Metabolic Response to Everolimus in Patient-derived Xenografts of Triple Negative Breast Cancer
Poster Dec 04, 2015
Leslie R. Euceda1, Deborah K. Hill1,2, Endre Stokke1, Elisabetta Marangoni3, Tone F. Bathen1, Siver A. Moestue1,2
Magnetic resonance based metabolic profiles of breast tumor tissue (n=103 samples) from triple negative breast cancer (TNBC) patient-derived xenografts (PDX) were used to evaluate treatment with the mTOR inhibitor Everolimus. Levels of 17 metabolites were calculated by integration and were used, along with lactate/glucose, taurine/creatine, and glycerophosphocholine/phosphocholine ratios, to build a multivariate PLS-DA model discriminating treated from control PDX, achieving an accuracy of 67% (p=0.003). Univariate linear mixed models revealed a significant increase (q ≤ 0.05) in glucose, glutamine, alanine, and glycerophosphocholine/phosphocholine, and decrease in phosphocholine, and lactate/glucose in treated PDX compared to controls, in accordance with PLS-DA loadings. This suggests reduced glycolytic lactate production and glutaminolysis after treatment, consistent with PI3K/AKT/mTOR signaling pathway inhibition1. However, no differences could be detected between responders and non-responders. PLS-DA was additionally employed on spectral profiles to investigate metabolic differences between controls (n=53) expressing or not expressing the tumor suppressors INPP4B and PTEN (determined by immunohistochemistry), which negatively regulate mTOR. Expression of INPP4B was successfully discriminated with an accuracy of 69% (p=0.009), while PTEN expression discrimination approached significance (64% accuracy, p=0.055). INPP4B expression was associated with increased phosphocholine, glycine, creatine, alanine, and lactate, and decreased glycerophosphocholine and taurine, according to PLS-DA loadings.
We developed an automated sample preparation protocol based on a robotic platform PAL RTC (CTC Analytics AG, Zwingen Switzerland), which represent a modified Bligh and Dyer method producing samples for both hydrophilic metabolomics using GC-MS and lipidomics using SFC-MS simultaneously.READ MORE