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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...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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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. |
format | Online Article Text |
id | pubmed-9297806 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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