Subtype-specific signaling pathways and genomic aberrations associated with prognosis of glioblastoma

DNA methylation heterogeneity attributable to a complex tumor immune microenvironment prompts prognostic risk in glioma

Gliomas are malignant tumours of the human nervous system with different World Health Organization (WHO) classifications, glioblastoma (GBM) with higher grade and are more malignant than lower-grade glioma (LGG). To dissect how the DNA methylation heterogeneity in gliomas is influenced by the complex cellular composition of the tumour immune microenvironment, we first compared the DNA methylation profiles of purified human immune cells and bulk glioma tissue, stratifying three tumour immune microenvironmental subtypes for GBM and LGG samples from The Cancer Genome Atlas (TCGA). We found that more intermediate methylation sites were enriched in glioma tumour tissues, and used the Proportion of sites with Intermediate Methylation (PIM) to compare intertumoral DNA methylation heterogeneity. A larger PIM score reflected stronger DNA methylation heterogeneity. Enhanced DNA methylation heterogeneity was associated with stronger immune cell infiltration, better survival rates, and slower tumour progression in glioma patients. We then created a Cell-type-associated DNA Methylation Heterogeneity Contribution (CMHC) score to explore the impact of different immune cell types on heterogeneous CpG site (CpGct) in glioma tissues. We identified eight prognosis-related CpGct to construct a risk score: the Cell-type-associated DNA Methylation Heterogeneity Risk (CMHR) score. CMHR was positively correlated with cytotoxic T-lymphocyte infiltration (CTL), and showed better predictive performance for IDH status (AUC = 0.96) and glioma histological phenotype (AUC = 0.81). Furthermore, DNA methylation alterations of eight CpGct might be related to drug treatments of gliomas. In conclusion, we indicated that DNA methylation heterogeneity is associated with a complex tumour immune microenvironment, glioma phenotype, and patient's prognosis.

HSPA5 and FGFR1 genes in the mesenchymal subtype of glioblastoma can improve a treatment efficacy

Tyrosine kinase inhibitors (TKIs) have emerged as a potential treatment strategy for glioblastoma multiforme (GBM). However, their efficacy is limited by various drug resistance mechanisms. To devise more effective treatments for GBM, genetic characteristics must be considered in addition to pre-existing treatments. We performed an integrative analysis with heterogeneous GBM datasets of genomic, transcriptomic, and proteomic data from DepMap, TCGA and CPTAC. We found that poor prognosis was induced by co-upregulation of heat shock protein family A member 5 (HSPA5) and fibroblast growth factor receptor 1 (FGFR1). Co-up regulation of these two genes could regulate the PI3K/AKT pathway. GBM cell lines with co-upregulation of these two genes showed higher drug sensitivity to PI3K inhibitors. In the mesenchymal subtype, the co-upregulation of FGFR1 and HSPA5 resulted in the most malignant subtype of GBM. Furthermore, we found this newly discovered subtype was correlated with homologous recombination deficiency (HRD) In conclusion, we discovered novel druggable candidates within the group exhibiting co-upregulation of these two genes in GBM, suggest potential strategies for combination therapy.

Resveratrol ameliorates glioblastoma inflammatory response by reducing NLRP3 inflammasome activation through inhibition of the JAK2/STAT3 pathway

OBJECTIVES

The aim of this study was to investigate the anti-tumor effect of resveratrol (RSV) on glioblastoma (GBM) and its specific mechanism in improving the inflammatory response of the tumor microenvironment. The tumor microenvironment of GBM is highly neuroinflammatory, inducing tumor immunosuppression. Therefore, ameliorating the inflammatory response is an important focus for anti-tumor research.

METHODS

The anti-tumor effect of RSV on GBM was demonstrated through in vitro cellular assays, including CCK-8, EdU, PI staining, Transwell, wound healing assay, and flow cytometry. Potential mechanisms of RSV's anti-GBM effects were identified through network pharmacological analysis. In addition, the relationship of RSV with the JAK2/STAT3 signaling pathway and the inflammasome NLRP3 was verified using Western blot.

RESULTS

RSV significantly inhibited cell viability in GBM cell lines LN-229 and U87-MG. Furthermore, it inhibited the proliferation and invasive migration ability of GBM cells, while promoting apoptosis. Network pharmacological analysis revealed a close association between the anti-GBM effects of RSV and the JAK/STAT signaling pathway, as well as inflammatory responses. Western blot analysis confirmed that RSV inhibited the over-activation of the inflammasome NLRP3 through the JAK2/STAT3 signaling pathway. Partial reversal of RSV's inhibition of inflammasome NLRP3 was observed with the addition of the JAK/STAT agonist RO8191.

CONCLUSIONS

In vitro, RSV can exert anti-tumor effects on GBM and improve the inflammatory response in the GBM microenvironment by inhibiting the activation of the JAK2/STAT3 signaling pathway. These findings provide new insights into potential therapeutic targets for GBM.

Cellular signaling in glioblastoma: A molecular and clinical perspective

Glioblastoma multiforme (GBM) is the most aggressive brain tumor with an average life expectancy of less than 15 months. Such high patient mortality in GBM is pertaining to the presence of clinical and molecular heterogeneity attributed to various genetic and epigenetic alterations. Such alterations in critically important signaling pathways are attributed to aberrant gene signaling. Different subclasses of GBM show predominance of different genetic alterations and therefore, understanding the complex signaling pathways and their key molecular components in different subclasses of GBM is extremely important with respect to clinical management. In this book chapter, we summarize the common and important signaling pathways that play a significant role in different subclasses and discuss their therapeutic targeting approaches in terms of preclinical studies and clinical trials.

Small Molecule Tyrosine Kinase Inhibitors (TKIs) for Glioblastoma Treatment

In the last decade, many small molecules, usually characterized by heterocyclic scaffolds, have been designed and synthesized as tyrosine kinase inhibitors (TKIs). Among them, several compounds have been tested at preclinical and clinical levels to treat glioblastoma multiforme (GBM). GBM is the most common and aggressive type of cancer originating in the brain and has an unfavorable prognosis, with a median survival of 15-16 months and a 5-year survival rate of 5%. Despite recent advances in treating GBM, it represents an incurable disease associated with treatment resistance and high recurrence rates. For these reasons, there is an urgent need for the development of new pharmacological agents to fight this malignancy. In this review, we reported the compounds published in the last five years, which showed promising activity in GBM preclinical models acting as TKIs. We grouped the compounds based on the targeted kinase: first, we reported receptor TKIs and then, cytoplasmic and peculiar kinase inhibitors. For each small molecule, we included the chemical structure, and we schematized the interaction with the target for some representative compounds with the aim of elucidating the mechanism of action. Finally, we cited the most relevant clinical trials.

A prognostic matrix code defines functional glioblastoma phenotypes and niches

Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a "matrix code" for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better patient survival, respectively. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles provide potential biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification which could be applied to optimize treatment responses.

Mapping the tumor microenvironment in clear cell renal carcinoma by single-cell transcriptome analysis

Introduction: Clear cell renal cell carcinoma (ccRCC) is associated with unfavorable clinical outcomes. To identify viable therapeutic targets, a comprehensive understanding of intratumoral heterogeneity is crucial. In this study, we conducted bioinformatic analysis to scrutinize single-cell RNA sequencing data of ccRCC tumor and para-tumor samples, aiming to elucidate the intratumoral heterogeneity in the ccRCC tumor microenvironment (TME). Methods: A total of 51,780 single cells from seven ccRCC tumors and five para-tumor samples were identified and grouped into 11 cell lineages using bioinformatic analysis. These lineages included tumor cells, myeloid cells, T-cells, fibroblasts, and endothelial cells, indicating a high degree of heterogeneity in the TME. Copy number variation (CNV) analysis was performed to compare CNV frequencies between tumor and normal cells. The myeloid cell population was further re-clustered into three major subgroups: monocytes, macrophages, and dendritic cells. Differential expression analysis, gene ontology, and gene set enrichment analysis were employed to assess inter-cluster and intra-cluster functional heterogeneity within the ccRCC TME. Results: Our findings revealed that immune cells in the TME predominantly adopted an inflammatory suppression state, promoting tumor cell growth and immune evasion. Additionally, tumor cells exhibited higher CNV frequencies compared to normal cells. The myeloid cell subgroups demonstrated distinct functional properties, with monocytes, macrophages, and dendritic cells displaying diverse roles in the TME. Certain immune cells exhibited pro-tumor and immunosuppressive effects, while others demonstrated antitumor and immunostimulatory properties. Conclusion: This study contributes to the understanding of intratumoral heterogeneity in the ccRCC TME and provides potential therapeutic targets for ccRCC treatment. The findings emphasize the importance of considering the diverse functional roles of immune cells in the TME for effective therapeutic interventions.

