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

Xiaojie Lin, Rui Xu, Siying Mao, Yuzhu Zhang, Yan Dai, Qianqian Guo, Xue Song, Qingling Zhang, Li Li, Qianjun Chen


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.