Molecular Oncology (Jul 2022)

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

  • Caridad Díaz,
  • Carmen González‐Olmedo,
  • Leticia Díaz‐Beltrán,
  • José Camacho,
  • Patricia Mena García,
  • Ariadna Martín‐Blázquez,
  • Mónica Fernández‐Navarro,
  • Ana Laura Ortega‐Granados,
  • Fernando Gálvez‐Montosa,
  • Juan Antonio Marchal,
  • Francisca Vicente,
  • José Pérez del Palacio,
  • Pedro Sánchez‐Rovira

DOI
https://doi.org/10.1002/1878-0261.13216
Journal volume & issue
Vol. 16, no. 14
pp. 2658 – 2671

Abstract

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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.

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