A prognostic matrix code defines functional glioblastoma phenotypes and niches

Interactions among tumor, immune and vascular niches play major roles in driving glioblastoma (GBM) malignancy and treatment responses. The composition, heterogeneity, and localization of extracellular core matrix proteins (CMPs) that mediate such interactions, however, are not well understood. Here, we characterize functional and clinical relevance of genes encoding CMPs in GBM at bulk, single cell, and spatial anatomical resolution. We identify a "matrix code" for genes encoding CMPs whose expression levels categorize GBM tumors into matrisome-high and matrisome-low groups that correlate with worse and better survival, respectively, of patients. The matrisome enrichment is associated with specific driver oncogenic alterations, mesenchymal state, infiltration of pro-tumor immune cells and immune checkpoint gene expression. Anatomical and single cell transcriptome analyses indicate that matrisome gene expression is enriched in vascular and leading edge/infiltrative anatomic structures that are known to harbor glioma stem cells driving GBM progression. Finally, we identified a 17-gene matrisome signature that retains and further refines the prognostic value of genes encoding CMPs and, importantly, potentially predicts responses to PD1 blockade in clinical trials for GBM. The matrisome gene expression profiles may provide biomarkers of functionally relevant GBM niches that contribute to mesenchymal-immune cross talk and patient stratification to optimize treatment responses.

Systematic in vitro analysis of therapy resistance in glioblastoma cell lines by integration of clonogenic survival data with multi-level molecular data

Despite intensive basic scientific, translational, and clinical efforts in the last decades, glioblastoma remains a devastating disease with a highly dismal prognosis. Apart from the implementation of temozolomide into the clinical routine, novel treatment approaches have largely failed, emphasizing the need for systematic examination of glioblastoma therapy resistance in order to identify major drivers and thus, potential vulnerabilities for therapeutic intervention. Recently, we provided proof-of-concept for the systematic identification of combined modality radiochemotherapy treatment vulnerabilities via integration of clonogenic survival data upon radio(chemo)therapy with low-density transcriptomic profiling data in a panel of established human glioblastoma cell lines. Here, we expand this approach to multiple molecular levels, including genomic copy number, spectral karyotyping, DNA methylation, and transcriptome data. Correlation of transcriptome data with inherent therapy resistance on the single gene level yielded several candidates that were so far underappreciated in this context and for which clinically approved drugs are readily available, such as the androgen receptor (AR). Gene set enrichment analyses confirmed these results, and identified additional gene sets, including reactive oxygen species detoxification, mammalian target of rapamycin complex 1 (MTORC1) signaling, and ferroptosis/autophagy-related regulatory circuits to be associated with inherent therapy resistance in glioblastoma cells. To identify pharmacologically accessible genes within those gene sets, leading edge analyses were performed yielding candidates with functions in thioredoxin/peroxiredoxin metabolism, glutathione synthesis, chaperoning of proteins, prolyl hydroxylation, proteasome function, and DNA synthesis/repair. Our study thus confirms previously nominated targets for mechanism-based multi-modal glioblastoma therapy, provides proof-of-concept for this workflow of multi-level data integration, and identifies novel candidates for which pharmacological inhibitors are readily available and whose targeting in combination with radio(chemo)therapy deserves further examination. In addition, our study also reveals that the presented workflow requires mRNA expression data, rather than genomic copy number or DNA methylation data, since no stringent correlation between these data levels could be observed. Finally, the data sets generated in the present study, including functional and multi-level molecular data of commonly used glioblastoma cell lines, represent a valuable toolbox for other researchers in the field of glioblastoma therapy resistance.

Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study

OBJECTIVES

To develop and validate a deep learning imaging signature (DLIS) for risk stratification in patients with multiforme (GBM), and to investigate the biological pathways and genetic alterations underlying the DLIS.

METHODS

The DLIS was developed from multi-parametric MRI based on a training set (n = 600) and validated on an internal validation set (n = 164), an external test set 1 (n = 100), an external test set 2 (n = 161), and a public TCIA set (n = 88). A co-profiling framework based on a radiogenomics analysis dataset (n = 127) using multiscale high-dimensional data, including imaging, transcriptome, and genome, was established to uncover the biological pathways and genetic alterations underpinning the DLIS.

RESULTS

The DLIS was associated with survival (log-rank p < 0.001) and was an independent predictor (p < 0.001). The integrated nomogram incorporating the DLIS achieved improved C indices than the clinicomolecular nomogram (net reclassification improvement 0.39, p < 0.001). DLIS significantly correlated with core pathways of GBM (apoptosis and cell cycle-related P53 and RB pathways, and cell proliferation-related RTK pathway), as well as key genetic alterations (del_CDNK2A). The prognostic value of DLIS-correlated genes was externally confirmed on TCGA/CGGA sets (p < 0.01).

CONCLUSIONS

Our study offers a biologically interpretable deep learning predictor of survival outcomes in patients with GBM, which is crucial for better understanding GBM patient's prognosis and guiding individualized treatment.

KEY POINTS

• MRI-based deep learning imaging signature (DLIS) stratifies GBM into risk groups with distinct molecular characteristics. • DLIS is associated with P53, RB, and RTK pathways and del_CDNK2A mutation. • The prognostic value of DLIS-correlated pathway genes is externally demonstrated.

Glioblastoma Molecular Classification Tool Based on mRNA Analysis: From Wet-Lab to Subtype

Most glioblastoma studies incorporate the layer of tumor molecular subtype based on the four-subtype classification system proposed in 2010. Nevertheless, there is no universally recognized and convenient tool for glioblastoma molecular subtyping, and each study applies a different set of markers and/or approaches that cause inconsistencies in data comparability and reproducibility between studies. Thus, this study aimed to create an applicable user-friendly tool for glioblastoma classification, with high accuracy, while using a significantly smaller number of variables. The study incorporated a TCGA microarray, sequencing datasets, and an independent cohort of 56 glioblastomas (LUHS cohort). The models were constructed by applying the Agilent G4502 dataset, and they were tested using the Affymetrix HG-U133a and Illumina Hiseq cohorts, as well as the LUHS cases. Two classification models were constructed by applying a logistic regression classification algorithm, based on the mRNA levels of twenty selected genes. The classifiers were translated to a RT-qPCR assay and validated in an independent cohort of 56 glioblastomas. The classification accuracy of the 20-gene and 5-gene classifiers varied between 90.7-91% and 85.9-87.7%, respectively. With this work, we propose a cost-efficient three-class (classical, mesenchymal, and proneural) tool for glioblastoma molecular classification based on the mRNA analysis of only 5-20 genes, and we provide the basic information for classification performance starting from the wet-lab stage. We hope that the proposed classification tool will enable data comparability between different research groups.

Circulating tumor DNA mutation profile is associated with the prognosis and treatment response of Chinese patients with newly diagnosed diffuse large B-cell lymphoma

Background

Characterization of gene mutation profiles can provide new treatment options for patients with diffuse large B-cell lymphoma (DLBCL). However, this method is challenged by the limited source of tissue specimens, especially those of DLBCL patients at advanced stages. Therefore, in the current study, we aimed to describe the gene mutation landscape of DLBCL using circulating tumor DNA (ctDNA) samples obtained from patients’ blood samples, as well as to explore the relationship between ctDNA mutations and the prognosis and treatment response of patients with newly diagnosed DLBCL.

Methods

A total of 169 newly diagnosed Chinese DLBCL patients were included in this study, among which 85 patients were divided into a training set and 84 were assigned into a validation set. The mutation profile of a 59-gene panel was analyzed by targeted next generation sequencing (NGS) of the patients’ ctDNA samples. Differences in clinical factors between patients with and without ctDNA mutations were analyzed. In addition, we also explored gene mutation frequencies between GCB and non-GCB subtypes, and the relationship between gene mutation status, clinical factors, mean VAF (variant allele frequencies) and the patients’ overall survival (OS) and progression-free survival (PFS).

Results

ctDNA mutations were detected in 64 (75.3%) patients of the training set and 67 (79.8%) patients of the validation set. The most commonly mutated genes in both sets were PCLO, PIM1, MYD88, TP53, KMT2D, CD79B, HIST1H1E and LRP1B, with mutation frequencies of &gt;10%. Patients with detectable ctDNA mutations trended to present advanced Ann Arbor stages (III-IV), elevated LDH (lactate dehydrogenase) levels, shorter OS and PFS, and a lower complete response (CR) rate to the R-CHOP regimen compared with DLBCL patients without ctDNA mutations. In addition, mean VAF (≥4.94%) and PCLO mutations were associated with poor OS and PFS.

Conclusion

We investigated the ctDNA mutation landscape in Chinese patients with newly diagnosed DLBCL and found that ctDNA could reflect tumor burden and patients with detectable ctDNA mutations trended to have shorter OS and PFS and a lower CR rate.

