceptibility, we here evaluated the association between HOXB13 mutations and increased breast cancer risk within 81 studies of the international Breast Cancer Association Consortium containing 68,521 invasive breast cancer patients and 54,865 controls. Both HOXB13 p.G84E and p.R217C did not associate with the development of breast cancer in European women, neither in the overall analysis (OR = 1.035, 95% CI = 0.859-1.246, P = 0.718 and OR = 0.798, 95% CI = 0.482-1.322, P = 0.381 respectively), nor in specific high-risk subgroups or breast cancer subtypes. Thus, although involved in breast cancer progression, HOXB13 is not a material breast cancer susceptibility gene.

Published
2020

21. Aggregation tests identify new gene associations with breast cancer in populations with diverse ancestry

Author

Stefanie H. Mueller, Alvina G. Lai, Maria Valkovskaya, Kyriaki Michailidou, Manjeet K. Bolla, Qin Wang, Joe Dennis, Michael Lush, Zomoruda Abu-Ful, Thomas U. Ahearn, Irene L. Andrulis, Hoda Anton-Culver, Natalia N. Antonenkova, Volker Arndt, Kristan J. Aronson, Annelie Augustinsson, Thais Baert, Laura E. Beane Freeman, Matthias W. Beckmann, Sabine Behrens, Javier Benitez, Marina Bermisheva, Carl Blomqvist, Natalia V. Bogdanova, Stig E. Bojesen, Bernardo Bonanni, Hermann Brenner, Sara Y. Brucker, Saundra S. Buys, Jose E. Castelao, Tsun L. Chan, Jenny Chang-Claude, Stephen J. Chanock, Ji-Yeob Choi, Wendy K. Chung, NBCS Collaborators, Sarah V. Colonna, CTS Consortium, Sten Cornelissen, Fergus J. Couch, Kamila Czene, Mary B. Daly, Peter Devilee, Thilo Dörk, Laure Dossus, Miriam Dwek, Diana M. Eccles, Arif B. Ekici, A. Heather Eliassen, Christoph Engel, D. Gareth Evans, Peter A. Fasching, Olivia Fletcher, Henrik Flyger, Manuela Gago-Dominguez, Yu-Tang Gao, Montserrat García-Closas, José A. García-Sáenz, Jeanine Genkinger, Aleksandra Gentry-Maharaj, Felix Grassmann, Pascal Guénel, Melanie Gündert, Lothar Haeberle, Eric Hahnen, Christopher A. Haiman, Niclas Håkansson, Per Hall, Elaine F. Harkness, Patricia A. Harrington, Jaana M. Hartikainen, Mikael Hartman, Alexander Hein, Weang-Kee Ho, Maartje J. Hooning, Reiner Hoppe, John L. Hopper, Richard S. Houlston, Anthony Howell, David J. Hunter, Dezheng Huo, ABCTB Investigators, Hidemi Ito, Motoki Iwasaki, Anna Jakubowska, Wolfgang Janni, Esther M. John, Michael E. Jones, Audrey Jung, Rudolf Kaaks, Daehee Kang, Elza K. Khusnutdinova, Sung-Won Kim, Cari M. Kitahara, Stella Koutros, Peter Kraft, Vessela N. Kristensen, Katerina Kubelka-Sabit, Allison W. Kurian, Ava Kwong, James V. Lacey, Diether Lambrechts, Loic Le Marchand, Jingmei Li, Martha Linet, Wing-Yee Lo, Jirong Long, Artitaya Lophatananon, Arto Mannermaa, Mehdi Manoochehri, Sara Margolin, Keitaro Matsuo, Dimitrios Mavroudis, Usha Menon, Kenneth Muir, Rachel A. Murphy, Heli Nevanlinna, William G. Newman, Dieter Niederacher, Katie M. O’Brien, Nadia Obi, Kenneth Offit, Olufunmilayo I. Olopade, Andrew F. Olshan, Håkan Olsson, Sue K. Park, Alpa V. Patel, Achal Patel, Charles M. Perou, Julian Peto, Paul D. P. Pharoah, Dijana Plaseska-Karanfilska, Nadege Presneau, Brigitte Rack, Paolo Radice, Dhanya Ramachandran, Muhammad U. Rashid, Gad Rennert, Atocha Romero, Kathryn J. Ruddy, Matthias Ruebner, Emmanouil Saloustros, Dale P. Sandler, Elinor J. Sawyer, Marjanka K. Schmidt, Rita K. Schmutzler, Michael O. Schneider, Christopher Scott, Mitul Shah, Priyanka Sharma, Chen-Yang Shen, Xiao-Ou Shu, Jacques Simard, Harald Surowy, Rulla M. Tamimi, William J. Tapper, Jack A. Taylor, Soo Hwang Teo, Lauren R. Teras, Amanda E. Toland, Rob A. E. M. Tollenaar, Diana Torres, Gabriela Torres-Mejía, Melissa A. Troester, Thérèse Truong, Celine M. Vachon, Joseph Vijai, Clarice R. Weinberg, Camilla Wendt, Robert Winqvist, Alicja Wolk, Anna H. Wu, Taiki Yamaji, Xiaohong R. Yang, Jyh-Cherng Yu, Wei Zheng, Argyrios Ziogas, Elad Ziv, Alison M. Dunning, Douglas F. Easton, Harry Hemingway, Ute Hamann, and Karoline B. Kuchenbaecker

Subjects
Breast cancer susceptibility, Diverse ancestry, Rare variants, Gene regulation, Genome-wide association study, Medicine, Genetics, QH426-470
Abstract

Abstract Background Low-frequency variants play an important role in breast cancer (BC) susceptibility. Gene-based methods can increase power by combining multiple variants in the same gene and help identify target genes. Methods We evaluated the potential of gene-based aggregation in the Breast Cancer Association Consortium cohorts including 83,471 cases and 59,199 controls. Low-frequency variants were aggregated for individual genes’ coding and regulatory regions. Association results in European ancestry samples were compared to single-marker association results in the same cohort. Gene-based associations were also combined in meta-analysis across individuals with European, Asian, African, and Latin American and Hispanic ancestry. Results In European ancestry samples, 14 genes were significantly associated (q

Published
2023
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23. Relevance of the MHC region for breast cancer susceptibility in Asians

Author

Ho, Peh Joo, Khng, Alexis Jiaying, Tan, Benita Kiat-Tee, Tan, Ern Yu, Tan, Su-Ming, Tan, Veronique Kiak Mien, Lim, Geok Hoon, Aronson, Kristan J., Chan, Tsun L., Choi, Ji-Yeob, Dennis, Joe, Ho, Weang-Kee, Hou, Ming-Feng, Ito, Hidemi, Iwasaki, Motoki, John, Esther M., Kang, Daehee, Kim, Sung-Won, Kurian, Allison W., Kwong, Ava, Lophatananon, Artitaya, Matsuo, Keitaro, Mohd-Taib, Nur Aishah, Muir, Kenneth, Murphy, Rachel A., Park, Sue K., Shen, Chen-Yang, Shu, Xiao-Ou, Teo, Soo Hwang, Wang, Qin, Yamaji, Taiki, Zheng, Wei, Bolla, Manjeet K., Dunning, Alison M., Easton, Douglas F., Pharoah, Paul D. P., Hartman, Mikael, and Li, Jingmei

Published
2022
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24. An Integrative Pancreatic Cancer Risk Prediction Model in the UK Biobank

Author

Te-Min Ke, Artitaya Lophatananon, and Kenneth R. Muir

Subjects
pancreatic cancer, polygenic score, risk prediction model, nomogram, random forest model, UK Biobank cohort, Biology (General), QH301-705.5
Abstract

Pancreatic cancer (PaCa) is a lethal cancer with an increasing incidence, highlighting the need for early prevention strategies. There is a lack of a comprehensive PaCa predictive model derived from large prospective cohorts. Therefore, we have developed an integrated PaCa risk prediction model for PaCa using data from the UK Biobank, incorporating lifestyle-related, genetic-related, and medical history-related variables for application in healthcare settings. We used a machine learning-based random forest approach and a traditional multivariable logistic regression method to develop a PaCa predictive model for different purposes. Additionally, we employed dynamic nomograms to visualize the probability of PaCa risk in the prediction model. The top five influential features in the random forest model were age, PRS, pancreatitis, DM, and smoking. The significant risk variables in the logistic regression model included male gender (OR = 1.17), age (OR = 1.10), non-O blood type (OR = 1.29), higher polygenic score (PRS) (Q5 vs. Q1, OR = 2.03), smoking (OR = 1.82), alcohol consumption (OR = 1.27), pancreatitis (OR = 3.99), diabetes (DM) (OR = 2.57), and gallbladder-related disease (OR = 2.07). The area under the receiver operating curve (AUC) of the logistic regression model is 0.78. Internal validation and calibration performed well in both models. Our integrative PaCa risk prediction model with the PRS effectively stratifies individuals at future risk of PaCa, aiding targeted prevention efforts and supporting community-based cancer prevention initiatives.

Published
2023
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25. Two truncating variants in FANCC and breast cancer risk.

Author

Dörk, Thilo, Peterlongo, Paolo, Mannermaa, Arto, Bolla, Manjeet K, Wang, Qin, Dennis, Joe, Ahearn, Thomas, Andrulis, Irene L, Anton-Culver, Hoda, Arndt, Volker, Aronson, Kristan J, Augustinsson, Annelie, Freeman, Laura E Beane, Beckmann, Matthias W, Beeghly-Fadiel, Alicia, Behrens, Sabine, Bermisheva, Marina, Blomqvist, Carl, Bogdanova, Natalia V, Bojesen, Stig E, Brauch, Hiltrud, Brenner, Hermann, Burwinkel, Barbara, Canzian, Federico, Chan, Tsun L, Chang-Claude, Jenny, Chanock, Stephen J, Choi, Ji-Yeob, Christiansen, Hans, Clarke, Christine L, Couch, Fergus J, Czene, Kamila, Daly, Mary B, Dos-Santos-Silva, Isabel, Dwek, Miriam, Eccles, Diana M, Ekici, Arif B, Eriksson, Mikael, Evans, D Gareth, Fasching, Peter A, Figueroa, Jonine, Flyger, Henrik, Fritschi, Lin, Gabrielson, Marike, Gago-Dominguez, Manuela, Gao, Chi, Gapstur, Susan M, García-Closas, Montserrat, García-Sáenz, José A, Gaudet, Mia M, Giles, Graham G, Goldberg, Mark S, Goldgar, David E, Guénel, Pascal, Haeberle, Lothar, Haiman, Christopher A, Håkansson, Niclas, Hall, Per, Hamann, Ute, Hartman, Mikael, Hauke, Jan, Hein, Alexander, Hillemanns, Peter, Hogervorst, Frans BL, Hooning, Maartje J, Hopper, John L, Howell, Tony, Huo, Dezheng, Ito, Hidemi, Iwasaki, Motoki, Jakubowska, Anna, Janni, Wolfgang, John, Esther M, Jung, Audrey, Kaaks, Rudolf, Kang, Daehee, Kapoor, Pooja Middha, Khusnutdinova, Elza, Kim, Sung-Won, Kitahara, Cari M, Koutros, Stella, Kraft, Peter, Kristensen, Vessela N, Kwong, Ava, Lambrechts, Diether, Marchand, Loic Le, Li, Jingmei, Lindström, Sara, Linet, Martha, Lo, Wing-Yee, Long, Jirong, Lophatananon, Artitaya, Lubiński, Jan, Manoochehri, Mehdi, Manoukian, Siranoush, Margolin, Sara, Martinez, Elena, Matsuo, Keitaro, Mavroudis, Dimitris, and Meindl, Alfons

