Construction of Interferon-Gamma-Related Gene Signature to Characterize the Immune-Inflamed Phenotype of Glioblastoma and Predict Prognosis, Efficacy of Immunotherapy and Radiotherapy

TGFBI: A novel therapeutic target for cancer

TGFBI, an extracellular matrix protein induced by transforming growth factor β, has been found to exhibit aberrant expression in various types of cancer. TGFBI plays a crucial role in tumor cell proliferation, angiogenesis, and apoptosis. It also facilitates invasion and metastasis in various types of cancer, including colon, head and neck squamous, renal, and prostate cancers. TGFBI, a prominent p-EMT marker, strongly correlates with lymph node metastasis. TGFBI demonstrates immunosuppressive effects within the tumor immune microenvironment. Targeted therapy directed at TGFBI shows promise as a potential strategy to combat cancer. Hence, a comprehensive review was conducted to examine the impact of TGFBI on various aspects of tumor biology, including cell proliferation, angiogenesis, invasion, metastasis, apoptosis, and the immune microenvironment. This review also delved into the underlying biochemical mechanisms to enhance our understanding of the research advancements related to TGFBI in the context of tumors.

Therapeutic Implications of Targeting YY1 in Glioblastoma

The transcription factor Yin Yang 1 (YY1) plays a pivotal role in the pathogenesis of glioblastoma multiforme (GBM), an aggressive form of brain tumor. This review systematically explores the diverse roles of YY1 overexpression and activities in GBM, including its impact on the tumor microenvironment (TME) and immune evasion mechanisms. Due to the poor response of GBM to current therapies, various findings of YY1-associated pathways in the literature provide valuable insights into novel potential targeted therapeutic strategies. Moreover, YY1 acts as a significant regulator of immune checkpoint molecules and, thus, is a candidate therapeutic target in combination with immune checkpoint inhibitors. Different therapeutic implications targeting YY1 in GBM and its inherent associated challenges encompass the use of nanoparticles, YY1 inhibitors, targeted gene therapy, and exosome-based delivery systems. Despite the inherent complexities of such methods, the successful targeting of YY1 emerges as a promising avenue for reshaping GBM treatment strategies, presenting opportunities for innovative therapeutic approaches and enhanced patient outcomes.

Immune features are associated with response to neoadjuvant chemo-immunotherapy for muscle-invasive bladder cancer

Neoadjuvant cisplatin-based chemotherapy is standard of care for muscle-invasive bladder cancer (MIBC). Immune checkpoint inhibition (ICI) alone, and ICI in combination with chemotherapy, have demonstrated promising pathologic response ( Collapse

Key Words

• predictive markers
• bladder cancer
• immunotherapy
• tumour immunology

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MESH Headings

• Humans
• Urinary Bladder Neoplasms/drug therapy
• Urinary Bladder Neoplasms/immunology
• Urinary Bladder Neoplasms/therapy
• Urinary Bladder Neoplasms/pathology
• Neoadjuvant Therapy/methods
• Deoxycytidine/analogs & derivatives
• Deoxycytidine/therapeutic use
• Deoxycytidine/administration & dosage
• Gemcitabine
• Immunotherapy/methods
• Male
• Cisplatin/therapeutic use
• Cisplatin/administration & dosage
• Female
• Antibodies, Monoclonal, Humanized/therapeutic use
• Antineoplastic Combined Chemotherapy Protocols/therapeutic use
• Immune Checkpoint Inhibitors/therapeutic use
• Aged
• Middle Aged
• Neoplasm Invasiveness
• Interleukin-9/metabolism
• B7-H1 Antigen/metabolism
• Biomarkers, Tumor/blood
• Treatment Outcome

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Grants

• F30 CA278317 NCI NIH HHS
• 1F30CA278317-01A1 U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
• Merck & Co., Inc. | Merck Sharp and Dohme (Merck Sharp & Dohme)
• U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)

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Affiliation(s)

Wolfgang Beckabir Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
Mi Zhou Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jin Seok Lee Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, USA
Steven P Vensko Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Mark G Woodcock Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Hsing-Hui Wang Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
Sara E Wobker Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Gatphan Atassi Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Alec D Wilkinson Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Kenneth Fowler Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Leah M Flick Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jeffrey S Damrauer Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Michael R Harrison Division of Medical Oncology, Department of Medicine, Duke Cancer Institute, Duke University, Durham, NC, USA
Karen P McKinnon Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
Tracy L Rose Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Matthew I Milowsky Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jonathan S Serody Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA. Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
William Y Kim Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Division of Hematology, Department of Medicine, UNC School of Medicine, Chapel Hill, NC, USA.
Benjamin G Vincent Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, USA. Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Computational Medicine Program, UNC School of Medicine, Chapel Hill, NC, USA.

