Comprehensive genomic characterization defines human glioblastoma genes and core pathways

A novel tool for multi-omics network integration and visualization: A study of glioma heterogeneity

Gliomas are highly heterogeneous tumors with generally poor prognoses. Leveraging multi-omics data and network analysis holds great promise in uncovering crucial signatures and molecular relationships that elucidate glioma heterogeneity. However, the complexity of the problem and the high dimensionality of the data increase the challenges of integrating information across various biological levels. This study develops a comprehensive framework aimed at identifying potential glioma-type-specific biomarkers through innovative variable selection and integrated network visualization. We designed a two-step framework for variable selection using sparse network estimation across various omics datasets. This framework incorporates MINGLE (Multi-omics Integrated Network for GraphicaL Exploration), a novel methodology designed to merge distinct multi-omics information into a single network, enabling the identification of underlying relations through an innovative integrated visualization. The analysis was conducted using glioma omics datasets, with patients grouped based on the latest glioma classification guidelines. Our investigation of the glioma data led to the identification of variables potentially serving as glioma-type-specific biomarkers. The integration of multi-omics data into a single network through MINGLE facilitated the discovery of molecular relationships that reflect glioma heterogeneity, supporting the biological interpretation. Scripts and files for reproducing the analysis or adapting it to other applications, are available in R software.

Allele age estimators designed for whole genome datasets show only a moderate reduction in performance when applied to whole exome datasets

Personalized genomics in the healthcare system is becoming increasingly accessible as the costs of sequencing decreases. With the increase in the number of genomes, larger numbers of rare variants are being discovered, leading to important initiatives in identifying the functional impacts in relation to disease phenotypes. One way to characterize these variants is to estimate the time the mutation entered the population. However, allele age estimators such as those implemented in the programs Relate, Genealogical Estimator of Variant Age (GEVA), and Runtc, were developed based on the assumption that datasets include the entire genome. We examined the performance of each of these estimators on simulated exome data under a neutral constant population size model, as well as under population expansion and background selection models. We found that each provides usable estimates of allele age from whole-exome datasets. Relate performs the best amongst all three estimators with Pearson coefficients of 0.83 and 0.73 (with respect to true simulated values, for neutral constant and expansion population model, respectively) with a 12 percent and 20 percent decrease in correlation between whole genome and whole exome estimations. Of the three estimators, Relate is best able to parallelize to yield quick results with little resources, however, Relate is currently only able to scale to thousands of samples making it unable to match the hundreds of thousands of samples being currently released. While more work is needed to expand the capabilities of current methods of estimating allele age, these methods show a modest decrease in performance in the estimation of the age of mutations.

Article Summary

Increasing availability of whole exome sequencing yields large numbers of rare variants that have direct impact on disease phenotypes. Many methods of identifying the functional impact of mutations exist including the estimation of the time a mutation entered a population. Popular methods of estimating this time assume whole genome data in the estimate of the allele age based on haplotypes. We simulated genome and exome data under a constant and expansion population demography model and found that there is a decrease in performance in all three methods on exome data of 15-30% depending on the method. Testing the robustness of the best performing method, Relate, further simulations introducing background selection and varying the sample size were also undertaken with similar results.

The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers

BACKGROUND

Glioblastoma is a highly heterogeneous brain tumor, posing challenges for precision therapies and patient stratification in clinical trials. Understanding how genetic mutations influence tumor imaging may improve patient management and treatment outcomes. This study investigates the relationship between imaging features, spatial patterns of tumor location, and genetic alterations in IDH-wildtype glioblastoma, as well as the likely sequence of mutational events.

METHODS

We conducted a retrospective analysis of 357 IDH-wildtype glioblastomas with pre-operative multiparametric MRI and targeted genetic sequencing data. Radiogenomic signatures and spatial distribution maps were generated for key mutations in genes such as EGFR, PTEN, TP53, and NF1 and their corresponding pathways. Machine and deep learning models were used to identify imaging biomarkers and stratify tumors based on their genetic profiles and molecular heterogeneity.

RESULTS

Here, we show that glioblastoma mutations produce distinctive imaging signatures, which are more pronounced in tumors with less molecular heterogeneity. These signatures provide insights into how mutations affect tumor characteristics such as neovascularization, cell density, invasion, and vascular leakage. We also found that tumor location and spatial distribution correlate with genetic profiles, revealing associations between tumor regions and specific oncogenic drivers. Additionally, imaging features reflect the cross-sectionally inferred evolutionary trajectories of glioblastomas.

CONCLUSIONS

This study establishes clinically accessible imaging biomarkers that capture the molecular composition and oncogenic drivers of glioblastoma. These findings have potential implications for noninvasive tumor profiling, personalized therapies, and improved patient stratification in clinical trials.

Tonabersat enhances temozolomide-mediated cytotoxicity in glioblastoma by disrupting intercellular connectivity through connexin 43 inhibition

Glioblastoma cells rely on connexin 43 (Cx43)-based gap junctions (GJs) for intercellular communication, enabling them to integrate into a widely branched malignant network. Although there are promising prospects for new targeted therapies, the lack of clinically feasible GJ inhibitors has impeded their adoption in clinical practice. In the present study, we investigated tonabersat (TO), a blood-brain-barrier-penetrating drug with GJ-inhibitory properties, in regard to its potential to disassemble intercellular connectivity in glioblastoma networks. Fluorescence-guided measurements of calcein cell-to-cell transfer were used to study functional intercellular connectivity. Specific DNA fragmentation rates of propidium iodide-stained nuclei were measured as a surrogate readout for cell death using flow cytometry. CRISPR/Cas9-mediated gene editing of Cx43 served as a validation tool of cellular effects related to Cx43 GJ inhibition. 3' mRNA sequencing was performed for molecular downstream analysis. We found that TO reduced intercellular GJ-mediated cytosolic traffic and yielded a significant reduction of tumor microtube (TM) length. TO-mediated inhibition of cellular tumor networks was accompanied by a synergistic effect for temozolomide-induced cell death. CRISPR/Cas9 Cx43-knockout revealed similar results, indicating that TO-mediated inhibitory effects rely on the inhibition of Cx43-based GJs. Gene set enrichment analyses found that GJ-mediated synergistic cytotoxic effects were linked to a significant upregulation of cell death signaling pathways. In conclusion, TO disrupts TM-based network connectivity via GJ inhibition and renders glioblastoma cells more susceptible to cytotoxic therapy. Given its previous use in clinical trials for migraine therapy, TO might harbor the potential of bridging the idea of a GJ-targeted therapeutic approach from bench to bedside.

Exploring the relationship between MGAT2 and glioblastoma: A Mendelian Randomization and bioinformatics approach

BACKGROUND

Mannosyl-glycoprotein beta-1,2-N-acetylglucosaminyltransferase 2 (MGAT2) and tumors' relevant research was in full swing recently. Therefore, we employed Mendelian Randomization (MR) alongside bioinformatics to thoroughly investigate the possible relationship between MGAT2 and glioblastoma (GBM).

METHODS

We utilized the summary statistics of genome-wide association studies (GWAS) for MGAT2 (N = 35,559 from deCODE) and glioblastoma (N = 379,155 from FinnGen). MR was used to assess the causal relationship between MGAT2 and GBM. Bioinformatics was used for a more in-depth exploration of the relationship between MGAT2 and GBM.

RESULTS

MR analysis demonstrated a causal relationship, showing that elevated levels of MGAT2 are associated with an increased risk of GBM (OR = 2.59, 95 % CI: 1.13-5.91, p = 0.023). Further investigation revealed significant differences in MGAT2 expression across normal tissue, tumor tissue, and gliomas of different types. Additionally, we found that MGAT2 may influence GBM through immune-related pathways, particularly through the role of macrophages. Proteins associated with MGAT2 were also identified in the PPI network.

CONCLUSION

This study first validated the causal relationship between MGAT2 and glioblastoma, and used bioinformatics to explore the relationship from multiple perspectives. Additionally, we proposed hypotheses for further research to investigate the potential mechanisms underlying this connection.

Pathological diagnosis of central nervous system tumours in adults: what's new?