Subjects
ABCTB Investigators, NBCS Collaborators, Humans, Breast Neoplasms, Fanconi Anemia, Genetic Predisposition to Disease, BRCA1 Protein, BRCA2 Protein, Case-Control Studies, Sequence Deletion, Female, Fanconi Anemia Complementation Group C Protein, Genetic Variation, Clinical Research, Breast Cancer, Genetics, Cancer, 2.1 Biological and endogenous factors, Biochemistry and Cell Biology, Other Physical Sciences
Abstract

Fanconi anemia (FA) is a genetically heterogeneous disorder with 22 disease-causing genes reported to date. In some FA genes, monoallelic mutations have been found to be associated with breast cancer risk, while the risk associations of others remain unknown. The gene for FA type C, FANCC, has been proposed as a breast cancer susceptibility gene based on epidemiological and sequencing studies. We used the Oncoarray project to genotype two truncating FANCC variants (p.R185X and p.R548X) in 64,760 breast cancer cases and 49,793 controls of European descent. FANCC mutations were observed in 25 cases (14 with p.R185X, 11 with p.R548X) and 26 controls (18 with p.R185X, 8 with p.R548X). There was no evidence of an association with the risk of breast cancer, neither overall (odds ratio 0.77, 95%CI 0.44-1.33, p = 0.4) nor by histology, hormone receptor status, age or family history. We conclude that the breast cancer risk association of these two FANCC variants, if any, is much smaller than for BRCA1, BRCA2 or PALB2 mutations. If this applies to all truncating variants in FANCC it would suggest there are differences between FA genes in their roles on breast cancer risk and demonstrates the merit of large consortia for clarifying risk associations of rare variants.

Published
2019

26. Reasons for Discontinuing Active Surveillance: Assessment of 21 Centres in 12 Countries in the Movember GAP3 Consortium

Author

Van Hemelrijck, Mieke, Ji, Xi, Helleman, Jozien, Roobol, Monique J, van der Linden, Wim, Nieboer, Daan, Bangma, Chris H, Frydenberg, Mark, Rannikko, Antti, Lee, Lui S, Gnanapragasam, Vincent J, Kattan, Mike W, Trock, Bruce, Ehdaie, Behfar, Carroll, Peter, Filson, Christopher, Kim, Jeri, Logothetis, Christopher, Morgan, Todd, Klotz, Laurence, Pickles, Tom, Hyndman, Eric, Moore, Caroline M, Gnanapragasam, Vincent, Dasgupta, Prokar, Bangma, Chris, Roobol, Monique, Villers, Arnauld, Valdagni, Riccardo, Perry, Antoinette, Hugosson, Jonas, Rubio-Briones, Jose, Bjartell, Anders, Hefermehl, Lukas, Shiong, Lee Lui, Kakehi, Yoshiyuki, Chung, Byung Ha, van der Kwast, Theo, Obbink, Henk, Hulsen, Tim, de Jonge, Cees, Kattan, Mike, Xinge, Ji, Muir, Kenneth, Lophatananon, Artitaya, Fahey, Michael, Steyerberg, Ewout, Zhang, Liying, Santa Olalla, Aida, Beckmann, Kerri, Denton, Brian, Hayen, Andrew, Boutros, Paul, Guo, Wei, Benfante, Nicole, Cowan, Janet, Patil, Dattatraya, Tolosa, Emily, Kim, Tae-Kyung, Mamedov, Alexandre, La Pointe, Vincent, Crump, Trafford, Kimberly-Duffell, Jenna, Santaolalla, Aida, Olivier, Jonathan, Rancati, Tiziana, Ahlgren, Helén, Mascarós, Juanma, Löfgren, Annica, Lehmann, Kurt, Lin, Catherine Han, Hirama, Hiromi, Lee, Kwang Suk, Jenster, Guido, Auvinen, Anssi, Haider, Masoom, van Bochove, Kees, Carter, Ballentine, Gledhill, Sam, Buzza, Mark, and Bruinsma, Sophie

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Oncology and Carcinogenesis, Prostate Cancer, Aging, Prevention, Urologic Diseases, Cancer, Good Health and Well Being, Aged, Asia, Australia, Biopsy, Cause of Death, Clinical Decision-Making, Databases, Factual, Disease Progression, Early Detection of Cancer, Europe, Humans, Kallikreins, Male, Middle Aged, North America, Patient Dropouts, Predictive Value of Tests, Prostate-Specific Antigen, Prostatic Neoplasms, Risk Assessment, Risk Factors, Time Factors, Watchful Waiting, Prostate cancer, Active surveillance, Discontinuation, Worldwide, Members of the Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance GAP3 consortium, Urology & Nephrology, Clinical sciences
Abstract

BackgroundCareful assessment of the reasons for discontinuation of active surveillance (AS) is required for men with prostate cancer (PCa).ObjectiveUsing Movember's Global Action Plan Prostate Cancer Active Surveillance initiative (GAP3) database, we report on reasons for AS discontinuation.Design, setting, and participantsWe compared data from 10296 men on AS from 21 centres across 12 countries.Outcome measurements and statistical analysisCumulative incidence methods were used to estimate the cumulative incidence rates of AS discontinuation.Results and limitationsDuring 5-yr follow-up, 27.5% (95% confidence interval [CI]: 26.4-28.6%) men showed signs of disease progression, 12.8% (95% CI: 12.0-13.6%) converted to active treatment without evidence of progression, 1.7% (95% CI: 1.5-2.0%) continued to watchful waiting, and 1.7% (95% CI: 1.4-2.1%) died from other causes. Of the 7049 men who remained on AS, 2339 had follow-up for >5yr, 4561 had follow-up for

Published
2019

27. Assessing the impact of MRI based diagnostics on pre-treatment disease classification and prognostic model performance in men diagnosed with new prostate cancer from an unscreened population

Author

Artitaya Lophatananon, Matthew H. V. Byrne, Tristan Barrett, Anne Warren, Kenneth Muir, Ibifuro Dokubo, Fanos Georgiades, Mostafa Sheba, Lisa Bibby, and Vincent J. Gnanapragasam

Subjects
Prostate cancer, Prognostic groups, MRI Reclassification, Overall survival, Cancer specific survival, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Introduction Pre-treatment risk and prognostic groups are the cornerstone for deciding management in non-metastatic prostate cancer. All however, were developed in the pre-MRI era. Here we compared categorisation of cancers using either only clinical parameters or with MRI enhanced information in men referred for suspected prostate cancer from an unscreened population. Patient and methods Data from men referred from primary care to our diagnostic service and with both clinical (digital rectal examination [DRE] and systematic biopsies) and MRI enhanced attributes (MRI stage and combined systematic/targeted biopsies) were used for this study. Clinical vs MRI data were contrasted for clinico-pathological and risk group re-distribution using the European Association of Urology (EAU), American Urological Association (AUA) and UK National Institute for Health Care Excellence (NICE) Cambridge Prognostic Group (CPG) models. Differences were retrofitted to a population cohort with long-term prostate cancer mortality (PCM) outcomes to simulate impact on model performance. We further contrasted individualised overall survival (OS) predictions using the Predict Prostate algorithm. Results Data from 370 men were included (median age 66y). Pre-biopsy MRI stage reassignments occurred in 7.8% (versus DRE). Image-guided biopsies increased Grade Group 2 and ≥ Grade Group 3 assignments in 2.7% and 2.9% respectively. The main change in risk groups was more high-risk cancers (6.2% increase in the EAU and AUA system, 4.3% increase in CPG4 and 1.9% CPG5). When extrapolated to a historical population-based cohort (n = 10,139) the redistribution resulted in generally lower concordance indices for PCM. The 5-tier NICE-CPG system outperformed the 4-tier AUA and 3-tier EAU models (C Index 0.70 versus 0.65 and 0.64). Using an individualised prognostic model, changes in predicted OS were small (median difference 1% and 2% at 10- and 15-years’ respectively). Similarly, estimated treatment survival benefit changes were minimal (1% at both 10- and 15-years’ time frame). Conclusion MRI guided diagnostics does change pre-treatment risk groups assignments but the overall prognostic impact appears modest in men referred from unscreened populations. Particularly, when using more granular tiers or individualised prognostic models. Existing risk and prognostic models can continue to be used to counsel men about treatment option until long term survival outcomes are available.

Published
2022
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29. Breast cancer risks associated with missense variants in breast cancer susceptibility genes

Author

Dorling, Leila, Carvalho, Sara, Allen, Jamie, Parsons, Michael T., Fortuno, Cristina, González-Neira, Anna, Heijl, Stephan M., Adank, Muriel A., Ahearn, Thomas U., Andrulis, Irene L., Auvinen, Päivi, Becher, Heiko, Beckmann, Matthias W., Behrens, Sabine, Bermisheva, Marina, Bogdanova, Natalia V., Bojesen, Stig E., Bolla, Manjeet K., Bremer, Michael, Briceno, Ignacio, Camp, Nicola J., Campbell, Archie, Castelao, Jose E., Chang-Claude, Jenny, Chanock, Stephen J., Chenevix-Trench, Georgia, Collée, J. Margriet, Czene, Kamila, Dennis, Joe, Dörk, Thilo, Eriksson, Mikael, Evans, D. Gareth, Fasching, Peter A., Figueroa, Jonine, Flyger, Henrik, Gabrielson, Marike, Gago-Dominguez, Manuela, García-Closas, Montserrat, Giles, Graham G., Glendon, Gord, Guénel, Pascal, Gündert, Melanie, Hadjisavvas, Andreas, Hahnen, Eric, Hall, Per, Hamann, Ute, Harkness, Elaine F., Hartman, Mikael, Hogervorst, Frans B. L., Hollestelle, Antoinette, Hoppe, Reiner, Howell, Anthony, Jakubowska, Anna, Jung, Audrey, Khusnutdinova, Elza, Kim, Sung-Won, Ko, Yon-Dschun, Kristensen, Vessela N., Lakeman, Inge M. M., Li, Jingmei, Lindblom, Annika, Loizidou, Maria A., Lophatananon, Artitaya, Lubiński, Jan, Luccarini, Craig, Madsen, Michael J., Mannermaa, Arto, Manoochehri, Mehdi, Margolin, Sara, Mavroudis, Dimitrios, Milne, Roger L., Mohd Taib, Nur Aishah, Muir, Kenneth, Nevanlinna, Heli, Newman, William G., Oosterwijk, Jan C., Park, Sue K., Peterlongo, Paolo, Radice, Paolo, Saloustros, Emmanouil, Sawyer, Elinor J., Schmutzler, Rita K., Shah, Mitul, Sim, Xueling, Southey, Melissa C., Surowy, Harald, Suvanto, Maija, Tomlinson, Ian, Torres, Diana, Truong, Thérèse, van Asperen, Christi J., Waltes, Regina, Wang, Qin, Yang, Xiaohong R., Pharoah, Paul D. P., Schmidt, Marjanka K., Benitez, Javier, Vroling, Bas, Dunning, Alison M., Teo, Soo Hwang, Kvist, Anders, de la Hoya, Miguel, Devilee, Peter, Spurdle, Amanda B., Vreeswijk, Maaike P. G., and Easton, Douglas F.

