Proteomics of gliomas: initial biomarker discovery and evolution of technology

Extracellular Matrix Structure and Interaction with Immune Cells in Adult Astrocytic Tumors

The extracellular matrix (ECM) is a dynamic set of molecules produced by the cellular component of normal and pathological tissues of the embryo and adult. ECM acts as critical regulator in various biological processes such as differentiation, cell proliferation, angiogenesis, and immune control. The most frequent primary brain tumors are gliomas and by far the majority are adult astrocytic tumors (AATs). The prognosis for patients with these neoplasms is poor and the treatments modestly improves survival. In the literature, there is a fair number of studies concerning the composition of the ECM in AATs, while the number of studies relating the composition of the ECM with the immune regulation is smaller. Circulating ECM proteins have emerged as a promising biomarker that reflect the general immune landscape of tumor microenvironment and may represent a useful tool in assessing disease activity. Given the importance it can have for therapeutic and prognostic purposes, the aim of our study is to summarize the biological properties of ECM components and their effects on the tumor microenvironment and to provide an overview of the interactions between major ECM proteins and immune cells in AATs. As the field of immunotherapy in glioma is quickly expanding, we retain that current data together with future studies on ECM organization and functions in glioma will provide important insights into the tuning of immunotherapeutic approaches.

The dynamic impact of location and resection on the glioma CSF proteome

While serial sampling of glioma tissue is rarely performed prior to recurrence, cerebrospinal fluid (CSF) is an underutilized longitudinal source of candidate glioma biomarkers for understanding therapeutic impacts. However, the impact of key variables to consider in longitudinal CSF samples, including anatomical location and post-surgical changes, remains unknown. To that end, pre- versus post-resection intracranial CSF samples were obtained at early (1-16 days; n=20) or delayed (86-153 days; n=11) timepoints for patients with glioma. Paired lumbar-versus-intracranial glioma CSF samples were also obtained (n=14). Using aptamer-based proteomics, we identify significant differences in the CSF proteome between lumbar, subarachnoid, and ventricular CSF. Our analysis of serial intracranial CSF samples suggests the early potential for disease monitoring and evaluation of pharmacodynamic impact of targeted therapies. Importantly, we found that resection had a significant, evolving longitudinal impact on the CSF proteome. Proteomic data are provided with individual clinical annotations as a resource for the field.

One Sentence Summary

Glioma cerebrospinal fluid (CSF) accessed intra-operatively and longitudinally via devices can reveal impacts of treatment and anatomical location.

MGMT ProFWise: Unlocking a New Application for Combined Feature Selection and the Rank-Based Weighting Method to Link MGMT Methylation Status to Serum Protein Expression in Patients with Glioblastoma

Glioblastoma (GBM) is a fatal brain tumor with limited treatment options. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status is the central molecular biomarker linked to both the response to temozolomide, the standard chemotherapy drug employed for GBM, and to patient survival. However, MGMT status is captured on tumor tissue which, given the difficulty in acquisition, limits the use of this molecular feature for treatment monitoring. MGMT protein expression levels may offer additional insights into the mechanistic understanding of MGMT but, currently, they correlate poorly to promoter methylation. The difficulty of acquiring tumor tissue for MGMT testing drives the need for non-invasive methods to predict MGMT status. Feature selection aims to identify the most informative features to build accurate and interpretable prediction models. This study explores the new application of a combined feature selection (i.e., LASSO and mRMR) and the rank-based weighting method (i.e., MGMT ProFWise) to non-invasively link MGMT promoter methylation status and serum protein expression in patients with GBM. Our method provides promising results, reducing dimensionality (by more than 95%) when employed on two large-scale proteomic datasets (7k SomaScan® panel and CPTAC) for all our analyses. The computational results indicate that the proposed approach provides 14 shared serum biomarkers that may be helpful for diagnostic, prognostic, and/or predictive operations for GBM-related processes, given further validation.

Proteomic landscape of primary and metastatic brain tumors for heterogeneity discovery

PURPOSE

Despite recent advancements in our understanding of driver gene mutations and heterogeneity within brain tumors, whether primary or metastatic (also known as secondary), our comprehension of proteomic changes remains inadequate. The aim of this study is to provide an informative source for brain tumor researches, and distinguish primary brain tumors and secondary brain tumors from extracranial origins based on proteomic analysis.

EXPERIMENTAL DESIGN

We assembled the most frequent brain tumors as follows: gliomas from WHO grade 2 to 4, with IDH1 mutations and wildtypes; brain metastases (BrMs) originating from lung cancer (LC), breast cancer (BC), ovarian cancer (OC), and colorectal cancer (CC). A total of 29 tissue samples were analyzed by label free quantitative mass spectrometry-based proteomics.

RESULTS

In total, 8165 protein groups were quantified, of which 4383 proteins were filtered at 50% valid intensity values for downstream analysis. Proteomic analysis of BrMs reveals conserved features shared among multiple origins. While proteomic heterogeneities were found for discriminating different grades of gliomas, as well as IDH1 mutant and wildtype gliomas. In addition, notable distinctions were observed at the pathway level between BrMs and gliomas. Specifically, BrMs exhibited characteristic pathways focused on proliferation and immunomodulation after colonizing the brain, whereas gliomas primarily engaged in invasion processes.

CONCLUSIONS AND CLINICAL RELEVANCE

We characterized an extensive proteomic landscape of BrMs and gliomas. These findings have promising implications for the development of targeted therapies for BrMs and gliomas.

Mechanistic insight into glioma through spatially multidimensional proteomics

Characterizing the tumor microenvironment at the molecular level is essential for understanding the mechanisms of tumorigenesis and evolution. However, the specificity of the blood proteome in localized region of the tumor and its linkages with other systems is difficult to investigate. Here, we propose a spatially multidimensional comparative proteomics strategy using glioma as an example. The blood proteome signature of tumor microenvironment was specifically identified by in situ collection of arterial and venous blood from the glioma region of the brain for comparison with peripheral blood. Also, by integrating with different dimensions of tissue and peripheral blood proteomics, the information on the genesis, migration, and exchange of glioma-associated proteins was revealed, which provided a powerful method for tumor mechanism research and biomarker discovery. The study recruited multidimensional clinical cohorts, allowing the proteomic results to corroborate each other, reliably revealing biological processes specific to gliomas, and identifying highly accurate biomarkers.

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.

