Abstract B052: Ovarian cancer risk prediction: a systematic assessment of the state of the art and development of a new model
Authors: Wentzensen Nicolas , Tworoger Shelley S. , Reid Brett M. , O'Donnell Thomas , Terry Kathryn , El-Naqa Issam , Pal Choudhury Parichoy 
Published: March 2024

Introduction: Ovarian cancer is a rare and highly heterogeneous disease usually detected at late stages when outcomes are poor. Population-based screening approaches have not been successful at reducing ovarian cancer mortality. Ovarian cancer risk prediction models may allow identification of populations at increased risk of ovarian cancer for preventive interventions or targeted early detection. We assessed ovarian cancer risk factors and published ovarian cancer risk prediction models to evaluate the level of risk stratification and potential clinical uses. We will develop a new ovarian cancer risk prediction model accounting for heterogeneity by subtype and integrating risk factors and biomarkers to improve ovarian cancer risk prediction. Methods: We conducted a systematic assessment of ovarian cancer risk prediction models based on epidemiologic risk factors and/or common single nucleotide polymorphisms (SNPs) and evaluated their absolute risk stratification. We are developing a new ovarian cancer risk prediction model integrating risk factors and genetic variants for US populations with the individualized Coherent Absolute Risk Estimators (iCARE) approach. We are using risk estimates from the literature, risk factors distributions from large health surveys and published literature, and hysterectomy-corrected ovarian cancer age-specific incidence rates from SEER, accounting for competing mortality. We identified ovarian histotype-specific relative risks for 18 exposures and assessed their distribution in population surveys. The iCARE ovarian cancer model will be validated in the Ovarian Cancer Cohort Consortium (OC3). Results: We identified five published ovarian cancer risk prediction models for the general population. The models were based on varying sets of risk factors with or without SNPs. Models achieved modest risk stratification with area under the curve (AUC) around 0.60. While common SNPs did not add meaningfully to risk stratification achieved with risk factors, a model based on SNPs alone had a similar AUC as a risk factor-based model. The absolute risk achieved for highest risk percentiles ranged from 2-3%, which is below currently accepted thresholds for increased surveillance or prophylactic oophorectomy. Histotype-specific risk factors showed high heterogeneity by subtype with stronger associations observed for the less common endometrioid and clear-cell tumors. Initial results from the risk-factor based iCARE model currently in development will be presented at the conference. Discussion: Achieving high absolute risk prediction for ovarian cancer is challenging due to the rarity of the disease in the population and the strong heterogeneity of risk associations across subtypes, highlighting the importance to evaluate histotype-specific risk prediction. With the iCARE approach, we are developing ovarian cancer risk prediction models integrating information from genetics, exposures, and biomarkers to evaluate the achievable risk stratification for ovarian cancer in the OC3, taking into context possible clinical actions.

Citation Format: Nicolas Wentzensen, Shelley S. Tworoger, Brett M. Reid, Thomas O'Donnell, Kathryn Terry, Issam El-Naqa, Parichoy Pal Choudhury. Ovarian cancer risk prediction: a systematic assessment of the state of the art and development of a new model [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 B052.