In the course of the last decade, the pathological diagnosis of many tumours of the central nervous system (CNS) has transitioned from a purely histological to a combined histological and molecular approach, resulting in a more precise 'histomolecular diagnosis'. Unfortunately, translation of this refinement in CNS tumour diagnostics into more effective treatment strategies is lagging behind. There is hope though that incorporating the assessment of predictive markers in the pathological evaluation of CNS tumours will help to improve this situation. The present review discusses some novel aspects with regard to the pathological diagnosis of the most common CNS tumours in adults. After a brief update on recognition of clinically meaningful subgroups in adult-type diffuse gliomas and the value of assessing predictive markers in these tumours, more detailed information is provided on predictive markers of (potential) relevance for immunotherapy especially for glioblastomas, IDH-wildtype. Furthermore, recommendations for improved grading of meningiomas by using molecular markers are briefly summarised, and an overview is given on (predictive) markers of interest in metastatic CNS tumours. In the last part of this review, some 'emerging new CNS tumour types' that may occur especially in adults are presented in a table. Hopefully, this review provides useful information on 'what's new' for practising pathologists diagnosing CNS tumours in adults.

Metabolism and signaling crosstalk in glioblastoma progression and therapy resistance

Glioblastoma is the most common form of primary malignant brain tumor in adults and one of the most lethal human cancers, with high recurrence and therapy resistance. Glioblastoma cells display extensive genetic and cellular heterogeneity, which precludes a unique and common therapeutic approach. The standard of care in glioblastoma patients includes surgery followed by radiotherapy plus concomitant temozolomide. As in many other cancers, cell signaling is deeply affected due to mutations or alterations in the so-called molecular drivers. Moreover, glioblastoma cells undergo metabolic adaptations to meet the new demands in terms of energy and building blocks, with an increasing amount of evidence connecting metabolic transformation and cell signaling deregulation in this type of aggressive brain tumor. In this review, we summarize some of the most common alterations both in cell signaling and metabolism in glioblastoma, presenting an integrative discussion about how they contribute to therapy resistance. Furthermore, this review aims at providing a comprehensive overview of the state-of-the-art of therapeutic approaches and clinical trials exploiting signaling and metabolism in glioblastoma.

Mitochondrial DNA copy number alterations: Key players in the complexity of glioblastoma (Review)

Renowned as a highly invasive and lethal tumor derived from neural stem cells in the central nervous system, glioblastoma (GBM) exhibits substantial histopathological variation and genomic complexity, which drive its rapid progression and therapeutic resistance. Alterations in mitochondrial DNA (mtDNA) copy number (CN) serve a crucial role in GBM development and progression, affecting various aspects of tumor biology, including energy production, oxidative stress regulation and cellular adaptability. Fluctuations in mtDNA levels, whether elevated or diminished, can impair mitochondrial function, potentially disrupting oxidative phosphorylation and amplifying reactive oxygen species generation, thereby fueling tumor growth and influencing treatment responses. Understanding the mechanisms of mtDNA‑CN variations, and their interplay with genetic and environmental elements in the tumor microenvironment, is essential for advancing diagnostic and therapeutic strategies. Targeting mtDNA alterations could strengthen treatment efficacy, mitigate resistance and ultimately enhance the prognosis of patients with this aggressive brain tumor. The present review summarizes the existing literature on mtDNA alterations, specifically emphasizing variations in mtDNA‑CN and their association with GBM by surveying articles published between 1996 and 2024, sourced from databases such as Scopus, PubMed and Google Scholar. In addition, the review provides a brief overview of mitochondrial genome architecture, knowledge regarding the regulation of mtDNA integrity and CN, and how mitochondria significantly impact GBM tumorigenesis. This review further presents information on therapeutic approaches for restoring mtDNA‑CN that contribute to optimized mitochondrial function and improved health outcomes.

Association of tumor microbiome with survival in resected early-stage PDAC

The pancreas tumor microbiota may influence tumor microenvironment and influence survival in early-stage pancreatic ductal adenocarcinoma (PDAC); however, current studies are limited and small. We investigated the relationship of tumor microbiota to survival in 201 surgically resected patients with localized PDAC (Stages I-II), from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts. We characterized the tumor microbiome using RNA-sequencing data. We examined the association of the tumor microbiome with overall survival (OS), via meta-analysis with the Cox PH model. A microbial risk score (MRS) was calculated from the OS-associated microbiota. We further explored whether the OS-associated microbiota is related to host tumor immune infiltration. PDAC tumor microbiome α- and β-diversities were not associated with OS; however, 11 bacterial species, including species of Gammaproteobacteria, confirmed by extensive resampling, were significantly associated with OS (all Q < 0.05). The MRS summarizing these bacteria was related to a threefold change in OS (hazard ratio = 2.96 per standard deviation change in the MRS, 95% confidence interval = 2.26-3.86). This result was consistent across the two cohorts and in stratified analyses by adjuvant therapy (chemotherapy/radiation). Identified microbiota and the MRS also exhibited association with memory B cells and naïve CD4+ T cells, which may be related to the immune landscape through BCR and TCR signaling pathways. Our study shows that a unique tumor microbiome structure, potentially affecting the tumor immune microenvironment, is associated with poorer survival in resected early-stage PDAC. These findings suggest that microbial mechanisms may be involved in PDAC survival, potentially informing PDAC prognosis and guiding personalized treatment strategies.IMPORTANCEMuch of the available data on the PDAC tumor microbiome and survival are derived from relatively small and heterogeneous studies, including those involving patients with advanced stages of pancreatic cancer. There is a critical knowledge gap in terms of the tumor microbiome and survival in early-stage patients treated by surgical resection; we expect that advancements in survival may initially be best achieved in these patients who are treated with curative intent.

Glioblastoma multiforme: insights into pathogenesis, key signaling pathways, and therapeutic strategies

Glioblastoma multiforme (GBM) is the most prevalent and aggressive primary brain tumor in adults, characterized by a poor prognosis and significant resistance to existing treatments. Despite progress in therapeutic strategies, the median overall survival remains approximately 15 months. A hallmark of GBM is its intricate molecular profile, driven by disruptions in multiple signaling pathways, including PI3K/AKT/mTOR, Wnt, NF-κB, and TGF-β, critical to tumor growth, invasion, and treatment resistance. This review examines the epidemiology, molecular mechanisms, and therapeutic prospects of targeting these pathways in GBM, highlighting recent insights into pathway interactions and discovering new therapeutic targets to improve patient outcomes.

Long-Chain Cyclic Arylguanidines as Multifunctional Serotonin Receptor Ligands with Antiproliferative Activity

Recent investigations have shown serotonin's stimulatory effect on several types of cancers and carcinoid tumors. Nowadays there has been a significant increase in interest in 5-HT7 and 5-HT5A receptors in the context of cancer treatment. The possible role of 5-HT6R in the pathogenesis and progression of glioma remains an interesting and relatively unexplored issue. We developed a new group of long-chain 2-aminoquinazoline sulfonamides as new multifunctional serotonin receptor ligands, focused on 5-HT6R. The chosen group was further evaluated for antiproliferative effects on 1321N1 astrocytoma cells, along with U87MG, U-251, and LN-229 glioblastoma cell lines. Certain compounds were subjected to in vitro absorption, distribution, metabolism, excretion, and toxicity (ADMET) testing, for assessing factors such as lipophilicity, plasma protein binding, phospholipid affinity, potential for drug-drug interactions (DDI), membrane permeability (PAMPA), metabolic stability, and hepatotoxicity. Additionally, in vivo testing was performed using the Danio rerio model. The developed group includes the selective 5-HT6R antagonist PP 15, dual ligand for 5-HT1AR/5-HT6R PP 13, and dual ligand for 5-HT5AR/5-HT6R PP 10. The use of multifunctional ligands was associated with high anticancer activity both against selected glioma cell lines and other cancers (IC50 < 25 μM).

Harnessing ferroptosis for precision oncology: challenges and prospects

The discovery of diverse molecular mechanisms of regulated cell death has opened new avenues for cancer therapy. Ferroptosis, a unique form of cell death driven by iron-catalyzed peroxidation of membrane phospholipids, holds particular promise for targeting resistant cancer types. This review critically examines current literature on ferroptosis, focusing on its defining features and therapeutic potential. We discuss how molecular profiling of tumors and liquid biopsies can generate extensive multi-omics datasets, which can be leveraged through machine learning-based analytical approaches for patient stratification. Addressing these challenges is essential for advancing the clinical integration of ferroptosis-driven treatments in cancer care.