Self-Functionalized Superlattice Nanosensor Enables Glioblastoma Diagnosis Using Liquid Biopsy

Glioblastoma (GBM), the most aggressive and lethal brain cancer, is detected only in the advanced stage, resulting in a median survival rate of 15 months. Therefore, there is an urgent need to establish GBM diagnosis tools to identify the tumor accurately. The clinical relevance of the current liquid biopsy techniques for GBM diagnosis remains mostly undetermined, owing to the challenges posed by the blood-brain barrier (BBB) that restricts biomarkers entering the circulation, resulting in the unavailability of clinically validated circulating GBM markers. GBM-specific liquid biopsy for diagnosis and prognosis of GBM has not yet been developed. Here, we introduce extracellular vesicles of GBM cancer stem cells (GBM CSC-EVs) as a previously unattempted, stand-alone GBM diagnosis modality. As GBM CSCs are fundamental building blocks of tumor initiation and recurrence, it is desirable to investigate these reliable signals of malignancy in circulation for accurate GBM diagnosis. So far, there are no clinically validated circulating biomarkers available for GBM. Therefore, a marker-free approach was essential since conventional liquid biopsy relying on isolation methodology was not viable. Additionally, a mechanism capable of trace-level detection was crucial to detecting the rare GBM CSC-EVs from the complex environment in circulation. To break these barriers, we applied an ultrasensitive superlattice sensor, self-functionalized for surface-enhanced Raman scattering (SERS), to obtain holistic molecular profiling of GBM CSC-EVs with a marker-free approach. The superlattice sensor exhibited substantial SERS enhancement and ultralow limit of detection (LOD of attomolar 10-18 M concentration) essential for trace-level detection of invisible GBM CSC-EVs directly from patient serum (without isolation). We detected as low as 5 EVs in 5 μL of solution, achieving the lowest LOD compared to existing SERS-based studies. We have experimentally demonstrated the crucial role of the signals of GBM CSC-EVs in the precise detection of glioblastoma. This was evident from the unique molecular profiles of GBM CSC-EVs demonstrating significant variation compared to noncancer EVs and EVs of GBM cancer cells, thus adding more clarity to the current understanding of GBM CSC-EVs. Preliminary validation of our approach was undertaken with a small amount of peripheral blood (5 μL) derived from GBM patients with 100% sensitivity and 97% specificity. Identification of the signals of GBM CSC-EV in clinical sera specimens demonstrated that our technology could be used for accurate GBM detection. Our technology has the potential to improve GBM liquid biopsy, including real-time surveillance of GBM evolution in patients upon clinical validation. This demonstration of liquid biopsy with GBM CSC-EV provides an opportunity to introduce a paradigm potentially impacting the current landscape of GBM diagnosis.

Metabolomic and Lipidomic Characterization of Meningioma Grades Using LC-HRMS and Machine Learning

Meningiomas are among the most common brain tumors that arise from the leptomeningeal cover of the brain and spinal cord and account for around 37% of all central nervous system tumors. According to the World Health Organization, meningiomas are classified into three histological subtypes: benign, atypical, and anaplastic. Sometimes, meningiomas with a histological diagnosis of benign tumors show clinical characteristics and behavior of aggressive tumors. In this study, we examined the metabolomic and lipidomic profiles of meningioma tumors, focusing on comparing low-grade and high-grade tumors and identifying potential markers that can discriminate between benign and malignant tumors. High-resolution mass spectrometry coupled to liquid chromatography was used for untargeted metabolomics and lipidomics analyses of 85 tumor biopsy samples with different meningioma grades. We then applied feature selection and machine learning techniques to find the features with the highest information to aid in the diagnosis of meningioma grades. Three biomarkers were identified to differentiate low- and high-grade meningioma brain tumors. The use of mass-spectrometry-based metabolomics and lipidomics combined with machine learning analyses to prospect and characterize biomarkers associated with meningioma grades may pave the way for elucidating potential therapeutic and prognostic targets.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma

Glioblastomas (GBM) are rapidly growing, aggressive, nearly uniformly fatal, and the most common primary type of brain cancer. They exhibit significant heterogeneity and resistance to treatment, limiting the ability to analyze dynamic biological behavior that drives response and resistance, which are central to advancing outcomes in glioblastoma. Analysis of the proteome aimed at signal change over time provides a potential opportunity for non-invasive classification and examination of the response to treatment by identifying protein biomarkers associated with interventions. However, data acquired using large proteomic panels must be more intuitively interpretable, requiring computational analysis to identify trends. Machine learning is increasingly employed, however, it requires feature selection which has a critical and considerable effect on machine learning problems when applied to large-scale data to reduce the number of parameters, improve generalization, and find essential predictors. In this study, using 7k proteomic data generated from the analysis of serum obtained from 82 patients with GBM pre- and post-completion of concurrent chemoirradiation (CRT), we aimed to select the most discriminative proteomic features that define proteomic alteration that is the result of administering CRT. Thus, we present a novel rank-based feature weighting method (RadWise) to identify relevant proteomic parameters using two popular feature selection methods, least absolute shrinkage and selection operator (LASSO) and the minimum redundancy maximum relevance (mRMR). The computational results show that the proposed method yields outstanding results with very few selected proteomic features, with higher accuracy rate performance than methods that do not employ a feature selection process. While the computational method identified several proteomic signals identical to the clinical intuitive (heuristic approach), several heuristically identified proteomic signals were not selected while other novel proteomic biomarkers not selected with the heuristic approach that carry biological prognostic relevance in GBM only emerged with the novel method. The computational results show that the proposed method yields promising results, reducing 7k proteomic data to 8 selected proteomic features with a performance value of 96.364%, comparing favorably with techniques that do not employ feature selection.

Spatial analysis of the glioblastoma proteome reveals specific molecular signatures and markers of survival

Molecular heterogeneity is a key feature of glioblastoma that impedes patient stratification and leads to large discrepancies in mean patient survival. Here, we analyze a cohort of 96 glioblastoma patients with survival ranging from a few months to over 4 years. 46 tumors are analyzed by mass spectrometry-based spatially-resolved proteomics guided by mass spectrometry imaging. Integration of protein expression and clinical information highlights three molecular groups associated with immune, neurogenesis, and tumorigenesis signatures with high intra-tumoral heterogeneity. Furthermore, a set of proteins originating from reference and alternative ORFs is found to be statistically significant based on patient survival times. Among these proteins, a 5-protein signature is associated with survival. The expression of these 5 proteins is validated by immunofluorescence on an additional cohort of 50 patients. Overall, our work characterizes distinct molecular regions within glioblastoma tissues based on protein expression, which may help guide glioblastoma prognosis and improve current glioblastoma classification.

