Serum biomarkers identification by iTRAQ and verification by MRM: S100A8/S100A9 levels predict tumor-stroma involvement and prognosis in Glioblastoma

Sexual-biased necroinflammation is revealed as a predictor of bevacizumab benefit in glioblastoma

BACKGROUND

Glioblastoma (GBM) is a highly malignant brain tumor that affects men more often than women. In addition, the former shows a poorer survival prognosis. To date, the reason for this sex-specific aggressiveness remains unclear. Therefore, the aim of this study is to investigate tumor processes that explain these sex differences.

METHODS

This was a retrospective study of GBM patients which was stratified according to sex. A cohort with 73 tumors was analyzed with immunohistochemistry, RNA-seq and RT-qPCR to characterize differences in vascular and immunological profiles. Transcriptomic profiling, gene set enrichment analysis, and pathway enrichment analysis were used for discovering molecular pathways predominant in each group. We further investigated the therapeutic effect of bevacizumab (vascular endothelial growth factor A (VEGFA) blocking antibody) in a retrospective GBM cohort (36 tumors) based on sex differences.

RESULTS

We found that under hypoxic tumor conditions, 2 distinct tumor immuno-angiogenic ecosystems develop linked to sex differences and ESR1 expression is generated. One of these subgroups, which includes male patients with low ESR1 expression, is characterized by vascular fragility associated with the appearance of regions of necrosis and high inflammation (called necroinflamed tumors). This male-specific tumor subtype shows high inflammation related to myeloid-derived suppressor cells infiltration. Using this stratification, we identified a possible group of patients who could respond to bevacizumab (BVZ) and revealed a genetic signature that may find clinical applications as a predictor of those who may benefit most from this treatment.

CONCLUSIONS

This study provides a stratification based on the sexual differences in GBM, which associates the poor prognosis with the presence of immunosuppressive myeloid cells in the necrotic areas. This new stratification could change the current prognosis of GBM and identifies those who respond to BVZ treatment.

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.

The S100 family is a prognostic biomarker and correlated with immune cell infiltration in pan-cancer

BACKGROUND

The S100 protein family is a group of small molecular EF-hand calcium-binding proteins that play critical roles in various biological processes, including promotion of growth, metastasis and immune evasion of tumor. However, the potential roles of S100 protein family expression in tumor microenvironment (TME) cell infiltration in pan-cancer remain elusive.

METHODS

Herein, we conducted a comprehensive assessment of the expression patterns of the S100 protein family in pan-cancer, meticulously examining their correlation with characteristics of TME cell infiltration. The S100 score was constructed to quantify S100 family expression patterns of individual tumors.

RESULTS

The S100 family was a potent risk factor in many cancers. Clustering analysis based on the transcriptome patterns of S100 protein family identified two cancer clusters with distinct immunophenotypes and clinical characteristics. Cluster A, with lower S100 expression, exhibited lower immune infiltration, whereas, Cluster B, with higher S100 expression, featured higher immune infiltration. Interestingly, Cluster B had a poorer prognosis, likely due to an immune-excluded phenotype resulting from stromal activation. The analysis revealed robust enrichment of the TGFb and EMT pathways in the cohort exhibiting high S100 score, alongside a positive correlation between the S100 score and Treg levels, suggesting the manifestation of an immune-excluded phenotype in this group. Moreover, S100 families were associated with the prognosis of 22 different cancers and a noteworthy association was observed between high S100 score and an unfavorable response to anti-PD-1/L1 immunotherapy. Consistent findings across two independent immunotherapy cohorts substantiated the advantageous therapeutic outcomes and clinical benefits in patients displaying lower S100score.

CONCLUSION

Our analysis demonstrated the role of S100 family in formation of TME diversity and complexity, enabling deeper cognition of TME infiltration characterization and the development of personalized immunotherapy strategies targeting S100 family for unique tumor types.

Deciphering Glioblastoma: Fundamental and Novel Insights into the Biology and Therapeutic Strategies of Gliomas

Gliomas constitute a diverse and complex array of tumors within the central nervous system (CNS), characterized by a wide range of prognostic outcomes and responses to therapeutic interventions. This literature review endeavors to conduct a thorough investigation of gliomas, with a particular emphasis on glioblastoma (GBM), beginning with their classification and epidemiological characteristics, evaluating their relative importance within the CNS tumor spectrum. We examine the immunological context of gliomas, unveiling the intricate immune environment and its ramifications for disease progression and therapeutic strategies. Moreover, we accentuate critical developments in understanding tumor behavior, focusing on recent research breakthroughs in treatment responses and the elucidation of cellular signaling pathways. Analyzing the most novel transcriptomic studies, we investigate the variations in gene expression patterns in glioma cells, assessing the prognostic and therapeutic implications of these genetic alterations. Furthermore, the role of epigenetic modifications in the pathogenesis of gliomas is underscored, suggesting that such changes are fundamental to tumor evolution and possible therapeutic advancements. In the end, this comparative oncological analysis situates GBM within the wider context of neoplasms, delineating both distinct and shared characteristics with other types of tumors.