MET variants with activating N-lobe mutations identified in hereditary papillary renal cell carcinomas still require ligand stimulation

In hereditary papillary renal cell carcinoma (HPRCC), the hepatocyte growth factor receptor (MET) receptor tyrosine kinase (RTK) mutations recorded to date are located in the kinase domain and lead to constitutive MET activation. This contrasts with MET mutations identified in non-small-cell lung cancer (NSCLC), which lead to exon 14 skipping and deletion of a regulatory domain: In this latter case, the mutated receptor still requires ligand stimulation. Sequencing of MET in samples from 158 HPRCC and 2808 NSCLC patients revealed 10 uncharacterized mutations. Four of these, all found in HPRCC and leading to amino acid substitutions in the N-lobe of the MET kinase, proved able to induce cell transformation, which was further enhanced by hepatocyte growth factor (HGF) stimulation: His1086Leu, Ile1102Thr, Leu1130Ser, and Cis1125Gly. Similar to the variant resulting in MET exon 14 skipping, the two N-lobe MET variants His1086Leu and Ile1102Thr were found to require stimulation by HGF in order to strongly activate downstream signaling pathways and epithelial cell motility. The Ile1102Thr mutation also displayed transforming potential, promoting tumor growth in a xenograft model. In addition, the N-lobe-mutated MET variants were found to trigger a common HGF-stimulation-dependent transcriptional program, consistent with an observed increase in cell motility and invasion. Altogether, this functional characterization revealed that N-lobe variants still require ligand stimulation, in contrast to other RTK variants. This suggests that HGF expression in the tumor microenvironment is important for tumor growth. The sensitivity of these variants to MET inhibitors opens the way for use of targeted therapies for patients harboring the corresponding mutations.

Heterogeneous Clustering of Multiomics Data for Breast Cancer Subgroup Classification and Detection

The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation-maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene analysis and pathway enrichment for BRCA patients. The analysis compared 302 tumor samples to 113 normal samples across 60,660 genes. The highly upregulated gene COL10A1, promoting breast cancer progression and poor prognosis, and the consistently downregulated gene CDG300LG, linked to brain metastatic cancer, were identified. Pathway enrichment analysis revealed similarities in cellular matrix organization pathways across subtypes, with notable differences in functions like cell proliferation regulation and endocytosis by host cells. GO Semantic Similarity analysis quantified gene relationships in each subtype, identifying potential biomarkers like MATN2, similar to COL10A1. These insights suggest deeper relationships within clusters and highlight personalized treatment potential based on subtypes.

A pathogenic subpopulation of human glioma associated macrophages linked to glioma progression

Malignant gliomas follow two distinct natural histories: de novo high grade tumors such as glioblastoma, or lower grade tumors with a propensity to transform into high grade disease. Despite differences in tumor genotype, both entities converge on a common histologically aggressive phenotype, and the basis for this progression is unknown. Glioma associated macrophages (GAM) have been implicated in this process, however GAMs are ontologically and transcriptionally diverse, rendering isolation of pathogenic subpopulations challenging. Since macrophage contextual gene programs are orchestrated by transcription factors acting on cis -acting promoters and enhancers in gene regulatory networks (GRN), we hypothesized that functional populations of GAMs can be resolved through GRN inference. Here we show via parallel single cell RNA and ATAC sequencing that a subpopulation of human GAMs can be defined by a GRN centered around the Activator Protein-1 transcription factor FOSL2 preferentially enriched in high grade tumors. Using this GRN we nominate ANXA1 and HMOX1 as surrogate cell surface markers for activation, thus permitting prospective isolation and functional validation in human GAMs. These cells, termed malignancy associated GAMs (mGAMs) are pro-invasive, pro-angiogenic, pro-proliferative, possess intact antigen presentation but skew T-cells towards a CD4+FOXP3+ phenotype under hypoxia. Ontologically, mGAMs share somatic mitochondrial mutations with peripheral blood monocytes, and their presence correlates with high grade disease irrespective of underlying tumor mutation status. Furthermore, spatio-temporally mGAMs occupy distinct metabolic niches; mGAMs directly induce proliferation and mesenchymal transition of low grade glioma cells and accelerate tumor growth in vivo upon co-culture. Finally mGAMs are preferentially enriched in patients with newly transformed regions in human gliomas, supporting the view that mGAMs play a pivotal role in glioma progression and may represent a plausible therapeutic target in human high-grade glioma.

Advancing Glioblastoma Research with Innovative Brain Organoid-Based Models

Glioblastoma (GBM) is a relatively rare but highly aggressive form of brain cancer characterized by rapid growth, invasiveness, and resistance to standard therapies. Despite significant progress in understanding its molecular and cellular mechanisms, GBM remains one of the most challenging cancers to treat due to its high heterogeneity and complex tumor microenvironment. To address these obstacles, researchers have employed a range of models, including in vitro cell cultures and in vivo animal models, but these often fail to replicate the complexity of GBM. As a result, there has been a growing focus on refining these models by incorporating human-origin cells, along with advanced genetic techniques and stem cell-based bioengineering approaches. In this context, a variety of GBM models based on brain organoids were developed and confirmed to be clinically relevant and are contributing to the advancement of GBM research at the preclinical level. This review explores the preparation and use of brain organoid-based models to deepen our understanding of GBM biology and to explore novel therapeutic approaches. These innovative models hold significant promise for improving our ability to study this deadly cancer and for advancing the development of more effective treatments.

An integrated perspective on single-cell and spatial transcriptomic signatures in high-grade gliomas

High-grade gliomas (HGG) are incurable brain malignancies in children and adults. Breakthrough advances in transcriptomic technologies unveiled the intricate diversity of cellular states and their spatial organization within HGGs. We qualitatively integrated 55 neoplastic transcriptomic signatures described in 17 single-cell and spatial RNA sequencing-based studies. Our review delineates a spectrum of cellular states, represented by the expression of specific genes, which can be conceptualized along a "reactive-developmental programs" axis. Additionally, we discussed the potential cues influencing these cellular states, including how spatial organization may impact transcriptomic dynamics. Leveraging these insightful discoveries, we discussed a novel, evolutive way to integrate the different transcriptomic signatures in two or three dimensions, incorporating developmental states, their proliferative capacity, and their possible transition towards reactive states. This integrated analysis illuminates the diverse cellular landscape of HGGs and provides a valuable resource for further elucidation of malignant mechanisms, and for the design of therapeutic endeavors.

Low & Anaplastic Grade Glioma Umbrella Study of MOlecular Guided TherapieS (LUMOS-2): study protocol for a phase 2, prospective, multicentre, open-label, multiarm, biomarker-directed, signal-seeking, umbrella, clinical trial for recurrent IDH mutant, grade 2/3 glioma

INTRODUCTION

All grade 2/3 gliomas are incurable and at the time of inevitable relapse, patients have significant unmet needs with few effective treatments. This study aims to improve outcomes by molecular profiling of patients at relapse, then matching them with the best available drug based on their molecular profile, maximising the chances of patient benefit while simultaneously testing multiple novel drugs.

METHODS AND ANALYSIS

Low & Anaplastic Grade Glioma Umbrella Study of MOlecular Guided TherapieS (LUMOS-2) will be an international, phase 2, multicentre, open-label, biomarker-directed, umbrella clinical trial for recurrent isocitrate dehydrogenase mutant, histologically grade 2/3 gliomas. Investigational treatment will be assigned based on molecular profiling of contemporaneous tissue obtained at disease relapse using next-generation sequencing. LUMOS-2 will begin with three therapeutic treatment arms: paxalisib, cadonilimab and selinexor. Patient molecular profiles will be assessed by an expert, multidisciplinary Molecular Tumour Advisory Panel. Patients whose molecular profile is considered suitable for a targeted agent like paxalisib will be allocated to that arm, others will be randomised to the available arms of the trial. The primary endpoint is progression-free survival at 6 months. Secondary objectives include assessment of overall survival, response rate, safety and quality of life measures. Two additional therapeutic arms are currently in development.

ETHICS AND DISSEMINATION

Central ethics approval was obtained from the Sydney Local Health District Ethics Review Committee, Royal Prince Alfred Hospital Zone, Sydney, Australia (Approval: 2022/ETH02230). Other clinical sites will provide oversight through local governance processes, including obtaining informed consent from suitable participants. A report describing the results of the study will be submitted to international meetings and peer-reviewed journals.

TRIAL REGISTRATION NUMBER

ACTRN12623000096651.

EZH2 functional dichotomy in reactive oxygen species-stratified glioblastoma

BACKGROUND

Enhancer of zeste homolog 2 (EZH2), well known for its canonical methyltransferase activity in transcriptional repression in many cancers including glioblastoma (GBM), has an understudied noncanonical function critical for sustained tumor growth. Recent GBM consortial efforts reveal complex molecular heterogeneity for which therapeutic vulnerabilities correlated with subtype stratification remain relatively unexplored. Current enzymatic EZH2 inhibitors (EZH2inh) targeting its canonical su(var)3-9, enhancer-of-zeste and trithorax domain show limited efficacy and lack durable response, suggesting that underlying differences in the noncanonical pathway may yield new knowledge. Here, we unveiled dual roles of the EZH2 CXC domain in therapeutically distinct, reactive oxygen species (ROS)-stratified tumors.

