EGFR mutation: novel prognostic factor associated with immune infiltration in lower-grade glioma; an exploratory study

Genetic Prognostic Factors in Adult Diffuse Gliomas: A 10-Year Experience at a Single Institution

Gliomas are primary brain lesions involving cerebral structures without well-defined boundaries and constitute the most prevalent central nervous system (CNS) neoplasms. Among gliomas, glioblastoma (GB) is a glioma of the highest grade and is associated with a grim prognosis. We examined how clinical variables and molecular profiles may have affected overall survival (OS) over the past ten years. A retrospective study was conducted at Sina Hospital in Tehran, Iran and examined patients with confirmed glioma diagnoses between 2012 and 2020. We evaluated the correlation between OS in GB patients and sociodemographic as well as clinical factors and molecular profiling based on IDH1, O-6-Methylguanine-DNA Methyltransferase (MGMT), TERTp, and epidermal growth factor receptor (EGFR) amplification (EGFR-amp) status. Kaplan-Meier and multivariate Cox regression models were used to assess patient survival. A total of 178 patients were enrolled in the study. The median OS was 20 months, with a 2-year survival rate of 61.0%. Among the 127 patients with available IDH measurements, 100 (78.7%) exhibited mutated IDH1 (IDH1-mut) tumors. Of the 127 patients with assessed MGMT promoter methylation (MGMTp-met), 89 (70.1%) had MGMT methylated tumors. Mutant TERTp (TERTp-mut) was detected in 20 out of 127 cases (15.7%), while wildtype TERTp (wildtype TERTp-wt) was observed in 107 cases (84.3%). Analyses using multivariable models revealed that age at histological grade (p < 0.0001), adjuvant radiotherapy (p < 0.018), IDH1 status (p < 0.043), and TERT-p status (p < 0.014) were independently associated with OS. Our study demonstrates that patients with higher tumor histological grades who had received adjuvant radiotherapy exhibited IDH1-mut or presented with TERTp-wt experienced improved OS. Besides, an interesting finding showed an association between methylation of MGMTp and TERTp status with tumor location.

Machine learning unveils immune-related signature in multicenter glioma studies

In glioma molecular subtyping, existing biomarkers are limited, prompting the development of new ones. We present a multicenter study-derived consensus immune-related and prognostic gene signature (CIPS) using an optimal risk score model and 101 algorithms. CIPS, an independent risk factor, showed stable and powerful predictive performance for overall and progression-free survival, surpassing traditional clinical variables. The risk score correlated significantly with the immune microenvironment, indicating potential sensitivity to immunotherapy. High-risk groups exhibited distinct chemotherapy drug sensitivity. Seven signature genes, including IGFBP2 and TNFRSF12A, were validated by qRT-PCR, with higher expression in tumors and prognostic relevance. TNFRSF12A, upregulated in GBM, demonstrated inhibitory effects on glioma cell proliferation, migration, and invasion. CIPS emerges as a robust tool for enhancing individual glioma patient outcomes, while IGFBP2 and TNFRSF12A pose as promising tumor markers and therapeutic targets.

Exosomal DNA: Role in Reflecting Tumor Genetic Heterogeneity, Diagnosis, and Disease Monitoring

Extracellular vesicles (EVs), with exosomes at the forefront, are key in transferring cellular information and assorted biological materials, including nucleic acids. While exosomal RNA has been thoroughly examined, exploration into exosomal DNA (exoDNA)-which is stable and promising for cancer diagnostics-lags behind. This hybrid genetic material, combining contributions from both nuclear and mitochondrial DNA (mtDNA), is rooted in the cytoplasm. The enigmatic process concerning its cytoplasmic encapsulation continues to captivate researchers. Covering the entire genetic landscape, exoDNA encases significant oncogenic alterations in genes like TP53, ALK, and IDH1, which is vital for clinical assessment. This review delves into exosomal origins, the ins and outs of DNA encapsulation, and exoDNA's link to tumor biology, underscoring its superiority to circulating tumor DNA in the biomarker arena for both detection and therapy. Amidst scientific progress, there are complexities in the comprehension and practical application of the exoDNA surface. Reflecting on these nuances, we chart the prospective research terrain and potential pitfalls, forging a path for future inquiry. By illuminating both the known and unknown facets of exoDNA, the objective of this review is to provide guidance to the field of liquid biopsy (LB) while minimizing the occurrence of avoidable blind spots and detours.

Genetic alterations shape innate immune cells to foster immunosuppression and cancer immunotherapy resistance

Cancer immunotherapy, particularly immune checkpoint inhibitors, has opened a new avenue for cancer treatment following the durable clinical benefits. Despite the clinical successes across several cancer types, primary or acquired resistance might eventually lead to cancer progression in patients with clinical responses. Hence, to broaden the clinical applicability of these treatments, a detailed understanding of the mechanisms limiting the efficacy of cancer immunotherapy is needed. Evidence provided thus far has implicated immunosuppressive innate immune cells infiltrating the tumor microenvironment as key players in immunotherapy resistance. According to the available data, genetic alterations can shape the innate immune response to promote immunotherapy resistance and tumor progression. Herein, this review has discussed the current understanding of the underlying mechanisms where genetic alterations modulate the innate immune milieu to drive immunosuppression and immunotherapy resistance.

