Search Results (3,260)

A woman in her 40s with a history of ER/PR+, HER2-negative breast cancer presented with a seizure three years after mastectomy. Magnetic resonance imaging (MRI) revealed a right caudate head mass, which was concerning for either high-grade glioma or metastatic disease, but biopsy was deemed too high risk. Cerebrospinal fluid (CSF) tumor-derived DNA (tDNA) analysis by next-generation sequencing (NGS) was ordered, revealing a gain-of-function variant in PIK3CA, ERBB2 copy number gain, and high aneuploidy, findings consistent with breast cancer brain metastasis. Based on these results, the patient was treated with stereotactic radiosurgery (SRS) followed by trastuzumab deruxtecan, a HER2-targeted therapy. This case highlights the diagnostic and therapeutic value of CSF tDNA analysis in central nervous system (CNS) lesions when biopsy is not feasible. The report also illustrates how clonal evolution, such as acquired ERBB2 amplification, can occur in metastatic disease and influence treatment decisions. Full article

Pediatric-type diffuse high-grade gliomas (pHGGs) tend to have a dismal prognosis. Some of these gliomas feature alterations in genes such as ROS1, ALK, MET, and NTRK1–3. Despite development of targeted agents, the therapeutic application of these agents in pHGGs is still unclear. The aim of this retrospective case series is to report the outcome of two patients with pHGGs who were treated at Arkansas Children’s Hospital with targeted agents (Cabozantinib for a MET fusion in patient 1 and Lorlatinib for an ALK fusion in patient 2) with an initial, objective response followed by treatment resistance. Each diagnosis was determined based on histology, targeted tumor sequencing, and methylation profiling. In both cases, relapse occurred while on targeted inhibition. Recurrent tumor sequencing for patient 2 revealed a MET copy gain suggesting a mechanism of resistance in this patient. Pediatric high-grade gliomas with targetable alterations can show objective responses to pathway inhibition. Relapse after initial response may warrant additional surgical samples to identify new alterations which can lead to changes in therapy. Larger prospective cohorts are needed to study targeted agents in this population, and earlier integration of these agents may be beneficial. Full article

Gliomas present as highly heterogeneous and aggressive central nervous system (CNS) tumors with challenging diagnosis and management. Traditional and current therapies are lacking efficacy in overcoming the complex and dynamic behavior of gliomas and the local tumor microenvironment. Emerging research highlights the significant role of innate immune receptors including Toll-like, NOD-like and RIG-like receptors, as well as cGAS-STING receptors, scavenger and C-type lectin receptors in glioma development and progression. These receptors can both impact immune modulation as well as facilitate tumor growth through interactions with tumor-associated macrophages, myeloid-derived suppressor cells and cytokine networks, contributing to immune evasion in the tumor microenvironment. Herein, we discuss the main signaling pathways induced through innate immune receptors in gliomas along with their functional properties in glioma pathology while exploring current applications to treatment. Utilizing innate immune receptors as therapeutic targets holds great promise, especially when used along with traditional chemotherapy and radiation schemes, strengthening immune responses. Future studies focusing on the deeper understanding of innate immune receptors signaling and complexity are highly required to enable novel immunoregulatory treatment schemes for gliomas. Full article

Gliomas are aggressive brain tumours with diverse histological and molecular features, complicating accurate diagnosis and treatment. Dysregulated lipid metabolism contributes to glioma progression, and analysing lipid profiles in plasma and tissue may enhance diagnostic and prognostic accuracy. This study investigated lipid dysregulation to identify key lipid signatures that distinguish glioma from other brain diseases and examined the associations between lipid biomarkers in glioma tissue and plasma. Biospecimens from 11 controls and 72 glioma patients of varying grades underwent lipidomic profiling using liquid chromatography-mass spectrometry. Univariate and multivariate analyses identified differentially abundant lipids, and correlation analysis evaluated the associations between tissue and plasma biomarkers. Lipidomic analysis revealed distinct lipid profiles in the tissues and plasma of glioma patients compared to those of controls. Prominent lipid metabolites in glioma tissues included LPC 21:3 (AUC = 0.925), DG 43:11 (AUC = 0.906), and PC 33:1 (AUC = 0.892), which served as effective biomarkers. Conversely, in plasma, lipid metabolites such as phosphatidylethanolamine (PE 21:3, AUC = 0.862), ceramide-1-phosphate (CerP 26:1, AUC = 0.861), and sphingomyelin (SM 24:3, AUC = 0.858) were identified as the most promising lipid biomarkers. Significant positive and negative correlations were observed between the tissue and plasma lipid biomarkers of glioma patients. Lipidomic profiling revealed aberrant lipid classes and pathways in glioma tissues and plasma, enhancing understanding of glioma heterogeneity and potential clinical applications. Full article

