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

Nasim Mavaddat; Kyriaki Michailidou; Joe Dennis; Michael Lush; Laura Fachal; Andrew Lee; Jonathan P Tyrer; Ting-Huei Chen; Qin Wang; Manjeet K Bolla; Xin Yang; Muriel A Adank; Thomas Ahearn; Kristiina Aittomäki; Jamie Allen; Irene L Andrulis; Hoda Anton-Culver; Natalia N Antonenkova; Volker Arndt; Kristan J Aronson; Paul L Auer; Päivi Auvinen; Myrto Barrdahl; Laura E Beane Freeman; Matthias W Beckmann; Sabine Behrens; Javier Benitez; Marina Bermisheva; Leslie Bernstein; Carl Blomqvist; Natalia V Bogdanova; Stig E. Bojesen; Bernardo Bonanni; Anne-Lise Børresen-Dale; Hiltrud Brauch; Michael Bremer; Hermann Brenner; Adam Brentnall; Ian W Brock; Angela Brooks-Wilson; Sara Y Brucker; Thomas Brüning; Barbara Burwinkel; Daniele Campa; Brian D Carter; Jose E Castelao; Hans Christiansen; Henrik Flyger; Sune F Nielsen; Børge G. Nordestgaard; ABCTB Investigators

doi:10.1016/j.ajhg.2018.11.002

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

Nasim Mavaddat^*, Kyriaki Michailidou, Joe Dennis, Michael Lush, Laura Fachal, Andrew Lee, Jonathan P Tyrer, Ting-Huei Chen, Qin Wang, Manjeet K Bolla, Xin Yang, Muriel A Adank, Thomas Ahearn, Kristiina Aittomäki, Jamie Allen, Irene L Andrulis, Hoda Anton-Culver, Natalia N Antonenkova, Volker Arndt, Kristan J AronsonPaul L Auer, Päivi Auvinen, Myrto Barrdahl, Laura E Beane Freeman, Matthias W Beckmann, Sabine Behrens, Javier Benitez, Marina Bermisheva, Leslie Bernstein, Carl Blomqvist, Natalia V Bogdanova, Stig E. Bojesen, Bernardo Bonanni, Anne-Lise Børresen-Dale, Hiltrud Brauch, Michael Bremer, Hermann Brenner, Adam Brentnall, Ian W Brock, Angela Brooks-Wilson, Sara Y Brucker, Thomas Brüning, Barbara Burwinkel, Daniele Campa, Brian D Carter, Jose E Castelao, Hans Christiansen, Henrik Flyger, Sune F Nielsen, Børge G. Nordestgaard, ABCTB Investigators

^*Corresponding author af dette arbejde

Institut for Klinisk Medicin

201 Citationer (Scopus)

17 Downloads (Pure)

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.

Originalsprog	Engelsk
Tidsskrift	American Journal of Human Genetics
Vol/bind	104
Udgave nummer	1
Sider (fra-til)	21-34
Antal sider	14
ISSN	0002-9297
DOI	https://doi.org/10.1016/j.ajhg.2018.11.002
Status	Udgivet - 3 jan. 2019

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10.1016/j.ajhg.2018.11.002Licens: CC BY-NC-ND

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer SubtypesForlagets udgivne version, 1,37 MBLicens: CC BY-NC-ND

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Mavaddat, N., Michailidou, K., Dennis, J., Lush, M., Fachal, L., Lee, A., Tyrer, J. P., Chen, T.-H., Wang, Q., Bolla, M. K., Yang, X., Adank, M. A., Ahearn, T., Aittomäki, K., Allen, J., Andrulis, I. L., Anton-Culver, H., Antonenkova, N. N., Arndt, V., ... ABCTB Investigators (2019). Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes. American Journal of Human Genetics, 104(1), 21-34. https://doi.org/10.1016/j.ajhg.2018.11.002

Mavaddat, N, Michailidou, K, Dennis, J, Lush, M, Fachal, L, Lee, A, Tyrer, JP, Chen, T-H, 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, A-L, 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, Christiansen, H, Flyger, H, Nielsen, SF, Nordestgaard, BG & ABCTB Investigators 2019, 'Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes', American Journal of Human Genetics, bind 104, nr. 1, s. 21-34. https://doi.org/10.1016/j.ajhg.2018.11.002

