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Prediction of Breast Cancer Risk Based on Profiling With Common Genetic Variants

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Prediction of Breast Cancer Risk Based on Profiling With Common Genetic Variants



Prediction of Breast Cancer Risk Based on Profiling With Common Genetic Variants

  1. Montserrat Garcia-Closas*
+Author Affiliations
  1. Affiliations of authors: Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of CambridgeCambridge, UK (NM, PDPP, KM, MKB, QW, JD, RL, JBr, DFE); Centre for Cancer Genetic Epidemiology, Department of Oncology, University of CambridgeCambridge, UK (PDPP, JT, AMD, MS, CL, CB, SA, MM, CSH, DFE); Division of Genetics and Epidemiology, The Institute of Cancer ResearchLondon, UK (MNB, ASw, MJS); Copenhagen General Population Study, Herlev Hospital, Copenhagen University HospitalCopenhagen, Denmark (SEB, BGN, SFN); Department of Clinical Biochemistry, Herlev Hospital, Copenhagen University HospitalCopenhagen, Herlev, Denmark (SEB, BGN, SFN); Faculty of Health and Medical Sciences, Copenhagen University HospitalCopenhagen, Herlev, Denmark (SEB, BGN); Department of Breast Surgery, Herlev Hospital, Copenhagen University Hospital,Copenhagen, Herlev, Denmark (HF); Department of Medical Epidemiology and Biostatistics, Karolinska InstitutetStockholm, Sweden (KC, HD, ME, KH, PHa); Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical MedicineLondon, UK (JP, IdSS, FD); Breakthrough Breast Cancer Research Centre, The Institute of Cancer ResearchLondon, UK (NJ, AA, NO, MGC); Netherlands Cancer Institute, Antoni van Leeuwenhoek hospitalAmsterdam, the Netherlands (MKS, AB, SV, EJR); Division of Breast Cancer Research, Institute of Cancer ResearchLondon, UK (ASw); Division of Cancer Epidemiology and Genetics, National Cancer InstituteRockville, MD (JF, SJC, LB, ASi, MD); Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie Memorial Cancer Center and Institute of OncologyWarsaw, Poland (JLis); Department of Laboratory Medicine and Pathology, Mayo ClinicRochester, MN (FJC); Department of Health Sciences Research, Mayo ClinicRochester, MN (JEO, CV, VSP, SS); Vesalius Research Center, VIBLeuven, Belgium (DL); Laboratory for Translational Genetics, Department of Oncology, University of LeuvenLeuven, Belgium (DL); Department of General Medical Oncology, University Hospitals Leuven, and Department of Oncology, KU LeuvenLeuven, Belgium (HW); Department of Radiation Oncology, University Hospital Gasthuisberg, Leuven, Belgium (EVL); Department of Radiology, University Hospital Gasthuisberg, Leuven, Belgium (CVO)Department of Genetics, Institute for Cancer Research, Oslo University HospitalRadiumhospitalet, Oslo, Norway (VK, GGA, SN, ALBD); Institute of Clinical Medicine, University of OsloOslo, Norway (VK, ALBD); Department of Clinical Molecular Biology, University of OsloOslo, Norway (VK); Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Central HospitalHelsinki, HUS, Finland (HN, TAM); Department of Clinical Genetics, University of Helsinki and Helsinki University Central HospitalHelsinki, HUS, Finland (KA); Department of Oncology, University of Helsinki and Helsinki University Central HospitalHelsinki, HUS, Finland (CB); Division of Cancer Epidemiology, German Cancer Research CenterHeidelberg, Germany (JCC, AR, PS, UE); Department of Cancer Epidemiology/Clinical Cancer Registry, University Clinic Hamburg-EppendorfHamburg, Germany (DFJ); University Breast Center Franconia, Department of Gynecology and Obstetrics, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMNErlangen, Germany (PAF, LH, MWB); David