Abstract 3464: Development and prospective validation of an estrogen receptor positive breast cancer risk model to identify women who could benefit for risk-reducing therapies
Authors: Ahearn Thomas U. , Mukhopadhyay Srijon , Balasubramanian Jeya , Chatterjee Nilanjan , García-Closas Montserrat , Choudhury Parichoy Pal 
Published: March 2024

Background: Breast cancer risk-reducing therapies such as tamoxifen and aromatase inhibitors are effective for ER-positive (ER+) but not for ER-negative disease. As these therapies have side effects it is important to identify women at risk of ER+ disease. We developed and validated ER-specific risk prediction models in a prospective cohort and evaluated the expected gains of predicting ER+ vs overall disease.

Methods: The iCARE-Lit model integrates established questionnaire-based risk factors and a 313 variant polygenic risk score. We reparametrized the iCARE-Lit model to estimate risk of ER+ and ER- disease separately by obtaining relative risks from published literature for ages at menarche, menopause, first childbirth, and parity to account for their heterogeneous associations with risk of ER-defined subtypes. We estimated 5-year (yr) absolute risk (AR) for ER+ disease using an overall and ER+ model. We evaluated model performance in the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial prospective cohort. This included evaluation of calibration (expected-to-observed (E/O) ratio) and discrimination (area under the receiver operating characteristic curve (AUC)), among 46,571 self-reported non-Hispanic White women aged 50-75 yrs (1,052 ER+ cases with 5 yrs of follow-up from enrollment). We compared projected risk stratification of the overall and ER+ models among eligible US women in terms of proportions of women and future ER+ cases identified above the 3% 5-yr AR threshold recommended for risk-reducing interventions in the US.

Results: In predicting ER+ disease in PLCO, on average the overall and ER+ AR were well calibrated; however, models overpredicted risk at the ≥3% 5-yr AR threshold, (E/O = 1.31 (1.20, 1. 42) and 1.23 (1.13, 1.34), respectively. Risk discrimination was similar from the overall and ER+ models (AUC=0.65 (0.64-0.67) and 0.66 (0.64-0.67), respectively. The overall model projected that 16% of women in the US could be eligible for risk-reducing therapies, compared to 11% when using ER+ model, resulting in ~1.4 million less women crossing the risk threshold when using the ER+ score. However, the percentage of ER+ cases in the US population projected to occur within 5-yrs among eligible women was larger for the overall (38%) than ER+ (32%) risk score.

Conclusion: Findings suggest that ER-specific risk scores might not offer a substantial advantage compared to overall risk scores to identify women eligible for risk-reducing therapies. Further risk-benefit analyses are needed to quantify the potential gains of predicting ER+ vs overall breast cancer.

Citation Format: Thomas U. Ahearn, Srijon Mukhopadhyay, Jeya Balasubramanian, Nilanjan Chatterjee, Montserrat García-Closas, Parichoy Pal Choudhury. Development and prospective validation of an estrogen receptor positive breast cancer risk model to identify women who could benefit for risk-reducing therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3464.