Immunomodulatory Effects of Tryptophan Metabolism in the Glioma Tumor Microenvironment

Glioblastoma Therapy: Past, Present and Future

Glioblastoma (GB) stands out as the most prevalent and lethal form of brain cancer. Although great efforts have been made by clinicians and researchers, no significant improvement in survival has been achieved since the Stupp protocol became the standard of care (SOC) in 2005. Despite multimodality treatments, recurrence is almost universal with survival rates under 2 years after diagnosis. Here, we discuss the recent progress in our understanding of GB pathophysiology, in particular, the importance of glioma stem cells (GSCs), the tumor microenvironment conditions, and epigenetic mechanisms involved in GB growth, aggressiveness and recurrence. The discussion on therapeutic strategies first covers the SOC treatment and targeted therapies that have been shown to interfere with different signaling pathways (pRB/CDK4/RB1/P16ink4, TP53/MDM2/P14arf, PI3k/Akt-PTEN, RAS/RAF/MEK, PARP) involved in GB tumorigenesis, pathophysiology, and treatment resistance acquisition. Below, we analyze several immunotherapeutic approaches (i.e., checkpoint inhibitors, vaccines, CAR-modified NK or T cells, oncolytic virotherapy) that have been used in an attempt to enhance the immune response against GB, and thereby avoid recidivism or increase survival of GB patients. Finally, we present treatment attempts made using nanotherapies (nanometric structures having active anti-GB agents such as antibodies, chemotherapeutic/anti-angiogenic drugs or sensitizers, radionuclides, and molecules that target GB cellular receptors or open the blood-brain barrier) and non-ionizing energies (laser interstitial thermal therapy, high/low intensity focused ultrasounds, photodynamic/sonodynamic therapies and electroporation). The aim of this review is to discuss the advances and limitations of the current therapies and to present novel approaches that are under development or following clinical trials.

Tryptophanylation of insulin receptor by WARS attenuates insulin signaling

Increased circulating amino acid levels have been linked to insulin resistance and development of type 2 diabetes (T2D), but the underlying mechanism remains largely unknown. Herein, we show that tryptophan modifies insulin receptor (IR) to attenuate insulin signaling and impair glucose uptake. Mice fed with tryptophan-rich chow developed insulin resistance. Excessive tryptophan promoted tryptophanyl-tRNA synthetase (WARS) to tryptophanylate lysine 1209 of IR (W-K1209), which induced insulin resistance by inhibiting the insulin-stimulated phosphorylation of IR, AKT, and AS160. SIRT1, but not other sirtuins, detryptophanylated IRW-K1209 to increase the insulin sensitivity. Collectively, we unveiled the mechanisms of how tryptophan impaired insulin signaling, and our data suggested that WARS might be a target to attenuate insulin resistance in T2D patients.

Comprehensive multi-omics analysis of tryptophan metabolism-related gene expression signature to predict prognosis in gastric cancer

Introduction: The 5-year survival of gastric cancer (GC) patients with advanced stage remains poor. Some evidence has indicated that tryptophan metabolism may induce cancer progression through immunosuppressive responses and promote the malignancy of cancer cells. The role of tryptophan and its metabolism should be explored for an in-depth understanding of molecular mechanisms during GC development. Material and methods: We utilized the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset to screen tryptophan metabolism-associated genes via single sample gene set enrichment analysis (ssGSEA) and correlation analysis. Consensus clustering analysis was employed to construct different molecular subtypes. Most common differentially expressed genes (DEGs) were determined from the molecular subtypes. Univariate cox analysis as well as lasso were performed to establish a tryptophan metabolism-associated gene signature. Gene Set Enrichment Analysis (GSEA) was utilized to evaluate signaling pathways. ESTIMATE, ssGSEA, and TIDE were used for the evaluation of the gastric tumor microenvironment. Results: Two tryptophan metabolism-associated gene molecular subtypes were constructed. Compared to the C2 subtype, the C1 subtype showed better prognosis with increased CD4 positive memory T cells as well as activated dendritic cells (DCs) infiltration and suppressed M2-phenotype macrophages inside the tumor microenvironment. The immune checkpoint was downregulated in the C1 subtype. A total of eight key genes, EFNA3, GPX3, RGS2, CXCR4, SGCE, ADH4, CST2, and GPC3, were screened for the establishment of a prognostic risk model. Conclusion: This study concluded that the tryptophan metabolism-associated genes can be applied in GC prognostic prediction. The risk model established in the current study was highly accurate in GC survival prediction.

FXR1 promotes glioma progression by downregulating microRNA-124-3p through long noncoding RNA FGD5-AS1 upregulation

OBJECTIVE

As reported, glioma progression is affected by altered FXR1, long non-coding RNA FGD5-AS1, and microRNA (miR)-124-3p. However, relationships among these genes remain unclear. Accordingly, this paper ascertains whether FXR1 manipulates glioma progression via the FGD5-AS1/miR-124-3p axis.

