The predictive ability of the 313-variant-based polygenic risk score for contralateral breast cancer risk prediction in women of European ancestry with a heterozygote BRCA1 or BRCA2 pathogenic variant

Inge MM Lakeman; Alexandra J van den Broek; Juliёn AM Vos; Daniel R Barnes; Julian Adlard; Irene L Andrulis; Adalgeir Arason; Norbert Arnold; Banu K Arun; Judith Balmaña; Daniel Barrowdale; Javier Benitez; Ake Borg; Trinidad Caldés; Maria A Caligo; Wendy K Chung; Kathleen BM Claes; GEMO Study Collaborators; EMBRACE Collaborators; J Margriet Collée; Fergus J Couch; Mary B Daly; Joe Dennis; Mallika Dhawan; Susan M Domchek; Ros Eeles; Christoph Engel; D Gareth Evans; Lidia Feliubadaló; Lenka Foretova; Eitan Friedman; Debra Frost; Patricia A Ganz; Judy Garber; Simon A Gayther; Anne-Marie Gerdes; Andrew K Godwin; David E Goldgar; Eric Hahnen; Christopher R Hake; Ute Hamann; Frans BL Hogervorst; Maartje J Hooning; John L Hopper; Peter J Hulick; Evgeny N Imyanitov; OCGN Investigators; HEBON Investigators; GEMO Investigators; Claudine Isaacs; Louise Izatt; Anna Jakubowska; Paul A James; Ramunas Janavicius; Uffe Birk Jensen; Yue Jiao; Esther M John; Vijai Joseph; Beth Y Karlan; Carolien M Kets; Irene Konstantopoulou; Ava Kwong; Clémentine Legrand; Goska Leslie; Fabienne Lesueur; Jennifer T Loud; Jan Lubiński; Siranoush Manoukian; Lesley McGuffog; Austin Miller; Denise Molina Gomes; Marco Montagna; Emmanuelle Mouret-Fourme; Katherine L Nathanson; Susan L Neuhausen; Heli Nevanlinna; Joanne Ngeow Yuen Yie; Edith Olah; Olufunmilayo I Olopade; Sue K Park; Michael T Parsons; Paolo Peterlongo; Marion Piedmonte; Paolo Radice; Johanna Rantala; Gad Rennert; Harvey A Risch; Rita K Schmutzler; Priyanka Sharma; Jacques Simard; Christian F Singer; Zsofia Stadler; Dominique Stoppa-Lyonnet; Christian Sutter; Yen Yen Tan; Manuel R Teixeira; Soo Hwang Teo; Alex Teulé; Mads Thomassen; Darcy L Thull

doi:10.1038/s41436-021-01198-7

. Author manuscript; available in PMC: 2021 Oct 7.

Published in final edited form as: Genet Med. 2021 Jun 10;23(9):1726–1737. doi: 10.1038/s41436-021-01198-7

The predictive ability of the 313-variant-based polygenic risk score for contralateral breast cancer risk prediction in women of European ancestry with a heterozygote BRCA1 or BRCA2 pathogenic variant

Inge MM Lakeman ^1,^2,^*, Alexandra J van den Broek ^3,^*, Juliёn AM Vos ³, Daniel R Barnes ⁴, Julian Adlard ⁵, Irene L Andrulis ^6,⁷, Adalgeir Arason ^8,⁹, Norbert Arnold ^10,¹¹, Banu K Arun ¹², Judith Balmaña ^13,¹⁴, Daniel Barrowdale ⁴, Javier Benitez ^15,¹⁶, Ake Borg ¹⁷, Trinidad Caldés ¹⁸, Maria A Caligo ¹⁹, Wendy K Chung ²⁰, Kathleen BM Claes ²¹; GEMO Study Collaborators^120,^121,^122,^123,^124,^125,^126,^127,^128,^129,^130,^131,^132,^133,^134,^135,^136,^137,^138,^139,^140,^141,^142,^143,^144,^145,^146,^147,^148,^149,^150,^151,^152,^153,^154,^155,^156,^157,^158,^159,^160,^161,^162,^163,^164,^165,^166,^167,^168,^169,^170,^171,^172,^173,^174,^175,^176,^177,^178,^179,^180,^181,^182,^183,^184,^185,^**; EMBRACE Collaborators^120,^121,^122,^123,^124,^125,^126,^127,^128,^129,^130,^131,^132,^133,^134,^135,^136,^137,^138,^139,^140,^141,^142,^143,^144,^145,^146,^147,^148,^149,^150,^151,^152,^153,^154,^155,^156,^157,^158,^159,^160,^161,^162,^163,^164,^165,^166,^167,^168,^169,^170,^171,^172,^173,^174,^175,^176,^177,^178,^179,^180,^181,^182,^183,^184,^185,^**, J Margriet Collée ²², Fergus J Couch ²³, Mary B Daly ²⁴, Joe Dennis ⁴, Mallika Dhawan ²⁵, Susan M Domchek ²⁶, Ros Eeles ²⁷, Christoph Engel ²⁸, D Gareth Evans ^29,³⁰, Lidia Feliubadaló ³¹, Lenka Foretova ³², Eitan Friedman ^33,³⁴, Debra Frost ⁴, Patricia A Ganz ³⁵, Judy Garber ³⁶, Simon A Gayther ³⁷, Anne-Marie Gerdes ³⁸, Andrew K Godwin ³⁹, David E Goldgar ⁴⁰, Eric Hahnen ^41,⁴², Christopher R Hake ⁴³, Ute Hamann ⁴⁴, Frans BL Hogervorst ⁴⁵, Maartje J Hooning ⁴⁶, John L Hopper ⁴⁷, Peter J Hulick ^48,⁴⁹, Evgeny N Imyanitov ⁵⁰; OCGN Investigators^120,^121,^122,^123,^124,^125,^126,^127,^128,^129,^130,^131,^132,^133,^134,^135,^136,^137,^138,^139,^140,^141,^142,^143,^144,^145,^146,^147,^148,^149,^150,^151,^152,^153,^154,^155,^156,^157,^158,^159,^160,^161,^162,^163,^164,^165,^166,^167,^168,^169,^170,^171,^172,^173,^174,^175,^176,^177,^178,^179,^180,^181,^182,^183,^184,^185,^**; HEBON Investigators^120,^121,^122,^123,^124,^125,^126,^127,^128,^129,^130,^131,^132,^133,^134,^135,^136,^137,^138,^139,^140,^141,^142,^143,^144,^145,^146,^147,^148,^149,^150,^151,^152,^153,^154,^155,^156,^157,^158,^159,^160,^161,^162,^163,^164,^165,^166,^167,^168,^169,^170,^171,^172,^173,^174,^175,^176,^177,^178,^179,^180,^181,^182,^183,^184,^185,^**; GEMO Investigators^120,^121,^122,^123,^124,^125,^126,^127,^128,^129,^130,^131,^132,^133,^134,^135,^136,^137,^138,^139,^140,^141,^142,^143,^144,^145,^146,^147,^148,^149,^150,^151,^152,^153,^154,^155,^156,^157,^158,^159,^160,^161,^162,^163,^164,^165,^166,^167,^168,^169,^170,^171,^172,^173,^174,^175,^176,^177,^178,^179,^180,^181,^182,^183,^184,^185,^**, Claudine Isaacs ⁵¹, Louise Izatt ⁵², Anna Jakubowska ^53,⁵⁴, Paul A James ^55,⁵⁶, Ramunas Janavicius ^57,⁵⁸, Uffe Birk Jensen ⁵⁹, Yue Jiao ^60,^61,⁶², Esther M John ^63,⁶⁴, Vijai Joseph ⁶⁵, Beth Y Karlan ⁶⁶, Carolien M Kets ⁴⁵, Irene Konstantopoulou ⁶⁷, Ava Kwong ^68,^69,⁷⁰, Clémentine Legrand ⁷¹, Goska Leslie ⁴, Fabienne Lesueur ^60,^61,⁶², Jennifer T Loud ⁷², Jan Lubiński ⁵³, Siranoush Manoukian ⁷³, Lesley McGuffog ⁴, Austin Miller ⁷⁴, Denise Molina Gomes ⁷⁵, Marco Montagna ⁷⁶, Emmanuelle Mouret-Fourme ⁷⁷, Katherine L Nathanson ²⁶, Susan L Neuhausen ⁷⁸, Heli Nevanlinna ⁷⁹, Joanne Ngeow Yuen Yie ^80,⁸¹, Edith Olah ⁸², Olufunmilayo I Olopade ⁸³, Sue K Park ^84,^85,⁸⁶, Michael T Parsons ⁸⁷, Paolo Peterlongo ⁸⁸, Marion Piedmonte ⁷⁴, Paolo Radice ⁸⁹, Johanna Rantala ⁹⁰, Gad Rennert ⁹¹, Harvey A Risch ⁹², Rita K Schmutzler ^41,^42,⁹³, Priyanka Sharma ⁹⁴, Jacques Simard ⁹⁵, Christian F Singer ⁹⁶, Zsofia Stadler ⁹⁷, Dominique Stoppa-Lyonnet ^77,^98,⁹⁹, Christian Sutter ¹⁰⁰, Yen Yen Tan ¹⁰¹, Manuel R Teixeira ^102,¹⁰³, Soo Hwang Teo ^104,¹⁰⁵, Alex Teulé ³¹, Mads Thomassen ¹⁰⁶, Darcy L Thull ¹⁰⁷, Marc Tischkowitz ^108,¹⁰⁹, Amanda E Toland ¹¹⁰, Nadine Tung ¹¹¹, Elizabeth J van Rensburg ¹¹², Ana Vega ^113,^114,¹¹⁵, Barbara Wappenschmidt ^41,⁴², Peter Devilee ^1,¹¹⁶, Christi J van Asperen ², Jonine L Bernstein ¹¹⁷, Kenneth Offit ^65,⁹⁷, Douglas F Easton ^4,¹¹⁸, Matti A Rookus ¹¹⁹, Georgia Chenevix-Trench ⁸⁷, Antonis C Antoniou ^4,^#, Mark Robson ^97,^#, Marjanka K Schmidt ^2,^3,^119,^#,^✉

¹Department of Human Genetics. In. Leiden University Medical Center. Vol P.O. Box 9600, 2300 RC. Leiden: The Netherlands; 2333 ZA

²Department of Clinical Genetics. In. Leiden University Medical Center. Vol P.O. Box 9600, 2300 RC. Leiden: The Netherlands; 2333 ZA

³Division of Molecular Pathology. In. The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital. Amsterdam: The Netherlands; 1066 CX

⁴Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care. In. University of Cambridge. Cambridge: UK; CB1 8RN

⁵Yorkshire Regional Genetics Service. In. Chapel Allerton Hospital. Leeds: UK; LS7 4SA

⁶Fred A. Litwin Center for Cancer Genetics. In. Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital. Toronto, ON: Canada; M5G 1X5

⁷Department of Molecular Genetics. In. University of Toronto. Toronto, ON: Canada; M5S 1A8

⁸Department of Pathology. In. Landspitali University Hospital. Reykjavik: Iceland; 101

⁹BMC (Biomedical Centre), Faculty of Medicine. In. University of Iceland. Reykjavik: Iceland; 101

¹⁰Department of Gynaecology and Obstetrics. In. University Hospital of Schleswig-Holstein, Campus Kiel, Christian-Albrechts University Kiel. Kiel: Germany; 24118

¹¹Institute of Clinical Molecular Biology. In. University Hospital of Schleswig-Holstein, Campus Kiel, Christian-Albrechts University Kiel. Kiel: Germany; 24118

