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

NBCS Collaborators; ABCTB Investigators; kConFab/AOCS Investigators

doi:10.1016/j.ajhg.2018.11.002

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

NBCS Collaborators
, ABCTB Investigators
, kConFab/AOCS Investigators

Surgery

Department of Research
Vestre Viken Hospital
Department of Cancer Genetics
Section for Breast and Endocrine Surgery
Ullevål University Hospital
Department of Radiology and Nuclear Medicine
Oslo University Hospital
Department of Pathology
Akershus University Hospital
Department of Tumor Biology
Department of Oncology
Faculty of Medicine
National Advisory Unit on Late Effects after Cancer Treatment
Department of Oncology
Breast Cancer Research Consortium
Australian Breast Cancer Tissue Bank
Sydney University Biological Informatics and Technology Centre (SUBIT)
Peter MacCallum Cancer Institute
Department of Public Health and Primary Care
Cyprus Institute of Neurology and Genetics
Department of Oncology
Université Laval
Antoni Van Leeuwenhoek Hospital
National Cancer Institute (NCI)
Helsinki University Central Hospital
University of Toronto/Lunenfeld-Tanenbaum Research Institute
University of Toronto Faculty of Medicine
University of California, Irvine
N.N. Alexandrov Research Institute of Oncology and Medical Radiology
German Cancer Research Center
Queen’s University
Fred Hutchinson Cancer Center
University of Wisconsin-Milwaukee
Kuopio University Hospital
University of Eastern Finland
University Hospital Erlangen
Cell Division and Cancer Group
CIBERER Spanish Network for Rare Diseases
Russian Academy of Sciences
City of Hope National Med Center
Orebro University Hospital
Hannover Medical School
Gentofte University Hospital
University of Copenhagen
Department of Experimental Oncology
University of Oslo
Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology
University of Tübingen
Barts and The London School of Medicine and Dentistry
University of Sheffield
British Columbia Cancer Agency
Simon Fraser University
University of Tübingen
Ruhr-Universität Bochum
Heidelberg University
University of Pisa
American Cancer Society
Xerencia de Xestion Integrada de Vigo-SERGAS
University of California, Los Angeles
Erasmus MC
Hormones and Women's Health Team
Mayo Clinic
University of Sheffield
Karolinska Institutet
Fox Chase Cancer Center
Leiden University Medical Center
London School of Hygiene and Tropical Medicine
Centre hospitalier universitaire de Québec
University of Southampton, Faculty of Medicine
University of Westminster
Harvard Medical School
Harvard School of Public Health
Lund University
University of Leipzig
Central Manchester University Hospitals NHS Foundation Trust
McGill University
University of Edinburgh
Cancer Research UK
Divisions of Molecular Pathology and Cancer Therapeutics
Lund University
Curtin University
Complejo Hospitalario Universitario de Santiago
University of California San Diego
Hospital Clinico San Carlos
University Hospital of Heraklion
Cancer Council Victoria
Melbourne School of Population and Global Health
Monash University
Bashkir State University
McGill University
McGill University Health Centre, Royal Victoria Hospital
Huntsman Cancer Institute
University Hospital of Oulu
Pomeranian Medical University in Szczecin
Université de Montréal
University Hospital Cologne
University of Cologne
Keck School of Medicine of USC
University of Massachusetts Amherst
Wythenshawe Hospital
University Hospital of South Manchester
The University of Western Australia
Erasmus MC Cancer Institute
University of Manchester
University of Oxford Medical Sciences Division
Macedonian Academy of Sciences and Arts
University Hospital Ulm
Stanford Cancer Institute
Tampere University Hospital
Jyväskylä Central Hospital
University of Toronto
Johanniter Krankenhaus
University Medical Center Hamburg-Eppendorf
VIB Center for the Biology of Disease
University of Leuven
The University of Hawaii Cancer Center
Carmel Medical Center and Technion Faculty of Medicine
Karolinska University Hospital
University of Washington
M. Sklodowska-Curie Memorial Cancer Center and Institute of Oncology
German Breast Group
Vanderbilt-Ingram Cancer Center
Fondazione IRCCS Istituto Nazionale dei Tumori, Milan
Department of Oncology
University of California San Diego
Alfred Hospital
Ludwig-Maximilians-University Munich
University College London
University Health Network
Université Paris Sorbonne Cité
Hospital do Meixoeiro
KU Leuven Cancer Institute
Memorial Sloan Kettering Cancer Center
Queen's University of Belfast
University of New Mexico
Hospital Monte Naranco
Hospital Puerta de Hierro
IFOM - The FIRC Institute of Molecular Oncology
Wayne State University School of Medicine
University of Oulu
Northern Finland Laboratory Centre Oulu
University Hospital of Larissa
National Institute of Environmental Health Sciences (NIEHS)
Guy's Hospital
Case Western Reserve University
John Hunter Hospital
University of Newcastle, College of Health, Medicine and Wellbeing
Mayo Clinic in Jacksonville, Florida
University of Melbourne
University of British Columbia
Saarland Cancer Registry
Mailman School of Public Health
University of Birmingham
Wellcome Trust Centre for Human Genetics
Frauenklinik der Stadtklinik Baden-Baden
Helios Clinics Berlin-Buch
Stanford University School of Medicine
Brigham and Women's Hospital
Uppsala University
Institute of Biosciences and Applications
QIMR Berghofer Medical Research Institute
Johns Hopkins University
The Johns Hopkins University School of Medicine

Research output: Contribution to a Journal (Peer & Non Peer) › Article › peer-review

858 Citations (Scopus)

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.

