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

dc.contributor.authorGiardiello, Daniele
dc.contributor.authorHooning, Maartje J.
dc.contributor.authorHauptmann, Michael
dc.contributor.authorKeeman, Renske
dc.contributor.authorHeemskerk-Gerritsen, B. A. M.
dc.contributor.authorBecher, Heiko
dc.contributor.authorBlomqvist, Carl
dc.contributor.authorBojesen, Stig E.
dc.contributor.authorBolla, Manjeet K.
dc.contributor.authorCamp, Nicola J.
dc.contributor.authorCzene, Kamila
dc.contributor.authorDevilee, Peter
dc.contributor.authorEccles, Diana M.
dc.contributor.authorFasching, Peter A.
dc.contributor.authorFigueroa, Jonine D.
dc.contributor.authorFlyger, Henrik
dc.contributor.authorGarcía-Closas, Montserrat
dc.contributor.authorHaiman, Christopher A.
dc.contributor.authorHamann, Ute
dc.contributor.authorHopper, John L.
dc.contributor.authorJakubowska, Anna
dc.contributor.authorLeeuwen, Floor E.
dc.contributor.authorLindblom, Annika
dc.contributor.authorLubiński, Jan
dc.contributor.authorMargolin, Sara
dc.contributor.authorMartinez, Maria Elena
dc.contributor.authorNevanlinna, Heli
dc.contributor.authorNevelsteen, Ines
dc.contributor.authorPelders, Saskia
dc.contributor.authorPharoah, Paul D. P.
dc.contributor.authorSiesling, Sabine
dc.contributor.authorSouthey, Melissa C.
dc.contributor.authorvan der Hout, Annemieke H.
dc.contributor.authorvan Hest, Liselotte P.
dc.contributor.authorChang-Claude, Jenny
dc.contributor.authorHall, Per
dc.contributor.authorEaston, Douglas F.
dc.contributor.authorSteyerberg, Ewout W.
dc.contributor.authorSchmidt, Marjanka K.
dc.description.abstractBackground 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.en
dc.identifier.citationBreast Cancer Research 24 (2022): 69. <>
dc.subjectContralateral breast cancer
dc.subjectRisk prediction
dc.subjectContralateral preventive mastectomy
dc.subjectClinical decision-making
dc.subjectBreast cancer genetic predisposition
dc.subjectBreast Cancer Association Consortium
dc.subjectPrediction performance
dc.subjectBRCA1/2 germline mutation
dc.subjectPolygenic risk score
dc.subject.ddcDDC Classification::6 Technik, Medizin, angewandte Wissenschaften :: 61 Medizin und Gesundheit :: 610 Medizin und Gesundheit
dc.titlePredictCBC-2.0: a contralateral breast cancer risk prediction model developed and validated in ~200,000 patientsen
dcterms.publisherFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
local.journal.titleBreast Cancer Research
local.subject.fakultaetMedizinische Fakultät
local.subject.sammlungUniversität Erlangen-Nürnberg / Open Access Artikel ohne Förderung / Open Access Artikel ohne Förderung 2022
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