Integrative Metabolomics Reveals Deep Tissue and Systemic Metabolic Remodeling in Glioblastoma

Mass Spectrometry Advances in Analysis of Glioblastoma

Some cancers such as glioblastoma (GBM), show minimal response to medical interventions, often only capable of mitigating tumor growth or alleviating symptoms. High metabolic activity in the tumor microenvironment marked by immune responses and hypoxia, is a crucial factor driving tumor progression. The many developments in mass spectrometry (MS) over the last decades have provided a pivotal tool for studying proteins, along with their posttranslational modifications. It is known that the proteomic landscape of GBM comprises a wide range of proteins involved in cell proliferation, survival, migration, and immune evasion. Combination of MS imaging and microscopy has potential to reveal the spatial and molecular characteristics of pathological tissue sections. Moreover, integration of MS in the surgical process in form of techniques such as DESI-MS or rapid evaporative ionization MS has been shown as an effective tool for rapid measurement of metabolite profiles, providing detailed information within seconds. In immunotherapy-related research, MS plays an indispensable role in detection and targeting of cancer antigens which serve as a base for antigen-specific therapies. In this review, we aim to provide detailed information on molecular profile in GBM and to discuss recent MS advances and their clinical benefits for targeting this aggressive disease.

Metabolic remodeling in glioblastoma: a longitudinal multi-omics study

Monitoring tumor evolution and predicting survival using non-invasive liquid biopsy is an unmet need for glioblastoma patients. The era of proteomics and metabolomics blood analyzes, may help in this context. A case-control study was conducted. Patients were included in the GLIOPLAK trial (ClinicalTrials.gov Identifier: NCT02617745), a prospective bicentric study conducted between November 2015 and December 2022. Patients underwent biopsy alone and received radiotherapy and temozolomide. Blood samples were collected at three different time points: before and after concomitant radiochemotherapy, and at the time of tumor progression. Plasma samples from patients and controls were analyzed using metabolomics and proteomics, generating 371 omics features. Descriptive, differential, and predictive analyses were performed to assess the relationship between plasma omics feature levels and patient outcome. Diagnostic performance and longitudinal variations were also analyzed. The study included 67 subjects (34 patients and 33 controls). A significant differential expression of metabolites and proteins between patients and controls was observed. Predictive models using omics features showed high accuracy in distinguishing patients from controls. Longitudinal analysis revealed temporal variations in a few omics features including CD22, CXCL13, EGF, IL6, GZMH, KLK4, and TNFRSP6B. Survival analysis identified 77 omics features significantly associated with OS, with ERBB2 and ITGAV consistently linked to OS at all timepoints. Pathway analysis revealed dynamic oncogenic pathways involved in glioblastoma progression. This study provides insights into the potential of plasma omics features as biomarkers for glioblastoma diagnosis, progression and overall survival. Clinical implication should now be explored in dedicated prospective trials.

MALDI-MSI-LC-MS/MS Workflow for Single-Section Single Step Combined Proteomics and Quantitative Lipidomics

We introduce a novel approach for comprehensive molecular profiling in biological samples. Our single-section methodology combines quantitative mass spectrometry imaging (Q-MSI) and a single step extraction protocol enabling lipidomic and proteomic liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis on the same tissue area. The integration of spatially correlated lipidomic and proteomic data on a single tissue section allows for a comprehensive interpretation of the molecular landscape. Comparing Q-MSI and Q-LC-MS/MS quantification results sheds new light on the effect of MSI and related sample preparation. Performing MSI before Q-LC-MS on the same tissue section led to fewer protein identifications and a lower correlation between lipid quantification results. Also, the critical role and influence of internal standards in Q-MSI for accurate quantification is highlighted. Testing various slide types and the evaluation of different workflows for single-section spatial multiomics analysis emphasized the need for critical evaluation of Q-MSI data. These findings highlight the necessity for robust quantification methods comparable to current gold-standard LC-MS/MS techniques. The spatial information from MSI allowed region-specific insights within heterogeneous tissues, as demonstrated for glioblastoma multiforme. Additionally, our workflow demonstrated the efficiency of a single step extraction for lipidomic and proteomic analyses on the same tissue area, enabling the examination of significantly altered proteins and lipids within distinct regions of a single section. The integration of these insights into a lipid-protein interaction network expands the biological information attainable from a tissue section, highlighting the potential of this comprehensive approach for advancing spatial multiomics research.

