BACKGROUND:Glioblastoma is the most aggressive primary brain tumor, with poor prognosis and limited treatment options. Natural killer (NK) cell therapy is a promising immunotherapeutic strategy, yet its efficacy remains limited. We evaluated FT538, a clinical-grade NK product derived from induced pluripotent stem cells (iPSCs), in glioblastoma models. METHODS:FT538, engineered with a high-affinity non-cleavable CD16 Fc receptor, a membrane-bound IL-15/IL-15Rα fusion protein, and CD38 knockout, was tested against 13 patient-derived glioblastoma stem-like cells (GSCs) in vitro and in orthotopic xenograft models. Intracranial persistence and neurotoxicity were assessed in mice. Surface proteomics identified therapeutic targets, and a B7-H3-targeted tri-specific killer engager (TriKE) was evaluated with FT538 and NKG2C+ adaptive NK cells. RESULTS:GSCs were classified as sensitive (38%), moderately sensitive (38%), or resistant (23%) to FT538. Intracranial administration in mice was well tolerated, persisted for at least 35 days, and caused no neurotoxicity. A single intratumoral dose induced complete regression in sensitive xenografts. Surface profiling identified B7-H3 as a target to overcome resistance. Combination therapy with FT538 and a B7-H3 TriKE enhanced antitumor efficacy in resistant models, an effect also observed with adaptive NK cells. CONCLUSIONS:FT538 exhibits potent tumoricidal activity in 77% of GSC lines (NK-sensitive and moderately sensitive), with curative potential in sensitive models, and demonstrates favorable persistence and tolerability in vivo. B7-H3-targeted TriKE restores NK sensitivity in resistant tumors. These findings provide a strong preclinical rationale for further clinical evaluation of FT538, alone or combined with B7-H3-targeted TriKE, for glioblastoma and other solid tumors.

Radiation therapy (RT) is one of the most effective and widely used treatment methods for glioblastoma multiforme (GBM). However, its efficacy is often compromised by the inherent radioresistance of tumor cells, while the restrictive nature of the blood-brain barrier (BBB) specifically impedes the delivery of radiosensitizer. Thus, we constructed and characterized polyethylene glycol (PEG)-functionalized silver-gold core-shell nanoparticles (PSGNPs) targeting both BBB (TfRA4) and GBM (DNA1) (TDSGNPs). Afterwards, studies conducted both in vitro and in vivo were employed to assess the BBB penetration capabilities, abilities of GBM targeting and radiosensitization effect. Transmission electron microscope images of PSGNPs showed a core-shell structure, and the results of ultraviolet-visible absorption spectroscopy and dynamic light scattering displayed that TDSGNPs were successfully constructed with excellent dispersion properties. TDSGNPs could be specifically taken up by U87MG cells and the uptake peaked at 24 h. TDSGNPs combined with RT obviously increased the apoptosis proportion of the cells. It was shown by the in vitro and in vivo investigations that TDSGNPs could target U87MG cells after crossing the BBB, and further study revealed that TDSGNPs showed an uptake peak in the tumor sites after 3 h intravenous injection. The radiosensitization of TDSGNPs was better than that of the nanoparticles modified with single aptamers and the median survival of tumor-bearing mice was greatly extended. This study demonstrated that TDSGNPs could penetrate BBB to target GBM, functioning as a promising radiosensitizer for the targeted therapy of GBM.

