Investigation of serum proteome alterations in human glioblastoma multiforme

Proteomics of tumor and serum samples from isocitrate dehydrogenase-wildtype glioblastoma patients: is the detoxification of reactive oxygen species associated with shorter survival?

Proteomics has been little used for the identification of novel prognostic and/or therapeutic markers in isocitrate dehydrogenase (IDH)-wildtype glioblastoma (GB). In this study, we analyzed 50 tumor and 30 serum samples from short- and long-term survivors of IDH-wildtype GB (STS and LTS, respectively) by data-independent acquisition mass spectrometry (DIA-MS)-based proteomics, with the aim of identifying such markers. DIA-MS identified 5422 and 826 normalized proteins in tumor and serum samples, respectively, with only three tumor proteins and 26 serum proteins displaying significant differential expression between the STS and LTS groups. These dysregulated proteins were principally associated with the detoxification of reactive oxygen species (ROS). In particular, GB patients in the STS group had high serum levels of malate dehydrogenase 1 (MDH1) and ribonuclease inhibitor 1 (RNH1) and low tumor levels of fatty acid-binding protein 7 (FABP7), which may have enabled them to maintain low ROS levels, counteracting the effects of the first-line treatment with radiotherapy plus concomitant and adjuvant temozolomide. A blood score built on the levels of MDH1 and RNH1 expression was found to be an independent prognostic factor for survival based on the serum proteome data for a cohort of 96 IDH-wildtype GB patients. This study highlights the utility of circulating MDH1 and RNH1 biomarkers for determining the prognosis of patients with IDH-wildtype GB. Furthermore, the pathways driven by these biomarkers, and the tumor FABP7 pathway, may constitute promising therapeutic targets for blocking ROS detoxification to overcome resistance to chemoradiotherapy in potential GB STS.

Zonulin as Gatekeeper in Gut-Brain Axis: Dysregulation in Glioblastoma

Novel biomarkers and therapeutic strategies for glioblastoma, the most common malignant brain tumor with an extremely unfavorable prognosis, are urgently needed. Recent studies revealed a significant upregulation of the protein zonulin in glioblastoma, which correlates with patient survival. Originally identified as pre-haptoglobin-2, zonulin modulates both the intestinal barrier and the blood-brain barrier by disassembling tight junctions. An association of zonulin with various neuroinflammatory diseases has been observed. It can be suggested that zonulin links a putative impairment of the gut-brain barrier with glioblastoma carcinogenesis, leading to an interaction of the gut microbiome, the immune system, and glioblastoma. We therefore propose three interconnected hypotheses: (I) elevated levels of zonulin in glioblastoma contribute to its aggressiveness; (II) upregulated (serum-) zonulin increases the permeability of the microbiota-gut-brain barrier; and (III) this creates a carcinogenic and immunosuppressive microenvironment preventing the host from an effective antitumor response. The role of zonulin in glioblastoma highlights a promising field of research that could yield diagnostic and therapeutic options for glioblastoma patients and other diseases with a disturbed microbiota-gut-brain barrier.

Variability of haptoglobin beta-chain proteoforms

Existing knowledge on changes of the haptoglobin (Hp) molecule suggests that it may exist in multiple proteoforms, which obviously exhibit different functions. Using two-dimensional electrophoresis (2DE) in combination with mass spectrometry and immunodetection, we have analyzed blood plasma samples from both healthy donors and patients with primary grade IV glioblastoma (GBM), and obtained a detailed composite 2DE distribution map of β-chain proteoforms, as well as the full-length form of Hp (zonulin). Although the total level of plasma Hp exceeded normal values in cancer patients (especially patients with GBM), the presence of particuar proteoforms, detected by their position on the 2DE map, was very individual. Variability was found in both zonulin and the Hp β-chain. The presence of an alkaline form of zonulin in plasma can be considered a conditional, but insufficient, GBM biomarker. In other words, we found that at the level of minor proteoforms of Hp, even in normal conditions, there was a high individual variability. On the one hand, this raises questions about the reasons for such variability, if it is present not only in Hp, but also in other proteins. On the other hand, this may explain the discrepancy between the number of experimentally detected proteoforms and the theoretically possible ones not only in Hp, but also in other proteins.

Comparative Analysis of Amino Acid Profiles in Patients with Glioblastoma and Meningioma Using Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometry (LC-ESI-MS/MS)

Brain tumors account for 1% of all cancers diagnosed de novo. Due to the specificity of the anatomical area in which they grow, they can cause significant neurological disorders and lead to poor functional status and disability. Regardless of the results of biochemical markers of intracranial neoplasms, they are currently of no diagnostic significance. The aim of the study was to use LC-ESI-MS/MS in conjunction with multivariate statistical analyses to examine changes in amino acid metabolic profiles between patients with glioblastoma, meningioma, and a group of patients treated for osteoarthritis of the spine as a control group. Comparative analysis of amino acids between patients with glioblastoma, meningioma, and the control group allowed for the identification of statistically significant differences in the amino acid profile, including both exogenous and endogenous amino acids. The amino acids that showed statistically significant differences (lysine, histidine, α-aminoadipic acid, phenylalanine) were evaluated for diagnostic usefulness based on the ROC curve. The best results were obtained for phenylalanine. Classification trees were used to build a model allowing for the correct classification of patients into the study group (patients with glioblastoma multiforme) and the control group, in which cysteine turned out to be the most important amino acid in the decision-making algorithm. Our results indicate amino acids that may prove valuable, used alone or in combination, toward improving the diagnosis of patients with glioma and meningioma. To better assess the potential utility of these markers, their performance requires further validation in a larger cohort of samples.

Glial cell proteome using targeted quantitative methods for potential multi-diagnostic biomarkers

Glioblastoma is one of the most malignant primary brain cancer. Despite surgical resection with modern technology followed by chemo-radiation therapy with temozolomide, resistance to the treatment and recurrence is common due to its aggressive and infiltrating nature of the tumor with high proliferation index. The median survival time of the patients with glioblastomas is less than 15 months. Till now there has been no report of molecular target specific for glioblastomas. Early diagnosis and development of molecular target specific for glioblastomas are essential for longer survival of the patients with glioblastomas. Development of biomarkers specific for glioblastomas is most important for early diagnosis, estimation of the prognosis, and molecular target therapy of glioblastomas. To that end, in this study, we have conducted a comprehensive proteome study using primary cells and tissues from patients with glioblastoma. In the discovery stage, we have identified 7429 glioblastoma-specific proteins, where 476 proteins were quantitated using Tandem Mass Tag (TMT) method; 228 and 248 proteins showed up and down-regulated pattern, respectively. In the validation stage (20 selected target proteins), we developed quantitative targeted method (MRM: Multiple reaction monitoring) using stable isotope standards (SIS) peptide. In this study, five proteins (CCT3, PCMT1, TKT, TOMM34, UBA1) showed the significantly different protein levels (t-test: p value ≤ 0.05, AUC ≥ 0.7) between control and cancer groups and the result of multiplex assay using logistic regression showed the 5-marker panel showed better sensitivity (0.80 and 0.90), specificity (0.92 and 1.00), error rate (10 and 2%), and AUC value (0.94 and 0.98) than the best single marker (TOMM34) in primary cells and tissues, respectively. Although we acknowledge that the model requires further validation in a large sample size, the 5 protein marker panel can be used as baseline data for the discovery of novel biomarkers of the glioblastoma.

An overview of glioblastoma multiforme and temozolomide resistance: can LC-MS-based proteomics reveal the fundamental mechanism of temozolomide resistance?

