Browsing by Author "Bund, C."

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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.
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.
Open Access
Metabolomic characterization of human hippocampus from drug-resistant epilepsy with mesial temporal seizure
(Wiley-Blackwell Publishing, 2018-03) Detour, J.; Bund, C.; Behr, C.; Cebula, H.; Çiçek, A. Ercüment; Valenti-Hirsch, Maria-Paola; Lannes, B.; Lhermitte, B.; Nehlig, A.; Kehrli, P.; Proust, F.; Hirsch, E.; Namer, Izzie-Jacques
Within a complex systems biology perspective, we wished to assess whether hippocampi with established neuropathological features have distinct metabolome. Apparently normal hippocampi with no signs of sclerosis (noHS), were compared to hippocampal sclerosis (HS) type 1 (HS1) and/or type 2 (HS2). Hippocampus metabolome from patients with epilepsy-associated neuroepithelial tumors (EANTs), namely, gangliogliomas (GGs) and dysembryoplastic neuroepithelial tumors (DNTs), was also compared to noHS epileptiform tissue. Methods: All patients underwent standardized temporal lobectomy. We applied 1H high-resolution magic angle spinning nuclear magnetic resonance (HRMAS NMR) spectroscopy to 48 resected human hippocampi. NMR spectra allowed quantification of 21 metabolites. Data were analyzed using multivariate analysis based on mutual information. Results: Clear distinct metabolomic profiles were observed between all studied groups. Sixteen and 18 expected metabolite levels out of 21 were significantly different for HS1 and HS2, respectively, when compared to noHS. Distinct concentration variations for glutamine, glutamate, and N-acetylaspartate (NAA) were observed between HS1 and HS2. Hippocampi from GG and DNT patients showed 7 and 11 significant differences in metabolite concentrations when compared to the same group, respectively. GG and DNT had a clear distinct metabolomic profile, notably regarding choline compounds, glutamine, glutamate, aspartate, and taurine. Lactate and acetate underwent similar variations in both groups. Significance: HRMAS NMR metabolomic analysis was able to disentangle metabolic profiles between HS, noHS, and epileptic hippocampi associated with EANT. HRMAS NMR metabolomic analysis may contribute to a better identification of abnormal biochemical processes and neuropathogenic combinations underlying mesial temporal lobe epilepsy.
Open Access
Metabolomic profile of aggressive meningiomas by using highresolution magic angle spinning nuclear magnetic resonance
(American Chemical Society, 2020) Bender, L.; Somme, F.; Ruhland, E.; Çiçek, A. Ercüment; Bund, C.; Namer, I. J.
Meningiomas are in most cases benign brain tumors. The WHO 2016 classification defines three grades of meningiomas. This classification had a prognosis value because grade III meningiomas have a worse prognosis value compared to grades I and II meningiomas. However, some benign or atypical meningiomas can have a clinical aggressive behavior. There are currently no reliable markers which allow distinguishing between the meningiomas with a good prognosis and those which may recur. High-resolution magic angle spinning (HRMAS) spectrometry is a noninvasive method able to determine the metabolite profile of a tissue sample. We retrospectively analyzed 62 meningioma samples by using HRMAS spectrometry (43 metabolites). We described a metabolic profile defined by a high concentration for acetate, threonine, N-acetyl-lysine, hydroxybutyrate, myoinositol, ascorbate, scylloinositol, and total choline and a low concentration for aspartate, glucose, isoleucine, valine, adenosine, arginine, and alanine. This metabolomic signature was associated with poor prognosis histological markers [Ki-67 ≥ 40%, high histological grade and negative progesterone receptor (PR) expression]. We also described a similar metabolomic spectrum between grade III and grade I meningiomas. Moreover, all grade I meningiomas with a low Ki-67 expression and a positive PR expression did not have the same metabolomic profile. Metabolomic analysis could be used to determine an aggressive meningioma in order to discuss a personalized treatment. Further studies are needed to confirm these results and to correlate this metabolic profile with survival data.
Open Access
Metabolomics approaches in experimental allergic encephalomyelitis
(Elsevier, 2018) Battini, B.; Bund, C.; Moussallieh, F. M.; Çiçek, A. Ercüment; De Sèze, J.; Namer, I. J.
A myelin basic protein (MBP)-induced experimental allergic encephalomyelitis (EAE) involves paraplegia due to a reversible thoracolumbar spinal cord impairment. The aims of this study were thus to find significant metabolic biomarkers of inflammation and identify the site of inflammation in the central nervous system (CNS) during the acute signs in of the disease using metabolomics. All the EAE samples were associated with higher levels of lactate, ascorbate, glucose and amino acids, and decreased level of N-acetyl-aspartate (NAA) compared to the control group. A decreased NAA level has been particularly shown in lumbar spinal cord in relationship with the clinical signs.