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title = "Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes",

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.",

author = "Nasim Mavaddat and Kyriaki Michailidou and Joe Dennis and Michael Lush and Laura Fachal and Andrew Lee and Tyrer, {Jonathan P} and Ting-Huei Chen and Qin Wang and Bolla, {Manjeet K} and Xin Yang and Adank, {Muriel A} and Thomas Ahearn and Kristiina Aittom{\"a}ki and Jamie Allen and Andrulis, {Irene L} and Hoda Anton-Culver and Antonenkova, {Natalia N} and Volker Arndt and Aronson, {Kristan J} and Auer, {Paul L} and P{\"a}ivi Auvinen and Myrto Barrdahl and {Beane Freeman}, {Laura E} and Beckmann, {Matthias W} and Sabine Behrens and Javier Benitez and Marina Bermisheva and Leslie Bernstein and Carl Blomqvist and Bogdanova, {Natalia V} and Bojesen, {Stig E.} and Bernardo Bonanni and Anne-Lise B{\o}rresen-Dale and Hiltrud Brauch and Michael Bremer and Hermann Brenner and Adam Brentnall and Brock, {Ian W} and Angela Brooks-Wilson and Brucker, {Sara Y} and Thomas Br{\"u}ning and Barbara Burwinkel and Daniele Campa and Carter, {Brian D} and Castelao, {Jose E} and Hans Christiansen and Henrik Flyger and Nielsen, {Sune F} and Nordestgaard, {B{\o}rge G.} and {ABCTB Investigators}",

year = "2019",

month = jan,

day = "3",

doi = "10.1016/j.ajhg.2018.11.002",

language = "English",

volume = "104",

pages = "21--34",

journal = "American Journal of Human Genetics",

issn = "0002-9297",

publisher = "Cell Press",

number = "1",

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TY - JOUR

T1 - Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

AU - Mavaddat, Nasim

AU - Michailidou, Kyriaki

AU - Dennis, Joe

AU - Lush, Michael

AU - Fachal, Laura

AU - Lee, Andrew

AU - Tyrer, Jonathan P

AU - Chen, Ting-Huei

AU - Wang, Qin

AU - Bolla, Manjeet K

AU - Yang, Xin

AU - Adank, Muriel A

AU - Ahearn, Thomas

AU - Aittomäki, Kristiina

AU - Allen, Jamie

AU - Andrulis, Irene L

AU - Anton-Culver, Hoda

AU - Antonenkova, Natalia N

AU - Arndt, Volker

AU - Aronson, Kristan J

AU - Auer, Paul L

AU - Auvinen, Päivi

AU - Barrdahl, Myrto

AU - Beane Freeman, Laura E

AU - Beckmann, Matthias W

AU - Behrens, Sabine

AU - Benitez, Javier

AU - Bermisheva, Marina

AU - Bernstein, Leslie

AU - Blomqvist, Carl

AU - Bogdanova, Natalia V

AU - Bojesen, Stig E.

AU - Bonanni, Bernardo

AU - Børresen-Dale, Anne-Lise

AU - Brauch, Hiltrud

AU - Bremer, Michael

AU - Brenner, Hermann

AU - Brentnall, Adam

AU - Brock, Ian W

AU - Brooks-Wilson, Angela

AU - Brucker, Sara Y

AU - Brüning, Thomas

AU - Burwinkel, Barbara

AU - Campa, Daniele

AU - Carter, Brian D

AU - Castelao, Jose E

AU - Christiansen, Hans

AU - Flyger, Henrik

AU - Nielsen, Sune F

AU - Nordestgaard, Børge G.

AU - ABCTB Investigators

PY - 2019/1/3

Y1 - 2019/1/3

N2 - 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.

AB - 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.

U2 - 10.1016/j.ajhg.2018.11.002

DO - 10.1016/j.ajhg.2018.11.002

M3 - Journal article

C2 - 30554720

SN - 0002-9297

VL - 104

SP - 21

EP - 34

JO - American Journal of Human Genetics

JF - American Journal of Human Genetics

IS - 1

ER -

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

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