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Clin Cancer Res. 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.

Author information

1
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
2
Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany. r.fortner@dkfz-heidelberg.de.
3
Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark.
4
Unit of Diet, Genes and Environment, Danish Cancer Society Research Center, Copenhagen, Denmark.
5
INSERM, Centre for Research in Epidemiology and Population Health (CESP), U1018, Nutrition, Hormones and Women's Health team, Villejuif, France.
6
Université Paris Sud, UMRS 1018, Villejuif, France.
7
Gustave Roussy, Villejuif, France.
8
Human Genetics Foundation (HuGeF), Turin, Italy.
9
Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
10
Hellenic Health Foundation, Athens, Greece.
11
WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, School of Medicine, Department of Hygiene, Epidemiology and Medical Statistics, National and Kapodistrian University of Athens, Athens, Greece.
12
Cancer Risk Factors and Life-Style Epidemiology Unit, Cancer Research and Prevention Institute - ISPO, Florence, Italy.
13
Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
14
Cancer Registry and Histopathology Unit, "Civic - M.P. Arezzo" Hospital, ASP Ragusa, Italy.
15
Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital and Center for Cancer Prevention (CPO), Turin, Italy.
16
Dipartimento di Medicina Clinica e Sperimentale, Federico II University, Naples, Italy.
17
Department for Determinants of Chronic Diseases (DCD), National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands.
18
Department of Epidemiology and Biostatistics, The School of Public Health, Imperial College London, London, United Kingdom.
19
Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, the Netherlands.
20
MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, United Kingdom.
21
Public Health Directorate, Asturias, Spain.
22
Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology-IDIBELL. L'Hospitalet de Llobregat, Barcelona, Spain.
23
Escuela Andaluza de Salud Pública, Instituto de Investigación Biosanitaria ibs.Granada, Hospitales Universitarios de Granada/Universidad de Granada, Granada, Spain.
24
CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
25
Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia, Spain.
26
Department of Health and Social Sciences, Universidad de Murcia, Murcia, Spain.
27
Navarra Public Health Institute, Pamplona, Spain.
28
Navarra Institute for Health Research (IdiSNA), Pamplona, Spain.
29
Public Health Division and Biodonostia Research Institute - Ciberesp, Basque Regional Health Department, San Sebastian, Spain.
30
Cancer Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom.
31
Cancer Epidemiology Unit, University of Oxford, Oxford, United Kingdom.
32
Nutrition and Metabolism Section, International Agency for Research on Cancer, Lyon, France.
33
School of Public Health, Imperial College London, London, United Kingdom.

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.

PMID:
28246273
DOI:
10.1158/1078-0432.CCR-16-3011
[Indexed for MEDLINE]
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