Glioma-derived S100A9 polarizes M2 microglia to inhibit CD8+T lymphocytes for immunosuppression via αvβ3 integrin/AKT1/TGFβ1

Our previous studies showed that S100A9 was overexpressed in glioma and promoted tumor growth. However, S100A9 can also be secreted by tumor cells to regulate the tumor microenvironment (TME). In this study, we aimed to explore the functions of glioma derived-S100A9 in microglial M2 polarization, resulting in inhibition of CD8+ T lymphocytes and promotion of immunosuppression. We first showed that glioma exhibited higher expression and secretion of S100A9 than astrocytes. After knocking down S100A9 in two glioma cell lines, the secretion of S100A9 was repressed. Then, the medium was collected and considered as conditioned medium (CM), which was incubated with microglia. We found that glioma-derived S100A9 drove microglial M2 polarization and increased TGFβ1 secretion. These molecular mechanisms were related to the interaction of S100A9 with αvβ3 integrin and the subsequent activation of AKT1 in microglia. Furthermore, we demonstrated that S100A9-induced M2 microglia negatively affected cell viability, IL-2 and IFN-γ secretion, together with increased early apoptosis in CD8+T lymphocytes via TGFβ1. Additionally, glioma cells were implanted into mouse brains, and we confirmed that S100A9 stimulated microglial M2 polarization, enhanced TGFβ1 levels and repressed CD8+ T lymphocytes in orthotopically transplanted tumors. In human glioma samples, S100A9 expression was positively associated with CD206 expression, but negatively correlated with CD8+T lymphocyte accumulation in the TME. Our data indicated that glioma-derived S100A9 has a promising ability to manipulate non-malignant cells and promote immune evasion in the TME, providing valuable insight into the mechanism by which S100A9 participates in the progression of glioma.

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.

Targeted Workflow Investigating Variations in the Tear Proteome by Liquid Chromatography Tandem Mass Spectrometry

Proteins in tears have an important role in eye health and have been shown as a promising source of disease biomarkers. The goal of this study was to develop a robust, sensitive, and targeted method for profiling tear proteins to examine the variability within a group of healthy volunteers over three days. Inter-individual and inter-day variabilities were examined to contribute to understanding the normal variations in the tear proteome, as well as to establish which proteins may be better candidates as eventual biomarkers of specific diseases. Tear samples collected on Schirmer strips were subjected to bottom-up proteomics, and resulting peptides were analyzed using an optimized targeted method measuring 226 proteins by liquid chromatography-scheduled multiple reaction monitoring. This method was developed using an in-house database of identified proteins from tears compiled from high-resolution data-dependent liquid chromatography tandem mass spectrometry data. The measurement of unique peptide signals can help better understand the dynamics of each of these proteins in tears. Some interesting trends were seen in specific pathways or protein classes, including higher variabilities for those involved in glycolysis, glutathione metabolism, and cytoskeleton proteins and lower variation for those involving the degradation of the extracellular matrix. The overall aim of this study was to contribute to the field of tear proteomics with the development of a novel and targeted method that is highly amenable to the clinical laboratory using high flow LC and commonly used triple quadrupole mass spectrometry while ensuring that protein quantitation was reported based on unique peptides for each protein and robust peak areas with data normalization. These results report on variabilities on over 200 proteins that are robustly detected in tear samples from healthy volunteers with a simple sample preparation procedure.

A single protein to multiple peptides: Investigation of protein-peptide correlations using targeted alpha-2-macroglobulin analysis

Recent advances in proteomics technologies have enabled the analysis of thousands of proteins in a high-throughput manner. Mass spectrometry (MS) based proteomics uses a peptide-centric approach where biological samples undergo specific proteolytic digestion and then only unique peptides are used for protein identification and quantification. Considering the fact that a single protein may have multiple unique peptides and a number of different forms, it becomes essential to understand dynamic protein-peptide relationships to ensure robust and reliable peptide-centric protein analysis. In this study, we investigated the correlation between protein concentration and corresponding unique peptide responses under a conventional proteolytic digestion condition. Protein-peptide correlation, digestion efficiency, matrix-effect, and concentration-effect were evaluated. Twelve unique peptides of alpha-2-macroglobulin (A2MG) were monitored using a targeted MS approach to acquire insights into protein-peptide dynamics. Although the peptide responses were reproducible between replicates, protein-peptide correlation was moderate in protein standards and low in complex matrices. The results suggest that reproducible peptide signal could be misleading in clinical studies and a peptide selection could dramatically change the outcome at protein level. This is the first study investigating quantitative protein-peptide correlations in biological samples using all unique peptides representing the same protein and opens a discussion on peptide-based proteomics.

Proteomic analysis predicts anti-angiogenic resistance in recurred glioblastoma

BACKGROUND

Recurrence is common in glioblastoma multiforme (GBM) because of the infiltrative, residual cells in the tumor margin. Standard therapy for GBM consists of surgical resection followed by chemotherapy and radiotherapy, but the median survival of GBM patients remains poor (~ 1.5 years). For recurrent GBM, anti-angiogenic treatment is one of the common treatment approaches. However, current anti-angiogenic treatment modalities are not satisfactory because of the resistance to anti-angiogenic agents in some patients. Therefore, we sought to identify novel prognostic biomarkers that can predict the therapeutic response to anti-angiogenic agents in patients with recurrent glioblastoma.

METHODS

We selected patients with recurrent GBM who were treated with anti-angiogenic agents and classified them into responders and non-responders to anti-angiogenic therapy. Then, we performed proteomic analysis using liquid-chromatography mass spectrometry (LC-MS) with formalin-fixed paraffin-embedded (FFPE) tissues obtained from surgical specimens. We conducted a gene-ontology (GO) analysis based on protein abundance in the responder and non-responder groups. Based on the LC-MS and GO analysis results, we identified potential predictive biomarkers for anti-angiogenic therapy and validated them in recurrent glioblastoma patients.