¹²Department of Breast Medical Oncology. In. University of Texas MD Anderson Cancer Center. Houston, TX: USA; 77030

¹³Hereditary cancer Genetics Group. In. Vall d’Hebron Institute of Oncology. Barcelona: Spain; 08035

¹⁴Department of Medical Oncology, Vall d’Hebron Barcelona Hospital Campus. In. University Hospital of Vall d’Hebron. Barcelona: Spain; 08035

¹⁵In. Centro de Investigación en Red de Enfermedades Raras (CIBERER). Madrid: Spain; 28029

¹⁶Human Cancer Genetics Programme. In. Spanish National Cancer Research Centre (CNIO). Madrid: Spain; 28029

¹⁷Department of Oncology. In. Lund University and Skåne University Hospital. Lund: Sweden; 222 41

¹⁸Molecular Oncology Laboratory. In. CIBERONC, Hospital Clinico San Carlos, IdISSC (Instituto de Investigación Sanitaria del Hospital Clínico San Carlos). Madrid: Spain; 28040

¹⁹SOD Genetica Molecolare. In. University Hospital. Pisa: Italy

²⁰Departments of Pediatrics and Medicine. In. Columbia University. New York, NY: USA; 10032

²¹Centre for Medical Genetics. In. Ghent University. Gent: Belgium; 9000

²²Department of Clinical Genetics. In. Erasmus University Medical Center. Vol P.O. Box 2040, 3000 CA. Rotterdam: The Netherlands; 3015 CN

²³Department of Laboratory Medicine and Pathology. In. Mayo Clinic. Rochester, MN: USA; 55905

²⁴Department of Clinical Genetics. In. Fox Chase Cancer Center. Philadelphia, PA: USA; 19111

²⁵Cancer Genetics and Prevention Program. In. University of California San Francisco. San Francisco, CA: USA; 94143-1714

²⁶Basser Center for BRCA, Abramson Cancer Center. In. University of Pennsylvania. Philadelphia, PA: USA; 19066

²⁷Oncogenetics Team. In. The Institute of Cancer Research and Royal Marsden NHS Foundation Trust. London: UK; SM2 5NG

²⁸Institute for Medical Informatics, Statistics and Epidemiology. In. University of Leipzig. Leipzig: Germany; 04107

²⁹Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health. In. University of Manchester, Manchester Academic Health Science Centre. Manchester: UK; M13 9WL

³⁰North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine. In. St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre. Manchester: UK; M13 9WL

³¹Hereditary Cancer Program. In. ONCOBELL-IDIBELL-IGTP, Catalan Institute of Oncology, CIBERONC. Barcelona: Spain; 8908

³²Department of Cancer Epidemiology and Genetics. In. Masaryk Memorial Cancer Institute. Brno: Czech Republic; 65653

³³The Susanne Levy Gertner Oncogenetics Unit. In. Chaim Sheba Medical Center. Ramat Gan: Israel; 52621

³⁴Sackler Faculty of Medicine. In. Tel Aviv University. Ramat Aviv: Israel; 69978

³⁵Schools of Medicine and Public Health, Division of Cancer Prevention & Control Research. In. Jonsson Comprehensive Cancer Centre, UCLA. Los Angeles, CA: USA; 90096-6900

³⁶Cancer Risk and Prevention Clinic. In. Dana-Farber Cancer Institute. Boston, MA: USA; 02215

³⁷Center for Bioinformatics and Functional Genomics and the Cedars Sinai Genomics Core. In. Cedars-Sinai Medical Center. Los Angeles, CA: USA; 90048

³⁸Department of Clinical Genetics. In. Rigshospitalet, Copenhagen University Hospital. Copenhagen: Denmark; DK-2100

³⁹Department of Pathology and Laboratory Medicine. In. University of Kansas Medical Center. Kansas City, KS: USA; 66160

⁴⁰Department of Dermatology. In. Huntsman Cancer Institute, University of Utah School of Medicine. Salt Lake City, UT: USA; 84112

⁴¹Center for Familial Breast and Ovarian Cancer. In. Faculty of Medicine and University Hospital Cologne, University of Cologne. Cologne: Germany; 50937

⁴²Center for Integrated Oncology (CIO). In. Faculty of Medicine and University Hospital Cologne, University of Cologne. Cologne: Germany; 50937

⁴³In. Waukesha Memorial Hospital-Pro Health Care. Waukesha: USA; 53188

⁴⁴Molecular Genetics of Breast Cancer. In. German Cancer Research Center (DKFZ). Heidelberg: Germany; 69120

⁴⁵Family Cancer Clinic. In. The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital. Vol P.O. Box 90203, 1006 BE Amsterdam. Amsterdam: The Netherlands; 1066 CX

⁴⁶Department of Medical Oncology. In. Erasmus MC Cancer Institute. Vol P.O. Box 2040, 3000 CA. Rotterdam: The Netherlands; 3015 CN

⁴⁷Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health. In. The University of Melbourne. Melbourne, Victoria: Australia; 3010

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⁴⁹In. The University of Chicago Pritzker School of Medicine. Chicago, IL: USA; 60637

⁵⁰In. N.N. Petrov Institute of Oncology. St. Petersburg: Russia; 197758

⁵¹In. Lombardi Comprehensive Cancer Center, Georgetown University. Washington, DC: USA; 20007

⁵²Clinical Genetics. In. Guy’s and St Thomas’ NHS Foundation Trust. London: UK; SE1 9RT

⁵³Department of Genetics and Pathology. In. Pomeranian Medical University. Szczecin: Poland; 71-252

⁵⁴Independent Laboratory of Molecular Biology and Genetic Diagnostics. In. Pomeranian Medical University. Szczecin: Poland; 71-252

⁵⁵Parkville Familial Cancer Centre. In. Peter MacCallum Cancer Center. Melbourne, Victoria: Australia; 3000

⁵⁶Sir Peter MacCallum Department of Oncology. In. The University of Melbourne. Melbourne, Victoria: Australia; 3000

⁵⁷Hematology, oncology and transfusion medicine center, Dept. of Molecular and Regenerative Medicine. In. Vilnius University Hospital Santariskiu Clinics. Vilnius: Lithuania

⁵⁸In. State Research Institute Centre for Innovative Medicine. Vilnius: Lithuania

⁵⁹Department of Clinical Genetics. In. Aarhus University Hospital. Aarhus N: Denmark; 8200

⁶⁰Genetic Epidemiology of Cancer team. In. Inserm U900. Paris: France; 75005

⁶¹In. Institut Curie. Paris: France; 75005

⁶²In. Mines ParisTech. Fontainebleau: France; 77305

⁶³Department of Epidemiology & Population Health. In. Stanford University School of Medicine. Stanford, CA: USA; 94305

⁶⁴Department of Medicine, Division of Oncology. In. Stanford Cancer Institute, Stanford University School of Medicine. Stanford, CA: USA; 94304

⁶⁵Clinical Genetics Research Lab, Department of Cancer Biology and Genetics. In. Memorial Sloan Kettering Cancer Center. New York, NY: USA; 10065

⁶⁶David Geffen School of Medicine, Department of Obstetrics and Gynecology. In. University of California at Los Angeles. Los Angeles, CA: USA; 90095

⁶⁷Molecular Diagnostics Laboratory, INRASTES. In. National Centre for Scientific Research ‘Demokritos’. Athens: Greece; 15310

⁶⁸Hong Kong Hereditary Breast Cancer Family Registry. In. Cancer Genetics Centre. Happy Valley: Hong Kong

⁶⁹Department of Surgery. In. The University of Hong Kong. Pok Fu Lam: Hong Kong

⁷⁰Department of Surgery. In. Hong Kong Sanatorium and Hospital. Happy Valley: Hong Kong

⁷¹Département de Génétique. In. CHU de Grenoble. Grenoble: France; 38043

⁷²Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics. In. National Cancer Institute. Bethesda, MD: USA; 20850-9772

⁷³Unit of Medical Genetics, Department of Medical Oncology and Hematology. In. Fondazione IRCCS Istituto Nazionale dei Tumori di Milano. Milan: Italy; 20133

⁷⁴NRG Oncology, Statistics and Data Management Center. In. Roswell Park Cancer Institute. Buffalo, NY: USA; 14263

⁷⁵Service de Biologie de la reproduction, Cytogénétique et Génétique Médicale. In. CHI Poissy - Saint Germain. Poissy: France; 78303

⁷⁶Immunology and Molecular Oncology Unit. In. Veneto Institute of Oncology IOV - IRCCS. Padua: Italy; 35128

⁷⁷Service de Génétique. In. Institut Curie. Paris: France; 75005

⁷⁸Department of Population Sciences. In. Beckman Research Institute of City of Hope. Duarte, CA: USA; 91010

⁷⁹Department of Obstetrics and Gynecology, Helsinki University Hospital. In. University of Helsinki. Vol P.O. BOX 700 (Haartmaninkatu 8), 00029 HUS. Helsinki: Finland; 00290

⁸⁰Cancer Genetics Service. In. National Cancer Centre. Singapore: Singapore; 169610

⁸¹Lee Kong Chian School of Medicine. In. Nanyang Technological University. Singapore: Singapore; 308232

⁸²Department of Molecular Genetics. In. National Institute of Oncology. Budapest: Hungary; 1122

⁸³Center for Clinical Cancer Genetics. In. The University of Chicago. Chicago, IL: USA; 60637

⁸⁴Department of Preventive Medicine. In. Seoul National University College of Medicine. Seoul: Korea; 03080

⁸⁵Department of Biomedical Sciences. In. Seoul National University Graduate School. Seoul: Korea; 03080

⁸⁶Cancer Research Institute. In. Seoul National University. Seoul: Korea; 03080

⁸⁷Department of Genetics and Computational Biology. In. QIMR Berghofer Medical Research Institute. Vol Locked Bag 2000, Herston, QLD 4029. Brisbane, Queensland: Australia; 4006

⁸⁸Genome Diagnostics Program. In. IFOM - the FIRC Institute of Molecular Oncology. Milan: Italy; 20139

⁸⁹Unit of Molecular Bases of Genetic Risk and Genetic Testing, Department of Research. In. Fondazione IRCCS Istituto Nazionale dei Tumori (INT). Milan: Italy; 20133

⁹⁰Clinical Genetics. In. Karolinska Institutet. Stockholm: Sweden; 171 76

⁹¹Clalit National Cancer Control Center. In. Carmel Medical Center and Technion Faculty of Medicine. Haifa: Israel; 35254

⁹²Chronic Disease Epidemiology. In. Yale School of Public Health. New Haven, CT: USA; 06510

⁹³Center for Molecular Medicine Cologne (CMMC). In. Faculty of Medicine and University Hospital Cologne, University of Cologne. Cologne: Germany; 50931

⁹⁴Department of Internal Medicine, Division of Medical Oncology. In. University of Kansas Medical Center. Westwood, KS: USA; 66205

⁹⁵Genomics Center. In. Centre Hospitalier Universitaire de Québec - Université Laval Research Center. Québec City, QC: Canada; G1V 4G2

⁹⁶Dept of OB/GYN and Comprehensive Cancer Center. In. Medical University of Vienna. Vienna: Austria; 1090

⁹⁷Clinical Genetics Service, Department of Medicine. In. Memorial Sloan Kettering Cancer Center. New York, NY: USA; 10065

⁹⁸Department of Tumour Biology. In. INSERM U830. Paris: France; 75005

⁹⁹In. Université Paris Descartes. Paris: France; 75006

¹⁰⁰Institute of Human Genetics. In. University Hospital Heidelberg. Heidelberg: Germany; 69120