Glioblastoma multiforme (GBM) is a primary type of lethal brain tumor. Over the last two decades, temozolomide (TMZ) has remained the primary chemotherapy for GBM. However, TMZ resistance in GBM constitutes an underlying factor contributing to high rates of mortality. Despite intense efforts to understand the mechanisms of therapeutic resistance, there is currently a poor understanding of the molecular processes of drug resistance. For TMZ, several mechanisms linked to therapeutic resistance have been proposed. In the past decade, significant progress in the field of mass spectrometry-based proteomics has been made. This review article discusses the molecular drivers of GBM, within the context of TMZ resistance with a particular emphasis on the potential benefits and insights of using global proteomic techniques.

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.

Construction of machine learning-based models for cancer outcomes in low and lower-middle income countries: A scoping review

Background

The impact and utility of machine learning (ML)-based prediction tools for cancer outcomes including assistive diagnosis, risk stratification, and adjunctive decision-making have been largely described and realized in the high income and upper-middle-income countries. However, statistical projections have estimated higher cancer incidence and mortality risks in low and lower-middle-income countries (LLMICs). Therefore, this review aimed to evaluate the utilization, model construction methods, and degree of implementation of ML-based models for cancer outcomes in LLMICs.

Methods

PubMed/Medline, Scopus, and Web of Science databases were searched and articles describing the use of ML-based models for cancer among local populations in LLMICs between 2002 and 2022 were included. A total of 140 articles from 22,516 citations that met the eligibility criteria were included in this study.

Results

ML-based models from LLMICs were often based on traditional ML algorithms than deep or deep hybrid learning. We found that the construction of ML-based models was skewed to particular LLMICs such as India, Iran, Pakistan, and Egypt with a paucity of applications in sub-Saharan Africa. Moreover, models for breast, head and neck, and brain cancer outcomes were frequently explored. Many models were deemed suboptimal according to the Prediction model Risk of Bias Assessment tool (PROBAST) due to sample size constraints and technical flaws in ML modeling even though their performance accuracy ranged from 0.65 to 1.00. While the development and internal validation were described for all models included (n=137), only 4.4% (6/137) have been validated in independent cohorts and 0.7% (1/137) have been assessed for clinical impact and efficacy.

Conclusion

Overall, the application of ML for modeling cancer outcomes in LLMICs is increasing. However, model development is largely unsatisfactory. We recommend model retraining using larger sample sizes, intensified external validation practices, and increased impact assessment studies using randomized controlled trial designs.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=308345, identifier CRD42022308345.

Haptoglobin as a Biomarker

Haptoglobin (Hp) is a glycoprotein that binds free hemoglobin (Hb) in plasma and plays a critical role in tissue protection and prevention of oxidative damage. Besides, it has some regulatory functions. Haptoglobin is an acute-phase protein, its concentration in plasma changes in pathology, and the test for its concentration is part of normal clinical practice. Haptoglobin is a conservative protein synthesized mainly in the liver and lungs and is the subject of research as a potential biomarker of many diseases, including various forms of malignant neoplasms. Haptoglobin has several unique biophysical characteristics. The human Нр gene is polymorphic, has three structural alleles that control the synthesis of three major phenotypes of haptoglobin: homozygous Нр1-1 and Нр2-2, and heterozygous Нр2-1, determined by a combination of allelic variants that are inherited. Numerous studies indicate that the phenotype of haptoglobin can be used to judge the individual predisposition of a person to various diseases. In addition, Hp undergoes various post-translational modifications (PTMs). These are structural transformations (removal of the signal peptide, cutting off the Pre-Hp precursor molecule into two subunits, α and β, limited proteolysis of α-chains, formation of disulfide bonds, multimerization), as well as chemical modifications of α-chains and glycosylation of the β-chain. Glycosylation of the β-chain of haptoglobin at four Asn sites is the most important variable PTM that regulates the structure and function of the glycoprotein. The study of modified oligosaccharides of the β-chain of Hp has become the main direction in the study of pathological processes, including malignant neoplasms. These characteristics indicate the possibility of the existence of Hp in the form of a multitude of proteoforms, probably performing different functions. This review is devoted to the description of the structural and functional diversity and the potential use of Hp as a biomarker of various pathologies.

Evaluation of Haptoglobin and Its Proteoforms as Glioblastoma Markers

Haptoglobin (Hp) is a blood plasma glycoprotein that plays a critical role in tissue protection and the prevention of oxidative damage. Haptoglobin is an acute-phase protein, its concentration in plasma changes in pathology, and the test for its concentration is part of normal clinical practice. Haptoglobin is a conservative protein and is the subject of research as a potential biomarker of many diseases, including malignant neoplasms. The Human Hp gene is polymorphic and controls the synthesis of three major phenotypes-homozygous Hp1-1 and Hp2-2, and heterozygous Hp2-1, determined by a combination of allelic variants that are inherited. Numerous studies indicate that the phenotype of haptoglobin can be used to judge the individual's predisposition to various diseases. In addition, Hp undergoes various post-translational modifications (PTMs). Glioblastoma multiform (GBM) is the most malignant primary brain tumor. In our study, we have analyzed the state of Hp proteoforms in plasma and cells using 1D (SDS-PAGE) and 2D electrophoresis (2DE) with the following mass spectrometry (LC ES-MS/MS) or Western blotting. We found that the levels of α2- and β-chain proteoforms are up-regulated in the plasma of GBM patients. An unprocessed form of Hp2-2 (PreHp2-2, zonulin) with unusual biophysical parameters (pI/Mw) was also detected in the plasma of GBM patients and glioblastoma cells. Altogether, this data shows the possibility to use proteoforms of haptoglobin as a potential GBM-specific plasma biomarker.

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.

Extracellular Vesicles as a Novel Liquid Biopsy-Based Diagnosis for the Central Nervous System, Head and Neck, Lung, and Gastrointestinal Cancers: Current and Future Perspectives

Simple Summary

To improve clinical outcomes, early diagnosis is mandatory in cancer patients. Several diagnostic approaches have been proposed, however, the main drawback relies on the invasive procedures required. Extracellular vesicles (EVs) are bilayer lipid membrane structures released by almost all cells and transferred to remote sites via the bloodstream. The observation that their cargo reflects the cell of origin has opened a new frontier for non-invasive biomarker discovery in oncology. Moreover, since EVs can be recovered from different body fluids, their impact as a Correctdiagnostic tool has gained particular interest. Hence, in the last decade, several studies using different biological fluids have been performed, showing the valuable contributions of EVs as tumour biomarkers, and their improved diagnostic power when combined with currently available tumour markers. In this review, the most relevant data on the diagnostic relevance of EVs, alone or in combination with the well-established tumour markers, are discussed.

Abstract

Early diagnosis, along with innovative treatment options, are crucial to increase the overall survival of cancer patients. In the last decade, extracellular vesicles (EVs) have gained great interest in biomarker discovery. EVs are bilayer lipid membrane limited structures, released by almost all cell types, including cancer cells. The EV cargo, which consists of RNAs, proteins, DNA, and lipids, directly mirrors the cells of origin. EVs can be recovered from several body fluids, including blood, cerebral spinal fluid (CSF), saliva, and Broncho-Alveolar Lavage Fluid (BALF), by non-invasive or minimally invasive approaches, and are therefore proposed as feasible cancer diagnostic tools. In this review, methodologies for EV isolation and characterization and their impact as diagnostics for the central nervous system, head and neck, lung, and gastrointestinal cancers are outlined. For each of these tumours, recent data on the potential clinical applications of the EV’s unique cargo, alone or in combination with currently available tumour biomarkers, have been deeply discussed.

