Abstract
We developed and validated a hybrid risk classifier combining serum markers and epidemiologic risk factors to identify post-menopausal women at elevated risk for invasive fallopian tube, primary peritoneal, and ovarian epithelial carcinoma.
To select epidemiologic risk factors for use in the classifier, Cox proportional hazards analyses were conducted using 74,786 Women's Health Initiative (WHI) Observational Study (OS) participants. To construct a combination classifier, 210 WHI OS cases and 536 matched controls with serum marker measurements were analyzed; validation employed 143 cases and 725 matched controls from the WHI Clinical Trial (CT) with similar data.
Analyses identified a combination risk classifier composed of two elevated-risk groups: 1) women with CA125 or HE4 exceeding a 98% specificity threshold; and 2) women with intact fallopian tubes, prior use of menopausal hormone therapy for at least two years, and either a first degree relative with breast or ovarian cancer or a personal history of breast cancer. In the WHI OS population, it classified 13% of women as elevated risk, identifying 30% of ovarian cancers diagnosed up to 7.8years post-enrollment (Hazard Ratio [HR]=2.6, p<0.001). In the WHI CT validation population, it classified 8% of women as elevated risk, identifying 31% of cancers diagnosed within 7years of enrollment (HR=4.6, p<0.001).
CA125 and HE4 contributed significantly to a risk prediction classifier combining serum markers with epidemiologic risk factors. The hybrid risk classifier may be useful to identify post-menopausal women who would benefit from timely surgical intervention to prevent epithelial ovarian cancer.
•Risk for ovarian cancer was predicted using data from the Women's Health Initiative.•Predictors include serum markers as well as epidemiologic risk factors.•At-risk post-menopausal women were identified, independent of deleterious mutations.•8% of women were classified as elevated risk, and 31% of cancers were identified.•A125 and HE4 contributed significantly to risk prediction.