RESULTS

In the mass spectrometry-based approach, 4957 unique proteins were quantified with high confidence across clinical parameters. Unsupervised clustering analysis highlighted distinct proteomic patterns (n = 269 proteins) between responders and non-responders. The GO term enrichment analysis revealed a cluster of genes related to immune cell-related pathways (e.g., TMEM173, FADD, CD99) in the responder group, whereas the non-responder group had a high expression of genes related to nuclear replisome (POLD) and damaged DNA binding (ERCC2). Immunohistochemistry of these biomarkers showed that the expression levels of TMEM173 and FADD were significantly associated with the overall survival and progression-free survival of patients with recurrent GBM.

CONCLUSIONS

The candidate biomarkers identified in our protein analysis may be useful for predicting the clinical response to anti-angiogenic agents in patients with recurred GBM.

The prognostic significance of tumor-associated neutrophils and circulating neutrophils in glioblastoma (WHO CNS5 classification)

BACKGROUND

Tumor-associated neutrophils (TANs) in the tumor microenvironment are prognostic biomarkers in many malignancies. However, it is unclear whether TANs can serve as a prognostic marker for clinical outcomes in patients with glioblastoma (GBM), as classified according to World Health Organization Classification of Tumors of the Central Nervous System, fifth edition (CNS5). In the present study, we analyzed correlations of TANs and peripheral blood neutrophils prior to radiotherapy with overall survival (OS) in GBM (CNS5).

METHODS

RNA-seq expression profiles of patients with newly diagnosed GBM (CNS5) were extracted from The Cancer Genome Atlas (TCGA), and The Chinese Glioma Genome Atlas (CGGA). TAN infiltration was inferred using CIBERSORTx algorithm. Neutrophil counts prior to radiotherapy in newly diagnosed GBM (CNS5) were obtained from the First Affiliated Hospital of Fujian Medical University. The prognostic value of TANs and peripheral blood neutrophils before radiotherapy was investigated using Kaplan-Meier analysis and Cox proportional hazards models. The robustness of these findings was evaluated by sensitivity analysis, and E values were calculated.

RESULTS

A total of 146 and 173 individuals with GBM (CNS5) were identified from the TCGA and CGGA cohorts, respectively. High infiltration of TANs was of prognostic of poor OS in TCGA (HR = 1.621, 95% CI: 1.004-2.619) and CGGA (HR = 1.546, 95% CI: 1.029-2.323). Levels of peripheral blood neutrophils before radiotherapy (HR = 2.073, 95% CI: 1.077-3.990) were independently associated with poor prognosis. Sensitivity analysis determined that the E-value of high TANs infiltration was 2.140 and 2.465 in the TCGA and CGGA cohorts.

CONCLUSIONS

TANs and peripheral blood neutrophil levels before radiotherapy are prognostic of poor outcomes in GBM (CNS5).

Urinary Exosomal Tissue TIMP and Angiopoietin-1 Are Preoperative Novel Biomarkers of Well-Differentiated Thyroid Cancer

Finding non-invasive and sensitive biomarkers for early screening of high-risk patients remains important in clinical practice. A higher concentration of urine exosomal thyroglobulin protein was found in late-stage patients with thyroid carcinoma compared to those with early stage in our previous study. This prospective study aims to find new prognostic biomarkers before surgery for decision-making with this platform. We enrolled patients newly diagnosed with papillary and follicular cancer from 2017 to 2018. Preoperative urine samples were collected and the exosomal proteins were analyzed. The association of the concentration of urine exosomal proteins with lymph node metastasis and MACIS score (metastasis, age, completeness of resection, invasion, and size) was analyzed with multiple logistic regression. In total, 21 patients were included, with a mean age of 51.29 ± 10.29 years and a majority of female patients (85.71%). The concentration of urine exosomal TIMP (tissue inhibitor of metalloproteinase) was significantly higher in patients with lymph node metastasis (p = 0.01). Multiple logistic regression analysis showed association of urine exosomal TIMP (adjusted odds ratio (aOR): 3.09, 95% confidence interval (CI): 0.99-9.6, p = 0.052), angiopoietin-1 (aOR: 2.24, 95% CI: 0.97-5.15, p = 0.058) with lymph node metastasis. However, no association was noted between MACIS score and various urine exosomal protein candidates. Preoperative urine exosomal data could suggest certain peptides having the potential as prognostic indicators for screening patients with high-risk before surgery. Further study with a large cohort and long follow-up is needed to identify the application of urine exosomal proteins on prognostic prediction.

Quantitative Proteomic Analysis of Serum Reveals MST1 as a Potential Candidate Biomarker in Spontaneously Diabetic Cynomolgus Monkeys

The prevalence of type 2 diabetes (T2DM) is increasing globally, creating essential demands for T2DM animal models for the study of disease pathogenesis, prevention, and therapy. A non-human primate model such as cynomolgus monkeys can develop T2DM spontaneously in an age-dependent way similar to humans. In this study, a data-independent acquisition-based quantitative proteomics strategy was employed to investigate the serum proteomic profiles of spontaneously diabetic cynomolgus monkeys compared with healthy controls. The results revealed significant differences in protein abundances. A total of 95 differentially expressed proteins (DEPs) were quantitatively identified in the current study, among which 31 and 64 proteins were significantly upregulated and downregulated, respectively. Bioinformatic analysis revealed that carbohydrate digestion and absorption was the top enriched pathway by the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Protein-protein interaction network analysis demonstrated that MST1 was identified as the most connected protein in the network and could be considered as the hub protein. MST1 was significantly and inversely associated with FSG and HbA1c. Furthermore, recent lines of evidence also indicate that MST1 acts as a crucial regulator in regulating hepatic gluconeogenesis to maintain metabolic homeostasis while simultaneously suppressing the inflammatory processes. In conclusion, our study provides novel insights into serum proteome changes in spontaneously diabetic cynomolgus monkeys and points out that the dysregulation of several DEPs may play an important role in the pathogenesis of T2DM.

