Daniele Giardiello1,2,3, Maartje J Hooning4, Michael Hauptmann5, Renske Keeman1, B A M Heemskerk-Gerritsen4, Heiko Becher6, Carl Blomqvist7,8, Stig E Bojesen9,10,11, Manjeet K Bolla12, Nicola J Camp13, Kamila Czene14, Peter Devilee15,16, Diana M Eccles17, Peter A Fasching18,19, Jonine D Figueroa20,21,22, Henrik Flyger23, Montserrat García-Closas22, Christopher A Haiman24, Ute Hamann25, John L Hopper26, Anna Jakubowska27,28, Floor E Leeuwen29, Annika Lindblom30,31, Jan Lubiński27, Sara Margolin32,33, Maria Elena Martinez34,35, Heli Nevanlinna36, Ines Nevelsteen37, Saskia Pelders4, Paul D P Pharoah12,38, Sabine Siesling39,40, Melissa C Southey41,42,43, Annemieke H van der Hout44, Liselotte P van Hest45, Jenny Chang-Claude46,47, Per Hall14,32, Douglas F Easton12,38, Ewout W Steyerberg2,48, Marjanka K Schmidt49,50. 1. Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands. 2. Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands. 3. Institute of Biomedicine, EURAC Research Affiliated Institute of the University of Lübeck, Bolzano, Italy. 4. Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands. 5. Brandenburg Medical School, Institute of Biostatistics and Registry Research, Neuruppin, Germany. 6. Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. 7. Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland. 8. Department of Oncology, Örebro University Hospital, Örebro, Sweden. 9. Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark. 10. Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark. 11. Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. 12. Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK. 13. Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA. 14. Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. 15. Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands. 16. Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands. 17. Faculty of Medicine, University of Southampton, Southampton, UK. 18. Division of Hematology and Oncology, Department of Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, CA, USA. 19. Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nuremberg (FAU), Erlangen, Germany. 20. Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh, Edinburgh, UK. 21. Cancer Research UK Edinburgh Centre, The University of Edinburgh, Edinburgh, UK. 22. Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. 23. Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark. 24. Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. 25. Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany. 26. Melbourne School of Population and Global Health, Centre for Epidemiology and Biostatistics, The University of Melbourne, Melbourne, VIC, Australia. 27. Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland. 28. Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland. 29. Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands. 30. Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden. 31. Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden. 32. Department of Oncology, Södersjukhuset, Stockholm, Sweden. 33. Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden. 34. Moores Cancer Center, University of California San Diego, La Jolla, CA, USA. 35. Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA. 36. Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland. 37. Department of Oncology, Leuven Multidisciplinary Breast Center, Leuven Cancer Institute, University Hospitals Leuven, Louven, Belgium. 38. Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK. 39. Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands. 40. Department of HealthTechnology and Services Research, Technical Medical Centre, University of Twente, Enschede, The Netherlands. 41. Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia. 42. Department of Clinical Pathology, The University of Melbourne, Melbourne, VIC, Australia. 43. Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia. 44. Department of Genetics, University Medical Center Groningen, University Groningen, Groningen, The Netherlands. 45. Clinical Genetics, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. 46. Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany. 47. Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany. 48. Department of Public Health, Erasmus MC Cancer Institute, Rotterdam, The Netherlands. 49. Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands. mk.schmidt@nki.nl. 50. Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni Van Leeuwenhoek Hospital, Amsterdam, The Netherlands. mk.schmidt@nki.nl.
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.
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:
BCAC; BRCA1/2 germline mutation; Breast Cancer Association Consortium; Breast cancer genetic predisposition; Clinical decision-making; Contralateral breast cancer; Contralateral preventive mastectomy; Polygenic risk score; Prediction performance; Risk prediction
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