Development and validation of circulating CA125 prediction models in postmenopausal women
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 im...
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BMC
2019-11-01
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Series: | Journal of Ovarian Research |
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Online Access: | http://link.springer.com/article/10.1186/s13048-019-0591-4 |
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language |
English |
format |
Article |
sources |
DOAJ |
author |
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 |
spellingShingle |
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 Development and validation of circulating CA125 prediction models in postmenopausal women Journal of Ovarian Research Ovarian cancer Early detection CA125 Prediction model Postmenopausal |
author_facet |
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 |
author_sort |
Naoko Sasamoto |
title |
Development and validation of circulating CA125 prediction models in postmenopausal women |
title_short |
Development and validation of circulating CA125 prediction models in postmenopausal women |
title_full |
Development and validation of circulating CA125 prediction models in postmenopausal women |
title_fullStr |
Development and validation of circulating CA125 prediction models in postmenopausal women |
title_full_unstemmed |
Development and validation of circulating CA125 prediction models in postmenopausal women |
title_sort |
development and validation of circulating ca125 prediction models in postmenopausal women |
publisher |
BMC |
series |
Journal of Ovarian Research |
issn |
1757-2215 |
publishDate |
2019-11-01 |
description |
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. |
topic |
Ovarian cancer Early detection CA125 Prediction model Postmenopausal |
url |
http://link.springer.com/article/10.1186/s13048-019-0591-4 |
work_keys_str_mv |
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doaj-31979de161694e43ac98261dd90413c92020-11-25T00:46:04ZengBMCJournal of Ovarian Research1757-22152019-11-0112111210.1186/s13048-019-0591-4Development and validation of circulating CA125 prediction models in postmenopausal womenNaoko Sasamoto0Ana Babic1Bernard A. Rosner2Renée T. Fortner3Allison F. Vitonis4Hidemi Yamamoto5Raina N. Fichorova6Linda J. Titus7Anne Tjønneland8Louise Hansen9Marina Kvaskoff10Agnès Fournier11Francesca Romana Mancini12Heiner Boeing13Antonia Trichopoulou14Eleni Peppa15Anna Karakatsani16Domenico Palli17Sara Grioni18Amalia Mattiello19Rosario Tumino20Valentina Fiano21N. Charlotte Onland-Moret22Elisabete Weiderpass23Inger T. Gram24J. Ramón Quirós25Leila Lujan-Barroso26Maria-Jose Sánchez27Sandra Colorado-Yohar28Aurelio Barricarte29Pilar Amiano30Annika Idahl31Eva Lundin32Hanna Sartor33Kay-Tee Khaw34Timothy J. Key35David Muller36Elio Riboli37Marc Gunter38Laure Dossus39Britton Trabert40Nicolas Wentzensen41Rudolf Kaaks42Daniel W. Cramer43Shelley S. Tworoger44Kathryn L. Terry45Obstetrics and Gynecology Epidemiology Center, Brigham and Women’s Hospital and Harvard Medical SchoolDepartment of Medical Oncology, Dana-Farber Cancer InstituteChanning Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical SchoolDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ)Obstetrics and Gynecology Epidemiology Center, Brigham and Women’s Hospital and Harvard Medical SchoolLaboratory of Genital Tract Biology, Department of Obstetrics, Gynecology and Reproductive Biology, Brigham and Women’s HospitalLaboratory of Genital Tract Biology, Department of Obstetrics, Gynecology and Reproductive Biology, Brigham and Women’s HospitalDepartments of Epidemiology and Pediatrics, Geisel School of Medicine at Dartmouth and Norris Cotton Cancer CenterDiet, Genes and Environment, Danish Cancer Society Research CenterDiet, Genes and Environment, Danish Cancer Society Research CenterCESP, Fac. de médecine - Univ. Paris-Sud, Fac. de médecine - UVSQ, INSERM, Université Paris-SaclayCESP, Fac. de médecine - Univ. Paris-Sud, Fac. de médecine - UVSQ, INSERM, Université Paris-SaclayCESP, Fac. de médecine - Univ. Paris-Sud, Fac. de médecine - UVSQ, INSERM, Université Paris-SaclayDepartment of Epidemiology, German Institute of Human Nutrition Potsdam-RehbrueckeHellenic Health FoundationHellenic Health FoundationHellenic Health FoundationCancer Risk Factors and Life-Style Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network - ISPROEpidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di MilanoDipartimento Di Medicina Clinica E Chirurgia, Federico II UniversityCancer Registry and Histopathology Department, “Civic - M.P. Arezzo”Hospital, ASPUnit of Cancer Epidemiology– CeRMS, Department of Medical Sciences, University of TurinDepartment of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht UniversityInternational Agency for Research on CancerDepartment of Community Medicine, University of Tromsø, The Arctic University of NorwayPublic Health DirectorateUnit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO-IDIBELL)Andalusian School of Public Health (EASP)CIBER of Epidemiology and Public Health (CIBERESP)CIBER of Epidemiology and Public Health (CIBERESP)CIBER of Epidemiology and Public Health (CIBERESP)Department of Clinical Sciences, Obstetrics and Gynecology, Umeå UniversityDepartment of Medical Biosciences, Pathology, Umeå UniversityDepartment of Medical Imaging and Physiology, Skåne University HospitalCancer Epidemiology Unit, University of CambridgeCancer Epidemiology Unit, Nuffield Department of Population Health, University of OxfordDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College LondonDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College LondonInternational Agency for Research on CancerInternational Agency for Research on CancerDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of HealthDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of HealthDivision of Cancer Epidemiology, German Cancer Research Center (DKFZ)Obstetrics and Gynecology Epidemiology Center, Brigham and Women’s Hospital and Harvard Medical SchoolDepartment of Cancer Epidemiology, Moffitt Cancer CenterObstetrics and Gynecology Epidemiology Center, Brigham and Women’s Hospital and Harvard Medical SchoolAbstract 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.http://link.springer.com/article/10.1186/s13048-019-0591-4Ovarian cancerEarly detectionCA125Prediction modelPostmenopausal |