WNT signaling at the intersection between neurogenesis and brain tumorigenesis

Neurogenesis and tumorigenesis share signaling molecules/pathways involved in cell proliferation, differentiation, migration, and death. Self-renewal of neural stem cells is a tightly regulated process that secures the accuracy of cell division and eliminates cells that undergo mitotic errors. Abnormalities in the molecular mechanisms controlling this process can trigger aneuploidy and genome instability, leading to neoplastic transformation. Mutations that affect cell adhesion, polarity, or migration enhance the invasive potential and favor the progression of tumors. Here, we review recent evidence of the WNT pathway’s involvement in both neurogenesis and tumorigenesis and discuss the experimental progress on therapeutic opportunities targeting components of this pathway.

Patient-derived tumor organoids as a platform of precision treatment for malignant brain tumors

Malignant brain tumors consist of malignancies originated primarily within the brain and the metastatic lesions disseminated from other organs. In spite of intensive studies, malignant brain tumors remain to be a medical challenge. Patient-derived organoid (PDO) can recapitulate the biological features of the primary tumor it was derived from and has emerged as a promising drug-screening model for precision therapy. Here we show a proof-of-concept based on early clinical study entailing the organoids derived from the surgically resected tumors of 26 patients with advanced malignant brain tumors enrolled during December 2020 to October 2021. The tumors included nine glioma patients, one malignant meningioma, one primary lymphoma patient, and 15 brain metastases. The primary tumor sites of the metastases included five from the lungs, three from the breasts, two from the ovaries, two from the colon, one from the testis, one of melanoma origin, and one of chondrosarcoma. Out of the 26 tissues, 13 (50%) organoids were successfully generated with a culture time of about 2 weeks. Among these patients, three were further pursued to have the organoids derived from their tumor tissues tested for the sensitivity to different therapeutic drugs in parallel to their clinical care. Our results showed that the therapeutic effects observed by the organoid models were consistent to the responses of these patients to their treatments. Our study suggests that PDO can recapitulate patient responses in the clinic with high potential of implementation in personalized medicine of malignant brain tumors.

Clinical and NGS predictors of response to regorafenib in recurrent glioblastoma

Predictive factors for response to regorafenib in recurrent glioblastoma, IDH-wildtype, are scarcely recognized. The objective of this study was to identify molecular predictive factors for response to regorafenib using a clinically available platform. We analyzed a prospective cohort of 30 patients harboring recurrent glioblastoma, IDH-wildtype, and treated with regorafenib. Next-generation sequencing (NGS) analysis was performed on DNA extracted from paraffin-embedded tissues using a clinically available platform. Moreover, MGMT methylation and EGFRvIII expression analyses were performed. Six-month progression-free survival (PFS) was 30% and median overall survival (OS) was 7.5 months, in line with literature data. NGS analysis revealed a mutation in the EGFR pathway in 18% of cases and a mutation in the mitogen-activated protein-kinase (MAPK) pathway in 18% of cases. In the remaining cases, no mutations were detected. Patients carrying MAPK pathway mutation had a poor response to regorafenib treatment, with a significantly shorter PFS and a nonsignificantly shorter OS compared to EGFR-mutated patients (for PFS, 2.5 vs 4.5 months, p = 0.0061; for OS, 7 vs 9 months, p = 0.1076). Multivariate analysis confirmed that MAPK pathway mutations independently predicted a shorter PFS after regorafenib treatment (p = 0.0188). The negative prognostic role of MAPK pathway alteration was reinforced when we combined EGFR-mutated with EGFRvIII-positive cases. Recurrent glioblastoma tumors with an alteration in MAPK pathway could belong to the mesenchymal subtype and respond poorly to regorafenib treatment, while EGFR-altered cases have a better response to regorafenib. We thus provide a molecular selection criterion easy to implement in the clinical practice.

The Role of Natural Products as Inhibitors of JAK/STAT Signaling Pathways in Glioblastoma Treatment

The permeability of glioblastoma, as well as its escaping the immune system, makes them one of the most deadly human malignancies. By avoiding programmed cell death (apoptosis), unlimited cell growth and metastatic ability could dramatically affect the immune system. Genetic mutations, epigenetic changes, and overexpression of oncogenes can cause this process. On the other hand, the blood-brain barrier (BBB) and intratumor heterogeneity are important factors causing resistance to therapy. Several signaling pathways have been identified in this field, including the Janus tyrosine kinase (JAK) converter and signal transducer and activator of transcription (STAT) activator pathways, which are closely related. In addition, the JAK/STAT signaling pathway contributes to a wide array of tumorigenesis functions, including replication, anti-apoptosis, angiogenesis, and immune suppression. Introducing this pathway as the main tumorigenesis and treatment resistance center can give a better understanding of how it operates. In light of this, it is an important goal in treating many disorders, particularly cancer. The inhibition of this signaling pathway is being considered an approach to the treatment of glioblastoma. The use of natural products alternatively to conventional therapies is another area of research interest among researchers. Some natural products that originate from plants or natural sources can interfere with JAK/STAT signaling in human malignant cells, also by stopping the progression and phosphorylation of JAK/STAT, inducing apoptosis, and stopping the cell cycle. Natural products are a viable alternative to conventional chemotherapy because of their cost-effectiveness, wide availability, and almost no side effects.

Regulatory pattern of abnormal promoter CpG island methylation in the glioblastoma multiforme classification

Glioblastoma (GBM) is characterized by extensive genetic and phenotypic heterogeneity. However, it remains unexplored primarily how CpG island methylation abnormalities in promoter mediate glioblastoma typing. First, we presented a multi-omics scale map between glioblastoma sample clusters constructed based on promoter CpG island (PCGI) methylation-driven genes, using datasets including methylation profiles, expression profiles, and single-cell sequencing data from multiple highly annotated public clinical cohorts. Second, we identified differences in the tumor microenvironment between the two glioblastoma sample clusters and resolved key signaling pathways between cell clusters at the single-cell level based on comprehensive comparative analyses to investigate the reasons for survival differences between two of these clusters. Finally, we developed a diagnostic map and a prediction model for glioblastoma, and compared theoretical differences of drug sensitivity between two glioblastoma sample clusters. In summary, this study established a classification system for dissecting promoter CpG island methylation heterogeneity in glioblastoma and provides a new perspective for the diagnosis and treatment of glioblastoma.

Oncogenic signaling pathway dysregulation landscape reveals the role of pathways at multiple omics levels in pan-cancer

Dysregulation of signaling pathways plays an essential role in cancer. However, there is not a comprehensive understanding on how oncogenic signaling pathways affect the occurrence and development with a common molecular mechanism of pan-cancer. Here, we investigated the oncogenic signaling pathway dysregulation by using multi-omics data on patients from TCGA from a pan-cancer perspective to identify commonalities across different cancer types. First, the pathway dysregulation profile was constructed by integrating typical oncogenic signaling pathways and the gene expression of TCGA samples, and four molecular subtypes with significant phenotypic and clinical differences induced by different oncogenic signaling pathways were identified: TGF-β+ subtype; cell cycle, MYC, and NF2− subtype; cell cycle and TP53+ subtype; and TGF-β and TP53− subtype. Patients in the TGF-β+ subtype have the best prognosis; meanwhile, the TGF-β+ subtype is associated with hypomethylation. Moreover, there is a higher level of immune cell infiltration but a slightly worse survival prognosis in the cell cycle, MYC, and NF2− subtype patients due to the effect of T-cell dysfunction. Then, the prognosis and subtype classifiers constructed by differential genes on a multi-omics level show great performance, indicating that these genes can be considered as biomarkers with potential therapeutic and prognostic significance for cancers. In summary, our study identified four oncogenic signaling pathway–driven patterns presented as molecular subtypes and their related potential prognostic biomarkers by integrating multiple omics data. Our discovery provides a perspective for understanding the role of oncogenic signaling pathways in pan-cancer.

Current Opportunities for Targeting Dysregulated Neurodevelopmental Signaling Pathways in Glioblastoma

Glioblastoma (GBM) is the most common and highly lethal type of brain tumor, with poor survival despite advances in understanding its complexity. After current standard therapeutic treatment, including tumor resection, radiotherapy and concomitant chemotherapy with temozolomide, the median overall survival of patients with this type of tumor is less than 15 months. Thus, there is an urgent need for new insights into GBM molecular characteristics and progress in targeted therapy in order to improve clinical outcomes. The literature data revealed that a number of different signaling pathways are dysregulated in GBM. In this review, we intended to summarize and discuss current literature data and therapeutic modalities focused on targeting dysregulated signaling pathways in GBM. A better understanding of opportunities for targeting signaling pathways that influences malignant behavior of GBM cells might open the way for the development of novel GBM-targeted therapies.