Published
2022
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31. Breast cancer risks associated with missense variants in breast cancer susceptibility genes

Author

Leila Dorling, Sara Carvalho, Jamie Allen, Michael T. Parsons, Cristina Fortuno, Anna González-Neira, Stephan M. Heijl, Muriel A. Adank, Thomas U. Ahearn, Irene L. Andrulis, Päivi Auvinen, Heiko Becher, Matthias W. Beckmann, Sabine Behrens, Marina Bermisheva, Natalia V. Bogdanova, Stig E. Bojesen, Manjeet K. Bolla, Michael Bremer, Ignacio Briceno, Nicola J. Camp, Archie Campbell, Jose E. Castelao, Jenny Chang-Claude, Stephen J. Chanock, Georgia Chenevix-Trench, NBCS Collaborators, J. Margriet Collée, Kamila Czene, Joe Dennis, Thilo Dörk, Mikael Eriksson, D. Gareth Evans, Peter A. Fasching, Jonine Figueroa, Henrik Flyger, Marike Gabrielson, Manuela Gago-Dominguez, Montserrat García-Closas, Graham G. Giles, Gord Glendon, Pascal Guénel, Melanie Gündert, Andreas Hadjisavvas, Eric Hahnen, Per Hall, Ute Hamann, Elaine F. Harkness, Mikael Hartman, Frans B. L. Hogervorst, Antoinette Hollestelle, Reiner Hoppe, Anthony Howell, kConFab Investigators, SGBCC Investigators, Anna Jakubowska, Audrey Jung, Elza Khusnutdinova, Sung-Won Kim, Yon-Dschun Ko, Vessela N. Kristensen, Inge M. M. Lakeman, Jingmei Li, Annika Lindblom, Maria A. Loizidou, Artitaya Lophatananon, Jan Lubiński, Craig Luccarini, Michael J. Madsen, Arto Mannermaa, Mehdi Manoochehri, Sara Margolin, Dimitrios Mavroudis, Roger L. Milne, Nur Aishah Mohd Taib, Kenneth Muir, Heli Nevanlinna, William G. Newman, Jan C. Oosterwijk, Sue K. Park, Paolo Peterlongo, Paolo Radice, Emmanouil Saloustros, Elinor J. Sawyer, Rita K. Schmutzler, Mitul Shah, Xueling Sim, Melissa C. Southey, Harald Surowy, Maija Suvanto, Ian Tomlinson, Diana Torres, Thérèse Truong, Christi J. van Asperen, Regina Waltes, Qin Wang, Xiaohong R. Yang, Paul D. P. Pharoah, Marjanka K. Schmidt, Javier Benitez, Bas Vroling, Alison M. Dunning, Soo Hwang Teo, Anders Kvist, Miguel de la Hoya, Peter Devilee, Amanda B. Spurdle, Maaike P. G. Vreeswijk, and Douglas F. Easton

Subjects
Breast cancer, Genetic epidemiology, Risk prediction, Missense variants, Medicine, Genetics, QH426-470
Abstract

Abstract 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.

Published
2022
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32. Exploring the Relationships between Lifestyle Patterns and Epigenetic Biological Age Measures in Men.

Author

Ke, Te-Min, Lophatananon, Artitaya, and Muir, Kenneth R.

Subjects
PEARSON correlation (Statistics), BODY mass index, BIOLOGICAL rhythms, DNA methylation, VITAMIN D
Abstract

DNA methylation, validated as a surrogate for biological age, is a potential tool for predicting future morbidity and mortality outcomes. This study aims to explore how lifestyle patterns are associated with epigenetic changes in British men. Five biological age clocks were utilised to investigate the relationship between these epigenetic markers and lifestyle-related factors in a prospective study involving 221 participants. Spearman's correlation test, Pearson's correlation test, and univariate linear regression were employed for analysis. The results indicate that higher consumption of saturated fat and total daily calories, and a higher body mass index (BMI) are associated with accelerated biological aging. Conversely, higher vitamin D intake and a higher healthy lifestyle index (HLI) are linked to decelerated biological aging. These findings highlight the potential impact of specific lifestyle-related factors on biological aging and can serve as a reference for applying healthy lifestyle improvements in future disease prevention studies. [ABSTRACT FROM AUTHOR]

Published
2024
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33. iHELP: Personalised Health Monitoring and Decision Support Based on Artificial Intelligence and Holistic Health Records.

Author

George Manias, Harm op den Akker, Ainhoa Azqueta-Alzúaz, Diego Burgos-Sancho, Nikola Dino Capocchiano, Borja Llobell Crespo, Athanasios Dalianis, Andrea Damiani, Krasimir Filipov, Giorgos Giotis, Maritini Kalogerini, Rostislav Kostadinov, Pavlos Kranas, Dimosthenis Kyriazis, Artitaya Lophatananon, Shwetambara Malwade, George Marinos, Fabio Melillo, Vicent Moncho Mas, Kenneth Muir, Marzena Nieroda, Antonio De Nigro, Claudia Pandolfo, Marta Patiño-Martínez, Florin Picioroaga, Aristodemos Pnevmatikakis, Shabbir Syed-Abdul, Tanja Tomson, Dilyana Vicheva, and Usman Wajid

Published
2021
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34. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci

Author

Schumacher, Fredrick R, Al Olama, Ali Amin, Berndt, Sonja I, Benlloch, Sara, Ahmed, Mahbubl, Saunders, Edward J, Dadaev, Tokhir, Leongamornlert, Daniel, Anokian, Ezequiel, Cieza-Borrella, Clara, Goh, Chee, Brook, Mark N, Sheng, Xin, Fachal, Laura, Dennis, Joe, Tyrer, Jonathan, Muir, Kenneth, Lophatananon, Artitaya, Stevens, Victoria L, Gapstur, Susan M, Carter, Brian D, Tangen, Catherine M, Goodman, Phyllis J, Thompson, Ian M, Batra, Jyotsna, Chambers, Suzanne, Moya, Leire, Clements, Judith, Horvath, Lisa, Tilley, Wayne, Risbridger, Gail P, Gronberg, Henrik, Aly, Markus, Nordström, Tobias, Pharoah, Paul, Pashayan, Nora, Schleutker, Johanna, Tammela, Teuvo LJ, Sipeky, Csilla, Auvinen, Anssi, Albanes, Demetrius, Weinstein, Stephanie, Wolk, Alicja, Håkansson, Niclas, West, Catharine ML, Dunning, Alison M, Burnet, Neil, Mucci, Lorelei A, Giovannucci, Edward, Andriole, Gerald L, Cussenot, Olivier, Cancel-Tassin, Géraldine, Koutros, Stella, Beane Freeman, Laura E, Sorensen, Karina Dalsgaard, Orntoft, Torben Falck, Borre, Michael, Maehle, Lovise, Grindedal, Eli Marie, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Martin, Richard M, Travis, Ruth C, Key, Tim J, Hamilton, Robert J, Fleshner, Neil E, Finelli, Antonio, Ingles, Sue Ann, Stern, Mariana C, Rosenstein, Barry S, Kerns, Sarah L, Ostrer, Harry, Lu, Yong-Jie, Zhang, Hong-Wei, Feng, Ninghan, Mao, Xueying, Guo, Xin, Wang, Guomin, Sun, Zan, Giles, Graham G, Southey, Melissa C, MacInnis, Robert J, FitzGerald, Liesel M, Kibel, Adam S, Drake, Bettina F, Vega, Ana, Gómez-Caamaño, Antonio, Szulkin, Robert, Eklund, Martin, Kogevinas, Manolis, Llorca, Javier, Castaño-Vinyals, Gemma, Penney, Kathryn L, Stampfer, Meir, Park, Jong Y, Sellers, Thomas A, Lin, Hui-Yi, Stanford, Janet L, and Cybulski, Cezary

Subjects
Cancer, Aging, Prostate Cancer, Genetics, Urologic Diseases, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Case-Control Studies, Genetic Loci, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Male, Polymorphism, Single Nucleotide, Prostatic Neoplasms, Risk, Profile Study, Australian Prostate Cancer BioResource, IMPACT Study, Canary PASS Investigators, Breast and Prostate Cancer Cohort Consortium, PRACTICAL (Prostate Cancer Association Group to Investigate Cancer-Associated Alterations in the Genome) Consortium, Cancer of the Prostate in Sweden, Prostate Cancer Genome-wide Association Study of Uncommon Susceptibility Loci, Genetic Associations and Mechanisms in Oncology (GAME-ON)/Elucidating Loci Involved in Prostate Cancer Susceptibility (ELLIPSE) Consortium, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

Genome-wide association studies (GWAS) and fine-mapping efforts to date have identified more than 100 prostate cancer (PrCa)-susceptibility loci. We meta-analyzed genotype data from a custom high-density array of 46,939 PrCa cases and 27,910 controls of European ancestry with previously genotyped data of 32,255 PrCa cases and 33,202 controls of European ancestry. Our analysis identified 62 novel loci associated (P C, p.Pro1054Arg) in ATM and rs2066827 (OR = 1.06; P = 2.3 × 10-9; T>G, p.Val109Gly) in CDKN1B. The combination of all loci captured 28.4% of the PrCa familial relative risk, and a polygenic risk score conferred an elevated PrCa risk for men in the ninetieth to ninety-ninth percentiles (relative risk = 2.69; 95% confidence interval (CI): 2.55-2.82) and first percentile (relative risk = 5.71; 95% CI: 5.04-6.48) risk stratum compared with the population average. These findings improve risk prediction, enhance fine-mapping, and provide insight into the underlying biology of PrCa1.

