Abstract C020: Identification of metabolic and molecular mechanisms contributing to ER+ cancer disparities using a machine-learning pipeline

Abstract

Abstract Background: African American (AA) women are less likely to develop breast cancer but when they do, their mortality rates are 40% higher compared to Non-Hispanic White (NHW) women. This disparity is particularly striking among ER+ breast cancer cases. The purpose of this study is to examine whether there are racial differences in metabolic and molecular pathways typically activated in patients with ER+ positive breast cancer. Methods: We collected plasma from AA and NHW cases and controls to conduct an untargeted metabolomics analysis using gas chromatography-mass spectrometry (GC-MS) to identify metabolites that are possibly altered in the different race 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 on AA patients with breast cancer. The clinical significance of the findings was further examined in the PanCancer Atlas breast cancer data set. Results: We identified differential metabolic signatures between NHW and AA patients. In AA patients, we observed disturbed amino acid metabolism, while fatty acid metabolism was significant in NHW patients. By mapping these metabolites to genes, this study identified significant relations with regulators of metabolism such as methionine adenosyltransferase 1A (MAT1A), DNA Methyltransferases, Histone methyltransferases for AA individuals, and Fatty acid Synthase (FASN) and Monoacylglycerol lipase (MGL) for NHW individuals. Specific histone methyltransferase NELFE was overexpressed and associated with poor survival exclusively in AA individuals. Conclusion: We employ a comprehensive and novel approach that integrates multiple machine learning methods, and statistical methods, coupled with human functional pathway analyses. This metabolic profile of serum samples might be used to assess risk progression in AA individuals with ER+ breast cancer. To our knowledge, this is a novel finding that describes metabolic alterations in AA breast cancer and emphasizes a potential biological basis for breast cancer health disparities. Citation Format: Ashlie Santaliz Casiano, Zeynep Madak-Erdogan, Dhruv Meta, Jonna Frasor, Garth Rauscher, Kent Hoskins. Identification of metabolic and molecular mechanisms contributing to ER+ cancer disparities using a machine-learning pipeline [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C020.