METHODS

We analyzed differentially expressed genes between ROS classes by examining cis-regulatory elements as well as clustering of activities and pathways to identify EZH2 as the key mediator in ROS-stratified cohorts. Pull-down assays and CRISPR knockout of EZH2 domains were used to dissect the distinct functions of EZH2 in ROS-stratified GBM cells. The efficacy of NF-κB-inducing kinase inhibitor (NIKinh) and standard-of-care temozolomide was evaluated using orthotopic patient-derived GBM xenografts.

RESULTS

In ROS(+) tumors, CXC-mediated co-interaction with RelB drives constitutive activation of noncanonical NF-κB2 signaling, sustaining the ROS(+) chemoresistant phenotype. In contrast, in ROS(-) subtypes, Polycomb Repressive Complex 2 methyltransferase activity represses canonical NF-κB. Addressing the lack of EZH2inh targeting its nonmethyltransferase roles, we utilized a brain-penetrant NIKinh that disrupts EZH2-RelB binding, consequently prolonging survival in orthotopic ROS(+)-implanted mice.

CONCLUSIONS

Our findings highlight the functional dichotomy of the EZH2 CXC domain in governing ROS-stratified therapeutic resistance, thereby advocating for the development of therapeutic approaches targeting its noncanonical activities and underscoring the significance of patient stratification methodologies.

Classification of non-TCGA cancer samples to TCGA molecular subtypes using compact feature sets

Molecular subtypes, such as defined by The Cancer Genome Atlas (TCGA), delineate a cancer's underlying biology, bringing hope to inform a patient's prognosis and treatment plan. However, most approaches used in the discovery of subtypes are not suitable for assigning subtype labels to new cancer specimens from other studies or clinical trials. Here, we address this barrier by applying five different machine learning approaches to multi-omic data from 8,791 TCGA tumor samples comprising 106 subtypes from 26 different cancer cohorts to build models based upon small numbers of features that can classify new samples into previously defined TCGA molecular subtypes-a step toward molecular subtype application in the clinic. We validate select classifiers using external datasets. Predictive performance and classifier-selected features yield insight into the different machine-learning approaches and genomic data platforms. For each cancer and data type we provide containerized versions of the top-performing models as a public resource.

MethylProphet: A Generalized Gene-Contextual Model for Inferring Whole-Genome DNA Methylation Landscape

DNA methylation (DNAm), an epigenetic modification, regulates gene expression, influences phenotypes, and encodes inheritable information, making it critical for disease diagnosis, treatment, and prevention. While human genome contains approximately 28 million CpG sites where DNAm can be measured, only 1-3% of these sites are typically available in most datasets due to complex experimental protocols and high costs, hindering insights from DNAm data. Leveraging the relationship between gene expression and DNAm offers promise for computational inference, but existing statistical, machine learning, and masking-based generative Transformers face critical limitations: they cannot infer DNAm at unmeasured CpGs or in new samples effectively. To overcome these challenges, we introduce MethylProphet, a gene-guided, context-aware Transformer model designed for DNAm inference. MethylProphet employs a Bottleneck MLP for efficient gene profile compression and a specialized DNA sequence tokenizer, integrating global gene expression patterns with local CpG context through a Transformer encoder architecture. Trained on whole-genome bisulfite sequencing data from ENCODE (1.6B training CpG-sample pairs; 322B tokens), MethylProphet demonstrates strong performance in hold-out evaluations, effectively inferring DNAm for unmeasured CpGs and new samples. In addition, its application to 10842 pairs of gene expression and DNAm samples at TCGA chromosome 1 (450M training CpGsample pairs; 91B tokens) highlights its potential to facilitate pan-cancer DNAm landscape inference, offering a powerful tool for advancing epigenetic research and precision medicine. All codes, data, protocols, and models are publicly available via https://github.com/xk-huang/methylprophet/ .

Research trends in glioma chemoradiotherapy resistance: a bibliometric analysis (2003-2023)

Background

Glioma is the most aggressive primary malignant tumor of the central nervous system, characterized by high recurrence rates and resistance to chemoradiotherapy, making therapeutic resistance a major challenge in neuro-oncology. Recent research emphasizes the role of the tumor microenvironment (TME) and immune modulation in glioma progression and resistance. Despite these advances, a comprehensive bibliometric analysis of research trends in glioma chemoradiotherapy resistance over the past two decades is lacking. This study aims to systematically evaluate the research landscape, identify emerging hotspots, and provide guidance for future investigations.

Methods

Articles on glioma chemoradiotherapy resistance published between 2003 and 2023 were retrieved from the Web of Science Core Collection, resulting in 4,528 publications. Bibliometric tools, including VOSviewer, CiteSpace, and R packages such as bibliometrix and ggplot2, were used to analyze co-authorship networks, keyword evolution, and citation bursts to identify collaboration patterns, thematic developments, and influential contributions.

Results

Publication output increased significantly between 2013 and 2022, peaking at 650 articles in 2022. Over 1,000 institutions from 88 countries contributed to this research. The United States, Switzerland, and Germany showed the highest citation impact, while China led in publication volume but demonstrated relatively lower citation influence. The research focus has shifted from traditional topics such as the "MGMT gene" to emerging areas including the "tumor microenvironment," "immune infiltration," and "nanoparticles." The androgen receptor was identified as a promising but underexplored therapeutic target.

Conclusions

Research on glioma chemoradiotherapy resistance has seen substantial growth, with increasing emphasis on immune modulation, the tumor microenvironment, and novel therapeutic targets such as the androgen receptor. This study represents the first comprehensive bibliometric analysis of this field, providing a detailed overview of research trends and potential directions for future studies. The findings highlight the need for strengthened international collaboration and multidisciplinary approaches to address the challenges of therapeutic resistance in glioma.

Individualized patient tumor organoids faithfully preserve human brain tumor ecosystems and predict patient response to therapy

Tumor organoids are important tools for cancer research, but current models have drawbacks that limit their applications for predicting response to therapy. Here, we developed a fast, efficient, and complex culture system (IPTO, individualized patient tumor organoid) that accurately recapitulates the cellular and molecular pathology of human brain tumors. Patient-derived tumor explants were cultured in induced pluripotent stem cell (iPSC)-derived cerebral organoids, thus enabling culture of a wide range of human tumors in the central nervous system (CNS), including adult, pediatric, and metastatic brain cancers. Histopathological, genomic, epigenomic, and single-cell RNA sequencing (scRNA-seq) analyses demonstrated that the IPTO model recapitulates cellular heterogeneity and molecular features of original tumors. Crucially, we showed that the IPTO model predicts patient-specific drug responses, including resistance mechanisms, in a prospective patient cohort. Collectively, the IPTO model represents a major breakthrough in preclinical modeling of human cancers, which provides a path toward personalized cancer therapy.

The role of lncRNAs in the interplay of signaling pathways and epigenetic mechanisms in glioma

Gliomas, highly aggressive tumors of the central nervous system, present overwhelming challenges due to their heterogeneity and therapeutic resistance. Glioblastoma multiforme (GBM), the most malignant form, underscores this clinical urgency due to dismal prognosis despite aggressive treatment regimens. Recent advances in cancer research revealed signaling pathways and epigenetic mechanisms that intricately govern glioma progression, offering multifaceted targets for therapeutic intervention. This review explores the dynamic interplay between signaling events and epigenetic regulation in the context of glioma, with a particular focus on the crucial roles played by non-coding RNAs (ncRNAs). Through direct and indirect epigenetic targeting, ncRNAs emerge as key regulators shaping the molecular landscape of glioblastoma across its various stages. By dissecting these intricate regulatory networks, novel and patient-tailored therapeutic strategies could be devised to improve patient outcomes with this devastating disease.

