Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach

Abstract

We are especially grateful to all the patients and their families who contributed the data that made this study possible. In addition, we thank the staff of the Clinical Research Unit of the Medical Oncology Service of the University Hospital of Jaen for their time and assistance, and the Fundacion MEDINA for their technical support. Lastly, we thank the Fundacion Bancaria Unicaja for the financial support. Jose Camacho is partly supported by the Agencia Andaluza del Conocimiento, Regional Government of Andalucia, in Spain, and ERDF (European Regional Development Fund) funds through project B-TIC-136-UGR20. ; Neoadjuvant chemotherapy (NACT) outcomes vary according to breast cancer (BC) subtype. Since pathologic complete response is one of the most important target endpoints of NACT, further investigation of NACT outcomes in BC is crucial. Thus, identifying sensitive and specific predictors of treatment response for each phenotype would enable early detection of chemoresistance and residual disease, decreasing exposures to ineffective therapies and enhancing overall survival rates. We used liquid chromatography high-resolution mass spectrometry (LC-HRMS)-based untargeted metabolomics to detect molecular changes in plasma of three different BC subtypes following the same NACT regimen, with the aim of searching for potential predictors of response. The metabolomics data set was analyzed by combining univariate and multivariate statistical strategies. By using ANOVA–simultaneous component analysis (ASCA), we were able to determine the prognostic value of potential biomarker candidates of response to NACT in the triple-negative (TN) subtype. Higher concentrations of docosahexaenoic acid and secondary bile acids were found at basal and presurgery samples, respectively, in the responders group. In addition, the glycohyocholic and glycodeoxycholic acids were able to classify TN patients according to response to treatment and overall survival with an area under the curve model > 0.77. In relation to luminal B (LB) and HER2+ subjects, it should be noted that significant differences were related to time and individual factors. Specifically, tryptophan was identified to be decreased over time in HER2+ patients, whereas LysoPE (22:6) appeared to be increased, but could not be associated with response to NACT. Therefore, the combination of untargeted-based metabolomics along with longitudinal statistical approaches may represent a very useful tool for the improvement of treatment and in administering a more personalized BC follow-up in the clinical practice. ; Fundacion MEDINA ; Fundacion Bancaria Unicaja ; Agencia Andaluza del Conocimiento, Regional Government of Andalucia ; European Commission B-TIC-136-UGR20