EGFR alterations in glioblastoma play a role in antitumor immunity regulation

The epidermal growth factor receptor (EGFR) is the most frequently altered gene in glioblastoma (GBM), which plays an important role in tumor development and anti-tumor immune response. While current molecular targeted therapies against the EGFR signaling pathway and its downstream key molecules have not demonstrated favorable clinical outcomes in GBM. Whereas tumor immunotherapies, especially immune checkpoint inhibitors, have shown durable antitumor responses in many cancers. However, the clinical efficacy is limited in patients carrying EGFR alterations, indicating that EGFR signaling may involve tumor immune response. Recent studies reveal that EGFR alterations not only promote GBM cell proliferation but also influence immune components in the tumor microenvironment (TME), leading to the recruitment of immunosuppressive cells (e.g., M2-like TAMs, MDSCs, and Tregs), and inhibition of T and NK cell activation. Moreover, EGFR alterations upregulate the expression of immunosuppressive molecules or cytokines (such as PD-L1, CD73, TGF-β). This review explores the role of EGFR alterations in establishing an immunosuppressive TME and hopes to provide a theoretical basis for combining targeted EGFR inhibitors with immunotherapy for GBM.

Constructing metabolism-protein interaction relationship to identify glioma prognosis using deep learning

Glioma is heterogeneous disease that requires classification into subtypes with similar clinical phenotypes, prognosis or treatment responses. Metabolic-protein interaction (MPI) can provide meaningful insights into cancer heterogeneity. Moreover, the potential of lipids and lactate for identifying prognostic subtypes of glioma remains relatively unexplored. Therefore, we proposed a method to construct an MPI relationship matrix (MPIRM) based on a triple-layer network (Tri-MPN) combined with mRNA expression, and processed the MPIRM by deep learning to identify glioma prognostic subtypes. These Subtypes with significant differences in prognosis were detected in glioma (p-value < 2e-16, 95% CI). These subtypes had a strong correlation in immune infiltration, mutational signatures and pathway signatures. This study demonstrated the effectiveness of node interaction from MPI networks in understanding the heterogeneity of glioma prognosis.

Role of COL6A2 in malignant progression and temozolomide resistance of glioma

BACKGROUND

Gliomas are one of the most common primary malignant tumors of the central nervous system, and have an unfavorable prognosis. Even combining precise surgery, chemotherapy and radiotherapy, the survival rate is still unsatisfactory. Chemotherapy resistance is one of main reasons for its adverse prognosis. As shown by several studies, glioma stem cells (GSCs) were correlated with radiotherapy/chemotherapy resistance and high relapse rate. This study aimed to find a new biomarker related to GSCs and chemotherapy resistance.

METHODS

TCGA, CGGA, GSE16011, GSE23806 and GDSC datasets were used to screen the genes related to GSCs, Temozolomide (TMZ) resistance, and survival. In the TCGA, GTEx, GSE16011 and CGGA datasets, mRNA level, prognostic value, and correlation with immune infiltration in the selected genes were analyzed through methods including Kaplan-Meier analysis, Cox analysis, the ESTIMATE algorithm, and the CIBERSORT algorithm. The expression of COL6A2 mRNA and protein in different groups was detected by RT-qPCR and western blot. A MTT assay and flow cytometry were used to measure the effect of COL6A2 on proliferation and apoptosis of glioma cells.

RESULTS

COL6A2 was positively correlated with glioma stemness and TMZ resistance. Its expression was up-regulated in GBM, and high expression was correlated with adverse prognosis. As shown by Cox analysis, COL6A2 was an independent prognostic factor for glioma. COL6A2 mRNA was increased with the glioma grade. It was over-expressed in MGMT non-methylation and IDH wild-type specimens. The results of in vitro experiments showed that COL6A2 promots proliferation of glioma cells and inhibits their apoptosis. Meanwhile, the expression of COL6A2 in TMZ-resistant glioma cells was significantly higher than that in ordinary glioma cells. As shown by GO and KEGG pathway analysis, COL6A2 was correlated with glioma proliferation, migration, invasion, and immunity. In particular, it was significantly positively correlated with PD-1, PD-L2, PD-L1, B7-H3, CTLA-4, IDO1 and TIM-3 expression, further verifying that it may play an important role in immune response. In addition, COL6A2 might influence immune cell infiltration in the glioma microenvironment.

CONCLUSION

COL6A2 high-expression is an indicator for adverse glioma prognosis, and is correlated with TMZ-resistant and immune response. Meanwhile, it may be a prospective biomarker for treatment.

Shared Cancer Dataset Analysis Identifies and Predicts the Quantitative Effects of Pan-Cancer Somatic Driver Variants

Driver mutations endow tumors with selective advantages and produce an array of pathogenic effects. Determining the function of somatic variants is important for understanding cancer biology and identifying optimal therapies. Here, we compiled a shared dataset from several cancer genomic databases. Two measures were applied to 535 cancer genes based on observed and expected frequencies of driver variants as derived from cancer-specific rates of somatic mutagenesis. The first measure comprised a binary classifier based on a binomial test; the second was tumor variant amplitude (TVA), a continuous measure representing the selective advantage of individual variants. TVA outperformed all other computational tools in terms of its correlation with experimentally derived functional scores of cancer mutations. TVA also highly correlated with drug response, overall survival, and other clinical implications in relevant cancer genes. This study demonstrates how a selective advantage measure based on a large cancer dataset significantly impacts our understanding of the spectral effect of driver variants in cancer. The impact of this information will increase as cancer treatment becomes more precise and personalized to tumor-specific mutations.

SIGNIFICANCE

A new selective advantage estimation assists in oncogenic driver identification and relative effect measurements, enabling better prognostication, therapy selection, and prioritization.

Identification of m6A-related lncRNAs for thyroid cancer recurrence

Background

Although the prognosis of thyroid cancer (THCA) is generally good, many patients have a high risk of recurrence after treatment. N6-methyladenosine (m6A)-related long noncoding RNAs (lncRNAs) have been extensively studied in recent years. However, the potential of m6A-related lncRNAs to predict recurrence in THCA is unknown.

