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

Published version
Peer-reviewed

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Authors

Giardiello, Daniele 
Hooning, Maartje J 
Hauptmann, Michael 
Keeman, Renske 
Heemskerk-Gerritsen, BAM 

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.

Description

Keywords

Research, Contralateral breast cancer, Risk prediction, Contralateral preventive mastectomy, Clinical decision-making, Breast cancer genetic predisposition, Breast Cancer Association Consortium, BCAC, Prediction performance, BRCA1/2 germline mutation, Polygenic risk score

Journal Title

Breast Cancer Res

Conference Name

Journal ISSN

1465-5411
1465-542X

Volume Title

Publisher

Springer Science and Business Media LLC
Sponsorship
European Commission (223175)
European Commission Horizon 2020 (H2020) Societal Challenges (634935)
European Commission Horizon 2020 (H2020) Societal Challenges (633784)
Cancer Research UK (A16563)
Cancer Research UK (A10118)