Quantitative magnetic resonance imaging (qMRI) denotes MRI methods that estimate physical tissue parameters in units, rather than relative signal. Typical readouts include T1/T2 relaxation (ms; or R1/R2 in s⁻¹), proton density (%), diffusion metrics (e.g., ADC in mm²/s, FA), magnetic susceptibility (χ, ppm), perfusion (e.g., CBF in mL/100 g/min; rCBV; K^trans), and regional brain volumes (cm³; cortical thickness). This review synthesizes brain qMRI across T1/T2 relaxometry, myelin/MT (MWF, MTR/MTsat/qMT), diffusion (DWI/DTI/DKI/IVIM), susceptibility imaging (SWI/QSM), perfusion (DSC/DCE/ASL), and volumetry using a unified framework: physics and signal model, acquisition and key parameters, outputs and units, validation/repeatability, clinical applications, limitations, and future directions. Our scope is the adult brain in neurodegenerative, neuro-inflammatory, neuro-oncologic, and cerebrovascular disease. Representative utilities include tracking demyelination and repair (T1, MWF/MTsat), grading and therapy monitoring in gliomas (rCBV, K^trans), penumbra and tissue-at-risk assessment (DWI/DKI/ASL), iron-related pathology (QSM), and early dementia diagnosis with normative volumetry. Persistent barriers to routine adoption are protocol standardization, vendor-neutral post-processing/QA, phantom-based and multicenter repeatability, and clinically validated cut-offs. We highlight consensus efforts and AI-assisted pipelines, and outline opportunities for multiparametric integration of complementary qMRI biomarkers. As methodological convergence and clinical validation mature, qMRI is poised to complement conventional MRI as a cornerstone of precision neuroimaging. Full article

Background/Objectives: Gliomas are the most common tumours of the central nervous system (CNS), classified into grades I to IV based on their malignancy. Genetic and epigenetic alterations play a crucial role in glioma progression. DNA methyltransferases (DNMTs) are vital enzymes responsible for DNA methylation, with DNMT1 and DNMT3 catalysing the addition of a methyl group to the 5-carbon of cytosine in CpG dinucleotides. Targeting DNMTs with DNA methyltransferase inhibitors (DNMTi) has become a promising therapeutic approach in tumour treatment. In this study, in silico screening tools were employed to evaluate potential inhibitors of DNMT1, DNMT3A, and DNMT3B for the treatment of glioblastoma multiforme (GBM). Methods: The Gene2Drug platform was used to screen compounds and rank them based on their capacity to dysregulate DNMT genes. PRISM viability assays were performed on 68 cell lines, and DepMap data were analyzed to assess the antitumor activities of these compounds and their target genes. Candidate drug similarity was evaluated using DSEA, and compounds with p < 1 × 10⁻³ were considered statistically significant. Gene-compound interactions for DNMT1, DNMT3A, and DNMT3B were confirmed using Expression Public 24Q2, while Prism Repositioning Public data were analyzed via DepMap. Results: Glioblastoma cell lines showed sensitivity to compounds including droperidol, demeclocycline, benzthiazide, ozagrel, pizotifen, tracazolate, norcyclobenzaprine, monocrotaline, dydrogesterone, 6-benzylaminopurine, and nifedipine. SwissTargetPrediction was utilised to identify alternative molecular targets for selected compounds, revealing high-probability matches for droperidol, pizotifen, tracazolate, monocrotaline, dydrogesterone, and nifedipine. Conclusions: Integrating computational approaches with biological insights and conducting tissue-specific and experimental validations may significantly enhance the development of DNMT-targeted therapies for gliomas. Full article