Methods

RNA sequencing (RNA-seq) data and clinical information for THCA were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DELs) were identified using the R package DESeq2. A coexpression network based on m6A-related genes and lncRNAs was constructed. The CIBERSORT algorithm and gene set enrichment analysis (GSEA) were used for immune-infiltrating cell estimation and clustering functional enrichment analysis, respectively. A Kaplan-Meier plot was used for prognostic analysis based on m6A-associated lncRNA risk patterns. The expression of lncRNAs in recurrent and nonrecurrent THCA tissues was analyzed by real-time quantitative polymerase chain reaction (RT-qPCR).

Results

A network of m6A-related lncRNAs containing 8 lncRNAs was constructed with good predictive power for recurrence in THCA. A total of 3 clusters were obtained, and cluster 1 was most associated with THCA recurrence. We found significantly lower levels of CD8 T cells and follicular helper T cells, and significantly higher levels of dendritic cells (DCs), M2 macrophages, resting DCs, regulatory T cells, and mast cells in cluster 1 patients. Pathway analysis revealed significant enrichment in natural killer cell-mediated cytotoxicity, butyrate metabolism, and cell adhesion molecules in cluster 1. The m6A-related lncRNA risk model was effective in predicting progression-free survival (PFS) in patients with THCA recurrence. RT-qPCR analysis based on 40 THCA clinical samples from our center found the risk model to be a good predictor of recurrence in THCA patients.

Conclusions

In summary, m6A-related lncRNAs may provide a novel predictive method for prognostic relapse in THCA patients.

Irigenin inhibits glioblastoma progression through suppressing YAP/β-catenin signaling

Glioblastoma (GBM) is the most malignant glioma in brain tumors with low survival and high recurrence rate. Irigenin, as an isoflavone compound extracted from Shegan, has shown many pharmacological functions such as antioxidant, anti-inflammatory and anti-tumor. However, the effects of irigenin on GBM cells and the related molecular mechanisms remain unexplored. In this study, we found that irigenin inhibited the proliferation of GBM cells in a dose-dependent manner by several assays in vitro. Subsequently, we found that irigenin arrested cell cycle at G2/M phase and induced apoptosis of GBM cells in vitro. In addition, irigenin inhibited the migration of GBM cells. Mechanically, we found that irigenin treatment decreased the expression of YAP (yes-associated protein), suppressed β-catenin signaling. Furthermore, overexpression of YAP partially restored the anti-tumor effects of irigenin on GBM cells in vitro. Finally, we found that irigenin inhibited the growth of tumor in GBM xenograft mice model through inactivation of YAP. Taken together, these results suggest that irigenin exerts its anticancer effects on GBM via inhibiting YAP/β-catenin signaling, which may provide a new strategy for the treatment of GBM.

Basement-Membrane-Related Gene Signature Predicts Prognosis in WHO Grade II/III Gliomas

Gliomas that are classified as grade II or grade III lesions by the World Health Organization (WHO) are highly aggressive, and some may develop into glioblastomas within a short period, thus portending the conferral of a poor prognosis for patients. Previous studies have implicated basement membrane (BM)-related genes in glioma development. In this study, we constructed a prognostic model for WHO grade II/III gliomas in accordance with the risk scores of BM-related genes. Differentially expressed genes (DEGs) in the glioma samples relative to normal samples were screened from the GEO database, and five prognostically relevant BM-related genes, including NELL2, UNC5A, TNC, CSPG4, and SMOC1, were selected using Cox regression analyses for the risk score model. The median risk score was calculated, based on which high- and low-risk groups of patients were generated. The clinical information, pathological information, and risk group were combined to establish a prognostic nomogram. Both the nomogram and risk score model performed well in the independent CGGA cohort. Gene set enrichment analysis (GSEA) and immune profile, drug sensitivity, and tumor mutation burden (TMB) analyses were performed in the two risk groups. A significant enrichment of 'Autophagy-other', 'Collecting duct acid secretion', 'Glycosphingolipid biosynthesis-lacto and neolacto series', 'Valine, leucine, and isoleucine degradation', 'Vibrio cholerae infection', and other pathways were observed for patients with high risk. In addition, higher proportions of monocytes and resting CD4 memory T cells were observed in the low- and high-risk groups, respectively. In conclusion, the BM-related gene risk score model can guide the clinical management of WHO grade II and III gliomas.

Comprehensive characterization of clonality of driver genes revealing their clinical relevance in colorectal cancer

Background

Genomic studies of colorectal cancer have revealed the complex genomic heterogeneity of the tumor. The acquisition and selection of genomic alterations may be critical to understanding the initiation and progression of this disease.

Methods

In this study, we have systematically characterized the clonal architecture of 97 driver genes in 536 colorectal cancer patients from TCGA.

Results

A high proportion of clonal mutations in 93 driver genes were observed. 40 genes showed significant associations between their clonality and multiple clinicopathologic factors. Kaplan–Meier analysis suggested that the mutation clonality of ANK1, CASP8, SMAD2, and ARID1A had a significant impact on the CRC patients' outcomes. Multivariable analysis revealed that subclonal ANK1 mutations, clonal CASP8 mutations, and clonal SMAD2 mutations independently predicted for shorter overall survival after adjusting for clinicopathological factors. The poor outcome of the subclonal ANK1 mutation may be caused by upregulation of IL4I1, IDO1, IFNG and MAPK12 which showed potential roles in tumor immune evasion through accumulation of immunosuppressive cells such as regulatory T cells and myeloid derived suppressor cells.

Conclusion

These results suggested that the clonality of driver genes could act as prognostic markers and potential therapeutic targets in human colorectal cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03529-x.

