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1. Polygenic risk modeling for prediction of epithelial ovarian cancer risk.

Author

Dareng EO, Tyrer JP, Barnes DR, Jones MR, Yang X, Aben KKH, Adank MA, Agata S, Andrulis IL, Anton-Culver H, Antonenkova NN, Aravantinos G, Arun BK, Augustinsson A, Balmaña J, Bandera EV, Barkardottir RB, Barrowdale D, Beckmann MW, Beeghly-Fadiel A, Benitez J, Bermisheva M, Bernardini MQ, Bjorge L, Black A, Bogdanova NV, Bonanni B, Borg A, Brenton JD, Budzilowska A, Butzow R, Buys SS, Cai H, Caligo MA, Campbell I, Cannioto R, Cassingham H, Chang-Claude J, Chanock SJ, Chen K, Chiew YE, Chung WK, Claes KBM, Colonna S, Cook LS, Couch FJ, Daly MB, Dao F, Davies E, de la Hoya M, de Putter R, Dennis J, DePersia A, Devilee P, Diez O, Ding YC, Doherty JA, Domchek SM, Dörk T, du Bois A, Dürst M, Eccles DM, Eliassen HA, Engel C, Evans GD, Fasching PA, Flanagan JM, Fortner RT, Machackova E, Friedman E, Ganz PA, Garber J, Gensini F, Giles GG, Glendon G, Godwin AK, Goodman MT, Greene MH, Gronwald J, Hahnen E, Haiman CA, Håkansson N, Hamann U, Hansen TVO, Harris HR, Hartman M, Heitz F, Hildebrandt MAT, Høgdall E, Høgdall CK, Hopper JL, Huang RY, Huff C, Hulick PJ, Huntsman DG, Imyanitov EN, Isaacs C, Jakubowska A, James PA, Janavicius R, Jensen A, Johannsson OT, John EM, Jones ME, Kang D, Karlan BY, Karnezis A, Kelemen LE, Khusnutdinova E, Kiemeney LA, Kim BG, Kjaer SK, Komenaka I, Kupryjanczyk J, Kurian AW, Kwong A, Lambrechts D, Larson MC, Lazaro C, Le ND, Leslie G, Lester J, Lesueur F, Levine DA, Li L, Li J, Loud JT, Lu KH, Lubiński J, Mai PL, Manoukian S, Marks JR, Matsuno RK, Matsuo K, May T, McGuffog L, McLaughlin JR, McNeish IA, Mebirouk N, Menon U, Miller A, Milne RL, Minlikeeva A, Modugno F, Montagna M, Moysich KB, Munro E, Nathanson KL, Neuhausen SL, Nevanlinna H, Yie JNY, Nielsen HR, Nielsen FC, Nikitina-Zake L, Odunsi K, Offit K, Olah E, Olbrecht S, Olopade OI, Olson SH, Olsson H, Osorio A, Papi L, Park SK, Parsons MT, Pathak H, Pedersen IS, Peixoto A, Pejovic T, Perez-Segura P, Permuth JB, Peshkin B, Peterlongo P, Piskorz A, Prokofyeva D, Radice P, Rantala J, Riggan MJ, Risch HA, Rodriguez-Antona C, Ross E, Rossing MA, Runnebaum I, Sandler DP, Santamariña M, Soucy P, Schmutzler RK, Setiawan VW, Shan K, Sieh W, Simard J, Singer CF, Sokolenko AP, Song H, Southey MC, Steed H, Stoppa-Lyonnet D, Sutphen R, Swerdlow AJ, Tan YY, Teixeira MR, Teo SH, Terry KL, Terry MB, Thomassen M, Thompson PJ, Thomsen LCV, Thull DL, Tischkowitz M, Titus L, Toland AE, Torres D, Trabert B, Travis R, Tung N, Tworoger SS, Valen E, van Altena AM, van der Hout AH, Van Nieuwenhuysen E, van Rensburg EJ, Vega A, Edwards DV, Vierkant RA, Wang F, Wappenschmidt B, Webb PM, Weinberg CR, Weitzel JN, Wentzensen N, White E, Whittemore AS, Winham SJ, Wolk A, Woo YL, Wu AH, Yan L, Yannoukakos D, Zavaglia KM, Zheng W, Ziogas A, Zorn KK, Kleibl Z, Easton D, Lawrenson K, DeFazio A, Sellers TA, Ramus SJ, Pearce CL, Monteiro AN, Cunningham J, Goode EL, Schildkraut JM, Berchuck A, Chenevix-Trench G, Gayther SA, Antoniou AC, and Pharoah PDP

Subjects
Bayes Theorem, Carcinoma, Ovarian Epithelial genetics, Female, Genetic Predisposition to Disease, Humans, Polymorphism, Single Nucleotide, Prospective Studies, Risk Factors, Breast Neoplasms, Ovarian Neoplasms epidemiology, Ovarian Neoplasms genetics
Abstract

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs., (© 2021. The Author(s).)