Immune checkpoint inhibitors for glioblastoma: emerging science, clinical advances, and future directions

Glioblastoma (GBM), the most common and aggressive primary central nervous system (CNS) tumor in adults, continues to have a dismal prognosis. Across hundreds of clinical trials, few novel approaches have translated to clinical practice while survival has improved by only a few months over the past three decades. Randomized controlled trials of immune checkpoint inhibitors (ICIs), which have seen impressive success for advanced or metastatic extracranial solid tumors, have so far failed to demonstrate a clinical benefit for patients with GBM. This has been secondary to GBM heterogeneity, the unique immunosuppressive CNS microenvironment, immune-evasive strategies by cancer cells, and the rapid evolution of tumor on therapy. This review aims to summarize findings from major clinical trials of ICIs for GBM, review historic failures, and describe currently promising avenues of investigation. We explore the biological mechanisms driving ICI responses, focusing on the role of the tumor microenvironment, immune evasion, and molecular biomarkers. Beyond conventional monotherapy approaches targeting PD-1, PD-L1, CTLA-4, we describe emerging approaches for GBM, such as dual-agent ICIs, and combination of ICIs with oncolytic virotherapy, antigenic peptide vaccines, chimeric antigenic receptor (CAR) T-cell therapy, along with nanoparticle-based delivery systems to enhance ICI efficacy. We highlight potential strategies for improving patient selection and treatment personalization, along with real-time, longitudinal monitoring of therapeutic responses through advanced imaging and liquid biopsy techniques. Integrated radiomics, tissue, and plasma-based analyses, may potentially uncover immunotherapeutic response signatures, enabling early, adaptive therapeutic adjustments. By specifically targeting current therapeutic challenges, outcomes for GBM patients may potentially be improved.

A prospective study comparing highly qualified Molecular Tumor Boards with AI-powered software as a medical device

BACKGROUND

The implementation of cancer precision medicine in Japan is deeply intertwined with insurance reimbursement policies and requires case-by-case reviews by Molecular Tumor Boards (MTBs), which impose considerable operational burdens on healthcare facilities. The extensive preparation and review times required by MTBs hinder their ability to efficiently assess comprehensive genomic profiling (CGP) test results. Despite attempts to optimize MTB operations, significant challenges remain. This study aims to evaluate the effectiveness of QA Commons, an artificial intelligence-driven system designed to improve treatment planning using CGP analysis. QA Commons utilizes a comprehensive knowledge base of drugs, regulatory approvals, and clinical trials linked to genetic biomarkers, thereby enabling the delivery of consistent and standardized treatment recommendations. Initial assessments revealed that the QA Commons' recommendations closely matched the ideal treatment recommendations (consensus annotations), outperforming the average results of MTBs at Cancer Genomic Medicine Core Hospitals.

METHODS

A clinical performance evaluation study will be conducted by comparing the QA Commons' treatment recommendations with those of the Academia Assembly, which includes medical professionals from the Cancer Genomic Medicine Core and Hub Hospitals. One hundred cases selected from the "Registry of the Academia Assembly," based on defined inclusion and exclusion criteria, will be analyzed to assess the concordance of recommendations.

CONCLUSION

The expected outcomes suggest that QA Commons could reduce the workload of MTB members, standardize the quality of MTB discussions, and provide consistent outcomes in repeated patient consultations. In addition, the global expansion of QA Commons could promote worldwide adoption of Japan's pioneering precision oncology system.

Precision neurology

Over the past several decades, high-resolution brain imaging, blood and cerebrospinal fluid analyses, and other advanced technologies have changed diagnosis from an exercise depending primarily on the history and physical examination to a computer- and online resource-aided process that relies on larger and larger quantities of data. In addition, randomized controlled trials (RCT) at a population level have led to many new drugs and devices to treat neurological disease, including disease-modifying therapies. We are now at a crossroads. Combinatorially profound increases in data about individuals has led to an alternative to population-based RCTs. Genotyping and comprehensive "deep" phenotyping can sort individuals into smaller groups, enabling precise medical decisions at a personal level. In neurology, precision medicine that includes prediction, prevention and personalization requires that genomic and phenomic information further incorporate imaging and behavioral data. In this article, we review the genomic, phenomic, and computational aspects of precision medicine for neurology. After defining biological markers, we discuss some applications of these "-omic" and neuroimaging measures, and then outline the role of computation and ultimately brain simulation. We conclude the article with a discussion of the relation between precision medicine and value-based care.

Actionable alterations in glioblastoma: Insights from the implementation of genomic profiling as the standard of care from 2016 to 2023

Background

In 2016, genomic profiling was implemented for patients with grade 4 primary brain tumors at Rigshospitalet, Denmark. The aim of this study was to discover actionable alterations and to match these with targeted therapies.

Methods

Between January 2016 and December 2023, 483 brain tumor patients were profiled. We retrieved clinical data and molecular data. Whole exome, whole genome, or panel sequencing, along with SNP array analyses, and RNA-seq were performed on resected primary tumor tissue. Alterations were classified according to the European Society for Medical Oncology (ESMO) Scale for Clinical Actionability of Molecular Targets (ESCAT) following the European Association of Neuro-Oncology (EANO) guideline on rational molecular testing.

Results

A total of 200 (41.4%) patients' tumors harbored an alteration of interest according to the EANO guideline. Twenty (4.1%) patients had an ESCAT high-tier alteration (tier I or II), while 155 patients (32.1%) had an alteration corresponding to ESCAT IIIA. Thirty-five patients (7.2%) had an actionable alteration, and 15 (3.1%) received targeted therapy. The treated targets were BRAFV600E mutations, FGFR alterations, NTRK fusions, PDGFRA fusions, PTPRZ1-MET fusions, and TMB-high. The overall response rate was 20%, with a median duration of response of 12 months, and 47% achieved stable disease as the best response.

Conclusions

Genomic profiling uncovers alterations of interest in a substantial number of patients, but only a minority are considered by the Danish National Molecular Tumor Board to have actionable alterations, and even fewer receive targeted therapy. Nevertheless, factors, such as promising targets and the increasing availability of trials, may contribute to a future increase in the number of patients benefiting from targeted therapies based on genomic profiling.

Network analyses of brain tumor multiomic data reveal pharmacological opportunities to alter cell state transitions

Glioblastoma Multiforme (GBM) remains a particularly difficult cancer to treat, and survival outcomes remain poor. In addition to the lack of dedicated drug discovery programs for GBM, extensive intratumor heterogeneity and epigenetic plasticity related to cell-state transitions are major roadblocks to successful drug therapy in GBM. To study these phenomenon, publicly available snRNAseq and bulk RNAseq data from patient samples were used to categorize cells from patients into four cell states (i.e., phenotypes), namely: (i) neural progenitor-like (NPC-like), (ii) oligodendrocyte progenitor-like (OPC-like), (iii) astrocyte-like (AC-like), and (iv) mesenchymal-like (MES-like). Patients were subsequently grouped into subpopulations based on which cell-state was the most dominant in their respective tumor. By incorporating phosphoproteomic measurements from the same patients, a protein-protein interaction network (PPIN) was constructed for each cell state. These four-cell state PPINs were pooled to form a single Boolean network that was used for in silico protein knockout simulations to investigate mechanisms that either promote or prevent cell state transitions. Simulation results were input into a boosted tree machine learning model which predicted the cell states or phenotypes of GBM patients from an independent public data source, the Glioma Longitudinal Analysis (GLASS) Consortium. Combining the simulation results and the machine learning predictions, we generated hypotheses for clinically relevant causal mechanisms of cell state transitions. For example, the transcription factor TFAP2A can be seen to promote a transition from the NPC-like to the MES-like state. Such protein nodes and the associated signaling pathways provide potential drug targets that can be further tested in vitro and support cell state-directed (CSD) therapy.

Recent advances in development and delivery of non-viral nucleic acid therapeutics for brain tumor therapy

High grade gliomas (HGG) are a group of CNS tumors refractory to currently existing therapies, which routinely leads to early recurrence and a dismal prognosis. Recent advancements in nucleic acid-based therapy using a wide variety of different molecular targets and non-viral nanocarrier systems suggest that this approach holds significant potential to meet the urgent demand for improved therapeutic options for the treatment of these tumors. This review provides a comprehensive and up-to-date overview on the current landscape and progress of preclinical and clinical developments in this rapidly evolving and exciting field of research, including optimized nanocarrier delivery systems, promising therapeutic targets and tailor-made therapeutic strategies for individualized HGG patient treatment.

Single-nucleus and spatial landscape of the sub-ventricular zone in human glioblastoma

The sub-ventricular zone (SVZ) is the most well-characterized neurogenic area in the mammalian brain. We previously showed that in 65% of patients with glioblastoma (GBM), the SVZ is a reservoir of cancer stem-like cells that contribute to treatment resistance and the emergence of recurrence. Here, we build a single-nucleus RNA-sequencing-based microenvironment landscape of the tumor mass and the SVZ of 15 patients and two histologically normal SVZ samples as controls. We identify a ZEB1-centered mesenchymal signature in the tumor cells of the SVZ. Moreover, the SVZ microenvironment is characterized by tumor-supportive microglia, which spatially coexist and establish crosstalks with tumor cells. Last, differential gene expression analyses, predictions of ligand-receptor and incoming/outgoing interactions, and functional assays reveal that the interleukin (IL)-1β/IL-1RAcP and Wnt-5a/Frizzled-3 pathways represent potential therapeutic targets in the SVZ. Our data provide insights into the biology of the SVZ in patients with GBM and identify potential targets of this microenvironment.