Cysteine cathepsin C: a novel potential biomarker for the diagnosis and prognosis of glioma

Background

Cysteine cathepsin C encoded by the CTSC gene is an important member of the cysteine cathepsin family that plays a key role regulation of many types of tumors. However, whether CTSC is involved in the pathological process of glioma has not yet been reported. We comprehensively analyzed data from multiple databases and for the first time revealed a role and specific mechanism of action of CTSC in glioma, identifying it as a novel and efficient biomarker for the diagnosis and treatment of this brain tumor.

Methods

The expression of CTSC in glioma and its relationship with clinical characteristics and prognosis of patients with glioma were analyzed at different levels by using clinical sample information from several databases. CTSC expression levels in glioma and normal brain tissues, as well as in glioma cells and normal brain cells, was validated by real-time quantitative polymerase chain reaction (RT-qPCR). Gene set enrichment analysis (GSEA) was used to reveal the signaling pathways that CTSC may participate in. The connectivity map was used to reveal small molecules that may inhibit CTSC expression in glioma, and the putative effect of these compounds was verified by RT-qPCR.

Results

Our analyses showed that the expression of CTSC in glioma was higher than that in non-cancerous cells. GSEA showed that CTSC expression may regulate the malignant development of glioma through Toll-like receptor signaling pathways, pathways in cancer, and extracellular matrix receptor interaction signaling pathways. And we proved piperlongumine and scopoletin could inhibit CTSC expression in glioma cells.

Conclusions

CTSC may serve as an efficient molecular target for the diagnosis and therapy of glioma, thereby improving the poor prognosis of patients with glioma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02417-6.

Plasma-Derived Extracellular Vesicles Reveal Galectin-3 Binding Protein as Potential Biomarker for Early Detection of Glioma

Gliomas are the most common type of the malignant brain tumor, which arise from glial cells. They make up about 40% of all primary brain tumors and around 70% of all primary malignant brain tumors. They can occur anywhere in the central nervous system (CNS) and have a poor prognosis. The average survival of glioma patients is approximately 6-15 months with poor aspects of life. In this edge, identification of proteins secreted by cancer cells is of special interest because it may provide a better understanding of tumor progression and provide early diagnosis of the diseases. Extracellular vesicles (EVs) were isolated from pooled plasma of healthy controls (n=03) and patients with different grades of glioma (Grade I or II or III, n=03 each). Nanoparticle tracking analysis, western blot, and flow cytometry were performed to determine the size, morphology, the concentration of glioma-derived vesicles and EV marker, CD63. Further, iTRAQ-based LC-MS/MS analysis of EV protein was performed to determine the differential protein abundance in extracellular vesicles across different glioma grades. We further verified galectin-3 binding protein (LGALS3BP) by ELISA in individual blood plasma and plasma-derived vesicles from control and glioma patients (n=40 each). Analysis by Max Quant identified 123 proteins from the pooled patient exosomes, out of which 34, 21, and 14 proteins were found to be differentially abundant by more than 1.3-fold in the different grades of glioma grade I, pilocytic astrocytoma; grade II, diffuse astrocytoma; grade III, anaplastic astrocytoma, respectively, in comparison with the control samples. A total of seven proteins-namely, CRP, SAA2, SERPINA3, SAA1, C4A, LV211, and KV112-showed differential abundance in all the three grades. LGALS3BP was seen to be upregulated across the different grades, and ELISA analysis from individual blood plasma and plasma-derived extracellular vesicles confirmed the increased expression of LGALS3BP in glioma patients (p<0.001). The present study provides LGALS3BP as a potential biomarker for early detection of glioma and improve survival outcome of the patient. The present study further provides the information of progression and monitoring the tumor grades (grade 1, grade II, grade III).

The Big Picture of Glioblastoma Malignancy: A Meta-Analysis of Glioblastoma Proteomics to Identify Altered Biological Pathways

Glioblastoma is a highly malignant cancer with no effective treatment. It is vital to elucidate the mechanisms which drive glioblastoma in order to identify therapeutic targets. The differences in protein expression between glioblastoma, grade I-III glioma, and normal brain tissue reflect the functional alterations driving malignancy. However, proteomic analysis of glioblastoma has been hampered by the heterogeneity of glioblastoma and the variety of methodology used in its study. To reduce these inconsistencies, we performed a meta-analysis of the literature published since 2015, including 14 datasets from eight papers comparing the whole proteome of glioblastoma to normal brain or grade I-III glioma. We found that 154 proteins were commonly upregulated and 116 proteins were commonly downregulated in glioblastoma compared to normal brain. Meanwhile, 240 proteins were commonly upregulated and 125 proteins were commonly downregulated in glioblastoma compared to grade I-III glioma. Functional enrichment analysis revealed upregulation of proteins involved in mRNA splicing and the immune system and downregulation of proteins involved in synaptic signaling and glucose and glutamine metabolism. The identification of these altered biological pathways provides a basis for deeper investigation in the pursuit of an effective treatment for glioblastoma.

BICD Cargo Adaptor 1 (BICD1) Downregulation Correlates with a Decreased Level of PD-L1 and Predicts a Favorable Prognosis in Patients with IDH1-Mutant Lower-Grade Gliomas

Simple Summary

The hypoxic inducible factor 1A (HIF1A) pathway has been known to play an important role in tumor progression in various cancers, including lower-grade (Grade II/III) gliomas (LGGs). An in silico analysis using 34 genes associated with the activity of the HIF1A pathway demonstrated that the BICD cargo adaptor 1 (BICD1) gene is a potential prognostic marker in LGGs. Moreover, BICD1 gene (BICD1) expression was positively correlated with CD274, GSK3B, HGF, and STAT3 expression in LGGs. Importantly, BICD1 downregulation was significantly associated with well-known favorable prognostic markers, such as a higher Karnofsky performance score (KPS), IDH1/TP53/ATRX mutations, wild-type EGFR and younger patient age, in LGGs. Therefore, our findings present BICD1 as a new prognostic biomarker to more precisely predict the clinical outcomes of LGG patients in coordination with those well-known biomarkers.