Our research aims to develop an automated method for segmenting brain CT images in healthy 2-year-old children using the ResU-Net deep learning model. Building on this model, we aim to quantify the volumes of specific brain regions and establish a normative reference database for clinical and research applications. In this retrospective study, we included 1,487 head CT scans of 2-year-old children showing normal radiological findings, which were divided into training (n = 1,041) and testing (n = 446) sets. We preprocessed the Brain CT images by resampling, intensity normalization, and skull stripping. Then, we trained the ResU-Net model on the training set and validated it on the testing set. In addition, we compared the performance of the ResU-Net model with different kernel sizes (3 × 3 × 3 and 1 × 3 × 3 convolution kernels) against the baseline model, which was the standard 3D U-Net. The performance of the model was evaluated using the Dice similarity score. Once the segmentation model was established, we derived the regional volume parameters. We then conducted statistical analyses to evaluate differences in brain volumes by sex and hemisphere, and performed a Spearman correlation analysis to assess the relationship between brain volume and age. The ResU-Net model we proposed achieved a Dice coefficient of 0.94 for the training set and 0.96 for the testing set, demonstrating robust segmentation performance. When comparing different models, ResU-Net (3,3,3) model achieved the highest Dice coefficient of 0.96 in the testing set, followed by ResU-Net (1,3,3) model with 0.92, and the baseline 3D U-Net with 0.88. Statistical analysis showed that the brain volume of males was significantly larger than that of females in all brain regions (p < 0.05), and age was positively correlated with the volume of each brain region. In addition, specific structural asymmetries were observed between the right and left hemispheres. This study highlights the effectiveness of deep learning for automatic brain segmentation in pediatric CT imaging, providing a reliable reference for normative brain volumes in 2-year-old children. The findings may serve as a benchmark for clinical assessment and research, complementing existing MRI-based reference data and addressing the need for accessible, population-based standards in pediatric neuroimaging.

The fight against lung adenocarcinoma (LUAD) is challenged by tumor microenvironment (TME)-mediated drug resistance, which limits effective treatment. This study examines the LUAD TME and identifies four distinct subtypes through multi-omics profiling: immune-rich, immune-exhausted, stromal-dominant, and TME-desert. Each subtype has unique molecular features, tumor diversity, and links to clinical outcomes. Immune-rich subtypes respond better to immune checkpoint inhibitors, while stromal-dominant and TME-desert subtypes show resistance to treatment and poor prognosis. Molecular analysis uncovers subtype-specific mutations, chromosomal instability, and altered signaling pathways, pointing to potential therapeutic targets. In silico drug screening identifies promising treatments for resistant subtypes. These findings, validated in independent cohorts, highlight the critical role of the TME in drug resistance and treatment response, providing insights for personalized treatment strategies in LUAD.

In patients with subtotal resection (STR) of WHO grade I skull base meningiomas, treatment strategies of adjuvant radiation versus observation with salvage radiation, if necessary, were compared using progression-free survival (PFS) and radiation failure-free survival (RFFS). Patients with newly diagnosed WHO grade I skull base meningioma who underwent radiographically confirmed STR between 1995 and 2021 were included. PFS was measured from last treatment date. RFFS was measured from surgery date to first radiation failure. Multivariable Cox regression, adjusted for propensity score (PS) and inverse probability treatment weighted (IPTW), was performed. Of 179 patients, 25 (14.0

Rationale and Objectives: This study was designed to investigate the value of nomograms based on MRI radiomics and clinical semantic features in identifying pleomorphic xanthoastrocytoma (PXA) and ganglioglioma (GG) as well as predicting BRAFV600E expression. Materials and Methods: This study included 265 patients histologically diagnosed with PXA (n = 113) and GG (n = 152). T1WI, T2WI, and CET1 sequences were utilized to extract radiomics features. Univariate analysis, Spearman correlation analysis, and the least absolute shrinkage and selection operator were used for dimensionality reduction and feature selection. Following this, logistic regression was utilized to establish the radiomics model. Univariate and multivariate analyses of clinical semantic features were applied, and clinical models were constructed. The nomograms were established by merging radiomics and clinical features. Furthermore, ROC curve analysis was used for examining the model performance, whereas the decision curve analysis (DCA) examined the clinical utility of the nomograms. Results: Nomograms achieved the best predictive efficacy compared to clinical and radiomics models alone. Concerning the differentiation between PXA and GG, the area under the curve (AUC) values of the nomogram were 0.879 (0.828-0.930) and 0.887 (0.805-0.969) for the training and testing cohorts, respectively. For predicting BRAFV600E expression, the AUC values of the nomogram were 0.873 (0.811-0.936) and 0.851 (0.740-0.963) for the training and testing cohorts, respectively. DCA confirmed the clinical utility of the nomograms. Conclusion: Nomograms based on radiomics and clinical semantic features were noninvasive tools for differential diagnosis of PXA and GG and predicting BRAFV600E expression, which may be helpful for assessing patient prognosis and developing individualized treatment strategies.