Original language	English
Pages (from-to)	21-34
Number of pages	14
Journal	American Journal of Human Genetics
Volume	104
Issue number	1
DOIs	https://doi.org/10.1016/j.ajhg.2018.11.002
Publication status	Published - 3 Jan 2019

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

breast
cancer
epidemiology
genetic
polygenic
prediction
risk
score
screening
stratification

Access to Document

10.1016/j.ajhg.2018.11.002

Cite this

@article{a9965a69c2ab42f7a9d5b103d0c316da,

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.",

keywords = "breast, cancer, epidemiology, genetic, polygenic, prediction, risk, score, screening, stratification",

author = "\{NBCS Collaborators\} and \{ABCTB Investigators\} and \{kConFab/AOCS Investigators\} and Nasim Mavaddat and Kyriaki Michailidou and Joe Dennis and Michael Lush and Laura Fachal and Andrew Lee and Tyrer, \{Jonathan P.\} and Chen, \{Ting Huei\} 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 B{\o}rresen-Dale, \{Anne Lise\} 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 Chanock, \{Stephen J.\} and Rowan Chlebowski and Hans Christiansen and Clarke, \{Christine L.\} and Coll{\'e}e, \{J. Margriet\} and Emilie Cordina-Duverger and Sten Cornelissen and Couch, \{Fergus J.\} and Angela Cox and Cross, \{Simon S.\} and Kamila Czene and Daly, \{Mary B.\} and Peter Devilee and Thilo D{\"o}rk and Isabel dos-Santos-Silva and Martine Dumont and Lorraine Durcan and Miriam Dwek and Eccles, \{Diana M.\} and Ekici, \{Arif B.\} and Eliassen, \{A. Heather\} and Carolina Ellberg and Christoph Engel and Mikael Eriksson and Evans, \{D. Gareth\} and Fasching, \{Peter A.\} and Jonine Figueroa and Olivia Fletcher and Henrik Flyger and Asta F{\"o}rsti and Lin Fritschi and Marike Gabrielson and Manuela Gago-Dominguez and Gapstur, \{Susan M.\} and Garc{\'i}a-S{\'a}enz, \{Jos{\'e} A.\} and Gaudet, \{Mia M.\} and Vassilios Georgoulias and Giles, \{Graham G.\} and Gilyazova, \{Irina R.\} and Gord Glendon and Goldberg, \{Mark S.\} and Goldgar, \{David E.\} and Anna Gonz{\'a}lez-Neira and \{Grenaker Aln{\ae}s\}, \{Grethe I.\} and Mervi Grip and Jacek Gronwald and Anne Grundy and Pascal Gu{\'e}nel and Lothar Haeberle and Eric Hahnen and Haiman, \{Christopher A.\} and Niclas H{\aa}kansson and Kerin, \{Michael J.\} and Nicola Miller",

note = "Publisher Copyright: {\textcopyright} 2018 The Authors",

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",

}

TY - JOUR

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

AU - NBCS Collaborators

AU - ABCTB Investigators

AU - kConFab/AOCS Investigators

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 - Chanock, Stephen J.

AU - Chlebowski, Rowan

AU - Christiansen, Hans

AU - Clarke, Christine L.

AU - Collée, J. Margriet

AU - Cordina-Duverger, Emilie

AU - Cornelissen, Sten

AU - Couch, Fergus J.

AU - Cox, Angela

AU - Cross, Simon S.

AU - Czene, Kamila

AU - Daly, Mary B.

AU - Devilee, Peter

AU - Dörk, Thilo

AU - dos-Santos-Silva, Isabel

AU - Dumont, Martine

AU - Durcan, Lorraine

AU - Dwek, Miriam

AU - Eccles, Diana M.

AU - Ekici, Arif B.

AU - Eliassen, A. Heather

AU - Ellberg, Carolina

AU - Engel, Christoph

AU - Eriksson, Mikael

AU - Evans, D. Gareth

AU - Fasching, Peter A.

AU - Figueroa, Jonine

AU - Fletcher, Olivia

AU - Flyger, Henrik

AU - Försti, Asta

AU - Fritschi, Lin

AU - Gabrielson, Marike

AU - Gago-Dominguez, Manuela

AU - Gapstur, Susan M.

AU - García-Sáenz, José A.

AU - Gaudet, Mia M.

AU - Georgoulias, Vassilios

AU - Giles, Graham G.

AU - Gilyazova, Irina R.

AU - Glendon, Gord

AU - Goldberg, Mark S.

AU - Goldgar, David E.

AU - González-Neira, Anna

AU - Grenaker Alnæs, Grethe I.

AU - Grip, Mervi

AU - Gronwald, Jacek

AU - Grundy, Anne

AU - Guénel, Pascal

AU - Haeberle, Lothar

AU - Hahnen, Eric

AU - Haiman, Christopher A.

AU - Håkansson, Niclas

AU - Kerin, Michael J.

AU - Miller, Nicola

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.

KW - breast

KW - cancer

KW - epidemiology

KW - genetic

KW - polygenic

KW - prediction

KW - risk

KW - score

KW - screening

KW - stratification

UR - https://www.scopus.com/pages/publications/85059498503

U2 - 10.1016/j.ajhg.2018.11.002

DO - 10.1016/j.ajhg.2018.11.002

M3 - Article

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

Abstract

UN SDGs

Keywords

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