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Breast Cancer Res. 2018 Dec 3;20(1):147. doi: 10.1186/s13058-018-1073-0.

Risk prediction for estrogen receptor-specific breast cancers in two large prospective cohorts.

Author information

1
Nutritional Methodology and Biostatistics Group, Nutrition and Metabolism Section, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372, Lyon Cedex 08, France.
2
Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, USA.
3
Breast and Gynaecologic Cancer Registry of Côte d'Or, Georges-François Leclerc Comprehensive Cancer Care Centre, Dijon, France.
4
EA 4184, Medical School, University of Burgundy, Dijon, France.
5
CESP, INSERM U1018, Univ. Paris-Sud, UVSQ, Université Paris-Saclay, Villejuif, France.
6
Gustave Roussy, Villejuif, France.
7
Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.
8
Cancer Registry and Histopathology Department, "Civic-M. P.Arezzo" Hospital, ASP, Ragusa, Italy.
9
Escuela Andaluza de Salud Pública, Instituto de Investigación Biosanitaria ibs. GRANADA, Hospitales Universitarios de Granada/ Universidad de Granada, Granada, Spain.
10
CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain.
11
Navarra Public Health Institute, Pamplona, Spain.
12
IdiSNA, Navarra Institute for Health Research, Pamplona, Spain.
13
Department of Epidemiology, Regional Health Council, IMIB-Arrixaca, Murcia, Spain.
14
Department of Health and Social Sciences, Universidad de Murcia, Murcia, Spain.
15
Unit of Nutrition and Cancer. Cancer Epidemiology Research Program, Catalan Institute of Oncology-IDIBELL. L'Hospitalet de Llobregat, Barcelona, Spain.
16
Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK.
17
Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
18
Department for Determinants of Chronic Diseases, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
19
Department of Gastroenterology and Hepatology, University Medical Centre, Utrecht, The Netherlands.
20
Department of Social & Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.
21
Hellenic Health Foundation, Athens, Greece.
22
WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Department of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece.
23
Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany.
24
Department of Population Health, New York University School of Medicine, New York, USA.
25
Department of Environmental Medicine, New York University School of Medicine, New York, USA.
26
Perlmutter Cancer Center, New York University School of Medicine, New York, USA.
27
Department of Public Health and Clinical Medicine, Nutritional Research, Umeå University, Umeå, Sweden.
28
Department of Surgical and Perioperative Sciences, Umeå University, Umeå, Sweden.
29
Section for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark.
30
Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark.
31
Department of Nutrition, Bjørknes University College, Oslo, Norway.
32
Department of Research, Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo, Norway.
33
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
34
Genetic Epidemiology Group, Folkhälsan Research Center, Helsinki, Finland.
35
Department of Community Medicine, University of Tromsø, The Arctic University of Norway, Tromsø, Norway.
36
Nutritional Epidemiology Group, Nutrition and Metabolism Section, International Agency for Research on Cancer, Lyon, France.
37
Biomarkers Group, Nutrition and Metabolism Section, International Agency for Research on Cancer, Lyon, France.
38
Nutritional Methodology and Biostatistics Group, Nutrition and Metabolism Section, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372, Lyon Cedex 08, France. ferrarip@iarc.fr.

Abstract

BACKGROUND:

Few published breast cancer (BC) risk prediction models consider the heterogeneity of predictor variables between estrogen-receptor positive (ER+) and negative (ER-) tumors. Using data from two large cohorts, we examined whether modeling this heterogeneity could improve prediction.

METHODS:

We built two models, for ER+ (ModelER+) and ER- tumors (ModelER-), respectively, in 281,330 women (51% postmenopausal at recruitment) from the European Prospective Investigation into Cancer and Nutrition cohort. Discrimination (C-statistic) and calibration (the agreement between predicted and observed tumor risks) were assessed both internally and externally in 82,319 postmenopausal women from the Women's Health Initiative study. We performed decision curve analysis to compare ModelER+ and the Gail model (ModelGail) regarding their applicability in risk assessment for chemoprevention.

RESULTS:

Parity, number of full-term pregnancies, age at first full-term pregnancy and body height were only associated with ER+ tumors. Menopausal status, age at menarche and at menopause, hormone replacement therapy, postmenopausal body mass index, and alcohol intake were homogeneously associated with ER+ and ER- tumors. Internal validation yielded a C-statistic of 0.64 for ModelER+ and 0.59 for ModelER-. External validation reduced the C-statistic of ModelER+ (0.59) and ModelGail (0.57). In external evaluation of calibration, ModelER+ outperformed the ModelGail: the former led to a 9% overestimation of the risk of ER+ tumors, while the latter yielded a 22% underestimation of the overall BC risk. Compared with the treat-all strategy, ModelER+ produced equal or higher net benefits irrespective of the benefit-to-harm ratio of chemoprevention, while ModelGail did not produce higher net benefits unless the benefit-to-harm ratio was below 50. The clinical applicability, i.e. the area defined by the net benefit curve and the treat-all and treat-none strategies, was 12.7 × 10- 6 for ModelER+ and 3.0 × 10- 6 for ModelGail.

CONCLUSIONS:

Modeling heterogeneous epidemiological risk factors might yield little improvement in BC risk prediction. Nevertheless, a model specifically predictive of ER+ tumor risk could be more applicable than an omnibus model in risk assessment for chemoprevention.

KEYWORDS:

Breast cancer; EPIC; Estrogen receptor; Prospective cohort; Risk prediction; WHI

PMID:
30509329
PMCID:
PMC6276150
DOI:
10.1186/s13058-018-1073-0
[Indexed for MEDLINE]
Free PMC Article

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