Published
2022
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2. Better life expectancy in women with BRCA2 compared with BRCA1 mutations is attributable to lower frequency and later onset of ovarian cancer.

Author

Byrd LM, Shenton A, Maher ER, Woodward E, Belk R, Lim C, Lalloo F, Howell A, Jayson GC, and Evans GD

Subjects
Aged, Aged, 80 and over, England epidemiology, Female, Humans, Incidence, Middle Aged, Mutation, Ovarian Neoplasms epidemiology, Ovarian Neoplasms pathology, Risk Factors, Genes, BRCA1, Genes, BRCA2, Life Expectancy, Ovarian Neoplasms genetics
Abstract

Purpose: No formal assessment of life expectancy in women with BRCA1 and BRCA2 mutations in these genes has been reported previously. We have evaluated life expectancy using actuarial analysis and assessed the effect of breast and ovarian cancers on premature death in >1,000 BRCA1/2 carriers., Methods: Families with pathogenic mutations in BRCA1 and BRCA2 have been ascertained in a 10-million population region of United Kingdom since 1996. Mutation carriers and their first-degree relatives were used in an analysis of breast and ovarian cancer incidence and mortality as well as to derive and compare an actuarial assessment of life expectancy., Results: Six hundred twelve BRCA1 and 482 BRCA2 female mutation carriers were identified from 482 families. Life expectancy was significantly reduced for BRCA1 carriers compared with BRCA2 (P = 0.0002). This effect was attributable to an increased death rate from ovarian cancer (P = 0.04). Kaplan-Meier analysis revealed a better long-term survival from early-stage ovarian cancer in BRCA2 carriers but no significant differences in deaths from breast cancer or from women presenting with late-stage ovarian cancer. There was no other major contributing cause to death other than breast/ovarian cancer in BRCA1/2 female carriers., Conclusion: Interventions to reduce ovarian cancer incidence are likely to have a greater effect on life expectancy in BRCA1 compared with BRCA2 carriers.

Published
2008
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3. Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Author