Dissecting and analyzing the Subclonal Mutations Associated with Poor Prognosis in Diffuse Glioma

The prognostic and therapeutic implications in diffuse gliomas are still challenging. In this study, we first performed an integrative framework to infer the clonal status of mutations in glioblastomas (GBMs) and low-grade gliomas (LGGs) by using exome sequencing data from TCGA and observed both clonal and subclonal mutations for most mutant genes. Based on the clonal status of a given gene, we systematically investigated its prognostic value in GBM and LGG, respectively. Focusing on the subclonal mutations, our results showed that they were more likely to contribute to the poor prognosis, which could be hardly figured out without considering clonal status. These risk subclonal mutations were associated with some specific genomic features, such as genomic instability and intratumor heterogeneity, and their accumulation could enhance the prognostic value. By analyzing the regulatory mechanisms underlying the risk subclonal mutations, we found that the subclonal mutations of AHNAK and AHNAK2 in GBM and those of NF1 and PTEN in LGG could influence some important molecules and functions associated with glioma progression. Furthermore, we dissected the role of risk subclonal mutations in tumor evolution and found that advanced subclonal mutations showed poorer overall survival. Our study revealed the importance of clonal status in prognosis analysis, highlighting the role of the subclonal mutation in glioma prognosis.

Characterization of aging tumor microenvironment with drawing implications in predicting the prognosis and immunotherapy response in low-grade gliomas

Aging tumor microenvironment (aging TME) is emerging as a hot spot in cancer research for its significant roles in regulation of tumor progression and tumor immune response. The immune and stromal scores of low-grade gliomas (LGGs) from TCGA and CGGA databases were determined by using ESTIMATE algorithm. Differentially expressed genes (DEGs) between high and low immune/stromal score groups were identified. Subsequently, weighted gene co-expression network analysis (WGCNA) was conducted to screen out aging TME related signature (ATMERS). Based on the expression patterns of ATMERS, LGGs were classified into two clusters with distinct prognosis via consensus clustering method. Afterwards, the aging TME score for each sample was calculated via gene set variation analysis (GSVA). Furthermore, TME components were quantified by MCP counter and CIBERSORT algorithm. The potential response to immunotherapy was evaluated by Tumor Immune Dysfunction and Exclusion analysis. We found that LGG patients with high aging TME scores showed poor prognosis, exhibited an immunosuppressive phenotype and were less likely to respond to immunotherapy compared to those with low scores. The predictive performance of aging TME score was verified in three external datasets. Finally, the expression of ATMERS in LGGs was confirmed at protein level through the Human Protein Atlas website and western blot analysis. This novel aging TME-based scoring system provided a robust biomarker for predicting the prognosis and immunotherapy response in LGGs.

The Prognostic Value of the Prognostic Nutritional Index in Operable High-Grade Glioma Patients and the Establishment of a Nomogram

Background

Studies confirmed the predictive value of the prognostic nutrition index (PNI) in many malignant tumors. However, it did not reach a consensus in glioma. Therefore, this study investigated the prognostic value of preoperative PNI in operable high-grade glioma and established a nomogram.

Methods

Clinical data of high-grade glioma patients were retrospectively analyzed. The primary endpoint was overall survival (OS). Survival analysis was conducted by the Kaplan–Meier method, log-rank test, and Cox regression analysis. A nomogram was established. The prediction effect of the nomogram covering PNI was verified by area under the curve (AUC).

Results

A total of 91 operable high-grade glioma patients were included. Kaplan–Meier analysis showed that among grade IV gliomas (n = 55), patients with higher PNI (>44) showed a trend of OS benefit (p = 0.138). In grade III glioma (n = 36), patients with higher PNI (>47) had longer OS (p = 0.023). However, the intersecting Kaplan–Meier curve suggested that there may be some confounding factors. Cox regression analysis showed that higher PNI was an independent prognostic factor for grade IV glioma (HR = 0.388, p = 0.040). In grade III glioma, there was no statistically relationship between PNI levels and prognosis. When evaluating the prognostic ability of PNI alone by ROC, the AUC in grade III and IV gliomas was low, indicating that PNI alone had poor predictive power for OS. Interestingly, we found that the nomogram including preoperative PNI, age, extent of resection, number of gliomas, and MGMT methylation status could predict the prognosis of patients with grade IV glioma well.

Conclusion

The PNI level before surgery was an independent prognostic factor for patients with grade IV glioma. The nomogram covering PNI in patients with grade IV glioma also proved the value of PNI. However, the value of PNI in grade III glioma needs to be further evaluated. More prospective studies are needed to verify this conclusion.

Glioblastoma: Relationship between Metabolism and Immunosuppressive Microenvironment

Glioblastoma (GBM) is the most aggressive brain tumor in adults and is characterized by an immunosuppressive microenvironment. Different factors shaping this tumor microenvironment (TME) regulate tumor initiation, progression, and treatment response. Genetic alterations and metabolism pathways are two main elements that influence tumor immune cells and TME. In this manuscript, we review how both factors can contribute to an immunosuppressive state and overview the strategies being tested.

