| Title: |
Polygenic Risk Modelling for Prediction of Epithelial Ovarian Cancer Risk |
| Authors: |
Dareng, Eileen O; Tyrer, Jonathan P; Barnes, Daniel R; Jones, Michelle R; Yang, Xin; Aben, Katja KH; 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 BM; Colanna, Sarah; Collaborators, GEMO Study; Collaborators, GC-HBOC study; Collaborators, EMBRACE; Cook, Linda S; Couch, Fergus J; Daly, Mary B; Dao, Fanny; Davies, Eleanor; 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; Eccles, Diana M; Eliassen, Heather A; Engel, Christoph; Evans, D Gareth; Fasching, Peter A; Flanagan, James M; Foretova, Lenka; Fortner, Renée T; 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; Group, OPAL Study; Group, AOCS; Hahnen, Eric; Haiman, Christopher A; Håkansson, Niclas; Hamann, Ute; Hansen, Thomas VO; Harris, Holly R; Hartman, Mikael; Heitz, Florian; Hildebrandt, Michelle AT; Høgdall, Estrid; Høgdall, Claus K; Hopper, John L; Huang, Ruea-Yea; Huff, Chad; Hulick, Peter J; Huntsman, David G; Imyanitov, Evgeny N; Investigators, KConFab; Investigators, HEBON; 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; Lesueur, Fabienne; Levine, Douglas A; Li, Lian; Li, Jingmei; Loud, Jennifer T; Lu, Karen H; Lubiński, Jan; Machackova, Eva; L., Phuong; Manoukian, Siranoush; Marks, Jeffrey R; Matsuno, Rayna Kim; Matsuo, Keitaro; May, Taymaa; McGuffog, Lesley; McLaughlin, John R; McNeish, Iain A; Mebirouk, Noura; 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; Schmutzler, Rita K; 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; Thomassen, Mads; Thompson, Pamela J; Thomsen, Liv Cecilie Vestrheim; Thull, Darcy L; Tischkowitz, Marc; Titus, Linda; Toland, Amanda E; Torres, Diana; Trabert, Britton; Travis, Ruth; Tung, Nadine; Tworoger, Shelley S; Valen, Ellen; van Altena, Anne M; van der Hout, Annemieke H; Van Nieuwenhuysen, Els; van Rensburg, Elizabeth J; Vega, Ana; Edwards, Digna Velez; Vierkant, Robert A; Wang, Frances; Wappenschmidt, Barbara; Webb, Penelope M; Weinberg, Clarice R; Weitzel, Jeffrey N; Wentzensen, Nicolas; White, Emily; Whittemore, Alice S; Winham, Stacey J; Wolk, Alicja; Woo, Yin-Ling; Wu, Anna H; Yan, Li; Yannoukakos, Drakoulis; Zavaglia, Katia M; Zheng, Wei; Ziogas, Argyrios; Zorn, Kristin K; Easton, Douglas; Lawrenson, Kate; DeFazio, Anna; Sellers, Thomas A; Ramus, Susan J; Pearce, Celeste L; Monteiro, Alvaro N; Cunningham, Julie; Goode, Ellen L; Schildkraut, Joellen M; Berchuck, Andrew; Chenevix-Trench, Georgia; Gayther, Simon A; Antoniou, Antonis C; Pharoah, Paul DP |
| Publisher Information: |
Cold Spring Harbor Laboratory; //doi.org/10.1101/2020.11.30.20219220 |
| Publication Year: |
2022 |
| Collection: |
Apollo - University of Cambridge Repository |
| Subject Terms: |
31 Biological Sciences; 32 Biomedical and Clinical Sciences; 3105 Genetics; 3211 Oncology and Carcinogenesis; Prevention; Rare Diseases; Ovarian Cancer; Breast Cancer; Cancer; Minority Health; Genetics; Health Disparities; Health Disparities and Racial or Ethnic Minority Health Research; Women's Health |
| Description: |
Funder: Funding details are provided in the Supplementary Material ; 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 ancestry; 7,669 women of East Asian ancestry; 1,072 women of African ancestry, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestry. 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 ancestry; 1.14(95%CI:1.08–1.19,AUC:0.538) in women of East Asian ancestry; 1.38(95%CI:1.21-1.58,AUC:0.593) in women of African ancestry; hazard ratios of 1.37(95%CI:1.30–1.44,AUC:0.592) in BRCA1 pathogenic variant carriers and 1.51(95%CI:1.36-1.67,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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf; text/xml; application/zip |
| Language: |
English |
| Relation: |
987; https://www.repository.cam.ac.uk/handle/1810/335373 |
| DOI: |
10.17863/CAM.82802 |
| Availability: |
https://www.repository.cam.ac.uk/handle/1810/335373; https://doi.org/10.17863/CAM.82802 |
| Accession Number: |
edsbas.7F494AAF |
| Database: |
BASE |