Metabolomics profiling in plasma samples from glioma patients correlates with tumor phenotypes

Circulating Liquid Biopsy Biomarkers in Glioblastoma: Advances and Challenges

Gliomas, particularly glioblastoma (GBM), represent the most prevalent and aggressive tumors of the central nervous system (CNS). Despite recent treatment advancements, patient survival rates remain low. The diagnosis of GBM traditionally relies on neuroimaging methods such as magnetic resonance imaging (MRI) or computed tomography (CT) scans and postoperative confirmation via histopathological and molecular analysis. Imaging techniques struggle to differentiate between tumor progression and treatment-related changes, leading to potential misinterpretation and treatment delays. Similarly, tissue biopsies, while informative, are invasive and not suitable for monitoring ongoing treatments. These challenges have led to the emergence of liquid biopsy, particularly through blood samples, as a promising alternative for GBM diagnosis and monitoring. Presently, blood and cerebrospinal fluid (CSF) sampling offers a minimally invasive means of obtaining tumor-related information to guide therapy. The idea that blood or any biofluid tests can be used to screen many cancer types has huge potential. Tumors release various components into the bloodstream or other biofluids, including cell-free nucleic acids such as microRNAs (miRNAs), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), proteins, extracellular vesicles (EVs) or exosomes, metabolites, and other factors. These factors have been shown to cross the blood-brain barrier (BBB), presenting an opportunity for the minimally invasive monitoring of GBM as well as for the real-time assessment of distinct genetic, epigenetic, transcriptomic, proteomic, and metabolomic changes associated with brain tumors. Despite their potential, the clinical utility of liquid biopsy-based circulating biomarkers is somewhat constrained by limitations such as the absence of standardized methodologies for blood or CSF collection, analyte extraction, analysis methods, and small cohort sizes. Additionally, tissue biopsies offer more precise insights into tumor morphology and the microenvironment. Therefore, the objective of a liquid biopsy should be to complement and enhance the diagnostic accuracy and monitoring of GBM patients by providing additional information alongside traditional tissue biopsies. Moreover, utilizing a combination of diverse biomarker types may enhance clinical effectiveness compared to solely relying on one biomarker category, potentially improving diagnostic sensitivity and specificity and addressing some of the existing limitations associated with liquid biomarkers for GBM. This review presents an overview of the latest research on circulating biomarkers found in GBM blood or CSF samples, discusses their potential as diagnostic, predictive, and prognostic indicators, and discusses associated challenges and future perspectives.

Metabolomics for searching validated biomarkers in cancer studies: a decade in review

INTRODUCTION

In the dynamic landscape of modern healthcare, the ability to anticipate and diagnose diseases, particularly in cases where early treatment significantly impacts outcomes, is paramount. Cancer, a complex and heterogeneous disease, underscores the critical importance of early diagnosis for patient survival. The integration of metabolomics information has emerged as a crucial tool, complementing the genotype-phenotype landscape and providing insights into active metabolic mechanisms and disease-induced dysregulated pathways.

AREAS COVERED

This review explores a decade of developments in the search for biomarkers validated within the realm of cancer studies. By critically assessing a diverse array of research articles, clinical trials, and studies, this review aims to present an overview of the methodologies employed and the progress achieved in identifying and validating biomarkers in metabolomics results for various cancer types.

EXPERT OPINION

Through an exploration of more than 800 studies, this review has allowed to establish a general idea about state-of-art in the search of biomarkers in metabolomics studies involving cancer which include certain level of results validation. The potential for metabolites as diagnostic markers to reach the clinic and make a real difference in patient health is substantial, but challenges remain to be explored.

Metabolic Contrasts: Fatty Acid Oxidation and Ketone Bodies in Healthy Brains vs. Glioblastoma Multiforme

The metabolism of glucose and lipids plays a crucial role in the normal homeostasis of the body. Although glucose is the main energy substrate, in its absence, lipid metabolism becomes the primary source of energy. The main means of fatty acid oxidation (FAO) takes place in the mitochondrial matrix through β-oxidation. Glioblastoma (GBM) is the most common form of primary malignant brain tumor (45.6%), with an incidence of 3.1 per 100,000. The metabolic changes found in GBM cells and in the surrounding microenvironment are associated with proliferation, migration, and resistance to treatment. Tumor cells show a remodeling of metabolism with the use of glycolysis at the expense of oxidative phosphorylation (OXPHOS), known as the Warburg effect. Specialized fatty acids (FAs) transporters such as FAT, FABP, or FATP from the tumor microenvironment are overexpressed in GBM and contribute to the absorption and storage of an increased amount of lipids that will provide sufficient energy used for tumor growth and invasion. This review provides an overview of the key enzymes, transporters, and main regulatory pathways of FAs and ketone bodies (KBs) in normal versus GBM cells, highlighting the need to develop new therapeutic strategies to improve treatment efficacy in patients with GBM.

Alterations in cellular metabolism under different grades of glioma staging identified based on a multi-omics analysis strategy

Glioma is a type of brain tumor closely related to abnormal cell metabolism. Firstly, multiple combinatorial sequencing studies have revealed this relationship. Genomic studies have identified gene mutations and gene expression disorders related to the development of gliomas, which affect cell metabolic pathways. In addition, transcriptome studies have revealed the genes and regulatory networks that regulate cell metabolism in glioma tissues. Metabonomics studies have shown that the metabolic pathway of glioma cells has changed, indicating their distinct energy and nutritional requirements. This paper focuses on the retrospective analysis of multiple groups combined with sequencing to analyze the changes in various metabolites during metabolism in patients with glioma. Finally, the changes in genes, regulatory networks, and metabolic pathways regulating cell metabolism in patients with glioma under different metabolic conditions were discussed. It is also proposed that multi-group metabolic analysis is expected to better understand the mechanism of abnormal metabolism of gliomas and provide more personalized methods and guidance for early diagnosis, treatment, and prognosis evaluation of gliomas.

Liquid biopsy: creating opportunities in brain space

In recent years, liquid biopsy has emerged as an alternative method to diagnose and monitor tumors. Compared to classical tissue biopsy procedures, liquid biopsy facilitates the repetitive collection of diverse cellular and acellular analytes from various biofluids in a non/minimally invasive manner. This strategy is of greater significance for high-grade brain malignancies such as glioblastoma as the quantity and accessibility of tumors are limited, and there are collateral risks of compromised life quality coupled with surgical interventions. Currently, blood and cerebrospinal fluid (CSF) are the most common biofluids used to collect circulating cells and biomolecules of tumor origin. These liquid biopsy analytes have created opportunities for real-time investigations of distinct genetic, epigenetic, transcriptomics, proteomics, and metabolomics alterations associated with brain tumors. This review describes different classes of liquid biopsy biomarkers present in the biofluids of brain tumor patients. Moreover, an overview of the liquid biopsy applications, challenges, recent technological advances, and clinical trials in the brain have also been provided.

A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors

Metabolomics combined with machine learning methods (MLMs), is a powerful tool for searching novel diagnostic panels. This study was intended to use targeted plasma metabolomics and advanced MLMs to develop strategies for diagnosing brain tumors. Measurement of 188 metabolites was performed on plasma samples collected from 95 patients with gliomas (grade I-IV), 70 with meningioma, and 71 healthy individuals as a control group. Four predictive models to diagnose glioma were prepared using 10 MLMs and a conventional approach. Based on the cross-validation results of the created models, the F1-scores were calculated, then obtained values were compared. Subsequently, the best algorithm was applied to perform five comparisons involving gliomas, meningiomas, and controls. The best results were obtained using the newly developed hybrid evolutionary heterogeneous decision tree (EvoHDTree) algorithm, which was validated using Leave-One-Out Cross-Validation, resulting in an F1-score for all comparisons in the range of 0.476-0.948 and the area under the ROC curves ranging from 0.660 to 0.873. Brain tumor diagnostic panels were constructed with unique metabolites, which reduces the likelihood of misdiagnosis. This study proposes a novel interdisciplinary method for brain tumor diagnosis based on metabolomics and EvoHDTree, exhibiting significant predictive coefficients.

