Development and validation of circulating CA125 prediction models in postmenopausal women

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

doi:10.1186/s13048-019-0591-4

Development and validation of circulating CA125 prediction models in postmenopausal women

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 MattielloRosario 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

Section of Environmental Health

6 Citations (Scopus)

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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.

Original language	English
Journal	Journal of Ovarian Research
Volume	12
ISSN	1757-2215
DOIs	https://doi.org/10.1186/s13048-019-0591-4
Publication status	Published - 26 Nov 2019

Keywords

Ovarian cancer
Early detection
CA125
Prediction model
Postmenopausal

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Development and validation of circulating CA125 prediction models in postmenopausal womenFinal published version, 556 KBLicence: CC BY

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Sasamoto, N., Babic, A., Rosner, B. A., Fortner, R. T., Vitonis, A. F., Yamamoto, H., Fichorova, R. N., Titus, L. J., Tjønneland, A., Hansen, L., Kvaskoff, M., Fournier, A., Mancini, F. R., Boeing, H., Trichopoulou, A., Peppa, E., Karakatsani, A., Palli, D., Grioni, S., ... Terry, K. L. (2019). Development and validation of circulating CA125 prediction models in postmenopausal women. Journal of Ovarian Research, 12. https://doi.org/10.1186/s13048-019-0591-4

Sasamoto, N, Babic, A, Rosner, BA, Fortner, RT, Vitonis, AF, Yamamoto, H, Fichorova, RN, Titus, LJ, Tjønneland, A, Hansen, L, Kvaskoff, M, Fournier, A, Mancini, FR, Boeing, H, Trichopoulou, A, Peppa, E, Karakatsani, A, Palli, D, Grioni, S, Mattiello, A, Tumino, R, Fiano, V, Onland-Moret, NC, Weiderpass, E, Gram, IT, Quirós, JR, Lujan-Barroso, L, Sánchez, M-J, Colorado-Yohar, S, Barricarte, A, Amiano, P, Idahl, A, Lundin, E, Sartor, H, Khaw, K-T, Key, TJ, Muller, D, Riboli, E, Gunter, M, Dossus, L, Trabert, B, Wentzensen, N, Kaaks, R, Cramer, DW, Tworoger, SS & Terry, KL 2019, 'Development and validation of circulating CA125 prediction models in postmenopausal women', Journal of Ovarian Research, vol. 12. https://doi.org/10.1186/s13048-019-0591-4

@article{0a85735a2ede46a6bf7a139679d9cd40,

title = "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 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.",

keywords = "Ovarian cancer, Early detection, CA125, Prediction model, Postmenopausal",

author = "Naoko Sasamoto and Ana Babic and Rosner, {Bernard A.} and Fortner, {Ren{\'e}e T.} and Vitonis, {Allison F.} and Hidemi Yamamoto and Fichorova, {Raina N.} and Titus, {Linda J.} and Anne Tj{\o}nneland and Louise Hansen and Marina Kvaskoff and Agn{\`e}s Fournier and Mancini, {Francesca Romana} and Heiner Boeing and Antonia Trichopoulou and Eleni Peppa and Anna Karakatsani and Domenico Palli and Sara Grioni and Amalia Mattiello and Rosario Tumino and Valentina Fiano and Onland-Moret, {N. Charlotte} and Elisabete Weiderpass and Gram, {Inger T.} and Quir{\'o}s, {J. Ram{\'o}n} and Leila Lujan-Barroso and Maria-Jose S{\'a}nchez and Sandra Colorado-Yohar and Aurelio Barricarte and Pilar Amiano and Annika Idahl and Eva Lundin and Hanna Sartor and Kay-Tee Khaw and Key, {Timothy J.} and David Muller and Elio Riboli and Marc Gunter and Laure Dossus and Britton Trabert and Nicolas Wentzensen and Rudolf Kaaks and Cramer, {Daniel W.} and Tworoger, {Shelley S.} and Terry, {Kathryn L.}",

year = "2019",

month = nov,

day = "26",

doi = "10.1186/s13048-019-0591-4",

language = "English",

volume = "12",

journal = "Journal of Ovarian Research",

issn = "1757-2215",

publisher = "BioMed Central Ltd.",

}

TY - JOUR

T1 - Development and validation of circulating CA125 prediction models in postmenopausal women

AU - Sasamoto, Naoko

AU - Babic, Ana

AU - Rosner, Bernard A.

AU - Fortner, Renée T.

AU - Vitonis, Allison F.

AU - Yamamoto, Hidemi

AU - Fichorova, Raina N.

AU - Titus, Linda J.

AU - Tjønneland, Anne

AU - Hansen, Louise

AU - Kvaskoff, Marina

AU - Fournier, Agnès

AU - Mancini, Francesca Romana

AU - Boeing, Heiner

AU - Trichopoulou, Antonia

AU - Peppa, Eleni

AU - Karakatsani, Anna

AU - Palli, Domenico

AU - Grioni, Sara

AU - Mattiello, Amalia

AU - Tumino, Rosario

AU - Fiano, Valentina

AU - Onland-Moret, N. Charlotte

AU - Weiderpass, Elisabete

AU - Gram, Inger T.

AU - Quirós, J. Ramón

AU - Lujan-Barroso, Leila

AU - Sánchez, Maria-Jose

AU - Colorado-Yohar, Sandra

AU - Barricarte, Aurelio

AU - Amiano, Pilar

AU - Idahl, Annika

AU - Lundin, Eva

AU - Sartor, Hanna

AU - Khaw, Kay-Tee

AU - Key, Timothy J.

AU - Muller, David

AU - Riboli, Elio

AU - Gunter, Marc

AU - Dossus, Laure

AU - Trabert, Britton

AU - Wentzensen, Nicolas

AU - Kaaks, Rudolf

AU - Cramer, Daniel W.

AU - Tworoger, Shelley S.

AU - Terry, Kathryn L.

PY - 2019/11/26

Y1 - 2019/11/26

N2 - 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.

AB - 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.

KW - Ovarian cancer

KW - Early detection

KW - CA125

KW - Prediction model

KW - Postmenopausal

U2 - 10.1186/s13048-019-0591-4

DO - 10.1186/s13048-019-0591-4

M3 - Journal article

C2 - 31771659

SN - 1757-2215

VL - 12

JO - Journal of Ovarian Research

JF - Journal of Ovarian Research

ER -

Development and validation of circulating CA125 prediction models in postmenopausal women

Abstract

Keywords

UN SDGs

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