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SPIN: sex-specific and pathway-based interpretable neural network for sexual dimorphism analysis

Sexual dimorphism in prevalence, severity and genetic susceptibility exists for most common diseases. However, most genetic and clinical outcome studies are designed in sex-combined framework considering sex as a covariate. Few sex-specific studies have analyzed males and females separately, which failed to identify gene-by-sex interaction. Here, we propose a novel unified biologically interpretable deep learning-based framework (named SPIN) for sexual dimorphism analysis. We demonstrate that SPIN significantly improved the C-index up to 23.6% in TCGA cancer datasets, and it was further validated using asthma datasets. In addition, SPIN identifies sex-specific and -shared risk loci that are often missed in previous sex-combined/-separate analysis. We also show that SPIN is interpretable for explaining how biological pathways contribute to sexual dimorphism and improve risk prediction in an individual level, which can result in the development of precision medicine tailored to a specific individual's characteristics.

Upregulation of the canonical signaling pathway of interferon-gamma is associated with glioblastoma progression

BACKGROUND

Glioblastoma is a brain malignant tumor grade IV, highly invasive. Alterations in several signaling pathways are involved in glioblastoma development. In this work, we evaluated the IFN-γ canonical signaling pathway in glioblastoma cells and its effect on cell viability and migration.

METHODS

The levels of STAT1/pSTAT1, IRF1, and PD-L1 in LN-18 glioblastoma cells were analyzed using western blotting. Cell viability was evaluated by calcein-AM/propidium iodide assays, and a wound healing assay was used to study the migration of glioblastoma cells treated with IFN-γ. Our aim was to determine the expression of IFN-γ signaling elements in cell lines and tissue from glioblastoma samples and examine the relationship between these elements and the survival of glioblastoma patients. The following platforms were utilized for analysis: the CCLE (Cancer Cell Line Encyclopedia), UALCAN (University of Alabama at Birmingham Cancer data analysis Portal), GEPIA (Gene Expression Profiling Interactive Analysis), and GENT2 (Gene Expression patterns across Normal and Tumor tissues).

RESULTS

Our results evidenced that IFN-γ signaling increases non-phosphorylated and phosphorylated STAT1 levels and promotes the upregulation of IRF1 and PD-L1 in glioblastoma cells. The activation of IFN-γ signaling increased cell migration without affecting the viability of glioblastoma cells. Furthermore, in silico analysis showed that the elements of IFN-γ signaling pathways (IFNGR1/IFNGR2/STAT1/IRF1) are upregulated in human glioblastoma samples. The upregulation of IFN-γ signaling was associated with shorter survival in glioblastoma patients.

CONCLUSION

IFN-γ signaling pathway is upregulated in glioblastoma, displaying pro-tumor activity. Thus, IFN-γ signaling elements may be potential biomarkers and targets for treating glioblastoma.

A co-formulation of interferons alpha2b and gamma distinctively targets cell cycle in the glioblastoma-derived cell line U-87MG

BACKGROUND

HeberFERON is a co-formulation of α2b and γ interferons, based on their synergism, which has shown its clinical superiority over individual interferons in basal cell carcinomas. In glioblastoma (GBM), HeberFERON has displayed promising preclinical and clinical results. This led us to design a microarray experiment aimed at identifying the molecular mechanisms involved in the distinctive effect of HeberFERON compared to the individual interferons in U-87MG model.

METHODS

Transcriptional expression profiling including a control (untreated) and three groups receiving α2b-interferon, γ-interferon and HeberFERON was performed using an Illumina HT-12 microarray platform. Unsupervised methods for gene and sample grouping, identification of differentially expressed genes, functional enrichment and network analysis computational biology methods were applied to identify distinctive transcription patterns of HeberFERON. Validation of most representative genes was performed by qPCR. For the cell cycle analysis of cells treated with HeberFERON for 24 h, 48 and 72 h we used flow cytometry.

RESULTS

The three treatments show different behavior based on the gene expression profiles. The enrichment analysis identified several mitotic cell cycle related events, in particular from prometaphase to anaphase, which are exclusively targeted by HeberFERON. The FOXM1 transcription factor network that is involved in several cell cycle phases and is highly expressed in GBMs, is significantly down regulated. Flow cytometry experiments corroborated the action of HeberFERON on the cell cycle in a dose and time dependent manner with a clear cellular arrest as of 24 h post-treatment. Despite the fact that p53 was not down-regulated, several genes involved in its regulatory activity were functionally enriched. Network analysis also revealed a strong relationship of p53 with genes targeted by HeberFERON. We propose a mechanistic model to explain this distinctive action, based on the simultaneous activation of PKR and ATF3, p53 phosphorylation changes, as well as its reduced MDM2 mediated ubiquitination and export from the nucleus to the cytoplasm. PLK1, AURKB, BIRC5 and CCNB1 genes, all regulated by FOXM1, also play central roles in this model. These and other interactions could explain a G2/M arrest and the effect of HeberFERON on the proliferation of U-87MG.