Open Access
PiDeeL: metabolic pathway-informed deep learning model for survival analysis and pathological classification of gliomas
(Oxford University Press, 2023-11-11) Kaynar, Gün; Çakmakçı, D.; Bund, C.; Todeschi, J.; Namer, I. J.; Çiçek, A. Ercüment; Martelli, Pier Luigi
Online assessment of tumor characteristics during surgery is important and has the potential to establish an intra-operative surgeon feedback mechanism. With the availability of such feedback, surgeons could decide to be more liberal or conservative regarding the resection of the tumor. While there are methods to perform metabolomics-based tumor pathology prediction, their model complexity predictive performance is limited by the small dataset sizes. Furthermore, the information conveyed by the feedback provided on the tumor tissue could be improved both in terms of content and accuracy. Results In this study, we propose a metabolic pathway-informed deep learning model (PiDeeL) to perform survival analysis and pathology assessment based on metabolite concentrations. We show that incorporating pathway information into the model architecture substantially reduces parameter complexity and achieves better survival analysis and pathological classification performance. With these design decisions, we show that PiDeeL improves tumor pathology prediction performance of the state-of-the-art in terms of the Area Under the ROC Curve by 3.38% and the Area Under the Precision–Recall Curve by 4.06%. Similarly, with respect to the time-dependent concordance index (c-index), PiDeeL achieves better survival analysis performance (improvement of 4.3%) when compared to the state-of-the-art. Moreover, we show that importance analyses performed on input metabolite features as well as pathway-specific neurons of PiDeeL provide insights into tumor metabolism. We foresee that the use of this model in the surgery room will help surgeons adjust the surgery plan on the fly and will result in better prognosis estimates tailored to surgical procedures.
Open Access
Targeted metabolomics analyses for brain tumor margin assessment during surgery
(Oxford University Press, 2022-06-15) Çakmakçı, D.; Kaynar, Gün; Bund, C.; Piotto, M.; Proust, F.; Namer, I. J.; Çiçek, A. Ercüment
Motivation: Identification and removal of micro-scale residual tumor tissue during brain tumor surgery are key for survival in glioma patients. For this goal, High-Resolution Magic Angle Spinning Nuclear Magnetic Resonance (HRMAS NMR) spectroscopy-based assessment of tumor margins during surgery has been an effective method. However, the time required for metabolite quantification and the need for human experts such as a pathologist to be present during surgery are major bottlenecks of this technique. While machine learning techniques that analyze the NMR spectrum in an untargeted manner (i.e. using the full raw signal) have been shown to effectively automate this feedback mechanism, high dimensional and noisy structure of the NMR signal limits the attained performance. Results: In this study, we show that identifying informative regions in the HRMAS NMR spectrum and using them for tumor margin assessment improves the prediction power. We use the spectra normalized with the ERETIC (electronic reference to access in vivo concentrations) method which uses an external reference signal to calibrate the HRMAS NMR spectrum. We train models to predict quantities of metabolites from annotated regions of this spectrum. Using these predictions for tumor margin assessment provides performance improvements up to 4.6% the Area Under the ROC Curve (AUC-ROC) and 2.8% the Area Under the Precision-Recall Curve (AUC-PR). We validate the importance of various tumor biomarkers and identify a novel region between 7.97 ppm and 8.09 ppm as a new candidate for a glioma biomarker. Availability and implementation: The code is released at https://github.com/ciceklab/targeted_brain_tumor_margin_ assessment. The data underlying this article are available in Zenodo, at https://doi.org/10.5281/zenodo.5781769.
Open Access
What does reduced FDG uptake mean in high-grade gliomas?
(NLM (Medline), 2019) Bund, C.; Lhermitte, B.; Çiçek, A. Ercüment; Ruhland, E.; Proust, F.; Namer, I. J.
Purpose: As well as in many others cancers, FDG uptake is correlated with the degree of malignancy in gliomas, that is, commonly high FDG uptake in high-grade gliomas. However, in clinical practice, it is not uncommon to observe high-grade gliomas with low FDG uptake. Our aim was to explore the tumor metabolism in 2 populations of high-grade gliomas presenting high or low FDG uptake. Methods: High-resolution magic-angle spinning nuclear magnetic resonance spectroscopy was realized on tissue samples from 7 high-grade glioma patients with high FDG uptake and 5 high-grade glioma patients with low FDG uptake. Tumor metabolomics was evaluated from 42 quantified metabolites and compared by network analysis. Results: Whether originating from astrocytes or oligodendrocytes, the highgrade gliomas with low FDG avidity represent a subgroup of high-grade gliomas presenting common characteristics: low aspartate, glutamate, and creatine levels, which are probably related to the impaired electron transport chain in mitochondria; high serine/glycine metabolism and so one-carbon metabolism; low glycerophosphocholine-phosphocholine ratio in membrane metabolism, which is associated with tumor aggressiveness; and finally negative MGMT methylation status. Conclusions: It seems imperative to identify this subgroup of high-grade gliomas with low FDG avidity, which is especially aggressive. Their identification could be important for early detection for a possible personalized treatment, such as antifolate treatment.