Abstract

Although several biomarkers have been identified to predict the prognosis of lower-grade (Grade II/III) gliomas (LGGs), we still need to identify new markers to facilitate those well-known markers to obtain more accurate prognosis prediction in LGGs. Bioinformatics data from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and the Cancer Cell Line Encyclopedia (CCLE) datasets were used as the research materials. In total, 34 genes associated with the HIF1A pathway were analyzed using the hierarchical method to search for the most compatible gene. The BICD cargo adaptor 1 (BICD1) gene (BICD1) was shown to be significantly correlated with The hypoxic inducible factor 1A (HIF1A) expression, the World Health Organization (WHO) grade, and IDH1 mutation status. In addition, BICD1 downregulation was significantly correlated with a higher Karnofsky performance score (KPS), IDH1/TP53/ATRX mutations, wild-type EGFR, and younger patient age in the enrolled LGG cohort. Moreover, BICD1 expression was significantly upregulated in wild-type IDH1 LGGs with EGFR mutations. Kaplan–Meier survival analysis revealed that BICD1 downregulation predicts a favorable overall survival (OS) in LGG patients, especially in those with IDH1 mutations. Intriguingly, we found a significant correlation between BICD1 downregulation and a decreased level of CD274, GSK3B, HGF, or STAT3 in LGGs. Our findings suggest that BICD1 downregulation could be a potential biomarker for a favorable prognosis of LGGs.

Novel Post-Translational Modifications and Molecular Substrates in Glioma Identified by Bioinformatics

Glioma is the most common type of brain cancer that originates from the glial cells. It constitutes about one-third of all brain cancers. Recently, transcriptomics, proteomics, and multiomics approaches have been harnessed to discover potential biomarkers and therapeutic targets in glioma. Moreover, post-translational modifications (PTMs) of proteins play a major role in cell biology and function and offer new avenues of research in cancer. Using unbiased multi-PTM bioinformatics analyses of two proteomic datasets of glioma available in the public domain, we identified 866 proteins with common PTMs from both studies. Out of these 866 proteins, 19 proteins were identified with the common PTMs, with the same site modifications pertaining to glioma. Importantly, the identified PTMs belonged to proteins involved in integrin PI3K/Akt/mTOR, JAK/STAT, and Ras/Raf/MAPK pathways. These pathways are essential for cell proliferation in tumor cells and thus involved in glioma progression. Taken together, these findings call for validation in larger datasets in glioma and brain cancers and with an eye to future drug discovery and diagnostic innovation. Bioinformatics-guided discovery of novel PTMs from the publicly available proteomic data can offer new avenues for innovation in cancer research.

A Systems Approach to Brain Tumor Treatment

Brain tumors are among the most lethal tumors. Glioblastoma, the most frequent primary brain tumor in adults, has a median survival time of approximately 15 months after diagnosis or a five-year survival rate of 10%; the recurrence rate is nearly 90%. Unfortunately, this prognosis has not improved for several decades. The lack of progress in the treatment of brain tumors has been attributed to their high rate of primary therapy resistance. Challenges such as pronounced inter-patient variability, intratumoral heterogeneity, and drug delivery across the blood-brain barrier hinder progress. A comprehensive, multiscale understanding of the disease, from the molecular to the whole tumor level, is needed to address the intratumor heterogeneity resulting from the coexistence of a diversity of neoplastic and non-neoplastic cell types in the tumor tissue. By contrast, inter-patient variability must be addressed by subtyping brain tumors to stratify patients and identify the best-matched drug(s) and therapies for a particular patient or cohort of patients. Accomplishing these diverse tasks will require a new framework, one involving a systems perspective in assessing the immense complexity of brain tumors. This would in turn entail a shift in how clinical medicine interfaces with the rapidly advancing high-throughput (HTP) technologies that have enabled the omics-scale profiling of molecular features of brain tumors from the single-cell to the tissue level. However, several gaps must be closed before such a framework can fulfill the promise of precision and personalized medicine for brain tumors. Ultimately, the goal is to integrate seamlessly multiscale systems analyses of patient tumors and clinical medicine. Accomplishing this goal would facilitate the rational design of therapeutic strategies matched to the characteristics of patients and their tumors. Here, we discuss some of the technologies, methodologies, and computational tools that will facilitate the realization of this vision to practice.

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.

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.

Quantitative MRI Biomarkers of Stereotactic Radiotherapy Outcome in Brain Metastasis

About 20-40% of cancer patients develop brain metastases, causing significant morbidity and mortality. Stereotactic radiation treatment is an established option that delivers high dose radiation to the target while sparing the surrounding normal tissue. However, up to 20% of metastatic brain tumours progress despite stereotactic treatment, and it can take months before it is evident on follow-up imaging. An early predictor of radiation therapy outcome in terms of tumour local failure (LF) is crucial, and can facilitate treatment adjustments or allow for early salvage treatment. In this study, an MR-based radiomics framework was proposed to derive and investigate quantitative MRI (qMRI) biomarkers for the outcome of LF in brain metastasis patients treated with hypo-fractionated stereotactic radiation therapy (SRT). The qMRI biomarkers were constructed through a multi-step feature extraction/reduction/selection framework using the conventional MR imaging data acquired from 100 patients (133 lesions), and were applied in conjunction with machine learning techniques for outcome prediction and risk assessment. The results indicated that the majority of the features in the optimal qMRI biomarkers characterize the heterogeneity in the surrounding regions of tumour including edema and tumour/lesion margins. The optimal qMRI biomarker consisted of five features that predict the outcome of LF with an area under the curve (AUC) of 0.79, and a cross-validated sensitivity and specificity of 81% and 79%, respectively. The Kaplan-Meier analyses showed a statistically significant difference in local control (p-value < 0.0001) and overall survival (p = 0.01). Findings from this study are a step towards using qMRI for early prediction of local failure in brain metastasis patients treated with SRT. This may facilitate early adjustments in treatment, such as surgical resection or salvage radiation, that can potentially improve treatment outcomes. Investigations on larger cohorts of patients are, however, required for further validation of the technique.

