Abstract PS5-36: Computational modeling of androgen receptor (AR) and estrogen receptor as predictive biomarkers of response to AR agonists and antagonists

Abstract

Abstract Background: Androgen receptor (AR) is expressed in 60%-90% of breast cancers. The role of AR in breast cancer largely depends on estrogen receptor (ER) status and remains controversial. In ER+ cancers, AR expression is associated with improved prognosis. Enzalutamide (ENZ), an AR antagonist, impairs AR signaling, inhibits ER+/AR+ breast cancer cell proliferation and has been shown to mitigate resistance to anti-estrogen therapies. A recent study identified that RAD140, an AR agonist, suppressed the growth of ER+/AR+ breast cancer via stimulating AR signaling, resulting in down regulation of ERα. Both AR agonists and antagonists are effective in treating ER+ breast cancers. Identifying the subgroup of patients who most likely will benefit from an individual drug is important in clinical practice. Here, we sought to characterize the relative AR to ER levels and their relationships to drug response in ER+ breast cancers.Methods: We evaluated AR and ER expression levels among 68 samples with ER+ breast cancers. On each tumor, IHC staining for AR and ER were performed and results were scored by multiplying the percentage of positive cells by the intensity. TCGA-RPPA (reverse phase protein array) dataset was used to verify AR protein distribution in ER+ cancers. We created MCF7 and T47D cells with doxycycline-inducible AR and ER expression system to manipulate the relative AR to ER ratios and determined the antitumor activity of ENZ and RAD140 by cell proliferation assays. The levels of AR and ER in cells were measured by western blot. Linear regression was used to test the association between dose-response area under curve (AUC) and AR, ER levels. Drug preference was modeled against AR/ER expression levels (AR/ER ratios) using logistic regression.Results: Among 68 ER+ breast cancers, 69.12% were AR-positive. More than 2/3 cases (48 of 68) had more ER than AR expressed. The AR to ER ratios varied from 0 to 6. In the TCGA cohort, 84.15% of 347 ER+ patients had AR/ER ratios less than 1.The range of AR to ER ratios in TCGA dataset were comparable with our data (0.004 to 4.210). In our cell line models, the AR/ER ratios were controlled between 0.19 to 4.05. We found that the AUC of RAD140 was negatively associated with AR protein levels (P=0.008) and AR/ER ratios (P=0.013), and not significantly associated with ER expression levels. On the other hand, the AUC of ENZ was significantly negatively associated with ER protein (P=0.0016) but postiviely associated with AR/ER ratio (p=0.037). Preferred treatment comparing efficacy of the two drug can be best determined at extremes of AR/ER ratios. Using clinically relevant dosages, our model predicted that ENZ (10µM) would be a preferred treatment choice and have a better treatment efficacy compared with RAD140 when the AR/ER ratio was ≤ 0.42, whereas RAD140 (1µM) would be the preferred choice with a better treatment efficacy compared with ENZ when AR/ER ratios were ≥ 3.1 The efficacy preference of the two drugs are equivocal for AR/ER ratios between 0.42 and 3.10. Conclusion: We developed preclinical models using AR and ER expression levels to predict AR-targeting drug response. The results support the use of RAD140 in AR high patients and those with an AR/ER ratio >3.10, and enzalutamide in AR low patients and those with an AR/ER ratio <0.42. Equipoise on choice of drug was found for AR/ER ratios of 0.42-3.10. RAD140 and enzalutamide are compelling candidates for monotherapy or combination with anti-estrogen therapies in ER+/AR+ breast cancer. Future clinical validation of the models and therapeutic effect is warranted. Citation Format: Lixuan Wei, Huanyao Gao, Jia Yu, Duan Liu, Huan Zhang, Thanh Nguyen, Marie R Passow, Jodi M Carter, Richard M Weinshilboum, James N Ingle, Liewei Wang. Computational modeling of androgen receptor (AR) and estrogen receptor as predictive biomarkers of response to AR agonists and antagonists [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS5-36.