PODNL1 Methylation Serves as a Prognostic Biomarker and Associates with Immune Cell Infiltration and Immune Checkpoint Blockade Response in Lower-Grade Glioma

Lower-grade glioma (LGG) is a diffuse infiltrative tumor of the central nervous system, which lacks targeted therapy. We investigated the role of Podocan-like 1 (PODNL1) methylation in LGG clinical outcomes using the TCGA-LGG transcriptomics dataset. We identified four PODNL1 CpG sites, cg07425555, cg26969888, cg18547299, and cg24354933, which were associated with unfavorable overall survival (OS) and disease-free survival (DFS) in univariate and multivariate analysis after adjusting for age, gender, tumor-grade, and IDH1-mutation. In multivariate analysis, the OS and DFS hazard ratios ranged from 0.44 to 0.58 (p < 0.001) and 0.62 to 0.72 (p < 0.001), respectively, for the four PODNL1 CpGs. Enrichment analysis of differential gene and protein expression and analysis of 24 infiltrating immune cell types showed significantly increased infiltration in LGGs and its histological subtypes with low-methylation levels of the PODNL1 CpGs. High PODNL1 expression and low-methylation subgroups of the PODNL1 CpG sites were associated with significantly increased PD-L1, PD-1, and CTLA4 expressions. PODNL1 methylation may thus be a potential indicator of immune checkpoint blockade response, and serve as a biomarker for determining prognosis and immune subtypes in LGG.

SMRT: Randomized Data Transformation for Cancer Subtyping and Big Data Analysis

Cancer is an umbrella term that includes a range of disorders, from those that are fast-growing and lethal to indolent lesions with low or delayed potential for progression to death. The treatment options, as well as treatment success, are highly dependent on the correct subtyping of individual patients. With the advancement of high-throughput platforms, we have the opportunity to differentiate among cancer subtypes from a holistic perspective that takes into consideration phenomena at different molecular levels (mRNA, methylation, etc.). This demands powerful integrative methods to leverage large multi-omics datasets for a better subtyping. Here we introduce Subtyping Multi-omics using a Randomized Transformation (SMRT), a new method for multi-omics integration and cancer subtyping. SMRT offers the following advantages over existing approaches: (i) the scalable analysis pipeline allows researchers to integrate multi-omics data and analyze hundreds of thousands of samples in minutes, (ii) the ability to integrate data types with different numbers of patients, (iii) the ability to analyze un-matched data of different types, and (iv) the ability to offer users a convenient data analysis pipeline through a web application. We also improve the efficiency of our ensemble-based, perturbation clustering to support analysis on machines with memory constraints. In an extensive analysis, we compare SMRT with eight state-of-the-art subtyping methods using 37 TCGA and two METABRIC datasets comprising a total of almost 12,000 patient samples from 28 different types of cancer. We also performed a number of simulation studies. We demonstrate that SMRT outperforms other methods in identifying subtypes with significantly different survival profiles. In addition, SMRT is extremely fast, being able to analyze hundreds of thousands of samples in minutes. The web application is available at http://SMRT.tinnguyen-lab.com. The R package will be deposited to CRAN as part of our PINSPlus software suite.

Glioma: molecular signature and crossroads with tumor microenvironment

In patients with glioblastoma, the average survival time with current treatments is short, mainly due to recurrences and resistance to therapy. This insufficient treatment success is, in large parts, due to the tremendous molecular heterogeneity of gliomas, which affects the overall prognosis and response to therapies and plays a vital role in gliomas’ grading. In addition, the tumor microenvironment is a major player for glioma development and resistance to therapy. Active communication between glioma cells and local or neighboring healthy cells and the immune environment promotes the cancerogenic processes and contributes to establishing glioma stem cells, which drives therapy resistance. Besides genetic alterations in the primary tumor, tumor-released factors, cytokines, proteins, extracellular vesicles, and environmental influences like hypoxia provide tumor cells the ability to evade host tumor surveillance machinery and promote disease progression. Moreover, there is increasing evidence that these players affect the molecular biological properties of gliomas and enable inter-cell communication that supports pro-cancerogenic cell properties. Identifying and characterizing these complex mechanisms are inevitably necessary to adapt therapeutic strategies and to develop novel measures. Here we provide an update about these junctions where constant traffic of biomolecules adds complexity in the management of glioblastoma.

Classification of Estrogen Receptor-Positive Breast Cancer Based on Immunogenomic Profiling and Validation at Single-Cell Resolution

Background: The aim of this paper was to identify an immunotherapy-sensitive subtype for estrogen receptor-positive breast cancer (ER+ BC) patients by exploring the relationship between cancer genetic programs and antitumor immunity via multidimensional genome-scale analyses. Methods: Multidimensional ER+ BC high-throughput data (raw count data) including gene expression profiles, copy number variation (CNV) data, single-nucleotide polymorphism mutation data, and relevant clinical information were downloaded from The Cancer Genome Atlas to explore an immune subtype sensitive to immunotherapy using the Consensus Cluster Plus algorithm based on multidimensional genome-scale analyses. One ArrayExpress dataset and eight Gene Expression Omnibus (GEO) datasets (GEO-meta dataset) as well as the Molecular Taxonomy of Breast Cancer International Consortium dataset were used as validation sets to confirm the findings regarding the immune profiles, mutational features, and survival outcomes of the three identified immune subtypes. Moreover, the development trajectory of ER+ BC patients from the single-cell resolution level was also explored. Results: Through comprehensive bioinformatics analysis, three immune subtypes of ER+ BC (C1, C2, and C3, designated the immune suppressive, activation, and neutral subtypes, respectively) were identified. C2 was associated with up-regulated immune cell signatures and immune checkpoint genes. Additionally, five tumor-related pathways (transforming growth factor, epithelial-mesenchymal transition, extracellular matrix, interferon-γ, and WNT signaling) tended to be more activated in C2 than in C1 and C3. Moreover, C2 was associated with a lower tumor mutation burden, a decreased neoantigen load, and fewer CNVs. Drug sensitivity analysis further showed that C2 may be more sensitive to immunosuppressive agents. Conclusion: C2 (the immune activation subtype) may be sensitive to immunotherapy, which provides new insights into effective treatment approaches for ER+ BC.

