Metabolomic Profiling of Gastrointestinal Stromal Tumor (GIST) in Human Tissue Samples and Xenografts.
Journal of clinical oncology(2014)
摘要
10516 Background: There is a growing need for novel agents to treat resistant GIST. The goal of this study was to characterize and identify potential therapeutic targets in the metabolic profile of GIST. Methods: Tumor, adjacent control tissue samples from 7 patients (five exposed to imatinib) and two tumor samples from xenografts in nude mice models (non-exposed) were extracted for analysis. 1H-NMR spectra, using a 500MHz spectrometer equipped with a cryoprobe head, were acquired and processed. Energy state, glucose (cytosolic glycolysis versus mitochondrial Krebs cycle), protein and lipid metabolism (indicators of proliferation and invasiveness) metabolomic profiles were assessed globally in correlation with clinical and histopathologic findings. Results: Findings suggest shift from cytosolic glycolysis (glucose decrease and increased lactate with minimal changes in pyruvate) towards the mitochondrial Krebs cycle (elevated glutamine and glutamate synthesis) in all tumor samples. Aspartate, myoinositol, and cell membrane phospholipids such as phosphocholine/glycerophosphocholine were greater in untreated GIST xenografts compared to treated tumors. Alanine, taurine, proline, ADP and phosphocholine were significantly higher in tumor tissue extracts compared to control (imatinib treated) (p<0.05). Tumor stage, mitotic index or mutation type did not correlate with metabolomic profile. Differences were accentuated among untreated patients. Partial Least Squares-Discriminant Analysis (PLS-DA) model successfully separated tumor tissues from controls (R2Y=0.56, Q2Y=0.22). Conclusions: Metabolomic profiling of patient and xenograft GIST samples exposed to KIT inhibitors suggest that cytosolic glycolysis and phospholipid biology may be important not only in the GIST phenotype but may be ameliorated with KIT inhibition by imatinib. Further understanding of GIST metabolomics may lead to the identification of new therapeutic targets.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要