Combinations of Single-Gene Biomarkers Can Precisely Stratify 1,028 Adult Gliomas for Prognostication

Advanced genomic techniques have now been incorporated into diagnostic practice in neuro-oncology in the literature. However, these assays are expensive and time-consuming and demand bioinformatics expertise for data interpretation. In contrast, single-gene tests can be run much more cheaply, with a short turnaround time, and are available in general pathology laboratories. The objective of this study was to establish a molecular grading scheme for adult gliomas using combinations of commonly available single-gene tests. We retrospectively evaluated molecular diagnostic data of 1,275 cases of adult diffuse gliomas from three institutions where we were testing for IDH1/2 mutation, TERTp mutation, 1p19q codeletion, EGFR amplification, 10q deletion, BRAF V600E, and H3 mutations liberally in our regular diagnostic workup. We found that a molecular grading scheme of Group 1 (1p19q codeleted, IDH mutant), Group 2 (IDH mutant, 1p19q non-deleted, TERT mutant), Group 3 (IDH mutant, 1p19q non-deleted, TERT wild type), Group 4 (IDH wild type, BRAF mutant), Group 5 (IDH wild type, BRAF wild type and not possessing the criteria of Group 6), and Group 6 (IDH wild type, and any one of TERT mutant, EGFR amplification, 10q deletion, or H3 mutant) could significantly stratify this large cohort of gliomas for risk. A total of 1,028 (80.6%) cases were thus classifiable with sufficient molecular data. There were 270 cases of molecular Group 1, 59 cases of molecular Group 2, 248 cases of molecular Group 3, 27 cases of molecular Group 4, 117 cases of molecular Group 5, and 307 cases of molecular Group 6. The molecular groups were independent prognosticators by multivariate analyses and in specific instances, superseded conventional histological grades. We were also able to validate the usefulness of the Groups with a cohort retrieved from The Cancer Genome Atlas (TCGA) where similar molecular tests were liberally available. We conclude that a single-gene molecular stratification system, useful for fine prognostication, is feasible and can be adopted by a general pathology laboratory.

Tracing the origins of glioblastoma by investigating the role of gliogenic and related neurogenic genes/signaling pathways in GBM development: a systematic review

Background

Glioblastoma is one of the most aggressive tumors. The etiology and the factors determining its onset are not yet entirely known. This study investigates the origins of GBM, and for this purpose, it focuses primarily on developmental gliogenic processes. It also focuses on the impact of the related neurogenic developmental processes in glioblastoma oncogenesis. It also addresses why glial cells are at more risk of tumor development compared to neurons.

Methods

Databases including PubMed, MEDLINE, and Google Scholar were searched for published articles without any date restrictions, involving glioblastoma, gliogenesis, neurogenesis, stemness, neural stem cells, gliogenic signaling and pathways, neurogenic signaling and pathways, and astrocytogenic genes.

Results

The origin of GBM is dependent on dysregulation in multiple genes and pathways that accumulatively converge the cells towards oncogenesis. There are multiple layers of steps in glioblastoma oncogenesis including the failure of cell fate-specific genes to keep the cells differentiated in their specific cell types such as p300, BMP, HOPX, and NRSF/REST. There are genes and signaling pathways that are involved in differentiation and also contribute to GBM such as FGFR3, JAK-STAT, and hey1. The genes that contribute to differentiation processes but also contribute to stemness in GBM include notch, Sox9, Sox4, c-myc gene overrides p300, and then GFAP, leading to upregulation of nestin, SHH, NF-κB, and others. GBM mutations pathologically impact the cell circuitry such as the interaction between Sox2 and JAK-STAT pathway, resulting in GBM development and progression.

Conclusion

Glioblastoma originates when the gene expression of key gliogenic genes and signaling pathways become dysregulated. This study identifies key gliogenic genes having the ability to control oncogenesis in glioblastoma cells, including p300, BMP, PAX6, HOPX, NRSF/REST, LIF, and TGF beta. It also identifies key neurogenic genes having the ability to control oncogenesis including PAX6, neurogenins including Ngn1, NeuroD1, NeuroD4, Numb, NKX6-1 Ebf, Myt1, and ASCL1. This study also postulates how aging contributes to the onset of glioblastoma by dysregulating the gene expression of NF-κB, REST/NRSF, ERK, AKT, EGFR, and others.

A Novel TNFSF-Based Signature Predicts the Prognosis and Immunosuppressive Status of Lower-Grade Glioma

Purpose

Tumour necrosis factor (TNF) superfamilies play important roles in cell proliferation, migration, differentiation, and apoptosis. We believe that TNF has a huge potential and might cast new insight into antitumour therapies. Therefore, we established this signature based on TNF superfamilies.

Results

A six-gene signature derived from the TNF superfamilies was established. The Riskscore correlated significantly with the expression of immune checkpoint genes and infiltrating M2 macrophages in the tumour specimen. This signature was also associated with mutations in genes that regulate tumour cell proliferation. Univariate and multivariate regression analyses further confirmed the Riskscore, TNFRSF11b, and TNFRSF12a as independent risk factors in The Cancer Genome Atlas and Chinese Glioma Genome Atlas datasets.

Conclusion

Our signature could accurately predict the prognosis of lower-grade gliomas (LGG). In addition, this six-gene signature could predict the immunosuppressive status of LGG and provide evidence that TNF superfamilies had correlations with some critical mutations that could be effectively targeted now.

A Deep Learning-Based Framework for Supporting Clinical Diagnosis of Glioblastoma Subtypes

Understanding molecular features that facilitate aggressive phenotypes in glioblastoma multiforme (GBM) remains a major clinical challenge. Accurate diagnosis of GBM subtypes, namely classical, proneural, and mesenchymal, and identification of specific molecular features are crucial for clinicians for systematic treatment. We develop a biologically interpretable and highly efficient deep learning framework based on a convolutional neural network for subtype identification. The classifiers were generated from high-throughput data of different molecular levels, i.e., transcriptome and methylome. Furthermore, an integrated subsystem of transcriptome and methylome data was also used to build the biologically relevant model. Our results show that deep learning model outperforms the traditional machine learning algorithms. Furthermore, to evaluate the biological and clinical applicability of the classification, we performed weighted gene correlation network analysis, gene set enrichment, and survival analysis of the feature genes. We identified the genotype-phenotype relationship of GBM subtypes and the subtype-specific predictive biomarkers for potential diagnosis and treatment.

GZ17-6.02 Inhibits the Growth of EGFRvIII+ Glioblastoma

Epidermal Growth Factor Receptor (EGFR) is amplified in over 50% of glioblastomas and promotes tumor formation and progression. However, attempts to treat glioblastoma with EGFR tyrosine kinase inhibitors have been unsuccessful thus far. The current standard of care is especially poor in patients with a constitutively active form of EGFR, EGFRvIII, which is associated with shorter survival time. This study examined the effect of GZ17-6.02, a novel anti-cancer agent undergoing phase 1 studies, on two EGFRvIII+ glioblastoma stem cells: D10-0171 and D317. In vitro analyses showed that GZ17-6.02 inhibited the growth of both D10-0171 and D317 cells with IC₅₀ values of 24.84 and 28.28 µg/mL respectively. RNA sequencing and reverse phase protein array analyses revealed that GZ17-6.02 downregulates pathways primarily related to steroid synthesis and cell cycle progression. Interestingly, G17-6.02’s mechanism of action involves the downregulation of the recently identified glioblastoma super-enhancer genes WSCD1, EVOL2, and KLHDC8A. Finally, a subcutaneous xenograft model showed that GZ17-6.02 inhibits glioblastoma growth in vivo. We conclude that GZ17-6.02 is a promising combination drug effective at inhibiting the growth of a subset of glioblastomas and our data warrants further preclinical studies utilizing xenograft models to identify patients that may respond to this drug.

Construction and Validation of an Immune-Related Risk Score Model for Survival Prediction in Glioblastoma

Background

As one of the most important brain tumors, glioblastoma (GBM) has a poor prognosis, especially in adults. Immune-related genes (IRGs) and immune cell infiltration are responsible for the pathogenesis of GBM. This study aimed to identify new tumor markers to predict the prognosis of patients with GBM.

Methods

The Cancer Genome Atlas (TCGA) database and ImmPort database were used for model construction. The Wilcoxon rank-sum test was applied to identify the differentially expressed IRGs (DEIRGs) between the GBM and normal samples. Univariate Cox regression analysis and Kaplan–Meier analysis was performed to investigate the relationship between each DEIRG and overall survival. Next, multivariate Cox regression analysis was exploited to further explore the prognostic potential of DEIRGs. A risk-score model was constructed based on the above results. The area under the curve (AUC) values were calculated to assess the effect of the model prediction. Furthermore, the Chinese Glioma Genome Atlas (CGGA) dataset was used for model validation. STRING database and functional enrichment analysis were used for exploring the gene interactions and the underlying functions and pathways. The CIBERSORT algorithm was used for correlation analysis of the marker genes and the tumor-infiltrating immune cells.