Upregulation of leucine-rich alpha-2 glycoprotein: A key regulator of inflammation and joint fibrosis in patients with severe knee osteoarthritis

Introduction

Osteoarthritis (OA) is a degenerative disease of the joints mainly affecting older individuals. Since the etiology behind the progression of OA is not well understood, several associated consequences, such as synovial joint stiffness and its progression due to joint fibrosis, are still poorly understood. Although a lot of developments have been achieved in the diagnosis and management of OA, synovial fibrosis remains one of the major challenging consequences. The present study was therefore focused on understanding the mechanism of synovial fibrosis, which may further contribute to improving symptomatic treatments, leading to overall improvements in the treatment outcomes of patients with OA.

Methods

We used advanced proteomic techniques including isobaric tag for relative and absolute quantitation and sequential window acquisition of all theoretical mass spectra for the identification of differentially expressed proteins in the plasma samples of patients with OA. An in silico study was carried out to evaluate the association of the identified proteins with their biological processes related to fibrosis and remodeling of the extracellular matrix (ECM). The most significantly upregulated protein was then validated by Western blot and enzyme-linked immunosorbent assay. The target protein was then further investigated for its role in inflammation and joint fibrosis using an in vitro study model.

Results

Leucine-rich alpha-2 glycoprotein (LRG1) was found to be the most highly differentially expressed upregulated (9.4-fold) protein in the plasma samples of patients with OA compared to healthy controls. The knockdown of LRG1 followed by in vitro studies revealed that this protein promotes the secretion of the ECM in synovial cells and actively plays a role in wound healing and cell migration. The knockdown of LRG1 further confirmed the reduction of the inflammatory- and fibrosis-related markers in primary cells.

Conclusion

LRG1 was identified as a highly significant upregulated protein in the plasma samples of patients with OA. It was found to be associated with increased fibrosis and cell migration, leading to enhanced inflammation and joint stiffness in OA pathogenesis.

The Role of the S100 Protein Family in Glioma

The S100 protein family consists of 25 members and share a common structure defined in part by the Ca²⁺ binding EF-hand motif. Multiple members' dysregulated expression is associated with progression, diagnosis and prognosis in a broad range of diseases, especially in tumors. They could exert wide range of functions both in intracellular and extracellular, including cell proliferation, cell differentiation, cell motility, enzyme activities, immune responses, cytoskeleton dynamics, Ca²⁺ homeostasis and angiogenesis. Gliomas are the most prevalent primary tumors of the brain and spinal cord with multiple subtypes that are diagnosed and classified based on histopathology. Up to now the role of several S100 proteins in gliomas have been explored. S100A8, S100A9 and S100B were highly expression in serum and may present as a marker correlated with survival and prognosis of glioma patients. Individual member was confirmed as a new regulator of glioma stem cells (GSCs) and a mediator of mesenchymal transition in glioblastoma (GBM). Additionally, several members up- or downregulation have been reported to involve in the development of glioma by interacting with signaling pathways and target proteins. Here we detail S100 proteins that are associated with glioma, and discuss their potential effects on progression, diagnosis and prognosis.

Screening the Significant Hub Genes by Comparing Tumor Cells, Normoxic and Hypoxic Glioblastoma Stem-like Cell Lines Using Co-Expression Analysis in Glioblastoma

Glioblastoma multiforme (GBM) is categorized by rapid malignant cellular growth in the central nervous system (CNS) tumors. It is one of the most prevailing primary brain tumors, particularly in human male adults. Even though the combination therapy comprises surgery, chemotherapy, and adjuvant therapies, the survival rate is on average 14.6 months. Glioma stem cells (GSCs) have key roles in tumorigenesis, progression, and counteracting chemotherapy and radiotherapy. In our study, firstly, the gene expression dataset GSE45117 was retrieved and differentially expressed genes (DEGs) were spotted. The co-expression network analysis was employed on DEGs to find the significant modules. The most significant module resulting from co-expression analysis was the turquoise module. The turquoise module related to the tumor cells, hypoxia, normoxic treatments of glioblastoma tumor (GBT), and GSCs were screened. Sixty-one common genes in the turquoise module were selected generated through the co-expression analysis and protein–protein interaction (PPI) network. Moreover, the GO and KEGG pathway enrichment results were studied. Twenty common hub genes were screened by the NetworkAnalyst web instrument constructed on the PPI network through the STRING database. After survival analysis via the Kaplan–Meier (KM) plotter from The Cancer Genome Atlas (TCGA) database, we identified the five most significant hub genes strongly related to the progression of GBM. We further observed these five most significant hub genes also up-regulated in another GBM gene expression dataset. The protein–protein interaction (PPI) network of the turquoise module genes was constructed and a KEGG pathway enrichments study of the turquoise module genes was performed. The VEGF signaling pathway was emphasized because of the strong link with GBM. A gene–disease association network was further constructed to demonstrate the information of the progression of GBM and other related brain neoplasms. All hub genes assessed through this study would be potential markers for the prognosis and diagnosis of GBM.

