Neuro-oncology advances最新文献

Background: We previously created a glioblastoma (GBM) DrugBank containing curated information on chemical structure, molecular target activity, and chemical biology for 500 compounds. This study expands the dataset to 1103 compounds, including molecular bioactivity, cellular dose-response, CRISPR-Cas9 knockout data, potential toxicity, and links to clinical trials and patents.

Methods: We gathered information from literature on compounds and models, allowing direct comparisons between compounds, their targets, and biological effects. We also included our own dose-response and drug-induced gene expression data across various glioblastoma cell culture models. Compounds were curated for their effect in preclinical GBM models, and these parameters were projected onto an ECFP_6-based UMAP visualization.

Results: The visualization facilitates comparisons of bioactivities, CRISPR-Cas9 effects in GBM, and potential toxicity in nontransformed models. The analysis highlights the strengths and weaknesses of GBM drug discovery, emphasizing the trade-offs between effectiveness, toxicity, and specificity. It also provides insights for optimizing targeting based on compound structure and characteristics, targets, and putative toxicity through cheminformatic or experimental approaches.

Conclusions: The GBMdrug1000 dataset is a public state-of-the-art resource for drug discovery and cheminformatics analysis, complemented by patent information and links to clinical trial data. This curated resource forms a framework for future prioritization of targets or their combinations.

Background: Fibroblast growth factor-inducible 14 (Fn14) belongs to the TNFR superfamily. Fn14 overexpression can drive receptor-autonomous signaling, increase both cell invasion and tumor-associated macrophages/microglia (TAMMs) recruitment, and correlates with reduced survival in glioblastoma (GBM) patients and rat gliomas. While prior studies report Fn14 expression in non-tumor cells within the GBM tumor microenvironment (TME), their relative contributions to glioma pathobiology remain unclear.

Methods: Using tumor-host pairings of Fn14-positive and -knockout (-KO) cells and mice, we examined the role of Fn14 in glioma biology. Mouse glioma and human GBM datasets were analyzed at the cellular, protein, and transcriptomic levels to assess Fn14-associated changes in the glioma TME and survival outcomes.

Results: Fn14 was found to be highly expressed in tumor cells and TAMMs in human GBM and 2 well-characterized murine glioma models. Fn14 KO in both tumor and host cells increased overall survival. Notably, this survival benefit was greater in the glioma model characterized by a more immunologically activated TME. Immunophenotyping revealed that Fn14 loss reshapes the tumor-immune landscape, reducing the presence of immunosuppressive macrophages and exhausted T-cells, suggesting that Fn14 modulates both innate and adaptive immune responses. These findings were supported by analyses of human GBM datasets, where high Fn14 expression correlated with immunosuppressive shifts and poor patient responses to immune checkpoint inhibitor therapy.

Conclusions: This study provides the first description of the contributions of both tumor- and host-derived Fn14 expression to tumor immunity and survival and identifies Fn14 as an important mediator of innate and adaptive immune responses in gliomas.

Background: Precise glioma segmentation in magnetic resonance imaging (MRI) is essential for accurate diagnosis, optimal treatment planning, and advancing clinical research. However, most deep learning approaches require complete, standardized MRI protocols that are frequently unavailable in routine clinical practice. This study presents and evaluates GlioMODA, a robust deep learning framework designed for automated glioma segmentation that delivers consistent high performance across varied and incomplete MRI protocols.

Methods: GlioMODA was trained and validated on the BraTS 2021 dataset (1251 training, 219 testing cases), systematically assessing performance across 11 clinically relevant MRI protocol combinations. Segmentation accuracy was evaluated using Dice similarity coefficients (DSC) and panoptic quality metrics. Volumetric accuracy was benchmarked against manual ground truth, and statistical significance was established via Wilcoxon signed‑rank tests with Benjamini-Yekutieli correction.

Results: GlioMODA demonstrated state-of-the-art segmentation accuracy across tumor subregions, maintaining robust performance with incomplete or heterogeneous MRI protocols. Protocols including both T1-weighted contrast-enhanced and T2-FLAIR sequences yielded volumetric differences vs manual ground truth that were not statistically significant for enhancing tumor (median difference 55 mm³, P = .157) and whole tumor (median difference -7 mm³, P = 1.0), and exhibited median DSC differences close to zero relative to the 4‑sequence reference protocol. Omitting either sequence led to substantial and significant volumetric errors.

Conclusions: GlioMODA facilitates reliable, automated glioma segmentation using a streamlined 2‑sequence protocol (T1‑contrast + T2‑FLAIR), supporting clinical workflow optimization and broader implementation of quantitative volumetry compatible with RANO 2.0 criteria. GlioMODA is published as an open-source, easy-to-use Python package at https://github.com/BrainLesion/GlioMODA/.

