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

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 r...

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Autores principales: Díaz, Caridad, González‐Olmedo, Carmen, Díaz‐Beltrán, Leticia, Camacho, José, Mena García, Patricia, Martín‐Blázquez, Ariadna, Fernández‐Navarro, Mónica, Ortega‐Granados, Ana Laura, Gálvez‐Montosa, Fernando, Marchal, Juan Antonio, Vicente, Francisca, Pérez del Palacio, José, Sánchez‐Rovira, Pedro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9297806/
https://www.ncbi.nlm.nih.gov/pubmed/35338693
http://dx.doi.org/10.1002/1878-0261.13216
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author Díaz, Caridad
González‐Olmedo, Carmen
Díaz‐Beltrán, Leticia
Camacho, José
Mena García, Patricia
Martín‐Blázquez, Ariadna
Fernández‐Navarro, Mónica
Ortega‐Granados, Ana Laura
Gálvez‐Montosa, Fernando
Marchal, Juan Antonio
Vicente, Francisca
Pérez del Palacio, José
Sánchez‐Rovira, Pedro
author_facet Díaz, Caridad
González‐Olmedo, Carmen
Díaz‐Beltrán, Leticia
Camacho, José
Mena García, Patricia
Martín‐Blázquez, Ariadna
Fernández‐Navarro, Mónica
Ortega‐Granados, Ana Laura
Gálvez‐Montosa, Fernando
Marchal, Juan Antonio
Vicente, Francisca
Pérez del Palacio, José
Sánchez‐Rovira, Pedro
author_sort Díaz, Caridad
collection PubMed
description 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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spelling pubmed-92978062022-07-22 Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach Díaz, Caridad González‐Olmedo, Carmen Díaz‐Beltrán, Leticia Camacho, José Mena García, Patricia Martín‐Blázquez, Ariadna Fernández‐Navarro, Mónica Ortega‐Granados, Ana Laura Gálvez‐Montosa, Fernando Marchal, Juan Antonio Vicente, Francisca Pérez del Palacio, José Sánchez‐Rovira, Pedro Mol Oncol Research Articles 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. John Wiley and Sons Inc. 2022-04-14 2022-07 /pmc/articles/PMC9297806/ /pubmed/35338693 http://dx.doi.org/10.1002/1878-0261.13216 Text en © 2022 The Authors. Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Díaz, Caridad
González‐Olmedo, Carmen
Díaz‐Beltrán, Leticia
Camacho, José
Mena García, Patricia
Martín‐Blázquez, Ariadna
Fernández‐Navarro, Mónica
Ortega‐Granados, Ana Laura
Gálvez‐Montosa, Fernando
Marchal, Juan Antonio
Vicente, Francisca
Pérez del Palacio, José
Sánchez‐Rovira, Pedro
Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach
title Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach
title_full Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach
title_fullStr Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach
title_full_unstemmed Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach
title_short Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach
title_sort predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9297806/
https://www.ncbi.nlm.nih.gov/pubmed/35338693
http://dx.doi.org/10.1002/1878-0261.13216
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