Identification of Astrocytoma Blood Serum Protein Profile

High-grade astrocytomas are some of the most common and aggressive brain cancers, whose signs and symptoms are initially non-specific. Up to the present date, there are no diagnostic tools to observe the early onset of the disease. Here, we analyzed the combination of blood serum proteins, which may play key roles in the tumorigenesis and the progression of glial tumors. Fifty-nine astrocytoma patients and 43 control serums were analyzed using Custom Human Protein Antibody Arrays, including ten targets: ANGPT1, AREG, IGF1, IP10, MMP2, NCAM1, OPN, PAI1, TGFβ1, and TIMP1. The decision tree analysis indicates that serums ANGPT1, TIMP1, IP10, and TGFβ1 are promising combinations of targets for glioma diagnostic applications. The accuracy of the decision tree algorithm was 73.5% (75/102), which correctly classified 79.7% (47/59) astrocytomas and 65.1% (28/43) healthy controls. The analysis revealed that the relative value of osteopontin (OPN) protein level alone predicted the 12-month survival of glioblastoma (GBM) patients with the specificity of 84%, while the inclusion of the IP10 protein increased model predictability to 92.3%. In conclusion, the serum protein profiles of ANGPT1, TIMP1, IP10, and TGFβ1 were associated with the presence of astrocytoma independent of its malignancy grade, while OPN and IP10 were associated with GBM patient survival.

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

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

Seven genes for the prognostic prediction in patients with glioma

PURPOSE

Glioma is a common malignant tumor of the central nervous system, which is characterized by a low cure rate, high morbidity, and high recurrence rate. Consequently, it is imperative to explore some indicators for prognostic prediction in glioma.

METHODS

We obtained glioma data from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were obtained by R software from TCGA data sets. Through Cox regression analysis, risk scores were obtained to assess the weighted gene-expression levels, which could predict the prognosis of patients with glioma. The validity and the prognostic value of this model in glioma were confirmed by the manifestation of receiver-operating characteristic (ROC) curves, area under the curve (AUC), and 5-year overall survival (OS).

RESULTS

In total, 920 DEGs of transcriptome genes in glioma were extracted from the TCGA database. We identified a novel seven-gene signature associated with glioma. Among them, AL118505.1 and SMOC1 were positively related to the 5-year OS of patients with glioma, showing a better prognosis for glioma; however, RAB42, SHOX2, IGFBP2, HIST1H3G, and IGF2BP3 were negatively related to 5-year OS, displaying a worse prognosis. In addition, according to risk scores, AL118505.1 was also a protective factor, while others were risk factors. Furthermore, the expression levels of SHOX2, IGFBP2, and IGF2BP3 were significantly positively correlated with glioma grades. Receiver-operating characteristic (ROC) curve assessed the accuracy and sensitivity of the gene signature. Each of the seven genes for patients with the distribution of the risk score was presented in the heat map.

CONCLUSION

We identified a novel seven-gene signature in patients with glioma, which could be used as a predictor for the prognosis of patients with glioma in the future.

Discovery and Validation of Clinical Biomarkers of Cancer: A Review Combining Metabolomics and Proteomics

Early detection and diagnosis of cancer can allow timely medical intervention, which greatly improves chances of survival and enhances quality of life. Biomarkers play an important role in assisting clinicians and health care providers in cancer diagnosis and treatment follow-up. In spite of years of research and the discovery of thousands of candidate cancer biomarkers, only a few have transitioned to routine usage in the clinic. This review highlights advances in proteomics technologies that have enabled high rates of discovery of candidate cancer biomarkers and evaluates integration with other omics technologies to improve their progress through to validation and clinical translation. Furthermore, it gauges the role of metabolomics technology in cancer biomarker research and assesses it as a complementary tool in aiding cancer biomarker discovery and validation.

Enhanced proteasomal activity is essential for long term survival and recurrence of innately radiation resistant residual glioblastoma cells

Therapy resistance and recurrence in Glioblastoma is due to the presence of residual radiation resistant cells. However, because of their inaccessibility from patient biopsies, the molecular mechanisms driving their survival remain unexplored. Residual Radiation Resistant (RR) and Relapse (R) cells were captured using cellular radiation resistant model generated from patient derived primary cultures and cell lines. iTRAQ based quantitative proteomics was performed to identify pathways unique to RR cells followed by in vitro and in vivo experiments showing their role in radio-resistance. 2720 proteins were identified across Parent (P), RR and R population with 824 and 874 differential proteins in RR and R cells. Unsupervised clustering showed proteasome pathway as the most significantly deregulated pathway in RR cells. Concordantly, the RR cells displayed enhanced expression and activity of proteasome subunits, which triggered NFkB signalling. Pharmacological inhibition of proteasome activity led to impeded NFkB transcriptional activity, radio-sensitization of RR cells in vitro, and significantly reduced capacity to form orthotopic tumours in vivo. We demonstrate that combination of proteasome inhibitor with radio-therapy abolish the inaccessible residual resistant cells thereby preventing GBM recurrence. Furthermore, we identified first proteomic signature of RR cells that can be exploited for GBM therapeutics.

Tissue Proteome Analysis of Different Grades of Human Gliomas Provides Major Cues for Glioma Pathogenesis

Gliomas are heterogeneous and most commonly occurring brain tumors. Blood-brain barrier restricts the entry of brain tumor proteins into blood stream thus limiting the usage of serum or plasma for proteomic analysis. Our study aimed at understanding the molecular basis of aggressiveness of various grades of brain tumors using isobaric tagging for relative and absolute quantification (iTRAQ) based mass spectrometry. Tissue proteomic analysis of various grades of gliomas was performed using four-plex iTRAQ. We labeled five sets (each set consists of control, grade-II, III, and IV tumor samples) of individual glioma patients using iTRAQ reagents. Significantly altered proteins were subjected to bioinformatics analysis using Database for Annotation, Visualization and Integrated Discovery (DAVID). Various metabolic pathways like glycolysis, TCA-cycle, electron transport chain, lactate metabolism, and blood coagulation pathways were majorly observed to be perturbed in gliomas. Most of the identified proteins involved in redox reactions, protein folding, pre-messenger RNA (mRNA) processing, antiapoptosis, and blood coagulation were found to be upregulated in gliomas. Transcriptomics data of glioblastoma multiforme (GBM), low-grade gliomas (LGGs), and controls were downloaded from The Cancer Genome Atlas (TCGA) data portal and further analyzed using BRB-Array tools. Expression levels of a few significantly altered proteins like lactate dehydrogenase, alpha-1 antitrypsin, fibrinogen alpha chain, nucleophosmin, annexin A5, thioredoxin, ferritin light chain, thymosin beta-4-like protein 3, superoxide dismutase-2, and peroxiredoxin-1 and 6 showed a positive correlation with increasing grade of gliomas thereby offering an insight into molecular basis behind their aggressive nature. Several proteins identified in different grades of gliomas are potential grade-specific markers, and perturbed pathways provide comprehensive overview of molecular cues involved in glioma pathogenesis.

