THU486 Identification Of Metabolic And Molecular Mechanisms Driving Estrogen Receptor Positive Breast Cancer Disparities Using A Machine-Learning Pipeline

Abstract

Abstract Disclosure: A. Santaliz Casiano: None. D. Mehta: None. H. Patel: None. G. Rauscher: None. J. Kim: None. J.M. Frasor: None. K. Hoskins: None. Background: African American (AA) women in the United States have a 40% higher breast cancer mortality rate compared with Non-Hispanic White (NHW) women. The survival disparity is particularly striking among ER+ breast cancer cases. The purpose of this study is to examine whether there are racial differences in metabolic pathways typically activated in patients with ER+ positive breast cancer. Methods: We collected pretreatment plasma from AA and NHW ER+ breast cancer cases and cancer-free controls to conduct an untargeted metabolomics analysis using gas chromatography mass spectrometry (GC-MS) to identify metabolites that may be altered in the different racial groups. Statistical methods combined with multiple feature selection and prediction models were employed to identify race-specific altered metabolic signatures. This was followed by the identification of altered metabolic pathways with a focus in AA patients with breast cancer. The clinical relevance of the identified pathways was further examined in PanCancer Atlas breast cancer data set from TCGA. Results: We identified differential metabolic signatures between NHW and AA patients. In AA patients, we observed changes in metabolites associated with amino acid metabolism, while fatty acid metabolism was significantly enriched in NHW patients. By mapping these metabolites to potential epigenetic regulatory mechanisms, this study identified significant association with regulators of metabolism such as methionine adenosyltransferase 1A (MAT1A), DNA Methyltransferases and Histone methyltransferases for AA individuals, and Fatty acid Synthase (FASN) and Monoacylglycerol lipase (MGL) for NHW individuals. Specific gene NELFE with histone methyltransferase activity was associated with poor survival exclusively for AA individuals. Conclusion: We employed a comprehensive and novel approach that integrates multiple machine learning methods and statistical methods, coupled with human functional pathway analyses. The metabolic profile of plasma samples identified may help elucidate underlying molecular drivers of disproportionately aggressive ER+ tumor biology in AA women, and may ultimately lead to identification of novel therapeutic targets. To our knowledge, this is a novel finding that describes a link between metabolic alterations and epigenetic regulation in AA breast cancer, and underscores the need for detailed investigations into the biological underpinnings of breast cancer health disparities. Presentation: Thursday, June 15, 2023