Results

There were 198 DEIRGs in GBM, including 153 upregulated genes and 45 downregulated genes. Seven marker genes (LYNX1, PRELID1P4, MMP9, TCF12, RGS14, RUNX1, and CCR2) were filtered out by sequential screening for DEIRGs. The regression coefficients (0.0410, 1.335, 0.005, −0.021, 0.123, 0.142, and −0.329) and expression data of the marker genes were used to construct the model. The AUC values for 1, 2, and 3 years were 0.744, 0.737, and 0.749 in the TCGA–GBM cohort and 0.612, 0.602, and 0.594 in the CGGA-GBM cohort, respectively, which indicated a high predictive power. The results of enrichment analysis revealed that these genes were enriched in the activation of T cell and cytokine receptor interaction pathways. The interaction network map demonstrated a close relationship between the marker genes MMP9 and CCR2. Infiltration analysis of the immune cells showed that dendritic cells (DCs) could identify GBM, while LYNX1, RUNX1, and CCR2 were significantly positively correlated with DCs expression.

Conclusion

This study analyzed the expression of IRGs in GBM and identified seven marker genes for the construction of an immune-related risk score model. These marker genes were found to be associated with DCs and were enriched in similar immune response pathways. These findings are likely to provide new insights for the immunotherapy of patients with GBM.

Bisulfite profiling of the MGMT promoter and comparison with routine testing in glioblastoma diagnostics

Background

Promoter methylation of the DNA repair gene O⁶-methylguanine-DNA methyltransferase (MGMT) is an acknowledged predictive epigenetic marker in glioblastoma multiforme and anaplastic astrocytoma. Patients with methylated CpGs in the MGMT promoter benefit from treatment with alkylating agents, such as temozolomide, and show an improved overall survival and progression-free interval. A precise determination of MGMT promoter methylation is of importance for diagnostic decisions. We experienced that different methods show partially divergent results in a daily routine. For an integrated neuropathological diagnosis of malignant gliomas, we therefore currently apply a combination of methylation-specific PCR assays and pyrosequencing.

Results

To better rationalize the variation across assays, we compared these standard techniques and assays to deep bisulfite sequencing results in a cohort of 80 malignant astrocytomas. Our deep analysis covers 49 CpG sites of the expanded MGMT promoter, including exon 1, parts of intron 1 and a region upstream of the transcription start site (TSS). We observed that deep sequencing data are in general in agreement with CpG-specific pyrosequencing, while the most widely used MSP assays published by Esteller et al. (N Engl J Med 343(19):1350–1354, 2000. 10.1056/NEJM200011093431901) and Felsberg et al. (Clin Cancer Res 15(21):6683–6693, 2009. 10.1158/1078-0432.CCR-08-2801) resulted in partially discordant results in 22 tumors (27.5%). Local deep bisulfite sequencing (LDBS) revealed that CpGs located in exon 1 are suited best to discriminate methylated from unmethylated samples. Based on LDBS data, we propose an optimized MSP primer pair with 83% and 85% concordance to pyrosequencing and LDBS data. A hitherto neglected region upstream of the TSS, with an overall higher methylation compared to exon 1 and intron 1 of MGMT, is also able to discriminate the methylation status.

Conclusion

Our integrated analysis allows to evaluate and redefine co-methylation domains within the MGMT promoter and to rationalize the practical impact on assays used in daily routine diagnostics.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01244-4.

Glioblastoma Clinical Trials: Current Landscape and Opportunities for Improvement

Therapeutic advances for glioblastoma have been minimal over the past 2 decades. In light of the multitude of recent phase III trials that have failed to meet their primary endpoints following promising preclinical and early-phase programs, a Society for Neuro-Oncology Think Tank was held in November 2020 to prioritize areas for improvement in the conduct of glioblastoma clinical trials. Here, we review the literature, identify challenges related to clinical trial eligibility criteria and trial design in glioblastoma, and provide recommendations from the Think Tank. In addition, we provide a data-driven context with which to frame this discussion by analyzing key study design features of adult glioblastoma clinical trials listed on ClinicalTrials.gov as "recruiting" or "not yet recruiting" as of February 2021.

Chloride intracellular channel 1 activity is not required for glioblastoma development but its inhibition dictates glioma stem cell responsivity to novel biguanide derivatives

Background

Chloride intracellular channel-1 (CLIC1) activity controls glioblastoma proliferation. Metformin exerts antitumor effects in glioblastoma stem cells (GSCs) inhibiting CLIC1 activity, but its low potency hampers its translation in clinical settings.

Methods

We synthesized a small library of novel biguanide-based compounds that were tested as antiproliferative agents for GSCs derived from human glioblastomas, in vitro using 2D and 3D cultures and in vivo in the zebrafish model. Compounds were compared to metformin for both potency and efficacy in the inhibition of GSC proliferation in vitro (MTT, Trypan blue exclusion assays, and EdU labeling) and in vivo (zebrafish model), migration (Boyden chamber assay), invasiveness (Matrigel invasion assay), self-renewal (spherogenesis assay), and CLIC1 activity (electrophysiology recordings), as well as for the absence of off-target toxicity (effects on normal stem cells and toxicity for zebrafish and chick embryos).

Results

We identified Q48 and Q54 as two novel CLIC1 blockers, characterized by higher antiproliferative potency than metformin in vitro, in both GSC 2D cultures and 3D spheroids. Q48 and Q54 also impaired GSC self-renewal, migration and invasion, and displayed low systemic in vivo toxicity. Q54 reduced in vivo proliferation of GSCs xenotransplanted in zebrafish hindbrain. Target specificity was confirmed by recombinant CLIC1 binding experiments using microscale thermophoresis approach. Finally, we characterized GSCs from GBMs spontaneously expressing low CLIC1 protein, demonstrating their ability to grow in vivo and to retain stem-like phenotype and functional features in vitro. In these GSCs, Q48 and Q54 displayed reduced potency and efficacy as antiproliferative agents as compared to high CLIC1-expressing tumors. However, in 3D cultures, metformin and Q48 (but not Q54) inhibited proliferation, which was dependent on the inhibition dihydrofolate reductase activity.

Conclusions

These data highlight that, while CLIC1 is dispensable for the development of a subset of glioblastomas, it acts as a booster of proliferation in the majority of these tumors and its functional expression is required for biguanide antitumor class-effects. In particular, the biguanide-based derivatives Q48 and Q54, represent the leads to develop novel compounds endowed with better pharmacological profiles than metformin, to act as CLIC1-blockers for the treatment of CLIC1-expressing glioblastomas, in a precision medicine approach.

OUP accepted manuscript

BACKGROUND

Seizures can present at any time before or after the diagnosis of a glioma. Roughly, 25%-30% of glioblastoma (GBM) patients initially present with seizures, and an additional 30% develop seizures during the course of the disease. Early studies failed to show an effect of general administration of antiepileptic drugs for glioblastoma patients, since they were unable to stratify patients into high- or low-risk seizure groups.

METHODS

111 patients, who underwent surgery for a GBM, were included. Genome-wide DNA methylation profiling was performed, before methylation subclasses and copy number changes inferred from methylation data were correlated with clinical characteristics. Independently, global gene expression was analyzed in GBM methylation subclasses from TCGA datasets (n = 68).

RESULTS

Receptor tyrosine Kinase (RTK) II GBM showed a significantly higher incidence of seizures than RTK I and mesenchymal (MES) GBM (P &lt; .01). Accordingly, RNA expression datasets revealed an upregulation of genes involved in neurotransmitter synapses and vesicle transport in RTK II glioblastomas. In a multivariate analysis, temporal location (P = .02, OR 5.69) and RTK II (P = .03, OR 5.01) were most predictive for preoperative seizures. During postoperative follow-up, only RTK II remained significantly associated with the development of seizures (P &lt; .01, OR 8.23). Consequently, the need for antiepileptic medication and its increase due to treatment failure was highly associated with the RTK II methylation subclass (P &lt; .01).

CONCLUSION

Our study shows a strong correlation of RTK II glioblastomas with preoperative and long-term seizures. These results underline the benefit of molecular glioblastoma profiling with important implications for postoperative seizure control.

A Functional Precision Medicine Pipeline Combines Comparative Transcriptomics and Tumor Organoid Modeling to Identify Bespoke Treatment Strategies for Glioblastoma

Li Fraumeni syndrome (LFS) is a hereditary cancer predisposition syndrome caused by germline mutations in TP53. TP53 is the most common mutated gene in human cancer, occurring in 30-50% of glioblastomas (GBM). Here, we highlight a precision medicine platform to identify potential targets for a GBM patient with LFS. We used a comparative transcriptomics approach to identify genes that are uniquely overexpressed in the LFS GBM patient relative to a cancer compendium of 12,747 tumor RNA sequencing data sets, including 200 GBMs. STAT1 and STAT2 were identified as being significantly overexpressed in the LFS patient, indicating ruxolitinib, a Janus kinase 1 and 2 inhibitors, as a potential therapy. The LFS patient had the highest level of STAT1 and STAT2 expression in an institutional high-grade glioma cohort of 45 patients, further supporting the cancer compendium results. To empirically validate the comparative transcriptomics pipeline, we used a combination of adherent and organoid cell culture techniques, including ex vivo patient-derived organoids (PDOs) from four patient-derived cell lines, including the LFS patient. STAT1 and STAT2 expression levels in the four patient-derived cells correlated with levels identified in the respective parent tumors. In both adherent and organoid cultures, cells from the LFS patient were among the most sensitive to ruxolitinib compared to patient-derived cells with lower STAT1 and STAT2 expression levels. A spheroid-based drug screening assay (3D-PREDICT) was performed and used to identify further therapeutic targets. Two targeted therapies were selected for the patient of interest and resulted in radiographic disease stability. This manuscript supports the use of comparative transcriptomics to identify personalized therapeutic targets in a functional precision medicine platform for malignant brain tumors.