Identification of Plasma Membrane Glycoproteins Specific to Human Glioblastoma Multiforme Cells Using Lectin Arrays and LC-MS/MS

Glioblastoma, also known as glioblastoma multiforme (GBM), is the most malignant type of brain cancer and has poor prognosis with a median survival of less than one year. While the structural changes of tumor cell surface carbohydrates are known to be associated with invasive behavior of tumor cells, the cell surface glycoproteins to differentiate the low- and high-grade glioma cells can be potential diagnostic markers and therapeutic targets for GBMs. In the present study, lectin arrays consisting of eight lectins were employed to explore cell surface carbohydrate expression patterns on low-grade oligodendroglioma cells (Hs683) and GBM cells (T98G). Griffonia simplicifolia I (GS I) was found to selectively bind to T98G cells and not to Hs683 cells. For identification of the glioblastoma-specific cell surface markers, the glycoproteins from each cell type were captured by a GS I lectin column and analyzed by LC-MS/MS. The identified proteins from the two cell types were quantified using label-free quantitative analysis based on spectral counting. Of cell surface glycoproteins showing significant increases in T98G cells, five proteins were selected for verification of both protein and glycosylation level changes using Western blot and GS I lectin-based immunosorbent assay.

BICD1 expression, as a potential biomarker for prognosis and predicting response to therapy in patients with glioblastomas

There is variation in the survival and therapeutic outcome of patients with glioblastomas (GBMs). Therapy resistance is an important challenge in the treatment of GBM patients. The aim of this study was to identify Temozolomide (TMZ) related genes and confirm their clinical relevance. The TMZ-related genes were discovered by analysis of the gene-expression profiling in our cell-based microarray. Their clinical relevance was verified by in silico meta-analysis of the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) datasets. Our results demonstrated that BICD1 expression could predict both prognosis and response to therapy in GBM patients. First, high BICD1 expression was correlated with poor prognosis in the TCGA GBM cohort (n=523) and in the CGGA glioma cohort (n=220). Second, high BICD1 expression predicted poor outcome in patients with TMZ treatment (n=301) and radiation therapy (n=405). Third, multivariable Cox regression analysis confirmed BICD1 expression as an independent factor affecting the prognosis and therapeutic response of TMZ and radiation in GBM patients. Additionally, age, MGMT and BICD1 expression were combinedly utilized to stratify GBM patients into more distinct risk groups, which may provide better outcome assessment. Finally, we observed a strong correlation between BICD1 expression and epithelial-mesenchymal transition (EMT) in GBMs, and proposed a possible mechanism of BICD1-associated survival or therapeutic resistance in GBMs accordingly. In conclusion, our study suggests that high BICD1 expression may result in worse prognosis and could be a predictor of poor response to TMZ and radiation therapies in GBM patients.

Cerebrospinal fluid and serum IL-8, CCL2, and ICAM-1 concentrations in astrocytic brain tumor patients

BACKGROUND

The aim of the study was the evaluation of serum and CSF concentrations of CCL2, IL-8, and sICAM-1 in patients with astrocytic tumors as compared to a group of non-tumoral patients.

METHODS

Chemokine concentrations were measured using the ELISA method.

RESULTS

Regardless of the parameter tested and the patient group (brain tumor or non-tumoral patients), statistical differences (P < 0.05) were found between concentrations obtained in CSF compared to values obtained in serum for all proteins tested. CSF IL-8 concentrations were significantly elevated in CNS tumor patients as compared to non-tumoral individuals (P = 0.000); serum CCL2 and sICAM-1 concentrations were significantly decreased in CNS tumors in comparison with the comparative group (P = 0.002 and P = 0.026, respectively). Among proteins tested in the serum, a higher area under the ROC curve (AUC) revealed CCL2 compared to sICAM-1 in differentiating subjects with CNS brain tumors from non-tumoral subjects. AUC for CSF IL-8 was higher than for its index (CSF IL-8/serum IL-8).

CONCLUSIONS

For individual biomarkers (IL-8 and CCL2, sICAM-1), measured in CNS brain tumor patients, the appropriate material, respectively CSF or serum, should be chosen and quantitatively tested. Increased cerebrospinal fluid IL-8 with decreased serum CCL2 create a pattern of biomarkers, which may be helpful in the management of CNS astrocytic brain tumors.

Label-free quantitative proteomics unravels the importance of RNA processing in glioma malignancy

Glioma, one of the most common cancers in human, is classified to different grades according to the degrees of malignancy. Glioblastoma (GBM) is known to be the most malignant (Grade IV) whereas low-grade astrocytoma (LGA, Grade II) is relatively benign. The mechanism underlying the pathogenesis and progression of glioma malignancy remains unclear. Here we report a quantitative proteomic study to elucidate the differences between GBM and LGA using liquid chromatography and tandem mass spectrometry followed by label-free quantification. A total of 136 proteins were differentially expressed in GBM for at least five folds in comparison with LGA. Ontological analysis revealed a close correlation between GBM-associated proteins and RNA processing. Interaction network analysis indicated that the GBM-associated proteins in the RNA processing were linked to crucial signaling transduction modulators including epidermal growth factor receptor (EGFR), signal transducer and activator of transcription 1 (STAT1), and mitogen-activated protein kinase 1 (MAPK1), which were further connected to the proteins important for neuronal structural integrity, development and functions. Upregulation of 40S ribosomal protein S5 (RPS5), Ferritin Heavy chain (FTH1) and STAT1, and downregulation of tenascin R (TNR) were validated as representatives by immune assays. In summary, we revealed a panel of GBM-associated proteins and the important modulators centered at the RNA-processing network in glioma malignancy that may become novel biomarkers and help elucidate the underlying mechanism.