Abstract BACKGROUND While directionally rotating Tumor Treating Fields (TTF) therapy has garnered considerable clinical interest in recent years, there has been comparatively less focus on directionally non-rotating electric field therapy (dnEFT). OBJECTIVE Here, we explore dnEFT generated through implanted electrodes as a glioblastoma therapeutic platform, with the goal of facilitating clinical translation. METHODS Using custom-designed electrode arrays to study the effect of dnEFT using in vitro and in vivo glioblastoma models. RESULTS In vitro, dnEFT generated using a clinical grade spinal cord stimulator showed anti-neoplastic activity against independent glioblastoma cell lines. In support of the results obtained using the clinical grade electrode, dnEFT delivered through a customized, two-electrode array induced glioblastoma apoptosis. To characterize this effect in vivo, a custom-designed four-electrode array was fabricated such that tumor cells can be implanted into murine cerebrum through a center channel equidistant from the electrodes. After implantation with this array and luciferase expressing murine GL261 glioblastoma cells, mice were randomized to dnEFT or placebo. Relative to placebo treated mice, dnEFT reduced tumor growth (measured by bioluminescence) and prolonged survival (median survival gain of 6.5 days). Analysis of brain sections following dnEFT showed a notable increase in the accumulation of peri-tumoral macrophage/microglia with increased expression of M1 genes (IFNγ, TNFα, IL-6) and decreased expression of M2 genes (CD206, Arg, IL-10) relative to placebo tumors. CONCLUSIONS Our results suggest therapeutic potential in glioblastoma for dnEFT delivered through implanted electrodes as a novel immunotherapy platform, supporting the concept of a proof-of-principle clinical trial using commercially available deep brain stimulator electrodes.

Background:While directionally rotating tumor-treating fields (TTF) therapy has garnered considerable clinical interest in recent years, there has been comparatively less focus on directionally non-rotating electric field therapy (dnEFT). Methods:We explored dnEFT generated through customized electrodes as a glioblastoma therapy in in vitro and in vivo preclinical models. The effects of dnEFT on tumor apoptosis and microglia/macrophages in the tumor microenvironment were tested using flow-cytometric and qPCR assays. Results:In vitro, dnEFT generated using a clinical-grade spinal cord stimulator showed antineoplastic activity against independent glioblastoma cell lines. In support of the results obtained using the clinical-grade electrode, dnEFT delivered through a customized, 2-electrode array induced glioblastoma apoptosis. To characterize this effect in vivo, a custom-designed 4-electrode array was fabricated such that tumor cells can be implanted into murine cerebrum through a center channel equidistant from the electrodes. After implantation with this array and luciferase-expressing murine GL261 glioblastoma cells, mice were randomized to dnEFT or placebo. Relative to placebo-treated mice, dnEFT reduced tumor growth (measured by bioluminescence) and prolonged survival (median survival gain of 6.5 days). Analysis of brain sections following dnEFT showed a notable increase in the accumulation of peritumoral macrophage/microglia with increased expression of M1 genes (IFNγ, TNFα, and IL-6) and decreased expression of M2 genes (CD206, Arg, and IL-10) relative to placebo-treated tumors. Conclusions:Our results suggest therapeutic potential in glioblastoma for dnEFT delivered through implanted electrodes, supporting the development of a proof-of-principle clinical trial using commercially available deep brain stimulator electrodes.