Immune Resistance Mechanisms and the Road to Personalized Immunotherapy

What does the future of cancer immunotherapy look like and how do we get there? Find out where we've been and where we're headed in A Report on Resistance: The Road to personalized immunotherapy.

Spatial metabolic heterogeneity of oligodendrogliomas at single-cell resolution

Oligodendrogliomas are a type of rare and incurable gliomas whose metabolic profiles have yet to be fully examined. The present study examined the spatial differences in metabolic landscapes underlying oligodendrogliomas and should provide unique insights into the metabolic characteristics of these uncommon tumors. Single-cell RNA-sequencing expression profiles from 4044 oligodendroglioma cells derived from tumors resected from four locations frontal, temporal, parietal, and frontotemporoinsular) and in which 1p/19q co-deletion and IDH1 or IDH2 mutations were confirmed were computationally analyzed through a robust workflow to elucidate relative differences in metabolic pathway activities among the different locations. Dimensionality reduction using metabolic expression profiles exhibited clustering corresponding to each location subgroup. From the 80 metabolic pathways examined, over 70 pathways had significantly different activity scores between location subgroups. Further analysis of metabolic heterogeneity suggests that mitochondrial oxidative phosphorylation accounts for considerable metabolic variation within the same locations. Steroid and fatty acid metabolism pathways were also found to be major contributors to heterogeneity. Oligodendrogliomas display distinct spatial metabolic differences in addition to intra-location metabolic heterogeneity.

A pilot study: Metabolic profiling of plasma and saliva samples from newly diagnosed glioblastoma patients

BACKGROUND

Despite aggressive treatment, more than 90% of glioblastoma (GBM) patients experience recurrences. GBM response to therapy is currently assessed by imaging techniques and tissue biopsy. However, difficulties with these methods may cause misinterpretation of treatment outcomes. Currently, no validated therapy response biomarkers are available for monitoring GBM progression. Metabolomics holds potential as a complementary tool to improve the interpretation of therapy responses to help in clinical interventions for GBM patients.

METHODS

Saliva and blood from GBM patients were collected pre and postoperatively. Patients were stratified conforming their progression-free survival (PFS) into favourable or unfavourable clinical outcomes (>9 months or PFS ≤ 9 months, respectively). Analysis of saliva (whole-mouth and oral rinse) and plasma samples was conducted utilising LC-QqQ-MS and LC-QTOF-MS to determine the metabolomic and lipidomic profiles. The data were investigated using univariate and multivariate statistical analyses and graphical LASSO-based graphic network analyses.

RESULTS

Altogether, 151 metabolites and 197 lipids were detected within all saliva and plasma samples. Among the patients with unfavourable outcomes, metabolites such as cyclic-AMP, 3-hydroxy-kynurenine, dihydroorotate, UDP and cis-aconitate were elevated, compared to patients with favourable outcomes during pre-and post-surgery. These metabolites showed to impact the pentose phosphate and Warburg effect pathways. The lipid profile of patients who experienced unfavourable outcomes revealed a higher heterogeneity in the abundance of lipids and fewer associations between markers in contrast to the favourable outcome group.

CONCLUSION

Our findings indicate that changes in salivary and plasma metabolites in GBM patients can potentially be employed as less invasive prognostic biomarkers/biomarker panel but validation with larger cohorts is required.

Application of Machine Learning to Metabolomic Profile Characterization in Glioblastoma Patients Undergoing Concurrent Chemoradiation

We here characterize changes in metabolite patterns in glioblastoma patients undergoing surgery and concurrent chemoradiation using machine learning (ML) algorithms to characterize metabolic changes during different stages of the treatment protocol. We examined 105 plasma specimens (before surgery, 2 days after surgical resection, before starting concurrent chemoradiation, and immediately after chemoradiation) from 36 patients with isocitrate dehydrogenase (IDH) wildtype glioblastoma. Untargeted GC-TOF mass spectrometry-based metabolomics was used given its superiority in identifying and quantitating small metabolites; this yielded 157 structurally identified metabolites. Using Multinomial Logistic Regression (MLR) and GradientBoostingClassifier (GB Classifier), ML models classified specimens based on metabolic changes. The classification performance of these models was evaluated using performance metrics and area under the curve (AUC) scores. Comparing post-radiation to pre-radiation showed increased levels of 15 metabolites: glycine, serine, threonine, oxoproline, 6-deoxyglucose, gluconic acid, glycerol-alpha-phosphate, ethanolamine, propyleneglycol, triethanolamine, xylitol, succinic acid, arachidonic acid, linoleic acid, and fumaric acid. After chemoradiation, a significant decrease was detected in 3-aminopiperidine 2,6-dione. An MLR classification of the treatment phases was performed with 78% accuracy and 75% precision (AUC = 0.89). The alternative GB Classifier algorithm achieved 75% accuracy and 77% precision (AUC = 0.91). Finally, we investigated specific patterns for metabolite changes in highly correlated metabolites. We identified metabolites with characteristic changing patterns between pre-surgery and post-surgery and post-radiation samples. To the best of our knowledge, this is the first study to describe blood metabolic signatures using ML algorithms during different treatment phases in patients with glioblastoma. A larger study is needed to validate the results and the potential application of this algorithm for the characterization of treatment responses.

MALDI Imaging Mass Spectrometry of High-Grade Gliomas: A Review of Recent Progress and Future Perspective

Glioblastoma (GBM) is the most common malignancy of the brain with a relatively short median survival and high mortality. Advanced age, high socioeconomic status, exposure to ionizing radiation, and other factors have been correlated with an increased incidence of GBM, while female sex hormones, history of allergies, and frequent use of specific drugs might exert protective effects against this disease. However, none of these explain the pathogenesis of GBM. The most recent WHO classification of CNS tumors classifies neoplasms based on their histopathological and molecular characteristics. Modern laboratory techniques, such as matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry, enable the comprehensive metabolic analysis of the tissue sample. MALDI imaging is able to characterize the spatial distribution of a wide array of biomolecules in a sample, in combination with histological features, without sacrificing the tissue integrity. In this review, we first provide an overview of GBM epidemiology, risk, and protective factors, as well as the recent WHO classification of CNS tumors. We then provide an overview of mass spectrometry workflow, with a focus on MALDI imaging, and recent advances in cancer research. Finally, we conclude the review with studies of GBM that utilized MALDI imaging and offer our perspective on future research.

Discovering Glioma Tissue through Its Biomarkers' Detection in Blood by Raman Spectroscopy and Machine Learning

The most commonly occurring malignant brain tumors are gliomas, and among them is glioblastoma multiforme. The main idea of the paper is to estimate dependency between glioma tissue and blood serum biomarkers using Raman spectroscopy. We used the most common model of human glioma when continuous cell lines, such as U87, derived from primary human tumor cells, are transplanted intracranially into the mouse brain. We studied the separability of the experimental and control groups by machine learning methods and discovered the most informative Raman spectral bands. During the glioblastoma development, an increase in the contribution of lactate, tryptophan, fatty acids, and lipids in dried blood serum Raman spectra were observed. This overlaps with analogous results of glioma tissues from direct Raman spectroscopy studies. A non-linear relationship between specific Raman spectral lines and tumor size was discovered. Therefore, the analysis of blood serum can track the change in the state of brain tissues during the glioma development.