Multiple Reaction Monitoring-Based Targeted Assays for the Validation of Protein Biomarkers in Brain Tumors

The emergence of omics technologies over the last decade has helped in advancement of research and our understanding of complex diseases like brain cancers. However, barring genomics, no other omics technology has been able to find utility in clinical settings. The recent advancements in mass spectrometry instrumentation have resulted in proteomics technologies becoming more sensitive and reliable. Targeted proteomics, a relatively new branch of mass spectrometry-based proteomics has shown immense potential in addressing the shortcomings of the standard molecular biology-based techniques like Western blotting and Immunohistochemistry. In this study we demonstrate the utility of Multiple reaction monitoring (MRM), a targeted proteomics approach, in quantifying peptides from proteins like Apolipoprotein A1 (APOA1), Apolipoprotein E (APOE), Prostaglandin H2 D-Isomerase (PTGDS), Vitronectin (VTN) and Complement C3 (C3) in cerebrospinal fluid (CSF) collected from Glioma and Meningioma patients. Additionally, we also report transitions for peptides from proteins - Vimentin (VIM), Cystatin-C (CST3) and Clusterin (CLU) in surgically resected Meningioma tissues; Annexin A1 (ANXA1), Superoxide dismutase (SOD2) and VIM in surgically resected Glioma tissues; and Microtubule associated protein-2 (MAP-2), Splicing factor 3B subunit 2 (SF3B2) and VIM in surgically resected Medulloblastoma tissues. To our knowledge, this is the first study reporting the use of MRM to validate proteins from three types of brain malignancies and two different bio-specimens. Future studies involving a large cohort of samples aimed at accurately detecting and quantifying peptides of proteins with roles in brain malignancies could potentially result in a panel of proteins showing ability to classify and grade tumors. Successful application of these techniques could ultimately offer alternative strategies with increased accuracy, sensitivity and lower turnaround time making them translatable to the clinics.

Circulating Biomarkers for Glioma: A Review

Accurate circulating biomarkers have potential clinical applications in population screening, tumor subclassification, monitoring tumor status, and the delivery of individualized treatments resulting from tumor genotyping. Recently, significant progress has been made within this field in several cancer types, but despite the many potential benefits, currently there is no validated circulating biomarker test for patients with glioma. A number of circulating factors have been examined, including circulating tumor cells, cell-free DNA, microRNA, exosomes, and proteins from both peripheral blood and cerebrospinal fluid with variable results. In the following article, we provide a narrative review of the current evidence pertaining to circulating biomarkers in patients with glioma, including discussion of the advantages and challenges encountered with the current methods used for discovery. Additionally, the potential clinical applications are described with reference to the literature.

Liquid Biomarkers for Improved Diagnosis and Classification of CNS Tumors

Liquid biopsy, as a non-invasive technique for cancer diagnosis, has emerged as a major step forward in conquering tumors. Current practice in diagnosis of central nervous system (CNS) tumors involves invasive acquisition of tumor biopsy upon detection of tumor on neuroimaging. Liquid biopsy enables non-invasive, rapid, precise and, in particular, real-time cancer detection, prognosis and treatment monitoring, especially for CNS tumors. This approach can also uncover the heterogeneity of these tumors and will likely replace tissue biopsy in the future. Key components of liquid biopsy mainly include circulating tumor cells (CTC), circulating tumor nucleic acids (ctDNA, miRNA) and exosomes and samples can be obtained from the cerebrospinal fluid, plasma and serum of patients with CNS malignancies. This review covers current progress in application of liquid biopsies for diagnosis and monitoring of CNS malignancies.

[Haptoglobin as a biomarker]

Haptoglobin (Hp) is a blood plasma glycoprotein that binds free hemoglobin (Hb) and plays a critical role in tissue protection and the prevention of oxidative damage. In addition, it has a number of regulatory functions. Haptoglobin is an acute phase protein, its concentration in plasma changes in pathology, and the test for its concentration is part of normal clinical practice. Haptoglobin is a conservative protein synthesized mainly in the liver and lungs and is the subject of research as a potential biomarker of many diseases, including various forms of malignant neoplasms. Haptoglobin has several unique biophysical characteristics. Only in humans, the Hp gene is polymorphic, has three structural alleles that control the synthesis of three major phenotypes of Hp, homozygous Hp1-1 and Hp2-2, and heterozygous Hp2-1, determined by a combination of allelic variants that are inherited. Numerous studies indicate that the phenotype of haptoglobin can be used to judge the individual's predisposition to various diseases. In addition, Hp undergoes various post-translational modifications (PTMs). These are structural transformations (removal of the signal peptide, cutting of the Pre-Hp precursor molecule into two subunits, α and β, limited proteolysis of α-chains, formation of disulfide bonds, multimerization), as well as chemical modifications of α-chains and glycosylation of the β-chain. Glycosylation of the β-chain of haptoglobin at four Asn sites is the most important variable PTM that regulates the structure and function of the glycoprotein. The study of modified oligosaccharides of the Hp β-chain has become the main direction in the study of pathological processes, including malignant neoplasms. Many studies are focused on the identification of PTM and changes in the level of the α2-chain of this protein in pathology. These characteristics of Hp indicate the possibility of the existence of this protein as different proteoforms, probably with different functions. This review is devoted to the description of the structural and functional diversity of Hp and its potential use as a biomarker of various pathologies.

Raman Spectral Signatures of Serum-Derived Extracellular Vesicle-Enriched Isolates May Support the Diagnosis of CNS Tumors

Investigating the molecular composition of small extracellular vesicles (sEVs) for tumor diagnostic purposes is becoming increasingly popular, especially for diseases for which diagnosis is challenging, such as central nervous system (CNS) malignancies. Thorough examination of the molecular content of sEVs by Raman spectroscopy is a promising but hitherto barely explored approach for these tumor types. We attempt to reveal the potential role of serum-derived sEVs in diagnosing CNS tumors through Raman spectroscopic analyses using a relevant number of clinical samples. A total of 138 serum samples were obtained from four patient groups (glioblastoma multiforme, non-small-cell lung cancer brain metastasis, meningioma and lumbar disc herniation as control). After isolation, characterization and Raman spectroscopic assessment of sEVs, the Principal Component Analysis-Support Vector Machine (PCA-SVM) algorithm was performed on the Raman spectra for pairwise classifications. Classification accuracy (CA), sensitivity, specificity and the Area Under the Curve (AUC) value derived from Receiver Operating Characteristic (ROC) analyses were used to evaluate the performance of classification. The groups compared were distinguishable with 82.9-92.5% CA, 80-95% sensitivity and 80-90% specificity. AUC scores in the range of 0.82-0.9 suggest excellent and outstanding classification performance. Our results support that Raman spectroscopic analysis of sEV-enriched isolates from serum is a promising method that could be further developed in order to be applicable in the diagnosis of CNS tumors.

Small Extracellular Vesicles Isolated from Serum May Serve as Signal-Enhancers for the Monitoring of CNS Tumors

Liquid biopsy-based methods to test biomarkers (e.g., serum proteins and extracellular vesicles) may help to monitor brain tumors. In this proteomics-based study, we aimed to identify a characteristic protein fingerprint associated with central nervous system (CNS) tumors. Overall, 96 human serum samples were obtained from four patient groups, namely glioblastoma multiforme (GBM), non-small-cell lung cancer brain metastasis (BM), meningioma (M) and lumbar disc hernia patients (CTRL). After the isolation and characterization of small extracellular vesicles (sEVs) by nanoparticle tracking analysis (NTA) and atomic force microscopy (AFM), liquid chromatography -mass spectrometry (LC-MS) was performed on two different sample types (whole serum and serum sEVs). Statistical analyses (ratio, Cohen's d, receiver operating characteristic; ROC) were carried out to compare patient groups. To recognize differences between the two sample types, pairwise comparisons (Welch's test) and ingenuity pathway analysis (IPA) were performed. According to our knowledge, this is the first study that compares the proteome of whole serum and serum-derived sEVs. From the 311 proteins identified, 10 whole serum proteins and 17 sEV proteins showed the highest intergroup differences. Sixty-five proteins were significantly enriched in sEV samples, while 129 proteins were significantly depleted compared to whole serum. Based on principal component analysis (PCA) analyses, sEVs are more suitable to discriminate between the patient groups. Our results support that sEVs have greater potential to monitor CNS tumors, than whole serum.