Dareng, Eileen O., Tyrer, Jonathan P., Barnes, Daniel R., Jones, Michelle R., Yang, Xin, Aben, Katja K. H., Adank, Muriel A., Agata, Simona, Andrulis, Irene L., Anton-Culver, Hoda, Antonenkova, Natalia N., Aravantinos, Gerasimos, Arun, Banu K., Augustinsson, Annelie, Balmaña, Judith, Bandera, Elisa V., Barkardottir, Rosa B., Barrowdale, Daniel, Beckmann, Matthias W., Beeghly-Fadiel, Alicia, Benitez, Javier, Bermisheva, Marina, Bernardini, Marcus Q., Bjorge, Line, Black, Amanda, Bogdanova, Natalia V., Bonanni, Bernardo, Borg, Ake, Brenton, James D., Budzilowska, Agnieszka, Butzow, Ralf, Buys, Saundra S., Cai, Hui, Caligo, Maria A., Campbell, Ian, Cannioto, Rikki, Cassingham, Hayley, Chang-Claude, Jenny, Chanock, Stephen J., Chen, Kexin, Chiew, Yoke-Eng, Chung, Wendy K., Claes, Kathleen, Colonna, Sarah, Lesueur, Fabienne, Mebirouk, Noura, Engel, Christoph, Schmutzler, Rita K., Davies, Eleanor, Eccles, Diana M., Evans, D. Gareth, Cook, Linda S., Couch, Fergus J., Daly, Mary B., Dao, Fanny, de la Hoya, Miguel, de Putter, Robin, Dennis, Joe, DePersia, Allison, Devilee, Peter, Diez, Orland, Ding, Yuan Chun, Doherty, Jennifer A., Domchek, Susan M., Dörk, Thilo, du Bois, Andreas, Dürst, Matthias, Eliassen, Heather A., Evans, Gareth D., Fasching, Peter A., Flanagan, James M., Fortner, Renée T., Machackova, Eva, Friedman, Eitan, Ganz, Patricia A., Garber, Judy, Gensini, Francesca, Giles, Graham G., Glendon, Gord, Godwin, Andrew K., Goodman, Marc T., Greene, Mark H., Gronwald, Jacek, Hahnen, Eric, Haiman, Christopher A., Håkansson, Niclas, Hamann, Ute, Hansen, Thomas V. O., Harris, Holly R., Hartman, Mikael, Heitz, Florian, Hildebrandt, Michelle A. T., Høgdall, Estrid, Høgdall, Claus K., Hopper, John L., Huang, Ruea-Yea, Huff, Chad, Hulick, Peter J., Huntsman, David G., Imyanitov, Evgeny N., Chenevix-Trench, Georgia, van der Hout, Annemieke H., Isaacs, Claudine, Jakubowska, Anna, James, Paul A., Janavicius, Ramunas, Jensen, Allan, Johannsson, Oskar Th., John, Esther M., Jones, Michael E., Kang, Daehee, Karlan, Beth Y., Karnezis, Anthony, Kelemen, Linda E., Khusnutdinova, Elza, Kiemeney, Lambertus A., Kim, Byoung-Gie, Kjaer, Susanne K., Komenaka, Ian, Kupryjanczyk, Jolanta, Kurian, Allison W., Kwong, Ava, Lambrechts, Diether, Larson, Melissa C., Lazaro, Conxi, Le, Nhu D., Leslie, Goska, Lester, Jenny, Levine, Douglas A., Li, Lian, Li, Jingmei, Loud, Jennifer T., Lu, Karen H., Lubiński, Jan, Mai, Phuong L., Manoukian, Siranoush, Marks, Jeffrey R., Matsuno, Rayna Kim, Matsuo, Keitaro, May, Taymaa, McGuffog, Lesley, McLaughlin, John R., McNeish, Iain A., Menon, Usha, Miller, Austin, Milne, Roger L., Minlikeeva, Albina, Modugno, Francesmary, Montagna, Marco, Moysich, Kirsten B., Munro, Elizabeth, Nathanson, Katherine L., Neuhausen, Susan L., Nevanlinna, Heli, Yie, Joanne Ngeow Yuen, Nielsen, Henriette Roed, Nielsen, Finn C., Nikitina-Zake, Liene, Odunsi, Kunle, Offit, Kenneth, Olah, Edith, Olbrecht, Siel, Olopade, Olufunmilayo I., Olson, Sara H., Olsson, Håkan, Osorio, Ana, Papi, Laura, Park, Sue K., Parsons, Michael T., Pathak, Harsha, Pedersen, Inge Sokilde, Peixoto, Ana, Pejovic, Tanja, Perez-Segura, Pedro, Permuth, Jennifer B., Peshkin, Beth, Peterlongo, Paolo, Piskorz, Anna, Prokofyeva, Darya, Radice, Paolo, Rantala, Johanna, Riggan, Marjorie J., Risch, Harvey A., Rodriguez-Antona, Cristina, Ross, Eric, Rossing, Mary Anne, Runnebaum, Ingo, Sandler, Dale P., Santamariña, Marta, Soucy, Penny, Setiawan, V. Wendy, Shan, Kang, Sieh, Weiva, Simard, Jacques, Singer, Christian F., Sokolenko, Anna P., Song, Honglin, Southey, Melissa C., Steed, Helen, Stoppa-Lyonnet, Dominique, Sutphen, Rebecca, Swerdlow, Anthony J., Tan, Yen Yen, Teixeira, Manuel R., Teo, Soo Hwang, Terry, Kathryn L., Terry, Mary Beth, Thompson, Pamela J., Thomsen, Liv Cecilie Vestrheim, Titus, Linda, Trabert, Britton, Travis, Ruth, Tworoger, Shelley S., Valen, Ellen, van Altena, Anne M., Van Nieuwenhuysen, Els, Edwards, Digna Velez, Vierkant, Robert A., Wang, Frances, Webb, Penelope M., Weinberg, Clarice R., Wentzensen, Nicolas, White, Emily, Whittemore, Alice S., Winham, Stacey J., Wolk, Alicja, Woo, Yin-Ling, Wu, Anna H., Yan, Li, Yannoukakos, Drakoulis, Zheng, Wei, Ziogas, Argyrios, Lawrenson, Kate, deFazio, Anna, Ramus, Susan J., Pearce, Celeste L., Monteiro, Alvaro N., Cunningham, Julie M., Goode, Ellen L., Schildkraut, Joellen M., Berchuck, Andrew, Gayther, Simon A., Pharoah, Paul D. P., Claes, Kathleen B. M., Thomassen, Mads, Thull, Darcy L., Tischkowitz, Marc, Toland, Amanda E., Torres, Diana, Tung, Nadine, van Rensburg, Elizabeth J., Vega, Ana, Wappenschmidt, Barbara, Weitzel, Jeffrey N., Zavaglia, Katia M., Zorn, Kristin K., Sellers, Thomas A., Antoniou, Antonis C., Kleibl, Zdenek, Easton, Douglas, DeFazio, Anna, Cunningham, Julie, GEMO Study Collaborators, [missing], GC-HBOC Study Collaborators, [missing], EMBRACE Collaborators, [missing], OPAL Study Group, [missing], AOCS Group, [missing], KConFab Investigators, [missing], HEBON Investigators, [missing], The OCAC Consortium, [missing], The CIMBA Consortium, [missing], Clinicum, Department of Pathology, HUSLAB, Department of Obstetrics and Gynecology, HUS Gynecology and Obstetrics, Faculteit Medische Wetenschappen/UMCG, Institut Català de la Salut, [Dareng EO, Barnes DR, Yang X] University of Cambridge, Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, Cambridge, UK. [Tyrer JP] University of Cambridge, Centre for Cancer Genetic Epidemiology, Department of Oncology, Cambridge, UK. [Jones MR] Center for Bioinformatics and Functional Genomics, Cedars-Sinai Medical Center, Los Angeles, CA, USA. [Aben KKH] Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands. Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands. [Balmaña J] Hereditary cancer Genetics Group, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. Servei d’Oncologia Mèdica, Vall d’Hebron Hospital Universitari, Barcelona, Spain. [Diez O] Oncogenetics Group, Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain. Àrea de Genètica Clínica i Molecular, Vall d’Hebron Hospital Universitari, Barcelona, Spain, Vall d'Hebron Barcelona Hospital Campus, Ovarian Cancer Action, and National Institute for Health Research

