Glioma Patients Research Articles

A ferroptosis-associated prognostic model correlated with immune landscape and radiotherapy response in low-grade gliomas (LGGs)

Despite receiving comprehensive treatment, the prognosis for low-grade gliomas (LGGs) patients varies considerably. Recent studies have focused extensively on ferroptosis, across a range of tumor types. Nevertheless, methodologies to evaluate the efficacy of radiotherapy for LGGs, from the perspective of ferroptosis-related genes (FRGs), remain strikingly rare. In this study, we conducted a retrospective study on the transcriptional profiles of LGG patients from the public databases and a local cohort. An FRG model was developed and validated, exhibits heightened robustness when contrasted with the traditional ssGSEA model. Patients demonstrating higher FRG scores were identified as a high-risk group, displaying a worse prognosis. By incorporating the FRG score alongside other prognosis-associated clinical indicators, we formulated an enhanced nomogram to achieve a higher level of prediction performance. Additionally, among LGG patients receiving radiotherapy, a poorer prognosis was observed in the high-risk group. Further investigation revealed that samples from the high-risk group generally exhibit a TME in an immuno-suppressive state. Collectively, we developed an FRG model and a robust nomogram for LGG prognostication. This study suggests that a high FRG score, indicative of an immunosuppressive TME, could potentially lead to a less favorable prognosis for certain LGG patients receiving radiotherapy.

Baicalein inhibits cell proliferation and induces apoptosis in brain glioma cells by downregulating the LGR4-EGFR pathway.

Patients diagnosed with brain glioma have a poor prognosis and limited therapeutic options. LGR4 is overexpressed in brain glioma and involved in the tumorigenesis of many tumors. Baicalein (BAI) is a kind of flavonoid that has exhibited anti-tumor effects in various tumors. Nevertheless, the functions and associations of BAI and LGR4 in brain glioma remain unclear. In this study, Gene Expression Profiling Interactive Analysis and Human Protein Atlas databases were used to perform expression and survival analysis of LGR4 in brain glioma patients. Subsequently, the significance of LGR4-EGFR in brain glioma cells (HS683 and KNS89) and brain glioma animal models was explored by RNA interference and subcutaneous transplantation. Additionally, brain glioma cells were treated with BAI to explore the roles and mechanisms of BAI in brain glioma. The results showed that LGR4 was highly expressed in brain glioma and was related to a poor prognosis. LGR4 knockdown repressed the proliferation and EGFR phosphorylation but induced apoptosis in brain glioma cells. However, these effects were reversed by EGFR overexpression and CBL knockdown. In contrast, both in vitro and in vivo experiments revealed that LGR4 overexpression facilitated brain glioma cell malignant behavior and promoted tumor development, but these effects were rescued by BAI and an EGFR inhibitor. Furthermore, si-LGR4 accelerated EGFR protein degradation, while oe-LGR4 exhibited the opposite effect. Without affecting normal cellular viability, BAI inhibited malignant behavior, interacted with LGR4, and blocked the LGR4-EGFR pathway for brain glioma cells. In conclusion, our data suggested that BAI inhibited brain glioma cell proliferation and induced apoptosis by downregulating the LGR4-EGFR pathway, which provides a novel strategy and potential therapeutic targets to treat brain glioma.

Impact of Antidepressant Use on Survival Outcomes in Glioma Patients: A Systematic Review and Meta-Analysis

