PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients

Breast Cancer Research

Research

Michael Hauptmann¹  Liselotte P. van Hest²  Stig E. Bojesen³  Ewout W. Steyerberg⁴  Henrik Flyger⁵  Jan Lubiński⁶  Anna Jakubowska⁷  Annemieke H. van der Hout⁸  Nicola J. Camp⁹  Kamila Czene^1,10  Per Hall^1,11  Saskia Pelders^1,12  Maartje J. Hooning^1,12  B. A. M. Heemskerk-Gerritsen^1,12  Annika Lindblom^1,13  Heli Nevanlinna^1,14  Carl Blomqvist^1,15  Ines Nevelsteen^1,16  Sara Margolin^1,17  Peter Devilee^1,18  Christopher A. Haiman^1,19  Manjeet K. Bolla^2,20  Paul D. P. Pharoah^2,21  Douglas F. Easton^2,21  Sabine Siesling^2,22  Montserrat García-Closas^2,23  Jenny Chang-Claude^2,24  Peter A. Fasching^2,25  Renske Keeman^2,26  Daniele Giardiello^2,27  Marjanka K. Schmidt^2,28  Floor E. Leeuwen^2,29  Diana M. Eccles^3,30  Heiko Becher^3,31  John L. Hopper^3,32  Ute Hamann^3,33  Maria Elena Martinez^3,34  Melissa C. Southey^3,35  Jonine D. Figueroa^3,36 

[1] Brandenburg Medical School, Institute of Biostatistics and Registry Research, Neuruppin, Germany;Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands;Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark;Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark;Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark;Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands;Department of Public Health, Erasmus MC Cancer Institute, Rotterdam, The Netherlands;Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark;Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland;Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland;Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland;Department of Genetics, University Medical Center Groningen, University Groningen, Groningen, The Netherlands;Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA;Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden;Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden;Department of Oncology, Södersjukhuset, Stockholm, Sweden;Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands;Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden;Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden;Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland;Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland;Department of Oncology, Örebro University Hospital, Örebro, Sweden;Department of Oncology, Leuven Multidisciplinary Breast Center, Leuven Cancer Institute, University Hospitals Leuven, Louven, Belgium;Department of Oncology, Södersjukhuset, Stockholm, Sweden;Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden;Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands;Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands;Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA;Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK;Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK;Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK;Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands;Department of HealthTechnology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands;Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA;Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany;Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany;Division of Hematology and Oncology, Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA;Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg (FAU), Erlangen, Germany;Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands;Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands;Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands;Institute of Biomedicine, EURAC Research Affiliated Institute of the University of Lübeck, Bolzano, Italy;Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands;Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands;Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands;Faculty of Medicine, University of Southampton, Southampton, UK;Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany;Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC, Australia;Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany;Moores Cancer Center, University of California San Diego, La Jolla, CA, USA;Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA;Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia;Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC, Australia;Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia;Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK;Cancer Research UK Edinburgh Centre, The University of Edinburgh, Edinburgh, UK;Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA;

关键词: Contralateral breast cancer;    Risk prediction;    Contralateral preventive mastectomy;    Clinical decision-making;    Breast cancer genetic predisposition;    Breast Cancer Association Consortium;    BCAC;    Prediction performance;    BRCA1/2;    Polygenic risk score;   

DOI  :  10.1186/s13058-022-01567-3

 received in 2022-03-14, accepted in 2022-10-07,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundPrediction of contralateral breast cancer (CBC) risk is challenging due to moderate performances of the known risk factors. We aimed to improve our previous risk prediction model (PredictCBC) by updated follow-up and including additional risk factors.MethodsWe included data from 207,510 invasive breast cancer patients participating in 23 studies. In total, 8225 CBC events occurred over a median follow-up of 10.2 years. In addition to the previously included risk factors, PredictCBC-2.0 included CHEK2 c.1100delC, a 313 variant polygenic risk score (PRS-313), body mass index (BMI), and parity. Fine and Gray regression was used to fit the model. Calibration and a time-dependent area under the curve (AUC) at 5 and 10 years were assessed to determine the performance of the models. Decision curve analysis was performed to evaluate the net benefit of PredictCBC-2.0 and previous PredictCBC models.ResultsThe discrimination of PredictCBC-2.0 at 10 years was higher than PredictCBC with an AUC of 0.65 (95% prediction intervals (PI) 0.56–0.74) versus 0.63 (95%PI 0.54–0.71). PredictCBC-2.0 was well calibrated with an observed/expected ratio at 10 years of 0.92 (95%PI 0.34–2.54). Decision curve analysis for contralateral preventive mastectomy (CPM) showed the potential clinical utility of PredictCBC-2.0 between thresholds of 4 and 12% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.ConclusionsAdditional genetic information beyond BRCA1/2 germline mutations improved CBC risk prediction and might help tailor clinical decision-making toward CPM or alternative preventive strategies. Identifying patients who benefit from CPM, especially in the general breast cancer population, remains challenging.

【 授权许可】

CC BY   
© The Author(s) 2022

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