Evaluation of non-supervised MALDI mass spectrometry imaging combined with microproteomics for glioma grade III classification

An integrated diagnosis using molecular features is recommended in the 2016 World Health Organization (WHO) classification. Our aim was to explore non-targeted molecular classification using MALDI mass spectrometry imaging (MALDI MSI) associated to microproteomics in order to classify anaplastic glioma by integration of clinical data. We used fresh-frozen tissue sections to perform MALDI MSI of proteins based on their digestion peptides after in-situ trypsin digestion of the tissue sections and matrix deposition by micro-spraying. The generated 70μm spatial resolution image datasets were further processed by individual or global segmentation in order to cluster the tissues according to their molecular protein signature. The clustering gives 3 main distinct groups. Within the tissues the ROIs (regions of interest) defined by these groups were used for microproteomics by micro-extraction of the tryptic peptides after on-tissue enzymatic digestion. More than 2500 proteins including 22 alternative proteins (AltProt) are identified by the Shotgun microproteomics. Statistical analysis on the basis of the label free quantification of the proteins shows a similar classification to the MALDI MSI segmentation into 3 groups. Functional analysis performed on each group reveals sub-networks related to neoplasia for group 1, glioma with inflammation for group 2 and neurogenesis for group 3. This demonstrates the interest on these new non-targeted large molecular data combining both MALDI MSI and microproteomics data, for tumor classification. This analysis provides new insights into grade III glioma organization. This specific information could allow a more accurate classification of the biopsies according to the prognosis and the identification of potential new targeted therapeutic options. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Differential proteomics analysis of low- and high-grade of astrocytoma using iTRAQ quantification

Astrocytoma is one of the most common types of brain tumor, which is histologically and clinically classified into four grades (I–IV): I (pilocytic astrocytoma), II (diffuse astrocytoma), III (anaplastic astrocytoma), and IV (glioblastoma multiforme). A higher grade astrocytoma represents a worse prognosis and is more aggressive. In this study, we compared the differential proteome profile of astrocytoma from grades I to IV. The protein samples from clinical specimens of grades I, II, III, and IV astrocytoma were analyzed by two-dimensional liquid chromatography–tandem mass spectrometry and isobaric tags for relative and absolute quantitation and quantification. A total of 2,190 proteins were identified. Compared to grade I astrocytoma, 173 (12.4%), 304 (14%), and 462 (21.2%) proteins were aberrantly expressed in grades II, III, and IV, respectively. By bioinformatics analysis, the cell proliferation, invasion, and angiogenesis-related pathways increase from low- to high-grade of astrocytoma. Five differentially expressed proteins were validated by Western blot. Within them, matrix metalloproteinase-9 and metalloproteinase inhibitor 1 were upregulated in glioblastoma multiforme group; whereas fibulin-2 and -5 were downregulated in grade II/III/IV astrocytoma, and the negative expression was significantly associated with advanced clinical stage. Functional analysis showed that both fibulin-2 and -5 may exert an antitumor effect by inhibiting cell proliferation, in vitro migration/invasion in glioma cells. New molecular biomarkers are likely to be used for accurate classification of astrocytoma and likely to be the target for drug development.

A review of novel analytical diagnostics for liquid biopsies: spectroscopic and spectrometric serum profiling of primary and secondary brain tumors

INTRODUCTION

Spectroscopic and spectrometric analysis of biological samples is regarded as quick, cost effective, easy to operate, and spectroscopic sample preparation involves minimal sample preparation.

RESULTS

Techniques like infrared (IR) spectroscopy, surface-enhanced laser desorption/ionization (SELDI)-mass spectroscopy (MS), and matrix-assisted laser desorption/ionization (MALDI) -MS could enable early diagnosis of cancer, disease monitoring, and assessment of treatment responses allowing refinement, if required.

DISCUSSION

Carrying out analytical testing within outpatient clinics would dramatically cut the time spent by patients attending different appointments, at different locations, save hospital time and resources but importantly would theoretically enable a reduction in mortality and morbidity. While the advantages of such a prospect seem obvious, this review aims to evaluate the use of human serum spectroscopic and spectrometric analysis as a diagnostic tool for brain cancers, creating a platform for the future of cancer diagnostics.

Matricellular proteins in drug delivery: Therapeutic targets, active agents, and therapeutic localization

Extracellular matrix is composed of a complex array of molecules that together provide structural and functional support to cells. These properties are mainly mediated by the activity of collagenous and elastic fibers, proteoglycans, and proteins such as fibronectin and laminin. ECM composition is tissue-specific and could include matricellular proteins whose primary role is to modulate cell-matrix interactions. In adults, matricellular proteins are primarily expressed during injury, inflammation and disease. Particularly, they are closely associated with the progression and prognosis of cardiovascular and fibrotic diseases, and cancer. This review aims to provide an overview of the potential use of matricellular proteins in drug delivery including the generation of therapeutic agents based on the properties and structures of these proteins as well as their utility as biomarkers for specific diseases.

Multifunctional nanoreactor for comprehensive characterization of membrane proteins based on surface functionalized mesoporous foams

An integrated protocol is proposed here for efficient analysis of membrane proteins based on surface functionalized mesoporous graphene foams (MGF). The inherent hydrophobic nature of MGF and surface modification with hydrophilic chitosan (CS) make it highly suitable for the enrichment of hydrophobic membrane proteins from organic solvent, while remaining well-dispersed in aqueous solution for subsequent proteolysis. Therefore, such a multifunctional reactor ensures a facile solvent adjustment route. Furthermore, as a chitosan modified nanoporous reactor, it also provides a biocompatible nanoenvironment that can maintain the stability and activity of enzymes to realize efficient in situ digestion of the enriched membrane proteins. The concept was first proved with a standard hydrophobic membrane protein, bacteriorhodopsin, where a high number of identified peptides and amino acid sequence coverage were achieved even at extremely low protein concentration. The mesoporous reaction system was further applied to the analysis of complex real-case proteome samples, where 931 membrane proteins were identified in triplicate analyses by 2D LC-MS/MS. In contrast, with in-solution proteolysis, only 73 membrane proteins were identified from the same sample by the same 2D LC-MS/MS. The identified membrane proteins by the MGF-CS protocol include many biomarkers of the cell line. These results suggest that the multifunctional MGF-CS protocol is of great value to facilitate the comprehensive characterization of membrane proteins in the proteome research.

