Browsing by Author "Chenard, M.-P."
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Open Access An integrated genomic and metabolomic approach for defining survival time in adult oligodendrogliomas patients(Springer, 2019) Bund, C.; Guergova‑Kuras, M.; Çiçek, A. Ercüment; Moussallieh, F.-M.; Dali‑Youcef, N.; Piotto, M.; Schneider, P.; Heller, R.; Entz‑Werle, N.; Lhermitte, B.; Chenard, M.-P.; Schott, R.; Proust, F.; Noel, G.; Namer, I. J.Introduction The identification of frequent acquired mutations shows that patients with oligodendrogliomas have divergent biology with differing prognoses regardless of histological classification. A better understanding of molecular features as well as their metabolic pathways is essential. Objectives The aim of this study was to examine the relationship between the tumor metabolome, six genomic aberrations (isocitrate dehydrogenase1 [IDH1] mutation, 1p/19q codeletion, tumor protein p53 [TP53] mutation, O6-methylguanin-DNA methyltransferase [MGMT] promoter methylation, epidermal growth factor receptor [EGFR] amplification, phosphate and tensin homolog [PTEN] methylation), and the patients’ survival time. Methods We applied 1H high-resolution magic-angle spinning (HRMAS) nuclear magnetic resonance (NMR) spectroscopy to 72 resected oligodendrogliomas. Results The presence of IDH1, TP53, 1p19q codeletion, MGMT promoter methylation reduced the relative risk of death, whereas PTEN methylation and EGFR amplification were associated with poor prognosis. Increased concentration of 2-hydroxyglutarate (2HG), N-acetyl-aspartate (NAA), myo-inositol and the glycerophosphocholine/phosphocholine (GPC/ PC) ratio were good prognostic factors. Increasing the concentration of serine, glycine, glutamate and alanine led to an increased relative risk of death. Conclusion HRMAS NMR spectroscopy provides accurate information on the metabolomics of oligodendrogliomas, making it possible to find new biomarkers indicative of survival. It enables rapid characterization of intact tissue and could be used as an intraoperative method.Item Open Access Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy(Public Library of Science, 2020) Çakmakçı, Doruk; Karakaşlar, Emin Onur; Ruhland, E.; Chenard, M.-P.; Proust, F.; Piotto, M.; Namer, I. J.; Çicek, A. ErcümentComplete resection of the tumor is important for survival in glioma patients. Even if the gross total resection was achieved, left-over micro-scale tissue in the excision cavity risks recurrence. High Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) technique can distinguish healthy and malign tissue efficiently using peak intensities of biomarker metabolites. The method is fast, sensitive and can work with small and unprocessed samples, which makes it a good fit for real-time analysis during surgery. However, only a targeted analysis for the existence of known tumor biomarkers can be made and this requires a technician with chemistry background, and a pathologist with knowledge on tumor metabolism to be present during surgery. Here, we show that we can accurately perform this analysis in real-time and can analyze the full spectrum in an untargeted fashion using machine learning. We work on a new and large HRMAS NMR dataset of glioma and control samples (n = 565), which are also labeled with a quantitative pathology analysis. Our results show that a random forest based approach can distinguish samples with tumor cells and controls accurately and effectively with a median AUC of 85.6% and AUPR of 93.4%. We also show that we can further distinguish benign and malignant samples with a median AUC of 87.1% and AUPR of 96.1%. We analyze the feature (peak) importance for classification to interpret the results of the classifier. We validate that known malignancy biomarkers such as creatine and 2-hydroxyglutarate play an important role in distinguishing tumor and normal cells and suggest new biomarker regions. The code is released at http://github.com/ciceklab/HRMAS_NC.Item Open Access Matrix Metalloproteinase-11 promotes early mouse mammary gland tumor growth through metabolic reprogramming and increased IGF1/AKT/FoxO1 signaling pathway, enhanced ER stress and alteration in mitochondrial UPR(MDPI AG, 2020-08) Tan, B.; Jaulin, A.; Bund, C.; Outilaft, H.; Wendling, C.; Chenard, M.-P.; Alpy, F.; Çiçek, A. Ercüment; Namer, I. J.; Tomasetto, C.; Dali-Youcef, N.Matrix metalloproteinase 11 (MMP11) is an extracellular proteolytic enzyme belonging to the matrix metalloproteinase (MMP11) family. These proteases are involved in extracellular matrix (ECM) remodeling and activation of latent factors. MMP11 is a negative regulator of adipose tissue development and controls energy metabolism in vivo. In cancer, MMP11 expression is associated with poorer survival, and preclinical studies in mice showed that MMP11 accelerates tumor growth. How the metabolic role of MMP11 contributes to cancer development is poorly understood. To address this issue, we developed a series of preclinical mouse mammary gland tumor models by genetic engineering. Tumor growth was studied in mice either deficient (Loss of Function-LOF) or overexpressing MMP11 (Gain of Function-GOF) crossed with a transgenic model of breast cancer induced by the polyoma middle T antigen (PyMT) driven by the murine mammary tumor virus promoter (MMTV) (MMTV-PyMT). Both GOF and LOF models support roles for MMP11, favoring early tumor growth by increasing proliferation and reducing apoptosis. Of interest, MMP11 promotes Insulin-like Growth Factor-1 (IGF1)/protein kinase B (AKT)/Forkhead box protein O1 (FoxO1) signaling and is associated with a metabolic switch in the tumor, activation of the endoplasmic reticulum stress response, and an alteration in the mitochondrial unfolded protein response with decreased proteasome activity. In addition, high resonance magic angle spinning (HRMAS) metabolomics analysis of tumors from both models established a metabolic signature that favors tumorigenesis when MMP11 is overexpressed. These data support the idea that MMP11 contributes to an adaptive metabolic response, named metabolic flexibility, promoting cancer growth.