The development and clinical applications of proteomics: an Indian perspective

INTRODUCTION

Proteomic research has been extensively used to identify potential biomarkers or targets for various diseases. Advances in mass spectrometry along with data analytics have led proteomics to become a powerful tool for exploring the critical molecular players associated with diseases, thereby, playing a significant role in the development of proteomic applications for the clinic.

AREAS COVERED

This review presents recent advances in the development and clinical applications of proteomics in India toward understanding various diseases including cancer, metabolic diseases, and reproductive diseases. Keywords combined with 'clinical proteomics in India' 'proteomic research in India' and 'mass spectrometry' were used to search PubMed.

EXPERT OPINION

The past decade has seen a significant increase in research in clinical proteomics in India. This approach has resulted in the development of proteomics-based marker technologies for disease management in the country. The majority of these investigations are still in the discovery phase and efforts have to be made to address the intended clinical use so that the identified potential biomarkers reach the clinic. To move toward this necessity, there is a pressing need to establish some key infrastructure requirements and meaningful collaborations between the clinicians and scientists which will enable more effective solutions to address health issues specific to India.

Glioma tumor proteomics: clinically useful protein biomarkers and future perspectives

Introduction: Despite being rare cancers, gliomas account for a significant number of cancer-related deaths. Identification and treatment of these tumors at an early stage would greatly improve the therapeutic outcomes. There is an urgent need for diagnostic and prognostic markers, which can identify disease early and discriminate the subtypes of these tumors thereby improving the existing treatment modalities.Areas covered: In this article, we have reviewed published literature on proteomics biomarkers for gliomas and their importance in diagnosis or prognosis. Proteomic studies for the discovery of protein, autoantibody biomarkers, and biological pathway alterations in serum, CSF and tumor biopsies have been discussed in this review.Expert opinion: The rapid development in the field of mass spectrometry and increased sensitivity and reproducibility in assays has led to the identification and quantification of large number of proteins very precisely. Though genomic markers are the prime focus in the classification of gliomas, incorporating protein markers would further improve the existing classification. In this regard, data mining and studies on large cohorts of glioma patients would help in the identification of diagnostic and prognostic markers ultimately translating to the clinics.

Machine learning approaches to study glioblastoma: A review of the last decade of applications

BACKGROUND

Glioblastoma (GB, formally glioblastoma multiforme) is a malignant type of brain cancer that currently has no cure and is characterized by being highly heterogeneous with high rates of re-incidence and therapy resistance. Thus, it is urgent to characterize the mechanisms of GB pathogenesis to help researchers identify novel therapeutic targets to cure this devastating disease. Recently, a promising approach to identifying novel therapeutic targets is the integration of tumor omics data with clinical information using machine learning (ML) techniques.

RECENT FINDINGS

ML has become a valuable addition to the researcher's toolbox, thanks to its flexibility, multidimensional approach, and a growing community of users. The goal of this review is to introduce basic concepts and applications of ML for studying GB to clinicians and practitioners who are new to data science. ML applications include exploring large data sets, finding new relevant patterns, predicting outcomes, or merely understanding associations of the complex molecular networks presented within the tumor. Here, we review ML applications published between 2008 and 2018 and discuss ML strategies intending to identify new potential therapeutic targets to improve the management and treatment of GB.

CONCLUSIONS

ML applications to study GB vary in purpose and complexity, with positive results. In GB studies, ML is often used to analyze high-dimensional datasets with prediction or classification as a primary goal. Despite the strengths of ML techniques, they are not fail-safe and methodological issues can occur in GB studies that use them. This is why researchers need to be aware of these issues when planning and appraising studies that apply ML to the study of GB.

Circulating Tumor Biomarkers in Meningiomas Reveal a Signature of Equilibrium Between Tumor Growth and Immune Modulation

Meningiomas are primary central nervous system (CNS) tumors that originate from the arachnoid cells of the meninges. Recurrence occurs in higher grade meningiomas and a small subset of Grade I meningiomas with benign histology. Currently, there are no established circulating tumor markers which can be used for diagnostic and prognostic purposes in a non-invasive way for meningiomas. Here, we aimed to identify potential biomarkers of meningioma in patient sera. For this purpose, we collected preoperative (n = 30) serum samples from the meningioma patients classified as Grade I (n = 23), Grade II (n = 4), or Grade III (n = 3). We used a high-throughput, multiplex immunoassay cancer panel comprising of 92 cancer-related protein biomarkers to explore the serum protein profiles of meningioma patients. We detected 14 differentially expressed proteins in the sera of the Grade I meningioma patients in comparison to the age- and gender-matched control subjects (n = 12). Compared to the control group, Grade I meningioma patients showed increased serum levels of amphiregulin (AREG), CCL24, CD69, prolactin, EGF, HB-EGF, caspase-3, and decreased levels of VEGFD, TGF-α, E-Selectin, BAFF, IL-12, CCL9, and GH. For validation studies, we utilized an independent set of meningioma tumor tissue samples (Grade I, n = 20; Grade II, n = 10; Grade III, n = 6), and found that the expressions of amphiregulin and Caspase3 are significantly increased in all grades of meningiomas either at the transcriptional or protein level, respectively. In contrast, the gene expression of VEGF-D was significantly lower in Grade I meningioma tissue samples. Taken together, our study identifies a meningioma-specific protein signature in blood circulation of meningioma patients and highlights the importance of equilibrium between tumor-promoting factors and anti-tumor immunity.

Proteomic Advances in Glial Tumors through Mass Spectrometry Approaches

Being the fourth leading cause of cancer-related death, glial tumors are highly diverse tumor entities characterized by important heterogeneity regarding tumor malignancy and prognosis. However, despite the identification of important alterations in the genome of the glial tumors, there remains a gap in understanding the mechanisms involved in glioma malignancy. Previous research focused on decoding the genomic alterations in these tumors, but due to intricate cellular mechanisms, the genomic findings do not correlate with the functional proteins expressed at the cellular level. The development of mass spectrometry (MS) based proteomics allowed researchers to study proteins expressed at the cellular level or in serum that may provide new insights on the proteins involved in the proliferation, invasiveness, metastasis and resistance to therapy in glial tumors. The integration of data provided by genomic and proteomic approaches into clinical practice could allow for the identification of new predictive, diagnostic and prognostic biomarkers that will improve the clinical management of patients with glial tumors. This paper aims to provide an updated review of the recent proteomic findings, possible clinical applications, and future research perspectives in diffuse astrocytic and oligodendroglial tumors, pilocytic astrocytomas, and ependymomas.

Liquid Biopsy in Glioblastoma: Opportunities, Applications and Challenges

Liquid biopsy represents a minimally invasive procedure that can provide similar information from body fluids to what is usually obtained from a tissue biopsy sample. Its implementation in the clinical setting might significantly renew the field of medical oncology, facilitating the introduction of the concepts of precision medicine and patient-tailored therapies. These advances may be useful in the diagnosis of brain tumors that currently require surgery for tissue collection, or to perform genetic tumor profiling for disease classification and guidance of therapy. In this review, we will summarize the most recent advances and putative applications of liquid biopsy in glioblastoma, the most common and malignant adult brain tumor. Moreover, we will discuss the remaining challenges and hurdles in terms of technology and biology for its clinical application.