Biologic Pathways Underlying Prognostic Radiomics Phenotypes from Paired MRI and RNA Sequencing in Glioblastoma

Background The biologic meaning of prognostic radiomics phenotypes remains poorly understood, hampered in part by lack of multicenter reproducible evidence. Purpose To uncover the biologic meaning of individual prognostic radiomics phenotypes in glioblastomas using paired MRI and RNA sequencing data and to validate the reproducibility of the identified radiogenomics linkages externally. Materials and Methods This retrospective multicenter study included four data sets gathered between January 2015 and December 2016. From a radiomics analysis set, a 13-feature radiomics signature was built using preoperative MRI for overall survival prediction. Using a radiogenomics training set with both MRI and RNA sequencing, biologic pathways were enriched and correlated with each of the 13 radiomics phenotypes. Radiomics-correlated key genes were identified to derive a prognostic radiomics gene expression (RadGene) score. The reproducibility of identified pathways and genes was validated with an external test set and a public data set (The Cancer Genome Atlas [TCGA]). A log-rank test was performed to assess prognostic significance. Results A total of 435 patients (mean age, 55 years ± 15 [standard deviation]; 263 men) were enrolled. The radiomics signature was associated with overall survival (hazard ratio [HR], 3.68; 95% CI: 2.08, 6.52; P < .001) in the radiomics validation subset. Four types of prognostic radiomics phenotypes were correlated with distinct pathways: immune, proliferative, treatment responsive, and cellular functions (false-discovery rate < 0.10). Thirty radiomics-correlated genes were identified. The prognostic significance of the RadGene score was confirmed in an external test set (HR, 2.02; 95% CI: 1.19, 3.41; P = .01) and a TCGA test set (HR, 1.43; 95% CI: 1.001, 2.04; P = .048). The radiomics-associated pathways and key genes can be replicated in an external test set. Conclusion Individual radiomics phenotypes on MRI scans predictive of overall survival were driven by distinct key pathways involved in immune regulation, tumor proliferation, treatment responses, and cellular functions in glioblastoma, which could be reproduced externally. © RSNA, 2021 Online supplemental material is available for this article.

Chromosome 10 abnormality predicts prognosis of neuroblastoma patients with bone marrow metastasis

BACKGROUND

Neuroblastoma (NB) is the most common extracranial solid tumor in children. It is known for high heterogeneity and concealed onset. In recent years, the mechanism of its occurrence and development has been gradually revealed. The purpose of this study is to summarize the clinical characteristics of children with NB and abnormal chromosome 10, and to investigate the relationship between the number and structure of chromosome 10 abnormalities and NB prognosis.

METHODS

Chromosome G-banding was used at the time of diagnosis to evaluate the genetics of chromosomes in patients with NB and track their clinical characteristics and prognosis. All participants were diagnosed with NB in the Medical Oncology Department of the Beijing Children's Hospital from May 2015 to December 2018 and were followed up with for at least 1 year.

RESULTS

Of all 150 patients with bone marrow metastases, 42 were clearly diagnosed with chromosomal abnormalities. Thirteen patients showed abnormalities in chromosome 10, and chromosome 10 was the most commonly missing chromosome. These 13 patients had higher LDH and lower OS and EFS than children with chromosomal abnormalities who did not have an abnormality in chromosome 10. Eight patients had both MYCN amplification and 1p36 deletion. Two patients had optic nerve damage and no vision, and one patient had left supraorbital metastases 5 months after treatment.

CONCLUSIONS

The results indicated that chromosome 10 might be a new prognostic marker for NB. MYCN amplification and 1p36 deletion may be related to chromosome 10 abnormalities in NB. Additionally, NB patients with abnormal chromosome 10 were prone to orbital metastases.

Oncogenesis, Microenvironment Modulation and Clinical Potentiality of FAP in Glioblastoma: Lessons Learned from Other Solid Tumors

Currently, glioblastoma (GBM) is the most common malignant tumor of the central nervous system in adults. Fibroblast activation protein (FAP) is a member of the dipeptidyl peptidase family, which has catalytic activity and is engaged in protein recruitment and scaffolds. Recent studies have found that FAP expression in different types of cells within the GBM microenvironment is typically upregulated compared with that in lower grade glioma and is most pronounced in the mesenchymal subtype of GBM. As a marker of cancer-associated fibroblasts (CAFs) with tumorigenic activity, FAP has been proven to promote tumor growth and invasion via hydrolysis of molecules such as brevican in the extracellular matrix and targeting of downstream pathways and substrates, such as fibroblast growth factor 21 (FGF21). In addition, based on its ability to suppress antitumor immunity in GBM and induce temozolomide resistance, FAP may be a potential target for immunotherapy and reversing temozolomide resistance; however, current studies on therapies targeting FAP are still limited. In this review, we summarized recent progress in FAP expression profiling and the understanding of the biological function of FAP in GBM and raised the possibility of FAP as an imaging biomarker and therapeutic target.

Mesenchymal and Proneural Subtypes of Glioblastoma Disclose Branching Based on GSC Associated Signature

Glioblastomas (GBM)—the most common, therapy-resistant, and lethal tumors driven by populations of glioma stem cells (GSCs) are still on the list of the most complicated pathologies. Thus, deeper understanding and characterization of GSCs is indispensable to find suitable targets and develop more effective therapies. In the present study, we applied native glioblastoma cells and GSCs sequencing, screened for GSC-specific targets and checked if the signature is related to GBM patient pathological, clinical data as well as molecular subtypes applying TCGA cohort. Data analysis revealed that tumors of proneural and mesenchymal subtypes are branching in separate clusters based on screened gene expression. Samples of the same subtype revealed significantly different patient survival prognosis as well as recurrence chance between the clusters. Recently, different subpopulations of mesenchymal GSC demonstrating different properties were shown, which indicates possible internal heterogeneity of GBM subtypes as well. Current findings also revealed branching of molecular GBM subtypes that were significantly linked to patient outcome and that might be decided by distinct GSC subpopulations.

Identification of molecular heterogeneity of hepatocellular carcinoma based on immune gene expression signatures

Although various molecular subtypes of hepatocellular carcinoma (HCC) have been investigated, most of these studies identify HCC subtype based on genomic profiling. Few studies have investigated the classification based on immune signatures, and none have classified HCC based on Immune activation and immunosuppressive. We performed immune gene expression of tumor tissue in 374 HCC patients from The Cancer Genome Atlas (TCGA) database and used unsupervised consensus clustering to stratify tumors. We then used HCC patients from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) as replication datasets. Based on the expression of 782 immune-related genes, HCC was stratified into four distinct immune subtypes. Tumors in one cluster (high immune activation; high-IA) indicate a higher level of Immune activation, which was characterized by higher anti-tumor immunity, higher pro-tumor immune-suppressive cell types, higher fractions of CD8+ T cells and M0 Macrophages compared with other subtypes. The high-IA also presents higher cancer-related hallmark signatures, such as epithelial-mesenchymal transition (EMT), angiogenesis, and apoptosis. We also found subpopulations of regulatory and exhaustion T lymphocyte were characterized by an opposite trend in high-IA, though samples in high-IA response to immunotherapy with better survival. The comparison of the immune profile in tumor and normal tissue indicates the activation of immune responses which only occurred in high-IA patients, while we conducted comparison of cirrhosis and non-cirrhosis tumor immune signatures, immune response activation was almost occurred in high-IA, but some of immune responses occurred in low-IA (low immune activation).

Radiomics-based machine learning model for efficiently classifying transcriptome subtypes in glioblastoma patients from MRI

BACKGROUND

In the field of glioma, transcriptome subtypes have been considered as an important diagnostic and prognostic biomarker that may help improve the treatment efficacy. However, existing identification methods of transcriptome subtypes are limited due to the relatively long detection period, the unattainability of tumor specimens via biopsy or surgery, and the fleeting nature of intralesional heterogeneity. In search of a superior model over previous ones, this study evaluated the efficiency of eXtreme Gradient Boosting (XGBoost)-based radiomics model to classify transcriptome subtypes in glioblastoma patients.

METHODS

This retrospective study retrieved patients from TCGA-GBM and IvyGAP cohorts with pathologically diagnosed glioblastoma, and separated them into different transcriptome subtypes groups. GBM patients were then segmented into three different regions of MRI: enhancement of the tumor core (ET), non-enhancing portion of the tumor core (NET), and peritumoral edema (ED). We subsequently used handcrafted radiomics features (n = 704) from multimodality MRI and two-level feature selection techniques (Spearman correlation and F-score tests) in order to find the features that could be relevant.