Prognostic and Predictive Biomarkers in Gliomas

Gliomas are the most common central nervous system tumors. New technologies, including genetic research and advanced statistical methods, revolutionize the therapeutic approach to the patient and reveal new points of treatment options. Moreover, the 2021 World Health Organization Classification of Tumors of the Central Nervous System has fundamentally changed the classification of gliomas and incorporated many molecular biomarkers. Given the rapid progress in neuro-oncology, here we compile the latest research on prognostic and predictive biomarkers in gliomas. In adult patients, IDH mutations are positive prognostic markers and have the greatest prognostic significance. However, CDKN2A deletion, in IDH-mutant astrocytomas, is a marker of the highest malignancy grade. Moreover, the presence of TERT promoter mutations, EGFR alterations, or a combination of chromosome 7 gain and 10 loss upgrade IDH-wildtype astrocytoma to glioblastoma. In pediatric patients, H3F3A alterations are the most important markers which predict the worse outcome. MGMT promoter methylation has the greatest clinical significance in predicting responses to temozolomide (TMZ). Conversely, mismatch repair defects cause hypermutation phenotype predicting poor response to TMZ. Finally, we discussed liquid biopsies, which are promising diagnostic, prognostic, and predictive techniques, but further work is needed to implement these novel technologies in clinical practice.

Lower-Grade Gliomas: Predicting DNA Methylation Subtyping and its Consequences on Survival with MR Features

RATIONALE AND OBJECTIVES

To explore associations between MR imaging features, DNA methylation subtyping, and survival in lower-grade gliomas (LGG).

MATERIALS AND METHODS

The MR data from 170 patients generated with the Cancer Imaging Archive were reviewed. The correlation was evaluated by Fisher's Exact Test, Pearson Chi-Square and binary regression analysis. Survival analysis was conducted by using time-dependent ROC analysis and the Kaplan-Meier method (the worst prognosis subgroup).

RESULTS

Identified were 9 (5.3%) M1-subtype, 18 (10.6%) M2-subtype, 48 (28.2%) M3-subtype, 31 (18.2%) M4-subtype and 64 (37.6%) M5-subtype. Patients with M4-subtype had the shortest median OS (49.3 vs. 28.4) months(p < 0.05). The time-dependent ROC for the M4-subtype was 0.83 (95% confidence interval 0.72-0.95) for survival at 12 months, 0.82 (95% confidence interval 0.70-0.94) for survival at 24 months, and 0.74 (95% confidence interval 0.62-0.86) for survival at 36 months. After uni- and multivariate analysis, a nomogram was built based on proportion contrast-enhanced (CE) tumor, extranodular growth, volume_{_cutoff_median}, and location. For the prediction of M4-subtype, the nomogram showed good discrimination, with an area under the curve (AUC) of 0.886 (95% CI: 0.820-952) and was well calibrated. On multivariate logistic regression analysis, volume ≥60cm³ (OR: 0.200; p < 0.001; 95%CI: 0.048-0.834) was associated with M1-subtype (AUC: 0.690). Hemorrhage (OR: 5.443; p = 0.002; 95%CI: 1.844-16.069) and volume > median (OR: 3.256; p = 0.05; 95%CI: 0.992-10.686) were associated with M2-subtype (AUC: 0.733). Proportion CE tumor<=5% (OR: 3.968; P=0.002; 95%CI: 1.634-9.635) was associated with M3-subtype (AUC: 0.632). Poorly-defined (OR: 2.258; p = 0.05; 95%CI: 1.000-5.101) and volume > median (OR: 2.447; p = 0.01; 95%CI: 1.244-4.813) were associated with M5-subtype (AUC: 0.645). Decision curve analysis indicated predictions for all models were clinically useful.

CONCLUSION

This preliminary radiogenomics analysis of lower-grade gliomas demonstrated associations between MR features and DNA methylation subtyping. The shortest survival was observed in patients with M4-subtype. And we have constructed nomogram that enables more accurate predictions of M4-subtype.

Endoplasmic Reticulum Stress Contributes to Gefitinib-Induced Apoptosis in Glioma

Adequate stress on the Endoplasmic Reticulum (ER) with the Unfolded Protein Response (UPR) could maintain glioma malignancy. Uncontrolled ER stress, on the other hand, predisposes an apoptosis-dominant UPR program. We studied here the proapoptotic actions of the Epidermal Growth Factor Receptor (EGFR) inhibitor gefitinib, with the focus on ER stress. The study models were human H4 and U87 glioma cell lines. We found that the glioma cell-killing effects of gefitinib involved caspase 3 apoptotic cascades. Three branches of ER stress, namely Activating Transcription Factor-6 (ATF6), Protein Kinase R (PKR)-Like ER Kinase (PERK), and Inositol-Requiring Enzyme 1 (IRE1), were activated by gefitinib, along with the elevation of intracellular free Ca²⁺, Reactive Oxygen Species (ROS), and NADPH Oxidase2/4 (NOX2/4). Specifically, elevated IRE1 phosphorylation, Tumor Necrosis Factor (TNF) Receptor-Associated Factor-2 (TRAF2) expression, Apoptosis Signal-Regulating Kinase-1 (Ask1) phosphorylation, c-Jun N-Terminal Kinase (JNK) phosphorylation, and Noxa expression appeared in gefitinib-treated glioma cells. Genetic, pharmacological, and biochemical studies further indicated an active ROS/ER stress/Ask1/JNK/Noxa axis causing the glioma apoptosis induced by gefitinib. The findings suggest that ER-stress-based therapeutic targeting could be a promising option in EGFR inhibitor glioma therapy, and may ultimately achieve a better patient response.