Proteomic-driven biomarker discovery in gestational diabetes mellitus: a review

Gestational diabetes mellitus (GDM) is defined as any degree of glucose intolerance with onset or first recognition during pregnancy and it affects 18% of pregnant women worldwide. GDM is considered a high-risk state which may lead to type II diabetes which is associated with an increase in a number of interrelated adverse perinatal outcomes. Given the fact that the progress of a successful pregnancy is dependent on the intricate communication between several biological molecules, identification of the proteomic profile perturbations in women with GDM is expected to help in understanding the disease pathogenesis and also discovery of clinical biomarker(s). In recent years, both gel-free and gel-based proteomics have been extensively investigated for improving maternal and child health. Although there are several reports integrating various aspects of proteomics in pregnancy related diseases such as preeclampsia, extensive Pubmed search shows no review so far on the application of proteomics in gestational diabetes. In this review, we focus on various high-throughput proteomic technologies for the identification of unique biosignatures and biomarkers responsible for the early prediction of GDM. Further, different analytical strategies and biological samples involved in proteomic analysis of this pregnancy-related disease are discussed.This article is part of a Special Issue entitled: Proteomics in India.

Beyond the World Health Organization grading of infiltrating gliomas: advances in the molecular genetics of glioma classification

BACKGROUND

Traditional classification of diffuse infiltrating gliomas (DIGs) as World Health Organization (WHO) grades II-IV is based on histological features of a heterogeneous population of tumors with varying prognoses and treatments. Over the last decade, research efforts have resulted in a better understanding of the molecular basis of glioma formation as well as the genetic alterations commonly identified in diffuse gliomas.

METHODS

A systematic review of the current literature related to advances in molecular phenotypes, mutations, and genomic analysis of gliomas was carried out using a PubMed search for these key terms. Data was studied and synthesized to generate a comprehensive review of glioma subclassification.

RESULTS

This new data helps supplement the existing WHO grading scale by subtyping gliomas into specific molecular groups. The emerging molecular profile of diffuse gliomas includes the studies of gene expression and DNA methylation in different glioma subtypes. The discovery of novel mutations in isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2) provides new biomarkers as points of stratification of gliomas based on prognosis and treatment response. Gliomas that harbor CpG island hypermethylator phenotypes constitute a subtype of glioma with improved survival. The difficulty of classifying oligodendroglial lineage of tumors can be aided with identification of 1p/19q codeletion. Glioblastomas (GBMs) previously described as primary or secondary can now be divided based on gene expression into proneural, mesenchymal, and classical subtypes and the identification of mutations in the promoter region of the telomerase reverse transcriptase (TERTp) have been correlated with poor prognosis in GBMs.

CONCLUSIONS

Incorporation of new molecular and genomic changes into the existing WHO grading of DIGs may provide better patient prognostication as well as advance the development of patient-specific treatments and clinical trials.

Total protein is an effective loading control for cerebrospinal fluid western blots

BACKGROUND

Cerebrospinal fluid (CSF) has been used to identify biomarkers of neurological disease. CSF protein biomarkers identified by high-throughput methods, however, require further validation. While Western blotting (WB) is well-suited to this task, the lack of a validated loading control for CSF WB limits the method's accuracy.

NEW METHOD

We investigated the use of total protein (TP) as a CSF WB loading control. Using iodine-based reversible membrane staining, we determined the linear range and consistency of the CSF TP signal. We then spiked green fluorescent protein (GFP) into CSF to create defined sample-to-sample differences in GFP levels that were measured by WB before and after TP loading correction. Levels of CSF complement C3 and cystatin C measured by WB with TP loading correction and ELISA in amyotrophic lateral sclerosis and healthy control CSF samples were then compared.

RESULTS

CSF WB with the TP loading control accurately detected defined differences in GFP levels and corrected for simulated loading errors. Individual CSF sample Western blot and ELISA measurements of complement C3 and cystatin C were significantly correlated and the methods showed a comparable ability to detect between-groups differences.

COMPARISON WITH EXISTING METHOD

CSF TP staining has a greater linear dynamic range and sample-to-sample consistency than albumin, a commonly used CSF loading control. The method accurately corrects for simulated errors in loading and improves the sensitivity of CSF WB compared to using no loading control.

CONCLUSIONS

The TP staining loading control improves the sensitivity and accuracy of CSF WB results.

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 novel tumor-associated cell surface sialoglycoproteins in human glioblastoma tumors using quantitative proteomics

Glioblastoma multiform (GBM) remains clinical indication with significant “unmet medical need”. Innovative new therapy to eliminate residual tumor cells and prevent tumor recurrences is critically needed for this deadly disease. A major challenge of GBM research has been the identification of novel molecular therapeutic targets and accurate diagnostic/prognostic biomarkers. Many of the current clinical therapeutic targets of immunotoxins and ligand-directed toxins for high-grade glioma (HGG) cells are surface sialylated glycoproteins. Therefore, methods that systematically and quantitatively analyze cell surface sialoglycoproteins in human clinical tumor samples would be useful for the identification of potential diagnostic markers and therapeutic targets for malignant gliomas. In this study, we used the bioorthogonal chemical reporter strategy (BOCR) in combination with label-free quantitative mass spectrometry (LFQ-MS) to characterize and accurately quantify the individual cell surface sialoproteome in human GBM tissues, in fetal, adult human astrocytes, and in human neural progenitor cells (NPCs). We identified and quantified a total of 843 proteins, including 801 glycoproteins. Among the 843 proteins, 606 (72%) are known cell surface or secreted glycoproteins, including 156 CD-antigens, all major classes of cell surface receptor proteins, transporters, and adhesion proteins. Our findings identified several known as well as new cell surface antigens whose expression is predominantly restricted to human GBM tumors as confirmed by microarray transcription profiling, quantitative RT-PCR and immunohistochemical staining. This report presents the comprehensive identification of new biomarkers and therapeutic targets for the treatment of malignant gliomas using quantitative sialoglycoproteomics with clinically relevant, patient derived primary glioma cells.

Circulating glioma biomarkers

Validated biomarkers for patients suffering from gliomas are urgently needed for standardizing measurements of the effects of treatment in daily clinical practice and trials. Circulating body fluids offer easily accessible sources for such markers. This review highlights various categories of tumor-associated circulating biomarkers identified in blood and cerebrospinal fluid of glioma patients, including circulating tumor cells, exosomes, nucleic acids, proteins, and oncometabolites. The validation and potential clinical utility of these biomarkers is briefly discussed. Although many candidate circulating protein biomarkers were reported, none of these have reached the required validation to be introduced for clinical practice. Recent developments in tracing circulating tumor cells and their derivatives as exosomes and circulating nuclear acids may become more successful in providing useful biomarkers. It is to be expected that current technical developments will contribute to the finding and validation of circulating biomarkers.