Xiaojie Lin1, Rui Xu1, Siying Mao1, Yuzhu Zhang1, Yan Dai1, Qianqian Guo1, Xue Song1, Qingling Zhang1, Li Li2, Qianjun Chen1. 1. Department of Mammary Disease, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou 510120, China. 2. Team of Molecular Biology and Systems Biology Research of Chinese Medicine, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou 510120, China.
Abstract
BACKGROUND: The effect of breast cancer neoadjuvant chemotherapy (NCT) is strongly associated with breast cancer long term survival, especially when patients get a pathological complete response (PCR). It always is still unknown which patient is the potential one to get a PCR in the NCT. Thus, we have seeded blood-derived metabolite biomarkers to predict the effect of NCT of breast cancer. METHODS: Patients who received either 6 or 8 cycles of anthracycline-docetaxel-based NCT (EC-T or TEC) had been assessed their response to chemotherapy-partial response (PR) (n=19) and stable disease (SD) (n=16). The serum samples had been collected before and after chemotherapy. Sixty-nine subjects were prospectively recruited with PR and SD patients before and after chemotherapy separately. Metabolomics profiles of serum samples were generated from 3,461 metabolites identified by liquid chromatography-mass spectrometry (LC-MS). RESULTS: Based on LC-MS metabolic profiling methods, nine metabolites were identified in this study: prostaglandin C1, ricinoleic acid, oleic acid amide, ethyl docosahexaenoic, hulupapeptide, lysophosphatidylethanolamine 0:0/22:4, cysteinyl-lysine, methacholine, and vitamin K2, which were used to make up a receiver operating characteristics (ROC) curve, a model for predicting chemotherapy response. With an area under the curve (AUC) of 0.957, the model has a specificity of 100% and sensitivity of 81.2% for predicting the response of PR and SD of breast cancer patients. CONCLUSIONS: A model with such good predictability would undoubtedly verify that the serum-derived metabolites be used for predicting the effect of breast cancer NCT. However, how identified metabolites work for prediction is still to be clearly understood. 2019 Annals of Translational Medicine. All rights reserved.
BACKGROUND: The effect of breast cancer neoadjuvant chemotherapy (NCT) is strongly associated with breast cancer long term survival, especially when patients get a pathological complete response (PCR). It always is still unknown which patient is the potential one to get a PCR in the NCT. Thus, we have seeded blood-derived metabolite biomarkers to predict the effect of NCT of breast cancer. METHODS: Patients who received either 6 or 8 cycles of anthracycline-docetaxel-based NCT (EC-T or TEC) had been assessed their response to chemotherapy-partial response (PR) (n=19) and stable disease (SD) (n=16). The serum samples had been collected before and after chemotherapy. Sixty-nine subjects were prospectively recruited with PR and SD patients before and after chemotherapy separately. Metabolomics profiles of serum samples were generated from 3,461 metabolites identified by liquid chromatography-mass spectrometry (LC-MS). RESULTS: Based on LC-MS metabolic profiling methods, nine metabolites were identified in this study: prostaglandin C1, ricinoleic acid, oleic acid amide, ethyl docosahexaenoic, hulupapeptide, lysophosphatidylethanolamine 0:0/22:4, cysteinyl-lysine, methacholine, and vitamin K2, which were used to make up a receiver operating characteristics (ROC) curve, a model for predicting chemotherapy response. With an area under the curve (AUC) of 0.957, the model has a specificity of 100% and sensitivity of 81.2% for predicting the response of PR and SD of breast cancer patients. CONCLUSIONS: A model with such good predictability would undoubtedly verify that the serum-derived metabolites be used for predicting the effect of breast cancer NCT. However, how identified metabolites work for prediction is still to be clearly understood. 2019 Annals of Translational Medicine. All rights reserved.
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