Development and validation of circulating CA125 prediction models in postmenopausal women View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-11-26

AUTHORS

Naoko Sasamoto, Ana Babic, Bernard A. Rosner, Renée T. Fortner, Allison F. Vitonis, Hidemi Yamamoto, Raina N. Fichorova, Linda J. Titus, Anne Tjønneland, Louise Hansen, Marina Kvaskoff, Agnès Fournier, Francesca Romana Mancini, Heiner Boeing, Antonia Trichopoulou, Eleni Peppa, Anna Karakatsani, Domenico Palli, Sara Grioni, Amalia Mattiello, Rosario Tumino, Valentina Fiano, N. Charlotte Onland-Moret, Elisabete Weiderpass, Inger T. Gram, J. Ramón Quirós, Leila Lujan-Barroso, Maria-Jose Sánchez, Sandra Colorado-Yohar, Aurelio Barricarte, Pilar Amiano, Annika Idahl, Eva Lundin, Hanna Sartor, Kay-Tee Khaw, Timothy J. Key, David Muller, Elio Riboli, Marc Gunter, Laure Dossus, Britton Trabert, Nicolas Wentzensen, Rudolf Kaaks, Daniel W. Cramer, Shelley S. Tworoger, Kathryn L. Terry

ABSTRACT

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. More... »

PAGES

116

Journal

TITLE

Journal of Ovarian Research

ISSUE

1

VOLUME

12

Author Affiliations

  • Obstetrics and Gynecology Epidemiology Center, Brigham and Women’s Hospital and Harvard Medical School, 221 Longwood Avenue, Boston, MA 02115 USA
  • 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
  • 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
  • Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
  • Diet, Genes and Environment, Danish Cancer Society Research Center, Copenhagen, Denmark
  • Gustave Roussy, Villejuif, France
  • Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
  • WHO Collaborating Center for Nutrition and Health, Unit of Nutritional Epidemiology and Nutrition in Public Health, Dept. of Hygiene, Epidemiology and Medical Statistics, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece
  • Hellenic Health Foundation, Athens, Greece
  • Pulmonary Medicine Department, School of Medicine, “ATTIKON” University Hospital, National and Kapodistrian University of Athens, Haidari, 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
  • CIBER of Epidemiology and Public Health (CIBERESP), Madrid, Spain
  • Research Group on Demography and Health, National Faculty of Public Health, University of Antioquia, Medellín, Colombia
  • Navarra Public Health Institute, Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
  • Public Health Division of Gipuzkoa, BioDonostia Research Institute, San Sebastian, Spain
  • Department of Clinical Sciences, Obstetrics and Gynecology, Umeå University, Umeå, Sweden
  • Department of Medical Biosciences, Pathology, Umeå University, Umeå, Sweden
  • Department of Translational Medicine, Lund University, 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 Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA USA
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13048-019-0591-4

    DOI

    http://dx.doi.org/10.1186/s13048-019-0591-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1122922626

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/31771659


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    18 schema:description 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.
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    26 CA125
    27 CA125 levels
    28 CA125 prediction models
    29 Cancer Screening Trial
    30 England Case Control Study
    31 European Prospective Investigation
    32 Health Study
    33 NEC
    34 NHS/NHSII
    35 NHSII
    36 New England Case-Control Study
    37 Nurses' Health Study
    38 Ovarian Cancer Screening Trial
    39 PLCO
    40 PLCO testing dataset
    41 Prospective Investigation
    42 Screening Trial
    43 age
    44 antigen 125
    45 available ovarian cancer
    46 biomarkers
    47 body mass index
    48 cancer
    49 cancer antigen 125
    50 case-control study
    51 characteristics
    52 colorectal
    53 control study
    54 correlation
    55 dataset
    56 development
    57 dichotomous CA125 prediction model
    58 dichotomous model
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    60 discriminatory performance
    61 duration
    62 duration of HT
    63 epic
    64 external validation dataset
    65 false-positive tests
    66 healthy women
    67 hormone therapy
    68 hysterectomy
    69 independent datasets
    70 index
    71 individuals
    72 investigation
    73 large population-based study
    74 levels
    75 linear CA125 prediction model
    76 linear prediction model
    77 low sensitivity
    78 lung
    79 mass index
    80 menopause
    81 model
    82 moderate discriminatory performance
    83 need
    84 normal variation
    85 novel predictor
    86 nutrition
    87 ovarian cancer
    88 parity
    89 part
    90 performance
    91 personal characteristics
    92 population-based study
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    94 positive test
    95 postmenopausal women
    96 prediction model
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    99 race
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