Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes. - PubMed - NCBI

2018 Dec 5. pii: S0002-9297(18)30405-1. doi: 10.1016/j.ajhg.2018.11.002. [Epub ahead of print]

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

Mavaddat N1, Michailidou K2, Dennis J3, Lush M3, Fachal L4, Lee A3, Tyrer JP4, Chen TH5, Wang Q3, Bolla MK3, Yang X3, Adank MA6, Ahearn T7, Aittomäki K8, Allen J3, Andrulis IL9, Anton-Culver H10, Antonenkova NN11, Arndt V12, Aronson KJ13, Auer PL14, Auvinen P15, Barrdahl M16, Beane Freeman LE7, Beckmann MW17, Behrens S16, Benitez J18, Bermisheva M19, Bernstein L20, Blomqvist C21, Bogdanova NV22, Bojesen SE23, Bonanni B24, Børresen-Dale AL25, Brauch H26, Bremer M27, Brenner H28, Brentnall A29, Brock IW30, Brooks-Wilson A31, Brucker SY32, Brüning T33, Burwinkel B34, Campa D35, Carter BD36, Castelao JE37, Chanock SJ7, Chlebowski R38, Christiansen H27, Clarke CL39, Collée JM40, Cordina-Duverger E41, Cornelissen S42, Couch FJ43, Cox A30, Cross SS44, Czene K45, Daly MB46, Devilee P47, Dörk T48, Dos-Santos-Silva I49, Dumont M50, Durcan L51, Dwek M52, Eccles DM53, Ekici AB54, Eliassen AH55, Ellberg C56, Engel C57, Eriksson M45, Evans DG58, Fasching PA59, Figueroa J60, Fletcher O61, Flyger H62, Försti A63, Fritschi L64, Gabrielson M45, Gago-Dominguez M65, Gapstur SM36, García-Sáenz JA66, Gaudet MM36, Georgoulias V67, Giles GG68, Gilyazova IR69, Glendon G70, Goldberg MS71, Goldgar DE72, González-Neira A73, Grenaker Alnæs GI74, Grip M75, Gronwald J76, Grundy A77, Guénel P41, Haeberle L17, Hahnen E78, Haiman CA79, Håkansson N80, Hamann U81, Hankinson SE82, Harkness EF83, Hart SN84, He W45, Hein A17, Heyworth J85, Hillemanns P48, Hollestelle A86, Hooning MJ86, Hoover RN7, Hopper JL87, Howell A88, Huang G81, Humphreys K45, Hunter DJ89, Jakimovska M90, Jakubowska A91, Janni W92, John EM93, Johnson N61, Jones ME94, Jukkola-Vuorinen A95, Jung A16, Kaaks R16, Kaczmarek K76, Kataja V96, Keeman R42, Kerin MJ97, Khusnutdinova E69, Kiiski JI98, Knight JA99, Ko YD100, Kosma VM101, Koutros S7, Kristensen VN25, Krüger U56, Kühl T102, Lambrechts D103, Le Marchand L104, Lee E79, Lejbkowicz F105, Lilyquist J84, Lindblom A106, Lindström S107, Lissowska J108, Lo WY109, Loibl S110, Long J111, Lubiński J76, Lux MP17, MacInnis RJ112, Maishman T51, Makalic E87, Maleva Kostovska I90, Mannermaa A101, Manoukian S113, Margolin S114, Martens JWM86, Martinez ME115, Mavroudis D67, McLean C116, Meindl A117, Menon U118, Middha P119, Miller N97, Moreno F66, Mulligan AM120, Mulot C121, Muñoz-Garzon VM122, Neuhausen SL20, Nevanlinna H98, Neven P123, Newman WG58, Nielsen SF124, Nordestgaard BG23, Norman A84, Offit K125, Olson JE84, Olsson H56, Orr N126, Pankratz VS127, Park-Simon TW48, Perez JIA128, Pérez-Barrios C129, Peterlongo P130, Peto J49, Pinchev M105, Plaseska-Karanfilska D90, Polley EC84, Prentice R131, Presneau N52, Prokofyeva D132, Purrington K133, Pylkäs K134, Rack B92, Radice P135, Rau-Murthy R136, Rennert G105, Rennert HS105, Rhenius V4, Robson M136, Romero A129, Ruddy KJ137, Ruebner M17, Saloustros E138, Sandler DP139, Sawyer EJ140, Schmidt DF141, Schmutzler RK78, Schneeweiss A142, Schoemaker MJ94, Schumacher F143, Schürmann P48, Schwentner L92, Scott C84, Scott RJ144, Seynaeve C86, Shah M4, Sherman ME145, Shrubsole MJ111, Shu XO111, Slager S84, Smeets A123, Sohn C142, Soucy P50, Southey MC146, Spinelli JJ147, Stegmaier C148, Stone J149, Swerdlow AJ150, Tamimi RM151, Tapper WJ152, Taylor JA153, Terry MB154, Thöne K102, Tollenaar RAEM155, Tomlinson I156, Truong T41, Tzardi M157, Ulmer HU158, Untch M159, Vachon CM84, van Veen EM58, Vijai J125, Weinberg CR160, Wendt C114, Whittemore AS161, Wildiers H123, Willett W162, Winqvist R134, Wolk A163, Yang XR7, Yannoukakos D164, Zhang Y12, Zheng W111, Ziogas A10; ABCTB Investigators165; kConFab/AOCS Investigators166; NBCS Collaborators167, Dunning AM4, Thompson DJ3, Chenevix-Trench G168, Chang-Claude J169, Schmidt MK170, Hall P171, Milne RL172, Pharoah PDP173, Antoniou AC3, Chatterjee N174, Kraft P175, García-Closas M7, Simard J50, Easton DF173.

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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.