Published
2018

35. Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants

Author

Dadaev, Tokhir, Saunders, Edward J, Newcombe, Paul J, Anokian, Ezequiel, Leongamornlert, Daniel A, Brook, Mark N, Cieza-Borrella, Clara, Mijuskovic, Martina, Wakerell, Sarah, Olama, Ali Amin Al, Schumacher, Fredrick R, Berndt, Sonja I, Benlloch, Sara, Ahmed, Mahbubl, Goh, Chee, Sheng, Xin, Zhang, Zhuo, Muir, Kenneth, Govindasami, Koveela, Lophatananon, Artitaya, Stevens, Victoria L, Gapstur, Susan M, Carter, Brian D, Tangen, Catherine M, Goodman, Phyllis, Thompson, Ian M, Batra, Jyotsna, Chambers, Suzanne, Moya, Leire, Clements, Judith, Horvath, Lisa, Tilley, Wayne, Risbridger, Gail, Gronberg, Henrik, Aly, Markus, Nordström, Tobias, Pharoah, Paul, Pashayan, Nora, Schleutker, Johanna, Tammela, Teuvo LJ, Sipeky, Csilla, Auvinen, Anssi, Albanes, Demetrius, Weinstein, Stephanie, Wolk, Alicja, Hakansson, Niclas, West, Catharine, Dunning, Alison M, Burnet, Neil, Mucci, Lorelei, Giovannucci, Edward, Andriole, Gerald, Cussenot, Olivier, Cancel-Tassin, Géraldine, Koutros, Stella, Freeman, Laura E Beane, Sorensen, Karina Dalsgaard, Orntoft, Torben Falck, Borre, Michael, Maehle, Lovise, Grindedal, Eli Marie, Neal, David E, Donovan, Jenny L, Hamdy, Freddie C, Martin, Richard M, Travis, Ruth C, Key, Tim J, Hamilton, Robert J, Fleshner, Neil E, Finelli, Antonio, Ingles, Sue Ann, Stern, Mariana C, Rosenstein, Barry, Kerns, Sarah, Ostrer, Harry, Lu, Yong-Jie, Zhang, Hong-Wei, Feng, Ninghan, Mao, Xueying, Guo, Xin, Wang, Guomin, Sun, Zan, Giles, Graham G, Southey, Melissa C, MacInnis, Robert J, FitzGerald, Liesel M, Kibel, Adam S, Drake, Bettina F, Vega, Ana, Gómez-Caamaño, Antonio, Fachal, Laura, Szulkin, Robert, Eklund, Martin, Kogevinas, Manolis, Llorca, Javier, Castaño-Vinyals, Gemma, Penney, Kathryn L, Stampfer, Meir, Park, Jong Y, and Sellers, Thomas A

Subjects
Genetics, Aging, Urologic Diseases, Prostate Cancer, Prevention, Human Genome, Cancer, Clinical Research, 2.1 Biological and endogenous factors, Aetiology, Algorithms, Bayes Theorem, Black People, Chromosome Mapping, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Molecular Sequence Annotation, Multivariate Analysis, Polymorphism, Single Nucleotide, Prostatic Neoplasms, Quantitative Trait Loci, Risk, White People, PRACTICAL (Prostate Cancer Association Group to Investigate Cancer-Associated Alterations in the Genome) Consortium
Abstract

Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling.

Published
2018

37. BRCA2 Hypomorphic Missense Variants Confer Moderate Risks of Breast Cancer

Author

Shimelis, Hermela, Mesman, Romy LS, Von Nicolai, Catharina, Ehlen, Asa, Guidugli, Lucia, Martin, Charlotte, Calléja, Fabienne MGR, Meeks, Huong, Hallberg, Emily, Hinton, Jamie, Lilyquist, Jenna, Hu, Chunling, Aalfs, Cora M, Aittomäki, Kristiina, Andrulis, Irene, Anton-Culver, Hoda, Arndt, Volker, Beckmann, Matthias W, Benitez, Javier, Bogdanova, Natalia V, Bojesen, Stig E, Bolla, Manjeet K, Borresen-Dale, Anne-Lise, Brauch, Hiltrud, Brennan, Paul, Brenner, Hermann, Broeks, Annegien, Brouwers, Barbara, Brüning, Thomas, Burwinkel, Barbara, Chang-Claude, Jenny, Chenevix-Trench, Georgia, Cheng, Ching-Yu, Choi, Ji-Yeob, Collée, J Margriet, Cox, Angela, Cross, Simon S, Czene, Kamila, Darabi, Hatef, Dennis, Joe, Dörk, Thilo, dos-Santos-Silva, Isabel, Dunning, Alison M, Fasching, Peter A, Figueroa, Jonine, Flyger, Henrik, García-Closas, Montserrat, Giles, Graham G, Glendon, Gord, Guénel, Pascal, Haiman, Christopher A, Hall, Per, Hamann, Ute, Hartman, Mikael, Hogervorst, Frans B, Hollestelle, Antoinette, Hopper, John L, Ito, Hidemi, Jakubowska, Anna, Kang, Daehee, Kosma, Veli-Matti, Kristensen, Vessela, Lai, Kah-Nyin, Lambrechts, Diether, Le Marchand, Loic, Li, Jingmei, Lindblom, Annika, Lophatananon, Artitaya, Lubinski, Jan, Machackova, Eva, Mannermaa, Arto, Margolin, Sara, Marme, Frederik, Matsuo, Keitaro, Miao, Hui, Michailidou, Kyriaki, Milne, Roger L, Muir, Kenneth, Neuhausen, Susan L, Nevanlinna, Heli, Olson, Janet E, Olswold, Curtis, Oosterwijk, Jan JC, Osorio, Ana, Peterlongo, Paolo, Peto, Julian, Pharoah, Paul DP, Pylkäs, Katri, Radice, Paolo, Rashid, Muhammad Usman, Rhenius, Valerie, Rudolph, Anja, Sangrajrang, Suleeporn, Sawyer, Elinor J, Schmidt, Marjanka K, Schoemaker, Minouk J, Seynaeve, Caroline, Shah, Mitul, Shen, Chen-Yang, and Shrubsole, Martha

Subjects
Breast Cancer, Cancer, Genetics, 2.1 Biological and endogenous factors, Aetiology, Aged, Amino Acid Substitution, Animals, BRCA2 Protein, Breast Neoplasms, Case-Control Studies, Female, Genotype, Germ-Line Mutation, Humans, Mice, Mutation, Missense, Risk, for kConFab/AOCS Investigators, for NBCS Collaborators, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

Breast cancer risks conferred by many germline missense variants in the BRCA1 and BRCA2 genes, often referred to as variants of uncertain significance (VUS), have not been established. In this study, associations between 19 BRCA1 and 33 BRCA2 missense substitution variants and breast cancer risk were investigated through a breast cancer case-control study using genotyping data from 38 studies of predominantly European ancestry (41,890 cases and 41,607 controls) and nine studies of Asian ancestry (6,269 cases and 6,624 controls). The BRCA2 c.9104A>C, p.Tyr3035Ser (OR = 2.52; P = 0.04), and BRCA1 c.5096G>A, p.Arg1699Gln (OR = 4.29; P = 0.009) variant were associated with moderately increased risks of breast cancer among Europeans, whereas BRCA2 c.7522G>A, p.Gly2508Ser (OR = 2.68; P = 0.004), and c.8187G>T, p.Lys2729Asn (OR = 1.4; P = 0.004) were associated with moderate and low risks of breast cancer among Asians. Functional characterization of the BRCA2 variants using four quantitative assays showed reduced BRCA2 activity for p.Tyr3035Ser compared with wild-type. Overall, our results show how BRCA2 missense variants that influence protein function can confer clinically relevant, moderately increased risks of breast cancer, with potential implications for risk management guidelines in women with these specific variants. Cancer Res; 77(11); 2789-99. ©2017 AACR.

Published
2017

38. Evaluating genetic variants associated with breast cancer risk in high and moderate-penetrance genes in Asians

Author

Han, Mi-Ryung, Zheng, Wei, Cai, Qiuyin, Gao, Yu-Tang, Zheng, Ying, Bolla, Manjeet K, Michailidou, Kyriaki, Dennis, Joe, Wang, Qin, Dunning, Alison M, Brennan, Paul, Chen, Shou-Tung, Choi, Ji-Yeob, Hartman, Mikael, Ito, Hidemi, Lophatananon, Artitaya, Matsuo, Keitaro, Miao, Hui, Muir, Kenneth, Sangrajrang, Suleeporn, Shen, Chen-Yang, Teo, Soo Hwang, Tseng, Chiu-chen, Wu, Anna H, Yip, Cheng Har, Kang, Daehee, Xiang, Yong-Bing, Easton, Douglas F, Shu, Xiao-Ou, and Long, Jirong

Subjects
Clinical Research, Breast Cancer, Cancer, Prevention, Genetics, Genetic Testing, 2.1 Biological and endogenous factors, Aetiology, Alleles, Asian People, Breast Neoplasms, Chromosome Mapping, Databases, Genetic, Female, Gene Frequency, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Variation, Humans, Penetrance, Polymorphism, Single Nucleotide, Population Surveillance, Risk, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

Over the past 20 years, high-penetrance pathogenic mutations in genes BRCA1, BRCA2, TP53, PTEN, STK11 and CDH1 and moderate-penetrance mutations in genes CHEK2, ATM, BRIP1, PALB2, RAD51C, RAD50 and NBN have been identified for breast cancer. In this study, we investigated whether there are additional variants in these 13 genes associated with breast cancer among women of Asian ancestry. We analyzed up to 654 single nucleotide polymorphisms (SNPs) from 6269 cases and 6624 controls of Asian descent included in the Breast Cancer Association Consortium (BCAC), and up to 236 SNPs from 5794 cases and 5529 controls included in the Shanghai Breast Cancer Genetics Study (SBCGS). We found three missense variants with minor allele frequency (MAF)

Published
2017

39. Prediction of breast cancer risk based on common genetic variants in women of East Asian ancestry

Author

Wen, Wanqing, Shu, Xiao-ou, Guo, Xingyi, Cai, Qiuyin, Long, Jirong, Bolla, Margaret K, Michailidou, Kyriaki, Dennis, Joe, Wang, Qin, Gao, Yu-Tang, Zheng, Ying, Dunning, Alison M, García-Closas, Montserrat, Brennan, Paul, Chen, Shou-Tung, Choi, Ji-Yeob, Hartman, MiKael, Ho, Hidemi, Lophatananon, Artitaya, Matsuo, Keitaro, Miao, Hui, Muir, Kenneth, Sangrajrang, Suleeporn, Shen, Chen-Yang, Teo, Soo H, Tseng, Chiu-chen, Wu, Anna H, Yip, Cheng Har, Simard, Jacques, Pharoah, Paul D. P., Hall, Per, Kang, Daehee, Xiang, Yongbing, Easton, Douglas F, and Zheng, Wei

Published
2016

40. Predicting risk of endometrial cancer in asymptomatic women (PRECISION): Model development and external validation.

Author

Kitson, Sarah J., Crosbie, Emma J., Evans, D. Gareth, Lophatananon, Aritaya, Muir, Kenneth R., Ashcroft, Darren, Kontopantelis, Evan, and Martin, Glen P.

