Breast Cancer Research (Oct 2022)
PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients
- Daniele Giardiello,
- Maartje J. Hooning,
- Michael Hauptmann,
- Renske Keeman,
- B. A. M. Heemskerk-Gerritsen,
- Heiko Becher,
- Carl Blomqvist,
- Stig E. Bojesen,
- Manjeet K. Bolla,
- Nicola J. Camp,
- Kamila Czene,
- Peter Devilee,
- Diana M. Eccles,
- Peter A. Fasching,
- Jonine D. Figueroa,
- Henrik Flyger,
- Montserrat García-Closas,
- Christopher A. Haiman,
- Ute Hamann,
- John L. Hopper,
- Anna Jakubowska,
- Floor E. Leeuwen,
- Annika Lindblom,
- Jan Lubiński,
- Sara Margolin,
- Maria Elena Martinez,
- Heli Nevanlinna,
- Ines Nevelsteen,
- Saskia Pelders,
- Paul D. P. Pharoah,
- Sabine Siesling,
- Melissa C. Southey,
- Annemieke H. van der Hout,
- Liselotte P. van Hest,
- Jenny Chang-Claude,
- Per Hall,
- Douglas F. Easton,
- Ewout W. Steyerberg,
- Marjanka K. Schmidt
Affiliations
- Daniele Giardiello
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital
- Maartje J. Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute
- Michael Hauptmann
- Brandenburg Medical School, Institute of Biostatistics and Registry Research
- Renske Keeman
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital
- B. A. M. Heemskerk-Gerritsen
- Department of Medical Oncology, Erasmus MC Cancer Institute
- Heiko Becher
- Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf
- Carl Blomqvist
- Department of Oncology, Helsinki University Hospital, University of Helsinki
- Stig E. Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital
- Manjeet K. Bolla
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge
- Nicola J. Camp
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah
- Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet
- Peter Devilee
- Department of Pathology, Leiden University Medical Center
- Diana M. Eccles
- Faculty of Medicine, University of Southampton
- Peter A. Fasching
- Division of Hematology and Oncology, Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles
- Jonine D. Figueroa
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh
- Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital
- Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health
- Christopher A. Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California
- Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ)
- John L. Hopper
- Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, The University of Melbourne
- Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University
- Floor E. Leeuwen
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital
- Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet
- Jan Lubiński
- Department of Genetics and Pathology, Pomeranian Medical University
- Sara Margolin
- Department of Oncology, Södersjukhuset
- Maria Elena Martinez
- Moores Cancer Center, University of California San Diego
- Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki
- Ines Nevelsteen
- Department of Oncology, Leuven Multidisciplinary Breast Center, Leuven Cancer Institute, University Hospitals Leuven
- Saskia Pelders
- Department of Medical Oncology, Erasmus MC Cancer Institute
- Paul D. P. Pharoah
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge
- Sabine Siesling
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL)
- Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University
- Annemieke H. van der Hout
- Department of Genetics, University Medical Center Groningen, University Groningen
- Liselotte P. van Hest
- Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam
- Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ)
- Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet
- Douglas F. Easton
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge
- Ewout W. Steyerberg
- Department of Biomedical Data Sciences, Leiden University Medical Center
- Marjanka K. Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital
- DOI
- https://doi.org/10.1186/s13058-022-01567-3
- Journal volume & issue
-
Vol. 24,
no. 1
pp. 1 – 14
Abstract
Abstract Background Prediction 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. Methods We 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. Results The 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. Conclusions Additional 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.
Keywords
- Contralateral breast cancer
- Risk prediction
- Contralateral preventive mastectomy
- Clinical decision-making
- Breast cancer genetic predisposition
- Breast Cancer Association Consortium
