Prediction of breast cancer risk based on profiling with common genetic variants

Research output: Contribution to journalJournal articleResearchpeer-review

Nasim Mavaddat, Paul D P Pharoah, Kyriaki Michailidou, Jonathan Tyrer, Mark N Brook, Manjeet K Bolla, Qin Wang, Joe Dennis, Alison M Dunning, Mitul Shah, Robert Luben, Judith Brown, Stig E Bojesen, Børge G Nordestgaard, Sune F Nielsen, Henrik Flyger, Kamila Czene, Hatef Darabi, Mikael Eriksson, Julian Peto & 192 others Isabel Dos-Santos-Silva, Frank Dudbridge, Nichola Johnson, Marjanka K Schmidt, Annegien Broeks, Senno Verhoef, Emiel J Rutgers, Anthony Swerdlow, Alan Ashworth, Nick Orr, Minouk J Schoemaker, Jonine Figueroa, Stephen J Chanock, Louise Brinton, Jolanta Lissowska, Fergus J Couch, Janet E Olson, Celine Vachon, Vernon S Pankratz, Diether Lambrechts, Hans Wildiers, Chantal Van Ongeval, Erik van Limbergen, Vessela Kristensen, Grethe Grenaker Alnæs, Silje Nord, Anne-Lise Borresen-Dale, Heli Nevanlinna, Taru A Muranen, Kristiina Aittomäki, Carl Blomqvist, Jenny Chang-Claude, Anja Rudolph, Petra Seibold, Dieter Flesch-Janys, Peter A Fasching, Lothar Haeberle, Arif B Ekici, Matthias W Beckmann, Barbara Burwinkel, Frederik Marme, Andreas Schneeweiss, Christof Sohn, Amy Trentham-Dietz, Polly Newcomb, Linda Titus, Kathleen M Egan, David J Hunter, Sara Lindstrom, Rulla M Tamimi, Peter Kraft, Nazneen Rahman, Clare Turnbull, Anthony Renwick, Sheila Seal, Jingmei Li, Jianjun Liu, Keith Humphreys, Javier Benitez, M Pilar Zamora, Jose Ignacio Arias Perez, Primitiva Menéndez, Anna Jakubowska, Jan Lubinski, Katarzyna Jaworska-Bieniek, Katarzyna Durda, Natalia V Bogdanova, Natalia N Antonenkova, Thilo Dörk, Hoda Anton-Culver, Susan L Neuhausen, Argyrios Ziogas, Leslie Bernstein, Peter Devilee, Robert A E M Tollenaar, Caroline Seynaeve, Christi J van Asperen, Angela Cox, Simon S Cross, Malcolm W R Reed, Elza Khusnutdinova, Marina Bermisheva, Darya Prokofyeva, Zalina Takhirova, Alfons Meindl, Rita K Schmutzler, Christian Sutter, Rongxi Yang, Peter Schürmann, Michael Bremer, Hans Christiansen, Tjoung-Won Park-Simon, Peter Hillemanns, Pascal Guénel, Thérèse Truong, Florence Menegaux, Marie Sanchez, Paolo Radice, Paolo Peterlongo, Siranoush Manoukian, Valeria Pensotti, John L Hopper, Helen Tsimiklis, Carmel Apicella, Melissa C Southey, Hiltrud Brauch, Thomas Brüning, Yon-Dschun Ko, Alice J Sigurdson, Michele M Doody, Ute Hamann, Diana Torres, Hans-Ulrich Ulmer, Asta Försti, Elinor J Sawyer, Ian Tomlinson, Michael J Kerin, Nicola Miller, Irene L Andrulis, Julia A Knight, Gord Glendon, Anna Marie Mulligan, Georgia Chenevix-Trench, Rosemary Balleine, Graham G Giles, Roger L Milne, Catriona McLean, Annika Lindblom, Sara Margolin, Christopher A Haiman, Brian E Henderson, Fredrick Schumacher, Loic Le Marchand, Ursula Eilber, Shan Wang-Gohrke, Maartje J Hooning, Antoinette Hollestelle, Ans M W van den Ouweland, Linetta B Koppert, Jane Carpenter, Christine Clarke, Rodney Scott, Arto Mannermaa, Vesa Kataja, Veli-Matti Kosma, Jaana M Hartikainen, Hermann Brenner, Volker Arndt, Christa Stegmaier, Aida Karina Dieffenbach, Robert Winqvist, Katri Pylkäs, Arja Jukkola-Vuorinen, Mervi Grip, Kenneth Offit, Joseph Vijai, Mark Robson, Rohini Rau-Murthy, Miriam Dwek, Ruth Swann, Katherine Annie Perkins, Mark S Goldberg, France Labrèche, Martine Dumont, Diana M Eccles, William J Tapper, Sajjad Rafiq, Esther M John, Alice S Whittemore, Susan Slager, Drakoulis Yannoukakos, Amanda E Toland, Song Yao, Wei Zheng, Sandra L Halverson, Anna González-Neira, Guillermo Pita, M Rosario Alonso, Nuria Álvarez, Daniel Herrero, Daniel C Tessier, Daniel Vincent, Francois Bacot, Craig Luccarini, Caroline Baynes, Shahana Ahmed, Mel Maranian, Catherine S Healey, Jacques Simard, Per Hall, Douglas F Easton, Montserrat Garcia-Closas

BACKGROUND: Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking.

METHODS: We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates.

RESULTS: There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer.

CONCLUSIONS: The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report.

Original languageEnglish
Article numberdjv036
JournalNational Cancer Institute. Journal (Print)
Volume107
Issue number5
Pages (from-to)1-15
Number of pages15
ISSN0027-8874
DOIs
Publication statusPublished - May 2015

    Research areas

  • Adult, Aged, Biomarkers, Tumor, Breast Neoplasms, Europe, Female, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Genetic Predisposition to Disease, Genotype, Humans, Middle Aged, Odds Ratio, Polymorphism, Single Nucleotide, Predictive Value of Tests, Receptors, Estrogen, Risk Assessment, Risk Factors

ID: 156340785