Browsing by Author "Ruhland, E."

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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. Ercüment
Complete 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.
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
Metabolomic profiling highlights the metabolic bases of acute-on-chronic and post-hepatectomy liver failure
(Elsevier, 2019) Faitot, F.; Ruhland, E.; Oncioiu, C.; Besch, C.; Addeo, P.; Çiçek, A. Ercüment; Bachellier, P.; Namer, I. -J.
Background Posthepatectomy liver failure (PHLF) is the main limitation to extending liver resection but its pathophysiology is not yet fully understood. The aim of the study was to describe the metabolic adaptations that occur with PHLF. Methods A retrospective study of 82 patients using nuclear magnetic resonance metabolomics to identify and quantify intra-hepatic metabolites was performed. The metabolite levels were compared using metabolic network analysis ADEMA between fatal PHLF (FLF) and non fatal PHLF and according to PHLF/ACLF grading. Results Metabolomic profiles were significantly different between patients presenting FLF and non FLF or grade 3 ACLF versus < grade 3 ACLF. In the patients undergoing hepatectomy, valine, alanine and glycerophosphocholine were identified as powerful biomarkers to predict FLF (AUROC 0.806, 0.802 and 0.856 respectively). Network analysis showed an activation of aerobic glycolysis with glutaminolysis as observed in highly proliferating systems. Inversely, ACLF3 showed deprivation of glucose and lactate compared to lower ACLF grade. Conclusion Clinical andbiological severity of ACLF and PHLF correlate with specific metabolic adaptations. Metabolomics can predict fatal liver failure after hepatectomy and underline significant differences in the metabolic patterns of ACLF and PHLF.
Open Access
Metabolomics of small intestine neuroendocrine tumors and related hepatic metastases
(MDPI AG, 2019-12) Çiçek, A. Ercüment; Imperiale, A.; Poncet, G.; Addeo, P.; Ruhland, E.; Roche, C.; Battini, S.; Chenard, M. P.; Hervieu, V.; Goichot, B.; Bachellier, P.; Walter, T.; Namer, I. J.
To assess the metabolomic fingerprint of small intestine neuroendocrine tumors (SI-NETs) and related hepatic metastases, and to investigate the influence of the hepatic environment on SI-NETs metabolome. Ninety-four tissue samples, including 46 SI-NETs, 18 hepatic NET metastases and 30 normal SI and liver samples, were analyzed using 1H-magic angle spinning (HRMAS) NMR nuclear magnetic resonance (NMR) spectroscopy. Twenty-seven metabolites were identified and quantified. Differences between primary NETs vs. normal SI and primary NETs vs. hepatic metastases, were assessed. Network analysis was performed according to several clinical and pathological features. Succinate, glutathion, taurine, myoinositol and glycerophosphocholine characterized NETs. Normal SI specimens showed higher levels of alanine, creatine, ethanolamine and aspartate. PLS-DA revealed a continuum-like distribution among normal SI, G1-SI-NETs and G2-SI-NETs. The G2-SI-NET distribution was closer and clearly separated from normal SI tissue. Lower concentration of glucose, serine and glycine, and increased levels of choline-containing compounds, taurine, lactate and alanine, were found in SI-NETs with more aggressive tumors. Higher abundance of acetate, succinate, choline, phosphocholine, taurine, lactate and aspartate discriminated liver metastases from normal hepatic parenchyma. Higher levels of alanine, ethanolamine, glycerophosphocholine and glucose was found in hepatic metastases than in primary SI-NETs. The present work gives for the first time a snapshot of the metabolomic characteristics of SI-NETs, suggesting the existence of complex metabolic reality, maybe characteristic of different tumor evolution.
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.