The role of proteomics in the multiplexed analysis of gene alterations in human cancer

INTRODUCTION

Proteomics has played a pivotal role in identifying proteins perturbed in disease conditions when compared with healthy samples. Study of dysregulated proteins aids in identifying diagnostic markers and potential therapeutic targets. Cancer is an outcome of interplay of several such disarrayed proteins and molecular pathways which perturb cellular homeostasis, resulting in transformation. In this review, we discuss various facets of proteomic approaches, including tools and technological advancements, aiding in understanding differentially expressed molecules and signaling mechanisms.

AREAS COVERED

In this review, we have taken the approach of documenting the different methods of proteomic studies, ranging from labeling techniques, data analysis methods, and the nature of molecule detected. We summarize each technique and provide a glimpse of cancer research carried out using them, highlighting the advantages and drawbacks in comparison with others. Literature search using online resources, such as PubMed and Google Scholar were carried out for this approach.

EXPERT OPINION

Technological advancements in proteomics studies have come a long way from the study of two-dimensional mapping of proteins separated on gels in the early 1970s. Higher precision in molecular identification and quantification (high throughput), and greater number of samples analyzed have been the focus of researchers.

A review of gliomas-related proteins. Characteristics of potential biomarkers

Brain tumors are one of the most commonly diagnosed cancers of the central nervous system. Of all diagnosed malignant tumors, 80% are gliomas. An unequivocal diagnosis of gliomas is not always simple, and there is a great need for research to find new treatment options and diagnostic approaches. This paper is focused on the glioma-related protein profiles as compared to healthy brain tissue, which is reflected in multiple correlations between biological aspects that influence proliferation, apoptosis evasion and the invasiveness of neoplastic cells. The work presents the possibilities of facilitating clinical practice with proteomic biomarkers, which offer a wider diagnostic spectrum and reduce the margin of mistake in histopathological or imaging diagnostic methods. In fact, many changes in the body's homeostasis can be overlooked due to the lack of symptoms or their non-specificity. Nevertheless, a single marker has limited reliability in distinguishing a particular tumor subtype, since the increased or decreased level of the protein of interest may differ between the stages or locations of the tumor. Moreover, the correlations between proposed proteins - presented in this paper - may help clinicians to choose the most optimal therapy, and estimate its effectiveness, or indicate new therapeutic targets affecting disrupted biochemical pathways.

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.

S100A gene family: immune-related prognostic biomarkers and therapeutic targets for low-grade glioma

BACKGROUND

Despite the better prognosis given by surgical resection and chemotherapy in low-grade glioma (LGG), progressive transformation is still a huge concern. In this case, the S100A gene family, being capable of regulating inflammatory responses, can promote tumor development.

METHODS

The analysis was carried out via ONCOMINE, GEPIA, cBioPortal, String, GeneMANIA, WebGestalt, LinkedOmics, TIMER, CGGA, R 4.0.2 and immunohistochemistry.

RESULTS

S100A2, S100A6, S100A10, S100A11, and S100A16 were up-regulated and S100A1 and S100A13 were down-regulated in LGG compared to normal tissues. S100A3, S100A4, S100A8, and S100A9 expression was up-regulated during the progression of glioma grade. In addition, genetic variation of the S100A family was high in LGG, and the S100A family genes mostly function through IL-17 signaling pathway, S100 binding protein, and inflammatory responses. The TIMER database also revealed a relationship between gene expression and immune cell infiltration. High expression of S100A2, S100A3, S100A4, S100A6, S100A8, S100A9, S100A10, S100A11, S100A13, and S100A16 was significantly associated with poor prognosis in LGG patients. S100A family genes S100A2, S100A3, S100A6, S100A10, and S100A11 may be prognosis-related genes in LGG, and were significantly associated with IDH mutation and 1p19q codeletion. The immunohistochemical staining results also confirmed that S100A2, S100A3, S100A6, S100A10, and S100A11 expression was upregulated in LGG.

CONCLUSION

The S100A family plays a vital role in LGG pathogenesis, presumably facilitating LGG progression via modulating inflammatory state and immune cell infiltration.

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.

Clinical Assay for the Early Detection of Colorectal Cancer Using Mass Spectrometric Wheat Germ Agglutinin Multiple Reaction Monitoring

Simple Summary

Colorectal cancer (CRC) is currently the third leading cause of cancer death worldwide. Early diagnosis of CRC is important for increasing the opportunity for treatment and receiving a good prognosis. The aim of our study was to develop a detection method that combined wheat germ agglutinin (WGA) chromatography with mass spectrometry (MS) for early detection of CRC. Further, machine learning algorithms and logistic regression were applied to combine multiple biomarkers we discovered. We validated in a population of 286 plasma samples the diagnostic performance of peptides corresponding to WGA-captured protein and its combination, which received a sensitivity of 84.5% and a specificity of 97.5% in the diagnoses of CRC. Proteomic biomarkers combined with algorithms can provide a powerful tool for discriminating patients with CRC and health controls (HCs). Measurements of WGA-captured PF4, ITIH4, and APOE with MS are then useful for early detection of CRC. Additionally, our study revealed the potential of applying lectin chromatography with MS for disease diagnosis.

