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. 2017 Aug 1;23(15):4181-4189.
doi: 10.1158/1078-0432.CCR-16-3011. Epub 2017 Feb 28.

Added Value of Serum Hormone Measurements in Risk Prediction Models for Breast Cancer for Women Not Using Exogenous Hormones: Results from the EPIC Cohort

Anika Hüsing  1 , Renée T Fortner  2 , Tilman Kühn  1 , Kim Overvad  3 , Anne Tjønneland  4 , Anja Olsen  4 , Marie-Christine Boutron-Ruault  5   6   7 , Gianluca Severi  5   6   7   8 , Agnes Fournier  5   6   7 , Heiner Boeing  9 , Antonia Trichopoulou  10   11 , Vassiliki Benetou  10   11 , Philippos Orfanos  10   11 , Giovanna Masala  12 , Valeria Pala  13 , Rosario Tumino  14 , Francesca Fasanelli  15 , Salvatore Panico  16 , H Bas Bueno de Mesquita  17   18 , Petra H Peeters  19   20 , Carla H van Gills  19 , J Ramón Quirós  21 , Antonio Agudo  22 , Maria-Jose Sánchez  23   24 , Maria-Dolores Chirlaque  24   25   26 , Aurelio Barricarte  24   27   28 , Pilar Amiano  29 , Kay-Tee Khaw  30 , Ruth C Travis  31 , Laure Dossus  32 , Kuanrong Li  32 , Pietro Ferrari  32 , Melissa A Merritt  33 , Ioanna Tzoulaki  33 , Elio Riboli  33 , Rudolf Kaaks  1
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Free article

Added Value of Serum Hormone Measurements in Risk Prediction Models for Breast Cancer for Women Not Using Exogenous Hormones: Results from the EPIC Cohort

Anika Hüsing et al. Clin Cancer Res. .
Free article

Abstract

Purpose: Circulating hormone concentrations are associated with breast cancer risk, with well-established associations for postmenopausal women. Biomarkers may represent minimally invasive measures to improve risk prediction models.Experimental Design: We evaluated improvements in discrimination gained by adding serum biomarker concentrations to risk estimates derived from risk prediction models developed by Gail and colleagues and Pfeiffer and colleagues using a nested case-control study within the EPIC cohort, including 1,217 breast cancer cases and 1,976 matched controls. Participants were pre- or postmenopausal at blood collection. Circulating sex steroids, prolactin, insulin-like growth factor (IGF) I, IGF-binding protein 3, and sex hormone-binding globulin (SHBG) were evaluated using backward elimination separately in women pre- and postmenopausal at blood collection. Improvement in discrimination was evaluated as the change in concordance statistic (C-statistic) from a modified Gail or Pfeiffer risk score alone versus models, including the biomarkers and risk score. Internal validation with bootstrapping (1,000-fold) was used to adjust for overfitting.Results: Among women postmenopausal at blood collection, estradiol, testosterone, and SHBG were selected into the prediction models. For breast cancer overall, model discrimination after including biomarkers was 5.3 percentage points higher than the modified Gail model alone, and 3.4 percentage points higher than the Pfeiffer model alone, after accounting for overfitting. Discrimination was more markedly improved for estrogen receptor-positive disease (percentage point change in C-statistic: 7.2, Gail; 4.8, Pfeiffer). We observed no improvement in discrimination among women premenopausal at blood collection.Conclusions: Integration of hormone measurements in clinical risk prediction models may represent a strategy to improve breast cancer risk stratification. Clin Cancer Res; 23(15); 4181-9. ©2017 AACR.

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