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

Giardiello D, Hooning MJ, Hauptmann M, Keeman R, Heemskerk-Gerritsen BAM, Becher H, Blomqvist C, Bojesen SE, Bolla MK, Camp NJ, Czene K, Devilee P, Eccles DM, Fasching P, Figueroa JD, Flyger H, Garcia-Closas M, Haiman CA, Hamann U, Hopper JL, Jakubowska A, Leeuwen FE, Lindblom A, Lubinski J, Margolin S, Martinez ME, Nevanlinna H, Nevelsteen I, Pelders S, Pharoah PDP, Siesling S, Southey MC, Van Der Hout AH, Van Hest LP, Chang-Claude J, Hall P, Easton DF, Steyerberg EW, Schmidt MK (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Book Volume: 24

Article Number: 69

Journal Issue: 1

DOI: 10.1186/s13058-022-01567-3

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.

Authors with CRIS profile

Involved external institutions

University Hospital Leuven (UZ) / Universitaire ziekenhuizen Leuven Belgium (BE) University of Southampton United Kingdom (GB) Pomeranian Medical University / Pomorski Uniwersytet Medyczny w Szczecinie (PMU) Poland (PL) Leiden University Medical Center Netherlands (NL) Erasmus University Medical Center (MC) Netherlands (NL) Helsingin yliopisto / University of Helsinki Finland (FI) University of Groningen / Rijksuniversiteit Groningen Netherlands (NL) Södersjukhuset Sweden (SE) University of California, San Diego United States (USA) (US) Karolinska Institute Sweden (SE) Netherlands Cancer Institute (NKI) Netherlands (NL) University of Cambridge United Kingdom (GB) University of Edinburgh United Kingdom (GB) Monash University Australia (AU) National Cancer Institute (NCI) United States (USA) (US) Vrije Universiteit Amsterdam (VU) / University Amsterdam Netherlands (NL) University of Southern California (USC) United States (USA) (US) Deutsches Krebsforschungszentrum (DKFZ) Germany (DE) Netherlands Comprehensive Cancer Organisation / Integraal Kankercentrum Nederland (IKNL) Netherlands (NL) The University of Melbourne Australia (AU) Copenhagen University Hospital Denmark (DK) University of Utah United States (USA) (US) Universitätsklinikum Hamburg-Eppendorf (UKE) Germany (DE) Medizinische Hochschule Brandenburg "Theodor Fontane" / Brandenburg Medical School "Theodor Fontane" Germany (DE)

How to cite

APA:

Giardiello, D., Hooning, M.J., Hauptmann, M., Keeman, R., Heemskerk-Gerritsen, B.A.M., Becher, H.,... Schmidt, M.K. (2022). PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients. Breast Cancer Research, 24(1). https://doi.org/10.1186/s13058-022-01567-3

MLA:

Giardiello, Daniele, et al. "PredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~ 200,000 patients." Breast Cancer Research 24.1 (2022).

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