CONCLUSIONS

We proposed molecular mechanisms underlying the distinctive behavior of HeberFERON compared to the treatments with the individual interferons in U-87MG model, where cell cycle related events were highly relevant.

Advanced immunotherapies for glioblastoma: tumor neoantigen vaccines in combination with immunomodulators

Glial-origin brain tumors, including glioblastomas (GBM), have one of the worst prognoses due to their rapid and fatal progression. From an oncological point of view, advances in complete surgical resection fail to eliminate the entire tumor and the remaining cells allow a rapid recurrence, which does not respond to traditional therapeutic treatments. Here, we have reviewed new immunotherapy strategies in association with the knowledge of the immune micro-environment. To understand the best lines for the future, we address the advances in the design of neoantigen vaccines and possible new immune modulators. Recently, the efficacy and availability of vaccine development with different formulations, especially liposome plus mRNA vaccines, has been observed. We believe that the application of new strategies used with mRNA vaccines in combination with personalized medicine (guided by different omic's strategies) could give good results in glioma therapy. In addition, a large part of the possible advances in new immunotherapy strategies focused on GBM may be key improving current therapies of immune checkpoint inhibitors (ICI), given the fact that this type of tumor has been highly refractory to ICI.

Tumor-secreted lactate contributes to an immunosuppressive microenvironment and affects CD8 T-cell infiltration in glioblastoma

Introduction

Glioblastoma is a malignant brain tumor with poor prognosis. Lactate is the main product of tumor cells, and its secretion may relate to immunocytes' activation. However, its role in glioblastoma is poorly understood.

Methods

This work performed bulk RNA-seq analysis and single cell RNA-seq analysis to explore the role of lactate in glioblastoma progression. Over 1400 glioblastoma samples were grouped into different clusters according to their expression and the results were validated with our own data, the xiangya cohort. Immunocytes infiltration analysis, immunogram and the map of immune checkpoint genes' expression were applied to analyze the potential connection between the lactate level with tumor immune microenvironment. Furthermore, machine learning algorithms and cell-cell interaction algorithm were introduced to reveal the connection of tumor cells with immunocytes. By co-culturing CD8 T cells with tumor cells, and performing immunohistochemistry on Xiangya cohort samples further validated results from previous analysis.

Discussion

In this work, lactate is proved that contributes to glioblastoma immune suppressive microenvironment. High level of lactate in tumor microenvironment can affect CD8 T cells' migration and infiltration ratio in glioblastoma. To step further, potential compounds that targets to samples from different groups were also predicted for future exploration.

Development of a TMErisk model based on immune infiltration in tumour microenvironment to predict prognosis of immune checkpoint inhibitor treatment in hepatocellular carcinoma

Immune checkpoint inhibitor (ICI) treatment has created the opportunity of improved outcome for patients with hepatocellular carcinoma (HCC). However, only a minority of HCC patients benefit from ICI treatment owing to poor treatment efficacy and safety concerns. There are few predictive factors that precisely stratify HCC responders to immunotherapy. In this study, we developed a tumour microenvironment risk (TMErisk) model to divide HCC patients into different immune subtypes and evaluated their prognosis. Our results indicated that virally mediated HCC patients who had more common tumour protein P53 (TP53) alterations with lower TMErisk scores were appropriate for ICI treatment. HCC patients with alcoholic hepatitis who more commonly harboured catenin beta 1 (CTNNB1) alterations with higher TMErisk scores could benefit from treatment with multi-tyrosine kinase inhibitors. The developed TMErisk model represents the first attempt to anticipate tumour tolerance of ICIs in the TME through the degree of immune infiltration in HCCs.

