Plasma Metabolome Signatures to Predict Responsiveness to Neoadjuvant Chemotherapy in Breast Cancer.

Autor: Silva AAR; MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil., Cardoso MR; Department of Obstetrics and Gynecology, Division of Gynecologic and Breast Oncology, Faculty of Medical Sciences, University of Campinas (UNICAMP-Universidade Estadual de Campinas), Campinas 13083881, São Paulo, Brazil.; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA., Oliveira DC; MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil., Godoy P; MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil., Talarico MCR; Department of Obstetrics and Gynecology, Division of Gynecologic and Breast Oncology, Faculty of Medical Sciences, University of Campinas (UNICAMP-Universidade Estadual de Campinas), Campinas 13083881, São Paulo, Brazil., Gutiérrez JM; MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil., Rodrigues Peres RM; MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil., de Carvalho LM; Post Graduate Program in Health Sciences, São Francisco University, Bragança Paulista 12916900, São Paulo, Brazil., Miyaguti NADS; MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil., Sarian LO; Department of Obstetrics and Gynecology, Division of Gynecologic and Breast Oncology, Faculty of Medical Sciences, University of Campinas (UNICAMP-Universidade Estadual de Campinas), Campinas 13083881, São Paulo, Brazil., Tata A; Laboratory of Experimental Chemistry, Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe), Viale Fiume 78, 36100 Vicenza, Italy., Derchain SFM; Department of Obstetrics and Gynecology, Division of Gynecologic and Breast Oncology, Faculty of Medical Sciences, University of Campinas (UNICAMP-Universidade Estadual de Campinas), Campinas 13083881, São Paulo, Brazil., Porcari AM; MS4Life Laboratory of Mass Spectrometry, Health Sciences Postgraduate Program, São Francisco University, Av. São Francisco de Assis, 218, Sala 211, Prédio 5, Bragança Paulista 12916900, São Paulo, Brazil.
Jazyk: angličtina
Zdroj: Cancers [Cancers (Basel)] 2024 Jul 06; Vol. 16 (13). Date of Electronic Publication: 2024 Jul 06.
DOI: 10.3390/cancers16132473
Abstrakt: Background: Neoadjuvant chemotherapy (NACT) has arisen as a treatment option for breast cancer (BC). However, the response to NACT is still unpredictable and dependent on cancer subtype. Metabolomics is a tool for predicting biomarkers and chemotherapy response. We used plasma to verify metabolomic alterations in BC before NACT, relating to clinical data.
Methods: Liquid chromatography coupled to mass spectrometry (LC-MS) was performed on pre-NACT plasma from patients with BC ( n = 75). After data filtering, an SVM model for classification was built and validated with 75%/25% of the data, respectively.
Results: The model composed of 19 identified metabolites effectively predicted NACT response for training/validation sets with high sensitivity (95.4%/93.3%), specificity (91.6%/100.0%), and accuracy (94.6%/94.7%). In both sets, the panel correctly classified 95% of resistant and 94% of sensitive females. Most compounds identified by the model were lipids and amino acids and revealed pathway alterations related to chemoresistance.
Conclusion: We developed a model for predicting patient response to NACT. These metabolite panels allow clinical gain by building precision medicine strategies based on tumor stratification.
Databáze: MEDLINE
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