¹⁰¹Dept of OB/GYN. In. Medical University of Vienna. Vienna: Austria; 1090

¹⁰²Department of Genetics. In. Portuguese Oncology Institute. Porto: Portugal; 4200072

¹⁰³Biomedical Sciences Institute (ICBAS). In. University of Porto. Porto: Portugal; 4050013

¹⁰⁴Breast Cancer Research Programme. In. Cancer Research Malaysia. Subang Jaya, Selangor: Malaysia; 47500

¹⁰⁵Department of Surgery, Faculty of Medicine. In. University of Malaya. Kuala Lumpur: Malaysia; 50603

¹⁰⁶Department of Clinical Genetics. In. Odense University Hospital. Odence C: Denmark; 5000

¹⁰⁷Department of Medicine. In. Magee-Womens Hospital, University of Pittsburgh School of Medicine. Pittsburgh, PA: USA; 15213

¹⁰⁸Program in Cancer Genetics, Departments of Human Genetics and Oncology. In. McGill University. Montréal, QC: Canada; H4A 3J1

¹⁰⁹Department of Medical Genetics. In. National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge. Vol Box 134, Level 6 Addenbrooke’s Treatment Centre, Addenbrooke’s Hosptital. Cambridge: UK; CB2 0QQ

¹¹⁰Department of Cancer Biology and Genetics. In. The Ohio State University. Columbus, OH: USA; 43210

¹¹¹Department of Medical Oncology. In. Beth Israel Deaconess Medical Center. Boston, MA: USA; 02215

¹¹²Department of Genetics. In. University of Pretoria. Arcadia: South Africa; 0007

¹¹³In. Centro de Investigación en Red de Enfermedades Raras (CIBERER). Madrid: Spain; 28029

¹¹⁴In. Fundación Pública Galega de Medicina Xenómica. Santiago de Compostela: Spain; 15706

¹¹⁵In. Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Complejo Hospitalario Universitario de Santiago, SERGAS. Santiago de Compostela: Spain; 15706

¹¹⁶Department of Pathology. In. Leiden University Medical Center. Leiden: The Netherlands; 2333 ZA

¹¹⁷Department of Epidemiology and Biostatistics. In. Memorial Sloan-Kettering Cancer Center. New York, NY: USA; 10065

¹¹⁸Centre for Cancer Genetic Epidemiology, Department of Oncology. In. University of Cambridge. Cambridge: UK; CB1 8RN

¹¹⁹Division of Psychosocial Research and Epidemiology. In. The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital. Amsterdam: The Netherlands; 1066 CX

¹²⁰In. Peter MacCallum Cancer Center. Melbourne, Victoria: Australia; 3000

¹²¹Department of Medicine, St Vincent’s Hospital. In. The University of Melbourne. Fitzroy, Victoria: Australia; 3065

¹²²North East Thames Regional Genetics Service. In. Great Ormond Street Hospital for Children NHS Trust. London: UK; WC1N 3JH

¹²³Leicestershire Clinical Genetics Service. In. University Hospitals of Leicester NHS Trust. Leicester: UK; LE1 5WW

¹²⁴North West Thames Regional Genetics Service, Kennedy Galton Centre. In. The North West London Hospitals NHS Trust. Middlesex: UK; HA1 3UJ

¹²⁵Breast Department. In. KK Women’s and Children’s Hospital. Singapore: Singapore; 229899

¹²⁶Department of Clinical Genetics. In. Royal Devon & Exeter Hospital. Exeter: UK; EX2 5DW

¹²⁷Sheffield Clinical Genetics Service. In. Sheffield Children’s Hospital. Sheffield: UK; S10 2TH

¹²⁸Department of Clinical Genetics. In. South Glasgow University Hospitals. Glasgow: UK; G51 4TF

¹²⁹Clinical Genetics Department. In. St Michael’s Hospital. Bristol: UK; BS2 8EG

¹³⁰Nottingham Clinical Genetics Service. In. Nottingham University Hospitals NHS Trust. Nottingham: UK; NG5 1PB

¹³¹Faculty of Medicine. In. University of Southampton. Southampton: UK; SO17 1BJ

¹³²North of Scotland Regional Genetics Service. In. NHS Grampian & University of Aberdeen, Foresterhill. Aberdeen: UK

¹³³Southwest Thames Regional Genetics Service. In. St George’s Hospital. London: UK; SW17 0QT

¹³⁴Institute of Genetic Medicine, Centre for Life. In. Newcastle Upon Tyne Hospitals NHS Trust. Newcastle upon Tyne: UK; NE1 3BZ

¹³⁵Department of Clinical Genetics. In. St George’s, University of London. London: UK; SW17 0RE

¹³⁶Academic Unit of Clinical and Molecular Oncology. In. Trinity College Dublin and St James’s Hospital. Dublin: Eire; 8

¹³⁷Department of Population Health Sciences. In. Weill Cornell Medicine. New York, NY: USA; 10065

¹³⁸Department of Clinical Genetics. In. Alder Hey Hospital. Liverpool: UK; L12 2AP

¹³⁹Northern Ireland Regional Genetics Centre. In. Belfast City Hospital. Belfast: UK; BT9 7AB

¹⁴⁰West Midlands Regional Genetics Service. In. Birmingham Women’s Hospital Healthcare NHS Trust. Birmingham: UK; B15 2TG

¹⁴¹In. SingHealth Duke-NUS Breast Centre. Singapore: Singapore; 168753

¹⁴²South East of Scotland Regional Genetics Service. In. Western General Hospital. Edinburgh: UK; EH4 2XU

¹⁴³All Wales Medical Genetics Services. In. University Hospital of Wales. Cardiff: UK; CF14 4XW

¹⁴⁴In. Princess Anne Hospital. Southampton: UK; SO16 5YA

¹⁴⁵Medical Genetics Unit. In. St George’s, University of London. London: UK; SW17 0RE

¹⁴⁶Oxford Regional Genetics Service. In. Churchill Hospital. Oxford: UK; OX3 7LJ

¹⁴⁷Oncogénétique. In. Institut Bergonié. Bordeaux: France; 33076

¹⁴⁸Département de Biopathologie. In. Centre François Baclesse. Caen: France; 14076

¹⁴⁹Laboratoire d’Oncologie moléculaire, Centre de Lutte Contre le Cancer. In. Centre Jean Perrin. Clermont-Ferrand: France; 63011

¹⁵⁰Unite de Prevention et d’Epidėmiologie Génétique. In. Centre Léon Bérard. Lyon: France; 69373

¹⁵¹Département de médicine - Oncogénétique. In. Gustave Roussy. Villejuif: France; 94805

¹⁵²In. Paris Sciences Lettres Research University. Paris: France; 75005

¹⁵³Service de Génétique Biologique. In. CHRU de Besançon. Besançon: France; 25030

¹⁵⁴Unité dOncogénétique. In. CHU Arnaud de Villeneuve. Montpellier: France; 34295

¹⁵⁵Oncogénétique. In. Institut de Cancérologie de l’Ouest siteRené Gauducheau. Saint Herblain: France; 44805

¹⁵⁶Unité d’oncogénétique, Centre de Lutte Contre le Cancer. In. Centre Georges-François Leclerc. Dijon: France; 21000

¹⁵⁷Centre de Génétique. In. CHU Dijon. Dijon: France

¹⁵⁸Laboratoire de Génétique Chromosomique. In. Hôtel Dieu Centre Hospitalier. Chambéry: France; 73011

¹⁵⁹Service Régional Oncogénétique Poitou-Charentes. In. CH Niort. Niort: France; 79021

¹⁶⁰Service de Génétique. In. Groupement Hospitalier Est, Hospices Civils de Lyon. Bron: France; F-69677

¹⁶¹Department of Genetics. In. F76000 and Normandy University, UNIROUEN, Inserm U1245, Normandy Centre for Genomic and Personalized Medicine, Rouen University Hospital. Rouen: France

¹⁶²Département d?ématologie-Oncologie Médicale. In. Centre Antoine Lacassagne. Nice: France; 06100

¹⁶³Lyon Neuroscience Research Center - CRNL, INSERM U1028, CNRS UMR5292. In. University of Lyon. Lyon: France; 69500

¹⁶⁴Service de Génétique. In. Hôpital Bretonneau - CHRU. Tours: France; 37004

¹⁶⁵Service de Génétique Clinique Chromosomique et Moléculaire. In. Hôpital Nord, CHU Saint Etienne. St Etienne: France; 42270

¹⁶⁶Unité dOncogénétique. In. Centre Paul Strauss. Vol BP 30042. Strasbourg: France; 67065

¹⁶⁷Département Oncologie Génétique, Prévention et Dépistage. In. Institut Paoli-Calmettes. Marseille: France; 13009

¹⁶⁸Marseille Medical School. In. Aix-Marseille University. Marseille: France

¹⁶⁹Laboratoire de génétique médicale. In. Nancy Université, Centre Hospitalier Régional et Universitaire. Vandoeuvre-les-Nancy: France; 54511

¹⁷⁰Department of Medical Oncology. In. CHU Dupuytren. Limoges: France; 87042

¹⁷¹Department of Clinical Genetics. In. Amsterdam UMC, location AMC. Vol P.O. Box 2260, 1100 DD. Amsterdam: The Netherlands; 1100 DD

¹⁷²Division Laboratories, Pharmacy and Biomedical Genetics, Department of Genetics. In. University Medical Center Utrecht. Utrecht: The Netherlands; 3508 AB

¹⁷³Department of Clinical Genetics. In. Maastricht University Medical Center. Vol P.O. Box 5800, 6202 AZ. Maastricht: The Netherlands; 6229 HX

¹⁷⁴Department of Surgical Oncology, Family Cancer Clinic. In. Erasmus MC Cancer Institute. Vol P.O. Box 2040, 3000 CA. Rotterdam: The Netherlands; 3015 CN

¹⁷⁵Department of Medical Genetics. In. University Medical Center. Vol P.O. Box 85090, 3508 AB. Utrecht: The Netherlands; 3594 CX

¹⁷⁶Department of Human Genetics. In. Radboud University Medical Center. Vol P.O. Box 9101, 6500 HB. Nijmegen: The Netherlands; 6525 GA

¹⁷⁷Department of Epidemiology. In. The Netherlands Cancer Institute. Vol P.O. Box 90203, 1006 BE. Amsterdam: The Netherlands; 1066 CX

¹⁷⁸Department of Genetics. In. University Medical Center Groningen, University Groningen. Groningen: The Netherlands; 9713 GZ

¹⁷⁹Department of Pathology. In. Erasmus University Medical Center. Vol P.O. Box 2040, 3000 CA. Rotterdam: The Netherlands; 3015 CN

¹⁸⁰Department of Gynaecology, Family Cancer Clinic. In. Erasmus MC Cancer Institute. Vol P.O. Box 5201, 3008 AE. Rotterdam: The Netherlands; 3015 CE

¹⁸¹Department of Clinical Genetics. In. VU University Medical Center. Vol P.O. Box 7057, 1007 MB. Amsterdam: The Netherlands; 1105 AZ

¹⁸²Clinical Genetics. In. Amsterdam UMC, Vrije Universiteit Amsterdam. Vol P.O. Box 7057. Amsterdam: The Netherlands; 1007 MB

¹⁸³Department of Human Genetics and Department of Clinical Genetics. In. Leiden University Medical Center. Leiden: The Netherlands; 2333 ZA

¹⁸⁴Department of Laboratory Medicine and Pathobiology. In. University of Toronto. Toronto, ON: Canada; M5S 1A8

¹⁸⁵Laboratory Medicine Program. In. University Health Network. Toronto, ON: Canada; M5G 2C4

^✉

Corresponding author: Marjanka K. Schmidt, Division of Molecular Pathology and Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands, mk.schmidt@nki.nl

Contributed equally to this manuscript

^**

A list of authors and their affiliations appears at the end of the paper.

