Dataset Information

Predicting Circulating CA125 Levels among Healthy Premenopausal Women.

ABSTRACT: BACKGROUND:Cancer antigen 125 (CA125) is the most promising ovarian cancer screening biomarker to date. Multiple studies reported CA125 levels vary by personal characteristics, which could inform personalized CA125 thresholds. However, this has not been well described in premenopausal women. METHODS:We evaluated predictors of CA125 levels among 815 premenopausal women from the New England Case Control Study (NEC). We developed linear and dichotomous (?35 U/mL) CA125 prediction models and externally validated an abridged model restricting to available predictors among 473 premenopausal women in the European Prospective Investigation into Cancer and Nutrition Study (EPIC). RESULTS:The final linear CA125 prediction model included age, race, tubal ligation, endometriosis, menstrual phase at blood draw, and fibroids, which explained 7% of the total variance of CA125. The correlation between observed and predicted CA125 levels based on the abridged model (including age, race, and menstrual phase at blood draw) had similar correlation coefficients in NEC (r = 0.22) and in EPIC (r = 0.22). The dichotomous CA125 prediction model included age, tubal ligation, endometriosis, prior personal cancer diagnosis, family history of ovarian cancer, number of miscarriages, menstrual phase at blood draw, and smoking status with AUC of 0.83. The abridged dichotomous model (including age, number of miscarriages, menstrual phase at blood draw, and smoking status) showed similar AUCs in NEC (0.73) and in EPIC (0.78). CONCLUSIONS:We identified a combination of factors associated with CA125 levels in premenopausal women. IMPACT:Our model could be valuable in identifying healthy women likely to have elevated CA125 and consequently improve its specificity for ovarian cancer screening.

SUBMITTER: Sasamoto N

PROVIDER: S-EPMC6548604 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Predicting Circulating CA125 Levels among Healthy Premenopausal Women.

Sasamoto Naoko N Babic Ana A Rosner Bernard A BA Fortner Renée T RT Vitonis Allison F AF Yamamoto Hidemi H Fichorova Raina N RN Tjønneland Anne A Hansen Louise L Overvad Kim K Kvaskoff Marina M Fournier Agnès A Romana Mancini Francesca F Boeing Heiner H Trichopoulou Antonia A Peppa Eleni E Karakatsani Anna A Palli Domenico D Pala Valeria V Mattiello Amalia A Tumino Rosario R Grasso Chiara C CC Onland-Moret N Charlotte NC Weiderpass Elisabete E Quirós J Ramón JR Lujan-Barroso Leila L Rodríguez-Barranco Miguel M Colorado-Yohar Sandra S Barricarte Aurelio A Dorronsoro Miren M Idahl Annika A Lundin Eva E Sartor Hanna H Khaw Kay-Tee KT Key Timothy J TJ Muller David D Riboli Elio E Gunter Marc J MJ Dossus Laure L Kaaks Rudolf R Cramer Daniel W DW Tworoger Shelley S SS Terry Kathryn L KL

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20190404 6

<h4>Background</h4>Cancer antigen 125 (CA125) is the most promising ovarian cancer screening biomarker to date. Multiple studies reported CA125 levels vary by personal characteristics, which could inform personalized CA125 thresholds. However, this has not been well described in premenopausal women.<h4>Methods</h4>We evaluated predictors of CA125 levels among 815 premenopausal women from the New England Case Control Study (NEC). We developed linear and dichotomous (≥35 U/mL) CA125 prediction mod ...[more]

PMID: 30948451

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Project description:BackgroundCancer 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.MethodsWe 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.ResultThe 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.ConclusionsThe 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.

Dataset Information

Predicting Circulating CA125 Levels among Healthy Premenopausal Women.

Publications

Predicting Circulating CA125 Levels among Healthy Premenopausal Women.

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