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Metabolic Response to Everolimus in Patient-Derived Triple-Negative Breast Cancer Xenografts

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Department of Circulation and Medical Imaging, NTNU, The Norwegian University of Science and Technology, Trondheim 7489, Norway
Department of Radiology, St. Olavs University Hospital, Trondheim 7030, Norway
§ Genetics Department, Institut Curie, PSL Research University, Paris CEDEX 05, France
Faculty of Pharmacy, Aleppo University, Aleppo 3355, Syria
Translational Research Department, Institut Curie, PSL Research University, Paris CEDEX 05, France
# EA7331, University of Paris Descartes, Paris CEDEX 06, France
Department of Laboratory Medicine, Children’s and Women’s Health, NTNU, The Norwegian University of Science and Technology, Trondheim 7489, Norway
*E-mail: [email protected]. Phone: +47 73597449. Fax: +47 73598613.
Cite this: J. Proteome Res. 2017, 16, 5, 1868–1879
Publication Date (Web):March 14, 2017
Copyright © 2017 American Chemical Society

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    Patients with triple-negative breast cancer (TNBC) are unresponsive to endocrine and anti-HER2 pharmacotherapy, limiting their therapeutic options to chemotherapy. TNBC is frequently associated with abnormalities in the PI3K/AKT/mTOR signaling pathway; drugs targeting this pathway are currently being evaluated in these patients. However, the response is variable, partly due to heterogeneity within TNBC, conferring a need to identify biomarkers predicting response and resistance to targeted therapy. In this study, we used a metabolomics approach to assess response to the mTOR inhibitor everolimus in a panel of TNBC patient-derived xenografts (PDX) (n = 103 animals). Tumor metabolic profiles were acquired using high-resolution magic angle spinning magnetic resonance spectroscopy. Partial least-squares-discriminant analysis on relative metabolite concentrations discriminated treated xenografts from untreated controls with an accuracy of 67% (p = 0.003). Multilevel linear mixed-effects models (LMM) indicated reduced glycolytic lactate production and glutaminolysis after treatment, consistent with PI3K/AKT/mTOR pathway inhibition. Although inherent metabolic heterogeneity between different PDX models seemed to hinder prediction of treatment response, the metabolic effects following treatment were more pronounced in responding xenografts compared to nonresponders. Additionally, the metabolic information predicted p53 mutation status, which may provide complementary insight into the interplay between PI3K signaling and other drivers of disease progression.

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.6b00918.

    • Supplementary methods, PCA scores and loadings plots of treated xenografts vs untreated controls, mean metabolite relative levels (log10 transformed) per treatment and response group, mean metabolite relative levels (log10 transformed) per treatment group within responding and nonresponding patient-derived xenograft (PDX) models (PDF)

    • Supplementary table containing metabolite relative levels per PDX model (XLSX)

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