Shared last authors

^✉

Corresponding author.

PMCID: PMC8460445 EMSID: EMS128250 PMID: 34113011

Abstract

Purpose

To evaluate the association between a previously published 313-variant-based breast cancer (BC) polygenic risk score (PRS₃₁₃) and contralateral breast cancer (CBC) risk, in BRCA1 and BRCA2 pathogenic variant heterozygotes.

Methods

We included women of European ancestry with a prevalent first primary invasive BC (BRCA1=6,591 with 1,402 prevalent CBC cases; BRCA2=4,208 with 647 prevalent CBC cases) from CIMBA, a large international retrospective series. Cox regression analysis was performed to assess the association between overall and ER-specific PRS₃₁₃ and CBC risk.

Results

For BRCA1 heterozygotes the estrogen receptor (ER)-negative PRS₃₁₃ showed the largest association with CBC risk, HR per SD=1.12, 95%CI [1.06-1.18], C-index=0.53; for BRCA2 heterozygotes, this was the ER-positive PRS₃₁₃, HR=1.15, 95%CI [1.07-1.25], C-index=0.57. Adjusting for family history, age at diagnosis, treatment or pathological characteristics for the first BC did not change association effect sizes. For women developing first BC <age 40 years, the cumulative PRS₃₁₃ 5^th and 95^th percentile 10-year CBC risks were 22% and 32% for BRCA1 and 13% and 23% for BRCA2 heterozygotes, respectively.

Conclusion

The PRS₃₁₃ can be used to refine individual CBC risks for BRCA1/2 heterozygotes of European ancestry, however the PRS₃₁₃ needs to be considered in the context of a multifactorial risk model to evaluate whether it might influence clinical-decision-making.

Introduction

Heterozygotes of germline pathogenic variants in BRCA1 or BRCA2 (henceforth: BRCA1/2 heterozygotes) have a higher risk of developing contralateral breast cancer than non-heterozygotes¹. The estimated cumulative 10-year contralateral breast cancer risk varies across studies between 18.5%-34.2% for BRCA1 heterozygotes and between 10.8%-29.2% for BRCA2 heterozygotes^1–6, compared to 4-6% in the population^7,8. Whether or not to undergo a risk-reducing contralateral mastectomy, which is an invasive intervention and associated with side effects such as postoperative surgical complications, inability to breast feed in the future and psychosocial burden⁹, is an important and difficult decision for BRCA1/2 heterozygotes that have been just confronted with their first breast cancer diagnosis. Precise individualized risk estimates could facilitate decision making for these women.

Two important factors influencing contralateral breast cancer risk in BRCA1/2 heterozygotes are the age at diagnosis of the first breast tumor and a family history of breast cancer^2,4,5,10. The effect of family history on contralateral breast cancer risk suggests a role for other genetic factors. In the last decade, more than 180 common low risk variants have been associated with breast cancer risk in Genome Wide Association Studies^11–13. Individually, these variants are associated with small increases in risk, but when combined as polygenic risk scores (PRS) they may improve disease-related risk stratification for women of European and Asian ancestry in the population^14–16. Limited number of studies have shown that variants associated with the risk of a first primary breast cancer are also associated with the risk of contralateral breast cancer^17–19. Furthermore, the PRS derived from the general population has also been shown to be associated with breast cancer risk in BRCA1/2 heterozygotes^20–24.

The most predictive, well validated PRS, for breast cancer in the general population is based on 313 breast cancer-associated variants (PRS₃₁₃); it showed an association with breast cancer in ten prospective studies with an odds ratio (OR) per standard deviation (SD) of 1.61 and an area under the receiver-operator characteristic curve of 0.630¹⁴. Among BRCA2 heterozygotes, this same PRS₃₁₃ was also associated with breast cancer risk, hazard ratio (HR) per SD=1.31, 95%CI [1.27-1.36]²⁴. Among BRCA1 heterozygotes, the largest association with breast cancer risk was found using the estrogen receptor (ER)-negative PRS₃₁₃ (which uses the same variants but with weights adapted to provide better prediction for ER-negative disease), HR=1.29, 95%CI [1.25-1.33]²⁴. Although these effect sizes were smaller than those for the general population, the 313-variant-based PRS could have a substantial impact on the high absolute risks²⁴, associated with BRCA1/2 pathogenic variants²⁵. Whether variants associated with breast cancer are associated with contralateral breast cancer risk for BRCA1/2 heterozygotes as well, individually or combined in a PRS, has not been investigated before. If so, the PRS may be useful to guide choices for risk management, especially regarding invasive risk-reducing contralateral mastectomy. In this study, we investigated whether the 313-variant-based PRS for breast cancer are associated with contralateral breast cancer risk among women of European ancestry with pathogenic variants in BRCA1/2 and explored the implications for contralateral breast cancer risk prediction for these women.

Materials and Methods

Study participants

We used retrospective cohort data from heterozygotes participating in the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA)²⁶. Briefly, CIMBA participants are heterozygotes of pathogenic variants in BRCA1 or BRCA2 who are 18 years or older at the time of inclusion and have phenotypic data available²⁶. CIMBA includes eighty-one individual studies of which the majority of the participants were ascertained through cancer genetics clinics²⁶. Although studies in CIMBA include individuals of non-European ancestry, our analyses were, due to power considerations (small numbers available for analyses and expected lower estimates for the PRS₃₁₃ in Asian ancestry based on results of women in the general breast cancer population¹⁹), restricted to women of European ancestry with available array genotyping data (31,195 women of 67 studies).

Women were eligible for this retrospective analysis if they developed an invasive primary breast tumor without metastatic disease at least 1 year before the baseline age. Women without information about metastatic disease were assumed to have no metastatic disease (n=9,242 of which 2,140 had a known negative lymph node status). Baseline age was defined as the age at local ascertainment (97%), or when this was not known, age at genetic testing (2%) or age at last follow-up (1%). Women were excluded if no information was available about the age at baseline or if they had developed synchronous contralateral breast cancer. Synchronous contralateral breast cancer was defined as contralateral breast cancer within one year after the first primary breast cancer, which was based on the exact date of cancer diagnosis or, if this was not available, on the age at diagnosis. A schematic overview of the selection is shown in Figure S1. In total, 6,591 women with BRCA1 and 4,208 women with BRCA2 pathogenic variants were included in this study, among whom 1,402 BRCA1 heterozygotes and 647 BRCA2 heterozygotes have had contralateral breast cancer. The diagnosis of primary and contralateral breast cancer was confirmed by pathology records, tumor registry data or medical records by the individual studies. Available phenotypic information for all participants is shown in Table 1, including the number of participants for whom the information was not available for each of the variables. Information about the ER-status of the first primary breast cancer compared to the contralateral breast cancer is shown in Table S1.

Table 1. Characteristics of the participants.

		BRCA1 heterozygotes		BRCA2 heterozygotes
		UBC, n (%)	CBC, n (%)	UBC, n (%)	CBC, n (%)
N		5,189	1,402	3,561	647
Genotyping Array	iCOGS	895 (17)	200 (14)	383 (11)	80 (12)
Genotyping Array	OncoArray	4,294 (83)	1,202 (86)	3,178 (89)	567 (88)
Birth cohort	<1920	25 (0.5)	8 (0.6)	23 (0.6)	9 (1)
	1920-1929	143 (3)	46 (3)	121 (3)	30 (5)
	1930-1939	392 (8)	130 (9)	341 (10)	99 (15)
	1940-1949	1,060 (20)	386 (28)	793 (22)	172 (27)
	1950-1959	1,540 (30)	452 (32)	1,104 (31)	202 (31)
	1960-1969	1,354 (26)	298 (21)	822 (23)	115 (18)
	≥1970	675 (13)	82 (6)	357 (10)	20 (3)
Variant class^a	I	3,354 (65)	904 (64)	3,207 (90)	570 (88)
	II	1,345 (26)	374 (27)	125 (4)	25 (4)
	III	490 (9)	124 (9)	229 (6)	52 (8)
BRRM		160 (3)	0	101 (3)	0
Deceased	N	44 (0.8)	12 (0.9)	19 (0.5)	2 (0.3)
Family history^b	No BC	583 (11)	175 (12)	289 (8)	78 (12)
	1 BC	906 (17)	270 (19)	760 (21)	127 (20)
	≥ 2 BC	1,250 (24)	363 (26)	1,120 (31)	210 (32)
	Unknown	2,450 (47)	594 (42)	1,392 (39)	232 (36)
Characteristics of first BC
Age at diagnosis	Mean	41.8	38.5	44.5	41.8
Age at diagnosis	Range	19-82	19-68	18-85	21-75
ER status	Positive	570 (11)	92 (7)	1,302 (37)	182 (28)
	Negative	1,738 (33)	402 (29)	424 (12)	61 (9)
	Unknown	2,881 (56)	908 (65)	1,835 (52)	404 (62)
Node status	Positive	797 (15)	182 (13)	781 (22)	119 (18)
	Negative	1,544 (30)	441 (31)	877 (25)	151 (23)
	Unknown	2,848 (55)	779 56)	1,903 (53)	377 (58)
Tumor size^c	T1	1,261 (24)	314 (22)	842 (24)	136 (21)
	T2	771 (15)	211 (15)	553 (16)	87 (13)
	T3	67 (13)	12 (0.9)	78 (2)	8 (1)
	T4	16 (0.5)	2 (0.1)	22 (0.6)	2 (0.3)
	Unknown	3,074 (59)	863 (62)	2,066 (58)	414 (64)
Chemotherapy^d	Yes	1,099 (21)	236 (17)	821 (23)	123 (19)
	No	576 (11)	212 (15)	503 (14)	129 (20)
	Unknown	3,514 (68)	954 (68)	2,237 (63)	395 (61)
Adjuvant	Yes	493 (10)	125 (9)	795 (22)	111 (17)
hormone	No	1,103 (21)	288 (21)	474 (13)	135 (21)
therapy	Unknown	3,593 (69)	989 (71)	2,292 (64)	401 (62)
Adjuvant	Yes	11 (0.2)	1 (0.1)	20 (0.6)	0 (0)
trastuzumab	No	1,161 (22)	351 (25)	983 (28)	218 (34)
therapy	Unknown	4,017 (77)	1,050 (75)	2,558 (72)	429 (66)
Radiotherapy	Yes	1,090 (21)	277 (20)	797 (22)	158 (24)
	No	535 (10)	141 (10)	420 (12)	84 (13)
	Unknown	3,564 (69)	984 (70)	2,344 (66)	405 (63)
Characteristics of CBC
Age at diagnosis	Mean	-	47.3	-	51.24
Age at diagnosis	Range	-	26-80.5	-	23.8-86
Invasiveness	Invasive	-	1,267 (90)	-	545 (84)
Invasiveness	Non-invasive	-	135 (10)	-	102 (16)
ER-status	Positive	-	101 (7)	-	197 (30)
	Negative	-	446 (32)	-	50 (8)
	Unknown	-	855 (61)	-	400 (62)
					PRS₃₁₃
Standardized PRS₃₁₃ mean (SD)	Overall BC	0.08 (1.01)	0.13 (1.01)	0.09 (1.02)	0.27 (1.04)
	ER-positive BC	0.07 (1.01)	0.09 (1.01)	0.08 (1.01)	0.27 (1.03)
	ER-negative BC	0.09 (1.00)	0.23 (0.99)	0.07 (1.02)	0.23 (1.07)

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Variant class: I=unstable or no protein, II= stable mutant protein, III= consequence unknown.