Abstract

Colorectal cancer (CRC) is currently the third leading cause of cancer-related mortality in the world. U.S. Food and Drug Administration-approved circulating tumor markers, including carcinoembryonic antigen, carbohydrate antigen (CA) 19-9 and CA125 were used as prognostic biomarkers of CRC that attributed to low sensitivity in diagnosis of CRC. Therefore, our purpose is to develop a novel strategy for novel clinical biomarkers for early CRC diagnosis. We used mass spectrometry (MS) methods such as nanoLC-MS/MS, targeted LC-MS/MS, and stable isotope-labeled multiple reaction monitoring (MRM) MS coupled to test machine learning algorithms and logistic regression to analyze plasma samples from patients with early-stage CRC, late-stage CRC, and healthy controls (HCs). On the basis of our methods, 356 peptides were identified, 6 differential expressed peptides were verified, and finally three peptides corresponding wheat germ agglutinin (WGA)-captured proteins were semi-quantitated in 286 plasma samples (80 HCs and 206 CRCs). The novel peptide biomarkers combination of PF4^54–62, ITIH4^429–438, and APOE^198–207 achieved sensitivity 84.5%, specificity 97.5% and an AUC of 0.96 in CRC diagnosis. In conclusion, our study demonstrated that WGA-captured plasma PF4^54–62, ITIH4^429–438, and APOE^198–207 levels in combination may serve as highly effective early diagnostic biomarkers for patients with CRC.

Role of RAGE and Its Ligands on Inflammatory Responses to Brain Tumors

Gliomas, the most common form of brain cancer, can range from relatively slow-growing low-grade to highly aggressive glioblastoma that has a median overall survival of only 15 months despite multimodal standard therapy. Although immunotherapy with checkpoint inhibitors has significantly improved patient survival for some cancers, to date, these agents have not shown consistent efficacy against malignant gliomas. Therefore, there is a pressing need to better understand the impact of host inflammatory responses on the efficacy of emerging immunotherapy approaches for these resistant tumors. RAGE is a multi-ligand pattern recognition receptor that is activated in various inflammatory states such as diabetes, Alzheimer's disease, cystic fibrosis, and cancer. Low levels of RAGE can be found under normal physiological conditions in neurons, immune cells, activated endothelial, and vascular smooth muscle cells, but it is over-expressed under chronic inflammation due to the accumulation of its ligands. RAGE binds to a range of damage-associated molecular pattern molecules (DAMPs) including AGEs, HMGB1, S100s, and DNA which mediate downstream cellular responses that promote tumor growth, angiogenesis, and invasion. Both in vitro and in vivo studies have shown that inhibition of RAGE signaling can disrupt inflammation and cancer progression and metastasis. Here, we will review our current understanding of the role of RAGE pathway on glioma progression and how it could be exploited to improve the efficacy of immunotherapy approaches.

Intracellular and extracellular S100A9 trigger epithelial-mesenchymal transition and promote the invasive phenotype of pituitary adenoma through activation of AKT1

Pituitary adenoma (PA) is mostly benign intracranial tumor, but it also displays invasive growth characteristics and provokes challenging clinical conditions. S100A9 protein enhances tumor progression. In this study, we firstly demonstrated that both intracellular and extracellular S100A9 promoted the expression of Vimentin and Intercellular cell adhesion molecule-1 (ICAM-1), coupled with reduced E-cadherin in PA. As a result, PA acquired the phenotype of Epithelial-Mesenchymal Transition (EMT), leading to proliferation, cell cycle progression, migration and invasion. In addition, we indicated S100A9-induced EMT was mediated by activation of AKT1. Furthermore, immunohistochemistry showed that S100A9 expression was higher in invasive PA than that in non-invasive PA. These data extended our understanding for the effects of S100A9 on PA invasion and contributed to further development of a promising therapeutic target for invasive PA.

Potential biomarkers and challenges in glioma diagnosis, therapy and prognosis

Gliomas are the most common central nervous system malignancies and present with significant morbidity and mortality. Treatment modalities are currently limited to surgical resection, chemotherapy and radiotherapy. Increases in survival rate over the previous decades are negligible, further pinpointing an unmet clinical need in this field. There is a continual struggle with the development of effective glioma diagnostics and therapeutics, largely due to a multitude of factors, including the presence of the blood–brain barrier and significant intertumoural and intratumoural heterogeneity. Importantly, there is a lack of reliable biomarkers for glioma, particularly in aiding tumour subtyping and measuring response to therapy. There is a need for biomarkers that would both overcome the complexity of the disease and allow for a minimally invasive means of detection and analysis. This is a comprehensive review evaluating the potential of current cellular, proteomic and molecular biomarker candidates for glioma. Significant hurdles faced in glioma diagnostics and therapy are also discussed here.

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.

Proteomic profiling of medulloblastoma reveals novel proteins differentially expressed within each molecular subgroup

OBJECTIVES

The objective of the study was to identify novel medulloblastoma (MB) biomarkers through proteomic profiling, correlate it with the molecular subgroups of MB and assess the clinical significance.

METHODS

Archived paraffin embedded tumor tissue blocks from 118 MB patients, operated at our institute were retrieved. Clinical information was documented from the hospital database. Tumours were stratified into molecular subgroups using the IHC markers- β Catenin, GAB-1, YAP-1 and p53. Six fresh MB tumour tissues and two control cerebellar tissues were subjected to proteomic profiling to study differential protein expression in molecular subgroups using high resolution mass spectrometry. Prominent signalling pathways activated in each subgroup were identified using the Panther pathway software.

RESULTS

Non WNT/SHH group was the most common (61.1 %), followed by SHH and WNT. p53 immunopositivity did not correlate with prognosis in any subgroup. Proteomic profiling revealed several novel proteins differentially expressed between MB molecular subgroups. Signalling pathways exclusively enriched in each molecular subgroup were also identified. The top upregulated proteins were PMEL and FBN2 in the WNT subgroup, SYNGR2 in the SHH subgroup and GFAP, IMPG2 and MAGEA10 in the Non WNT/Non SHH group. We validated GFAP by immunohistochemistry on the archived samples (n = 118) and noted two types of staining pattern in MBs - reactive (stellate) astrocytes and tumour cell staining. GFAP immunopositivity in tumor cells of SHH subgroup correlated with a better prognosis.

