Open AccessMetabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer
Author(s) -
Wei Siwei,
Liu Lingyan,
Zhang Jian,
Bowers Jeremiah,
Gowda G.A. Nagana,
Seeger Harald,
Fehm Tanja,
Neubauer Hans J.,
Vogel Ulrich,
Clare Susan E.,
Raftery Daniel
Publication year - 2013
Publication title -
molecular oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.332
H-Index - 88
eISSN - 1878-0261
pISSN - 1574-7891
DOI - 10.1016/j.molonc.2012.10.003
Subject(s) - breast cancer , metabolomics , chemotherapy , glutamine , medicine , metabolite , oncology , metabolome , biomarker , cancer , bioinformatics , biology , amino acid , biochemistry
Breast cancer is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. As an example, only some women will benefit from chemotherapy. Identifying patients who will respond to chemotherapy and thereby improve their long‐term survival has important implications to treatment protocols and outcomes, while identifying non responders may enable these patients to avail themselves of other investigational approaches or other potentially effective treatments. In this study, serum metabolite profiling was performed to identify potential biomarker candidates that can predict response to neoadjuvant chemotherapy for breast cancer. Metabolic profiles of serum from patients with complete (n = 8), partial (n = 14) and no response (n = 6) to chemotherapy were studied using a combination of nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography–mass spectrometry (LC–MS) and statistical analysis methods. The concentrations of four metabolites, three (threonine, isoleucine, glutamine) from NMR and one (linolenic acid) from LC–MS were significantly different when comparing response to chemotherapy. A prediction model developed by combining NMR and MS derived metabolites correctly identified 80% of the patients whose tumors did not show complete response to chemotherapy. These results show promise for larger studies that could result in more personalized treatment protocols for breast cancer patients.