Added value of serum hormone measurements in risk prediction models for breast cancer for women not using exogenous hormones: Results from the EPIC cohort

Publikation: Forskning - peer reviewTidsskriftartikel


  • Anika Hüsing
    Anika HüsingDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ).Tyskland
  • Renée T Fortner
    Renée T FortnerDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ)
  • Tilman Kühn
    Tilman KühnDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ).Tyskland
  • Kim Overvad
  • Anne Tjonneland
    Anne TjonnelandDiet, Genes and Environment, Danish Cancer Society Research Centre,Danmark
  • Anja Olsen
    Anja OlsenDiet, Genes and Environment. Danish Cancer Society Research Center Danmark
  • Marie-Christine Boutron-Ruault
    Marie-Christine Boutron-RuaultUniversité Paris-Saclay, Univ. Paris-Sud, UVSQ, Inserm, CESP, Generations and health.Frankrig
  • Gianluca Severi
    Gianluca SeveriHuGeF-Human Genetics FoundationFrankrig
  • Agnes Fournier
    Agnes FournierNutrition, Hormones and Women's Health Team, INSERM, CESP Centre for Research in Epidemiology and Population Health, U1018, Univ Paris Sud.Frankrig
  • Heiner Boeing
    Heiner BoeingDepartment of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke.Tyskland
  • Antonia Trichopoulou
    Antonia TrichopoulouBureau of Epidemiologic Research, Academy of Athens.Grækenland
  • Vassiliki Benetou
    Vassiliki BenetouDepartment of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece.Grækenland
  • Philippos Orfanos
    Philippos OrfanosHellenic Health Foundation, AthensGrækenland
  • Giovanna Masala
    Giovanna MasalaI S P O (Cancer Research and Prevention Institute), Molecular and Nutritional Epidemiology Unit.Italien
  • Valeria Pala
    Valeria PalaNutritional Epidemiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori.Italien
  • Rosario Tumino
    Rosario TuminoCancer Registry and Histopathology Unit, "Civic-M.P.Arezzo" Hospital, ASP, Via Dante N° 109, 97100 Ragusa, Italy.Italien
  • Francesca Fasanelli
    Francesca FasanelliDipartimento di Scienze Mediche, Università di Torino.Italien
  • Salvatore Panico
    Salvatore PanicoDepartment of Clinical Medicine and Surgery, Section of Endocrinology, Federico II University.Italien
  • Bas H Bueno-De-Mesquita
    Bas H Bueno-De-MesquitaNational Institute for Public Health and the Environment (RIVM)Holland
  • Petra Peeters
    Petra PeetersJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.Holland
  • Carla H van Gils
    Carla H van GilsJulius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.Holland
  • J Ramon Quiros
    J Ramon QuirosPublic Health Directorate, Asturias, Spain.Spanien
  • Antonio Agudo
    Antonio AgudoUnit of Nutrition, Environment and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO-IDIBELL), 08908 Barcelona, Spain.Spanien
  • Maria-Jose Sánchez
    Maria-Jose SánchezAndalucian School of Public Health, Research Institute Biosanitary Granada.Grenada
  • María-Dolores Chirlaque
    María-Dolores ChirlaqueCIBER en Epidemiologia y Salud Publica (CIBERESP), Epidemiology Department, Murcia Health Council.Spanien
  • Aurelio Barricarte
    Aurelio BarricarteCIBER Epidemiologia y Salud Publica (CIBERESP).Spanien
  • Pilar Amiano
    Pilar AmianoPublic Health Division of Gipuzkoa, Basque Government, Spain; Health Research Institute, Biodonostia, San Sebastián, Spain.Spanien
  • Kay-Tee Khaw
    Kay-Tee KhawClinical Gerontology Unit, School of Clinical Medicine, University of Cambridge, Addenbrooke's Hospital, Hills Rd, Cambridge CB2 0SP, UK.Storbritannien
  • Ruth C. Travis
    Ruth C. TravisCancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.Storbritannien
  • Laure Dossus
    Laure DossusIARC.Frankrig
  • Kuanrong Li
    Kuanrong LiInternational Agency for Research on Cancer (IARC-WHO).Frankrig
  • Pietro Ferrari
    Pietro FerrariSection of Nutrition and Metabolism, International Agency for Research on Cancer (IARC-WHO), Lyon, France.Frankrig
  • Melissa A Merritt
    Melissa A MerrittDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.Storbritannien
  • Ioanna Tzoulaki
    Ioanna TzoulakiDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom.Storbritannien
  • Elio Riboli
    Elio RiboliSchool of Public Health, Imperial College London, London, UK.Storbritannien
  • Rudolf Kaaks
    Rudolf KaaksDivision of Cancer Epidemiology, German Cancer Research Centre

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.

TidsskriftClinical Cancer Research
Sider (fra-til)4181-4189
Antal sider9
StatusUdgivet - 1 aug. 2017

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