Multiplex assay for multiomics advances in personalized-precision medicine

Building the future of precision medicine is the main focus in cancer domain. Clinical trials are moving toward an array of studies that are more adapted to precision medicine. In this domain, there is an enhanced need for biomarkers, monitoring devices, and data-analysis methods. Omics profiling using whole genome, epigenome, transcriptome, proteome, and metabolome can offer detailed information of the human body in an integrative manner. Omes profiles reflect more accurately real-time physiological status. Personalized omics analyses both disease as a whole and the main disease processes, for a better understanding of the individualized health. Through this, multi-omic approaches for health monitoring, preventative medicine, and personalized treatment can be targeted simultaneously and can lead clinicians to have a comprehensive view on the diseasome.

Application of 2D-DIGE and iTRAQ Workflows to Analyze CSF in Gliomas

Proteomics is an indispensable tool for disease biomarker discovery. It is widely used for the analysis of biological fluids such as cerebrospinal fluid (CSF), blood, and saliva, which further aids in our understanding of disease incidence and progression. CSF is often the biospecimen of choice in case of intracranial tumors, as rapid changes in the tumor microenvironment can be easily assessed due to its close proximity to the brain. On the contrary studies comprising of serum or plasma samples do not truly reflect the underlying molecular alterations due to the presence of protective blood-brain barrier. We have described in here the detailed workflows for two advanced proteomics techniques, namely, 2D-DIGE (two-dimensional difference in-gel electrophoresis) and iTRAQ (isobaric tag for relative and absolute quantitation), for CSF analysis. Both of these techniques are very sensitive and widely used for quantitative proteomics analysis.

Clinical Significance of Extracellular Vesicles in Plasma from Glioblastoma Patients

PURPOSE

Glioblastoma (GBM) is the most common primary brain tumor. The identification of blood biomarkers reflecting the tumor status represents a major unmet need for optimal clinical management of patients with GBM. Their high number in body fluids, their stability, and the presence of many tumor-associated proteins and RNAs make extracellular vesicles potentially optimal biomarkers. Here, we investigated the potential role of plasma extracellular vesicles from patients with GBM for diagnosis and follow-up after treatment and as a prognostic tool.

EXPERIMENTAL DESIGN

Plasma from healthy controls (n = 33), patients with GBM (n = 43), and patients with different central nervous system malignancies (n = 25) were collected. Extracellular vesicles were isolated by ultracentrifugation and characterized in terms of morphology by transmission electron microscopy, concentration, and size by nanoparticle tracking analysis, and protein composition by mass spectrometry. An orthotopic mouse model of human GBM confirmed human plasma extracellular vesicle quantifications. Associations between plasma extracellular vesicle concentration and clinicopathologic features of patients with GBM were analyzed. All statistical tests were two-sided.

RESULTS

GBM releases heterogeneous extracellular vesicles detectable in plasma. Plasma extracellular vesicle concentration was higher in GBM compared with healthy controls (P < 0.001), brain metastases (P < 0.001), and extra-axial brain tumors (P < 0.001). After surgery, a significant drop in plasma extracellular vesicle concentration was measured (P < 0.001). Plasma extracellular vesicle concentration was also increased in GBM-bearing mice (P < 0.001). Proteomic profiling revealed a GBM-distinctive signature.

CONCLUSIONS

Higher extracellular vesicle plasma levels may assist in GBM clinical diagnosis: their reduction after GBM resection, their rise at recurrence, and their protein cargo might provide indications about tumor, therapy response, and monitoring.

Identification of blood biomarkers in glioblastoma by SWATH mass spectrometry and quantitative targeted absolute proteomics

Molecular biomarkers in blood are needed to aid the early diagnosis and clinical assessment of glioblastoma (GBM). Here, in order to identify biomarker candidates in plasma of GBM patients, we performed quantitative comparisons of the plasma proteomes of GBM patients (n = 14) and healthy controls (n = 15) using SWATH mass spectrometry analysis. The results were validated by means of quantitative targeted absolute proteomics analysis. As a result, we identified eight biomarker candidates for GBM (leucine-rich alpha-2-glycoprotein (LRG1), complement component C9 (C9), C-reactive protein (CRP), alpha-1-antichymotrypsin (SERPINA3), apolipoprotein B-100 (APOB), gelsolin (GSN), Ig alpha-1 chain C region (IGHA1), and apolipoprotein A-IV (APOA4)). Among them, LRG1, C9, CRP, GSN, IGHA1, and APOA4 gave values of the area under the receiver operating characteristics curve of greater than 0.80. To investigate the relationships between the biomarker candidates and GBM biology, we examined correlations between plasma concentrations of biomarker candidates and clinical presentation (tumor size, progression-free survival time, or overall survival time) in GBM patients. The plasma concentrations of LRG1, CRP, and C9 showed significant positive correlations with tumor size (R² = 0.534, 0.495, and 0.452, respectively).

Multi-pronged proteomic analysis to study the glioma pathobiology using cerebrospinal fluid samples

PURPOSE

Gliomas are one of the most aggressive and lethal brain tumors arising from neoplastic transformation of astrocytes and oligodendrocytes. A comprehensive quantitative analysis of proteome level differences in cerebrospinal fluid (CSF) across different grades of gliomas for a better understanding of glioma pathobiology is carried out.

EXPERIMENTAL DESIGN

Glioma patients are diagnosed by radiology and histochemistry-based analyses. Differential proteomic analysis of high (n = 12) and low (n = 5) grade gliomas, and control (n = 3) samples is performed by using two complementary quantitative proteomic approaches; 2D-DIGE and iTRAQ. Further, comparative analysis of three IDH wild-type and five IDH mutants is performed to identify the proteome level differences between these two sub-classes.

RESULTS

Level of several proteins including haptoglobin, transthyretin, osteopontin, vitronectin, complement factor H and different classes of immunoglobulins are found to be considerably increased in CSF of higher grades of gliomas. Subsequent bioinformatics analysis indicated that many of the dysregulated CSF proteins are associated with metabolism of lipids and lipoproteins, complement and coagulation cascades and extracellular matrix remodeling in gliomas. Intriguingly, CSF of glioma patients with IDH mutations exhibite increased levels of multiple proteins involved in response to oxidative stress.

CONCLUSION AND CLINICAL RELEVANCE

To the best of our knowledge, this is the foremost proteome level investigation describing comprehensive proteome profiles of different grades of gliomas using proximal fluid (CSF); and thereby providing insights into disease pathobiology, which aided in identification of grade and sub-type specific alterations. Moreover, if validated in larger clinical cohorts, a panel of differentially abundant CSF proteins may serve as potential disease monitoring and prognostic markers for gliomas.

Integrative analysis to select cancer candidate biomarkers to targeted validation

Targeted proteomics has flourished as the method of choice for prospecting for and validating potential candidate biomarkers in many diseases. However, challenges still remain due to the lack of standardized routines that can prioritize a limited number of proteins to be further validated in human samples. To help researchers identify candidate biomarkers that best characterize their samples under study, a well-designed integrative analysis pipeline, comprising MS-based discovery, feature selection methods, clustering techniques, bioinformatic analyses and targeted approaches was performed using discovery-based proteomic data from the secretomes of three classes of human cell lines (carcinoma, melanoma and non-cancerous). Three feature selection algorithms, namely, Beta-binomial, Nearest Shrunken Centroids (NSC), and Support Vector Machine-Recursive Features Elimination (SVM-RFE), indicated a panel of 137 candidate biomarkers for carcinoma and 271 for melanoma, which were differentially abundant between the tumor classes. We further tested the strength of the pipeline in selecting candidate biomarkers by immunoblotting, human tissue microarrays, label-free targeted MS and functional experiments. In conclusion, the proposed integrative analysis was able to pre-qualify and prioritize candidate biomarkers from discovery-based proteomics to targeted MS.

