Prediction and clinical utility of a contralateral breast cancer risk model.

Giardiello D1,2, Steyerberg EW2,3, Hauptmann M4,5, Adank MA6, Akdeniz D7, Blomqvist C8,9, Bojesen SE10,11,12, Bolla MK13, Brinkhuis M14, Chang-Claude J15,16, Czene K17, Devilee P18,19, Dunning AM20, Easton DF13,20, Eccles DM21, Fasching PA22,23, Figueroa J24,25,26, Flyger H27, García-Closas M26,28, Haeberle L23, Haiman CA29, Hall P17,30, Hamann U31, Hopper JL32, Jager A33, Jakubowska A34,35, Jung A15, Keeman R1, Kramer I1, Lambrechts D36,37, Le Marchand L38, Lindblom A39,40, Lubiński J34, Manoochehri M31, Mariani L41, Nevanlinna H42, Oldenburg HSA43, Pelders S7, Pharoah PDP13,20, Shah M20, Siesling S44, Smit VTHBM18, Southey MC45,46, Tapper WJ47, Tollenaar RAEM48, van den Broek AJ1, van Deurzen CHM49, van Leeuwen FE50, van Ongeval C51, Van't Veer LJ1, Wang Q13, Wendt C52, Westenend PJ53, Hooning MJ7, Schmidt MK54,55.

Author information

1: Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
2: Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.
3: Department of Public Health, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
4: Institute of Biometry and Registry Research, Brandenburg Medical School, Neuruppin, Germany.
5: Department of Epidemiology and Biostatistics, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
6: The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Family Cancer Clinic, Amsterdam, The Netherlands.
7: Department of Medical Oncology, Family Cancer Clinic, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
8: Department of Oncology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland.
9: Department of Oncology, Örebro University Hospital, Örebro, Sweden.
10: Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark.
11: Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark.
12: Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
13: Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
14: East-Netherlands, Laboratory for Pathology, Hengelo, The Netherlands.
15: Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
16: Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
17: Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.
18: Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands.
19: Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.
20: Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK.
21: Cancer Sciences Academic Unit, Faculty of Medicine, University of Southampton, Southampton, UK.
22: Department of Medicine Division of Hematology and Oncology, University of California at Los Angeles, David Geffen School of Medicine, Los Angeles, CA, USA.
23: Department of Gynecology and Obstetrics, Comprehensive Cancer Center ER-EMN, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany.
24: Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh Medical School, Edinburgh, UK.
25: Cancer Research UK Edinburgh Centre, Edinburgh, UK.
26: Department of Health and Human Services, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
27: Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark.
28: Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK.
29: Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
30: Department of Oncology, Södersjukhuset, Stockholm, Sweden.
31: Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany.
32: Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia.
33: Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
34: Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland.
35: Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland.
36: VIB Center for Cancer Biology, VIB, Leuven, Belgium.
37: Laboratory for Translational Genetics, Department of Human Genetics, University of Leuven, Leuven, Belgium.
38: University of Hawaii Cancer Center, Epidemiology Program, Honolulu, HI, USA.
39: Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.
40: Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden.
41: Unit of Clinical Epidemiology and Trial Organization, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
42: Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland.
43: Department of Surgical Oncology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands.
44: Department of Research, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands.
45: Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.
46: Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia.
47: Faculty of Medicine, University of Southampton, Southampton, UK.
48: Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands.
49: Department of Pathology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands.
50: Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066, CX, Amsterdam, The Netherlands.
51: Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium.
52: Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden.
53: BOOG, Laboratory for Pathology Dordrecht, Dordrecht, The Netherlands.
54: Division of Molecular Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands. mk.schmidt@nki.nl.
55: Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Plesmanlaan 121, 1066, CX, Amsterdam, The Netherlands. mk.schmidt@nki.nl.

Abstract

BACKGROUND:

Breast cancer survivors are at risk for contralateral breast cancer (CBC), with the consequent burden of further treatment and potentially less favorable prognosis. We aimed to develop and validate a CBC risk prediction model and evaluate its applicability for clinical decision-making.

METHODS:

We included data of 132,756 invasive non-metastatic breast cancer patients from 20 studies with 4682 CBC events and a median follow-up of 8.8 years. We developed a multivariable Fine and Gray prediction model (PredictCBC-1A) including patient, primary tumor, and treatment characteristics and BRCA1/2 germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model without BRCA1/2 mutation status (PredictCBC-1B) since this information was available for only 6% of patients and is routinely unavailable in the general breast cancer population. Prediction performance was evaluated using calibration and discrimination, calculated by a time-dependent area under the curve (AUC) at 5 and 10 years after diagnosis of primary breast cancer, and an internal-external cross-validation procedure. Decision curve analysis was performed to evaluate the net benefit of the model to quantify clinical utility.

RESULTS:

In the multivariable model, BRCA1/2 germline mutation status, family history, and systemic adjuvant treatment showed the strongest associations with CBC risk. The AUC of PredictCBC-1A was 0.63 (95% prediction interval (PI) at 5 years, 0.52-0.74; at 10 years, 0.53-0.72). Calibration-in-the-large was -0.13 (95% PI: -1.62-1.37), and the calibration slope was 0.90 (95% PI: 0.73-1.08). The AUC of Predict-1B at 10 years was 0.59 (95% PI: 0.52-0.66); calibration was slightly lower. Decision curve analysis for preventive contralateral mastectomy showed potential clinical utility of PredictCBC-1A between thresholds of 4-10% 10-year CBC risk for BRCA1/2 mutation carriers and non-carriers.

CONCLUSIONS:

We developed a reasonably calibrated model to predict the risk of CBC in women of European-descent; however, prediction accuracy was moderate. Our model shows potential for improved risk counseling, but decision-making regarding contralateral preventive mastectomy, especially in the general breast cancer population where limited information of the mutation status in BRCA1/2 is available, remains challenging.