Prediction and clinical utility of a contralateral breast cancer risk model
Authors: Giardiello Daniele , Steyerberg Ewout W. , Hauptmann Michael , Adank Muriel A. , Akdeniz Delal , Blomqvist Carl , Bojesen Stig E. , Bolla Manjeet K. , Brinkhuis Mariël , Chang-Claude Jenny , Czene Kamila , Devilee Peter , Dunning Alison M. , Easton Douglas F. , Eccles Diana M. , Fasching Peter A. , Figueroa Jonine , Flyger Henrik , García-Closas Montserrat , Haeberle Lothar , Haiman Christopher A. , Hall Per , Hamann Ute , Hopper John L. , Jager Agnes , Jakubowska Anna , Jung Audrey , Keeman Renske , Kramer Iris , Lambrechts Diether , Le Marchand Loic , Lindblom Annika , Lubiński Jan , Manoochehri Mehdi , Mariani Luigi , Nevanlinna Heli , Oldenburg Hester S. A. , Pelders Saskia , Pharoah Paul D. P. , Shah Mitul , Siesling Sabine , Smit Vincent T. H. B. M. , Southey Melissa C. , Tapper William J. , Tollenaar Rob A. E. M. , van den Broek Alexandra J. , van Deurzen Carolien H. M. , van Leeuwen Flora E. , van Ongeval Chantal , Van’t Veer Laura J. , Wang Qin , Wendt Camilla , Westenend Pieter J. , Hooning Maartje J. , Schmidt Marjanka K. 
Published: December 2019

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 andBRCA1/2germline mutation status, accounting for the competing risks of death and distant metastasis. We also developed a model withoutBRCA1/2mutation 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,BRCA1/2germline 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 forBRCA1/2mutation 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 inBRCA1/2is available, remains challenging.