Identification of hub genes and regulatory factors of glioblastoma multiforme subgroups by RNA-seq data analysis

Glioblastoma multiforme (GBM) is the most common malignant brain tumor. This study aimed to identify the hub genes and regulatory factors of GBM subgroups by RNA sequencing (RNA-seq) data analysis, in order to explore the possible mechanisms responsbile for the progression of GBM. The dataset RNASeqV2 was downloaded by TCGA-Assembler, containing 169 GBM and 5 normal samples. Gene expression was calculated by the reads per kilobase per million reads measurement, and nor malized with tag count comparison. Following subgroup classification by the non-negative matrix factorization, the differentially expressed genes (DEGs) were screened in 4 GBM subgroups using the method of significance analysis of microarrays. Functional enrichment analysis was performed by DAVID, and the protein-protein interaction (PPI) network was constructed based on the HPRD database. The subgroup-related microRNAs (miRNAs or miRs), transcription factors (TFs) and small molecule drugs were predicted with predefined criteria. A cohort of 19,515 DEGs between the GBM and control samples was screened, which were predominantly enriched in cell cycle- and immunoreaction-related pathways. In the PPI network, lymphocyte cytosolic protein 2 (LCP2), breast cancer 1 (BRCA1), specificity protein 1 (Sp1) and chromodomain-helicase-DNA-binding protein 3 (CHD3) were the hub nodes in subgroups 1–4, respectively. Paired box 5 (PAX5), adipocyte protein 2 (aP2), E2F transcription factor 1 (E2F1) and cAMP-response element-binding protein-1 (CREB1) were the specific TFs in subgroups 1–4, respectively. miR-147b, miR-770-5p, miR-220a and miR-1247 were the particular miRNAs in subgroups 1–4, respectively. Natalizumab was the predicted small molecule drug in subgroup 2. In conclusion, the molecular regulatory mechanisms of GBM pathogenesis were distinct in the different subgroups. Several crucial genes, TFs, miRNAs and small molecules in the different GBM subgroups were identified, which may be used as potential markers. However, further experimental validations may be required.

Evaluation of potential circulating biomarkers for prediction of response to chemoradiation in patients with glioblastoma

Surgery followed by chemoradiation and adjuvant chemotherapy is standard of care for patients with a glioblastoma (GBM). Due to its limited benefit, an upfront method to predict dismal outcome would prevent unnecessary toxic treatment. We searched for a predictive blood derived biomarker in a cohort of 55 patients with GBM. Increasing age (HR 1.03, 95 % CI 1.01–1.06), and postoperative tumor residue (HR 1.07, 95 % CI 1.02–1.15) were independently associated with unfavourable progression free survival (PFS) in these patients. Corticosteroid use before start of chemoradiaton was strongly predictive for outcome (HR 3.26, 95 % CI 1.67–6.39) with a mean PFS and OS in patients using corticosteroids of 7.3 and 14.6 months, versus 16.1 and 21.6 months in patients not using corticosteroids (p = 0.0005, p < 0.0067 respectively). Despite earlier reports, blood concentrations of YKL-40, Fetuin-a and haptoglobin were not predictive for response. In addition, serum peptide profiles, determined by MALDI-TOF mass spectroscopy, were not predictive as well. In conclusion, further biomarker discovery studies are needed to predict treatment outcome for patients with GBM in the near future.

Label-Free LC-MS/MS Proteomic Analysis of Cerebrospinal Fluid Identifies Protein/Pathway Alterations and Candidate Biomarkers for Amyotrophic Lateral Sclerosis

Analysis of the cerebrospinal fluid (CSF) proteome has proven valuable to the study of neurodegenerative disorders. To identify new protein/pathway alterations and candidate biomarkers for amyotrophic lateral sclerosis (ALS), we performed comparative proteomic profiling of CSF from sporadic ALS (sALS), healthy control (HC), and other neurological disease (OND) subjects using label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS). A total of 1712 CSF proteins were detected and relatively quantified by spectral counting. Levels of several proteins with diverse biological functions were significantly altered in sALS samples. Enrichment analysis was used to link these alterations to biological pathways, which were predominantly related to inflammation, neuronal activity, and extracellular matrix regulation. We then used our CSF proteomic profiles to create a support vector machines classifier capable of discriminating training set ALS from non-ALS (HC and OND) samples. Four classifier proteins, WD repeat-containing protein 63, amyloid-like protein 1, SPARC-like protein 1, and cell adhesion molecule 3, were identified by feature selection and externally validated. The resultant classifier distinguished ALS from non-ALS samples with 83% sensitivity and 100% specificity in an independent test set. Collectively, our results illustrate the utility of CSF proteomic profiling for identifying ALS protein/pathway alterations and candidate disease biomarkers.

Circulating biomarkers for gliomas

Currently, gliomas are diagnosed by neuroimaging, and refined diagnosis requires resection or biopsy to obtain tumour tissue for histopathological classification and grading. Blood-derived biomarkers, therefore, would be useful as minimally invasive markers that could support diagnosis and enable monitoring of tumour growth and response to treatment. Such circulating biomarkers could distinguish true progression from therapy-associated changes such as radiation necrosis, and help evaluate the persistence or disappearance of a therapeutic target, such as an oncoprotein or a targetable gene mutation, after targeted therapy. Unlike for other tumours, circulating biomarkers for gliomas are still being defined and are not yet in use in clinical practice. Circulating tumour DNA (ctDNA) isolated from plasma has been shown to reflect the mutational status of glioblastoma, and extracellular vesicles (EVs) containing ctDNA, microRNA and proteins function as rapidly adapting reservoirs for glioma biomarkers such as typical DNA mutations, regulatory microRNAs and oncoproteins. Ideally, circulating tumour cells could enable profiling of the whole-tumour genome, but they are difficult to detect and can reflect only a single cell type of the heterogeneous tumour composition, whereas EVs reflect the complex heterogeneity of the whole tumour, as well as its adaptations to therapy. Although all categories of potential blood-derived biomarkers need to be developed further, findings from other tumour types suggest that EVs are the most promising biomarkers.

Targeted Proteomics to Assess the Response to Anti-Angiogenic Treatment in Human Glioblastoma (GBM)

Glioblastoma (GBM) is a highly aggressive primary brain tumor with dismal outcome for affected patients. Because of the significant neo-angiogenesis exhibited by GBMs, anti-angiogenic therapies have been intensively evaluated during the past years. Recent clinical studies were however disappointing, although a subpopulation of patients may benefit from such treatment. We have previously shown that anti-angiogenic targeting in GBM increases hypoxia and leads to a metabolic adaptation toward glycolysis, suggesting that combination treatments also targeting the glycolytic phenotype may be effective in GBM patients. The aim of this study was to identify marker proteins that are altered by treatment and may serve as a short term readout of anti-angiogenic therapy. Ultimately such proteins could be tested as markers of efficacy able to identify patient subpopulations responsive to the treatment. We applied a proteomics approach based on selected reaction monitoring (SRM) to precisely quantify targeted protein candidates, selected from pathways related to metabolism, apoptosis and angiogenesis. The workflow was developed in the context of patient-derived intracranial GBM xenografts developed in rodents and ensured the specific identification of human tumor versus rodent stroma-derived proteins. Quality control experiments were applied to assess sample heterogeneity and reproducibility of SRM assays at different levels. The data demonstrate that tumor specific proteins can be precisely quantified within complex biological samples, reliably identifying small concentration differences induced by the treatment. In line with previous work, we identified decreased levels of TCA cycle enzymes, including isocitrate dehydrogenase, whereas malectin, calnexin, and lactate dehydrogenase A were augmented after treatment. We propose the most responsive proteins of our subset as potential novel biomarkers to assess treatment response after anti-angiogenic therapy that warrant future analysis in clinical GBM samples.