Context-Dependent Glioblastoma-Macrophage/Microglia Symbiosis and Associated Mechanisms

Glioblastoma (GBM) is a lethal form of primary brain tumor in human adults. The impact of tumor-intrinsic alterations is not exclusively confined to cancer cells but can also be extended to the tumor microenvironment (TME). Glioblastoma-associated macrophages/microglia (GAMs) are a prominent type of immune cells that account for up to 50% of total cells in GBM. Emerging evidence suggests that context-dependent GBM-GAM symbiotic interactions are pivotal for tumor growth and progression. Here, we discuss how specific genetic alterations in GBM cells affect GAM biology and, reciprocally, how GAMs support GBM progression. We hypothesize that understanding context-dependent GBM-GAM symbiosis may reveal the molecular basis of GBM tumorigenesis and lead to novel candidate treatment approaches aiming to improve GBM patient outcomes.

Tumor cell and immune cell profiles in primary human glioblastoma: Impact on patient outcome

The distribution and role of tumor-infiltrating leucocytes in glioblastoma (GBM) remain largely unknown. Here, we investigated the cellular composition of 55 primary (adult) GBM samples by flow cytometry and correlated the tumor immune profile with patient features at diagnosis and outcome. GBM single-cell suspensions were stained at diagnosis (n = 44) and recurrence following radiotherapy and chemotherapy (n = 11) with a panel of 8-color monoclonal antibody combinations for the identification and enumeration of (GFAP+ CD45- ) tumor and normal astrocytic cells, infiltrating myeloid cells -i.e. microglial and blood-derived tumor-associated macrophages (TAM), M1-like, and M2-like TAM, neutrophils. and myeloid-derived suppressor cells (MDSC)- and tumor-infiltrating lymphocytes (TIL) -i.e. CD3+ T-cells and their TCD4+ , TCD8+ , TCD4- CD8- , and (CD25+ CD127lo ) regulatory (T-regs) subsets, (CD19+ CD20+ ) B-cells, and (CD16+ ) NK-cells-. Overall, GBM samples consisted of a major population (mean ± 1SD) of tumor and normal astrocytic cells (73% ± 16%) together with a significant but variable fraction of immune cells (24% ± 18%). Within myeloid cells, TAM predominated (13% ± 12%) including both microglial cells (10% ± 11%) and blood-derived macrophages (3% ± 5%), in addition to a smaller proportion of neutrophils (5% ± 9%) and MDSC (4% ± 8%). Lymphocytes were less represented and mostly included TCD4+ (0.5% ± 0.7%) and TCD8+ cells (0.6% ± 0.7%), together with lower numbers of TCD4- CD8- T-cells (0.2% ± 0.4%), T-regs (0.1% ± 0.2%), B-lymphocytes (0.1% ± 0.2%) and NK-cells (0.05% ± 0.05%). Overall, three distinct immune profiles were identified: cases with a minor fraction of leucocytes, tumors with a predominance of TAM and neutrophils, and cases with mixed infiltration by TAM, neutrophils, and T-lymphocytes. Untreated GBM patients with mixed myeloid and lymphoid immune infiltrates showed a significantly shorter patient overall survival versus the other two groups, in the absence of gains of the EGFR gene (p = 0.02). Here we show that immune cell infiltrates are systematically present in GBM, with highly variable levels and immune profiles. Patients with mixed myeloid and T-lymphoid infiltrates showed a worse outcome.

NVP-BEZ235 or JAKi Treatment leads to decreased survival of examined GBM and BBC cells

Cancer cells almost universally harbor constitutively active Phosphatidylinositol-3 Kinase (PI3K) Pathway activity via mutation of key signaling components and/or epigenetic mechanisms. Scores of PI3K Pathway inhibitors are currently under investigation as putative chemotherapeutics. However, feedback and stem cell mechanisms induced by PI3K Pathway inhibition can lead to reduced treatment efficacy. To address therapeutic barriers, we examined whether JAKi would reduce stem gene expression in a setting of PI3K Pathway inhibition in order to improve treatment efficacy. We targeted the PI3K Pathway with NVP-BEZ235 (dual PI3K and mTOR inhibitor) in combination with the Janus Kinase inhibitor JAKi in glioblastoma (GBM) and basal-like breast cancer (BBC) cell lines. We examined growth, gene expression, and apoptosis in cells treated with NVP-BEZ235 and/or JAKi. Growth and recovery assays showed no significant impact of dual treatment with NVP-BEZ235/JAKi compared to NVP-BEZ235 treatment alone. Gene expression and flow cytometry revealed that single and dual treatments induced apoptosis. Stem gene expression was retained in dual NVP-BEZ235/JAKi treatment samples. Future in vivo studies may give further insight into the impact of combined NVP-BEZ235/JAKi treatment in GBM and BBC.

Characterization of the Clinical Significance of PD-1/PD-Ls Expression and Methylation in Patients With Low-Grade Glioma

BACKGROUND

Immune checkpoints play crucial roles in the immune escape of cancer cells. However, the exact prognostic values of expression and methylation of programmed-death 1 (PD-1), programmed-death-ligand 1 (PD-L1) and PD-L2 in low-grade glioma (LGG) have not been well-defined yet.

METHODS

A total 514 LGG samples from the Cancer Genome Atlas (TCGA) dataset containing gene expression, DNA methylation, and survival data were enrolled in our study. Besides, a total of 137 primary LGG samples from the Chinese Glioma Genome Atlas (CGGA) database were also extracted for the survival analysis of the prognostic values of PD-1/PD-Ls expression.

