Christina C. Dahm

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

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Kuanrong Li, Nutritional Methodology and Biostatistics Group, Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC-WHO), 150, Cours Albert Thomas, 69372, Lyon Cedex 08, France. freislingh@iarc.fr., France
  • Garnet Anderson, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA., United States
  • Vivian Viallon, Nutritional Methodology and Biostatistics Group, Section of Nutrition and Metabolism, International Agency for Research on Cancer (IARC-WHO), 150, Cours Albert Thomas, 69372, Lyon Cedex 08, France. freislingh@iarc.fr., France
  • Patrick Arveux, Institut Gustave Roussy, Villejuif, France., France
  • Marina Kvaskoff, Institut Gustave Roussy, Villejuif, France., France
  • Agnès Fournier, Institut Gustave Roussy, Villejuif, France., France
  • Vittorio Krogh, 12 Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy., Italy
  • Rosario Tumino, Civic-M.P. Arezzo Hospital, Italy
  • Maria-Jose Sánchez, CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain., Spain
  • Eva Ardanaz, IdiSNA, Navarra Institute for Health Research, Pamplona, Spain., Spain
  • María-Dolores Chirlaque, Department of Epidemiology, Murcia Regional Health Authority, Murcia, Spain; Department of Health and Social Sciences, Universidad de Murcia, Murcia, Spain., Spain
  • Antonio Agudo, Unit of Nutrition and Cancer. Cancer Epidemiology Research Program. Catalan Institute of Oncology-IDIBELL. L'Hospitalet de Llobregat, Barcelona, Spain., Spain
  • David C Muller, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK., United Kingdom
  • Todd Smith, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK., United Kingdom
  • Ioanna Tzoulaki, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK., United Kingdom
  • Timothy J Key, Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK., United Kingdom
  • Bas Bueno-de-Mesquita, Julius Centre University of Malaya, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia;, Malaysia
  • Antonia Trichopoulou, 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., Greece
  • Christina Bamia, 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., Greece
  • Philippos Orfanos, 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., Greece
  • Rudolf Kaaks, Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany., Germany
  • Anika Hüsing, Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany., Germany
  • Renée T Fortner, Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany., Germany
  • Anne Zeleniuch-Jacquotte, Department of Public Health and Clinical Medicine,Nutritional Research, Umeå University,Umeå,Sweden., Sweden
  • Malin Sund, Department of Surgical and Perioperative Sciences, Umeå University, Umeå, Sweden., Sweden
  • Christina C Dahm
  • Kim Overvad
  • Dagfinn Aune, Department of Nutrition, Bjørknes University College, Oslo, Norway., Norway
  • Elisabete Weiderpass, Department of Community Medicine, University of Tromsø, The Arctic University of Norway, Tromsø, Norway., Norway
  • Isabelle Romieu, Nutritional Epidemiology Group, Section of Nutrition and Metabolism, International Agency for Research On Cancer (IARC-WHO), Lyon, France., France
  • Elio Riboli, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK., United Kingdom
  • Marc J Gunter, Nutritional Epidemiology Group, Section of Nutrition and Metabolism, International Agency for Research On Cancer (IARC-WHO), Lyon, France., France
  • Laure Dossus, Biomarkers Group, Nutrition and Metabolism Section, International Agency for Research on Cancer (IARC-WHO), 69372 Lyon, France. achaintred@iarc.fr., France
  • Ross Prentice, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA., United States
  • Pietro Ferrari, 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., France

BackgroundFew 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.MethodsWe built two models, for ER+ (Model(ER+)) and ER- tumors (Model(ER-)), 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 Model(ER+) and the Gail model (Model(Gail)) regarding their applicability in risk assessment for chemoprevention.ResultsParity, 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 Model(ER+) and 0.59 for Model(ER-). External validation reduced the C-statistic of Model(ER+) (0.59) and Model(Gail) (0.57). In external evaluation of calibration, Model(ER+) outperformed the Model(Gail): 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, Model(ER+) produced equal or higher net benefits irrespective of the benefit-to-harm ratio of chemoprevention, while Model(Gail) 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 12.7x10(-6) for Model(ER+) and 3.0x10(-6) for Model(Gail).ConclusionsModeling 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.

Original languageEnglish
Article number147
JournalBreast Cancer Research (Online Edition)
Volume20
Issue1
Pages (from-to)147
Number of pages16
ISSN1465-5411
DOIs
Publication statusPublished - 3 Dec 2018

    Research areas

  • breast cancer, EPIC, estrogen receptor, prospective cohort, risk prediction, WHI, Estrogen receptor, Prospective cohort, Risk prediction, Breast cancer, MORTALITY, PLUS PROGESTIN, VALIDATION, MAMMOGRAPHY, MODELS, HORMONE-REPLACEMENT THERAPY, POSTMENOPAUSAL WOMEN

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