Background: Surgical resection of skull base meningiomas is particularly challenging. Due to their proximity to critical structures such as cranial nerves, cerebral vessels, and brainstem, it often results in subtotal resections (STR). Following STR of a WHO grade I meningioma, there is an ongoing debate on whether to proceed immediately with adjuvant radiotherapy or to observe and utilize salvage radiotherapy for any tumor progression. Traditional progression-free survival (PFS) metrics do not consider the possible effectiveness of salvage radiation treatment. Additionally, a direct comparison of PFS between adjuvant and salvage radiation is confounded by selection bias, as salvage radiation is often reserved for more aggressive, recurring tumors. Therefore, an optimal and more nuanced metric and analysis method is critical.

RATIONALE AND OBJECTIVES:This study aimed to develop and validate a fusion model combining MRI deep transfer learning (DTL) and radiomics for discriminating between pilocytic astrocytoma (PA) and adamantinomatous craniopharyngioma (ACP) in the sellar region. METHODS:This study included 348 patients with histologically confirmed PA (n = 139) and ACP (n = 209). Data were randomly divided into training and testing cohorts in a 7:3 ratio. Pre-trained ResNet50 network was utilized to extract DTL features from T1WI, T2WI, and CET1, while radiomics features (Rad) were extracted from manually delineated images of the same modalities. The fusion feature set (DLR) was constructed by integrating these features. Semantic features were used to develop clinical models. Pearson rank correlation and The least absolute shrinkage and selection operator regression were used for feature selection, and K-nearest neighbor algorithm was applied to establish the model. The performance of the model was evaluated using receiver operating characteristic curve. DeLong's test was performed to assess differences between models, and decision curve analysis was conducted to evaluate the clinical utility of the models. RESULTS:The DLR model achieved AUC values of 0.945 (95% CI, 0.9149-0.9760) in the training cohort and 0.929 (95% CI, 0.8824-0.9762) in the testing cohort, significantly higher than those of models using DTL features, Rad features, or clinical features alone. CONCLUSION:The fusion model based on MRI deep transfer learning and radiomics (DLR) demonstrated high accuracy and clinical utility in discriminating between PA and ACP, providing an effective tool for the non-invasive diagnosis of these two diseases.

Introduction: Cerebral atrophy with leukoencephalopathy is a known morbidity after whole brain radiation therapy (WBRT), resulting in ex-vacuo ventriculomegaly with leukoencephalopathy (EVL). Here we studied the correlation between WBRT, stereotactic radiosurgery (SRS), and risk for EVL in brain metastases patients. Methods: In a retrospective study, we identified 195 patients (with 1,018 BM) who underwent SRS for BM (2007-2017) and had > 3 months of MRI follow-up. All patients who underwent ventriculoperitoneal shunting were excluded. Cerebral atrophy was measured by ex-vacuo-ventriculomegaly, defined based on Evans' criteria. Demographic and clinical variables were analyzed using logistic regression models. Results: Ex-vacuo ventriculomegaly was observed on pre-radiosurgery imaging in 29.7% (58/195) of the study cohort. On multivariate analysis, older age was the only variable associated with pre-radiosurgery ventriculomegaly. Of the 137 patients with normal ventricular size before radiosurgery, 27 (19.7 %) developed exvacuo ventriculomegaly and leukoencephalopathy (EVL) post-SRS. In univariate analysis, previous whole brain radiation therapy was the main factor associated with increased risk for developing EVL (OR = 5.08, p < 0.001). In bivariate models that included prior receipt of WBRT, both the number of SRS treatments (OR = 1.499, p = 0.025) and WBRT (OR = 11.321, p = 0.003 were independently associated with increased EVL risk. Conclusions: While repeat radiosurgery contributes to the risk of EVL in BM patients, this risk is similar to 20-fold lower than that associated with WBRT.

