Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

AUTHORS

Mavaddat N , Michailidou K , Dennis J , Lush M , Fachal L , Lee A , Tyrer JP , Chen TH , Wang Q , Bolla MK , Yang X , Adank MA , Ahearn T , Aittomäki K , Allen J , Andrulis IL , Anton-Culver H , Antonenkova NN , Arndt V , Aronson KJ , Auer PL , Auvinen P , Barrdahl M , Beane Freeman LE , Beckmann MW , Behrens S , Benitez J , Bermisheva M , Bernstein L , Blomqvist C , Bogdanova NV , Bojesen SE , Bonanni B , Børresen-Dale AL , Brauch H , Bremer M , Brenner H , Brentnall A , Brock IW , Brooks-Wilson A , Brucker SY , Brüning T , Burwinkel B , Campa D , Carter BD , Castelao JE , Chanock SJ , Chlebowski R , Christiansen H , Clarke CL , Collée JM , Cordina-Duverger E , Cornelissen S , Couch FJ , Cox A , Cross SS , Czene K , Daly MB , Devilee P , Dörk T , Dos-Santos-Silva I , Dumont M , Durcan L , Dwek M , Eccles DM , Ekici AB , Eliassen AH , Ellberg C , Engel C , Eriksson M , Evans DG , Fasching PA , Figueroa J , Fletcher O , Flyger H , Försti A , Fritschi L , Gabrielson M , Gago-Dominguez M , Gapstur SM , García-Sáenz JA , Gaudet MM , Georgoulias V , Giles GG , Gilyazova IR , Glendon G , Goldberg MS , Goldgar DE , González-Neira A , Grenaker Alnæs GI , Grip M , Gronwald J , Grundy A , Guénel P , Haeberle L , Hahnen E , Haiman CA , Håkansson N , Hamann U , Hankinson SE , Harkness EF , Hart SN , He W , Hein A , Heyworth J , Hillemanns P , Hollestelle A , Hooning MJ , Hoover RN , Hopper JL , Howell A , Huang G , Humphreys K , Hunter DJ , Jakimovska M , Jakubowska A , Janni W , John EM , Johnson N , Jones ME , Jukkola-Vuorinen A , Jung A , Kaaks R , Kaczmarek K , Kataja V , Keeman R , Kerin MJ , Khusnutdinova E , Kiiski JI , Knight JA , Ko YD , Kosma VM , Koutros S , Kristensen VN , Krüger U , Kühl T , Lambrechts D , Le Marchand L , Lee E , Lejbkowicz F , Lilyquist J , Lindblom A , Lindström S , Lissowska J , Lo WY , Loibl S , Long J , Lubiński J , Lux MP , MacInnis RJ , Maishman T , Makalic E , Maleva Kostovska I , Mannermaa A , Manoukian S , Margolin S , Martens JWM , Martinez ME , Mavroudis D , McLean C , Meindl A , Menon U , Middha P , Miller N , Moreno F , Mulligan AM , Mulot C , Muñoz-Garzon VM , Neuhausen SL , Nevanlinna H , Neven P , Newman WG , Nielsen SF , Nordestgaard BG , Norman A , Offit K , Olson JE , Olsson H , Orr N , Pankratz VS , Park-Simon TW , Perez JIA , Pérez-Barrios C , Peterlongo P , Peto J , Pinchev M , Plaseska-Karanfilska D , Polley EC , Prentice R , Presneau N , Prokofyeva D , Purrington K , Pylkäs K , Rack B , Radice P , Rau-Murthy R , . American journal of human genetics. 2018 12 05; ().

PMID: 30554720 [PubMed].

ABSTRACT

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.