METHODS

Glioma tissues were harvested, in which FGD5-AS1 and miR-124-3p levels were examined with qRT-PCR and FXR1 level was assessed with qRT-PCR and western blot. The interaction of miR-124-3p with FGD5-AS1 was analyzed by dual-luciferase reporter, RIP, and Pearson correlation coefficient assays, and that of FXR1 with FGD5-AS1 was assessed by RIP and Pearson correlation coefficient assays. Glioma cells were obtained, followed by qRT-PCR detection of miR-124-3p expression. After gain- or loss-of-function assays, EdU, Transwell, and tubule formation assays were performed to determine cell proliferation, invasion and migration, and angiogenesis. Next, the intracranial in situ graft tumor model was established for in vivo verification.

RESULTS

FGD5-AS1 and FXR1 levels were high, but miR-124-3p level was low in glioma tissues. Likewise, glioma cells had downregulated miR-124-3p expression. Mechanistically, FGD5-AS1 negatively bound to miR-124-3p, and FXR1 was positively correlated and interacted with FGD5-AS1. miR-124-3p overexpression or FGD5-AS1 or FXR1 knockdown restricted cell invasion, proliferation, migration, and angiogenesis in gliomas. miR-124-3p inhibition abrogated the repressive impacts of FXR1 knockdown on the malignant progression of gliomas. Also, FXR1 constrained tumor growth and angiogenesis in mice, which was counterweighed by inhibiting miR-124-3p.

CONCLUSION

FXR1 might act as an oncogene in gliomas by declining miR-124-3p through FGD5-AS1.

Investigational Microbiological Therapy for Glioma

Glioma is the most common primary malignancy of the central nervous system (CNS), and 50% of patients present with glioblastoma (GBM), which is the most aggressive type. Currently, the most popular therapies are progressive chemotherapy and treatment with temozolomide (TMZ), but the median survival of glioma patients is still low as a result of the emergence of drug resistance, so we urgently need to find new therapies. A growing number of studies have shown that the diversity, bioactivity, and manipulability of microorganisms make microbial therapy a promising approach for cancer treatment. However, the many studies on the research progress of microorganisms and their derivatives in the development and treatment of glioma are scattered, and nobody has yet provided a comprehensive summary of them. Therefore, in this paper, we review the research progress of microorganisms and their derivatives in the development and treatment of glioma and conclude that it is possible to treat glioma by exogenous microbial therapies and targeting the gut-brain axis. In this article, we discuss the prospects and pressing issues relating to these therapies with the aim of providing new ideas for the treatment of glioma.

Rewired Metabolism of Amino Acids and Its Roles in Glioma Pathology

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

Molecular Crosstalk between T Cells and Tumor Uncovers GBM-Specific T Cell Signatures in Blood: Noninvasive GBM Diagnosis Using Immunosensors

Glioblastoma (GBM) is the most common and aggressive stage IV brain cancer with a poor prognosis and survival rate. The blood-brain barrier (BBB) in GBM prevents the entry and exit of biomarkers, limiting its treatment options. Hence, GBM diagnosis is pivotal for timely clinical management. Currently, there exists no clinically validated biomarker for GBM diagnosis. T cells exhibit the potential to escape a leaky BBB in GBM patients. These T cells infiltrating the GBM interact with the heterogeneous population of tumor cells, display a symbiotic interaction resulting in intertwined molecular crosstalk, and display a GBM-associated signature while entering the peripheral circulation. Therefore, we hypothesize that studying these distinct molecular changes is critical to enable T cells to be a diagnostic marker for accurate detection of GBM from patient blood. We demonstrated this by utilizing the phenotypic and immunological landscape changes in T cells associated with glioblastoma tumors. GBM exhibits a high level of heterogeneity with diverse subtypes of cells within the tumor, enabling immune infiltration and different degrees of interactions with the tumor. To accurately detect these subtle molecular differences in T cells, we designed an immunosensor with a high detection sensitivity and repeatability. Hence in this study, we investigated the characteristic behavior of T cells to establish two preclinically validated biomarkers: GBM-associated T cells (GBMAT) and GBM stem cell-associated T cells (GSCAT). A comprehensive investigation was conducted by mimicking the tumor microenvironment in vitro by coculturing T cells with cancer cells and cancer stem cells to study the distinct variation in GBMAT and GSCAT. Preclinical investigation of T cells from GBM patient blood shows similar characteristics to our established biomarkers (GBMAT, GSCAT). Further evaluating the relative attributes of T cells in patient blood and tissue biopsy confirms the infiltrating ability of T cells across the BBB. A pilot validation using a SERS-based machine learning algorithm was accomplished by training the model with GBMAT and GSCAT as diagnostic markers. Using GBMAT as a biomarker, we achieved a sensitivity and specificity of 93.3% and 97.4%, respectively, whereas applying GSCAT yielded a sensitivity and specificity of 100% and 98.7%, respectively. We also validated this diagnostic methodology by using conventional biological assays to study the change in expression levels of T cell surface markers (CD4 and CD8) and cytokine levels in T cells (IL6, IL10, TNFα, INFγ) from GBM patients. This study introduces T cells as GBM-specific immune biomarkers to diagnose GBM using patient liquid biopsy. This preclinical validation study presents a better translatability into clinical reality that will enable rapid and noninvasive glioblastoma detection from patient blood.