Abstract Background Depression is common among glioma patients, and antidepressants are frequently prescribed to manage symptoms. Understanding the impact of antidepressants on glioma patient survival is crucial for informing treatment strategies. Methods A systematic search was conducted in PubMed and EMBASE databases for studies published from January 1994 to March 2024. The search strategy included terms related to overall survival, prognosis, antidepressants, and gliomas. A manual search was performed in the reference lists. According to PRISMA guideline, two authors independently extracted data. Statistical analysis was performed using Review Manager (version 5.4.1) software, employing random effects model based on study heterogeneity. The primary outcome was overall survival (OS). Hazard ratios (HRs) were used to present survival differences between the two arms. HRs after correcting for confounders were prioritized for extraction. Results Seven retrospective cohort studies involving 5579 patients were analyzed. Selective serotonin reuptake inhibitors (SSRIs) showed no significant survival difference in all glioma patients (HR=1.34, 95% CI: 0.66-2.70) and in GBM patients (HR=1.05, 95% CI: 0.45-2.46), while non-SSRIs had an unfavorable impact on OS in GBMs (HR=3.54, 95% CI: 2.51-4.99). When considering LGG, both SSRIs and non-SSRIs usage demonstrated associations with poorer survival outcomes (SSRIs: HR=3.26, 95%CI: 2.19-4.85; Non-SSRIs: HR=7.71, 95% CI: 4.25-14.00). Conclusions Antidepressant use was not significantly associated with better survival outcomes, emphasizing the need for reconsidering the real effects of antidepressants medication. Future clinical research should address patient heterogeneity to better clarify the effects of antidepressants on glioma survival.

Unite and Conquer: Association of Two G-Quadruplex Aptamers Provides Antiproliferative and Antimigration Activity for Cells from High-Grade Glioma Patients

Background: High-grade gliomas remain a virtually incurable form of brain cancer. Current therapies are unable to completely eradicate the tumor, and the tumor cells that survive chemotherapy or radiation therapy often become more aggressive and resistant to further treatment, leading to inevitable relapses. While the antiproliferative effects of new therapeutic molecules are typically the primary focus of research, less attention is given to their influence on tumor cell migratory activity, which can play a significant role in recurrence. A potential solution may lie in the synergistic effects of multiple drugs on the tumor. Objectives: In this study, we investigated the effect of combined exposure to bi-(AID-1-T), an anti-proliferative aptamer, and its analog bi-(AID-1-C), on the migratory activity of human GBM cells. Results: We examined the effects of various sequences of adding bi-(AID-1-T) and bi-(AID-1-C) on five human GBM cell cultures. Our findings indicate that certain sequences significantly reduced the ability of tumor cells to migrate and proliferate. Additionally, the expression of Nestin, PARP1, L1CAM, Caveolin-1, and c-Myc was downregulated in human GBM cells that survived exposure, suggesting that the treatment had a persistent antitumor effect on these cells.

Distributed parameter model of dynamic contrast-enhanced MRI in the identification of IDH mutation, 1p19q codeletion, and tumor cell proliferation in glioma patients

ObjectivesTo investigate the clinical value of hemodynamic parameters derived from dynamic contrast-enhanced MRI (DCE-MRI) in predicting glioma genotypes including isocitrate dehydrogenase (IDH) mutation, 1p/19q codeletion status and the tumor proliferation index (Ki-67) noninvasively. And to compare the diagnostic performance of parameters of distributed parameter (DP)model and extended Tofts (Ex-Tofts) model.Materials and methodsDynamic contrast-enhanced MRI (DCE-MRI) data of patients with glioma were prospectively enrolled from April 2021 to May 2023. The imaging data were analyzed using DP and Ex-Tofts model for evaluating the perfusion and permeability characteristics of glioma. Comparisons were performed according to IDH genotype in all glioma patients and 1p/19q codeletion in IDH mutation glioma patients. Receiver operating characteristic (ROC) curves were generated for DCE-MRI parameters. The Spearman rank correlation coefficients were calculated between DCE MRI parameters and Ki-67 index.ResultsIn IDH-mutation gliomas, a higher blood flow (F) was found in 1p/19q codeletion gliomas than in 1p/19q intact gliomas. No parameter derived from Ex-Tofts model showed significant differences in predicting 1p/19q status. Fractional volume of interstitial space (Ve) derived from both the DP and Ex-Tofts models exhibited optimal performance in predicting IDH genotype (AUC = 0.818, 0.828, respectively). Ve also showed the highest correlations with Ki-67 LI within their respective models in all gliomas (ρ = 0.62, 0.61), indicating comparable moderate positive associations. Ki-67ConclusionDP model showed a clear advantage in predicting 1p/19q status compared to Ex-Tofts model. The DP and Ex-Tofts models performed similarly in predicting IDH mutation and Ki-67 index.