CONCLUSIONS

Proteomic profile identified several novel proteins differentially regulated within the molecular subgroups that could serve as potential diagnostic /prognostic biomarkers. Notably, GFAP, which was derived from proteomics data, when validated by IHC, revealed a variable staining pattern in MB tumours. The prognostic significance of GFAP in SHH tumor patients further points at the heterogeneity of this subgroup. The study also throws light on the signaling pathways activated in MB and in turn its plausible role in the tumorigenesis.

Esophageal, gastric and colorectal cancers: Looking beyond classical serological biomarkers towards glycoproteomics-assisted precision oncology

Esophageal (OC), gastric (GC) and colorectal (CRC) cancers are amongst the digestive track tumors with higher incidence and mortality due to significant molecular heterogeneity. This constitutes a major challenge for patients' management at different levels, including non-invasive detection of the disease, prognostication, therapy selection, patient's follow-up and the introduction of improved and safer therapeutics. Nevertheless, important milestones have been accomplished pursuing the goal of molecular-based precision oncology. Over the past five years, high-throughput technologies have been used to interrogate tumors of distinct clinicopathological natures, generating large-scale biological datasets (e.g. genomics, transcriptomics, and proteomics). As a result, GC and CRC molecular subtypes have been established to assist patient stratification in the clinical settings. However, such molecular panels still require refinement and are yet to provide targetable biomarkers. In parallel, outstanding advances have been made regarding targeted therapeutics and immunotherapy, paving the way for improved patient care; nevertheless, important milestones towards treatment personalization and reduced off-target effects are also to be accomplished. Exploiting the cancer glycoproteome for unique molecular fingerprints generated by dramatic alterations in protein glycosylation may provide the necessary molecular rationale towards this end. Therefore, this review presents functional and clinical evidences supporting a reinvestigation of classical serological glycan biomarkers such as sialyl-Tn (STn) and sialyl-Lewis A (SLe^A) antigens from a tumor glycoproteomics perspective. We anticipate that these glycobiomarkers that have so far been employed in non-invasive cancer prognostication may hold unexplored value for patients' management in precision oncology settings.

An MRM-Based Multiplexed Quantification Assay for Human Adipokines and Apolipoproteins

Adipokines and apolipoproteins are key regulators and potential biomarkers in obesity and associated diseases and their quantitative assessment is crucial for functional analyses to understand disease mechanisms. Compared to routinely used ELISAs, multiple reaction monitoring (MRM)-based mass spectrometry allows multiplexing and detection of proteins for which antibodies are not available. Thus, we established an MRM method to quantify 9 adipokines and 10 apolipoproteins in human serum. We optimized sample preparation by depleting the two most abundant serum proteins for improved detectability of low abundant proteins. Intra-day and inter-day imprecision were below 16.5%, demonstrating a high accuracy. In 50 serum samples from participants with either normal weight or obesity, we quantified 8 adipokines and 10 apolipoproteins. Significantly different abundances were observed for five adipokines (adipsin, adiponectin, chemerin, leptin, vaspin) and four apolipoproteins (apo-B100/-C2/-C4/-D) between the body mass index (BMI) groups. Additionally, we applied our MRM assay to serum samples from normal weight children and human adipocyte cell culture supernatants to proof the feasibility for large cohort studies and distinct biological matrices. In summary, this multiplexed assay facilitated the investigation of relationships between adipokines or apolipoproteins and phenotypes or clinical parameters in large cohorts, which may contribute to disease prediction approaches in the future.

Protein Expression Profile in Rat Silicosis Model Reveals Upregulation of PTPN2 and Its Inhibitory Effect on Epithelial-Mesenchymal Transition by Dephosphorylation of STAT3

Silicosis is a chronic occupational lung disease caused by long-term inhalation of crystalline silica particulates. We created a rat model that closely approximates the exposure and development of silicosis in humans. Isobaric tags for relative and absolute quantitation (iTRAQ) technologies we used to identify proteins differentially expressed in activated rat lung tissue. We constructed three lentiviral knockdown vectors and an overexpression vector for the protein tyrosine phosphatase non-receptor type 2 (PTPN2) gene to achieve stable long-term expression. A total of 471 proteins were differentially expressed in the silicosis group compared with controls. Twenty upregulated, and eight downregulated proteins exhibited a ≥1.5-fold change relative to controls. We next found that the PTPN2, Factor B, and VRK1 concentrations in silicotic rats silicosis and SiO₂-stimulated MLE-12 cells were significantly higher than control groups. More importantly, we found that overexpression of PTPN2 simultaneously decreased the expression of phospho–signal transducer and activator of transcription 3 (p-STAT3) and Vimentin, while increasing E-cadherin expression. The opposite pattern was observed for PTPN2-gene silencing. We identified three proteins with substantially enhanced expression in silicosis. Our study also showed that PTPN2 can inhibit epithelial-mesenchymal transition by dephosphorylating STAT3 in silicosis fibrosis.

High-order radiomics features based on T2 FLAIR MRI predict multiple glioma immunohistochemical features: A more precise and personalized gliomas management

Objective

To investigate the performance of high-order radiomics features and models based on T2-weighted fluid-attenuated inversion recovery (T2 FLAIR) in predicting the immunohistochemical biomarkers of glioma, in order to execute a non-invasive, more precise and personalized glioma disease management.

