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 et al. and Pfeiffer et al. using a nested case-control study within the EPIC cohort including 1217 breast cancer cases and 1976 matched controls. Participants were pre- or postmenopausal at blood collection. Circulating sex steroids, prolactin, insulin-like growth factor 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 C-statistic from a modified Gail or Pfeiffer risk score alone vs. models including the biomarkers and risk score. Internal validation with bootstrapping (1000-fold) was used to adjust for over-fitting. RESULTS: Among women postmenopausal at blood collection, estradiol, testosterone and SHBG were selected into the prediction models. For breast cancer overall, discrimination 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 over-fitting. Discrimination was more markedly improved for estrogen receptor (ER)+ 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.

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Clin Cancer Res

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