Geffen School of Medicine, Department of Medicine Division of Hematology and Oncology, University of California at Los AngelesCA (PAF); Institute of Human Genetics, University Hospital Erlangen, Friedrich Alexander University Erlangen-NurembergErlangen, Germany (ABE); Department of Obstetrics and Gynecology, University of HeidelbergHeidelberg, Germany (BB, FM, ASc, CSohn, RY); Molecular Epidemiology Group, German Cancer Research CenterHeidelberg, Germany (BB, RY); National Center for Tumor Diseases, University of HeidelbergHeidelberg, Germany (FM, ASc); University of Wisconsin Carbone Cancer CenterMadison, WI (ATD, PN); Cancer Prevention Program, Fred Hutchinson Cancer Research CenterSeattle, WA (PN); Geisel School of Medicine at DartmouthHanover, NH (LT); Division of Population Sciences, Moffitt Cancer Center & Research InstituteTampa, FL (KE); Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health,Boston, MA (DJH, SL, PK)Department of Epidemiology, Harvard T.H. Chan School of Public HealthBoston, MA (DJH, SL, RMT, PK); Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical SchoolBoston, MA (RMT); Section of Cancer Genetics, Institute of Cancer ResearchLondon, UK (NR, CT, AR, SS); Human Genetics Division, Genome Institute of Singapore, Singapore (JLi, JLiu); Human Genotyping-CEGEN Unit, Human Cancer Genetics Programme, Spanish National Cancer Research CentreMadrid, Spain (JBe, AGN, GP, MRA, NA, DH); Centro de Investigación en Red de Enfermedades RarasValencia, Spain (JBe); Servicio de Oncología Médica, Hospital Universitario La PazMadrid, Spain (MPZ); Servicio de Cirugía General y Especialidades, Hospital Monte NarancoOviedo, Spain (JIAP); Servicio de Anatomía Patológica, Hospital Monte NarancoOviedo, Spain (PM); Department of Genetics and Pathology, Pomeranian Medical UniversitySzczecin, Poland (AJ, JLu, KJB, KD); Department of Obstetrics and Gynaecology, Hannover Medical SchoolHannover, Germany (NVB, TD, PS, TWPS, PHi); Department of Radiation Oncology, Hannover Medical SchoolHannover, Germany (NVB, MB, HC); NN Alexandrov Research Institute of Oncology and Medical RadiologyMinsk, Belarus (NNA); Department of Epidemiology, University of California IrvineIrvine, CA (HAC, AZ); Department of Population Sciences, Beckman Research Institute of City of HopeDuarte, CA (SLN, LB); Department of Human Genetics and Department of Pathology, Leiden University Medical CenterLeiden, the Netherlands (PD); Department of Surgical Oncology, Leiden University Medical CenterLeiden, the Netherlands (RAEMT); Department of Clinical Genetics, Leiden University Medical CenterLeiden, the Netherlands (CJvA); Sheffield Cancer Research, Department of Oncology, University of Sheffield, UK (AC, MWRR); Academic Unit of Pathology, Department of Neuroscience, University of SheffieldSheffield, UK (SSC); Institute of Biochemistry and Genetics, Ufa Scientific Center of Russian Academy of SciencesUfa, Russia (EK, MB, ZT); Department of Genetics and Fundamental Medicine of Bashkir State UniversityUfa, Russia (EK, DP); Division of Gynaecology and Obstetrics, Technische Universität MünchenMunich, Germany (AMe); Center for Hereditary Breast and Ovarian Cancer, University Hospital Cologne, Cologne, Germany (RKS); Center for Integrated Oncology, University Hospital Cologne, Cologne, Germany (RKS); Center for Molecular Medicine Cologne, University of Cologne, Cologne, Germany (RKS)Institute of Human Genetics, University HeidelbergHeidelberg, Germany (CSutter); National Institute of Health and Medical Research, Center for Research in Epidemiology and Population Health, U1018, Environmental Epidemiology of CancerVillejuif, France (PG, TT, FM, MS); University Paris-SudVillejuif, France (PG, TT, FM, MS); Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Preventive and Predictive Medicine, Fondazione IRCCS Istituto Nazionale TumoriMilan, Italy (PR); IFOM, Fondazione Istituto FIRC di Oncologia MolecolareMilan, Italy (PP, VP); Unit of Medical Genetics, Department of Preventive and Predictive Medicine, Fondazione IRCCS Istituto Nazionale TumoriMilan, Italy (SM); Cogentech Cancer Genetic Test LaboratoryMilan, Italy (VP); Centre for Epidemiology & Biostatistics, Melbourne School of Population and Global Health, University of MelbourneMelbourne, Victoria, Australia (JLH, CA, GGG, RLM); Department of Pathology, University of MelbourneMelbourne, Victoria, Australia (HT, MCS); Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany, for the GENICA Network (HB); University of Tübingen, Tübingen, Germany, for the GENICA Network (HB); German Cancer Consortium, German Cancer Research Center (DKFZ)Heidelberg, Germany (HBra, HBre, AKD); Institute for Prevention and Occupational Medicine of the German Social Accident Insurance, Institute of the Ruhr-Universität Bochum (IPA), Germany, for the GENICA Network (TB); Department of Internal Medicine, Evangelische Kliniken Bonn gGmbH, Johanniter KrankenhausBonn, Germany, for the GENICA Network (YDK); Molecular Genetics of Breast Cancer, German Cancer Research CenterHeidelberg, Germany, for the GENICA Network (UH); Institute of Human Genetics, Pontificia Universidad JaverianaBogota, Colombia (DT); Frauenklinik der Stadtklinik Baden-BadenBaden-Baden, Germany (HUU); Division of Molecular Genetic Epidemiology, German Cancer Research CenterHeidelberg, Germany (AF); Center for Primary Health Care Research, University of LundMalmö, Sweden (AF); Division of Cancer Studies, Kings College London, Guy’s HospitalLondon, UK (EJS); Wellcome Trust Centre for Human Genetics and Oxford Biomedical Research Centre, University of OxfordOxford, UK (IT); Clinical Science Institute, University Hospital GalwayGalway, Ireland (MJK, NM); Ontario Cancer Genetics Network, Lunenfeld-Tanenbaum Research Institute of Mount Sinai HospitalToronto, Ontario, Canada (ILA, GG); Department of Molecular Genetics, University of TorontoToronto, Ontario, Canada (ILA); Prosserman Centre for Health Research, Lunenfeld-Tanenbaum Research Institute, Mount Sinai HospitalToronto, Ontario, Canada (JAK); Division of Epidemiology, Dalla Lana School of Public Health, University of TorontoToronto, Ontario, Canada (JAK); Department of Laboratory Medicine and Pathobiology, University of TorontoToronto, Ontario, Canada (AMM); Laboratory Medicine Program, University Health Network, TorontoOntario, Canada (AMM); Department of Genetics, QIMR Berghofer Medical Research InstituteBrisbane, Australia, for the Australian Ovarian Cancer Study Group (GCT); Peter MacCallum Cancer Center, Melbourne, Victoria, Australia, for kConFab Investigators and the Australian Ovarian Cancer Study GroupWestmead Millenium Institute for Medical Research, University of SydneySydney, NSW, Australia (RB, CC); Western Sydney and Nepean Blue Mountains Local Health DistrictsSydney, Australia (RB); Cancer Epidemiology Centre, Cancer Council Victoria, MelbourneVictoria, Australia (GGG, RLM); Anatomical Pathology, The Alfred Hospital, MelbourneVictoria, Australia (CM); Department of Molecular Medicine and Surgery, Karolinska InstitutetStockholm, Sweden (AL); Department of Oncology - Pathology, Karolinska InstitutetStockholm, Sweden (SM); Department of Preventive Medicine, Keck School of Medicine, University of Southern CaliforniaLos Angeles, CA (CAH, BEH, FS); Epidemiology