Metabolic biomarkers of radiotherapy response in plasma and tissue of an IDH1 mutant astrocytoma mouse model

Astrocytomas are the most common subtype of brain tumors and no curative treatment exist. Longitudinal assessment of patients, usually via Magnetic Resonance Imaging (MRI), is crucial since tumor progression may occur earlier than clinical progression. MRI usually provides a means for monitoring the disease, but it only informs about the structural changes of the tumor, while molecular changes can occur as a treatment response without any MRI-visible change. Radiotherapy (RT) is routinely performed following surgery as part of the standard of care in astrocytomas, that can also include chemotherapy involving temozolomide. Monitoring the response to RT is a key factor for the management of patients. Herein, we provide plasma and tissue metabolic biomarkers of treatment response in a mouse model of astrocytoma that was subjected to radiotherapy. Plasma metabolic profiles acquired over time by Liquid Chromatography Mass Spectrometry (LC/MS) were subjected to multivariate empirical Bayes time-series analysis (MEBA) and Receiver Operating Characteristic (ROC) assessment including Random Forest as the classification strategy. These analyses revealed a variation of the plasma metabolome in those mice that underwent radiotherapy compared to controls; specifically, fumarate was the best discriminatory feature. Additionally, Nuclear Magnetic Resonance (NMR)-based 13C-tracing experiments were performed at end-point utilizing [U-13C]-Glutamine to investigate its fate in the tumor and contralateral tissues. Irradiated mice displayed lower levels of glycolytic metabolites (e.g. phosphoenolpyruvate) in tumor tissue, and a higher flux of glutamine towards succinate was observed in the radiation cohort. The plasma biomarkers provided herein could be validated in the clinic, thereby improving the assessment of brain tumor patients throughout radiotherapy. Moreover, the metabolic rewiring associated to radiotherapy in tumor tissue could lead to potential metabolic imaging approaches for monitoring treatment using blood draws.

Rewired Metabolism of Amino Acids and Its Roles in Glioma Pathology

Amino acids (AAs) are indispensable building blocks of diverse bio-macromolecules as well as functional regulators for various metabolic processes. The fact that cancer cells live with a voracious appetite for specific AAs has been widely recognized. Glioma is one of the most lethal malignancies occurring in the central nervous system. The reprogrammed metabolism of AAs benefits glioma proliferation, signal transduction, epigenetic modification, and stress tolerance. Metabolic alteration of specific AAs also contributes to glioma immune escape and chemoresistance. For clinical consideration, fluctuations in the concentrations of AAs observed in specific body fluids provides opportunities to develop new diagnosis and prognosis markers. This review aimed at providing an extra dimension to understanding glioma pathology with respect to the rewired AA metabolism. A deep insight into the relevant fields will help to pave a new way for new therapeutic target identification and valuable biomarker development.

Elevated Serum Lactate in Glioma Patients: Associated Factors

Introduction

Serum lactate levels in brain cancer patients correlate with tumor malignancy grading, and serum lactate has been suggested as a potential biomarker and prognostic factor. The purpose of this study was to identify potential sources of elevated serum lactate in patients with brain gliomas by examining factors of importance for serum lactate production and clearance.

Methods

In this cross-sectional study, data were collected from 261 glioma patients who underwent surgery from March 2011 to June 2015. We recorded patient gender, age, blood serum measures of lactate, glucose, pH, hemoglobin and base excess, patient health status, medications, and tumor characteristics. Patients with elevated and normal serum lactate levels were compared, and we explored if there were correlations between the variables. The association of serum lactate with the measured variables was investigated by simple and multivariable linear regression models.

Results and Discussion

Patients with elevated serum lactate had higher blood glucose, larger tumor volumes, and more tumor edema; more often needed pressor medication during surgery; and more often received corticosteroid treatment. The investigated variables were highly correlated. Multivariable linear regression indicated that gender, tumor volume, Charlson Comorbidity Index, hyperglycemia, and corticosteroid treatment were associated with serum lactate levels. Histopathology was not an independent factor. In conclusion, comorbidities, hyperglycemia, and presurgical corticosteroid treatment exhibited the strongest association with serum lactate in glioma patients.

Proline Metabolism in Malignant Gliomas: A Systematic Literature Review

Simple Summary

Studies of various types of cancers have found proline metabolism to be a key player in tumor development, involved in basic metabolic pathways, regulating cell proliferation, survival, and signaling. Here, we systematically searched the literature to find data on proline metabolism in malignant glial tumors. Despite limited availability, existing studies have found several ways in which proline metabolism may affect the development of gliomas, involving the maintenance of redox balance, providing essential glutamate, and affecting major signaling pathways. Metabolomic profiling has revealed the importance of proline as a link to basic cell metabolic cycles and shown it to be correlated with overall survival. Emerging knowledge on the role of proline in general oncology encourages further studies on malignant gliomas.

Abstract

Background: Proline has attracted growing interest because of its diverse influence on tumor metabolism and the discovery of the regulatory mechanisms that appear to be involved. In contrast to general oncology, data on proline metabolism in central nervous system malignancies are limited. Materials and Methods: We performed a systematic literature review of the MEDLINE and EMBASE databases according to PRISMA guidelines, searching for articles concerning proline metabolism in malignant glial tumors. From 815 search results, we identified 14 studies pertaining to this topic. Results: The role of the proline cycle in maintaining redox balance in IDH-mutated gliomas has been convincingly demonstrated. Proline is involved in restoring levels of glutamate, the main glial excitatory neurotransmitter. Proline oxidase influences two major signaling pathways: p53 and NF- κB. In metabolomics studies, the metabolism of proline and its link to the urea cycle was found to be a prognostic factor for survival and a marker of malignancy. Data on the prolidase concentration in the serum of glioblastoma patients are contradictory. Conclusions: Despite a paucity of studies in the literature, the available data are interesting enough to encourage further research, especially in terms of extrapolating what we have learned of proline functions from other neoplasms to malignant gliomas.

Proteomics and metabolomics approach in adult and pediatric glioma diagnostics

The diagnosis of glioma is mainly based on imaging methods that do not distinguish between stage and subtype prior to histopathological analysis. Patients with gliomas are generally diagnosed in the symptomatic stage of the disease. Additionally, healing scar tissue may be mistakenly identified based on magnetic resonance imaging (MRI) as a false positive tumor recurrence in postoperative patients. Current knowledge of molecular alterations underlying gliomagenesis and identification of tumoral biomarkers allow for their use as discriminators of the state of the organism. Moreover, a multiomics approach provides the greatest spectrum and the ability to track physiological changes and can serve as a minimally invasive method for diagnosing asymptomatic gliomas, preceding surgery and allowing for the initiation of prophylactic treatment. It is important to create a vast biomarker library for adults and pediatric patients due to their metabolic differences. This review focuses on the most promising proteomic, metabolomic and lipidomic glioma biomarkers, their pathways, the interactions, and correlations that can be considered characteristic of tumor grade or specific subtype.

Dysregulated Alanine as a Potential Predictive Marker of Glioma-An Insight from Untargeted HRMAS-NMR and Machine Learning Data

Metabolic alterations play a crucial role in glioma development and progression and can be detected even before the appearance of the fatal phenotype. We have compared the circulating metabolic fingerprints of glioma patients versus healthy controls, for the first time, in a quest to identify a panel of small, dysregulated metabolites with potential to serve as a predictive and/or diagnostic marker in the clinical settings. High-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HRMAS-NMR) was used for untargeted metabolomics and data acquisition followed by a machine learning (ML) approach for the analyses of large metabolic datasets. Cross-validation of ML predicted NMR spectral features was done by statistical methods (Wilcoxon-test) using JMP-pro16 software. Alanine was identified as the most critical metabolite with potential to detect glioma with precision of 1.0, recall of 0.96, and F1 measure of 0.98. The top 10 metabolites identified for glioma detection included alanine, glutamine, valine, methionine, N-acetylaspartate (NAA), γ-aminobutyric acid (GABA), serine, α-glucose, lactate, and arginine. We achieved 100% accuracy for the detection of glioma using ML algorithms, extra tree classifier, and random forest, and 98% accuracy with logistic regression. Classification of glioma in low and high grades was done with 86% accuracy using logistic regression model, and with 83% and 79% accuracy using extra tree classifier and random forest, respectively. The predictive accuracy of our ML model is superior to any of the previously reported algorithms, used in tissue- or liquid biopsy-based metabolic studies. The identified top metabolites can be targeted to develop early diagnostic methods as well as to plan personalized treatment strategies.