Background: This retrospective study used volumetric MRI assessment to compare 3 response assessment criteria, namely, RANO (response assessment in neuro-oncology), mRANO (modified response assessment in neuro-oncology), and RANO 2.0, in patients with newly diagnosed glioblastoma (GB) treated with standard-of-care therapy. We evaluated whether progression-free survival (PFS), defined by each criterion, serves as a prognostic marker for overall survival (OS), and evaluated the impact of progression on patient outcome using landmark analyses.

Methods: A total of 137 GB patients were included. Tumor volumes were assessed using semiautomatic 3D segmentation of contrast-enhancing and T2/fluid-attenuated inversion recovery lesions on serial MRI. The PFS was determined using RANO, mRANO, and RANO 2.0 criteria. Correlation between PFS and OS was evaluated using Spearman rank test. Differences in PFS and postprogression survival were tested with the Kruskal-Wallis test and corrected for multiple testing. Landmark analyses at 8 and 12 months were conducted to assess the prognostic effect of progression, with hazard ratios (HRs) derived from Cox models.

Results: Median PFS differed significantly across criteria: 7.9 months (RANO), 11.3 months (mRANO), and 9.7 months (RANO 2.0). The correlation of PFS with OS was best with mRANO (ρ = 0.70), followed by RANO 2.0 (ρ = 0.66) and RANO (ρ = 0.50). The highest HR for death in patients with progressive disease was seen with RANO 2.0 (HR = 3.6), followed by mRANO (HR = 3.3), and RANO (HR = 3.3) (8-month landmark).

Conclusion: mRANO and RANO 2.0 provided strong prognostic value in newly diagnosed GB patients. mRANO and RANO 2.0 showed strong correlation between PFS and OS, while RANO 2.0 demonstrated the strongest stratification of survival risk based on progression status at landmarks.

Background: Recent evidence has demonstrated a tight relationship between neuronal activity and glioblastoma (GBM) growth, involving novel mechanisms such as neuron-glioma synapses and tumor microtube networks. Seizure activity and antiepileptic drug (AED) usage are highly prevalent among GBM patients. In this study, we investigate the impact of AEDs and their mechanism of action on overall survival (OS) in a cohort of patients treated for GBM.

Methods: We performed a retrospective, single-center study of a cohort of histopathologically proven GBM patients at a tertiary center. Multivariate analyses were performed at 4 different timepoints by (1) patients who did and did not use AEDs, (2) use of individual AEDs, and (3) use of AEDs with the same mechanism of action.

Results: A total of 236 patients were included in the analysis, 178 of which were on anti-epileptics (75.4%). There was no significant impact of AEDs overall in OS-median survival was 16.2 months for patients taking AEDs and 13.8 months for patients not. Being on a voltage-gated sodium channel (VGNC) blocker seemed to confer a significant survival advantage at 24 months when compared with patients not on AEDs (hazard ratio [HR] = 0.67, P = .045). This significance was, however, lost when corrected for covariates (multivariable HR = 1.01, confidence interval [CI] = 0.67-1.54, P = .953). While patients on adjuvant treatment had a higher OS, this was only the case in patients -taking AEDs.

Conclusion: There was no significant effect of AEDs on OS, VGNC blockers trended toward significance at 24 months. However, a link may exist between AEDs and the impact of adjuvant treatments.

Background: Benefits of tumor resection in elderly glioblastoma multiforme (GBM) patients within the framework of multimodal treatment remains a matter of debate. This retrospective, multicenter analysis aims at the identification of patients who are most likely to benefit from microsurgical resection, and the development of a scoring system to improve future decision-making.

Methods: Demographic and outcome data of GBM patients aged 65 years and older, who underwent resection or biopsy at three centers between 2003 and 2022, were analyzed. Frailty was assessed by the Modified 5-Item Frailty Index (mFI-5).

Results: We studied 537 patients (median age 73.4 years); 369 (68.7%) underwent resection and 168 (31.3%) underwent biopsy. Resected patients were younger, were preoperatively more commonly functionally independent, were less frail, and had larger tumor volumes (all P < .001). The median overall survival was 9.1 [confidence interval (CI) 95% 7.9-10.5] months for resected and 2.8 (CI 95% 2.1-4.3) months for biopsied patients (P < .001). The strongest preoperatively determinable predictors for poor survival were older age, significant frailty (mFI-5 ≥ 2), and deep-seated tumor location for, both, resected and biopsied patients (P < .001). Based on this, a score (0-5 points) incorporating these parameters was developed. Among patients with assumed favorable prognosis (score ≤ 1) the median survival difference between resection and biopsy was 7.5 months, whereas in case of poor prognosis (score ≥ 4) the difference was 2.1 months. No long-term survivors ≥24 months had a score of ≥3 points.

Conclusions: Selected elderly glioblastoma patients might benefit from microsurgical resection. A score to guide neurosurgical treatment decision-making and patient counseling was developed.