Program, University of Hawaii Cancer CenterHonolulu, HI (LLM); Department of Obstetrics and Gynecology, University of UlmUlm, Germany (SWG); Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, the Netherlands (MJH, AH, CS)Department of Clinical Genetics, Erasmus University Medical CenterRotterdam, the Netherlands (AMWvdO); Department of Surgical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, the Netherlands (LBK); Australian Breast Cancer Tissue Bank, Westmead Millennium Institute, University of SydneySydney, NSW, Australia, for the ABCTB Investigators (JC); Division of Genetics, Hunter Area Pathology Service and University of NewcastleCallaghan, NSW, Australia (RSc); School of Medicine, Institute of Clinical Medicine, Pathology and Forensic Medicine (AMa, VMK, JMH); Cancer Center of Eastern Finland, University of Eastern Finland, Kuopio, Finland (AMa, VMK, JMH); Imaging Center, Department of Clinical Pathology, Kuopio University Hospital, Kuopio, Finland (AMa, VMK, JMH); Cancer Center, Kuopio University Hospital, Kuopio, Finland, and Jyvaskyla Central Hospital, Jyvaskyla, Finland (VK)Division of Clinical Epidemiology and Aging Research, German Cancer Research CenterHeidelberg, Germany (HB, VA, AKD); Saarland Cancer RegistrySaarbrücken, Germany (CStegmaier); Laboratory of Cancer Genetics and Tumor Biology, Department of Clinical Chemistry and Biocenter Oulu, University of Oulu, Northern Finland Laboratory Centre NordLabOulu, Finland (RW, KP); Department of Oncology, Oulu University Hospital, University of OuluOulu, Finland (AJV); Department of Surgery, Oulu University Hospital, University of OuluOulu, Finland (MG); Clinical Genetics Service, Department of Medicine, Memorial Sloan-Kettering Cancer CenterNew York, NY (KO, JV, MR, RRM); Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan-Kettering Cancer CenterNew York, NY (KO, JV); Department of Molecular and Applied Biosciences, Faculty of Science and Technology, University of WestminsterLondon, UK (MDw, RSw, KAP); Department of Medicine, McGill University, MontrealQuebec, Canada (MSG); Division of Clinical Epidemiology, McGill University Health Centre, Royal Victoria HospitalMontreal, Quebec, Canada (MSG); Département de médecine sociale et préventive, Département de santé environnementale et santé au travail, Université de MontréalMontreal, Quebec, Canada (FL); Cancer Genomics Laboratory for Genomics Centre, Centre Hospitalier Universitaire de Québec Research Centre and Laval UniversityQuébec CityQuebec, Canada (MDu, JS); Faculty of Medicine, University of Southampton, UK (DME, WJT, SR); Cancer Prevention Institute of CaliforniaFremont, CA (EMJ); Department of Health Research and Policy Stanford University School of Medicine Stanford CA (EMJ, ASW); Molecular Diagnostics Laboratory, IRRP, National Centre for Scientific Research “Demokritos”, Aghia Paraskevi AttikisAthens, Greece (DY); Department of Molecular Virology, Immunology and Medical Genetics, Comprehensive Cancer Center, The Ohio State UniversityColumbus, OH (AET); Department of Cancer Prevention and Control, Roswell Park Cancer InstituteBuffalo, NY (SY); Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of MedicineNashville, TN (WZ, SLH); McGill University and Génome Québec Innovation Centre, MontréalQuébec, Canada (DCT, DV, FB).
  1. *Authors contributed equally to this work.
  2. Correspondence to: Nasim Mavaddat, MBBS, PhD, PhD, Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Worts Causeway Cambridge, CB1 8RN, UK (e-mail:nm274@medschl.cam.ac.uk).
  • Received March 28, 2014.
  • Revision received December 1, 2014.
  • Accepted January 26, 2015.