Subjects
ENDOMETRIAL cancer, ASYMPTOMATIC patients, DISEASE risk factors, CANCER patients, AKAIKE information criterion
Abstract

Objectives: Develop an endometrial cancer risk prediction model and externally validate it for UK primary care use. Design: Cohort study. Setting: The UK Biobank was used for model development and a linked primary (Clinical Practice Research Datalink, CPRD) and secondary care (HES), mortality (ONS) and cancer register (NRCAS) dataset was used for external validation. Population: Women aged 45–60 years with no history of endometrial cancer or hysterectomy. Methods: Model development was performed using a flexible parametric survival model and stepwise backward selection aiming to minimise the Akaike information criterion. Model performance on external validation was assessed through flexible calibration plots, calculation of the expected to observed ratio and C‐statistic and decision curve analysis. Main outcome measures: Endometrial cancer diagnosis within 1–10 years of the index date. Results: Model development included 222 031 women (902 incident endometrial cancer cases) and external validation 3 094 371 women (8585 endometrial cancer cases). The final model (with equation provided) incorporated age, body mass index, waist circumference, age at menarche, menopause and last birth, hormone replacement, tamoxifen and oral contraceptive pill use, type 2 diabetes, smoking and family history of bowel cancer. It was well calibrated on external validation (calibration slope 1.14, 95% confidence interval [CI] 1.11–1.17, E/O 1.03, 95% CI 1.01–1.05), with moderate/good discrimination (C‐statistic 0.70, 95% CI 0.69–0.70) and had improved net benefit compared with previously developed models. Conclusions: The Predicting risk of endometrial cancer in asymptomatic women model (PRECISION), using easily measurable anthropometric, reproductive, personal and family history, accurately quantifies a woman's 10‐year risk of endometrial cancer. Its use could determine eligibility for primary endometrial cancer prevention trials and for targeted resource allocation in UK general practices. [ABSTRACT FROM AUTHOR]

Published
2024
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41. Adherence to Active Surveillance Protocols for Low-risk Prostate Cancer: Results of the Movember Foundation’s Global Action Plan Prostate Cancer Active Surveillance Initiative

Author

Trock, Bruce, Ehdaie, Behfar, Carroll, Peter, Filson, Christopher, Kim, Jeri, Logothetis, Christopher, Morgan, Todd, Klotz, Laurence, Pickles, Tom, Hyndman, Eric, Moore, Caroline M., Gnanapragasam, Vincent, Van Hemelrijck, Mieke, Dasgupta, Prokar, Bangma, Chris, Roobol, Monique, Villers, Arnauld, Rannikko, Antti, Valdagni, Riccardo, Perry, Antoinette, Hugosson, Jonas, Rubio-Briones, Jose, Bjartell, Anders, Hefermehl, Lukas, Shiong, Lee Lui, Frydenberg, Mark, Kakehi, Yoshiyuki, Chung, Byung Ha, van der Kwast, Theo, Obbink, Henk, van der Linden, Wim, Hulsen, Tim, Jonge, Cees de, Kattan, Mike, Xinge, Ji, Muir, Kenneth, Lophatananon, Artitaya, Fahey, Michael, Steyerberg, Ewout, Nieboer, Daan, Zhang, Liying, Beckmann, Kerri, Denton, Brian, Hayen, Andrew, Boutros, Paul, Guo, Wei, Benfante, Nicole, Cowan, Janet, Patil, Dattatraya, Tolosa, Emily, Kim, Tae-Kyung, Mamedov, Alexandre, LaPointe, Vincent, Crump, Trafford, Kimberly-Duffell, Jenna, Santaolalla, Aida, Olivier, Jonathan, Rancati, Tiziana, Ahlgren, Helén, Mascarós, Juanma, Löfgren, Annica, Lehmann, Kurt, Lin, Catherine Han, Hirama, Hiromi, Lee, Kwang Suk, Jenster, Guido, Auvinen, Anssi, Haider, Masoom, van Bochove, Kees, Carter, Ballentine, Gledhill, Sam, Buzza, Mark, Bruinsma, Sophie, Helleman, Jozien, Kalapara, Arveen A., Verbeek, Jan F.M., Lee, Lui Shiong, Bangma, Chris H., Steyerberg, Ewout W., Harkin, Tim, and Roobol, Monique J.

Published
2020
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42. Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction

Author

Conti, David V., Darst, Burcu F., Moss, Lilit C., Saunders, Edward J., Sheng, Xin, Chou, Alisha, Schumacher, Fredrick R., Olama, Ali Amin Al, Benlloch, Sara, Dadaev, Tokhir, Brook, Mark N., Sahimi, Ali, Hoffmann, Thomas J., Takahashi, Atushi, Matsuda, Koichi, Momozawa, Yukihide, Fujita, Masashi, Muir, Kenneth, Lophatananon, Artitaya, Wan, Peggy, Le Marchand, Loic, Wilkens, Lynne R., Stevens, Victoria L., Gapstur, Susan M., Carter, Brian D., Schleutker, Johanna, Tammela, Teuvo L. J., Sipeky, Csilla, Auvinen, Anssi, Giles, Graham G., Southey, Melissa C., MacInnis, Robert J., Cybulski, Cezary, Wokołorczyk, Dominika, Lubiński, Jan, Neal, David E., Donovan, Jenny L., Hamdy, Freddie C., Martin, Richard M., Nordestgaard, Børge G., Nielsen, Sune F., Weischer, Maren, Bojesen, Stig E., Røder, Martin Andreas, Iversen, Peter, Batra, Jyotsna, Chambers, Suzanne, Moya, Leire, Horvath, Lisa, Clements, Judith A., Tilley, Wayne, Risbridger, Gail P., Gronberg, Henrik, Aly, Markus, Szulkin, Robert, Eklund, Martin, Nordström, Tobias, Pashayan, Nora, Dunning, Alison M., Ghoussaini, Maya, Travis, Ruth C., Key, Tim J., Riboli, Elio, Park, Jong Y., Sellers, Thomas A., Lin, Hui-Yi, Albanes, Demetrius, Weinstein, Stephanie J., Mucci, Lorelei A., Giovannucci, Edward, Lindstrom, Sara, Kraft, Peter, Hunter, David J., Penney, Kathryn L., Turman, Constance, Tangen, Catherine M., Goodman, Phyllis J., Thompson, Jr., Ian M., Hamilton, Robert J., Fleshner, Neil E., Finelli, Antonio, Parent, Marie-Élise, Stanford, Janet L., Ostrander, Elaine A., Geybels, Milan S., Koutros, Stella, Freeman, Laura E. Beane, Stampfer, Meir, Wolk, Alicja, Håkansson, Niclas, Andriole, Gerald L., Hoover, Robert N., Machiela, Mitchell J., Sørensen, Karina Dalsgaard, Borre, Michael, Blot, William J., Zheng, Wei, Yeboah, Edward D., Mensah, James E., Lu, Yong-Jie, Zhang, Hong-Wei, Feng, Ninghan, Mao, Xueying, Wu, Yudong, Zhao, Shan-Chao, Sun, Zan, Thibodeau, Stephen N., McDonnell, Shannon K., Schaid, Daniel J., West, Catharine M. L., Burnet, Neil, Barnett, Gill, Maier, Christiane, Schnoeller, Thomas, Luedeke, Manuel, Kibel, Adam S., Drake, Bettina F., Cussenot, Olivier, Cancel-Tassin, Géraldine, Menegaux, Florence, Truong, Thérèse, Koudou, Yves Akoli, John, Esther M., Grindedal, Eli Marie, Maehle, Lovise, Khaw, Kay-Tee, Ingles, Sue A., Stern, Mariana C., Vega, Ana, Gómez-Caamaño, Antonio, Fachal, Laura, Rosenstein, Barry S., Kerns, Sarah L., Ostrer, Harry, Teixeira, Manuel R., Paulo, Paula, Brandão, Andreia, Watya, Stephen, Lubwama, Alexander, Bensen, Jeannette T., Fontham, Elizabeth T. H., Mohler, James, Taylor, Jack A., Kogevinas, Manolis, Llorca, Javier, Castaño-Vinyals, Gemma, Cannon-Albright, Lisa, Teerlink, Craig C., Huff, Chad D., Strom, Sara S., Multigner, Luc, Blanchet, Pascal, Brureau, Laurent, Kaneva, Radka, Slavov, Chavdar, Mitev, Vanio, Leach, Robin J., Weaver, Brandi, Brenner, Hermann, Cuk, Katarina, Holleczek, Bernd, Saum, Kai-Uwe, Klein, Eric A., Hsing, Ann W., Kittles, Rick A., Murphy, Adam B., Logothetis, Christopher J., Kim, Jeri, Neuhausen, Susan L., Steele, Linda, Ding, Yuan Chun, Isaacs, William B., Nemesure, Barbara, Hennis, Anselm J. M., Carpten, John, Pandha, Hardev, Michael, Agnieszka, De Ruyck, Kim, De Meerleer, Gert, Ost, Piet, Xu, Jianfeng, Razack, Azad, Lim, Jasmine, Teo, Soo-Hwang, Newcomb, Lisa F., Lin, Daniel W., Fowke, Jay H., Neslund-Dudas, Christine, Rybicki, Benjamin A., Gamulin, Marija, Lessel, Davor, Kulis, Tomislav, Usmani, Nawaid, Singhal, Sandeep, Parliament, Matthew, Claessens, Frank, Joniau, Steven, Van den Broeck, Thomas, Gago-Dominguez, Manuela, Castelao, Jose Esteban, Martinez, Maria Elena, Larkin, Samantha, Townsend, Paul A., Aukim-Hastie, Claire, Bush, William S., Aldrich, Melinda C., Crawford, Dana C., Srivastava, Shiv, Cullen, Jennifer C., Petrovics, Gyorgy, Casey, Graham, Roobol, Monique J., Jenster, Guido, van Schaik, Ron H. N., Hu, Jennifer J., Sanderson, Maureen, Varma, Rohit, McKean-Cowdin, Roberta, Torres, Mina, Mancuso, Nicholas, Berndt, Sonja I., Van Den Eeden, Stephen K., Easton, Douglas F., Chanock, Stephen J., Cook, Michael B., Wiklund, Fredrik, Nakagawa, Hidewaki, Witte, John S., Eeles, Rosalind A., Kote-Jarai, Zsofia, and Haiman, Christopher A.