Objective: To develop an effective machine learning model to preoperatively predict the occurrence of futile recanalization (FR) of acute basilar artery occlusion (ABAO) patients with endovascular treatment (EVT).Materials and Methods: Data from 132 ABAO patients (109 male [82.6 %]; mean age +/- standard deviation, 59.1 +/- 12.5 years) were randomly divided into the training (n = 106) and test cohort (n = 26) with a ratio of 8:2. FR is defined as a poor outcome [modified Rankin Scale (mRS) 4-6] despite a successful recanalization [modified Thrombolysis in Cerebral Infarction (mTICI) >= 2b]. A total of 1130 radiomics features were extracted from diffusion-weighted imaging (DWI) images. The least absolute shrinkage and selection operator (LASSO) regression method was applicated to select features. Support vector machine (SVM) was applicated to construct radiomics and clinical models. Finally, a radiomics-clinical model that combined clinical with radiomics features was developed. The models were evaluated by receiver operating characteristic (ROC) curve and decision curve. Results: The area under the receiver operating characteristic (ROC) curve (AUC) of the radiomics-clinical model was 0.897 (95 % confidence interval, 0.837-0.958) in the training cohort and 0.935 (0.833-1.000) in the test cohort. The AUC of the radiomics model was 0.887 (0.824-0.951) in the training cohort and 0.840 (0.680-1.000) in the test cohort. The AUC of the clinical model was 0.746 (0.652-0.840) in the training cohort and 0.766 (0.569-0.964) in the test cohort. The AUC of the radiomics-clinical model was significantly larger than the clinical model (p = 0.016). A radiomics-clinical nomogram was developed. The decision curve analysis indicated its clinical usefulness.Conclusion: The DWI-based radiomics-clinical machine learning model achieved satisfactory performance in predicting the FR of ABAO patients preoperatively.

PurposeTo predict chromosome 7 gain and chromosome 10 loss (+7/-10) in IDH wild-type (IDH-wt) histologically low-grade gliomas (LGG) by machine learning models based on MRI radiomics and semantic features.MethodsA total of 122 patients diagnosed as IDH-wt histologically LGG were retrospectively included in this study. The patients were randomly divided into a training group and a test group in a ratio of 7:3. The radiomics features were extracted from axial T1WI, T2WI, FLAIR and CET1 sequences, respectively. The distance correlation (DC) and least absolute shrinkage and selection operator (LASSO) were used to select the radiomics signatures. Three machine learning algorithms including neural network (NN), support vector machine (SVM), and linear discriminant analysis (LDA) were used to construct radiomics models. In addition, a nomogram was developed by combining the optimal radiomics signature with clinical risk factors, and the potential clinical utility of the nomogram was evaluated using decision curve analysis.ResultsThe LDA+DC model was identified as the optimal classifier among the six radiomics models. Necrosis was determined as a risk factor for +7/-10 in IDH-wt histologically LGG. The nomogram achieved the best performance, with an AUC of 0.854 and an accuracy of 0.778 in the independent test group. The decision curve of the nomogram confirmed its clinical usefulness in a wide range of thresholds.ConclusionThe nomogram combining radiomics and semantic features can predict the +7/-10 status effectively, which may contribute to the risk stratification and individualized treatment planning of patients with IDH-wt histologically LGG.

Background: The 2021 World Health Organization (WHO) classification considers a histological low grade glioma with specific molecular characteristics as molecular glioblastoma (mGBM). Accurate identification of mGBM will aid in risk stratification of glioma patients. Purpose: To explore the value of machine learning models based on magnetic resonance imaging (MRI) radiomics features in predicting mGBM. Material and Methods: In total, 166 patients histologically diagnosed as low-grade diffuse glioma (WHO II and III) were included in the study. Fifty-three cases were reclassified as mGBM based on molecular status. Four dimensionality reduction methods including distance correlation (DC), gradient boosted decision tree (GBDT), least absolute shrinkage and selection operator (LASSO) and minimal redundancy maximal relevance (MRMR) were used to select the optimal signatures. Six machine learning algorithms including support vector machine (SVM), linear discriminant analysis (LDA), neural network (NN), logistic regression (LR), K-nearest neighbour (KNN) and decision tree (DT) were used to develop the classifiers. The relative SD was used to evaluate the stability of the models, and the area under the curve values in the independent test group were used to evaluate their performances. Results: NN_DC was determined as the optimal classifier due to the highest area under the curve of 0.891 in the test group. The classification accuracy, sensitivity, specificity, positive predictive value and negative predictive value of NN_DC were 0.915, 0.842, 0.950, 0.889 and 0.927, respectively. Conclusion: Machine learning models can predict mGBM non-invasively, which may help to develop personalized treatment strategies for neurosurgeons and provide an effective tool for accurate stratification in clinical trials.