Radiogenomics and machine learning predict oncogenic signaling pathways in glioblastoma

BACKGROUND

Glioblastoma (GBM) is a highly aggressive brain tumor associated with a poor patient prognosis. The survival rate remains low despite standard therapies, highlighting the urgent need for novel treatment strategies. Advanced imaging techniques, particularly magnetic resonance imaging (MRI), are crucial in assessing GBM. Disruptions in various oncogenic signaling pathways, such as Receptor Tyrosine Kinase (RTK)-Ras-Extracellular signal-regulated kinase (ERK) signaling, Phosphoinositide 3- Kinases (PI3Ks), tumor protein p53 (TP53), and Neurogenic locus notch homolog protein (NOTCH), contribute to the development of different tumor types, each exhibiting distinct morphological and phenotypic features that can be observed at a microscopic level. However, identifying genetic abnormalities for targeted therapy often requires invasive procedures, prompting exploration into non-invasive approaches like radiogenomics. This study explores the utility of radiogenomics and machine learning (ML) in predicting these oncogenic signaling pathways in GBM patients.

METHODS

We collected post-operative MRI scans (T1w, T1c, FLAIR, T2w) from the BRATS-19 dataset, including scans from patients with both GBM and LGG, linked to genetic and clinical data via TCGA and CPTAC. Signaling pathway data was manually extracted from cBioPortal. Radiomic features were extracted from four MRI modalities using PyRadiomics. Dimensionality reduction and feature selection were applied and Data imbalance was addressed with SMOTE. Five ML models were trained to predict signaling pathways, with Grid Search optimizing hyperparameters and 5-fold cross-validation ensuring unbiased performance. Each model's performance was evaluated using various metrics on test data.

RESULTS

Our results showed a positive association between most signaling pathways and the radiomic features derived from MRI scans. The best models achieved high AUC scores, namely 0.7 for RTK-RAS, 0.8 for PI3K, 0.75 for TP53, and 0.4 for NOTCH, and therefore, demonstrated the potential of ML models in accurately predicting oncogenic signaling pathways from radiomic features, thereby informing personalized therapeutic approaches and improving patient outcomes.

CONCLUSION

We present a novel approach for the non-invasive prediction of deregulation in oncogenic signaling pathways in glioblastoma (GBM) by integrating radiogenomic data with machine learning models. This research contributes to advancing precision medicine in GBM management, highlighting the importance of integrating radiomics with genomic data to understand tumor behavior and treatment response better.

Immune checkpoint expression and therapeutic implications in IDH1-mutant and wild-type glioblastomas

Programmed cell death protein 1 (PDCD1) and cluster of differentiation 274 (CD274) expression is implicated in escaping tumors from immune surveillance. Immune checkpoint inhibitors show promise in cancer therapy, yet their efficacy in glioblastomas, particularly with IDH1 mutations, remains unclear. This study analyzed two independent NGS datasets (n = 577 and n = 153) from TCGA to investigate the expression of PDCD1 and CD274 in glioblastomas and their relationship with IDH1 mutations. We used cBioPortal for mutation analysis, RNA seq for expression analysis, miRDB and miRabel for differential expression of miRNAs, and Kaplan-Meier for survival prediction. We found that 5.4% of glioblastomas harbored IDH1 mutations, correlating with improved overall survival (OS) (p = 2.196e-3). Different glioblastoma cohorts showed a diverse IDH1 mutational prevalence (4-31%). Despite this, IDH1Mu was consistently associated with better OS (p = 8.235e-5). Notably, PDCD1 and CD274 were statistically significantly highly expressed in both IDH1Wt (p < 0.0001) and IDH1Mu tumors (p < 0.0001), with higher expression linked to poorer survival outcomes (PDCD1: p = 0.009; CD274: p = 0.02). Differential co-expression analyses revealed distinct gene and miRNA profiles for IDH1Wt and IDH1Mu glioblastomas, with specific upregulation of PTEN and downregulation of MUC16 in IDH1Wt, and upregulation of PIK3R1 in IDH1Mu. Additionally, PIK3R1 and ITGB2 emerged as critical druggable targets. Our findings indicate that PDCD1 and CD274 are highly expressed irrespective of IDH1 mutation statuses, suggesting that glioblastomas could benefit from immunotherapy. Moreover, IDH1Mu glioblastomas may require a combination of PI3K/AKT/mTOR inhibitors and immunotherapy due to PIK3R1 overexpression.

In Vitro Functional Validation of an Anti-FREM2 Nanobody for Glioblastoma Cell Targeting

Background/Objectives: Glioblastomas are the most common brain malignancies. Despite the implementation of multimodal therapy, patient life expectancy after diagnosis is barely 12 to 18 months. Glioblastomas are highly heterogeneous at the genetic and epigenetic level and comprise multiple different cell subpopulations. Therefore, small molecules such as nanobodies, able to target membrane proteins specific to glioblastoma cells or specific cell types within the tumor are being investigated as novel tools to treat glioblastomas. Methods: Here, we describe the identification of such a nanobody and its in silico and in vitro validation. NB3F18, as we named it, is directed against the membrane-associated protein FREM2, overexpressed in glioblastoma stem cells. Results: Three dimensional in silico modeling indicated that NB3F18 and FREM2 form a stable complex. Surface plasmon resonance confirmed their interaction with moderate affinity. As we demonstrated by flow cytometry, NB3F18 binds to glioblastoma stem cells to a greater extent than to differentiated glioblastoma cells and astrocytes. Immunocytochemistry revealed surface localization of NB3F18 on glioblastoma stem cells, whereas cytoplasmic localization of NB3F18 was observed in other cell lines. NB3F18 was detected by transmission electron microscopy on the plasma membrane and in various compartments of the endocytic pathway, from endocytic vesicles to multivesicular bodies (endosomes) and lysosomes. Interestingly, NB3F18 was cytotoxic to glioblastoma stem cells. Conclusions: Collectively, NB3F18 has been qualified as an interesting tool to target glioblastoma cells and as a potential vehicle to deliver biological or pharmaceutical agents to these cells.

Virtual Biopsy for the Prediction of MGMT Promoter Methylation in Gliomas: A Comprehensive Review of Radiomics and Deep Learning Approaches Applied to MRI

Background/Objectives: The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in gliomas has emerged as a critical biomarker for prognosis and treatment response. Conventional methods for assessing MGMT promoter methylation, such as methylation-specific PCR, are invasive and require tissue sampling. Methods: A comprehensive literature search was performed in compliance with the updated PRISMA 2020 guidelines within electronic databases MEDLINE/PubMed, Scopus, and IEEE Xplore. Search terms, including "MGMT", "methylation", "glioma", "glioblastoma", "machine learning", "deep learning", and "radiomics", were adopted in various MeSH combinations. Original studies in the English, Italian, German, and French languages were considered for inclusion. Results: This review analyzed 34 studies conducted in the last six years, focusing on assessing MGMT methylation status using radiomics (RD), deep learning (DL), or combined approaches. These studies utilized radiological data from the public (e.g., BraTS, TCGA) and private institutional datasets. Sixteen studies focused exclusively on glioblastoma (GBM), while others included low- and high-grade gliomas. Twenty-seven studies reported diagnostic accuracy, with fourteen achieving values above 80%. The combined use of DL and RD generally resulted in higher accuracy, sensitivity, and specificity, although some studies reported lower minimum accuracy compared to studies using a single model. Conclusions: The integration of RD and DL offers a powerful, non-invasive tool for precisely recognizing MGMT promoter methylation status in gliomas, paving the way for enhanced personalized medicine in neuro-oncology. The heterogeneity of study populations, data sources, and methodologies reflected the complexity of the pipeline and machine learning algorithms, which may require general standardization to be implemented in clinical practice.