RESULTS

After the feature selection approach, we identified 13 radiomics features that were the most meaningful ones that can be used to reach the optimal results. With these features, our XGBoost model reached the predictive accuracies of 70.9%, 73.3%, 88.4%, and 88.4% for classical, mesenchymal, neural, and proneural subtypes, respectively. Our model performance has been improved in comparison with the other models as well as previous works on the same dataset.

CONCLUSION

The use of XGBoost and two-level feature selection analysis (Spearman correlation and F-score) could be expected as a potential combination for classifying transcriptome subtypes with high performance and might raise public attention for further research on radiomics-based GBM models.

Pathogenetic Features and Current Management of Glioblastoma

Glioblastoma (GBM) is the most common form of primary malignant brain tumor with a devastatingly poor prognosis. The disease does not discriminate, affecting adults and children of both sexes, and has an average overall survival of 12-15 months, despite advances in diagnosis and rigorous treatment with chemotherapy, radiation therapy, and surgical resection. In addition, most survivors will eventually experience tumor recurrence that only imparts survival of a few months. GBM is highly heterogenous, invasive, vascularized, and almost always inaccessible for treatment. Based on all these outstanding obstacles, there have been tremendous efforts to develop alternative treatment options that allow for more efficient targeting of the tumor including small molecule drugs and immunotherapies. A number of other strategies in development include therapies based on nanoparticles, light, extracellular vesicles, and micro-RNA, and vessel co-option. Advances in these potential approaches shed a promising outlook on the future of GBM treatment. In this review, we briefly discuss the current understanding of adult GBM's pathogenetic features that promote treatment resistance. We also outline novel and promising targeted agents currently under development for GBM patients during the last few years with their current clinical status.

Correlation of mutational landscape and survival outcome of peripheral T-cell lymphomas

OBJECTIVE

To explore the correlation of mutation landscape with clinical outcomes in patients with peripheral T-cell lymphoma (PTCL).

METHODS

We retrospectively analyzed the clinicopathological and prognosis data of 53 patients with PTCL from November 2011 to December 2017. Targeted next-generation sequencing of a 659-gene panel was performed for tissues from 53 patients with PTCLs. The correlation of mutation landscape with clinical outcomes was analyzed.

RESULTS

TET2 was the most frequently mutated gene (64%), followed by RHOA (43%), PCLO (23%), DNMT3A (19%), IDH2 (17%), PIEZO1 (17%) and TP53 (15%). When mutated genes were categorized into functional groups, the most common mutations were those involved in epigenetic/chromatin modification (75%), T-cell activation (74%), and the DNA repair/TP53 pathway (64%). TET2/TP53 mutations were significantly associated with positive B symptoms (P = 0.045), and elevated lactate dehydrogenase (LDH) level (P = 0.011). Moreover, TET2/TP53 mutation was a risk factor for PTCL patient survival (HR 3.574, 95% CI 1.069 - 11.941, P = 0.039). The occurrence of JAK/STAT pathway mutations in angioimmunoblastic T-cell lymphoma (AITL) patients conferred a worse progression-free survival (HR 2.366, 95% CI 0.9130-6.129, P = 0.0334).

CONCLUSIONS

Heterogeneous gene mutations occur in PTCL, some of which have a negative impact on the survival outcome.

The Role and Therapeutic Targeting of JAK/STAT Signaling in Glioblastoma

Glioblastoma remains one of the deadliest and treatment-refractory human malignancies in large part due to its diffusely infiltrative nature, molecular heterogeneity, and capacity for immune escape. The Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway contributes substantively to a wide variety of protumorigenic functions, including proliferation, anti-apoptosis, angiogenesis, stem cell maintenance, and immune suppression. We review the current state of knowledge regarding the biological role of JAK/STAT signaling in glioblastoma, therapeutic strategies, and future directions for the field.

Rapalink-1 Targets Glioblastoma Stem Cells and Acts Synergistically with Tumor Treating Fields to Reduce Resistance against Temozolomide

Simple Summary

Glioblastoma (GBM) resistance to standard treatment is driven by stem-like cell behavior and epithelial-like-mesenchymal transition. The main purpose of this paper was to functionally validate a novel discovered pharmacological strategy to treat GBM, the dual mTOR pathway inhibitor Rapalink-1 (RL1) using relevant stem cell models of the disease to unravel mechanistic insights. Our approach also interrogates combination studies with clinical treatment options of tumor treating fields (TTFields) and the best standard of care chemotherapy, temozolomide (TMZ). We provided clinical relevance of our experimental work through in silico evaluation on molecular data of diverse patient samples. RL1 effectively impaired motility and clonogenicity of GBM stem cells and reduced the expression of stem cell molecules. We elucidated a synergistic therapeutic potential of the inhibitor with TTFields to minimize therapy resistance toward TMZ, which supports its consideration for further translational oriented studies.

Abstract

Glioblastoma (GBM) is a lethal disease with limited clinical treatment options available. Recently, a new inhibitor targeting the prominent cancer signaling pathway mTOR was discovered (Rapalink-1), but its therapeutic potential on stem cell populations of GBM is unknown. We applied a collection of physiological relevant organoid-like stem cell models of GBM and studied the effect of RL1 exposure on various cellular features as well as on the expression of mTOR signaling targets and stem cell molecules. We also undertook combination treatments with this agent and clinical GBM treatments tumor treating fields (TTFields) and the standard-of-care drug temozolomide, TMZ. Low nanomolar (nM) RL1 treatment significantly reduced cell growth, proliferation, migration, and clonogenic potential of our stem cell models. It acted synergistically to reduce cell growth when applied in combination with TMZ and TTFields. We performed an in silico analysis from the molecular data of diverse patient samples to probe for a relationship between the expression of mTOR genes, and mesenchymal markers in different GBM cohorts. We supported the in silico results with correlative protein data retrieved from tumor specimens. Our study further validates mTOR signaling as a druggable target in GBM and supports RL1, representing a promising therapeutic target in brain oncology.

The CNS and the Brain Tumor Microenvironment: Implications for Glioblastoma Immunotherapy

Glioblastoma (GBM) is the most common and aggressive malignant primary brain tumor in adults. Its aggressive nature is attributed partly to its deeply invasive margins, its molecular and cellular heterogeneity, and uniquely tolerant site of origin—the brain. The immunosuppressive central nervous system (CNS) and GBM microenvironments are significant obstacles to generating an effective and long-lasting anti-tumoral response, as evidenced by this tumor’s reduced rate of treatment response and high probability of recurrence. Immunotherapy has revolutionized patients’ outcomes across many cancers and may open new avenues for patients with GBM. There is now a range of immunotherapeutic strategies being tested in patients with GBM that target both the innate and adaptive immune compartment. These strategies include antibodies that re-educate tumor macrophages, vaccines that introduce tumor-specific dendritic cells, checkpoint molecule inhibition, engineered T cells, and proteins that help T cells engage directly with tumor cells. Despite this, there is still much ground to be gained in improving the response rates of the various immunotherapies currently being trialed. Through historical and contemporary studies, we examine the fundamentals of CNS immunity that shape how to approach immune modulation in GBM, including the now revamped concept of CNS privilege. We also discuss the preclinical models used to study GBM progression and immunity. Lastly, we discuss the immunotherapeutic strategies currently being studied to help overcome the hurdles of the blood–brain barrier and the immunosuppressive tumor microenvironment.

Current Opinion on Molecular Characterization for GBM Classification in Guiding Clinical Diagnosis, Prognosis, and Therapy

Glioblastoma (GBM) is highly invasive and the deadliest brain tumor in adults. It is characterized by inter-tumor and intra-tumor heterogeneity, short patient survival, and lack of effective treatment. Prognosis and therapy selection is driven by molecular data from gene transcription, genetic alterations and DNA methylation. The four GBM molecular subtypes are proneural, neural, classical, and mesenchymal. More effective personalized therapy heavily depends on higher resolution molecular subtype signatures, combined with gene therapy, immunotherapy and organoid technology. In this review, we summarize the principal GBM molecular classifications that guide diagnosis, prognosis, and therapeutic recommendations.

Eukaryotic translation initiation factor 5A in the pathogenesis of cancers

Cancer is the leading cause of death worldwide. The absence of obvious symptoms and insufficiently sensitive biomarkers in early stages of carcinoma limits early diagnosis. Cancer therapy agents and targeted therapy have been used extensively against tissues or organs of specific cancers. However, the intrinsic and/or acquired resistance to the agents or targeted drugs as well as the serious toxic side effects of the drugs would limit their use. Therefore, identifying biomarkers involved in tumorigenesis and progression represents a challenge for cancer diagnosis and therapeutic strategy development. The eukaryotic translation factor 5A (eIF5A), originally identified as an initiation factor, was later shown to promote translation elongation of iterated proline sequences. There are two eIF5A isoforms (eIF5A1 and eIF5A2). eIF5A2 protein consists of 153 residues, and shares 84% amino acid identity with eIF5A1. However, the biological functions of these two isoforms may be significantly different. Recently, it was demonstrated that eIF5Ais widely involved in the pathogenesis of a number of diseases, including cancers. In particular, eIF5A plays an important role in regulating tumor growth, invasion, metastasis and tumor microenvironment. It was also shown to serve as a potential biomarker and target for the diagnosis and treatment of cancers. The present review briefly discusses the latest findings of eIF5A in the pathogenesis of certain malignant cancers and evolving clinical applications.