Family history was defined as the number of first- or second- degree relatives affected with BC, ranging from 0 to ≥2.

Tumor size: T1=≤2cm (≤0.79in), T2=>2cm-5cm (>0.79-1.97in), T3=>5cm (>1.97in), T4=any size, with direct extension to the chest wall or skin.

Including neoadjuvant and adjuvant chemotherapy

Abbreviations: BC, Breast Cancer; BRRM, Bilateral Risk Reducing Mastectomy; CBC, Contralateral Breast Cancer; ER-status, Estrogen Receptor status of the tumor; N, Number; PRS, Polygenic Risk Score; SD, Standard Deviation; UBC, Unilateral Breast Cancer

Ethics Statement

All participants were recruited by the host institutions under protocols approved by local ethics review boards and provided written informed consent²⁴.

Genotyping and Polygenic Risk Score calculation

For most of the participants, genotyping was performed with the Illumina OncoArray²⁷. The remaining participants were genotyped with the Illumina iCOGS array¹¹. Details about the quality control procedures and correlation between the arrays have been described previously^{19,24,28–31}. European ancestry was determined using genetic data and multidimensional scaling. More detailed information about the genotyping and PRS calculation is provided in the supplementary methods.

We used the 313-variant-based PRS for breast cancer developed in an independent study using data from the general population as described previously¹⁴; correlation between PRS based on the two genotyping arrays was high¹⁹. The PRS for overall breast cancer (PRS₃₁₃) and two ER-specific PRS, the ER-positive PRS₃₁₃ and ER-negative PRS₃₁₃ were calculated. The variants and their corresponding weights used in the PRS as published previously¹⁴, and the imputation quality are listed in Table S2. The three PRS were standardized to the mean from all CIMBA participants, including both unaffected and affected women, and to the SD in BCAC population controls which were included in the validation dataset¹⁴. Using these SDs, the HR estimates for the associations of the standardized PRS₃₁₃ in our study are directly comparable with the OR estimates reported in the BCAC population-based study¹⁴ and the HR estimates reported for primary breast cancer in BRCA1 and BRCA2 heterozygotes²⁴.

Statistical analysis

To assess the associations between the three PRS and contralateral breast cancer risk in BRCA1/2 heterozygotes, Cox-regression analyses were performed. The time at risk was started one year after the first breast cancer diagnosis based on the exact date or if not available, on the age of developing the first breast tumor. Time at risk of participants was censored at age at baseline, i.e., end of follow-up in these analyses, prophylactic contralateral mastectomy, or death, whichever was earlier (Figure S2). Incidence of a metachronous contralateral breast cancer, invasive or in situ, before baseline was considered as an event in the main analyses. The proportional hazard assumption was evaluated by using Schoenfeld residuals against the transformed time. A sensitivity analysis was performed considering invasive contralateral breast cancer only as an event. Women who developed an in situ contralateral breast cancer were censored at the age at diagnosis of the in situ contralateral breast cancer. Furthermore, a sensitivity analysis was performed including information about distant relapse, which was available for 1,725 BRCA1 and 1,450 BRCA2 heterozygotes. In total 55 BRCA1 heterozygotes and 101 BRCA2 heterozygotes were censored at the age of distant relapse of which 13 and 11 women were excluded from the analyses, respectively, because they developed distant relapse in the year before the baseline age.

Analyses were stratified by country (Table S3), adjusted for birth cohort (quartiles of the observed distribution), and clustered on family membership using a unique family-identifier to account for the inclusion of related individuals. For BRCA1 and BRCA2 respectively, there were 5923 and 3752 clusters of which 554 and 362 clusters with more than one participant. The main analyses assessed the association with the PRS as a continuous covariate. We evaluated the linearity of the association using restricted cubic splines with three knots, which showed no evidence for violation of the linearity assumption. The discriminatory ability of the best performing PRS was evaluated by Harrell’s C-index³². C-indexes were calculated stratified by country and clustered on family membership.

The influence of possible confounding variables on the observed associations was assessed using the PRS exhibiting the largest associations. Possible confounding variables included breast cancer family history, age at diagnosis of the first breast cancer, pathological characteristics and treatment of the first breast cancer. Each variable was added to the model one by one and in addition, a full model which included all possible confounders together was fitted. If the addition of a variable resulted in a change of more than 10% in the log HR, the variable was retained as a covariate in the final Cox-regression model. To avoid excluding many participants with missing data for one of these included variables (Table 1), missing data were imputed using Multiple Imputation by Chained Equations (MICE)³³. Imputation was started with the least missing variable and progressed in order of increased amount of missing data. Using this method, 10 complete datasets for analyses were created and mean parameter estimates were derived.

Secondary analyses were performed for ER-positive and ER-negative cases only, based on the ER-status of the contralateral breast cancer, after imputation as described above. The average number of ER-positive and ER-negative cases in the 10 imputed datasets is shown in Table S4. In these analyses the event of interest was either ER-positive or ER-negative contralateral breast cancer. Contralateral breast cancer cases with the alternative ER-status were censored at the age of contralateral breast cancer.

The interaction between the PRS with the age at first breast cancer diagnosis was tested in the final model, treating the PRS as a continuous variable. Furthermore, the effect size of the PRS was evaluated for groups based on the age at first primary breast cancer diagnosis (<40 years; 40 to 50 years; ≥50 years)^1,20. The association of the PRS and contralateral breast cancer risk was tested separately for heterozygotes of pathogenic variants which lead to unstable or no protein (class I) and heterozygotes of pathogenic variants which lead to mutant stable protein (class II). Finally, analyses were performed to test the association between a categorized PRS and contralateral breast cancer risk to establish whether the results were consistent with those under a continuous PRS model. The categories were defined on the basis of the distribution of the PRS in unilateral breast cancer cases, using PRS percentiles (0-5^th, 5^th-10^th, 10^th-20^th, 20^th-40^th, 40^th-60^th (reference), 60^th-80^th, 80^th-90^th, 90^th-95^th, 95^th-100^th).

Cumulative risks

Absolute contralateral breast cancer risks were calculated at percentiles of the best-performing continuous PRS for both BRCA1 and BRCA2 heterozygotes, using the log HR per SD and including an interaction term with the continuous age at first breast cancer diagnosis (at age 35; 45 and 55 for the corresponding age groups as described below). For this purpose, we constrained the incidence of contralateral breast cancer, by age at first breast cancer and in years after the first breast cancer, and averaged over all PRS categories to agree with external contralateral breast cancer incidence estimates, as described previously²³. These external incidence estimates were based on prospective cohort data from three consortia on heterozygotes of pathogenic BRCA1 and BRCA2 variants¹, the International BRCA1/2 Carrier Cohort Study (IBCCS), the Breast Cancer Family Registry (BCFR), and the Kathleen Cuningham Foundation Consortium for Research Into Familial Breast Cancer (kConFab). Because the contralateral breast cancer incidences vary with the age of first breast cancer diagnosis, incidences were calculated for three different groups based on the age of the first breast cancer diagnosis (<40 years, 40 to 50 years, ≥50 years)¹.

All statistical tests were performed with R version 3.5.0³⁴. Statistical significance was defined as a two-sided p-value <0.05.

Results

In the analyses, 6,591 BRCA1 and 4,208 BRCA2 heterozygotes of European ancestry who had developed an invasive first primary breast cancer before entry in CIMBA were identified. The median follow-up time was 6.0 and 5.4 years for BRCA1 and BRCA2 heterozygotes, respectively. In total, 1,402 BRCA1 and 647 BRCA2 heterozygotes were diagnosed with a metachronous contralateral breast cancer before enrollment in CIMBA. The cumulative 10-year risk of developing contralateral breast cancer in this cohort was 25%, 95%CI [23.5%-26.4%] and 18.8%, 95%CI [17.1%-20.5%] for BRCA1 and BRCA2 heterozygotes, respectively (Figure S3). Patient and tumor characteristics as well as the PRS distributions are shown in Table 1 and Figure S4.

PRS and contralateral breast cancer risk

Results of the association analyses between the PRS and contralateral breast cancer risk are shown in Table 2, Table S4 and Figure 1.

Table 2. Results of association analyses between the PRS₃₁₃ and contralateral breast cancer risk.

		BRCA1 heterozygotes [ER-negative PRS₃₁₃]					BRCA2 heterozygotes [ER-positive PRS₃₁₃]
		UBC cases, n	CBC cases, n	HR^a	95% CI	P	UBC cases, n	CBC cases, n	HR^a	95% CI	P
PRS continuous	All CBC	5,189	1,402	1.12	1.06-1.18	5.98x10⁻⁵	3,561	647	1.15	1.07-1.25	1.94x10⁻⁴
PRS continuous	Invasive CBC	5,324	1,267	1.13	1.07-1.20	3.15x10⁻⁵	3,663	545	1.15	1.06-1.25	6.02x10⁻⁴
Categorical PRS percentiles	0-5	260	48	0.81	0.59-1.11	0.188	166	28	1.06	0.71-1.58	0.782
	5-10	259	54	0.77	0.57-1.03	0.082	198	26	0.68	0.44-1.04	0.074
	10-20	519	131	0.94	0.76-1.15	0.544	355	51	0.91	0.66-1.25	0.554
	20-40	1,038	230	0.83	0.70-0.98	0.031	697	108	0.87	0.68-1.13	0.295
	40-60 [reference]	1,037	282	1.00			695	123	1.00
	60-80	1,038	313	1.04	0.88-1.22	0.664	734	128	0.96	0.75-1.23	0.748
	80-90	519	170	1.11	0.92-1.34	0.255	358	90	1.35	1.03-1.77	0.030
	90-95	259	82	1.18	0.92-1.51	0.185	178	46	1.35	0.96-1.90	0.082
	95-100	260	92	1.24	0.98-1.56	0.074	180	47	1.31	0.94-1.82	0.116
PRS*age	Main effect	5,189	1,402	1.48	1.15-1.89	2.03x10⁻³	3,561	647	1.53	1.11-2.12	0.010
BC1 continuous	Interaction effect			0.99	0.99-1.00	0.025			0.99	0.99-1.00	0.089
PRS effect per age group	<40	2,339	815	1.22	1.14-1.31	4.79x10⁻⁸	1,238	268	1.23	1.09-1.38	5.78x10⁻⁴
	40-50	1,821	456	0.99	0.90-1.09	0.785	1,306	261	1.19	1.05-1.34	6.91x10⁻³
	≥50	1,029	131	1.03	0.86-1.24	0.715	1,017	118	0.97	0.81-1.15	0.698
Variant class^b	Class I	3,354	904	1.11	1.03-1.18	4.32x10⁻³	3,207	570	1.16	1.07-1.26	1.99x10⁻⁴
Variant class^b	Class II	1,345	374	1.15	1.04-1.28	4.75x10⁻³	125	25	0.91	0.65-1.28	0.594

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HRs for association with breast cancer and the continuous PRS₃₁₃ are reported per standard deviation of the PRS in population-based controls.

Class I pathogenic variants result in an unstable or no protein. Class II pathogenic variants yield stable mutant proteins.

Abbreviations: BC1, First primary Breast Cancer; CBC, Contralateral Breast Cancer; CI, Confidence Interval; HR, Hazard Ratio; PRS, Polygenic Risk Score; UBC, Unilateral Breast Cancer.