Discovery of paradoxical genes: reevaluating the prognostic impact of overexpressed genes in cancer

Oncogenes are typically overexpressed in tumor tissues and often linked to poor prognosis. However, recent advancements in bioinformatics have revealed that many highly expressed genes in tumors are associated with better patient outcomes. These genes, which act as tumor suppressors, are referred to as "paradoxical genes." Analyzing The Cancer Genome Atlas (TCGA) confirmed the widespread presence of paradoxical genes, and KEGG analysis revealed their role in regulating tumor metabolism. Mechanistically, discrepancies between gene and protein expression-affected by pre- and post-transcriptional modifications-may drive this phenomenon. Mechanisms like upstream open reading frames and alternative splicing contribute to these inconsistencies. Many paradoxical genes modulate the tumor immune microenvironment, exerting tumor-suppressive effects. Further analysis shows that the stage- and tumor-specific expression of these genes, along with their environmental sensitivity, influence their dual roles in various signaling pathways. These findings highlight the importance of paradoxical genes in resisting tumor progression and maintaining cellular homeostasis, offering new avenues for targeted cancer therapy.

Genetically Engineered Brain Organoids Recapitulate Spatial and Developmental States of Glioblastoma Progression

Glioblastoma (GBM) is an aggressive form of brain cancer that is highly resistant to therapy due to significant intra-tumoral heterogeneity. The lack of robust in vitro models to study early tumor progression has hindered the development of effective therapies. Here, this study develops engineered GBM organoids (eGBOs) harboring GBM subtype-specific oncogenic mutations to investigate the underlying transcriptional regulation of tumor progression. Single-cell and spatial transcriptomic analyses revealed that these mutations disrupt normal neurodevelopment gene regulatory networks resulting in changes in cellular composition and spatial organization. Upon xenotransplantation into immunodeficient mice, eGBOs form tumors that recapitulate the transcriptional and spatial landscape of human GBM samples. Integrative single-cell trajectory analysis of both eGBO-derived tumor cells and patient GBM samples reveal the dynamic gene expression changes in developmental cell states underlying tumor progression. This analysis of eGBOs provides an important validation of engineered cancer organoid models and demonstrates their utility as a model of GBM tumorigenesis for future preclinical development of therapeutics.

MET Activation in Lung Cancer and Response to Targeted Therapies

The hepatocyte growth factor receptor (MET) is a receptor tyrosine kinase (RTK) that mediates the activity of a variety of downstream pathways upon its activation. These pathways regulate various physiological processes within the cell, including growth, survival, proliferation, and motility. Under normal physiological conditions, this allows MET to regulate various development and regenerative processes; however, mutations resulting in aberrant MET activity and the consequent dysregulation of downstream signaling can contribute to cellular pathophysiology. Mutations within MET have been identified in a variety of cancers and have been shown to mediate tumorigenesis by increasing RTK activity and downstream signaling. In lung cancer specifically, a number of patients have been identified as possessing MET alterations, commonly receptor amplification (METamp) or splice site mutations resulting in loss of exon 14 (METex14). Due to MET's role in mediating oncogenesis, it has become an attractive clinical target and has led to the development of various targeted therapies, including MET tyrosine kinase inhibitors (TKIs). Unfortunately, these TKIs have demonstrated limited clinical efficacy, as patients often present with either primary or acquired resistance to these therapies. Mechanisms of resistance vary but often occur through off-target or bypass mechanisms that render downstream signaling pathways insensitive to MET inhibition. This review provides an overview of the therapeutic landscape for MET-positive cancers and explores the various mechanisms that contribute to therapeutic resistance in these cases.

Involvement of p38 MAPK and MAPKAPK2 in promoting cell death and the inflammatory response to ischemic stress associated with necrotic glioblastoma

The association of necrosis in tumors with poor prognosis implies a potential tumor-promoting role. However, the mechanisms underlying cell death in this context and how damaged tissue contributes to tumor progression remain unclear. Here, we identified p38 mitogen-activated protein kinases (p38 MAPK, a.k.a. p38) as a key player in promoting cell death and the inflammatory response to ischemic stress associated with necrotic tumors. We found that glioblastoma (GBM) cells expressing patient-derived Kirsten rat sarcoma (KRAS) or phosphoinositide-3-kinase (PI3K) active mutants showed enhanced cell death under ischemia-mimetic conditions in vitro and were more likely to develop into necrotic tumors in vivo. Cell death in both settings depended on p38, which is also required for tumor progression driven by KRAS or PI3K. Under ischemia-mimetic conditions, GBM cells undergo reactive oxygen species (ROS)-dependent cell death. Gene expression in these cells recapitulated multiple features observed in peri-necrotic tumors from patient GBM. Further studies showed the involvement of a positive feedback loop between the p38-MAPK-activated protein kinase 2 (MAPKAPK2, a.k.a. MK2) signaling axis and the unfolded protein response signaling components activating transcription factor 4 (ATF4) and inositol-requiring enzyme 1 (IRE1α) in driving ischemic tumor cell death. This signaling cascade was further potentiated by RAS or PI3K activation under ischemic conditions, contributing to the inflammatory gene expression response. Therefore, our study suggests that p38 could be targeted to relieve the inflammatory response in necrotic tumors and inhibit GBM progression.

Lipid droplets in central nervous system and functional profiles of brain cells containing lipid droplets in various diseases

Lipid droplets (LDs), serving as the convergence point of energy metabolism and multiple signaling pathways, have garnered increasing attention in recent years. Different cell types within the central nervous system (CNS) can regulate energy metabolism to generate or degrade LDs in response to diverse pathological stimuli. This article provides a comprehensive review on the composition of LDs in CNS, their generation and degradation processes, their interaction mechanisms with mitochondria, the distribution among different cell types, and the roles played by these cells-particularly microglia and astrocytes-in various prevalent neurological disorders. Additionally, we also emphasize the paradoxical role of LDs in post-cerebral ischemia inflammation and explore potential underlying mechanisms, aiming to identify novel therapeutic targets for this disease.

ATRX mutation modifies the DNA damage response in glioblastoma multiforme tumor cells and enhances patient prognosis

The presence of specific genetic mutations in patients with glioblastoma multiforme (GBM) is associated with improved survival outcomes. Disruption of the DNA damage response (DDR) pathway in tumor cells enhances the effectiveness of radiotherapy drugs, while increased mutational burden following tumor cell damage also facilitates the efficacy of immunotherapy. The ATRX gene, located on chromosome X, plays a crucial role in DDR. The aim of this research is to elucidate the correlation between ATRX mutations and GBM. Dataset obtained from TCGA-GBM were conducted an analysis on the genomic features, biological characteristics, immunopathological markers, and clinical prognosis of patients carrying ATRX mutations. Our findings revealed a significantly elevated level of microsatellite instability in individuals with ATRX mutants, along with significant alterations in the receptor-tyrosine kinase (RTK)-ras pathway among patients exhibiting combined ATRX mutations. TCGA-GBM patients with concurrent ATRX mutations exhibited sensitivity to 26 chemotherapeutic and anticancer drugs, which exerted their effects by modulating the DDR of tumor cells through highly correlated mechanisms involving the RTK-ras pathway. Additionally, we observed an enrichment of ATRX mutations in specific pathways associated with DDR among TCGA-GBM patients. Our model also demonstrated prolonged overall survival in patients carrying ATRX mutations, particularly showing strong predictive value for 3- and 5-year survival rates. Furthermore, additional protective factors such as younger age, female gender, combined IDH mutations, and TP53 mutations were identified. The results underscore the protective role and prognostic significance of ATRX mutations in GBM as a potential therapeutic target and biomarker for patient survival.

LDH Isoenzyme and GAA-BSA Nanoparticles: A Novel Therapy Approach for Proneural Subtype Glioblastoma Multiforme

Glioblastoma multiforme (GBM), whose pathogenesis involves proneural-to-mesenchymal transition (PMT), is the most malignant type of glioma and is associated with a bleak prognosis. Lactate dehydrogenase (LDH) comprises two major subunits, LDHA and LDHB, which can assemble into five different isoenzymes (LDH1-5). However, the role of LDH isoenzyme and its subunits in different GBM subtypes is largely unknown. Our findings reveal that LDHA and LDHB subunits correlated with mesenchymal and proneural subtype classification, and have prognostic and clinical significance in GBM patients. Moreover, it is demonstrated that LDH5, characterized by high LDHA and low LDHB levels, is highly expressed in mesenchymal subtype GBM cells and promotes proliferation, migration, and PMT. Conversely, proneural subtype GBM cells exhibited LDH1 dominance, and low LDHA and high LDHB levels. Notably, LDH1 played a pivotal role in the proliferation, migration, and PMT of proneural glioma cells. For treatment of proneural subtype GBM, gossypol-acetic acid (GAA)-bovine serum albumin (BSA) nanoparticles (GAA-BSA NPs) were developed to ameliorate PMT by targeting LDH1. These nanoparticles effectively suppress proneural subtype tumor growth both in vitro and in vivo, surpassing their efficacy against the mesenchymal subtype. The results offer several novel insights into the role of LDH isoenzyme in subtype classification between mesenchymal and proneural GBM and provide a promising therapeutic approach for proneural subtype GBM.