Complementary Nuclear Magnetic Resonance-Based Metabolomics Approaches for Glioma Biomarker Identification in a Drosophila melanogaster Model

Human malignant gliomas are the most common type of primary brain tumor. Composed of glial cells and their precursors, they are aggressive and highly invasive, leading to a poor prognosis. Due to the difficulty of surgically removing tumors and their resistance to treatments, novel therapeutic approaches are needed to improve patient life expectancy and comfort. Drosophila melanogaster is a compelling genetic model to better understanding human neurological diseases owing to its high conservation in signaling pathways and cellular content of the brain. Here, glioma has been induced in Drosophila by co-activating the epidermal growth factor receptor and the phosphatidyl-inositol-3 kinase signaling pathways. Complementary nuclear magnetic resonance (NMR) techniques were used to obtain metabolic profiles in the third instar larvae brains. Fresh organs were directly studied by ¹H high resolution-magic angle spinning (HR-MAS) NMR, and brain extracts were analyzed by solution-state ¹H-NMR. Statistical analyses revealed differential metabolic signatures, impacted metabolic pathways, and glioma biomarkers. Each method was efficient to determine biomarkers. The highlighted metabolites including glucose, myo-inositol, sarcosine, glycine, alanine, and pyruvate for solution-state NMR and proline, myo-inositol, acetate, and glucose for HR-MAS show very good performances in discriminating samples according to their nature with data mining based on receiver operating characteristic curves. Combining results allows for a more complete view of induced disturbances and opens the possibility of deciphering the biochemical mechanisms of these tumors. The identified biomarkers provide a means to rebalance specific pathways through targeted metabolic therapy and to study the effects of pharmacological treatments using Drosophila as a model organism.

Blood-Based Biomarkers for Glioma in the Context of Gliomagenesis: A Systematic Review

BACKGROUND

Gliomas are the most common and aggressive tumors of the central nervous system. A robust and widely used blood-based biomarker for glioma has not yet been identified. In recent years, a plethora of new research on blood-based biomarkers for glial tumors has been published. In this review, we question which molecules, including proteins, nucleic acids, circulating cells, and metabolomics, are most promising blood-based biomarkers for glioma diagnosis, prognosis, monitoring and other purposes, and align them to the seminal processes of cancer.

METHODS

The Pubmed and Embase databases were systematically searched. Biomarkers were categorized in the identified biomolecules and biosources. Biomarker characteristics were assessed using the area under the curve (AUC), accuracy, sensitivity and/or specificity values and the degree of statistical significance among the assessed clinical groups was reported.

RESULTS

7,919 references were identified: 3,596 in PubMed and 4,323 in Embase. Following screening of titles, abstracts and availability of full-text, 262 articles were included in the final systematic review. Panels of multiple biomarkers together consistently reached AUCs >0.8 and accuracies >80% for various purposes but especially for diagnostics. The accuracy of single biomarkers, consisting of only one measurement, was far more variable, but single microRNAs and proteins are generally more promising as compared to other biomarker types.

CONCLUSION

Panels of microRNAs and proteins are most promising biomarkers, while single biomarkers such as GFAP, IL-10 and individual miRNAs also hold promise. It is possible that panels are more accurate once these are involved in different, complementary cancer-related molecular pathways, because not all pathways may be dysregulated in cancer patients. As biomarkers seem to be increasingly dysregulated in patients with short survival, higher tumor grades and more pathological tumor types, it can be hypothesized that more pathways are dysregulated as the degree of malignancy of the glial tumor increases. Despite, none of the biomarkers found in the literature search seem to be currently ready for clinical implementation, and most of the studies report only preliminary application of the identified biomarkers. Hence, large-scale validation of currently identified and potential novel biomarkers to show clinical utility is warranted.

Metabolomics Monitoring of Treatment Response to Brain Tumor Immunotherapy

Immunotherapy has revolutionized care for many solid tissue malignancies, and is being investigated for efficacy in the treatment of malignant brain tumors. Identifying a non-invasive monitoring technique such as metabolomics monitoring to predict patient response to immunotherapy has the potential to simplify treatment decision-making and to ensure therapy is tailored based on early patient response. Metabolomic analysis of peripheral immune response is feasible due to large metabolic shifts that immune cells undergo when activated. The utility of this approach is under investigation. In this review, we discuss the metabolic changes induced during activation of an immune response, and the role of metabolic profiling to monitor immune responses in the context of immunotherapy for malignant brain tumors. This review provides original insights into how metabolomics monitoring could have an important impact in the field of tumor immunotherapy if achievable.

Metabolomics of Glioma

Metabolic reprogramming is an important characteristics of glioma, the most common form of malignant brain tumor. In this chapter, we aim to discuss some of the recently discovered metabolic alterations in glioma, including the dysregulated TCA cycle, amino acid, nucleotide, and lipid metabolism. We have also detailed some of the metabolomic applications in gliomas, particularly the analyses of body fluids and tissues of glioma patients. With new improvement of the technology, metabolomics will become a powerful tool to discover truly meaningful biomarkers for clinical applications in gliomas. Metabolomic studies of gliomas will also facilitate a better understanding of the molecular targets/pathways and the development of new therapeutic treatments for this devastating disease.

Roles of nitric oxide and polyamines in brain tumor growth

Nitric oxide (NO) and polyamines: putrescine, spermidine and spermine, are key arginine metabolites in mammalian tissues that play critical roles i.a. in regulation of vascular tone (NO), and cell cycle regulation (polyamines). In the brain, both classes of molecules additionally have neuromodulatory and neuroprotective potential, and NO also a neurotoxic potential. Here we review evidence that brain tumors use the NO- and polyamine-synthesizing machineries to the benefit of their differentiation and growth from healthy glia and neurons. With a few exceptions, brain tumors show increased activities of one or all of the three arginine (Arg) to NO-converting nitric oxide synthase (NOS) isoforms (iNOS, eNOS, nNOS), but also elevated activities of polyamines-generating and modifying enzymes: arginase I/II, ornithine decarboxylase and spermidine/spermine N¹-acetyltransferase. The degree of stimulation of NO- and polyamine synthesis often correlates with brain tumor malignancy. Excess NO, but also spermine, spermidine and their N¹-acetylated forms, are tumor- and context-dependently involved in angiogenesis, tumor initiation and growth, and resistance to chemo- or radiotherapy. Hypothetically, increased demand for NO and/or polyamines is likely to contribute to Arg auxotrophy of malignant brain tumors, albeit the causal nexus awaits experimental verification.

Diagnosis of Glioma Molecular Markers by Terahertz Technologies

This review considers glioma molecular markers in brain tissues and body fluids, shows the pathways of their formation, and describes traditional methods of analysis. The most important optical properties of glioma markers in the terahertz (THz) frequency range are also presented. New metamaterial-based technologies for molecular marker detection at THz frequencies are discussed. A variety of machine learning methods, which allow the marker detection sensitivity and differentiation of healthy and tumor tissues to be improved with the aid of THz tools, are considered. The actual results on the application of THz techniques in the intraoperative diagnosis of brain gliomas are shown. THz technologies’ potential in molecular marker detection and defining the boundaries of the glioma’s tissue is discussed.