Published
2021
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43. Evidence that the 5p12 Variant rs10941679 Confers Susceptibility to Estrogen-Receptor-Positive Breast Cancer through FGF10 and MRPS30 Regulation

Author

Ghoussaini, Maya, French, Juliet D, Michailidou, Kyriaki, Nord, Silje, Beesley, Jonathan, Canisus, Sander, Hillman, Kristine M, Kaufmann, Susanne, Sivakumaran, Haran, Marjaneh, Moradi, Lee, Jason S, Dennis, Joe, Bolla, Manjeet K, Wang, Qin, Dicks, Ed, Milne, Roger L, Hopper, John L, Southey, Melissa C, Schmidt, Marjanka K, Broeks, Annegien, Muir, Kenneth, Lophatananon, Artitaya, Fasching, Peter A, Beckmann, Matthias W, Fletcher, Olivia, Johnson, Nichola, Sawyer, Elinor J, Tomlinson, Ian, Burwinkel, Barbara, Marme, Frederik, Guénel, Pascal, Truong, Thérèse, Bojesen, Stig E, Flyger, Henrik, Benitez, Javier, González-Neira, Anna, Alonso, M Rosario, Pita, Guillermo, Neuhausen, Susan L, Anton-Culver, Hoda, Brenner, Hermann, Arndt, Volker, Meindl, Alfons, Schmutzler, Rita K, Brauch, Hiltrud, Hamann, Ute, Tessier, Daniel C, Vincent, Daniel, Nevanlinna, Heli, Khan, Sofia, Matsuo, Keitaro, Ito, Hidemi, Dörk, Thilo, Bogdanova, Natalia V, Lindblom, Annika, Margolin, Sara, Mannermaa, Arto, Kosma, Veli-Matti, Investigators, kConFab AOCS, Wu, Anna H, Van Den Berg, David, Lambrechts, Diether, Floris, Giuseppe, Chang-Claude, Jenny, Rudolph, Anja, Radice, Paolo, Barile, Monica, Couch, Fergus J, Hallberg, Emily, Giles, Graham G, Haiman, Christopher A, Le Marchand, Loic, Goldberg, Mark S, Teo, Soo H, Yip, Cheng Har, Borresen-Dale, Anne-Lise, Collaborators, NBCS, Zheng, Wei, Cai, Qiuyin, Winqvist, Robert, Pylkäs, Katri, Andrulis, Irene L, Devilee, Peter, Tollenaar, Rob AEM, García-Closas, Montserrat, Figueroa, Jonine, Hall, Per, Czene, Kamila, Brand, Judith S, Darabi, Hatef, Eriksson, Mikael, Hooning, Maartje J, Koppert, Linetta B, Li, Jingmei, Shu, Xiao-Ou, Zheng, Ying, Cox, Angela, Cross, Simon S, Shah, Mitul, and Rhenius, Valerie

Subjects
Prevention, Genetics, Cancer, Breast Cancer, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Alleles, Breast Neoplasms, Case-Control Studies, Cell Line, Tumor, Chromosomes, Human, Pair 5, Enhancer Elements, Genetic, Fibroblast Growth Factor 10, Genetic Predisposition to Disease, Haplotypes, Humans, Polymorphism, Single Nucleotide, Promoter Regions, Genetic, Quantitative Trait Loci, Receptor, Fibroblast Growth Factor, Type 2, Receptors, Estrogen, kConFab/AOCS Investigators, NBCS Collaborators, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Genome-wide association studies (GWASs) have revealed increased breast cancer risk associated with multiple genetic variants at 5p12. Here, we report the fine mapping of this locus using data from 104,660 subjects from 50 case-control studies in the Breast Cancer Association Consortium (BCAC). With data for 3,365 genotyped and imputed SNPs across a 1 Mb region (positions 44,394,495-45,364,167; NCBI build 37), we found evidence for at least three independent signals: the strongest signal, consisting of a single SNP rs10941679, was associated with risk of estrogen-receptor-positive (ER+) breast cancer (per-g allele OR ER+ = 1.15; 95% CI 1.13-1.18; p = 8.35 × 10-30). After adjustment for rs10941679, we detected signal 2, consisting of 38 SNPs more strongly associated with ER-negative (ER-) breast cancer (lead SNP rs6864776: per-a allele OR ER- = 1.10; 95% CI 1.05-1.14; p conditional = 1.44 × 10-12), and a single signal 3 SNP (rs200229088: per-t allele OR ER+ = 1.12; 95% CI 1.09-1.15; p conditional = 1.12 × 10-05). Expression quantitative trait locus analysis in normal breast tissues and breast tumors showed that the g (risk) allele of rs10941679 was associated with increased expression of FGF10 and MRPS30. Functional assays demonstrated that SNP rs10941679 maps to an enhancer element that physically interacts with the FGF10 and MRPS30 promoter regions in breast cancer cell lines. FGF10 is an oncogene that binds to FGFR2 and is overexpressed in ∼10% of human breast cancers, whereas MRPS30 plays a key role in apoptosis. These data suggest that the strongest signal of association at 5p12 is mediated through coordinated activation of FGF10 and MRPS30, two candidate genes for breast cancer pathogenesis.

Published
2016

44. Fine‐scale mapping of 8q24 locus identifies multiple independent risk variants for breast cancer

Author

Shi, Jiajun, Zhang, Yanfeng, Zheng, Wei, Michailidou, Kyriaki, Ghoussaini, Maya, Bolla, Manjeet K, Wang, Qin, Dennis, Joe, Lush, Michael, Milne, Roger L, Shu, Xiao‐Ou, Beesley, Jonathan, Kar, Siddhartha, Andrulis, Irene L, Anton‐Culver, Hoda, Arndt, Volker, Beckmann, Matthias W, Zhao, Zhiguo, Guo, Xingyi, Benitez, Javier, Beeghly‐Fadiel, Alicia, Blot, William, Bogdanova, Natalia V, Bojesen, Stig E, Brauch, Hiltrud, Brenner, Hermann, Brinton, Louise, Broeks, Annegien, Brüning, Thomas, Burwinkel, Barbara, Cai, Hui, Canisius, Sander, Chang‐Claude, Jenny, Choi, Ji‐Yeob, Couch, Fergus J, Cox, Angela, Cross, Simon S, Czene, Kamila, Darabi, Hatef, Devilee, Peter, Droit, Arnaud, Dork, Thilo, Fasching, Peter A, Fletcher, Olivia, Flyger, Henrik, Fostira, Florentia, Gaborieau, Valerie, García‐Closas, Montserrat, Giles, Graham G, Grip, Mervi, Guenel, Pascal, Haiman, Christopher A, Hamann, Ute, Hartman, Mikael, Miao, Hui, Hollestelle, Antoinette, Hopper, John L, Hsiung, Chia‐Ni, Investigators, kConFab, Ito, Hidemi, Jakubowska, Anna, Johnson, Nichola, Torres, Diana, Kabisch, Maria, Kang, Daehee, Khan, Sofia, Knight, Julia A, Kosma, Veli‐Matti, Lambrechts, Diether, Li, Jingmei, Lindblom, Annika, Lophatananon, Artitaya, Lubinski, Jan, Mannermaa, Arto, Manoukian, Siranoush, Le Marchand, Loic, Margolin, Sara, Marme, Frederik, Matsuo, Keitaro, McLean, Catriona, Meindl, Alfons, Muir, Kenneth, Neuhausen, Susan L, Nevanlinna, Heli, Nord, Silje, Børresen‐Dale, Anne‐Lise, Olson, Janet E, Orr, Nick, van den Ouweland, Ans MW, Peterlongo, Paolo, Putti, Thomas Choudary, Rudolph, Anja, Sangrajrang, Suleeporn, Sawyer, Elinor J, Schmidt, Marjanka K, Schmutzler, Rita K, Shen, Chen‐Yang, Hou, Ming‐Feng, Shrubsole, Matha J, and Southey, Melissa C

Subjects
Genetics, Cancer, Breast Cancer, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Alleles, Breast Neoplasms, Case-Control Studies, Chromosome Mapping, Chromosomes, Human, Pair 8, Female, Genetic Variation, Genome-Wide Association Study, Genotype, Haplotypes, Humans, Linkage Disequilibrium, Odds Ratio, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Risk, White People, breast cancer, genetic susceptibility, 8q24, fine-mapping, single nucleotide polymorphism, Mervi Grip, kConFab Investigators, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

Previous genome-wide association studies among women of European ancestry identified two independent breast cancer susceptibility loci represented by single nucleotide polymorphisms (SNPs) rs13281615 and rs11780156 at 8q24. A fine-mapping study across 2.06 Mb (chr8:127,561,724-129,624,067, hg19) in 55,540 breast cancer cases and 51,168 controls within the Breast Cancer Association Consortium was conducted. Three additional independent association signals in women of European ancestry, represented by rs35961416 (OR = 0.95, 95% CI = 0.93-0.97, conditional p = 5.8 × 10(-6) ), rs7815245 (OR = 0.94, 95% CI = 0.91-0.96, conditional p = 1.1 × 10(-6) ) and rs2033101 (OR = 1.05, 95% CI = 1.02-1.07, conditional p = 1.1 × 10(-4) ) were found. Integrative analysis using functional genomic data from the Roadmap Epigenomics, the Encyclopedia of DNA Elements project, the Cancer Genome Atlas and other public resources implied that SNPs rs7815245 in Signal 3, and rs1121948 in Signal 5 (in linkage disequilibrium with rs11780156, r(2)  = 0.77), were putatively functional variants for two of the five independent association signals. The results highlighted multiple 8q24 variants associated with breast cancer susceptibility in women of European ancestry.

Published
2016

45. Fine scale mapping of the 17q22 breast cancer locus using dense SNPs, genotyped within the Collaborative Oncological Gene-Environment Study (COGs).

Author

Darabi, Hatef, Beesley, Jonathan, Droit, Arnaud, Kar, Siddhartha, Nord, Silje, Moradi Marjaneh, Mahdi, Soucy, Penny, Michailidou, Kyriaki, Ghoussaini, Maya, Fues Wahl, Hanna, Bolla, Manjeet K, Wang, Qin, Dennis, Joe, Alonso, M Rosario, Andrulis, Irene L, Anton-Culver, Hoda, Arndt, Volker, Beckmann, Matthias W, Benitez, Javier, Bogdanova, Natalia V, Bojesen, Stig E, Brauch, Hiltrud, Brenner, Hermann, Broeks, Annegien, Brüning, Thomas, Burwinkel, Barbara, Chang-Claude, Jenny, Choi, Ji-Yeob, Conroy, Don M, Couch, Fergus J, Cox, Angela, Cross, Simon S, Czene, Kamila, Devilee, Peter, Dörk, Thilo, Easton, Douglas F, Fasching, Peter A, Figueroa, Jonine, Fletcher, Olivia, Flyger, Henrik, Galle, Eva, García-Closas, Montserrat, Giles, Graham G, Goldberg, Mark S, González-Neira, Anna, Guénel, Pascal, Haiman, Christopher A, Hallberg, Emily, Hamann, Ute, Hartman, Mikael, Hollestelle, Antoinette, Hopper, John L, Ito, Hidemi, Jakubowska, Anna, Johnson, Nichola, Kang, Daehee, Khan, Sofia, Kosma, Veli-Matti, Kriege, Mieke, Kristensen, Vessela, Lambrechts, Diether, Le Marchand, Loic, Lee, Soo Chin, Lindblom, Annika, Lophatananon, Artitaya, Lubinski, Jan, Mannermaa, Arto, Manoukian, Siranoush, Margolin, Sara, Matsuo, Keitaro, Mayes, Rebecca, McKay, James, Meindl, Alfons, Milne, Roger L, Muir, Kenneth, Neuhausen, Susan L, Nevanlinna, Heli, Olswold, Curtis, Orr, Nick, Peterlongo, Paolo, Pita, Guillermo, Pylkäs, Katri, Rudolph, Anja, Sangrajrang, Suleeporn, Sawyer, Elinor J, Schmidt, Marjanka K, Schmutzler, Rita K, Seynaeve, Caroline, Shah, Mitul, Shen, Chen-Yang, Shu, Xiao-Ou, Southey, Melissa C, Stram, Daniel O, Surowy, Harald, Swerdlow, Anthony, Teo, Soo H, Tessier, Daniel C, Tomlinson, Ian, Torres, Diana, and Truong, Thérèse

Abstract

Genome-wide association studies have found SNPs at 17q22 to be associated with breast cancer risk. To identify potential causal variants related to breast cancer risk, we performed a high resolution fine-mapping analysis that involved genotyping 517 SNPs using a custom Illumina iSelect array (iCOGS) followed by imputation of genotypes for 3,134 SNPs in more than 89,000 participants of European ancestry from the Breast Cancer Association Consortium (BCAC). We identified 28 highly correlated common variants, in a 53 Kb region spanning two introns of the STXBP4 gene, that are strong candidates for driving breast cancer risk (lead SNP rs2787486 (OR = 0.92; CI 0.90-0.94; P = 8.96 × 10(-15))) and are correlated with two previously reported risk-associated variants at this locus, SNPs rs6504950 (OR = 0.94, P = 2.04 × 10(-09), r(2) = 0.73 with lead SNP) and rs1156287 (OR = 0.93, P = 3.41 × 10(-11), r(2) = 0.83 with lead SNP). Analyses indicate only one causal SNP in the region and several enhancer elements targeting STXBP4 are located within the 53 kb association signal. Expression studies in breast tumor tissues found SNP rs2787486 to be associated with increased STXBP4 expression, suggesting this may be a target gene of this locus.

