Abstract P2-10-06: Validation of abridged breast cancer risk assessment model for the general population

Abstract The ability to efficiently and accurately risk stratify women can lead to improved risk-reduction measures. Actionable risk thresholds for both screening and risk-reduction guidance exist; however, despite several clinical risk models currently in existence, the utilization of such models is underwhelming. We have developed and validated an abridged clinical risk model, COSMO (Clinical pOlygenic denSity faMily mOdel), incorporating three of the most relevant epidemiological risk factors associated with sporadic and familial breast cancer in a format that is designed for “ease of use”. Polygenic risk is recognized as a promising epidemiological risk factor however, its value is greater when integrated with other phenotypic risk factors including 1st and 2nd family history, breast density menopausal status, BMI and age. We used 200,009 active UK Biobank (UKB) members who were female, genetically Caucasian, had SNP data for 313 breast cancer associated variants and aged 40 to 69 years at baseline assessment date. We used age and date of baseline assessment to calculate censoring age for those who had died before completing 5 years of follow-up and to determine the date at which 5 years of follow-up was completed for the others. We used both self-reported and linked cancer registry data (ICD9 code 174 or ICD10 code C50) to determine the earliest diagnosis of invasive breast cancer for each affected woman. In terms of discrimination, COSMO (AUC=0.651; 95% CI 0.642, 0.661) performed better than the Gail model (AUC=0.567; 95% CI=0.557, 0.567); P value for difference <0.0001. Overall calibration showed the number of breast cancers expected using COSMO was slightly higher than the number observed (SIR=0.94; 95% CI=0.91, 0.97), while for the Gail model, the number of breast cancers was lower than the number observed (SIR=1.07; 95% CI=1.04, 1.11). The modest over estimation may be explained by the “healthy volunteer” selection bias of the UKB. Importantly, COSMO significantly improved predicted risk classification of women compared to the Gail when stratified according to actionable thresholds. (Table) Limitations to the validation include the lack of mammographic breast density data in the UKB—a critical factor in COSMO. To this end, we have an ongoing validation in a Nurses’ Health Study population (1,269 cases and 1,812 controls) that includes breast density data. Ongoing model comparison studies will confirm the equivalence of COSMO to standard clinical models. If performance is equivalent, COSMO has an advantage in clinical implementation because the abridged model can be less time consuming to implement in a general practice or screening clinic setting due to the truncated pedigree. Furthermore, as multiethnic cohort data access continues to improve, the epidemiological components can be recalibrated for application across any ethnicity. COSMO integrates the most impactful epidemiological risk factors, is well calibrated for the general population and provides improved discrimination over a standard risk model. General population risk assessment like COSMO can lead to the identification of more women who would benefit from clinical intervention based on current risk-reduction guidelines. Beyond that, incidental by products of a general population risk assessment include promoting breast cancer awareness, empowering women to understand their own risk, and encouraging all women to consider their lifestyle habits. Citation Format: Erika Spaeth, Gillian Dite, Richard Allman. Validation of abridged breast cancer risk assessment model for the general population [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-10-06.