Original Article
Metabolic biomarker signature for predicting the effect of neoadjuvant chemotherapy of breast cancer
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.
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.