Extracellular vesicles (EVs) secreted by glioblastoma multiforme and other types of cancer cells are key factors contributing to the aggressiveness of the disease and its resistance to therapy. Chloroquine (CHQ), a lysosomal inhibitor, has shown potential as an enhancer of temozolomide (TMZ) cytotoxicity against glioblastoma cells. Since both CHQ and TMZ are known to modulate EV secretion, we sought to investigate their potential interplay in this process. Simultaneous treatment of TMZ-sensitive (U87-MG) and TMZ-resistant (U138-MG) glioblastoma cells with TMZ and CHQ led to a synergistic upregulation of EV secretion. Although CHQ did not enhance the TMZ cytotoxicity in U87-MG cells, it synergized with the latter to upregulate the release of extracellular nucleic acids implicating activation of unconventional secretory pathways. Synergistic upregulation of the autophagy markers LC3B-II and p62 by CHQ and TMZ in both cells and EVs indicates that secretory autophagy is likely involved in the observed unconventional secretion. Moreover, a significant enrichment of caveolin-1 in small EVs highlights their potential role in modulating tumor aggressiveness. The synergy in EV upregulation was not confined to the specific biological activity of TMZ and CHQ; similar effects were observed upon co-treatments with CHQ and etoposide (a topoisomerase inhibitor) and TMZ and Bafilomycin A1 (another lysosomal inhibitor). Heightened EV release was also observed in THP-1 monocytes and macrophages treated with Bafilomycin and TMZ, highlighting a broader, cell-type-independent mechanism. These findings indicate that combined DNA damage and lysosomal inhibition synergistically stimulate secretory autophagy and EV release, potentially impacting the tumor microenvironment and driving disease progression. Full article

Background: Primary spinal gliomas are rare in the pediatric population. Separately, FGFR1 genomic aberrations are also uncommon in spinal cord tumors. We report a case of a previously well adolescent who presented with progressive symptoms secondary to an intramedullary tumor with unique radiological and molecular characteristics. Case Presentation: A previously well 17-year-old male presented with worsening mid-back pain associated with lower limb long-tract signs. Magnetic resonance imaging (MRI) of his neuro-axis reported a long-segment intramedullary lesion with enhancing foci and a multi-septate syrinx containing hemorrhagic components from C4 to T12. The largest enhancement focus was centered at T7. Additional MRI sequences observed no intracranial involvement or vascular anomaly. He underwent an emergent laminoplasty and excision of the thoracic lesion. Intraoperative findings demonstrated a soft, grayish intramedullary tumor associated with extensive hematomyelia that had multiple septations. Active fenestration of the latter revealed blood products in various stages of resolution. Postoperatively, the patient recovered well, with neurological improvement. Final histology reported a circumscribed low-grade glial neoplasm. Further molecular interrogation via next-generation sequencing panels showed FGFR1 p.K656E and V561M alterations. The unique features of this case are presented and discussed in corroboration with a focused literature review. Conclusions: We highlight an interesting case of an intramedullary tumor with unusual radiological and pathological findings. Emphasis is on the importance of tissue sampling in corroboration with genomic investigations to guide clinical management. Full article