Subjects
Male, Biochemistry & Molecular Biology, OCAC Consortium, Genetic Phenomena::Genotype::Genetic Predisposition to Disease [PHENOMENA AND PROCESSES], Breast Neoplasms, Ovaris - Càncer - Aspectes genètics, Carcinoma, Ovarian Epithelial, SUSCEPTIBILITY, Polymorphism, Single Nucleotide, GEMO Study Collaborators, Risk Factors, OPAL Study Group, Other subheadings::Other subheadings::/genetics [Other subheadings], Medicine and Health Sciences, Genetics, Ovaris - Càncer - Propensió, Humans, Genetic Predisposition to Disease, Prospective Studies, EMBRACE Collaborators, GC-HBOC Study Collaborators, Genetics (clinical), HEBON Investigators, fenómenos genéticos::genotipo::predisposición genética a la enfermedad [FENÓMENOS Y PROCESOS], Medicinsk genetik, Ovarian Neoplasms, Genetics & Heredity, 0604 Genetics, Science & Technology, Otros calificadores::Otros calificadores::/genética [Otros calificadores], 1184 Genetics, developmental biology, physiology, Neoplasms::Neoplasms::Neoplasms by Site::Endocrine Gland Neoplasms::Ovarian Neoplasms::Carcinoma, Ovarian Epithelial [DISEASES], 1103 Clinical Sciences, Bayes Theorem, AOCS Group, neoplasias::neoplasias::neoplasias por localización::neoplasias de las glándulas endocrinas::neoplasias ováricas::carcinoma epitelial de ovario [ENFERMEDADES], Women's cancers Radboud Institute for Health Sciences [Radboudumc 17], Carcinoma, Ovarian Epithelial/genetics, Urological cancers Radboud Institute for Health Sciences [Radboudumc 15], CIMBA Consortium, 1182 Biochemistry, cell and molecular biology, Female, KConFab Investigators, Ovarian Neoplasms/epidemiology, Medical Genetics, Life Sciences & Biomedicine
Abstract

Clinical genetics; Genetic markers; Risk factors Genética clínica; Marcadores genéticos; Factores de riesgo Genètica clínica; Marcadors genètics; Factors de risc Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28–1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08–1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21–1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29–1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35–1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

Published
2022

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