Identification of EMT-Related Genes and Prognostic Signature With Significant Implications on Biological Properties and Oncology Treatment of Lower Grade Gliomas

The epithelial-mesenchymal transition (EMT) is an important process that drives progression, metastasis, and oncology treatment resistance in cancers. Also, the adjacent non-tumor tissue may affect the biological properties of cancers and have potential prognostic implications. Our study aimed to identify EMT-related genes in LGG samples, explore their impact on the biological properties of lower grade gliomas (LGG) through the multi-omics analysis, and reveal the potential mechanism by which adjacent non-tumor tissue participated in the malignant progression of LGG. Based on the 121 differentially expressed EMT-related genes between normal samples from the GTEx database and LGG samples in the TCGA cohort, we identified two subtypes and constructed EMTsig. Because of the genetic, epigenetic, and transcriptomic heterogeneity, malignant features including clinical traits, molecular traits, metabolism, anti-tumor immunity, and stemness features were different between samples with C1 and C2. In addition, EMTsig could also quantify the EMT levels, variation in prognosis, and oncology treatment sensitivity of LGG patients. Therefore, EMTsig could assist us in developing objective diagnostic tools and in optimizing therapeutic strategies for LGG patients. Notably, with the GSVA, we found that adjacent non-tumor tissue might participate in the progression, metastasis, and formation of the tumor microenvironment in LGG. Therefore, the potential prognostic implications of adjacent non-tumor tissue should be considered when performing clinical interventions for LGG patients. Overall, our study investigated and validated the effects of EMT-related genes on the biological properties from multiple perspectives, and provided new insights into the function of adjacent non-tumor tissue in the malignant progression of LGG.

Identification of Implications of Angiogenesis and m6A Modification on Immunosuppression and Therapeutic Sensitivity in Low-Grade Glioma by Network Computational Analysis of Subtypes and Signatures

Angiogenesis is a complex process in the immunosuppressed low-grade gliomas (LGG) microenvironment and is regulated by multiple factors. N⁶-methyladenosine (m6A), modified by the m6A modification regulators (“writers” “readers” and “erasers”), can drive LGG formation. In the hypoxic environment of intracranial tumor immune microenvironment (TIME), m6A modifications in glioma stem cells are predominantly distributed around neovascularization and synergize with complex perivascular pathological ecology to mediate the immunosuppressive phenotype of TIME. The exact mechanism of this phenomenon remains unknown. Herein, we elucidated the relevance of the angiogenesis-related genes (ARGs) and m6A regulators (MAGs) and their influencing mechanism from a macro perspective. Based on the expression pattern of MAGs, we divided patients with LGG into two robust categories via consensus clustering, and further annotated the malignant related mechanisms and corresponding targeted agents. The two subgroups (CL1, CL2) demonstrated a significant correlation with prognosis and clinical-pathology features. Moreover, WGCNA has also uncovered the hub genes and related mechanisms of MAGs affecting clinical characters. Clustering analysis revealed a synergistic promoting effect of M6A and angiogenesis on immunosuppression. Based on the expression patterns of MAGs, we established a high-performance gene-signature (MASig). MASig revealed somatic mutational mechanisms by which MAGs affect the sensitivity to treatment in LGG patients. In conclusion, the MAGs were critical participants in the malignant process of LGG, with a vital potential in the prognosis stratification, prediction of outcome, and therapeutic sensitivity of LGG. Findings based on these strategies may facilitate the development of objective diagnosis and treatment systems to quantify patient survival and other outcomes, and in some cases, to identify potential unexplored targeted therapies.

A 2-Gene Signature Related to Interferon-Gamma Predicts Prognosis and Responsiveness to Immune Checkpoint Blockade of Glioma

Background

Gliomas represent the most common and aggressive brain malignancy. Interferon-gamma (IFNG) is a potent inducer of immune response, developing IFNG-related gene signature may promote the diagnosis and treatment of this disease.

Methods

Bulk tumor and single-cell mRNA-seq datasets of glioma ranging from WHO grade II to IV with corresponding demographics were included. Multiple bioinformatics and machine learning algorithms were performed to develop an IFNG-related prognostic signature and evaluate immune checkpoint blockade (ICB) therapy response.

Results

IFNGR1 and IFNGR2 were used as concise IFNG-related gene signature based on which the IFNGR score well-characterized the IFNG response in the glioma microenvironment. Increased IFNGR score was associated with clinicopathological parameters relating to tumor malignancy and prevailing molecular pathological markers. Notably, K-M and Cox regression analysis found that the IFNGR score was an effective prognostic biomarker, and was associated with tumor relapse for a subset of patients. Notably, IFNGR1 and IFNGR2 were preferentially expressed by the Mono/Macro cells in the glioma microenvironment and were significantly correlated with M2 macrophage. Thus, the IFNGR score-high group had increased expression of immune checkpoints and had the potential to predict ICB responsiveness.

Conclusion

In conclusion, we have developed a concise IFNG-related gene signature of clinical significance, which may improve the current diagnosis and treatment of glioma.