Abstract INTRODUCTION Though alternating electric field therapy (EFT) for glioblastoma received FDA approval, biophysical modeling suggests that field strength generated by scalp electrodes may not sufficiently extend to deep, subcortical regions. Objective: We explore the anti-tumor activity of electric field (EF) generated by electrode directly implanted into glioblastomas, with the goal of repurposing deep brain stimulators as a therapeutic platform. Method: Laboratory characterization and murine modeling Result: In vitro, activation of leads of a deep brain stimulator induced tumoricidal activity within the region encompassed by the EF. To further characterized this tumoricidal activity, a customized two-electrode array was designed and fabricated to allow study of glioblastoma cells seeded at the center of the EF. Consistent with the observations made using the deep brain stimulator electrode, electric field therapy (EFT) induced both necrosis and apoptosis of glioblastoma cells. To characterize this effect in vivo, a four-electrode array was designed and fabricated such that tumor cells can be implanted through a center channel equidistant the electrodes. Mice were implanted with this array, followed by luciferase labelled murine glioblastomas through the center channel. After tumor engravement, mice were randomized to EFT or placebo. EFT was associated with significant diminishment of tumor growth (measured by bio- bioluminescence) and prolonged survival. Analysis of brain sections following EFT showed a notable increase in peri-tumoral microglia accumulation, suggesting potential of EFT as an immune-modulation platform. CONCLUSION Our results suggest therapeutic potential for repurposing of deep brain stimulator as glioblastoma therapy, with opportunities for therapeutic enhancement through novel electrode design and stimulation parameter modulation.

Abstract OBJECT Given that multiple co-morbidities are present in up to 80% of octogenarians, resection of benign tumors, such as meningiomas, is a challenging proposition, especially in the context of frailty naturally associated with aging. Here, we characterized the patterns of clinical practice, morbidity, and mortality associated with benign meningioma resection for octogenarian using the Surveillance, Epidemiology and End RESULTS (SEER), SEER-Medicare (SEER-M), National Cancer Database (NCDB), and National Readmission Database (NRD). METHODS Logistic regression, propensity-score matching (PSM)-method (covariates balanced using PSM: socio-demographics, facility features, and comorbidities), and accelerated failure time (AFT) models. RESULTS In SEER, the adjusted odds ratios of recommendation for surgery were 0.59 (95%CI=0.53-0.66, p< 0.001) and 0.17 (95%CI=0.15-0.21, p< 0.001) for benign meningioma patients age 60-79 and > 80, relative to patients age 40-59. Expectedly, the hazard of death increased with advancing aging strata in patients who underwent meningioma resection. However, in independent analyses of NCDB and SEER-M, likelihood of death after benign meningioma resection was not significantly elevated in patients age > 80 relative to age- and PSM-matched patient cohorts. In the NRD, age > 80 was associated with increased odds of 90-day medical readmission after meningioma resection (OR: 1.63, 95%CI=1.33-1.99) but not 90-day neurologic readmission (OR: 1.02, 95%CI:0.76-1.37) relative to age 40-59. CONCLUSION We found no increased risk for patients age > 80 who were selected for and underwent resection in terms of mortality and 90-day neurologic readmission risk. However, the octogenarians have a higher risk of 90-day medical readmission. These findings suggest that neurosurgeons, in general, exert sound judgment in the selection of elderly patients for benign meningioma resections.