Opportunities and challenges for sample preparation and enrichment in mass spectrometry for single-cell metabolomics

Single-cell heterogeneity in metabolism, drug resistance and disease type poses the need for analytical techniques for single-cell analysis. As the metabolome provides the closest view of the status quo in the cell, studying the metabolome at single-cell resolution may unravel said heterogeneity. A challenge in single-cell metabolome analysis is that metabolites cannot be amplified, so one needs to deal with picolitre volumes and a wide range of analyte concentrations. Due to high sensitivity and resolution, MS is preferred in single-cell metabolomics. Large numbers of cells need to be analysed for proper statistics; this requires high-throughput analysis, and hence automation of the analytical workflow. Significant advances in (micro)sampling methods, CE and ion mobility spectrometry have been made, some of which have been applied in high-throughput analyses. Microfluidics has enabled an automation of cell picking and metabolite extraction; image recognition has enabled automated cell identification. Many techniques have been used for data analysis, varying from conventional techniques to novel combinations of advanced chemometric approaches. Steps have been set in making data more findable, accessible, interoperable and reusable, but significant opportunities for improvement remain. Herein, advances in single-cell analysis workflows and data analysis are discussed, and recommendations are made based on the experimental goal.

Untangling the web of glioblastoma treatment resistance using a multi-omic and multidisciplinary approach

Glioblastoma (GBM), the most common human brain tumor, has been notoriously resistant to treatment. As a result, the dismal overall survival of GBM patients has not changed over the past three decades. GBM has been stubbornly resistant to checkpoint inhibitor immunotherapies, which have been remarkably effective in the treatment of other tumors. It is clear that GBM resistance to therapy is multifactorial. Although therapeutic transport into brain tumors is inhibited by the blood brain barrier, there is evolving evidence that overcoming this barrier is not the predominant factor. GBMs generally have a low mutation burden, exist in an immunosuppressed environment and they are inherently resistant to immune stimulation, all of which contribute to treatment resistance. In this review, we evaluate the contribution of multi-omic approaches (genomic and metabolomic) along with analyzing immune cell populations and tumor biophysical characteristics to better understand and overcome GBM multifactorial resistance to treatment.

Bioenergetic Profiling in Glioblastoma Multiforme Patients with Different Clinical Outcomes

The accumulation of cell biomass is associated with dramatically increased bioenergetic and biosynthetic demand. Metabolic reprogramming, once thought as an epiphenomenon, currently relates to disease progression, also in response to extracellular fate-decisive signals. Glioblastoma multiforme patients often suffer misdiagnosis, short survival time, low quality of life, and poor disease management options. Today, tumor genetic testing and histological analysis guide diagnosis and treatment. We and others appreciate that metabolites complement translational biomarkers and molecular signatures in disease profiling and phenotyping. Herein, we coupled a mixed-methods content analysis to a mass spectrometry-based untargeted metabolomic analysis on plasma samples from glioblastoma multiforme patients to delineate the role of metabolic remodeling in biological plasticity and, hence, disease severity. Following data processing and analysis, we established a bioenergetic profile coordinated by the mitochondrial function and redox state, lipids, and energy substrates. Our findings show that epigenetic modulators are key players in glioblastoma multiforme cell metabolism, in particular when microRNAs are considered. We propose that biological plasticity in glioblastoma multiforme is a mechanism of adaptation and resistance to treatment which is eloquently revealed by bioenergetics.

Assessing Metabolic Markers in Glioblastoma Using Machine Learning: A Systematic Review

Glioblastoma (GBM) is a common and deadly brain tumor with late diagnoses and poor prognoses. Machine learning (ML) is an emerging tool that can create highly accurate diagnostic and prognostic prediction models. This paper aimed to systematically search the literature on ML for GBM metabolism and assess recent advancements. A literature search was performed using predetermined search terms. Articles describing the use of an ML algorithm for GBM metabolism were included. Ten studies met the inclusion criteria for analysis: diagnostic (n = 3, 30%), prognostic (n = 6, 60%), or both (n = 1, 10%). Most studies analyzed data from multiple databases, while 50% (n = 5) included additional original samples. At least 2536 data samples were run through an ML algorithm. Twenty-seven ML algorithms were recorded with a mean of 2.8 algorithms per study. Algorithms were supervised (n = 24, 89%), unsupervised (n = 3, 11%), continuous (n = 19, 70%), or categorical (n = 8, 30%). The mean reported accuracy and AUC of ROC were 95.63% and 0.779, respectively. One hundred six metabolic markers were identified, but only EMP3 was reported in multiple studies. Many studies have identified potential biomarkers for GBM diagnosis and prognostication. These algorithms show promise; however, a consensus on even a handful of biomarkers has not yet been made.