The figure includes the effect size of the association between contralateral breast cancer and the three different PRS₃₁₃ after testing for covariates for the following selections: all contralateral breast cancer, invasive contralateral breast cancer only, ER-negative contralateral breast cancer, and ER-positive contralateral breast cancer. The numbers of unilateral and contralateral breast cancer cases and effect sizes are shown in Table 2 and Table S4.

Abbreviations: CBC, Contralateral Breast Cancer; ER, Estrogen Receptor; HR, Hazard Ratio; PRS, Polygenic Risk Score; SD, Standard Deviation.

BRCA1 heterozygotes

For BRCA1 heterozygotes the ER-negative PRS₃₁₃ showed the largest association with all contralateral breast cancer, HR per SD=1.12, 95%CI [1.06-1.18], p-value=6.0x10⁻⁵, C-index 0.53, 95%CI [0.51-0.55]. There was no evidence of violation of the proportional hazard assumption, p-value=0.840.

Neither sequential inclusion of possible confounders, nor including all these confounders in one model, changed the log HR estimate for the ER-negative PRS₃₁₃ association more than 10% when compared with the model with no confounders (Table S5).

Considering only invasive contralateral breast cancer as the event of interest resulted in a similar association with the ER-negative PRS₃₁₃, HR per SD=1.13, 95%CI [1.07-1.20], p-value=3.2x10⁻⁵.

Censoring at distant metastasis relapse, if applicable, did not change the effect size of the ER-negative PRS₃₁₃, HR per SD=1.12, 95%CI [1.06-1.18], p-value=4.9x10^-5.

The HR-estimates for association with contralateral breast cancer for different quantiles of the ER-negative PRS₃₁₃, were consistent with the predicted HRs from the model using the continuous ER-negative PRS₃₁₃ (Table 2 and Figure 2).

For ER-positive contralateral breast cancer as event, the PRS₃₁₃ showed the largest association, HR per SD=1.32, 95%CI [1.12-1.56], p-value=0.002. For ER-negative contralateral breast cancer as event, only the ER-negative PRS₃₁₃ showed a significant association, HR per SD=1.07, 95%CI [1.01-1.15], p-value=0.036 (Table S4).

BRCA2 heterozygotes

For BRCA2 heterozygotes the largest association was seen with the ER-positive PRS₃₁₃, HR per SD=1.15, 95%CI [1.07-1.25], p-value=1.9x10⁻⁴, C-index 0.57, 95%CI [0.54-0.59]. There was no evidence of violation of the proportional hazard assumption, p-value=0.300.

Neither sequential inclusion of possible confounders, nor including all these confounders in one model, changed the log HR estimate for the ER-positive PRS₃₁₃ association more than 10% when compared with the model with no confounders (Table S5).

Considering only invasive contralateral breast cancer as the event of interest resulted in a similar association, HR per SD for the ER-positive PRS₃₁₃=1.15, 95%CI [1.06-1.25], p-value=6.0x10⁻⁴.

Censoring at distant metastasis relapse, if applicable, did not change the effect size of the ER-positive PRS₃₁₃, HR per SD=1.15, 95%CI [1.07-1.24], p-value=2.1x10⁻⁴.

The HR estimates for association with contralateral breast cancer for different quantiles of the ER-positive PRS₃₁₃, were consistent with the predicted estimates using the continuous PRS₃₁₃ (Table 2 and Figure 2).

The ER-positive PRS₃₁₃ showed the largest association with ER-positive contralateral breast cancer for BRCA2 heterozygotes, HR per SD=1.22, 95%CI [1.11-1.33], p-value=2.2x10⁻⁵ (Table S4). None of the PRS showed significant associations with ER-negative contralateral breast cancer for BRCA2 heterozygotes, but the ER-negative PRS₃₁₃ exhibited the largest HR estimate, HR per SD=1.10, 95%CI [0.91-1.32], p-value=0.346.

Interaction with age at first breast cancer diagnosis

A significant interaction between the age at first breast cancer diagnosis and the ER-negative PRS₃₁₃ was found for BRCA1 heterozygotes: HR per year=0.99, 95%CI [0.99-1.00], p-value=0.025. For BRCA2 heterozygotes a similar magnitude of interaction was observed with the ER-positive PRS₃₁₃, although the interaction was not significant, HR per year=0.99, 95%CI [0.99-1.00], p-value=0.09.

Categorizing age at first breast cancer diagnosis for BRCA1 heterozygotes resulted in HRs per SD of the ER-negative PRS₃₁₃ of 1.22, 95%CI [1.14-1.31], 0.99, 95%CI [0.90-1.09] and 1.03, 95%CI [0.86-1.24] for ages <40 years, 40-50 years and ≥50 year respectively. For BRCA2 heterozygotes the corresponding estimates for ER-positive PRS₃₁₃ were 1.23, 95%CI [1.09-1.38], 1.19, 95%CI [1.05-1.34] and 0.97, 95%CI [0.81-1.15] respectively (Table 2).

Analyses by predicted variant effect on protein expression

For BRCA1 heterozygotes, the HRs for association between the ER-negative PRS₃₁₃ and contralateral breast cancer risk were similar for heterozygotes of pathogenic variants, which lead to a stable mutant protein (class II) compared with those leading to no protein or an unstable protein (class I). For BRCA2 heterozygotes, the ER-positive PRS₃₁₃ effect size for the association with contralateral breast cancer risk was non-significantly smaller among heterozygotes of a pathogenic variant which lead to a stable mutant protein, although statistical power to detect these associations was low and the confidence intervals overlap with the overall estimate (Table 2).

Cumulative risks

Estimate cumulative contralateral breast cancer risks, by categories of age at diagnosis of the first breast cancer are shown in Figure 3. The largest risk difference was seen for women with a first breast cancer diagnosis before the age of 40, with BRCA1 heterozygotes at the 5^th percentile of the ER-negative PRS₃₁₃ having a 10- and 20-year risk of 22% and 35% compared with 32% and 49% at the 95^th percentile, respectively. For BRCA2 heterozygotes, the 10- and 20-year risks in this category were 13% and 25% at the 5^th percentile of the ER-positive PRS₃₁₃ compared with 23% and 42% for women at the 95^th percentile.

Predicted absolute contralateral breast cancer risks by percentile of the continuous ER-negative PRS₃₁₃ for *BRCA1* heterozygotes and ER-positive PRS₃₁₃ for *BRCA2* heterozygotes. The assumed contralateral breast cancer incidences were from a study that estimated breast cancer incidence in a large prospective cohort of *BRCA1* and *BRCA2* heterozygotes²⁰. The age categories were based on the age at diagnosis of the first primary breast tumor. Risks were calculated including the interaction between the PRS and the continuous age of first breast cancer diagnosis. The lines for different percentiles of the PRS are overlapping for the age category ≥50 year for *BRCA1* heterozygotes.

Abbreviations: BC, Breast Cancer; CBC, Contralateral Breast Cancer; PRS, Polygenic Risk Score.

Discussion

In this study we investigated the associations between an established PRS based on 313 variants for primary first breast cancer and contralateral breast cancer risks among BRCA1 and BRCA2 heterozygotes of European ancestry enrolled in the large international retrospective CIMBA cohort. We showed significant albeit modest associations among both BRCA1 and BRCA2 heterozygotes between the PRS and contralateral breast cancer risk. For BRCA1 heterozygotes, the largest association was seen with the ER-negative PRS₃₁₃, while for BRCA2 heterozygotes, both the PRS₃₁₃ and ER-positive PRS₃₁₃ showed similar associations with contralateral breast cancer risk that were somewhat larger than the ER-negative PRS₃₁₃ association. These findings are consistent with previous studies on the effects of disease-specific PRS on the first breast cancers in BRCA1 and BRCA2 heterozygotes^20,24 and with the higher relative prevalence of ER-negative and ER-positive contralateral breast cancers respectively, in this cohort.

For both BRCA1 and BRCA2 heterozygotes, the strength of the association was greater for ER-positive contralateral breast cancers compared with ER-negative contralateral breast cancers (in the case of BRCA1, even if the ER-negative PRS was used), although most of the confidence intervals overlapped. The effect sizes for the PRS are also larger for ER-positive disease in the general population, perhaps because ER-positive disease is commoner and the power to identify genetic variants has been greater for ER-positive disease. With larger datasets, it should be possible to develop better subtype specific PRS for contralateral breast cancer.

Although we found clear associations between the PRS and contralateral breast cancer risk, the magnitude of these associations (expressed in terms of HRs) were smaller than previously reported for the first breast cancers. For BRCA1 heterozygotes, the HR per SD for the association between the ER-negative PRS₃₁₃ and breast cancer was 1.29, 95%CI [1.25-1.33]²⁴, compared with 1.12, 95%CI [1.06-1.18] for contralateral breast cancer in this study. For BRCA2 heterozygotes, the HR per SD for the association between the ER-positive PRS₃₁₃ and breast cancer was 1.31, 95%CI [1.26-1.36]²⁴, compared with 1.15, 95%CI [1.07-1.24] for contralateral breast cancer in this study. This lower relative risk is consistent with a general pattern of a lower relative risk in a higher risk population, as seen in, the lower relative risk for contralateral breast cancer than first breast cancer in the general population¹⁹, and the lower relative risk for the first cancer in BRCA1/2 heterozygotes than in the general population²⁴. The attenuated estimate might be explained by several factors, some of which are speculative. BRCA1/2 pathogenic variant heterozygotes in this study were selected based on having a first breast cancer; these women will have on average a higher PRS, but also higher frequencies of other genetic and non-genetic risk factors than women who do not develop breast cancer at all. This can lead to a weaker association with the PRS as women with the largest PRS may have lower risks due to other factors, a phenomenon related to index event bias³⁵. There could also be negative interactions between the PRS effect and other risk factors (for example, treatment factors). However, in this study, we have shown that adjustment for the known contralateral breast cancer risk factors did not change the effect size of the PRS, which was also shown in population-based studies^17,19. Finally, although we tried to exclude potential early metastases misdiagnosed as second primaries by excluding women who developed a contralateral breast cancer the first year after the primary diagnosis, it is possible that a small percentage of contralateral breast cancers were metastases³⁶.

A limitation of this study is that participants were recruited through clinical genetic centers, resulting in ascertainment bias, as individuals are more likely to have a strong family of breast cancer and/or be affected at a young age in order to be referred for testing. This was a historical cohort in which follow-up was prior to entry into CIMBA, so that all cases are prevalent. Therefore, the breast cancer patients included in the analyses are likely to be at higher contralateral breast cancer risk when compared with the general BRCA1/2 heterozygote breast cancer population. Indeed, the estimated 20-year risks of developing contralateral breast cancer in this study were higher compared to a previously published study with a prospective design¹: 47% versus 40% for BRCA1 heterozygotes and 40% versus 26% for BRCA2 heterozygotes, respectively. While this is unlikely to introduce a significant bias in the relative risk estimates, a prospective cohort would clearly be preferably, although this will take several years to achieve. Finally, the PRS was developed using datasets of women of European ancestry, since our dataset included insufficient samples of women of other ancestries, and our results were exclusively based on women of European ancestry. Therefore, caution is required when applying this to non-European ancestry populations. However, a population study found clear associations between the PRS, based on the same 313 variants or a subset of these variants, and (contralateral) breast cancer also in women of Asian ancestry. The effect size of these associations were slightly weaker, possibly reflecting the fact that this PRS was developed in a cohort of women of European ancestry^16,19. These results suggest that there might be an association with the PRS as well in BRCA1/2 heterozygotes of Asian ancestry. Future studies including a sufficient number of individuals of Asian ancestry are needed to confirm this statement.

