Abstract 604: Metabolomics in predicting breast cancer treatment responses

Abstract

Abstract Breast cancer is the most frequently diagnosed cancer in women and the second leading cause of cancer death in American women. Postoperative adjuvant radiotherapy (RT) significantly improve local-regional recurrence and survival. Therefore, there has been increasing usage of adjuvant RT in early-stage breast cancer patients. Although well tolerated by some patients, 48% patients in our study experience RT-induced early adverse skin reactions (EASRs) that negatively impact quality of life. Therefore, we evaluated urine metabolomics in predicting RT-induced EASRs and precision intervention. Based on the pilot data of 478 metabolites in 60 patients, we used the MetaboAnalyst 3.0 (www.metaboanalyst.ca) to conduct metabolomic data analysis, visualization and interpretation. Using KEGG pathway analysis, we have identified 7 pathways that are significantly associated with RT-induced EASRs (i.e., False Discovery Rate (FDR) p less than 0.05). The alanine, aspartate and glutamate metabolism pathway has the most significant FDR p-value and the highest impact value of 0.60 in predicting RT-induced EASRs. In summary, to the best of our knowledge, this is the first study in breast cancer patients associating glutamate metabolism pathway to RT-induced EASRs. Our research will have significant clinical impact because: (i) many tumor cells depend on glutamine-glutamate as energy source for growth and survival, (ii) breast cancer cells secrete high levels of glutamate often metastasize to bone with severe pain issue, (iii) glutaminase inhibitors enhance chemotherapy in triple negative breast cancer, (iv) two thirds of cancer patients receive RT, and (v) glutamate metabolism is associated with RT-induced EASRs. With increasing recent interest in targeting tumor metabolism, we anticipate that glutaminase inhibitors will also have application in preventing RT-induced EASRs in addition to their anticancer activities in triggering metabolic crisis in tumor cells and enhancing chemotherapy. Citation Format: Carolina Puyana Barcha, Eunkyung Lee, Cristiane Takita, Jean L. Wright, Jennifer Hu. Metabolomics in predicting breast cancer treatment responses [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 604.