Abstract

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.
Breast cancer is the most common cancer among Western women, with approximately 1.67 million cases diagnosed annually worldwide (1). Strategies such as endocrine risk–reducing medication and early detection by breast cancer screening can reduce the burden of disease but have disadvantages including side effects, overdiagnosis, and increased cost (2–4). Stratification of women according to the risk of developing breast cancer could improve risk reduction and screening strategies by targeting those most likely to benefit (5–8).
Both genetic and lifestyle factors are implicated in the aetiology of breast cancer. Women with a history of breast cancer in a first-degree relative are at approximately two-fold higher risk than women without a family history (9). Rare high-risk mutations particularly in the BRCA1 and BRCA2 genes explain less than 20% of the two-fold familial relative risk (FRR) (10) and account for a small proportion of breast cancer cases in the general population. Low frequency variants conferring intermediate risk, such as those in CHEK2ATM, and PALB2, explain 2% to 5% of the FRR. Genome-wide association studies (GWAS) have led to the discovery of multiple common, low-risk variants (single nucleotide polymorphisms [SNPs]) associated with breast cancer risk (11), many of which are differentially associated by estrogen receptor (ER) status (12,13). Recently, new risk-associated variants have been identified in a large-scale replication study conducted by the Breast Cancer Association Consortium (BCAC) as part of the Collaborative Oncological Gene-Environment Study (COGS). SNPs were genotyped in over 40 000 breast cancer cases and 40 000 control women, using a custom array (iCOGS). This experiment increased the number of SNPs robustly associated with breast cancer from 27 to more than 70 and identified additional variants specific to ER-negative breast cancer (14–17).
Risks conferred by SNPs are not sufficiently large to be useful in risk prediction individually. However, the combined effect of multiple SNPs could achieve a degree of risk discrimination that is useful for population-based programmes of breast cancer prevention and early detection (8,18). In this report, we investigated the value of using all 77 breast cancer susceptibility loci identified to date for risk stratification. Previous studies of polygenic risk have assumed a log-additive model for combining SNPs; however, this assumption needs to be evaluated empirically. We first assessed whether interaction between SNP pairs could influence the joint contribution of genetic factors on disease risk by testing for all possible pair-wise interactions between SNPs. We then constructed polygenic risk scores (PRSs) to capture the combined effects of the 77 SNPs on overall breast cancer risk, as well as on the risk of ER-positive and ER-negative disease separately. We estimated absolute risks of developing breast cancer for different levels of the PRS, accounting for the competing risk of mortality from other causes. Effect sizes were confirmed in one large study (pKARMA) that was not part of any SNP discovery set. We discuss the degree of breast cancer risk stratification obtained in women with and without a family history of breast cancer.

Methods

Study Subjects and Genotyping

Study participants for the primary analyses (set 1) were 89 049 women of European origin participating in 41 studies in BCAC. All studies were approved by the relevant institutional review boards, and all individuals gave written informed consent. Samples were genotyped using a custom Illumina iSelect array (iCOGS) comprising 211 155 SNPs (15). For some analyses, a further 72 014 women in BCAC genotyped for the relevant SNPs in earlier experiments were included (set 2). For PRS analyses (67 054 women), studies that oversampled breast cancer cases with a family history (21 995 women) were excluded. Supplementary Tables 1–3(available online) show study designs and numbers of breast cancer cases and control women included.
Analyses were based primarily on variants reported to be associated (at P< 5x10-8) by COGS or previous publications, with either breast cancer overall or ER-negative disease. SNPs and regions included are summarized in Supplementary Table 4 (available online).

Statistical Methods

Tests for pair-wise SNP*SNP interactions (departures from a multiplicative model) were carried out using logistic regression, with breast cancer as the outcome. The two SNPs were each coded as a categorical variable (ie, fitting a separate parameter for heterozygous and risk-allele homozygous genotypes), while the interaction term (SNP1*SNP2) was included as continuous covariate. All analyses were adjusted for study and seven principal components (PC) to account for population substructure (15). Additional interaction tests used are described in the Supplementary Methods (available online).
To investigate the association between breast cancer risk and the combined effects of 77 SNPs, a PRS was derived for each individual using the formula:
PRS=β1x1+β2x2+βkxk+βnxn
where β k is the per-allele log odds ratio (OR) for breast cancer associated with the minor allele for SNP k, and x k the number of alleles for the same SNP (0, 1, or 2), and n = 77 is the total number of SNPs. Thus, the PRS summarizes the combined effect of the SNPs, ignoring departures from a multiplicative model (18). SNPs and corresponding odds ratios used in derivation of PRSs are summarized in Supplementary Table 4 (available online).
Logistic regression models were used to estimate the odds ratios for breast cancer by percentile of the PRS, with the middle quintile category (40th to 60th percentile) as the reference. Observed odds ratios for breast cancer by percentile of the PRS were compared with predicted odds ratios under a multiplicative polygenic model of inheritance. Modification of the PRS by age or by family history of breast cancer in a first-degree relative was evaluated by fitting additional interaction terms in the model. All tests of statistical significance were two-sided. The thresholds for statistical significance are indicated below.
The absolute risk of overall breast cancer, ER-positive and ER-negative breast cancer for individuals in each risk category, was calculated taking into account the competing risk of dying from other causes apart from breast cancer. Approximate confidence limits for the absolute risk were derived from the variance-covariance matrix of the log (relative risk) parameters in the logistic regression analysis. Detailed methods are provided in Supplementary Methods (available online).

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