Although the relative risks of the PRS for contralateral breast cancer were modest, differences in the PRS may still have an important effect on the absolute risk, which is high. BRCA1 and BRCA2 heterozygotes aged under 40 at first breast cancer, at the 5^th and 95^th percentile of the PRS differed by 10% in 10-year contralateral breast cancer risk. These absolute risk differences are modest, but might be of relevance for the choices regarding preventive surgery if incorporated into a multifactorial model that includes other predictive factors, such as family history and adjuvant systemic treatment of the first breast cancer^37,38. In the context of such a comprehensive model, further research is needed to investigate whether the PRS would contribute to the choices that women make for follow-up or preventive surgery.

To summarize, we have investigated the associations between PRS based on 313 variants with contralateral breast cancer risk in a large international series of BRCA1/2 heterozygotes. We found that the PRS is associated with contralateral breast cancer risk in both BRCA1 and BRCA2 heterozygotes of European ancestry and that PRS can be used to refine estimates of contralateral breast cancer risks in these women. However, for women with a first breast cancer after the age of 50, PRS may be of less value in the prediction of the contralateral breast cancer risk. Incorporating risk factors other than PRS and including ER-specific estimates may further improve contralateral breast cancer risk prediction. Before implementation in a diagnostic setting, our results should be validated in a prospective cohort of BRCA1 and BRCA2 heterozygotes.

Supplementary Material

Supplementary (Appendix, online only material, etc.)

EMS128250-supplement-Supplementary___Appendix__online_only_material__etc__.pdf^{(729.7KB, pdf)}

Table S2

EMS128250-supplement-Table_S2.xlsx^{(41.4KB, xlsx)}

Acknowledgements

We acknowledge all the families, clinicians, family doctors, researchers, research nurses, research assistants, and technicians who contribute to the individual studies of which we used the data for this research and manuscript.

Funding

This work was supported by the Alpe d’HuZes/Dutch Cancer Society (KWF Kankerbestrijding) project 6253 and Dutch Cancer Society (KWF Kankerbestrijding) project UL2014-7473.

CIMBA: The CIMBA data management and data analysis were supported by Cancer Research – UK grants C12292/A20861, C12292/A11174. GCT and ABS are NHMRC Research Fellows. iCOGS: the European Community’s Seventh Framework Programme under grant agreement n° 223175 (HEALTH-F2-2009-223175) (COGS), Cancer Research UK (C1287/A10118, C1287/A 10710, C12292/A11174, C1281/A12014, C5047/A8384, C5047/A15007, C5047/A10692, C8197/A16565), the National Institutes of Health (CA128978) and Post-Cancer GWAS initiative (1U19 CA148537, 1U19 CA148065 and 1U19 CA148112 - the GAME-ON initiative), the Department of Defence (W81XWH-10-1-0341), the Canadian Institutes of Health Research (CIHR) for the CIHR Team in Familial Risks of Breast Cancer (CRN-87521), and the Ministry of Economic Development, Innovation and Export Trade (PSR-SIIRI-701), Komen Foundation for the Cure, the Breast Cancer Research Foundation, and the Ovarian Cancer Research Fund. OncoArray: the PERSPECTIVE and PERSPECTIVE I&I projects funded by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research, the ‘Ministère de l’Économie, de la Science et de l’Innovation du Québec’ through Genome Québec, and the Quebec Breast Cancer Foundation; the NCI Genetic Associations and Mechanisms in Oncology (GAME-ON) initiative and Discovery, Biology and Risk of Inherited Variants in Breast Cancer (DRIVE) project (NIH Grants U19 CA148065 and X01HG007492); and Cancer Research UK (C1287/A10118 and C1287/A16563).

BCFR: UM1 CA164920 from the National Cancer Institute. The content of this manuscript does not necessarily reflect the views or policies of the National Cancer Institute or any of the collaborating centers in the Breast Cancer Family Registry (BCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government or the BCFR. BFBOCC: Lithuania (BFBOCC-LT): Research Council of Lithuania grant SEN-18/2015. BIDMC: Breast Cancer Research Foundation. BMBSA: Cancer Association of South Africa (PI Elizabeth J. van Rensburg). BRI-COH: SLN is partially supported by the Morris and Horowitz Families Professorship. CNIO: Spanish Ministry of Health PI16/00440 supported by FEDER funds, the Spanish Ministry of Economy and Competitiveness (MINECO) SAF2014-57680-R and the Spanish Research Network on Rare diseases (CIBERER). COH-CCGCRN: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under grant number R25CA112486, and RC4CA153828 (PI: J. Weitzel) from the National Cancer Institute and the Office of the Director, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. CONSIT TEAM: Associazione Italiana Ricerca sul Cancro (AIRC; IG2015 no.16732) to P. Peterlongo. DEMOKRITOS: European Union (European Social Fund – ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF) - Research Funding Program of the General Secretariat for Research & Technology: SYN11_10_19 NBCA. Investing in knowledge society through the European Social Fund. DFKZ: German Cancer Research Center. EMBRACE: Cancer Research UK Grants C1287/A10118 and C1287/A11990. D. Gareth Evans and Fiona Lalloo are supported by an NIHR grant to the Biomedical Research Centre, Manchester. The Investigators at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust are supported by an NIHR grant to the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. Ros Eeles and Elizabeth Bancroft are supported by Cancer Research UK Grant C5047/A8385. Ros Eeles is also supported by NIHR support to the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. FCCC: A.K.G. was in part funded by the NCI (R01 CA214545), The University of Kansas Cancer Center Support Grant (P30 CA168524), The Kansas Institute for Precision Medicine (P20 GM130423), and the Kansas Bioscience Authority Eminent Scholar Program. A.K.G. is the Chancellors Distinguished Chair in Biomedical Sciences Professorship. FPGMX: A.Vega is supported by the Spanish Health Research Foundation, Instituto de Salud Carlos III (ISCIII), partially supported by FEDER funds through Research Activity Intensification Program (contract grant numbers: INT15/00070, INT16/00154, INT17/00133), and through Centro de Investigación Biomédica en Red de Enferemdades Raras CIBERER (ACCI 2016: ER17P1AC7112/2018); Autonomous Government of Galicia (Consolidation and structuring program: IN607B), and by the Fundación Mutua Madrileña. The German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC) is funded by the German Cancer Aid (#110837, #70111850, coordinator: Rita K. Schmutzler, Cologne) and the Ministry for Innovation, Science and Research of the State of North Rhine-Westphalia (#323-8.0302.16.02-132142). GEMO: initially funded by the French National Institute of Cancer (INCa, PHRC Ile de France, grant AOR 01 082, 2001-2003, grant 2013-1-BCB-01-ICH-1), the Association “Le cancer du sein, parlons-en!” Award (2004), the Association for International Cancer Research (2008-2010), and the Foundation ARC pour la recherche sur le cancer (grant PJA 20151203365). It also received support from the Canadian Institute of Health Research for the “CIHR Team in Familial Risks of Breast Cancer” program (2008-2013), and the European commission FP7, Project «Collaborative Ovarian, breast and prostate Gene-environment Study (COGS), Large-scale integrating project» (2009-2013). GEMO is currently supported by the INCa grant SHS-E-SP 18-015. GEORGETOWN: The Survey, Recruitment, and Biospecimen Collection Shared Resource at Georgetown University (NIH/NCI grant P30-CA051008), the Fisher Center for Hereditary Cancer and Clinical Genomics Research, and the Nina Hyde Center for Breast Cancer Research. G-FAST: Bruce Poppe is a senior clinical investigator of FWO. Mattias Van Heetvelde obtained funding from IWT. HCSC: Spanish Ministry of Health Pl15/00059, PI16/01292, and CB-161200301 CIBERONC from ISCIII (Spain), partially supported by European Regional Development FEDER funds. HEBCS: Helsinki University Hospital Research Fund, the Finnish Cancer Society and the Sigrid Juselius Foundation. The HEBON study is supported by the Dutch Cancer Society grants NKI1998-1854, NKI2004-3088, NKI2007-3756, the Netherlands Organisation of Scientific Research grant NWO 91109024, the Pink Ribbon grants 110005 and 2014-187.WO76, the BBMRI grant NWO 184.021.007/CP46 and the Transcan grant JTC 2012 Cancer 12-054. HRBCP: Hong Kong Sanatorium and Hospital, Dr Ellen Li Charitable Foundation, The Kerry Group Kuok Foundation, National Institute of Health1R 03CA130065, and North California Cancer Center. HUNBOCS: Hungarian Research Grants KTIA-OTKA CK-80745, NKFI_OTKA K-112228 and TUDFO/51757/2019-ITM. ICO: Contract grant sponsor: Supported by the Carlos III National Health Institute funded by FEDER funds - a way to build Europe - [PI16/00563, PI19/00553 and CIBERONC]; the Government of Catalonia [Pla estratègic de recerca i innovació en salut (PERIS) Project MedPerCan, 2017SGR1282 and 2017SGR496]; and CERCA program.IHCC: supported by Grant PBZ_KBN_122/P05/2004 and the program of the Minister of Science and Higher Education under the name “Regional Initiative of Excellence” in 2019-2022 project number 002/RID/2018/19 amount of financing 12 000 000 PLN. ILUH: Icelandic Association “Walking for Breast Cancer Research” and by the Landspitali University Hospital Research Fund. INHERIT: Canadian Institutes of Health Research for the “CIHR Team in Familial Risks of Breast Cancer” program - grant # CRN-87521 and the Ministry of Economic Development, Innovation and Export Trade - grant # PSR-SIIRI-701. IOVHBOCS: Ministero della Salute and “5x1000” Istituto Oncologico Veneto grant. IPOBCS: Liga Portuguesa Contra o Cancro. kConFab: The National Breast Cancer Foundation, and previously by the National Health and Medical Research Council (NHMRC), the Queensland Cancer Fund, the Cancer Councils of New South Wales, Victoria, Tasmania and South Australia, and the Cancer Foundation of Western Australia. KOHBRA: the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), and the National R&D Program for Cancer Control, Ministry of Health & Welfare, Republic of Korea (HI16C1127; 1020350; 1420190). KUMC: NIGMS P20 GM130423 (to AKG). MAYO: NIH grants CA116167, CA192393 and CA176785, an NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA116201), and a grant from the Breast Cancer Research Foundation. MCGILL: Jewish General Hospital Weekend to End Breast Cancer, Quebec Ministry of Economic Development, Innovation and Export Trade. Marc Tischkowitz is supported by the funded by the European Union Seventh Framework Program (2007Y2013)/European Research Council (Grant No. 310018). MODSQUAD: MH CZ - DRO (MMCI, 00209805) and LM2018125, MEYS - NPS I - LO1413 to LF, and by Charles University in Prague project UNCE204024 (MZ). MSKCC: the Breast Cancer Research Foundation, the Robert and Kate Niehaus Clinical Cancer Genetics Initiative, the Andrew Sabin Research Fund and a Cancer Center Support Grant/Core Grant (P30 CA008748). NAROD: 1R01 CA149429-01. NCI: the Intramural Research Program of the US National Cancer Institute, NIH, and by support services contracts NO2-CP-11019-50, N02-CP-21013-63 and N02-CP-65504 with Westat, Inc, Rockville, MD. NICCC: Clalit Health Services in Israel, the Israel Cancer Association and the Breast Cancer Research Foundation (BCRF), NY. NNPIO: the Russian Foundation for Basic Research (grants 17-00-00171, 18-515-45012 and 19-515-25001). NRG Oncology: U10 CA180868, NRG SDMC grant U10 CA180822, NRG Administrative Office and the NRG Tissue Bank (CA 27469), the NRG Statistical and Data Center (CA 37517) and the Intramural Research Program, NCI. OSUCCG: Ohio State University Comprehensive Cancer Center. PBCS: supported by the “Fondazione Pisa per la Scienza, project nr. 127/2016. Maria A Caligo was supported by the grant: “n. 127/16 Caratterizzazione delle varianti missenso nei geni BRCA1/2 per la valutazione del rischio di tumore al seno” by Fondazione Pisa, Pisa, Italy; SEABASS: Ministry of Science, Technology and Innovation, Ministry of Higher Education (UM.C/HlR/MOHE/06) and Cancer Research Initiatives Foundation. SMC: the Israeli Cancer Association. SWE-BRCA: the Swedish Cancer Society. UCHICAGO: NCI Specialized Program of Research Excellence (SPORE) in Breast Cancer (CA125183), R01 CA142996, 1U01CA161032 and by the Ralph and Marion Falk Medical Research Trust, the Entertainment Industry Fund National Women’s Cancer Research Alliance and the Breast Cancer research Foundation. OIO is an ACS Clinical Research Professor. UCLA: Jonsson Comprehensive Cancer Center Foundation; Breast Cancer Research Foundation. UCSF: UCSF Cancer Risk Program and Helen Diller Family Comprehensive Cancer Center. UKFOCR: Cancer Research h UK. UPENN: Breast Cancer Research Foundation; Susan G. Komen Foundation for the cure, Basser Research Center for BRCA. UPITT/MWH: Hackers for Hope Pittsburgh. VFCTG: Victorian Cancer Agency, Cancer Australia, National Breast Cancer Foundation. WCP: Dr Karlan is funded by the American Cancer Society Early Detection Professorship (SIOP-06-258-01-COUN) and the National Center for Advancing Translational Sciences (NCATS), Grant UL1TR000124.

