Abstract
Abstract Breast cancer is the most frequently diagnosed cancer in women and the second leading cause of cancer death in Americans. With more than 3 million breast cancer survivors in the US, a number that is projected to increase, it is important to identify targets for precision intervention to improve breast cancer prognosis. With the rapid advancement of technology for metabolomics, the results from several recent studies have shown that metabolomics may have applications in breast cancer diagnosis and subtype analysis, characterization of heterogeneity of breast cancer, and prognosis. In the current study, we performed a global urinary metabolomic analysis of 120 breast cancer patients: 60 progression-free (PF) cases as the reference group and 60 with progressive disease (PD: recurrence, second primary, metastasis, or death). The urine samples were collected immediately after radiotherapy. Using UPLC-MS/MS and GC-MS, Metabolon Inc. identified a robust set of 1,742 biochemicals (1,258 known and 484 unknown structure). The most notable differences between PF and PD patients involved multiple pathways and metabolites include: carbohydrate metabolism (e.g., glucose, sedoheptulose, and N6-carboxymethyllysine), branch-chain amino acid metabolism (e.g., alpha-hydroxyisocaproate and beta-hydroxyisovalerylglycine), phosphatidylcholine metabolism (e.g., 1-palmitoyl-2-oleoyl-GPC (16:0/18:1) and 1-palmitoyl-2-linoleoyl-GPC (16:0/18:2)), arginine metabolism (e.g., dimethylarginine, N-acetylcitrulline, and homocitrulline), oxidative stress-related metabolites (e.g., cysteine-glutathione disulfide, gamma-glutamylisoleucine, and gamma-glutamylthreonine), androgenic steroids (Dehydroepiandrosterone sulfate (DHEA-S) and 16a-hydroxy DHEA 3-sulfate), and nucleotide metabolism. Some of these identified metabolomic differences may serve as potential predictive biomarkers of breast cancer prognosis. In summary, with increasing interest in targeting tumor metabolism in precision medicine and our pilot data suggesting multiple metabolic pathways in predicting breast cancer prognosis, future research is warranted to validate our findings and identify metabolomic targets for precision interventions. Citation Format: Jennifer J. Hu, Cristiane Takita, Isildinha M. Reis, George Yang, Wei Zhao, Eunkyung Lee. Metabolomics pathways and biomarkers in predicting breast cancer prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2328.
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