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

dc.contributor.authorDiaz, Caridad
dc.contributor.authorGonzalez-Olmedo, Carmen
dc.contributor.authorDiaz-Beltran, Leticia
dc.contributor.authorCamacho, Jose
dc.contributor.authorMena Garcia, Patricia
dc.contributor.authorMartin-Blazquez, Ariadna
dc.contributor.authorFernandez-Navarro, Monica
dc.contributor.authorOrtega-Granados, Ana Laura
dc.contributor.authorGalvez-Montosa, Fernando
dc.contributor.authorMarchal, Juan Antonio
dc.contributor.authorVicente, Francisca
dc.contributor.authorPerez Del Palacio, Jose
dc.contributor.authorSanchez-Rovira, Pedro
dc.date.accessioned2023-05-03T13:26:08Z
dc.date.available2023-05-03T13:26:08Z
dc.date.issued2022-03-24
dc.description.abstractNeoadjuvant 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.
dc.description.versionSi
dc.identifier.citationDíaz 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.
dc.identifier.doi10.1002/1878-0261.13216
dc.identifier.essn1878-0261
dc.identifier.pmcPMC9297806
dc.identifier.pmid35338693
dc.identifier.pubmedURLhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9297806/pdf
dc.identifier.unpaywallURLhttps://doi.org/10.1002/1878-0261.13216
dc.identifier.urihttp://hdl.handle.net/10668/19508
dc.issue.number14
dc.journal.titleMolecular oncology
dc.journal.titleabbreviationMol Oncol
dc.language.isoen
dc.organizationFundación MEDINA (Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía)
dc.organizationInstituto de Investigación Biosanitaria de Granada (ibs.GRANADA)
dc.organizationHospital Universitario de Jaén
dc.organizationFundación MEDINA
dc.page.number2658-2671
dc.publisherJohn Wiley & Sons Ltd.
dc.pubmedtypeJournal Article
dc.pubmedtypeResearch Support, Non-U.S. Gov't
dc.relation.publisherversionhttps://onlinelibrary.wiley.com/doi/10.1002/1878-0261.13216
dc.rightsAttribution 4.0 International
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectASCA
dc.subjectLC-HRMS
dc.subjectbreast cancer
dc.subjectneoadjuvant chemotherapy
dc.subjectpersonalized medicine
dc.subjecttreatment response
dc.subject.decsFemenino
dc.subject.decsHumanos
dc.subject.decsMetabolómica
dc.subject.decsNeoplasias de la mama
dc.subject.decsProtocolos de quimioterapia combinada antineoplásica
dc.subject.decsTerapia neoadyuvante
dc.subject.meshAntineoplastic Combined Chemotherapy Protocols
dc.subject.meshBreast Neoplasms
dc.subject.meshFemale
dc.subject.meshHumans
dc.subject.meshMetabolomics
dc.subject.meshNeoadjuvant Therapy
dc.titlePredicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach.
dc.typeresearch article
dc.type.hasVersionVoR
dc.volume.number16
dspace.entity.typePublication

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