Exploring Metabolic Signatures of Ex Vivo Tumor Tissue Cultures for Prediction of Chemosensitivity in Ovarian Cancer

Simple Summary

Women diagnosed with ovarian cancer have 5-year survival rates below 45%. Prediction of patient’s outcome and the onset of drug resistance are still major challenges. The patient’s drug response is influenced by the environment that surrounds the tumor cells. We previously showed that patient-derived tumor tissue can be kept in the lab, alive and retaining aspects of that environment. In this study, we exposed tumor tissue derived from ovarian cancer patients to the chemotherapy patients receive and identified metabolites released by the tumor tissue after treatment (metabolic footprint). Using machine learning, we uncovered metabolic signatures that discriminate tumor tissues with higher vs. lower drug sensitivity. We propose potential biomarkers involved in the production of specific building blocks of cells and energy generation processes. Overall, we established a platform to explore metabolic features of the complex environment of each patient’s tumor that can underpin the discovery of biomarkers of drug response.

Abstract

Predicting patient response to treatment and the onset of chemoresistance are still major challenges in oncology. Chemoresistance is deeply influenced by the complex cellular interactions occurring within the tumor microenvironment (TME), including metabolic crosstalk. We have previously shown that ex vivo tumor tissue cultures derived from ovarian carcinoma (OvC) resections retain the TME components for at least four weeks of culture and implemented assays for assessment of drug response. Here, we explored ex vivo patient-derived tumor tissue cultures to uncover metabolic signatures of chemosensitivity and/or resistance. Tissue cultures derived from nine OvC cases were challenged with carboplatin and paclitaxel, the standard-of-care chemotherapeutics, and the metabolic footprints were characterized by LC-MS. Partial least-squares discriminant analysis (PLS-DA) revealed metabolic signatures that discriminated high-responder from low-responder tissue cultures to ex vivo drug exposure. As a proof-of-concept, a set of potential metabolic biomarkers of drug response was identified based on the receiver operating characteristics (ROC) curve, comprising amino acids, fatty acids, pyrimidine, glutathione, and TCA cycle pathways. Overall, this work establishes an analytical and computational platform to explore metabolic features of the TME associated with response to treatment, which can leverage the discovery of biomarkers of drug response and resistance in OvC.

New biomarker: the gene HLA-DRA associated with low-grade glioma prognosis

BACKGROUND

Low-grade gliomas (LGG) are WHO grade II tumors presenting as the most common primary malignant brain tumors in adults. Currently, LGG treatment involves either or a combination of surgery, radiation therapy, and chemotherapy. Despite the knowledge of constitutive genetic risk factors contributing to gliomas, the role of single genes as diagnostic and prognostic biomarkers is limited. The aim of the current study is to discover the predictive and prognostic genetic markers for LGG.

METHODS

Transcriptome data and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. We first performed the tumor microenvironment (TME) survival analysis using the Kaplan-Meier method. An analysis was undertaken to screen for differentially expressed genes. The function of these genes was studied by Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Following which a protein-protein interaction network (PPI) was constructed and visualized. Univariate and multivariate COX analyses were performed to obtain the probable prognostic genes. The key genes were selected by an intersection of core and prognostic genes. A clinical correlation analysis of single-gene expression was undertaken. GSEA enrichment analysis was performed to identify the function of key genes. Finally, a single gene-related correlation analysis was performed to identify the core immune cells involved in the development of LGG.

RESULTS

A total of 529 transcriptome data and 515 clinical samples were obtained from the TCGA. Immune cells and stromal cells were found to be significantly increased in the LGG microenvironment. The top five core genes intersected with the top 38 prognostically relevant genes and two key genes were identified. Our analysis revealed that a high expression of HLA-DRA was associated with a poor prognosis of LGG. Correlation analysis of immune cells showed that HLA-DRA expression level was related to immune infiltration, positively related to macrophage M1 phenotype, and negatively related to activation of NK cells.

CONCLUSIONS

HLA-DRA may be an independent prognostic indicator and an important biomarker for diagnosing and predicting survival in LGG patients. It may also be associated with the immune infiltration phenotype in LGG.