Omeprazole Inhibits Glioblastoma Cell Invasion and Tumor Growth

Background: The aryl hydrocarbon receptor (AhR) is expressed in gliomas and the highest staining is observed in glioblastomas. A recent study showed that the AhR exhibited tumor suppressor-like activity in established and patient-derived glioblastoma cells and genomic analysis showed that this was due, in part, to suppression of CXCL12, CXCR4 and MMP9. Methods: Selective AhR modulators (SAhRMs) including AhR-active pharmaceuticals were screened for their inhibition of invasion using a spheroid invasion assay in patient-derived AhR-expressing 15-037 glioblastoma cells and in AhR-silenced 15-037 cells. Invasion, migration and cell proliferation were determined using spheroid invasion, Boyden chambers and scratch assay, and XTT metabolic assays for cell growth. Changes in gene and gene product expression were determined by real-time PCR and Western blot assays, respectively. In vivo antitumorigenic activity of omeprazole was determined in SCID mice bearing subcutaneous patient-derived 15-037 cells. Results: Results of a screening assay using patient-derived 15-037 cells (wild-type and AhR knockout) identified the AhR-active proton pump inhibitor omeprazole as an inhibitor of glioblastoma cell invasion and migration only AhR-expressing cells but not in cells where the AhR was downregulated. Omeprazole also enhanced AhR-dependent repression of the pro-invasion CXCL12, CXCR4 and MMP9 genes, and interactions and effectiveness of omeprazole plus temozolomide were response-dependent. Omeprazole (100 mg/kg/injection) inhibited and delayed tumors in SCID mice bearing patient-derived 15-037 cells injected subcutaneously. Conclusion: Our results demonstrate that omeprazole enhances AhR-dependent inhibition of glioblastoma invasion and highlights a potential new avenue for development of a novel therapeutic mechanism-based approach for treating glioblastoma.

Identification of glioblastoma-specific prognostic biomarkers via an integrative analysis of DNA methylation and gene expression

Glioblastoma (GBM) is the most aggressive and lethal tumor of the central nervous system. The present study set out to identify reliable prognostic and predictive biomarkers for patients with GBM. RNA-sequencing data were obtained from The Cancer Genome Atlas database and DNA methylation data were downloaded using the University of California Santa Cruz-Xena database. The expression and methylation differences between patients with GBM, and survival times <1 and ≥1 year were investigated. A protein-protein interaction network was constructed and functional enrichment analyses of differentially expressed and methylated genes were performed. Hub genes were identified using the Cytoscape plug-in cytoHubba software. Survival analysis was performed using the survminer package, in order to determine the prognostic values of the hub genes. The present study identified 71 genes that were hypomethylated and expressed at high levels, and four genes that were hypermethylated and expressed at low levels in GBM. These genes were predominantly enriched in the ‘JAK-STAT signaling pathway’, ‘transcriptional misregulation in cancer’ and the ‘ECM-receptor interaction’, which are associated with GBM development. Among the 24 hub genes identified, 15 possessed potential prognostic value. An integrative analysis approach was implemented in order to analyze the association of DNA methylation with changes in gene expression and to assess the association of gene expression changes with GBM survival time. The results of the present study suggest that these 15 CpG-based genes may be useful and practical tools in predicting the prognosis of patients with GBM. However, future research on gene methylation and/or expression is required in order to develop personalized treatments for patients with GBM.

Graphene-Induced Transdifferentiation of Cancer Stem Cells as a Therapeutic Strategy against Glioblastoma

Glioblastoma (GBM) is an extremely malignant tumor of the central nervous system, characterized by low response to treatments and reoccurrence. This therapeutic resistance is believed to arise mostly from the presence of a subpopulation of tumorigenic stem cells, known as cancer stem cells (CSCs). In addition, the surrounding microenvironment is known to maintain CSCs, thus supporting tumor development and aggressiveness. This review focuses on a therapeutic strategy involving the stem cell trans-differentiating ability of graphene and its derivatives. Graphene distinguishes itself from other carbon-based nanomaterials due to an array of properties that makes it suitable for many purposes, from bioengineering to biomedical applications. Studies have shown that graphene is able to promote and direct the differentiation of CSCs. In addition, potential usage of graphene in GBM treatment represents a challenge in respect to its administration method. The present review also provides a general outlook of the potential side effects (e.g., cell toxicity) that graphene could have. Overall, this report discusses certain graphene-based therapeutic strategies targeting CSCs, which can be considered as prospective effective GBM treatments.

A risk signature with four autophagy-related genes for predicting survival of glioblastoma multiforme

Glioblastoma multiforme (GBM) is a devastating brain tumour without effective treatment. Recent studies have shown that autophagy is a promising therapeutic strategy for GBM. Therefore, it is necessary to identify novel biomarkers associated with autophagy in GBM. In this study, we downloaded autophagy-related genes from Human Autophagy Database (HADb) and Gene Set Enrichment Analysis (GSEA) website. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were performed to identify genes for constructing a risk signature. A nomogram was developed by integrating the risk signature with clinicopathological factors. Time-dependent receiver operating characteristic (ROC) curve and calibration plot were used to evaluate the efficiency of the prognostic model. Finally, four autophagy-related genes (DIRAS3, LGALS8, MAPK8 and STAM) were identified and were used for constructing a risk signature, which proved to be an independent risk factor for GBM patients. Furthermore, a nomogram was developed based on the risk signature and clinicopathological factors (IDH1 status, age and history of radiotherapy or chemotherapy). ROC curve and calibration plot suggested the nomogram could accurately predict 1-, 3- and 5-year survival rate of GBM patients. For function analysis, the risk signature was associated with apoptosis, necrosis, immunity, inflammation response and MAPK signalling pathway. In conclusion, the risk signature with 4 autophagy-related genes could serve as an independent prognostic factor for GBM patients. Moreover, we developed a nomogram based on the risk signature and clinical traits which was validated to perform better for predicting 1-, 3- and 5-year survival rate of GBM.

The Personalisation of Glioblastoma Treatment Using Whole Exome Sequencing: A Pilot Study

The molecular heterogeneity of glioblastoma has been linked to differences in survival and treatment response, while the development of personalised treatments may be a novel way of combatting this disease. Here we show for the first time that low passage number cells derived from primary tumours are greater than an 86% match genetically to the tumour tissue. We used these cells to identify eight genes that could be used for the personalisation of glioblastoma treatment and discovered a number of personalised drug combinations that were significantly more effective at killing glioblastoma cells and reducing recurrence than the individual drugs as well as the control and non-personalised combinations. This pilot study demonstrates for the first time that whole exome sequencing has the potential be used to improve the treatment of glioblastoma patients by personalising treatment. This novel approach could potentially offer a new avenue for treatment for this terrible disease.

PI3K/mTORC1/2 inhibitor PQR309 inhibits proliferation and induces apoptosis in human glioblastoma cells

Glioblastoma (GBM) is the most common type of primary central nervous system tumor in adults, which has high mortality and morbidity rates, and short survival time, namely <15 months after the diagnosis and application of standard therapy, which includes surgery, radiation therapy and chemotherapy; thus, novel therapeutic strategies are imperative. The activation of the PI3K/AKT signaling pathway plays an important role in GBM. In the present study, U87 and U251 GBM cells were treated with the PI3K/mTORC1/2 inhibitor PQR309, and its effect on glioma cells was investigated. Cell Counting Kit-8 assay, 5-ethynyl-2′-deoxyuridine and colony formation assays revealed dose- and time-dependent cytotoxicity in glioma cells that were treated with PQR309. Flow cytometry and western blotting revealed that PQR309 can significantly induce tumor cell apoptosis and arrest the cell cycle in the G1 phase. Furthermore, the expression levels of AKT, phosphorylated (p)-AKT, Bcl-2, Bcl-xL, Bad, Bax, cyclin D1, cleaved caspase-3, MMP-9 and MMP-2 were altered. In addition, the migration and invasion of glioma cells, as detected by wound healing, migration and Transwell invasion assays, exhibited a marked suppression after treating the cells with PQR309. These results indicated that PQR309 exerts an antitumor effect by inhibiting proliferation, inducing apoptosis, inducing G1 cell cycle arrest, and inhibiting invasion and migration in human glioma cells. The present study provides evidence supportive of further development of PQR309 for adjuvant therapy of GBM.