Development and validation of circulating CA125 prediction models in postmenopausal women.
- Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital and Harvard Medical School, 221 Longwood Avenue, Boston, MA, 02115, USA. nsasamoto@bwh.harvard.edu.
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital and Harvard Medical School, 221 Longwood Avenue, Boston, MA, 02115, USA.
- Laboratory of Genital Tract Biology, Department of Obstetrics, Gynecology and Reproductive Biology, Brigham and Women's Hospital, Boston, MA, USA.
- Departments of Epidemiology and Pediatrics, Geisel School of Medicine at Dartmouth and Norris Cotton Cancer Center, Hanover, NH, USA.
- Diet, Genes and Environment, Danish Cancer Society Research Center, Copenhagen, Denmark.
- CESP, Fac. de médecine - Univ. Paris-Sud, Fac. de médecine - UVSQ, INSERM, Université Paris-Saclay, Villejuif, France.
- Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
- Hellenic Health Foundation, Athens, Greece.
- Cancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network - ISPRO, Florence, Italy.
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milano, Italy.
- Dipartimento Di Medicina Clinica E Chirurgia, Federico II University, Naples, Italy.
- Cancer Registry and Histopathology Department, "Civic - M.P. Arezzo"Hospital, ASP, Ragusa, Italy.
- Unit of Cancer Epidemiology- CeRMS, Department of Medical Sciences, University of Turin, Turin, Italy.
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
- International Agency for Research on Cancer, Lyon, France.
- Department of Community Medicine, University of Tromsø, The Arctic University of Norway, Tromsø, Norway.
- Public Health Directorate, Astruias, Spain.
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO-IDIBELL), L' Hospitalet de Llobregat, Barcelona, Spain.
- Andalusian School of Public Health (EASP), Granada, Spain.
- CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain.
- Department of Clinical Sciences, Obstetrics and Gynecology, Umeå University, Umeå, Sweden.
- Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden.
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden.
- Cancer Epidemiology Unit, University of Cambridge, Cambridge, UK.
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Washington, D.C, USA.
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, Florida, USA.
BACKGROUND: Cancer Antigen 125 (CA125) is currently the best available ovarian cancer screening biomarker. However, CA125 has been limited by low sensitivity and specificity in part due to normal variation between individuals. Personal characteristics that influence CA125 could be used to improve its performance as screening biomarker.
METHODS: We developed and validated linear and dichotomous (≥35 U/mL) circulating CA125 prediction models in postmenopausal women without ovarian cancer who participated in one of five large population-based studies: Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO, n = 26,981), European Prospective Investigation into Cancer and Nutrition (EPIC, n = 861), the Nurses' Health Studies (NHS/NHSII, n = 81), and the New England Case Control Study (NEC, n = 923). The prediction models were developed using stepwise regression in PLCO and validated in EPIC, NHS/NHSII and NEC.
RESULT: The linear CA125 prediction model, which included age, race, body mass index (BMI), smoking status and duration, parity, hysterectomy, age at menopause, and duration of hormone therapy (HT), explained 5% of the total variance of CA125. The correlation between measured and predicted CA125 was comparable in PLCO testing dataset (r = 0.18) and external validation datasets (r = 0.14). The dichotomous CA125 prediction model included age, race, BMI, smoking status and duration, hysterectomy, time since menopause, and duration of HT with AUC of 0.64 in PLCO and 0.80 in validation dataset.
CONCLUSIONS: The linear prediction model explained a small portion of the total variability of CA125, suggesting the need to identify novel predictors of CA125. The dichotomous prediction model showed moderate discriminatory performance which validated well in independent dataset. Our dichotomous model could be valuable in identifying healthy women who may have elevated CA125 levels, which may contribute to reducing false positive tests using CA125 as screening biomarker.
- PLCO-82: Redefining normal: personalized CA125 cutpoints for ovarian cancer screening (Kathryn Terry - 2014)