Background/Objectives: Glioblastoma (GBM) is an aggressive primary brain tumor with limited therapeutic options despite multimodal treatment. Small interfering RNA (siRNA)-based therapeutics can silence tumor-promoting genes, but achieving efficient and tumor-specific delivery remains challenging. Lipid nanoparticles (LNPs) are promising siRNA carriers; however, conventional antibody conjugation can impair antigen recognition and complicate manufacturing. This study aimed to establish a modular Fc-binding peptide (FcBP)-mediated post-insertion strategy to enable PD-L1-targeted delivery of VEGF-siRNA via LNPs for GBM therapy. Methods: Preformed VEGF-siRNA-loaded LNPs were functionalized with FcBP–lipid conjugates, enabling non-covalent anchoring of anti-PD-L1 antibodies through Fc interactions. Particle characteristics were analyzed using dynamic light scattering and encapsulation efficiency assays. Targeted cellular uptake and VEGF gene silencing were evaluated in PD-L1-positive GL261 glioma cells. Anti-tumor efficacy was assessed in a subcutaneous GL261 tumor model following repeated intratumoral administration using tumor volume and bioluminescence imaging as endpoints. Results: FcBP post-insertion preserved LNP particle size (125.2 ± 1.3 nm), polydispersity, zeta potential, and siRNA encapsulation efficiency. Anti-PD-L1–FcBP-LNPs significantly enhanced cellular uptake (by ~50-fold) and VEGF silencing in PD-L1-expressing GL261 cells compared to controls. In vivo, targeted LNPs reduced tumor volume by 65% and markedly suppressed bioluminescence signals without inducing weight loss. Final tumor weight was reduced by 63% in the anti-PD-L1–FcBP–LNP group (656.9 ± 125.4 mg) compared to the VEGF-siRNA LNP group (1794.1 ± 103.7 mg). The FcBP-modified LNPs maintained antibody orientation and binding activity, enabling rapid functionalization with targeting antibodies. Conclusions: The FcBP-mediated post-insertion strategy enables site-specific, modular antibody functionalization of LNPs without compromising physicochemical integrity or antibody recognition. PD-L1-targeted VEGF-siRNA delivery demonstrated potent, selective anti-tumor effects in GBM murine models. This platform offers a versatile approach for targeted nucleic acid therapeutics and holds translational potential for treating GBM. Full article

Background/Objectives: Galectin-3 (Gal-3), encoded by LGALS3, is a β-galactoside-binding lectin involved in diverse tumor-associated processes, including immune modulation, cell cycle regulation, and stress adaptation. Despite its known roles in cancer biology, the full extent of its molecular functions and prognostic relevance across tumor types remains incompletely understood. This study aimed to systematically investigate the transcriptomic impact of LGALS3 deletion and assess its clinical significance in cancer. Methods: We analyzed CRISPR-Cas9 knockout transcriptomic data from the SigCom LINCS database to characterize the consensus gene signature associated with LGALS3 loss using functional enrichment analyses. Pan-cancer survival analyses were conducted using TIMER2.0. Differential Gal-3 protein levels in ductal adenocarcinoma and normal pancreatic tissues were evaluated using the Human Protein Atlas. Finally, functional analyses were performed in pancreatic ductal adenocarcinoma (PDAC). Results: LGALS3 deletion across multiple cancer cell lines led to transcriptomic changes involving mitotic progression, stress responses, and axonal guidance pathways. High LGALS3 expression was significantly associated with worse overall survival in lower-grade glioma, PDAC, uveal melanoma, and kidney renal papillary cell carcinoma. LGALS3 knockout in YAPC cells recapitulated the pan-cancer findings, linking LGALS3 to cell morphogenesis and proliferation. Conclusions: These findings identify Galectin-3 as a key regulator of oncogenic programs and a potential prognostic biomarker in PDAC and other malignancies, with implications for therapeutic targeting. Full article

Background: This narrative review summarizes recent progress in artificial intelligence (AI), especially radiomics and deep learning, for non-invasive diagnosis and molecular profiling of gliomas. Methodology: A thorough literature search was conducted on PubMed, Scopus, and Embase for studies published from January 2020 to July 2025, focusing on clinical and technical research. In key areas, these studies examine AI models’ predictive capabilities with multi-parametric Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Results: The domains identified in the literature include the advancement of radiomic models for tumor grading and biomarker prediction, such as Isocitrate Dehydrogenase (IDH) mutation, O6-methylguanine-dna methyltransferase (MGMT) promoter methylation, and 1p/19q codeletion. The growing use of convolutional neural networks (CNNs) and generative adversarial networks (GANs) in tumor segmentation, classification, and prognosis was also a significant topic discussed in the literature. Deep learning (DL) methods are evaluated against traditional radiomics regarding feature extraction, scalability, and robustness to imaging protocol differences across institutions. Conclusions: This review analyzes emerging efforts to combine clinical, imaging, and histology data within hybrid or transformer-based AI systems to enhance diagnostic accuracy. Significant findings include the application of DL to predict cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletion and chemokine CCL2 expression. These highlight the expanding capabilities of imaging-based genomic inference and the importance of clinical data in multimodal fusion. Challenges such as data harmonization, model interpretability, and external validation still need to be addressed. Full article