Plasma amino acids indicate glioblastoma with ATRX loss

Glioblastoma (GB) is the most common primary brain tumour in adults. The lack of molecular biomarker, non-specific symptoms and fast growth rate often result in a significant delay in diagnosis. Despite multimodal treatment, the prognosis remains poor. Here, we verified the hypothesis that amino acids (AA) regulating the critical metabolic pathways necessary for maintenance, growth, reproduction, and immunity of an organism, may constitute a favourable target in GB biomarker research. We measured the plasma amino acids levels in 18 GB patients and 15 controls and performed the quantitative and qualitative metabolomic analysis of free AA applying high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). We present both the raw data and the results of our statistical analysis. The majority of AA were lowered in the study group in comparison to the control group. Five of these (arginine, glutamic acid, glutamine, glycine, and histidine) differed significantly (all p < 10^-5 and AUC > 0.9). Plasma levels of leucine and phenylalanine decreased in the case of GB with lost alpha-thalassemia/mental retardation X-linked (ATRX) expression on immunohistochemistry (p = 0.003 and 0.045, respectively). We demonstrated for the first time that certain plasma-free AA levels of GB patients were significantly different from those in healthy volunteers. Target profiling of plasma-free AA, identified utilizing LC-QTOF-MS, may present prognostic value by indicating GB patients with lost ATRX expression. The on-going quest for glioma biomarkers still aims to determine the detailed metabolic profile and evaluate its impact on therapy and prognosis.

Machine learning assisted intraoperative assessment of brain tumor margins using HRMAS NMR spectroscopy

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.

Take Advantage of Glutamine Anaplerosis, the Kernel of the Metabolic Rewiring in Malignant Gliomas

Glutamine is a non-essential amino acid that plays a key role in the metabolism of proliferating cells including neoplastic cells. In the central nervous system (CNS), glutamine metabolism is particularly relevant, because the glutamine-glutamate cycle is a way of controlling the production of glutamate-derived neurotransmitters by tightly regulating the bioavailability of the amino acids in a neuron-astrocyte metabolic symbiosis-dependent manner. Glutamine-related metabolic adjustments have been reported in several CNS malignancies including malignant gliomas that are considered ‘glutamine addicted’. In these tumors, glutamine becomes an essential amino acid preferentially used in energy and biomass production including glutathione (GSH) generation, which is crucial in oxidative stress control. Therefore, in this review, we will highlight the metabolic remodeling that gliomas undergo, focusing on glutamine metabolism. We will address some therapeutic regimens including novel research attempts to target glutamine metabolism and a brief update of diagnosis strategies that take advantage of this altered profile. A better understanding of malignant glioma cell metabolism will help in the identification of new molecular targets and the design of new therapies.

Metabolomic profiling in neuroblastoma

BACKGROUND AND OBJECTIVES

Previous studies on several cancer types show that metabolomics provides a potentially useful noninvasive screening approach for outcome prediction and accurate response to treatment assessment. Neuroblastoma (NB) accounts for at least 15% of cancer-related deaths in children. Although current risk-based treatment approaches in NB have resulted in improved outcome, survival for high-risk patients remains poor. This study aims to evaluate the use of metabolomics for improving patients' risk-group stratification and outcome prediction in NB.

DESIGN AND METHODS

Plasma samples from 110 patients with NB were collected at diagnosis prior to starting therapy and at the end of treatment if available. Metabolomic analysis of samples was carried out by ultra-performance liquid chromatography-time of flight mass spectrometry (UPLC-MS).

RESULTS

The metabolomic analysis was able to identify different plasma metabolic profiles in high-risk and low-risk NB patients at diagnosis. The metabolic model correctly classified 16 high-risk and 15 low-risk samples in an external validation set providing 84.2% sensitivity (60.4-96.6, 95% CI) and 93.7% specificity (69.8-99.8, 95% CI). Metabolomic profiling could also discriminate high-risk patients with active disease from those in remission. Notably, a plasma metabolomic signature at diagnosis identified a subset of high-risk NB patients who progressed during treatment.

CONCLUSIONS

To the best of our knowledge, this is the largest NB study investigating the prognostic power of plasma metabolomics. Our results support the potential of metabolomic profiling for improving NB risk-group stratification and outcome prediction. Additional validating studies with a large cohort are needed.

Targeting Glutamine Addiction in Gliomas

The most common malignant brain tumors are those of astrocytic origin, gliomas, with the most aggressive glioblastoma (WHO grade IV) among them. Despite efforts, medicine has not made progress in terms of the prognosis and life expectancy of glioma patients. Behind the malignant phenotype of gliomas lies multiple genetic mutations leading to reprogramming of their metabolism, which gives those highly proliferating cells an advantage over healthy ones. The so-called glutamine addiction is a metabolic adaptation that supplements oxidative glycolysis in order to secure neoplastic cells with nutrients and energy in unfavorable conditions of hypoxia. The present review aims at presenting the research and clinical attempts targeting the different metabolic pathways involved in glutamine metabolism in gliomas. A brief description of the biochemistry of glutamine transport, synthesis, and glutaminolysis, etc. will forego a detailed comparison of the therapeutic strategies undertaken to inhibit glutamine utilization by gliomas.

Metabolic changes related to the IDH1 mutation in gliomas preserve TCA-cycle activity: An investigation at the protein level

The discovery of the IDH1 R132H (IDH1 mut) mutation in low-grade glioma and the associated change in function of the IDH1 enzyme has increased the interest in glioma metabolism. In an earlier study, we found that changes in expression of genes involved in the aerobic glycolysis and the TCA cycle are associated with IDH1 mut. Here, we apply proteomics to FFPE samples of diffuse gliomas with or without IDH1 mutations, to map changes in protein levels associated with this mutation. We observed significant changes in the enzyme abundance associated with aerobic glycolysis, glutamate metabolism, and the TCA cycle in IDH1 mut gliomas. Specifically, the enzymes involved in the metabolism of glutamate, lactate, and enzymes involved in the conversion of α-ketoglutarate were increased in IDH1 mut gliomas. In addition, the bicarbonate transporter (SLC4A4) was increased in IDH1 mut gliomas, supporting the idea that a mechanism preventing intracellular acidification is active. We also found that enzymes that convert proline, valine, leucine, and isoleucine into glutamate were increased in IDH1 mut glioma. We conclude that in IDH1 mut glioma metabolism is rewired (increased input of lactate and glutamate) to preserve TCA-cycle activity in IDH1 mut gliomas.

Hypoxic Environment and Paired Hierarchical 3D and 2D Models of Pediatric H3.3-Mutated Gliomas Recreate the Patient Tumor Complexity

BACKGROUND

Pediatric high-grade gliomas (pHGGs) are facing a very dismal prognosis and representative pre-clinical models are needed for new treatment strategies. Here, we examined the relevance of collecting functional, genomic, and metabolomics data to validate patient-derived models in a hypoxic microenvironment.

METHODS

From our biobank of pediatric brain tumor-derived models, we selected 11 pHGGs driven by the histone H3.3K28M mutation. We compared the features of four patient tumors to their paired cell lines and mouse xenografts using NGS (next generation sequencing), aCGH (array comparative genomic hybridization), RNA sequencing, WES (whole exome sequencing), immunocytochemistry, and HRMAS (high resolution magic angle spinning) spectroscopy. We developed a multicellular in vitro model of cell migration to mimic the brain hypoxic microenvironment. The live cell technology Incucyte© was used to assess drug responsiveness in variable oxygen conditions.

RESULTS

The concurrent 2D and 3D cultures generated from the same tumor sample exhibited divergent but complementary features, recreating the patient intra-tumor complexity. Genomic and metabolomic data described the metabolic changes during pHGG progression and supported hypoxia as an important key to preserve the tumor metabolism in vitro and cell dissemination present in patients. The neurosphere features preserved tumor development and sensitivity to treatment.