Published
2016

46. An intergenic risk locus containing an enhancer deletion in 2q35 modulates breast cancer risk by deregulating IGFBP5 expression

Author

Wyszynski, Asaf, Hong, Chi-Chen, Lam, Kristin, Michailidou, Kyriaki, Lytle, Christian, Yao, Song, Zhang, Yali, Bolla, Manjeet K, Wang, Qin, Dennis, Joe, Hopper, John L, Southey, Melissa C, Schmidt, Marjanka K, Broeks, Annegien, Muir, Kenneth, Lophatananon, Artitaya, Fasching, Peter A, Beckmann, Matthias W, Peto, Julian, dos-Santos-Silva, Isabel, Sawyer, Elinor J, Tomlinson, Ian, Burwinkel, Barbara, Marme, Frederik, Guénel, Pascal, Truong, Thérèse, Bojesen, Stig E, Nordestgaard, Børge G, Gonzalez-Neira, Anna, Benitez, Javier, Neuhausen, Susan L, Brenner, Hermann, Dieffenbach, Aida Karina, Meindl, Alfons, Schmutzler, Rita K, Brauch, Hiltrud, Nevanlinna, Heli, Khan, Sofia, Matsuo, Keitaro, Ito, Hidemi, Dörk, Thilo, Bogdanova, Natalia V, Lindblom, Annika, Margolin, Sara, Mannermaa, Arto, Kosma, Veli-Matti, Wu, Anna H, Van Den Berg, David, Lambrechts, Diether, Wildiers, Hans, Chang-Claude, Jenny, Rudolph, Anja, Radice, Paolo, Peterlongo, Paolo, Couch, Fergus J, Olson, Janet E, Giles, Graham G, Milne, Roger L, Haiman, Christopher A, Henderson, Brian E, Dumont, Martine, Teo, Soo Hwang, Wong, Tien Y, Kristensen, Vessela, Zheng, Wei, Long, Jirong, Winqvist, Robert, Pylkäs, Katri, Andrulis, Irene L, Knight, Julia A, Devilee, Peter, Seynaeve, Caroline, García-Closas, Montserrat, Figueroa, Jonine, Klevebring, Daniel, Czene, Kamila, Hooning, Maartje J, van den Ouweland, Ans M. W., Darabi, Hatef, Shu, Xiao-Ou, Gao, Yu-Tang, Cox, Angela, Blot, William, Signorello, Lisa B, Shah, Mitul, Kang, Daehee, Choi, Ji-Yeob, Hartman, Mikael, Miao, Hui, Hamann, Ute, Jakubowska, Anna, Lubinski, Jan, Sangrajrang, Suleeporn, McKay, James, Toland, Amanda E, Yannoukakos, Drakoulis, Shen, Chen-Yang, Wu, Pei-Ei, Swerdlow, Anthony, and Orr, Nick

Published
2016

47. Association of genetic susceptibility variants for type 2 diabetes with breast cancer risk in women of European ancestry.

Author

Arndt, Volker, Beckmann, Matthias, Beeghly-Fadiel, Alicia, Benitez, Javier, Blomqvist, Carl, Bogdanova, Natalia, Børresen-Dale, Anne-Lise, Brand, Judith, Brauch, Hiltrud, Brenner, Hermann, Burwinkel, Barbara, Cai, Qiuyin, Casey, Graham, Chenevix-Trench, Georgia, Couch, Fergus, Cox, Angela, Cross, Simon, Czene, Kamila, Dörk, Thilo, Dumont, Martine, Fasching, Peter, Figueroa, Jonine, Flesch-Janys, Dieter, Fletcher, Olivia, Flyger, Henrik, Fostira, Florentia, Gammon, Marilie, Giles, Graham, Guénel, Pascal, Haiman, Christopher, Hamann, Ute, Harrington, Patricia, Hartman, Mikael, Hooning, Maartje, Hopper, John, Jakubowska, Anna, Jasmine, Farzana, John, Esther, Johnson, Nichola, Kabisch, Maria, Khan, Sofia, Kibriya, Muhammad, Knight, Julia, Kosma, Veli-Matti, Kriege, Mieke, Kristensen, Vessela, Le Marchand, Loic, Lee, Eunjung, Li, Jingmei, Lindblom, Annika, Lophatananon, Artitaya, Luben, Robert, Lubinski, Jan, Malone, Kathleen, Mannermaa, Arto, Manoukian, Siranoush, Margolin, Sara, Marme, Frederik, McLean, Catriona, Meijers-Heijboer, Hanne, Meindl, Alfons, Miao, Hui, Muir, Kenneth, Neuhausen, Susan, Nevanlinna, Heli, Neven, Patrick, Olson, Janet, Perkins, Barbara, Peterlongo, Paolo, Phillips, Kelly-Anne, Pylkäs, Katri, Rudolph, Anja, Santella, Regina, Sawyer, Elinor, Schmutzler, Rita, Schoemaker, Minouk, Shah, Mitul, Shrubsole, Martha, Southey, Melissa, Swerdlow, Anthony, Toland, Amanda, Tomlinson, Ian, Torres, Diana, Truong, Thérèse, Ursin, Giske, Van Der Luijt, Rob, Verhoef, Senno, Wang-Gohrke, Shan, Whittemore, Alice, Winqvist, Robert, Pilar Zamora, M, Zhao, Hui, Dunning, Alison, Simard, Jacques, Hall, Per, Kraft, Peter, Pharoah, Paul, Hunter, David, Easton, Douglas, and Zheng, Wei

Subjects
Breast cancer, Epidemiology, GWAS, Genetic susceptibility, Type 2 diabetes, Breast Neoplasms, Case-Control Studies, Diabetes Mellitus, Type 2, Ethnicity, Female, Genetic Predisposition to Disease, Genetic Variation, Humans, Middle Aged, Odds Ratio, Polymorphism, Single Nucleotide, Risk Factors, White People
Abstract

PURPOSE: Type 2 diabetes (T2D) has been reported to be associated with an elevated risk of breast cancer. It is unclear, however, whether this association is due to shared genetic factors. METHODS: We constructed a genetic risk score (GRS) using risk variants from 33 known independent T2D susceptibility loci and evaluated its relation to breast cancer risk using the data from two consortia, including 62,328 breast cancer patients and 83,817 controls of European ancestry. Unconditional logistic regression models were used to derive adjusted odds ratios (ORs) and 95 % confidence intervals (CIs) to measure the association of breast cancer risk with T2D GRS or T2D-associated genetic risk variants. Meta-analyses were conducted to obtain summary ORs across all studies. RESULTS: The T2D GRS was not found to be associated with breast cancer risk, overall, by menopausal status, or for estrogen receptor positive or negative breast cancer. Three T2D associated risk variants were individually associated with breast cancer risk after adjustment for multiple comparisons using the Bonferroni method (at p < 0.001), rs9939609 (FTO) (OR 0.94, 95 % CI = 0.92-0.95, p = 4.13E-13), rs7903146 (TCF7L2) (OR 1.04, 95 % CI = 1.02-1.06, p = 1.26E-05), and rs8042680 (PRC1) (OR 0.97, 95 % CI = 0.95-0.99, p = 8.05E-04). CONCLUSIONS: We have shown that several genetic risk variants were associated with the risk of both T2D and breast cancer. However, overall genetic susceptibility to T2D may not be related to breast cancer risk.

Published
2016

48. Association of genetic susceptibility variants for type 2 diabetes with breast cancer risk in women of European ancestry

Author

Zhao, Zhiguo, Wen, Wanqing, Michailidou, Kyriaki, Bolla, Manjeet K, Wang, Qin, Zhang, Ben, Long, Jirong, Shu, Xiao-Ou, Schmidt, Marjanka K, Milne, Roger L, García-Closas, Montserrat, Chang-Claude, Jenny, Lindstrom, Sara, Bojesen, Stig E, Ahsan, Habibul, Aittomäki, Kristiina, Andrulis, Irene L, Anton-Culver, Hoda, Arndt, Volker, Beckmann, Matthias W, Beeghly-Fadiel, Alicia, Benitez, Javier, Blomqvist, Carl, Bogdanova, Natalia V, Børresen-Dale, Anne-Lise, Brand, Judith, Brauch, Hiltrud, Brenner, Hermann, Burwinkel, Barbara, Cai, Qiuyin, Casey, Graham, Chenevix-Trench, Georgia, Couch, Fergus J, Cox, Angela, Cross, Simon S, Czene, Kamila, Dörk, Thilo, Dumont, Martine, Fasching, Peter A, Figueroa, Jonine, Flesch-Janys, Dieter, Fletcher, Olivia, Flyger, Henrik, Fostira, Florentia, Gammon, Marilie, Giles, Graham G, Guénel, Pascal, Haiman, Christopher A, Hamann, Ute, Harrington, Patricia, Hartman, Mikael, Hooning, Maartje J, Hopper, John L, Jakubowska, Anna, Jasmine, Farzana, John, Esther M, Johnson, Nichola, Kabisch, Maria, Khan, Sofia, Kibriya, Muhammad, Knight, Julia A, Kosma, Veli-Matti, Kriege, Mieke, Kristensen, Vessela, Le Marchand, Loic, Lee, Eunjung, Li, Jingmei, Lindblom, Annika, Lophatananon, Artitaya, Luben, Robert, Lubinski, Jan, Malone, Kathleen E, Mannermaa, Arto, Manoukian, Siranoush, Margolin, Sara, Marme, Frederik, McLean, Catriona, Meijers-Heijboer, Hanne, Meindl, Alfons, Miao, Hui, Muir, Kenneth, Neuhausen, Susan L, Nevanlinna, Heli, Neven, Patrick, Olson, Janet E, Perkins, Barbara, Peterlongo, Paolo, Phillips, Kelly-Anne, Pylkäs, Katri, Rudolph, Anja, Santella, Regina, Sawyer, Elinor J, Schmutzler, Rita K, Schoemaker, Minouk, Shah, Mitul, Shrubsole, Martha, Southey, Melissa C, Swerdlow, Anthony J, Toland, Amanda E, and Tomlinson, Ian

