Abstract B047: Development and validation of an improved risk stratification model for ovarian cancer
Authors: Phung Minh Tung , Lee Alice W. , McLean Karen , Khoja Lilah , Anton-Culver Hoda , Bandera Elisa V. , Doherty Jennifer Anne , Fortner Renee T. , Goodman Marc T. , Modugno Francesmary , Pharoah Paul D. P. , Terry Kathryn L. , Webb Penelope M. , Wu Anna H. , Berchuck Andrew , Hanley Gillian E. , Mukherjee Bhramar , Pike Malcolm C. , Pearce Celeste Leigh , Trabert Britton 
Published: March 2024

Abstract
Based on previous models, among individuals who are not known to carry a pathogenic variant, lifetime risk of ovarian cancer ranges between ~0.1% and ~11%. Risk stratification to identify the people at the higher end of this lifetime risk range is of paramount importance for prevention efforts. Previous risk stratification models for ovarian cancer were based on a limited number of risk/protective factors. Further, we have also shown that risk estimates differ by menopausal status which most models have not considered. We aimed to develop and internally validate a risk stratification model for ovarian cancer that considers 15 unequivocal risk/protective factors and properly accounts for effect modification by menopausal status. We used data from nine studies (7,984 cases, 12,260 controls) participating in the Ovarian Cancer Association Consortium (OCAC). The data were split into a training set and a test set that comprised 80% and 20% of the OCAC dataset, respectively. Seven risk factors (body mass index, height, later age at menopause, menopausal hormonal therapy use, first-degree family history of ovarian cancer, endometriosis, and a polygenic score of 36 common genetic variants) and eight protective factors (later age at menarche, parity, breastfeeding, incomplete pregnancy, later age at last pregnancy, tubal ligation, combined oral contraceptive use and depot-medroxyprogesterone acetate use) were included. Other risk/protective factors such as talcum powder or aspirin use were not included due to high proportions of missing values. We fit multiplicative logistic regression models separately by menopausal status group in the training set to determine the associations between the factors and ovarian cancer. All models were adjusted for race/ethnicity, education level, age and OCAC study. In the test set, we calculated a summary relative risk for every combination of the 15 risk/protective factors (hereafter called a risk profile) based on the estimates from the training set. The summary relative risk was then translated into an absolute risk: the frequency-weighted average of all the profile-specific relative risks was scaled to the average absolute risk, and then this scaling factor was applied to each profile-specific relative risk and its confidence interval (CI). The range of absolute lifetime risks observed in the test set was 0.1%-7.2% using 15 factors, accounting for menopausal status. The area under the receiving operating curve (AUC) was 0.67 (95% CI 0.65-0.68). This is slightly higher than the previous risk stratification models (AUC=0.55-0.66). External validation of our risk stratification model in a longitudinal cohort is warranted, as our findings suggest that there is a subset of individuals at the higher end of the risk range who would be potential candidates for primary prevention strategies including salpingectomy.
Citation Format: Minh Tung Phung, Alice W. Lee, Karen McLean, Lilah Khoja, Hoda Anton-Culver, Elisa V. Bandera, Jennifer Anne Doherty, Renee T. Fortner, Marc T. Goodman, Francesmary Modugno, Paul D. P. Pharoah, Kathryn L. Terry, Penelope M. Webb, Anna H. Wu, Andrew Berchuck, Gillian E. Hanley, Bhramar Mukherjee, Malcolm C. Pike, Celeste Leigh Pearce, Britton Trabert. Development and validation of an improved risk stratification model for ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B047.