CONCLUSION

We proposed a novel multistep work for the development and validation of patient-derived models, considering the immature and differentiated content and the tumor microenvironment of pHGGs.

Metabolomic profiling of blood plasma in patients with primary brain tumours: Basal plasma metabolites correlated with tumour grade and plasma biomarker analysis predicts feasibility of the successful statistical discrimination from healthy subjects - a preliminary study

The brain tumours represent a complex tissue that has its own characteristic metabolic features and is interfaced with the whole organism. We investigated changes in basal blood plasma metabolites in the presence of primary brain tumour, their correlation with tumour grade, as well as the feasibility of statistical discrimination based on plasma metabolites. Together 60 plasma samples from patients with clinically defined glioblastoma, meningioma, oligodendrioglioma, astrocytoma, and non-specific glial tumour and plasma samples from 28 healthy volunteers without any cancer history were measured by NMR spectroscopy. In blood plasma of primary brain tumour patients, we found significantly increased levels of glycolytic metabolites glucose and pyruvate, and significantly decreased level of glutamine and also metabolites participating in tricarboxylic acid (TCA) cycle, citrate and succinate, when compared with controls. Further, plasma metabolites levels: tyrosine, phenylalanine, glucose, creatine and creatinine correlated significantly with tumour grade. In general, observed changes are parallel to the biochemistry expected for tumourous tissue and metabolic changes in plasma seem to follow the similar rules in all primary brain tumours, with very subtle variations among tumour types. Only two plasma metabolites tyrosine and phenylalanine were increased exclusively in blood plasma of patients with glioblastoma. Based on metabolite levels, an excellent discrimination between plasma from patient's tumours and controls was attainable. The metabolites creatine, pyruvate, glucose, formate, creatinine and citrate were of the highest discriminatory power.

Metabolic profiling of human gliomas assessed with NMR

Little is known about the underlying metabolic alterations of gliomas. The objective of this study was to analyze metabolomic profiles of gliomas diagnosed according to revised WHO classification to demonstrate metabolic signatures beyond isocitrate dehydrogenase (IDH) 1/2 mutation. ¹H NMR spectroscopy of tumor extracts was performed to analyze brain tumor metabolism. We detected 46 metabolites including 2-hydroxyglutarate from human brain tumors. Metabolic profiles obtained were analyzed using multivariate analysis and MetaboAnalyst 3.0, a pathway analysis tool. We found that lactate, glutamate, alanine, glutamine, 2-hydroxglutarate, serine, O-phosphocholine, glycine, glycerol, myo-inositol, aspartate, leucine, threonine, creatine, and valine had top-ranked VIP scores in metabolic pathway analyses of glioma. Major metabolism pathways perturbed in glioma included alanine/aspartate/glutamate metabolism, glycine/serine/threonine metabolism, pyruvate metabolism, taurine/hypotaurine metabolism, and d-glutamine/d-glutamate metabolism. Altered metabolites were defined between low-grade and high-grade gliomas. We identified metabolomics signatures of gliomas associated with 2-hydroxglutarate and glioma grade. Metabolic approach may lead to metabolomic cluster-precision strategy and development of metabolic anti-glioma therapy in the future.

Current and Future Trends on Diagnosis and Prognosis of Glioblastoma: From Molecular Biology to Proteomics

Glioblastoma multiforme is the most aggressive malignant tumor of the central nervous system. Due to the absence of effective pharmacological and surgical treatments, the identification of early diagnostic and prognostic biomarkers is of key importance to improve the survival rate of patients and to develop new personalized treatments. On these bases, the aim of this review article is to summarize the current knowledge regarding the application of molecular biology and proteomics techniques for the identification of novel biomarkers through the analysis of different biological samples obtained from glioblastoma patients, including DNA, microRNAs, proteins, small molecules, circulating tumor cells, extracellular vesicles, etc. Both benefits and pitfalls of molecular biology and proteomics analyses are discussed, including the different mass spectrometry-based analytical techniques, highlighting how these investigation strategies are powerful tools to study the biology of glioblastoma, as well as to develop advanced methods for the management of this pathology.

Influence of molecular classification in anaplastic glioma for determining outcome and future approach to management

INTRODUCTION

Assess survival of patients with anaplastic glioma (AG) and the relationship to molecular subtype.

METHODS

Patients with AG managed with IMRT between 2008 and 2014 were entered into a prospective database assessing relapse-free survival (RFS) and overall survival (OS). Isocitrate dehydrogenase (IDH) mutations were assessed prospectively from 2011, and subsequent testing of historical patients allowing categorisation under WHO 2016 classification as anaplastic astrocytoma IDH wild type (AAwt), anaplastic astrocytoma IDH mutated (AAmut), anaplastic oligodendroglioma (AOD) or other glial tumour (OTH). Kaplan-Meier estimates of survival distribution were calculated for the primary endpoint of overall survival and Log-rank test used to determine associated factors.

RESULTS

One hundred and fifty-six patients were included with median follow-up for survivors of 4.7 years. Fifty-six per cent were managed after initial diagnosis, whilst 18% received IMRT at second or later relapse. Seventy-three per cent had temozolomide as part of initial therapy. A total of 118 or 75% of patients had IDH mutated glioma, of which 61 were AOD and 57 AAmut. There were 68 relapses and 52 deaths for a 6yrRFS of 51.2% and 6yrOS of 62.5%. AAwt was associated with worse survival (P < 0.001); and delay of RT until second or later relapse (P = 0.03). Within the 118 patients with IDH mutated tumours, 6yrOS for AOD and AAmut were 90.0% and 62.5%, respectively (P = 0.003). Also two or more craniotomies (P < 0.001), delayed RT (P = 0.006) and age <40 years (P = 0.022) were associated with worse survival on univariate analysis but only AAmut subtype and number of craniotomies on multivariate analysis.

CONCLUSION

Within AG, molecular classification predicts for survival, and should influence current decision-making.

Tumour volume reduction following PET guided intensity modulated radiation therapy and temozolomide in IDH mutated anaplastic glioma

The role of maximal surgical debulking in isocitrate dehydrogenase (IDH) mutated anaplastic glioma prior to adjuvant radiation therapy remains uncertain. This study assessed the reduction in tumour volume following intensity modulated radiation therapy (IMRT) and temozolomide in this favourable and more responsive tumour pathology. 56 patients were managed from 2011 to 2014 and 53 had residual disease. To assess radiological response, tumour volumes were created on representative T1/T2Flair MRI sequences using identical slice-levels in three planes for pre-IMRT, month + 3 and month + 12 post-IMRT scans. Change in volumes was assessed between time periods. Progression-free survival (PFS) was calculated from start of radiotherapy. Median follow-up for survivors is 48.2 months. Pathology was anaplastic oligodendroglioma (AOD) and anaplastic astrocytoma IDH-mutated (AAmut) in 32 and 21 patients respectively. 93% received sequential chemotherapy. The median residual disease on T1 and T2Flair imaging was 9.7 cm³ and 20.6 cm³. 17 patients relapsed for projected 5 year PFS of 74.9%; with 8 isolated relapses within initial surgical site. On MRI at month + 3, the median volume for T1 and T2Flair reduced by 69.4% and 67.3% respectively; which further decreased to 82.4% and 81.3% at month + 12. By month + 12, 69.2% and 62.2% of patients had >75% volume reduction. Patients with AOD had superior reduction at month + 3 compared with AAmut (p = 0.02); but equivalent reduction at month + 12 (p = 0.14). Thus, in patients with anaplastic glioma harbouring an IDH mutation, where an attempt at near-total resection may be associated with unacceptable morbidity, this data suggests that the radiation therapy may provide effective cytoreduction of residual disease.

