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Metabolomics approach for predicting response to neoadjuvant chemotherapy for breast cancer
Authors:Siwei Wei  Lingyan Liu  Jian Zhang  Jeremiah Bowers  G.A. Nagana Gowda  Harald Seeger  Tanja Fehm  Hans J. Neubauer  Ulrich Vogel  Susan E. Clare  Daniel Raftery
Affiliation:1. Department of Chemistry, Purdue University, 560 Oval Dr., West Lafayette, IN 47907, USA;2. Weldon School of Biomedical Engineering, Purdue University, 206 S. Martin Jischke Dr., West Lafayette, IN 47907, USA;3. Department of Gynecology and Obstetrics, Eberhard-Karls-University of Tuebingen, Calwerstrasse 7/6, 72076 Tuebingen, Germany;4. Department of Pathology, Eberhard-Karls-University of Tuebingen, Liebermeisterstr. 8, 72076 Tuebingen, Germany;5. Department of Surgery, IU School of Medicine, 980 W. Walnut St., Indianapolis, IN 46202, USA
Abstract:
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.
Keywords:Breast cancer, Neoadjuvant chemotherapy, Therapy response, Metabolomics, NMR, LC‐  MS
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