Footnotes

Conflicts of interest disclosures

Claudine Isaacs is consultant to Astra Zeneca, Novartis, Pfizer, Genentech, PUMA, Seattle Genetics and received research support from Tesaro.

Author contribution statement

Conceptualization: ACC, MR, MKS; Formal analysis: IMML, AjvdB, JAMV, DRB; Resources: All authors; Supervision: ACC, MR, MKS; Writing – original draft: IMML, ACC, MR, MKS; Writing – review & editing: All authors

Data availability statement

The CIMBA data is available on request. To receive access to the data, a concept form must be submitted, which will then be reviewed by the CIMBA Data Access Coordination Committee (DACC). Please contact Lesley McGuffog (e-mail: ljm26@medschl.cam.ac.uk), to get access to these concept forms (http://cimba.ccge.medschl.cam.ac.uk/contact/).

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37.Akdeniz D, Schmidt MK, Seynaeve CM, et al. Risk factors for metachronous contralateral breast cancer: A systematic review and meta-analysis. Breast (Edinburgh, Scotland) 2019;44:1–14. doi: 10.1016/j.breast.2018.11.005. [DOI] [PubMed] [Google Scholar]
38.Giardiello D, Steyerberg EW, Hauptmann M, et al. Prediction and clinical utility of a contralateral breast cancer risk model. Breast cancer research : BCR. 2019;21(1):144. doi: 10.1186/s13058-019-1221-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary (Appendix, online only material, etc.)

EMS128250-supplement-Supplementary___Appendix__online_only_material__etc__.pdf^{(729.7KB, pdf)}

Table S2

EMS128250-supplement-Table_S2.xlsx^{(41.4KB, xlsx)}

Data Availability Statement

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[R17] 17.Robson ME, Reiner AS, Brooks JD, et al. Association of Common Genetic Variants With Contralateral Breast Cancer Risk in the WECARE Study. Journal of the National Cancer Institute. 2017;109(10) doi: 10.1093/jnci/djx051. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.Sawyer S, Mitchell G, McKinley J, et al. A role for common genomic variants in the assessment of familial breast cancer. JClinOncol. 2012;30(35):4330–4336. doi: 10.1200/JCO.2012.41.7469. [DOI] [PubMed] [Google Scholar]

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[R20] 20.Kuchenbaecker KB, McGuffog L, Barrowdale D, et al. Evaluation of Polygenic Risk Scores for Breast and Ovarian Cancer Risk Prediction in BRCA1 and BRCA2 Mutation Carriers. Journal of the National Cancer Institute. 2017;109(7) doi: 10.1093/jnci/djw302. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R21] 21.Antoniou AC, Sinilnikova OM, McGuffog L, et al. Common variants in LSP1, 2q35 and 8q24 and breast cancer risk for BRCA1 and BRCA2 mutation carriers. Human molecular genetics. 2009;18(22):4442–4456. doi: 10.1093/hmg/ddp372. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R22] 22.Antoniou AC, Spurdle AB, Sinilnikova OM, et al. Common breast cancer-predisposition alleles are associated with breast cancer risk in BRCA1 and BRCA2 mutation carriers. American journal of human genetics. 2008;82(4):937–948. doi: 10.1016/j.ajhg.2008.02.008. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R23] 23.Antoniou AC, Beesley J, McGuffog L, et al. Common breast cancer susceptibility alleles and the risk of breast cancer for BRCA1 and BRCA2 mutation carriers: implications for risk prediction. Cancer research. 2010;70(23):9742–9754. doi: 10.1158/0008-5472.CAN-10-1907. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R24] 24.Barnes DR, Rookus MA, McGuffog L, et al. Polygenic risk scores and breast and epithelial ovarian cancer risks for carriers of BRCA1 and BRCA2 pathogenic variants. Genetics in Medicine. 2020;22(10):1653–1666. doi: 10.1038/s41436-020-0862-x. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R25] 25.Gail MH, Pfeiffer RM. Breast Cancer Risk Model Requirements for Counseling, Prevention, and Screening. Journal of the National Cancer Institute. 2018;110(9):994–1002. doi: 10.1093/jnci/djy013. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R26] 26.Chenevix-Trench G, Milne RL, Antoniou AC, Couch FJ, Easton DF, Goldgar DE. An international initiative to identify genetic modifiers of cancer risk in BRCA1 and BRCA2 mutation carriers: the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA) Breast cancer research : BCR. 2007;9(2):104. doi: 10.1186/bcr1670. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R27] 27.Amos CI, Dennis J, Wang Z, et al. The OncoArray Consortium: A Network for Understanding the Genetic Architecture of Common Cancers. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2017;26(1):126–135. doi: 10.1158/1055-9965.EPI-16-0106. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R28] 28.Gaudet MM, Kuchenbaecker KB, Vijai J, et al. Identification of a BRCA2-specific modifier locus at 6p24 related to breast cancer risk. PLoS genetics. 2013;9(3):e1003173. doi: 10.1371/journal.pgen.1003173. [DOI] [PMC free article] [PubMed] [Google Scholar]

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[R31] 31.Milne RL, Kuchenbaecker KB, Michailidou K, et al. Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer. Nature genetics. 2017;49(12):1767–1778. doi: 10.1038/ng.3785. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R32] 32.Harrell FE, Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in medicine. 1996;15(4):361–387. doi: 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4. [DOI] [PubMed] [Google Scholar]

[R33] 33.Azur MJ, Stuart EA, Frangakis C, Leaf PJ. Multiple imputation by chained equations: what is it and how does it work? International journal of methods in psychiatric research. 2011;20(1):40–49. doi: 10.1002/mpr.329. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R34] 34.R_Core_Team_(2019) R Foundation for Statistical Computing. Vienna, Austria: R: A language and environment for statistical computing. [Google Scholar]

[R35] 35.Dahabreh IJ, Kent DM. Index Event Bias as an Explanation for the Paradoxes of Recurrence Risk Research. Jama. 2011;305(8):822–823. doi: 10.1001/jama.2011.163. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R36] 36.Begg CB, Ostrovnaya I, Geyer FC, et al. Contralateral breast cancers: Independent cancers or metastases? International journal of cancer. 2018;142(2):347–356. doi: 10.1002/ijc.31051. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R37] 37.Akdeniz D, Schmidt MK, Seynaeve CM, et al. Risk factors for metachronous contralateral breast cancer: A systematic review and meta-analysis. Breast (Edinburgh, Scotland) 2019;44:1–14. doi: 10.1016/j.breast.2018.11.005. [DOI] [PubMed] [Google Scholar]

[R38] 38.Giardiello D, Steyerberg EW, Hauptmann M, et al. Prediction and clinical utility of a contralateral breast cancer risk model. Breast cancer research : BCR. 2019;21(1):144. doi: 10.1186/s13058-019-1221-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

The predictive ability of the 313-variant-based polygenic risk score for contralateral breast cancer risk prediction in women of European ancestry with a heterozygote BRCA1 or BRCA2 pathogenic variant

Inge MM Lakeman, MD

Alexandra J van den Broek, PhD

Juliёn AM Vos, MD

Daniel R Barnes, PhD

Julian Adlard

Irene L Andrulis, PhD

Adalgeir Arason, BSc

Norbert Arnold, PhD

Banu K Arun, MD

Judith Balmaña, MD, PhD

Daniel Barrowdale, BSc

Javier Benitez, PhD

Ake Borg, PhD

Trinidad Caldés, MD

Maria A Caligo

Wendy K Chung

Kathleen BM Claes, PhD

J Margriet Collée, MD

Fergus J Couch, PhD

Mary B Daly, MD, PhD

Joe Dennis, MSc

Mallika Dhawan, MD

Susan M Domchek

Ros Eeles

Christoph Engel, MD

D Gareth Evans, MD

Lidia Feliubadaló, PhD

Lenka Foretova, MD, PhD

Eitan Friedman

Debra Frost

Patricia A Ganz

Judy Garber, MD MPH

Simon A Gayther, PhD

Anne-Marie Gerdes, MD

Andrew K Godwin

David E Goldgar, PhD

Eric Hahnen, PhD

Christopher R Hake

Ute Hamann, PhD

Frans BL Hogervorst, PhD

Maartje J Hooning, PhD

John L Hopper, PhD

Peter J Hulick, MD

Evgeny N Imyanitov

Claudine Isaacs, MD

Louise Izatt

Anna Jakubowska, PhD

Paul A James, MBBS, PhD

Ramunas Janavicius, MD, PhD

Uffe Birk Jensen

Yue Jiao, PhD

Esther M John, PhD

Vijai Joseph, PhD

Beth Y Karlan, MD

Carolien M Kets, MD, PhD

Irene Konstantopoulou, PhD

Ava Kwong

Clémentine Legrand, MD

Goska Leslie, MEng

Fabienne Lesueur, PhD

Jennifer T Loud, DNP, CRNP

Jan Lubiński, MD, PhD

Siranoush Manoukian, MD

Lesley McGuffog

Austin Miller, PhD

Denise Molina Gomes, MD

Marco Montagna, PhD

Emmanuelle Mouret-Fourme, MD

Katherine L Nathanson, PhD

Susan L Neuhausen, PhD

Heli Nevanlinna, PhD

Joanne Ngeow Yuen Yie, MBBS, MRCP

Edith Olah, PhD, DSc

Olufunmilayo I Olopade

Sue K Park, MD, PhD

Michael T Parsons, BSc

Paolo Peterlongo, PhD

Marion Piedmonte

Table 2. Results of association analyses between the PRS₃₁₃ and contralateral breast cancer risk.