The Role of Protein Disulfide Isomerase Inhibitors in Cancer Therapy

Protein disulfide isomerase (PDI) is a member of the mercaptan isomerase family, primarily located in the endoplasmic reticulum (ER). At least 21 PDI family members have been identified. PDI plays a key role in protein folding, correcting misfolded proteins, and catalyzing disulfide bond formation, rearrangement, and breaking. It also acts as a molecular chaperone. Dysregulation of PDI activity is thus linked to diseases such as cancer, infections, immune disorders, thrombosis, neurodegenerative diseases, and metabolic disorders. In particular, elevated intracellular PDI levels can enhance cancer cell proliferation, metastasis, and invasion, making it a potential cancer marker. Cancer cells require extensive protein synthesis, with disulfide bond formation by PDI being a critical producer. Thus, cancer cells have higher PDI levels than normal cells. Targeting PDI can induce ER stress and activate the Unfolded Protein Response (UPR) pathway, leading to cancer cell apoptosis. This review discusses the structure and function of PDI, PDI inhibitors in cancer therapy, and the limitations of current inhibitors, proposing especially future directions for developing new PDI inhibitors.

Lossless and reference-free compression of FASTQ/A files using GeneSqueeze

As sequencing becomes more accessible, there is an acute need for novel compression methods to efficiently store sequencing files. Omics analytics can leverage sequencing technologies to enhance biomedical research and individualize patient care, but sequencing files demand immense storage capabilities, particularly when sequencing is utilized for longitudinal studies. Addressing the storage challenges posed by these technologies is crucial for omics analytics to achieve their full potential. We present a novel lossless, reference-free compression algorithm, GeneSqueeze, that leverages the patterns inherent in the underlying components of FASTQ files to solve this need. GeneSqueeze's benefits include an auto-tuning compression protocol based on each file's distribution, lossless preservation of IUPAC nucleotides and read identifiers, and unrestricted FASTQ/A file attributes (i.e., read length, number of reads, or read identifier format). We compared GeneSqueeze to the general-purpose compressor, gzip, and to a domain-specific compressor, SPRING, to assess performance. Due to GeneSqueeze's current Python implementation, GeneSqueeze underperformed as compared to gzip and SPRING in the time domain. GeneSqueeze and gzip achieved 100% lossless compression across all elements of the FASTQ files (i.e. the read identifier, sequence, quality score and ' + ' lines). GeneSqueeze and gzip compressed all files losslessly, while both SPRING's traditional and lossless modes exhibited data loss of non-ACGTN IUPAC nucleotides and of metadata following the ' + ' on the separator line. GeneSqueeze showed up to three times higher compression ratios as compared to gzip, regardless of read length, number of reads, or file size, and had comparable compression ratios to SPRING across a variety of factors. Overall, GeneSqueeze represents a competitive and specialized compression method for FASTQ/A files containing nucleotide sequences. As such, GeneSqueeze has the potential to significantly reduce the storage and transmission costs associated with large omics datasets without sacrificing data integrity.

Deep learning methods for improving the accuracy and efficiency of pathological image analysis

This study presents a novel integration of two advanced deep learning models, U-Net and EfficientNetV2, to achieve high-precision segmentation and rapid classification of pathological images. A key innovation is the development of a new heatmap generation algorithm, which leverages meticulous image preprocessing, data enhancement strategies, ensemble learning, attention mechanisms, and deep feature fusion techniques. This algorithm not only produces highly accurate and interpretatively rich heatmaps but also significantly improves the accuracy and efficiency of pathological image analysis. Unlike existing methods, our approach integrates these advanced techniques into a cohesive framework, enhancing its ability to reveal critical features in pathological images. Rigorous experimental validation demonstrated that our algorithm excels in key performance indicators such as accuracy, recall rate, and processing speed, underscoring its potential for broader applications in pathological image analysis and beyond.

From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients

With the increasing demand for precision medicine in cancer patients, radiogenomics emerges as a promising frontier. Radiogenomics is originally defined as a methodology for associating gene expression information from high-throughput technologies with imaging phenotypes. However, with advancements in medical imaging, high-throughput omics technologies, and artificial intelligence, both the concept and application of radiogenomics have significantly broadened. In this review, the history of radiogenomics is enumerated, related omics technologies, the five basic workflows and their applications across tumors, the role of AI in radiogenomics, the opportunities and challenges from tumor heterogeneity, and the applications of radiogenomics in tumor immune microenvironment. The application of radiogenomics in positron emission tomography and the role of radiogenomics in multi-omics studies is also discussed. Finally, the challenges faced by clinical transformation, along with future trends in this field is discussed.

Novel Treatments for Brain Tumors

The blood-brain barrier and knowledge gaps in tumor biology remain significant obstacles to the development of effective treatments for brain tumors. The identification of shared molecular and genetic pathways that contribute to tumorigenesis in both dogs and people has been key to the discovery and translation of targeted pharmacologic and biologic therapies. Treatment approaches often utilize targeted or multifunctional antitumor agents, such as nanocarriers, molecularly targeted agents, immunotherapeutics, and oncolytic viruses in combination with alternative therapeutic delivery strategies. The article discusses about various treatments albeit none of the treatments discussed here are widely available or approved for clinical use.

Immunocompetent murine glioblastoma stem-like cell models exhibiting distinct phenotypes

Background

Glioblastoma (GBM) treatment is hindered by a dearth of representative mouse GBM preclinical models in immunocompetent mice. Here, we characterized 5 murine GBM stem-like cell (mGSC) models derived from lentivirus-induced tumors in transgenic mice that are driven by the activation of the Nf1-Ras signaling pathway and inactivation of Tp53.

Methods

MGSC lines (005, RIG, NF53, C1, and C3) were cultured as spheres in serum-free stem cell media. Whole exome sequencing (WES) was employed to quantify single nucleotide polymorphisms (SNPs). Stem cell properties were characterized by stemness in vitro and tumorigenicity after intracerebral implantation in C57BL/6 mice. Tumor phenotypes and the immune microenvironment were characterized by immunohistochemistry, flow cytometry, and RNA sequencing.

Results

WES revealed a large variation in coding sequence SNPs across mGSC lines (~20-fold), likely influenced by the mixed backgrounds of the parental mice. MGSCs exhibited variable clonogenic sphere formation and CD133 expression levels. In vivo, they consistently initiated lethal malignant gliomas, with median survival ranging from 29 to 82 days, and showed strong CD44 expression and variable invasiveness. The tumor microenvironment featured an abundance of CD68+ macrophages and uniform high PD-L1+ myeloid cells, while T-cell infiltration varied among the models, with low mutation burden C1 and C3 exhibiting fewer tumor-infiltrating T cells.

Conclusions

Upon orthotopic implantation in immunocompetent mice, mGSCs generate tumors characteristic of human GBM. Despite similar strategies to generate these mGSCs, they exhibited a range of phenotypes and immune profiles in mGSC-derived orthotopic tumors. These mGSCs provide new preclinical GBM models for developing GBM immunotherapies.

Collateral lethality: A unique type of synthetic lethality in cancers

Genetic interactions play crucial roles in cell-essential functions. Intrinsic genetic defects in tumors typically involve gain-of- and loss-of-function mutations in tumor suppressor genes (TSGs) and oncogenes, respectively, providing potential antitumor vulnerabilities. Moreover, tumor cells with TSG deficiencies exhibit heightened sensitivity to the inhibition of compensatory pathways. Synthetic and collateral lethality are two strategies used for exploiting novel drug targets in multiple types of cancer. Collateral lethality is a unique type of synthetic lethality that occurs when passenger genes are co-deleted in neighboring TSGs. Although synthetic lethality has already been successfully demonstrated in clinical practice, antitumor therapeutics based on collateral lethality are predominantly still in the preclinical phase. Therefore, screening for potential genetic interactions within the cancer genome has emerged as a promising approach for drug development. Here, the two conceptual therapeutic strategies that involve the deletion or inactivation of cancer-specific TSGs are discussed. Moreover, existing approaches for screening and identifying potential gene partners are also discussed. Particularly, this review highlights the current advances of "collateral lethality" in the preclinical phase and addresses the challenges involved in translating them into therapeutic applications. This review provides insights into these strategies as new opportunities for the development of personalized antitumor therapies.