Preclinical and Clinical Applications of Metabolomics and Proteomics in Glioblastoma Research

Glioblastoma (GB) is a primary malignancy of the central nervous system that is classified by the WHO as a grade IV astrocytoma. Despite decades of research, several aspects about the biology of GB are still unclear. Its pathogenesis and resistance mechanisms are poorly understood, and methods to optimize patient diagnosis and prognosis remain a bottle neck owing to the heterogeneity of the malignancy. The field of omics has recently gained traction, as it can aid in understanding the dynamic spatiotemporal regulatory network of enzymes and metabolites that allows cancer cells to adjust to their surroundings to promote tumor development. In combination with other omics techniques, proteomic and metabolomic investigations, which are a potent means for examining a variety of metabolic enzymes as well as intermediate metabolites, might offer crucial information in this area. Therefore, this review intends to stress the major contribution these tools have made in GB clinical and preclinical research and highlights the crucial impacts made by the integrative "omics" approach in reducing some of the therapeutic challenges associated with GB research and treatment. Thus, our study can purvey the use of these powerful tools in research by serving as a hub that particularly summarizes studies employing metabolomics and proteomics in the realm of GB diagnosis, treatment, and prognosis.

Lipid Alterations in Glioma: A Systematic Review

Gliomas are highly lethal tumours characterised by heterogeneous molecular features, producing various metabolic phenotypes leading to therapeutic resistance. Lipid metabolism reprogramming is predominant and has contributed to the metabolic plasticity in glioma. This systematic review aims to discover lipids alteration and their biological roles in glioma and the identification of potential lipids biomarker. This systematic review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Extensive research articles search for the last 10 years, from 2011 to 2021, were conducted using four electronic databases, including PubMed, Web of Science, CINAHL and ScienceDirect. A total of 158 research articles were included in this study. All studies reported significant lipid alteration between glioma and control groups, impacting glioma cell growth, proliferation, drug resistance, patients' survival and metastasis. Different lipids demonstrated different biological roles, either beneficial or detrimental effects on glioma. Notably, prostaglandin (PGE2), triacylglycerol (TG), phosphatidylcholine (PC), and sphingosine-1-phosphate play significant roles in glioma development. Conversely, the most prominent anti-carcinogenic lipids include docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and vitamin D3 have been reported to have detrimental effects on glioma cells. Furthermore, high lipid signals were detected at 0.9 and 1.3 ppm in high-grade glioma relative to low-grade glioma. This evidence shows that lipid metabolisms were significantly dysregulated in glioma. Concurrent with this knowledge, the discovery of specific lipid classes altered in glioma will accelerate the development of potential lipid biomarkers and enhance future glioma therapeutics.

Stress Reactivity, Susceptibility to Hypertension, and Differential Expression of Genes in Hypertensive Compared to Normotensive Patients

Although half of hypertensive patients have hypertensive parents, known hypertension-related human loci identified by genome-wide analysis explain only 3% of hypertension heredity. Therefore, mainstream transcriptome profiling of hypertensive subjects addresses differentially expressed genes (DEGs) specific to gender, age, and comorbidities in accordance with predictive preventive personalized participatory medicine treating patients according to their symptoms, individual lifestyle, and genetic background. Within this mainstream paradigm, here, we determined whether, among the known hypertension-related DEGs that we could find, there is any genome-wide hypertension theranostic molecular marker applicable to everyone, everywhere, anytime. Therefore, we sequenced the hippocampal transcriptome of tame and aggressive rats, corresponding to low and high stress reactivity, an increase of which raises hypertensive risk; we identified stress-reactivity-related rat DEGs and compared them with their known homologous hypertension-related animal DEGs. This yielded significant correlations between stress reactivity-related and hypertension-related fold changes (log2 values) of these DEG homologs. We found principal components, PC1 and PC2, corresponding to a half-difference and half-sum of these log2 values. Using the DEGs of hypertensive versus normotensive patients (as the control), we verified the correlations and principal components. This analysis highlighted downregulation of β-protocadherins and hemoglobin as whole-genome hypertension theranostic molecular markers associated with a wide vascular inner diameter and low blood viscosity, respectively.