Methods

51 pathologically confirmed gliomas patients committed in our hospital from March 2015 to June 2018 were retrospective analysis, and Ki-67, vimentin, S-100 and CD34 immunohistochemical data were collected. The volumes of interest (VOIs) were manually sketched and the radiomics features were extracted. Feature reduction was performed by ANOVA+ Mann-Whiney, spearman correlation analysis, least absolute shrinkage and selection operator (LASSO) and Gradient descent algorithm (GBDT). SMOTE technique was used to solve the data bias between two groups. Comprehensive binary logistic regression models were established. Area under the ROC curves (AUC), sensitivity, specificity and accuracy were used to evaluate the predict performance of models. Models reliability were decided according to the standard net benefit of the decision curves.

Results

Four clusters of significant features were screened out and four predicting models were constructed. AUC of Ki-67, S-100, vimentin and CD34 models were 0.713, 0.923, 0.854 and 0.745, respectively. The sensitivities were 0.692, 0.893, 0.875 and 0.556, respectively. The specificities were: 0.667, 0.905, 0.722, and 0.875, with accuracy of 0.660, 0.898, 0.738, and 0.667, respectively. According to the decision curves, the Ki-67, S-100 and vimentin models had reference values.

Conclusion

The radiomics features based on T2 FLAIR can potentially predict the Ki-67, S-100, vimentin and CD34 expression. Radiomics model were expected to be a computer-intelligent, non-invasive, accurate and personalized management method for gliomas.

Cancer-educated mesenchymal stem cells promote the survival of cancer cells at primary and distant metastatic sites via the expansion of bone marrow-derived-PMN-MDSCs

Bone marrow mesenchymal stem cells (BMSCs) are multipotent stromal cells that can differentiate into a variety of cell types. BMSCs are chemotactically guided towards the cancer cells and contribute to the formation of a cancer microenvironment. The homing of BMSCs was affected by various factors. Disseminated tumour cells (DTCs) in distant organs, especially in the bone marrow, are the source of cancer metastasis and cancer relapse. DTC survival is also determined by the microenvironment. Here we aim to elucidate how cancer-educated BMSCs promote the survival of cancer cells at primary tumour sites and distant sites. We highlight the dynamic change by identifying different gene expression signatures in intratumoral BMSCs and in BMSCs that move back in the bone marrow. Intratumoral BMSCs acquire high mobility and displayed immunosuppressive effects. Intratumoral BMSCs that ultimately home to the bone marrow exhibit a strong immunosuppressive function. Cancer-educated BMSCs promote the survival of lung cancer cells via expansion of MDSCs in bone marrow, primary tumour sites and metastatic sites. These Ly6G+ MDSCs suppress proliferation of T cells. CXCL5, nitric oxide and GM-CSF produced by cancer-educated BMSCs contribute to the formation of malignant microenvironments. Treatment with CXCL5 antibody, the iNOS inhibitor 1400w and GM-CSF antibody reduced MDSC expansion in the bone marrow, primary tumour sites and metastatic sites, and promoted the efficiency of PD-L1 antibody. Our study reveals that cancer-educated BMSCs are the component of the niche for primary lung cancer cells and DTCs, and that they can be the target for immunotherapy.

A New Era of Prostate Cancer Precision Medicine

Prostate cancer is the second most common male cancer affecting Western society. Despite substantial advances in the exploration of prostate cancer biomarkers and treatment strategies, men are over diagnosed with inert prostate cancer, while there is also a substantial mortality from the invasive disease. Precision medicine is the management of treatment profiles across different cancers predicting therapies for individual cancer patients. With strategies including individual genomic profiling and targeting specific cancer pathways, precision medicine for prostate cancer has the potential to impose changes in clinical practices. Some of the recent advances in prostate cancer precision medicine comprise targeting gene fusions, genome editing tools, non-coding RNA biomarkers, and the promise of liquid tumor profiling. In this review, we will discuss these recent scientific advances to scale up these approaches and endeavors to overcome clinical barriers for prostate cancer precision medicine.

Quantitative proteomics reveals reduction of endocytic machinery components in gliomas

Background

Gliomas are the most frequent and aggressive malignancies of the central nervous system. Decades of molecular analyses have demonstrated that gliomas accumulate genetic alterations that culminate in enhanced activity of receptor tyrosine kinases and downstream mediators. While the genetic alterations, like gene amplification or loss, have been well characterized, little information exists about changes in the proteome of gliomas of different grades.

Methods

We performed unbiased quantitative proteomics of human glioma biopsies by mass spectrometry followed by bioinformatic analysis.

Findings

Various pathways were found to be up- or downregulated. In particular, endocytosis as pathway was affected by a vast and concomitant reduction of multiple machinery components involved in initiation, formation, and scission of endocytic carriers. Both clathrin-dependent and -independent endocytosis were changed, since not only clathrin, AP-2 adaptins, and endophilins were downregulated, but also dynamin that is shared by both pathways. The reduction of endocytic machinery components caused increased receptor cell surface levels, a prominent phenotype of defective endocytosis. Analysis of additional biopsies revealed that depletion of endocytic machinery components was a common trait of various glioma grades and subclasses.

Interpretation

We propose that impaired endocytosis creates a selective advantage in glioma tumor progression due to prolonged receptor tyrosine kinase signaling from the cell surface.

Fund

This work was supported by Grants 316030-164105 (to P. Jenö), 31003A-162643 (to M. Spiess) and PP00P3-176974 (to G. Hutter) from the Swiss National Science Foundation. Further funding was received by the Department of Surgery from the University Hospital Basel.