Publication: Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach.
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Identifiers
Date
2022-03-24
Authors
Diaz, Caridad
Gonzalez-Olmedo, Carmen
Diaz-Beltran, Leticia
Camacho, Jose
Mena Garcia, Patricia
Martin-Blazquez, Ariadna
Fernandez-Navarro, Monica
Ortega-Granados, Ana Laura
Galvez-Montosa, Fernando
Marchal, Juan Antonio
Advisors
Journal Title
Journal ISSN
Volume Title
Publisher
John Wiley & Sons Ltd.
Abstract
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.
Description
MeSH Terms
Antineoplastic Combined Chemotherapy Protocols
Breast Neoplasms
Female
Humans
Metabolomics
Neoadjuvant Therapy
Breast Neoplasms
Female
Humans
Metabolomics
Neoadjuvant Therapy
DeCS Terms
Femenino
Humanos
Metabolómica
Neoplasias de la mama
Protocolos de quimioterapia combinada antineoplásica
Terapia neoadyuvante
Humanos
Metabolómica
Neoplasias de la mama
Protocolos de quimioterapia combinada antineoplásica
Terapia neoadyuvante
CIE Terms
Keywords
ASCA, LC-HRMS, breast cancer, neoadjuvant chemotherapy, personalized medicine, treatment response
Citation
Díaz C, González-Olmedo C, Díaz-Beltrán L, Camacho J, Mena García P, Martín-Blázquez A, et al. Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach. Mol Oncol. 2022 Jul;16(14):2658-2671.