Subjects
Genetics, Breast Cancer, Prevention, Diabetes, Aging, Cancer, Clinical Research, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Breast Neoplasms, Case-Control Studies, Diabetes Mellitus, Type 2, Ethnicity, Female, Genetic Predisposition to Disease, Genetic Variation, Humans, Middle Aged, Odds Ratio, Polymorphism, Single Nucleotide, Risk Factors, White People, Type 2 diabetes, Genetic susceptibility, GWAS, Breast cancer, Epidemiology, Oncology and Carcinogenesis, Public Health and Health Services
Abstract

PurposeType 2 diabetes (T2D) has been reported to be associated with an elevated risk of breast cancer. It is unclear, however, whether this association is due to shared genetic factors.MethodsWe constructed a genetic risk score (GRS) using risk variants from 33 known independent T2D susceptibility loci and evaluated its relation to breast cancer risk using the data from two consortia, including 62,328 breast cancer patients and 83,817 controls of European ancestry. Unconditional logistic regression models were used to derive adjusted odds ratios (ORs) and 95 % confidence intervals (CIs) to measure the association of breast cancer risk with T2D GRS or T2D-associated genetic risk variants. Meta-analyses were conducted to obtain summary ORs across all studies.ResultsThe T2D GRS was not found to be associated with breast cancer risk, overall, by menopausal status, or for estrogen receptor positive or negative breast cancer. Three T2D associated risk variants were individually associated with breast cancer risk after adjustment for multiple comparisons using the Bonferroni method (at p < 0.001), rs9939609 (FTO) (OR 0.94, 95 % CI = 0.92-0.95, p = 4.13E-13), rs7903146 (TCF7L2) (OR 1.04, 95 % CI = 1.02-1.06, p = 1.26E-05), and rs8042680 (PRC1) (OR 0.97, 95 % CI = 0.95-0.99, p = 8.05E-04).ConclusionsWe have shown that several genetic risk variants were associated with the risk of both T2D and breast cancer. However, overall genetic susceptibility to T2D may not be related to breast cancer risk.

Published
2016

49. No evidence that protein truncating variants in BRIP1 are associated with breast cancer risk: implications for gene panel testing.

Author

Easton, Douglas F, Lesueur, Fabienne, Decker, Brennan, Michailidou, Kyriaki, Li, Jun, Allen, Jamie, Luccarini, Craig, Pooley, Karen A, Shah, Mitul, Bolla, Manjeet K, Wang, Qin, Dennis, Joe, Ahmad, Jamil, Thompson, Ella R, Damiola, Francesca, Pertesi, Maroulio, Voegele, Catherine, Mebirouk, Noura, Robinot, Nivonirina, Durand, Geoffroy, Forey, Nathalie, Luben, Robert N, Ahmed, Shahana, Aittomäki, Kristiina, Anton-Culver, Hoda, Arndt, Volker, Australian Ovarian Cancer Study Group, Baynes, Caroline, Beckman, Matthias W, Benitez, Javier, Van Den Berg, David, Blot, William J, Bogdanova, Natalia V, Bojesen, Stig E, Brenner, Hermann, Chang-Claude, Jenny, Chia, Kee Seng, Choi, Ji-Yeob, Conroy, Don M, Cox, Angela, Cross, Simon S, Czene, Kamila, Darabi, Hatef, Devilee, Peter, Eriksson, Mikael, Fasching, Peter A, Figueroa, Jonine, Flyger, Henrik, Fostira, Florentia, García-Closas, Montserrat, Giles, Graham G, Glendon, Gord, González-Neira, Anna, Guénel, Pascal, Haiman, Christopher A, Hall, Per, Hart, Steven N, Hartman, Mikael, Hooning, Maartje J, Hsiung, Chia-Ni, Ito, Hidemi, Jakubowska, Anna, James, Paul A, John, Esther M, Johnson, Nichola, Jones, Michael, Kabisch, Maria, Kang, Daehee, kConFab Investigators, Kosma, Veli-Matti, Kristensen, Vessela, Lambrechts, Diether, Li, Na, Lifepool Investigators, Lindblom, Annika, Long, Jirong, Lophatananon, Artitaya, Lubinski, Jan, Mannermaa, Arto, Manoukian, Siranoush, Margolin, Sara, Matsuo, Keitaro, Meindl, Alfons, Mitchell, Gillian, Muir, Kenneth, NBCS Investigators, Nevelsteen, Ines, van den Ouweland, Ans, Peterlongo, Paolo, Phuah, Sze Yee, Pylkäs, Katri, Rowley, Simone M, Sangrajrang, Suleeporn, Schmutzler, Rita K, Shen, Chen-Yang, Shu, Xiao-Ou, Southey, Melissa C, Surowy, Harald, Swerdlow, Anthony, and Teo, Soo H

Subjects
Australian Ovarian Cancer Study Group, kConFab Investigators, Lifepool Investigators, NBCS Investigators, Humans, Breast Neoplasms, Genetic Predisposition to Disease, RNA Helicases, DNA-Binding Proteins, Risk, Cohort Studies, Mutation, Adult, Aged, Middle Aged, European Continental Ancestry Group, Female, Fanconi Anemia Complementation Group Proteins, Genetic Association Studies, Cancer: breast, Genetics & Heredity, Biological Sciences, Medical and Health Sciences
Abstract

BACKGROUND:BRCA1 interacting protein C-terminal helicase 1 (BRIP1) is one of the Fanconi Anaemia Complementation (FANC) group family of DNA repair proteins. Biallelic mutations in BRIP1 are responsible for FANC group J, and previous studies have also suggested that rare protein truncating variants in BRIP1 are associated with an increased risk of breast cancer. These studies have led to inclusion of BRIP1 on targeted sequencing panels for breast cancer risk prediction. METHODS:We evaluated a truncating variant, p.Arg798Ter (rs137852986), and 10 missense variants of BRIP1, in 48 144 cases and 43 607 controls of European origin, drawn from 41 studies participating in the Breast Cancer Association Consortium (BCAC). Additionally, we sequenced the coding regions of BRIP1 in 13 213 cases and 5242 controls from the UK, 1313 cases and 1123 controls from three population-based studies as part of the Breast Cancer Family Registry, and 1853 familial cases and 2001 controls from Australia. RESULTS:The rare truncating allele of rs137852986 was observed in 23 cases and 18 controls in Europeans in BCAC (OR 1.09, 95% CI 0.58 to 2.03, p=0.79). Truncating variants were found in the sequencing studies in 34 cases (0.21%) and 19 controls (0.23%) (combined OR 0.90, 95% CI 0.48 to 1.70, p=0.75). CONCLUSIONS:These results suggest that truncating variants in BRIP1, and in particular p.Arg798Ter, are not associated with a substantial increase in breast cancer risk. Such observations have important implications for the reporting of results from breast cancer screening panels.

Published
2016

50. Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170.

Author

Dunning, Alison M, Michailidou, Kyriaki, Kuchenbaecker, Karoline B, Thompson, Deborah, French, Juliet D, Beesley, Jonathan, Healey, Catherine S, Kar, Siddhartha, Pooley, Karen A, Lopez-Knowles, Elena, Dicks, Ed, Barrowdale, Daniel, Sinnott-Armstrong, Nicholas A, Sallari, Richard C, Hillman, Kristine M, Kaufmann, Susanne, Sivakumaran, Haran, Moradi Marjaneh, Mahdi, Lee, Jason S, Hills, Margaret, Jarosz, Monika, Drury, Suzie, Canisius, Sander, Bolla, Manjeet K, Dennis, Joe, Wang, Qin, Hopper, John L, Southey, Melissa C, Broeks, Annegien, Schmidt, Marjanka K, Lophatananon, Artitaya, Muir, Kenneth, Beckmann, Matthias W, Fasching, Peter A, Dos-Santos-Silva, Isabel, Peto, Julian, Sawyer, Elinor J, Tomlinson, Ian, Burwinkel, Barbara, Marme, Frederik, Guénel, Pascal, Truong, Thérèse, Bojesen, Stig E, Flyger, Henrik, González-Neira, Anna, Perez, Jose IA, Anton-Culver, Hoda, Eunjung, Lee, Arndt, Volker, Brenner, Hermann, Meindl, Alfons, Schmutzler, Rita K, Brauch, Hiltrud, Hamann, Ute, Aittomäki, Kristiina, Blomqvist, Carl, Ito, Hidemi, Matsuo, Keitaro, Bogdanova, Natasha, Dörk, Thilo, Lindblom, Annika, Margolin, Sara, Kosma, Veli-Matti, Mannermaa, Arto, Tseng, Chiu-Chen, Wu, Anna H, Lambrechts, Diether, Wildiers, Hans, Chang-Claude, Jenny, Rudolph, Anja, Peterlongo, Paolo, Radice, Paolo, Olson, Janet E, Giles, Graham G, Milne, Roger L, Haiman, Christopher A, Henderson, Brian E, Goldberg, Mark S, Teo, Soo H, Yip, Cheng Har, Nord, Silje, Borresen-Dale, Anne-Lise, Kristensen, Vessela, Long, Jirong, Zheng, Wei, Pylkäs, Katri, Winqvist, Robert, Andrulis, Irene L, Knight, Julia A, Devilee, Peter, Seynaeve, Caroline, Figueroa, Jonine, Sherman, Mark E, Czene, Kamila, Darabi, Hatef, Hollestelle, Antoinette, van den Ouweland, Ans MW, Humphreys, Keith, Gao, Yu-Tang, and Shu, Xiao-Ou

Subjects
EMBRACE, GEMO Study Collaborators, HEBON, kConFab Investigators, Chromosomes, Human, Pair 6, Humans, Breast Neoplasms, Genetic Predisposition to Disease, Carrier Proteins, Cell Cycle Proteins, Estrogen Receptor alpha, Risk Factors, Gene Expression, Gene Expression Regulation, Neoplastic, Base Sequence, Protein Binding, Phenotype, Polymorphism, Single Nucleotide, Female, Genetic Association Studies, Cancer, Clinical Research, Breast Cancer, Genetics, Aetiology, 2.1 Biological and endogenous factors, Biological Sciences, Medical and Health Sciences, Developmental Biology
Abstract

We analyzed 3,872 common genetic variants across the ESR1 locus (encoding estrogen receptor α) in 118,816 subjects from three international consortia. We found evidence for at least five independent causal variants, each associated with different phenotype sets, including estrogen receptor (ER(+) or ER(-)) and human ERBB2 (HER2(+) or HER2(-)) tumor subtypes, mammographic density and tumor grade. The best candidate causal variants for ER(-) tumors lie in four separate enhancer elements, and their risk alleles reduce expression of ESR1, RMND1 and CCDC170, whereas the risk alleles of the strongest candidates for the remaining independent causal variant disrupt a silencer element and putatively increase ESR1 and RMND1 expression.

Published
2016

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