A review of validated biomarkers obtained through metabolomics

INTRODUCTION

Studying changes in the whole set of small molecules, final products of biochemical reactions in living systems or metabolites, is extremely appealing because they represent the best approach to identifying what occurs in an organism when samples are collected. However, their usefulness as potential biomarkers is limited by discoveries obtained in small groups without proper validation or even confirmation of the chemical structure. Areas covered: During the past 5 years, more than 900 papers have been published on metabolomics for biomarker discovery, but the numbers are much lower when some criteria of validation are applied. In total, 102 papers have been included in this review. The most frequent disease areas in which these markers have been discovered include the following: cancer, diabetes, and related diseases and neurodegenerative, cardiovascular, autoimmune, liver, and kidney diseases. Expert commentary: Metabolomics has been demonstrated as rapidly growing due to the improvements in instrumentation, mainly mass spectrometry, and data mining software. For application in the clinic, the results should be validated in different stages, from analytical validation to validation in independent sets of samples, using thousands of samples from different sources.

Pharmacometabolomics Informs Quantitative Radiomics for Glioblastoma Diagnostic Innovation

Applications of omics systems biology technologies have enormous promise for radiology and diagnostics in surgical fields. In this context, the emerging fields of radiomics (a systems scale approach to radiology using a host of technologies, including omics) and pharmacometabolomics (use of metabolomics for patient and disease stratification and guiding precision medicine) offer much synergy for diagnostic innovation in surgery, particularly in neurosurgery. This synthesis of omics fields and applications is timely because diagnostic accuracy in central nervous system tumors still challenges decision-making. Considering the vast heterogeneity in brain tumors, disease phenotypes, and interindividual variability in surgical and chemotherapy outcomes, we believe that diagnostic accuracy can be markedly improved by quantitative radiomics coupled to pharmacometabolomics and related health information technologies while optimizing economic costs of traditional diagnostics. In this expert review, we present an innovation analysis on a systems-level multi-omics approach toward diagnostic accuracy in central nervous system tumors. For this, we suggest that glioblastomas serve as a useful application paradigm. We performed a literature search on PubMed for articles published in English between 2006 and 2016. We used the search terms "radiomics," "glioblastoma," "biomarkers," "pharmacogenomics," "pharmacometabolomics," "pharmacometabonomics/pharmacometabolomics," "collaborative informatics," and "precision medicine." A list of the top 4 insights we derived from this literature analysis is presented in this study. For example, we found that (i) tumor grading needs to be better refined, (ii) diagnostic precision should be improved, (iii) standardization in radiomics is lacking, and (iv) quantitative radiomics needs to prove clinical implementation. We conclude with an interdisciplinary call to the metabolomics, pharmacy/pharmacology, radiology, and surgery communities that pharmacometabolomics coupled to information technologies (chemoinformatics tools, databases, collaborative systems) can inform quantitative radiomics, thus translating Big Data and information growth to knowledge growth, rational drug development and diagnostics innovation for glioblastomas, and possibly in other brain tumors.

Identification of metabolites in plasma for predicting survival in glioblastoma

Circulating metabolomics profiling holds prognostic potential. However, such efforts have not been extensively carried out in glioblastoma. In this study, two-step (training and testing) metabolomics profiling was conducted from the plasma samples of 159 glioblastoma patients. Metabolomics profiling was tested for correlation with 2-year overall and disease-free survivals. Arginine, methionine, and kynurenate levels were significantly associated with 2-year overall survival in both the training and testing sets. In the combined sets, elevated levels of arginine and methionine were associated with a 34% and 37% increased probability whereas kynurenate was associated with a 55% decreased probability of 2-year overall survival. These three metabolites were also significantly associated with 2-year disease-free survival. Risk scores were generated using the linear combination of levels of these significant metabolites. Glioblastoma patients with a high-risk score exhibited a 2.41-fold decreased probability of 2-year overall survival (hazard ratio (HR) = 2.41; 95% Confidence Interval (CI) = 1.20-4.93) and a 3.17-fold decreased probability of 2-year disease free survival (HR = 3.17, 95%CI = 1.42-7.54) relative to those with a low-risk score. In conclusion, we identified a unique plasma metabolite profile that is predictive of glioblastoma prognosis.

A metabolomic study on early detection of steroid-induced avascular necrosis of the femoral head

The early and accurate diagnosis of steroid-induced avascular necrosis of the femoral head (SANFH) is appealing considering its irreversible progression and serious consequence for the patients. The purpose of this study was to investigate the metabolic change of SANFH for its early detection. Two stages were designed in this study, namely discovery and verification. Except the biochemical index anomaly and the accidental death, 30 adult healthy adult Japanese white rabbits were used for screening out the potential metabolites in discovery experiment and 13 rabbits were used in verification experiment. The femoral heads were assessed with magnetic resonance imaging and transmission electron microscopy. The metabolomic profiling of serum samples were analysis by UHPLC-MS/MS. Metabolomic cluster analysis enable us to differentiate the rabbits without and with injection of the glucocorticoid in 1 week even when there is no obvious abnormal symptom in behaviors or imaging diagnosis. The majority of differential metabolites were identified as phospholipids which were observed significant change after injection of glucocorticoid in 1, 2, 3 weeks. And the results obtained in verification experiment of 6 weeks showed that these differential metabolites exhibited consistent trends in late progression with that in early-stage. At the end of 6 weeks the damage of SANFH could be verified by pathological imaging. Therefore the finding of serum metabolite profile links to the progression of SANFH and provides the potential of early detection of SANFH.

Circulating metabolite profiles to predict overall survival in advanced non-small cell lung cancer patients receiving first-line chemotherapy

OBJECTIVES

The prognosis for advanced-stage non-small cell lung cancer (NSCLC) is usually poor. However, survival may be variable and difficult to predict. In the current study, we aimed to identify circulating metabolites as potential predictive biomarkers for overall survival of advanced-stage (III/IV) NSCLC patients treated with first-line platinum-based chemotherapy.

MATERIALS AND METHODS

Using two-stage study design, we performed global metabolomic profiling in blood of 220 advanced-stage NSCLC patients, including 110 with poor survival and 110 with good survival. Metabolomic profiling was conducted using Metabolon platform. The association of each metabolite with survival was assessed by Cox proportional hazard regression model with adjustment for covariates.

RESULTS AND CONCLUSION

We found levels of 4 metabolites, caffeine, paraxanthine, stachydrine, and methyl glucopyranoside (alpha+beta), differed significantly between NSCLC patients with poor and good survival in both discovery and validation phases (P<0.05). Interestingly, majority of the identified metabolites are involved in caffeine metabolism, and 2 metabolites are related to coffee intake. In fact, caffeine metabolism pathway was the only significant pathway identified which significantly differed between NSCLC patients with poor and good survival (P=1.48E-07) in the pathway analysis. We also found 4 metabolites whose levels were significantly associated with good survival in both discovery and validation phases. Strong cumulative effects on overall survival were observed for these 4 metabolites. In conclusion, we identified a panel of metabolites including metabolites in caffeine metabolism pathway that may predict survival outcome in advanced-stage NSCLC patients. The identified small metabolites may be useful biomarker candidates to help identify patients who may benefit from platinum-based chemotherapy.

In vivo Metabolic Profiles as Determined by 31P and short TE 1H MR-Spectroscopy : No Difference Between Patients with IDH Wildtype and IDH Mutant Gliomas

PURPOSE

Previous ex vivo spectroscopic data from tissue samples revealed differences in phospholipid metabolites between isocitrate dehydrogenase mutated (IDHmut) and IDH wildtype (IDHwt) gliomas. We investigated whether these changes can be found in vivo using ¹H-decoupled ³¹P magnetic resonance spectroscopic imaging (MRSI) with 3D chemical shift imaging (CSI) at 3 T in patients with low and high-grade gliomas.

