Abstract 3162: Genome-scale metabolic modeling suggests novel molecular targets in ER-positive breast cancer

Abstract

Abstract High expression of the epithelial splicing regulatory protein 1 (ESRP1) is correlated with poor prognosis in estrogen receptor positive (ER+) breast cancer and acquired resistance to tamoxifen/endocrine therapies. Our previous study revealed a novel functionality of ESRP1 in metabolic dysregulation by affecting lipid metabolism and oxidoreductase processes. To further understand how metabolic programming is affected by ESRP1 expression and identify novel therapeutic targets, we developed systems biology models to explore ER+ breast cancer metabolism. Flux balance analysis (FBA) is a computational approach to predict steady-state fluxes through metabolic reactions by posing cellular “objectives” such as maximization of biomass, ATP, or minimization of reactive oxygen species that reflect selective pressures posed by the tumor microenvironment or therapeutic insults. Our custom FBA pipeline leverages transcriptomic data, mutation information, and thermodynamic constraints to generate personalized genome-scale metabolic models. In this study, 100 ESRP1-high and 100 ESRP1-low tumors from the TCGA Breast Cancer Invasive Carcinoma dataset were simulated and compared; in a secondary analysis, we simulated metabolic rerouting of tamoxifen-resistant LCC2 cells and fulvestrant-resistant LCC9 cells upon ESRP1 knockdown. The intersection of statistically significant (p < 0.05) reactions that were altered in both the TCGA and cell line contexts suggested that changes in eicosanoid metabolism and androgen and estrogen synthesis pathways are highly involved in ESRP1's role in breast cancer prognosis and resistance to endocrine therapies. Eicosanoid-utilizing enzymes are consistently altered between the ESRP1-low and high conditions; Hematopoietic prostaglandin D synthase (HPGDS), prostaglandin I2 synthase (PTDIS), and thromboxane A synthase 1 (TBXAS1) are druggable. Furthermore, reactions associated with cytosolic and lysosomal 4-hydroxy-17beta-estradiol production, catalyzed by the enzymes myeloperoxidase, lactoperoxidase, and peroxiredoxin 6, also emerged as consistently altered across the groups. The findings from this methodology have suggested novel metabolic targets to overcome tamoxifen/endocrine therapy resistance in ER+ breast cancer. Experimental validation of these targets is currently underway. Citation Format: Zachary B. Mudge, Kristof Kovacs, Melissa L. Kemp, Sunil S. Badve, Yesim Gokmen-Polar. Genome-scale metabolic modeling suggests novel molecular targets in ER-positive breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3162.