Introduction: Focused ultrasound (FUS) is making rapid advances in the field of neurological sciences. While proven effective for tremor, pain and dystonia, FUS hold promises in experimental applications like blood-brain barrier opening (BBBO). BBBO is an emerging non-invasive option for intracranial brain tumor treatments by enhancing drug delivery and enabling liquid biopsies. Clinical trials underscore the need for consistent use of FUS for BBBO, which requires sophisticated facilities and standardized protocols to ensure efficacy and reproducibility. This manuscript outlines a single-center experience using MR-guided FUS (MRgFUS) for BBBO in patients with glioblastoma, incorporating technical insights and describing the evolution of a workflow mirroring an ongoing learning curve.

Methods: We developed an optimized protocol that includes comprehensive pre-procedural evaluations, a patient preparation process designed to minimize discomfort, and a sedation protocol utilizing dexmedetomidine, presented as a technical note involving 4 patients. The procedures were conducted using the Exablate Neuro 4000 Type 2 system, with MRI providing precise targeting and monitoring.

Results: Our optimized protocol significantly enhanced patient comfort and reduced procedure times. Employing a large treatment envelope and a 64-spot grid allowed for near-total brain coverage, minimizing the effort for pre-planning and reducing sonication time. Optimized MRI protocol contributed to the reduction in duration and confirmed successful BBBO, indicated by specific imaging signs and the resolution of these effects without any clinical complications.

Conclusion: The protocol we developed for MRgFUS-BBBO offers substantial improvements in terms of procedure efficiency, imaging quality, and patient comfort. This approach could be adopted in other FUS treatment settings to optimize workflows, reduce procedure times, and improve patient outcomes.

Background: Glioblastoma (GBM) in elderly patients (≥65 years) carries a poor prognosis and higher treatment toxicity. Consequently, many receive de-escalated regimens, often guided by MGMT promoter methylation. However, real-world outcomes across treatment strategies remain underreported and often lack molecular and functional detail.

Methods: We retrospectively analyzed 573 elderly GBM patients treated between 2009 and 2023 at 2 Israeli tertiary centers. Post-operative treatments included (1) chemoradiotherapy (CRT; 60Gy in 30 fractions or 40Gy in a15 fractions), (2) temozolomide (TMZ) monotherapy, (3) radiotherapy alone (RT), or (4) best supportive care. MGMT status and Karnofsky Performance Status (KPS) were analyzed where available. Survival was assessed using Kaplan-Meier and log-rank tests.

Results: Median overall survival (mOS) was longest with CRT (14 months), compared to 8 months for TMZ and RT monotherapies and 2 months for best supportive care. Among MGMT-methylated patients, CRT yielded mOS of 23 months versus 8 months for TMZ alone. Younger age, surgical resection, and higher KPS predicted longer survival. In the TMZ subgroup, toxicity was low (6% hematologic, 12% non-hematologic grade 3-4 events) and survival improved with increasing TMZ cycles. Salvage therapy after progression was also associated with longer survival. Limitations include retrospective design, incomplete molecular data, frailty and QOL data not collected routinely, potential selection bias, and evolving treatment practices.

Conclusion: Fit elderly patients with GBM may achieve benefit from full standard-of-care therapy. Treatment decisions should be guided by functional and clinical fitness rather than chronological age. These real-world data highlight the importance of integrating clinical and functional factors into management of elderly GBM.

Background: Meningiomas are the most common primary intracranial neoplasms. A significant unmet need exists for noninvasive biomarkers to risk-stratify patients and guide decision-making. The integrated risk score (IRS), which combines histopathological grade, DNA methylation class, and copy number variation (CNV), is a powerful predictor of risk of progression. We aimed to predict the molecular-morphologic IRS from pre-operative T1-weighted contrast-enhanced MRI scans.

Methods: We retrospectively analyzed 2 multi-institutional prospectively compiled datasets, including patients with histologically confirmed intracranial meningiomas that had matched DNA methylation and (CNV) profiles. We first developed a radiomics model and trained a support vector machine (SVM) classifier. We then developed a convolutional neural network based on ResNet101, and a vision transformer (ViT). Model performance was assessed using accuracy, area under the curve, precision, recall, and F1-scores.

Results: Seventy-six patients met the inclusion criteria. Distributions per WHO class and IRS were 53 (I), 22 (II), 1 (III), and 53 (low), 18 (intermediate) and 5 (high), respectively. The SVM model achieved an AUC of 80.4% and accuracy of 78.2%. The ResNet101 model achieved 85.3% accuracy. The ViT model outperformed, achieving 89.4% accuracy and 89.1% precision in predicting low versus intermediate/high IRS tumors.

Conclusions: This study represents the first application of the ViT architecture to predict, with high accuracy, the prognostically highly relevant IRS from routine pre-operative neuroimaging. This pipeline provides a biologically informed and clinically relevant model with potential for future use in early risk stratification and decision support in the management of patients with meningiomas.