METHODS

The study included 33 prospectively enrolled, mostly untreated patients who met spectral quality criteria according to the World Health Organization (WHO II n = 7, WHO III n = 17, WHO IV n = 9; 25 patients IDHmut, 8 patients IDHwt). The MRSI protocol included ¹H decoupled ³¹P MRSI with 3D CSI (3D ³¹P CSI), 2D ¹H CSI and a ¹H single voxel spectroscopy sequence (TE 30 ms) from the tumor area. For ¹H MRS, absolute metabolite concentration values were calculated (phantom replacement method). For ³¹P MRS, metabolite intensity ratios were calculated for the choline (C) and ethanolamine (E)-containing metabolites.

RESULTS

In our patient cohort we did not find significant differences for the ratio of phosphocholine (PC) and phosphoethanolamine (PE), PC/PE, (p = 0.24) for IDHmut compared to IDHwt gliomas. Furthermore, we found no elevated ratios of glycerophosphocholine (GPC) and glycerophosphoethanolamine (GPE), GPC/GPE, (p = 0.68) or GPC/PE (p = 0.12) for IDHmut gliomas. Even the ratio (PC+GPC)/(PE+GPE) showed no significant differences with respect to mutation status (p = 0.16). Nonetheless, changes related to tumor grade regarding intracellular pH (pH_i) and phospholipid metabolism as well as absolute metabolite concentrations of co-registered 2D ¹H CSI data for tumor and control tissue showed the anticipated results.

CONCLUSION

Using 3D-CSI data acquisition, in vivo ³¹P MR spectroscopic measurement of phospholipid metabolites could not distinguish between IDHmut and IDHwt.

Metabolomics Applications in Precision Medicine: An Oncological Perspective

Nowadays, cancer therapy remains limited by the conventional one-size-fits-all approach. In this context, treatment decisions are based on the clinical stage of disease but fail to ascertain the individual´s underlying biology and its role in driving malignancy. The identification of better therapies for cancer treatment is thus limited by the lack of sufficient data regarding the characterization of specific biochemical signatures associated with each particular cancer patient or group of patients. Metabolomics approaches promise a better understanding of cancer, a disease characterized by significant alterations in bioenergetic metabolism, by identifying changes in the pattern of metabolite expression in addition to changes in the concentration of individual metabolites as well as alterations in biochemical pathways. These approaches hold the potential of identifying novel biomarkers with different clinical applications, including the development of more specific diagnostic methods based on the characterization of metabolic subtypes, the monitoring of currently used cancer therapeutics to evaluate the response and the prognostic outcome with a given therapy, and the evaluation of the mechanisms involved in disease relapse and drug resistance. This review discusses metabolomics applications in different oncological processes underlining the potential of this omics approach to further advance the implementation of precision medicine in the oncology area.

Capillary electrophoresis - Mass spectrometry metabolomics analysis revealed enrichment of hypotaurine in rat glioma tissues

Glioma is one of the most lethal brain malignancies with unknown etiologies. Many metabolomics analysis aiming at diverse kinds of samples had been performed. Due to the varied adopted analytical platforms, the reported disease-related metabolites were not consistent across different studies. Comparable metabolomics results are more likely to be acquired by analyzing the same sample types with identical analytical platform. For tumor researches, tissue samples metabolomics analysis own the unique advantage that it can gain more direct insight into disease-specific pathological molecules. In this light, a previous reported capillary electrophoresis - mass spectrometry human tissues metabolomics analysis method was employed to profile the metabolome of rat C6 cell implantation gliomas and the corresponding precancerous tissues. It was found that 9 metabolites increased in the glioma tissues. Of them, hypotaurine was the only metabolite that enriched in the malignant tissues as what had been reported in the relevant human tissues metabolomics analysis. Furthermore, hypotaurine was also proved to inhibit α-ketoglutarate-dependent dioxygenases (2-KDDs) through immunocytochemistry staining and in vitro enzymatic activity assays by using C6 cell cultures. This study reinforced the previous conclusion that hypotaurine acted as a competitive inhibitor of 2-KDDs and proved the value of metabolomics in oncology studies.

A prospective study of serum metabolites and glioma risk

Malignant glioma is one of the most lethal adult cancers, yet its etiology remains largely unknown. We conducted a prospective serum metabolomic analysis of glioma based on 64 cases and 64 matched controls selected from Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Median time from collection of baseline fasting serum to diagnosis was nine years (inter-decile range 3-20 years). LC/MS-MS identified 730 known metabolites, and conditional logistic regression models estimated odds ratios for one-standard deviation differences in log-metabolite signals. Forty-three metabolites were associated with glioma at P<0.05. 2-Oxoarginine, cysteine, alpha-ketoglutarate, chenodeoxycholate and argininate yielded the strongest metabolite signals and were inversely related to overall glioma risk (0.0065≤P<0.0083). Also, seven xanthine metabolites related to caffeine metabolism were higher in cases than in controls (0.017≤P<0.042). Findings were mostly similar in high-grade glioma cases, although prominent inversely associated metabolites included the secondary bile acids glycocholenate sulfate and 3β-hydroxy-5-cholenoic acid, xenobiotic methyl 4-hydroxybenzoate sulfate, sex steroid 5alpha-pregnan-3beta, 20beta-diol-monosulfate, and cofactor/vitamin oxalate (0.0091≤P<0.021). A serum metabolomic profile of glioma identified years in advance of clinical diagnoses is characterized by altered signals in arginine/proline, antioxidant, and coffee-related metabolites. The observed pattern provides new potential leads regarding the molecular basis relevant to etiologic or sub-clinical biomarkers for glioma.

Intraoperative serum lactate is not a predictor of survival after glioblastoma surgery

BACKGROUND

Cancer cells can produce lactate in high concentrations. Two previous studies examined the clinical relevance of serum lactate as a biomarker in patients with brain tumors. Patients with high-grade tumors have higher serum concentrations of lactate than those with low-grade tumors. We hypothesized that serum lactic could be used of biomarker to predictor of survival in patients with glioblastoma (GB).

METHODS

This was a retrospective study. Demographic, lactate concentrations and imaging data from 275 adult patients with primary GB was included in the analysis. The progression free survival (PFS) and overall survival (OS) rates were compared in patients who had above and below the median concentrations of lactate. We also investigated the correlation between lactate concentrations and tumor volume. Multivariate analyses were conducted to test the association lactate, tumor volume and demographic variables with PFS and OS.

RESULTS

The median serum concentration of lactate was 2.3mmol/L. A weak correlation was found between lactate concentrations and tumor volume. Kaplan-Meier curves demonstrated similar survival in patients with higher or lower than 2.3mmol/L of lactate. The multivariate analysis indicated that the intraoperative levels of lactate were not independently associated with changes in survival. On another hand, a preoperative T1 volume was an independent predictor PFS (HR 95%CI: 1.41, 1.02-1.82, p=0.006) and OS (HR 95%CI: 1.47, 1.11-1.96, p=0.006).

CONCLUSION

This retrospective study suggests that the serum concentrations of lactate cannot be used as a biomarker to predict survival after GB surgery. To date, there are no clinically available serum biomarkers to determine prognosis in patients with high-grade gliomas. These tumors may produce high levels of lactic acid. We hypothesized that serum lactic could be used of biomarker to predictor of survival in patients with glioblastoma (GB). In this study, we collected perioperative and survival data from 275 adult patients with primary high-grade gliomas to determine whether intraoperative serum acid lactic concentrations can serve as a marker of prognosis. The median serum concentration of lactate was 2.3mmol/L. Our analysis indicated the intraoperative levels of lactate were not independently associated with changes in survival. This retrospective study suggests that the serum concentrations of lactate cannot be used as a biomarker to predict survival after GB surgery.