Diffuse gliomas are the most common primary brain tumors in adults in the Western world. According to the 2021 World Health Organization (WHO) classification of central nervous system (CNS) tumors, the assessment of isocitrate dehydrogenase (IDH1/2)-mutation status is essential for accurate patient stratification. In this study, we performed a comprehensive evaluation of IDH-mutation status in the intraoperative setting using the Idylla platform. The reference cohort comprised 30 formalin-fixed paraffin-embedded (FFPE) tissue samples with known IDH status, while the exploration cohort included 35 intraoperative snap-frozen and native-tissue specimens. The results were compared with those of a standard next-generation sequencing (NGS) analysis. Our findings demonstrate that the Idylla IDH-mutation assay provides 100% concordance compared with NGS analysis for both FFPE and intraoperative tissue samples. The Idylla system delivers results within approximately 90 min, significantly outperforming NGS, which requires between 7 and 27 days. This rapid turnaround facilitates timely interdisciplinary case discussions and enables timely therapy planning, within the framework of neuro-oncological molecular tumor boards. The ultra-fast intraoperative IDH-mutation analysis using the Idylla platform, in combination with intraoperative histopathological assessment, enables rapid patient stratification and treatment planning in diffuse gliomas. Full article

Astrocytoma is the most common type of brain glioma and is classified by the World Health Organization into four grades, providing prognostic insights and guiding treatment decisions. The accurate determination of astrocytoma grade is critical for patient management, especially in high-malignancy-grade cases. This study proposes a fully optimized Convolutional Neural Network (CNN) for the classification of astrocytoma MRI slices across the three malignant grades (G2–4). The training dataset consisted of 1284 pre-operative axial 2D MRI slices from T1-weighted contrast-enhanced and FLAIR sequences derived from 69 patients. To provide the best possible model performance, an extensive hyperparameter tuning was carried out through the Hyperband method, a variant of Successive Halving. Training was conducted using Repeated Hold-Out Validation across four randomized data splits, achieving a mean classification accuracy of 98.05%, low loss values, and an AUC of 0.997. Comparative evaluation against state-of-the-art pre-trained models using transfer learning demonstrated superior performance. For validation purposes, the proposed CNN trained on an altered version of the training set yielded 93.34% accuracy on unmodified slices, which confirms the model’s robustness and potential use for clinical deployment. Model interpretability was ensured through the application of two Explainable AI (XAI) techniques, SHAP and LIME, which highlighted the regions of the slices contributing to the decision-making process. Full article

Accurate brain tumor classification from MRI is often constrained by limited labeled data. We systematically compare conventional machine learning, deep learning, and few-shot learning (FSL) for four classes (glioma, meningioma, pituitary, no tumor) using a standardized pipeline. Models are trained on the Kaggle Brain Tumor MRI Dataset and evaluated across dataset regimes (100%→10%). We further test generalization on BraTS and quantify robustness to resolution changes, acquisition noise, and modality shift (T1→FLAIR). To support clinical trust, we add visual explanations (Grad-CAM/saliency) and report per-class results (confusion matrices). A fairness-aligned protocol (shared splits, optimizer, early stopping) and a complexity analysis (parameters/FLOPs) enable balanced comparison. With full data, Convolutional Neural Networks (CNNs)/Residual Networks (ResNets) perform strongly but degrade with 10% data; Model-Agnostic Meta-Learning (MAML) retains competitive performance (AUC-ROC ≥ 0.9595 at 10%). Under cross-dataset validation (BraTS), FSL—particularly MAML—shows smaller performance drops than CNN/ResNet. Variability tests reveal FSL’s relative robustness to down-resolution and noise, although modality shift remains challenging for all models. Interpretability maps confirm correct activations on tumor regions in true positives and explain systematic errors (e.g., “no tumor”→pituitary). Conclusion: FSL provides accurate, data-efficient, and comparatively robust tumor classification under distribution shift. The added per-class analysis, interpretability, and complexity metrics strengthen clinical relevance and transparency. Full article