Proteomics analysis of human brain glial cell proteome by 2D gel

INTRODUCTION

Proteomics is increasingly employed in both neurological and oncological research, and applied widely in every area of neuroscience research including brain cancer. Astrocytomas are the most common glioma and can occur in most parts of the brain and occasionally in the spinal cord. Patients with high-grade astrocytomas have a life expectancy of <1 year even after surgery, chemotherapy, and radiotherapy.

MATERIALS AND METHODS

We extracted proteins from tumors and normal brain tissues and then evaluated the protein purity by Bradford test and spectrophotometry method. In this study, we separated proteins by the two-dimensional (2DG) gel electrophoresis method, and the spots were analyzed and compared using statistical data.

RESULTS

On each analytical 2D gel, an average of 800 spots was observed. In this study, 164 spots exhibited up-regulation of expression level, whereas the remaining 179 spots decreased in astrocytoma tumor relative to normal tissue. RESULTS demonstrate that functional clustering and principal component analysis (PCA) has considerable merits in aiding the interpretation of proteomic data. Proteomics is a powerful tool in identifying multiple proteins that are altered following a neuropharmacological intervention in a disease of the central nervous system (CNS).

CONCLUSION

2-D gel and cluster analysis have important roles in the diagnostic management of astrocytoma patients, providing insight into tumor biology. The application of proteomics to CNS research has invariably been very successful in yielding large amounts of data.

Quantitative proteomic analysis of meningiomas for the identification of surrogate protein markers

Meningiomas are the most common non-glial tumors of the brain and spine. Pathophysiology and definite histological grading of meningiomas are frequently found to be deceptive due to their unusual morphological features and locations. Here for the first time we report a comprehensive serum proteomic analysis of different grades of meningiomas by using multiple quantitative proteomic and immunoassay-based approaches to obtain mechanistic insights about disease pathogenesis and identify grade specific protein signatures. In silico functional analysis revealed modulation of different vital physiological pathways including complement and coagulation cascades, metabolism of lipids and lipoproteins, immune signaling, cell growth and apoptosis and integrin signaling in meningiomas. ROC curve analysis demonstrated apolipoprotein E and A-I and hemopexin as efficient predictors for meningiomas. Identified proteins like vimentin, alpha-2-macroglobulin, apolipoprotein B and A-I and antithrombin-III, which exhibited a sequential increase in different malignancy grades of meningiomas, could serve as potential predictive markers.

Bayesian networks in neuroscience: a survey

Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty. Thanks to their generality, Bayesian networks can accommodate continuous and discrete variables, as well as temporal processes. In this paper we review Bayesian networks and how they can be learned automatically from data by means of structure learning algorithms. Also, we examine how a user can take advantage of these networks for reasoning by exact or approximate inference algorithms that propagate the given evidence through the graphical structure. Despite their applicability in many fields, they have been little used in neuroscience, where they have focused on specific problems, like functional connectivity analysis from neuroimaging data. Here we survey key research in neuroscience where Bayesian networks have been used with different aims: discover associations between variables, perform probabilistic reasoning over the model, and classify new observations with and without supervision. The networks are learned from data of any kind-morphological, electrophysiological, -omics and neuroimaging-, thereby broadening the scope-molecular, cellular, structural, functional, cognitive and medical- of the brain aspects to be studied.

Towards developing biomarkers for glioblastoma multiforme: a proteomics view

Glioblastoma multiforme (GBM) is one of the most aggressive and lethal forms of the primary brain tumors. With predominance of tumor heterogeneity and emergence of new subtypes, new approaches are needed to develop tissue-based markers for tumor typing or circulatory markers to serve as blood-based assays. Multi-omics data integration for GBM tissues would offer new insights on the molecular view of GBM pathogenesis useful to identify biomarker panels. On the other hand, mapping differentially expressed tissue proteins for their secretory potential through bioinformatics analysis or analysis of the tumor cell secretome or tumor exosomes would enhance our understanding of the tumor microenvironment and prospects for targeting circulatory biomarkers. In this review, the authors first present potential biomarker candidates for GBM that have been reported and then focus on plausible pipelines for multi-omic data integration to identify additional, high-confidence molecular panels for clinical applications in GBM.

Circulating biomarker panels for targeted therapy in brain tumors

An important goal of oncology is the development of cancer risk-identifier biomarkers that aid early detection and target therapy. High-throughput profiling represents a major concern for cancer research, including brain tumors. A promising approach for efficacious monitoring of disease progression and therapy could be circulating biomarker panels using molecular proteomic patterns. Tailoring treatment by targeting specific protein-protein interactions and signaling networks, microRNA and cancer stem cell signaling in accordance with tumor phenotype or patient clustering based on biomarker panels represents the future of personalized medicine for brain tumors. Gathering current data regarding biomarker candidates, we address the major challenges surrounding the biomarker field of this devastating tumor type, exploring potential perspectives for the development of more effective predictive biomarker panels.

Identification of plasma biomarker candidates in glioblastoma using an antibody-array-based proteomic approach

Background

Glioblastoma multiforme (GBM) is a brain tumour with a very high patient mortality rate, with a median survival of 47 weeks. This might be improved by the identification of novel diagnostic, prognostic and predictive therapy-response biomarkers, preferentially through the monitoring of the patient blood. The aim of this study was to define the impact of GBM in terms of alterations of the plasma protein levels in these patients.

Materials and methods.

We used a commercially available antibody array that includes 656 antibodies to analyse blood plasma samples from 17 healthy volunteers in comparison with 17 blood plasma samples from patients with GBM.

Results

We identified 11 plasma proteins that are statistically most strongly associated with the presence of GBM. These proteins belong to three functional signalling pathways: T-cell signalling and immune responses; cell adhesion and migration; and cell-cycle control and apoptosis. Thus, we can consider this identified set of proteins as potential diagnostic biomarker candidates for GBM. In addition, a set of 16 plasma proteins were significantly associated with the overall survival of these patients with GBM. Guanine nucleotide binding protein alpha (GNAO1) was associated with both GBM presence and survival of patients with GBM.

Conclusions

Antibody array analysis represents a useful tool for the screening of plasma samples for potential cancer biomarker candidates in small-scale exploratory experiments; however, clinical validation of these candidates requires their further evaluation in a larger study on an independent cohort of patients.

Proteomic analysis of cerebrospinal fluid: toward the identification of biomarkers for gliomas

Gliomas are the most common primary brain tumors in adults and, despite advances in the understandings of glioma pathogenesis in the genetic era, they are still ineradicable, justifying the need to develop more reliable diagnostic and prognostic biomarkers for this malignancy. Because changes in cerebrospinal fluid (CSF) are suggested to be capable of sensitively reflecting pathological processes, e.g., neoplastic conditions, in the central nervous system, CSF has been deemed a valuable source for potential biomarkers screening in this era of proteomics. This systematic review focused on the proteomic analysis of glioma CSF that has been published to date and identified a total of 19 differentially expressed proteins. Further functional and protein-protein interaction assessments were performed by using Protein Analysis Through Evolutionary Relationships (PANTHER) website and Ingenuity Pathway Analysis (IPA) software, which revealed several important protein networks (e.g., IL-6/STAT-3) and four novel focus proteins (IL-6, galanin (GAL), HSPA5, and WNT4) that might be involved in glioma pathogenesis. The concentrations of these focus proteins were subsequently determined by enzyme-linked immunosorbent assay (ELISA) in an independent set of CSF and tumor cyst fluid (CF) samples. Specifically, glioblastoma (GBM) CF had significantly lower GAL, HSPA5, and WNT4 levels than CSF from different grades of glioma. In contrast, IL-6 level was significantly higher in GBM CF when compared with CSF and, among different CSF groups, was highest in GBM CSF. Therefore, these candidate protein biomarkers, identified from both the literatures and in silico analysis, may have potentials in clinical diagnosis, prognosis evaluation, treatment response monitoring, and novel therapeutic targets identification for patients with glioma.