AbstractBackgroundGammaTile® (GT) is a recent U.S. Food and Drug Administration (FDA) cleared brachytherapy platform. Here, we report clinical outcomes for recurrent glioblastoma patients after GT treatment following maximal safe resection.MethodsWe prospectively followed twenty-two consecutive Isocitrate Dehydrogenase (IDH) wild-type glioblastoma patients (6 O6-Methylguanine-DNA methyltransferase methylated (MGMTm); sixteen MGMT unmethylated (MGMTu)) who underwent maximal safe resection of recurrent tumor followed by GT placement.ResultsThe cohort consisted of 14 second and eight third recurrences. In terms of procedural safety, there was one 30-day re-admission (4.5%) for an incisional cerebrospinal fluid leak, which resolved with lumbar drainage. No other wound complications were observed. Six patients (27.2%) declined in Karnofsky Performance Score (KPS) after surgery due to worsening existing deficits. One patient suffered a new-onset seizure postsurgery (4.5%). There was one (4.5%) 30-day mortality from intracranial hemorrhage secondary to heparinization for an ischemic limb. The mean follow-up was 733 days (range 279–1775) from the time of initial diagnosis. Six-month local control (LC6) and twelve-month local control (LC12) were 86 and 81%, respectively. Median progression-free survival (PFS) was comparable for MGMTu and MGMTm patients (~8.0 months). Median overall survival (OS) was 20.0 months for the MGMTu patients and 37.4 months for MGMTm patients. These outcomes compared favorably to data in the published literature and an independent glioblastoma cohort of comparable patients without GT treatment.ConclusionsThis clinical experience supports GT brachytherapy as a treatment option in a multi-modality treatment strategy for recurrent glioblastomas.

Glioblastoma is the most common malignant brain cancer with dismal survival and prognosis. Temozolomide (TMZ) is a first-line chemotherapeutic agent for glioblastoma, but the emergence of drug resistance limits its anti-tumor activity. We previously discovered that the interferon inducible guanylate binding protein 3 (GBP3) is highly elevated and promotes tumorigenicity of glioblastoma. Here, we show that TMZ treatment significantly upregulates the expression of GBP3 and stimulator of interferon genes (STING), both of which increase TMZ-induced DNA damage repair and reduce cell apoptosis of glioblastoma cells. Mechanistically, relying on its N-terminal GTPase domain, GBP3 physically interacts with STING to stabilize STING protein levels, which in turn induces expression of p62 (Sequestosome 1), nuclear factor erythroid 2 like 2 (NFE2L2, NRF2), and O6-methlyguanine-DNA-methyltransferase (MGMT), leading to the resistance to TMZ treatment. Reducing GBP3 levels by RNA interference in glioblastoma cells markedly increases the sensitivity to TMZ treatment in vitro and in murine glioblastoma models. Clinically, GBP3 expression is high and positively correlated with STING, NRF2, p62, and MGMT expression in human glioblastoma tumors, and is associated with poor outcomes. These findings provide novel insight into TMZ resistance and suggest that GBP3 may represent a novel potential target for the treatment of glioblastoma.

The recently introduced orientation selective deep brain stimulation (OS-DBS) technique freely controls the direction of the electric field’s spatial gradient by using multiple contacts with independent current sources within a multielectrode array. The goal of OS-DBS is to align the electrical field along the axonal track of interest passing through the stimulation site. Here we utilized OS-DBS with a planar 3-channel electrode for stimulating the rat entorhinal cortex (EC) and medial septal nucleus (MSN), two promising areas for DBS treatment of Alzheimer’s disease. The brain responses to OS-DBS were monitored by whole brain functional magnetic resonance imaging (fMRI) at 9.4 T with Multi-Band Sweep Imaging with Fourier Transformation (MB-SWIFT). Varying the in-plane OS-DBS stimulation angle in the EC resulted in activity modulation of multiple downstream brain areas involved in memory and cognition. Contrary to that, no angle dependence of brain activations was observed when stimulating the MSN, consistent with predictions based on the electrode configuration and on the main axonal directions of the targets derived from diffusion MRI tractography and histology. We conclude that tuning the OS-DBS stimulation angle modulates the activation of brain areas relevant to Alzheimer’s disease, thus holding great promise in the DBS treatment of the disease.