RESULTS

PD-1/PD-Ls had distinct co-expression patterns in LGG tissues. The expression and methylation level of PD-1/PD-Ls seemed to be various in different LGG subtypes. Besides, overexpression and hypo-methylation of PD-1/PD-Ls were associated with worse prognosis. In addition, PD-1/PD-Ls expression was positively associated with TIICs infiltration, while their methylation was negatively associated with TIICs infiltration. Moreover, PD-1/PD-Ls and their positively correlated gene mainly participated in immune response related biological processes.

CONCLUSION

To conclude, overexpression and hypo-methylation of PD-1/PD-Ls predicted unfavorable prognosis in LGG patients, suggesting those patients may benefit from PD1/PD-Ls checkpoint inhibitors treatment.

SLC39A1 contribute to malignant progression and have clinical prognostic impact in gliomas

BACKGROUND

Gliomas are one of the most common primary tumors of the central nervous system, and have an unfavorable prognosis. SLC39A1 is a zinc ion transport protein which inhibits the progression of prostate cancer. By studying the role and mechanism of SLC39A1 in the progression of gliomas, perhaps a new therapeutic target can be provided for their treatment.

METHOD

The TCGA, CCGA, GSE16011, GSE44971 and GSE11260 data sets were employed to evaluate the expression level of SLC39A1 in paracancerous and glioma tissues. In addition, Kaplan-Meier analysis, Cox analysis, and the ESTIMATE and CIBERSORT algorithms were used to analyze its prognostic value and immune infiltration correlation. A CCK-8 and flow cytometer were used to measure the effects of SLC39A1 on U87 cell proliferation or apoptosis; RT-qPCR and western blot were used to detect its effects on the expression of MMP2\MMP9.

RESULTS

SLC39A1 has up-regulated expression in glioma tissues. High SLC39A1 expression predicted significantly worse survival. Univariate and multivariate analysis show that SLC39A1 independently indicated poor prognosis in patients with gliomas. The expression of SLC39A1 is significantly correlated with clinical pathological parameters such as Grade, IDH mutation status, and 1p19q codeletion status. In vitro experimental results show that SLC39A1 promotes proliferation of glioma cells, inhibits their apoptosis, and promotes expression of MMP2\MMP9. In addition, it may affect infiltration of immune cells into the glioma microenvironment.

CONCLUSION

SLC39A1 may serve as a new prognostic biomarker and potential target for treatment of gliomas.

Cellular Plasticity and Tumor Microenvironment in Gliomas: The Struggle to Hit a Moving Target

Brain tumors encompass a diverse group of neoplasias arising from different cell lineages. Tumors of glial origin have been the subject of intense research because of their rapid and fatal progression. From a clinical point of view, complete surgical resection of gliomas is highly difficult. Moreover, the remaining tumor cells are resistant to traditional therapies such as radio- or chemotherapy and tumors always recur. Here we have revised the new genetic and epigenetic classification of gliomas and the description of the different transcriptional subtypes. In order to understand the progression of the different gliomas we have focused on the interaction of the plastic tumor cells with their vasculature-rich microenvironment and with their distinct immune system. We believe that a comprehensive characterization of the glioma microenvironment will shed some light into why these tumors behave differently from other cancers. Furthermore, a novel classification of gliomas that could integrate the genetic background and the cellular ecosystems could have profound implications in the efficiency of current therapies as well as in the development of new treatments.

Dissecting Molecular Features of Gliomas: Genetic Loci and Validated Biomarkers

Recently, several studies focused on the genetics of gliomas. This allowed identifying several germline loci that contribute to individual risk for tumor development, as well as various somatic mutations that are key for disease classification. Unfortunately, none of the germline loci clearly confers increased risk per se. Contrariwise, somatic mutations identified within the glioma tissue define tumor genotype, thus representing valid diagnostic and prognostic markers. Thus, genetic features can be used in glioma classification and guided therapy. Such copious genomic variabilities are screened routinely in glioma diagnosis. In detail, Sanger sequencing or pyrosequencing, fluorescence in-situ hybridization, and microsatellite analyses were added to immunohistochemistry as diagnostic markers. Recently, Next Generation Sequencing was set-up as an all-in-one diagnostic tool aimed at detecting both DNA copy number variations and mutations in gliomas. This approach is widely used also to detect circulating tumor DNA within cerebrospinal fluid from patients affected by primary brain tumors. Such an approach is providing an alternative cost-effective strategy to genotype all gliomas, which allows avoiding surgical tissue collection and repeated tumor biopsies. This review summarizes available molecular features that represent solid tools for the genetic diagnosis of gliomas at present or in the next future.

Identification of a Recurrence Signature and Validation of Cell Infiltration Level of Thyroid Cancer Microenvironment

Though many patients with thyroid cancer may be indolent, there are still about 50% lymph node metastases and 20% the recurrence rates. There is still no ideal method to predict its relapse. In this study, we analyzed the gene transcriptome profiles of eight Gene Expression Omnibus (GEO), and next screened 77 commonly differential expressed genes. Next, Least Absolute Shrinkage and Selection Operator (LASSO) regression model was performed and seven genes (i.e., FN1, PKIA, TMEM47, FXYD6, SDC2, CD44, and GGCT) were then identified, which is highly associated with recurrence data from the Cancer Genome Atlas (TCGA) database. These patients were then divided into low and high-risk groups with specific risk-score formula. Univariate and multivariate Cox regression further revealed that the 7-mRNA signature plays a functional causative role independent of clinicopathological characteristics. The 7-mRNA-signature integrated nomogram showed better discrimination, and decision curve analysis demonstrated that it is clinically useful. Besides, patient with lower risk score shows a relatively lower level of activated dendritic cells (DCs), resting DCs, regulatory T cells and γδT cells, and process of DCs apoptotic. In conclusion, our present immune-related classifier could produce a potential tool for predicting early-relapse.