Critical multiple angiogenic factors secreted by glioblastoma stem-like cells underline the need for combinatorial anti-angiogenic therapeutic strategies

Glioblastomas are the most frequent adult primary brain tumors that still remain fatal despite major clinical efforts. As in other solid tumors, populations of glioblastoma stem-like cells (GSCs) endowed with tumor initiating and therapeutic resistance properties have been identified. Glioblastomas are highly vascularized tumors resulting in a rich dialog between GSCs and endothelial cells. In one direction, endothelial cells and their secreted proteins are able to sustain GSC properties while, in turn, GSCs can promote neoangiogenesis, modulate endothelial cell functions and may even transdifferentiate into endothelial cells. Accordingly, targeting tumor vasculature seems a promising issue despite incomplete and transient results obtained from anti-vascular endothelial growth factor therapeutic trials. Recent findings of novel GSC-secreted molecules with pro-angiogenic properties (Semaphorin 3A, hepatoma-derived growth factor) open the path to the design of a concerted attack of glioblastoma vasculature that could overcome the development of resistance to single-targeted therapies while keeping away the toxicity of the treatments.

Multi-study integration of brain cancer transcriptomes reveals organ-level molecular signatures

We utilized abundant transcriptomic data for the primary classes of brain cancers to study the feasibility of separating all of these diseases simultaneously based on molecular data alone. These signatures were based on a new method reported herein – Identification of Structured Signatures and Classifiers (ISSAC) – that resulted in a brain cancer marker panel of 44 unique genes. Many of these genes have established relevance to the brain cancers examined herein, with others having known roles in cancer biology. Analyses on large-scale data from multiple sources must deal with significant challenges associated with heterogeneity between different published studies, for it was observed that the variation among individual studies often had a larger effect on the transcriptome than did phenotype differences, as is typical. For this reason, we restricted ourselves to studying only cases where we had at least two independent studies performed for each phenotype, and also reprocessed all the raw data from the studies using a unified pre-processing pipeline. We found that learning signatures across multiple datasets greatly enhanced reproducibility and accuracy in predictive performance on truly independent validation sets, even when keeping the size of the training set the same. This was most likely due to the meta-signature encompassing more of the heterogeneity across different sources and conditions, while amplifying signal from the repeated global characteristics of the phenotype. When molecular signatures of brain cancers were constructed from all currently available microarray data, 90% phenotype prediction accuracy, or the accuracy of identifying a particular brain cancer from the background of all phenotypes, was found. Looking forward, we discuss our approach in the context of the eventual development of organ-specific molecular signatures from peripheral fluids such as the blood.

From a multi-study, integrated transcriptomic dataset, we identified a marker panel for differentiating major human brain cancers at the gene-expression level. The ISSAC molecular signatures for brain cancers, composed of 44 unique genes, are based on comparing expression levels of pairs of genes, and phenotype prediction follows a diagnostic hierarchy. We found that sufficient dataset integration across multiple studies greatly enhanced diagnostic performance on truly independent validation sets, whereas signatures learned from only one dataset typically led to high error rate. Molecular signatures of brain cancers, when obtained using all currently available gene-expression data, achieved 90% phenotype prediction accuracy. Thus, our integrative approach holds significant promise for developing organ-level, comprehensive, molecular signatures of disease.

An extensive targeted proteomic analysis of disease-related protein biomarkers in urine from healthy donors

The analysis of protein biomarkers in urine is expected to lead to advances in a variety of clinical settings. Several characteristics of urine including a low-protein matrix, ease of testing and a demonstrated proteomic stability offer distinct advantages over current widely used biofluids, serum and plasma. Improvements in our understanding of the urine proteome and in methods used in its evaluation will facilitate the clinical development of urinary protein biomarkers. Multiplexed bead-based immunoassays were utilized to evaluate 211 proteins in urines from 103 healthy donors. An additional 25 healthy donors provided serial urine samples over the course of two days in order to assess temporal variation in selected biomarkers. Nearly one-third of the evaluated biomarkers were detected in urine at levels greater than 1 ng/ml, representing a diverse panel of proteins with respect to structure, function and biological role. The presence of several biomarkers in urine was confirmed by western blot. Several methods of data normalization were employed to assess impact on biomarker variability. A complex pattern of correlations with urine creatinine, albumin and beta-2-microglobulin was observed indicating the presence of highly specific mechanisms of renal filtration. Further investigation of the urinary protein biomarkers identified in this preliminary study along with a consideration of the underlying proteomic trends suggested by these findings should lead to an improved capability to identify candidate biomarkers for clinical development.

Extracellular vesicles as prospective carriers of oncogenic protein signatures in adult and paediatric brain tumours

Extracellular vesicles (EVs), including exosomes, act as biological effectors and as carriers of oncogenic signatures in human cancer. The molecular composition and accessibility of EVs in biofluids open unprecedented diagnostic opportunities in malignancies where tumour tissue is difficult to sample, especially in primary and metastatic brain tumours. The ongoing genetic discovery of driver mutations defines the ever increasing numbers of distinct molecular subtypes of brain tumours (orphan diseases), a complexity that may soon be translated into alterations in functional proteins and their oncogenic networks. This may likely be extended to real time changes engendered by the disease progression, tumour heterogeneity, inter-individual variations and therapeutic responses. Meeting these challenges through EV analysis is dependent on technological progress in such areas as generation of mutation- and phospho-specific antibodies, antibody array platforms, nanotechnology, microfluidics, NMR spectroscopy, MS and MRM approaches of quantitative proteomics, which should not be underestimated. Still, vesiculation emerges as a unique process that could be harnessed for the benefit of more individualised patient care.

Alterations in cerebrospinal fluid proteins in a presymptomatic primary glioma model

Background

Understanding the early relationship between brain tumor cells and their environment could lead to more sensitive biomarkers and new therapeutic strategies. We have been using a rodent model of neurocarcinogenesis in which all animals develop brain tumors by six months of age to establish two early landmarks in glioma development: the appearance of a nestin⁺ cell at thirty days of age and the appearance of cellular hyperplasia between 60 and 120 days of age. We now report an assessment of the CSF proteome to determine the changes in protein composition that occur during this period.

Materials and Methods

Nestin⁺ cell clusters and microtumors were assessed in 63 ethylnitrosourea-exposed rats on 30, 60, and 90 days of age. CSF was obtained from the cisterna magna from 101 exposed and control rats at 30, 60, and 90 days and then analyzed using mass spectrometry. Differentially expressed peaks were isolated and identified.

Results

Nestin⁺ cells were noted in all ethylnitrosourea-exposed rats assessed pathologically. Small microtumors were noted in 0%, 18%, and 67% of 30-, 60-, and 90-day old rats, respectively (p<0.05, Chi square). False Discovery Rate analysis of peak intensities showed that the number of true discoveries with p<0.05 increased markedly with increasing age. Isolation and identification of highly differentially detected proteins at 90 days of age revealed increases in albumin and a fragment of α1 macroglobulin and alterations in glutathionylated transthyretin.

Conclusions

The presence of increased albumin, fragments of cerebrospinal fluid proteins, and glutathione breakdown in temporal association with the development of cellular hyperplasia, suggests that, similar to many other systemic cancers, inflammation and oxidative stress is playing an important early role in the host’s response to brain tumor development and may be involved in affecting the early growth of brain tumor.