Factors Contributing to Variability in Neurocognitive Performance Before Glioma Neurosurgery

Abstract Background Cognitive impairment following anti-tumor treatment is a common concern for brain tumor patients. However, growing evidence indicates that significant impairments can be present even before treatment. The purpose of this study was to identify factors that explain variability in pre-treatment test performance, beyond that of tumor burden. Methods Using multi-step linear regression, we retrospectively probed the contribution of clinical-, tumor-, patient-, and self-reported factors to variance in performance among 96 treatment-naïve brain tumor patients across 13 objective neurocognitive tests. Agreement between subjective and objective reports of cognitive impairment was also examined. Results Clinically significant pre-operative impairments were observed in both objective and subjective domains. Estimated premorbid intelligence quotient (IQ), tumor volume, diagnosis of an astrocytoma, self-reported Depression and perceived Cognitive Functioning Scores were the most common predictors of objective neurocognitive performance prior to treatment, explaining 12.3%-58.3% of the variance. No association was identified between objective and subjective reports of cognitive impairment. Conclusions Glioma patients frequently exhibit objective and subjective impairments prior to treatment. Both tumor- and self-reported factors were identified as predictors of performance, after correcting for estimated premorbid IQ. Nevertheless, more than 41.7% of the variance in cognitive performance remained unexplained, indicating a substantial role for additional, as yet unaccounted for, clinical factors. Notable disparity between objective- and subjective cognitive impairment status re-emphasizes the importance of assessing both domains to ascertain a patient’s overall functioning in the context of treatment outcomes.

Histone deacetylase inhibition disrupts the molecular signature of the glioblastoma secretome related to extracellular vesicle-associated proteins and targets RAB7a and RAB14 in vitro

Glioblastoma (GBM) is the most aggressive brain tumor with a poor prognosis. While Histone Deacetylase inhibitors have shown promising results in inhibiting cancer cell invasion and promoting apoptosis, their effects on GBM secretion, specifically focusing on extracellular vesicles (EVs) secretion, remain largely unexplored. Using label-free NANOLC-MS/MS methodology, we identified significant changes in the abundance of membrane traffic regulatory proteins in the secretome of U87MG cells after the treatment with the HDAC inhibitor Trichostatin A (TSA). In silico analysis showed that TSA treatment disrupted the secretion pattern of EVs-associated proteins and cellular signaling pathways, both qualitatively and quantitatively. Notably, RAB14/RAB7a interaction was only observed in the secretome of cells treated with TSA. In vitro assays revealed that TSA treatment of glioma cells increased EVs secretion and intracellular protein levels of RAB7a and RAB14 without affecting gene expression, suggesting a role of these two EVs-associated proteins in grade IV glioma cells. Additionally, an integrative approach using clinical data highlighted a correlation between DNA mutations affecting vesicle traffic coding-genes and clinical and phenotypic outcomes in glioma patients. These findings provide insights into the interplay between epigenetics and GBM intracellular trafficking, potentially leading to improved strategies for targeting and modifying the complex signaling network established between GBM cells and the tumor cell microenvironment.

P11.05.B LONGITUDINAL MODIFICATIONS OF GRADIENTS AND GRAPH-THEORY MEASURES OF FUNCTIONAL MRI CONNECTIVITY IN GLIOMA PATIENTS AS POTENTIAL MARKERS OF POST-SURGICAL LONG-TERM COGNITIVE OUTCOME

Abstract BACKGROUND Integrating connectomics and functional brain activity into the study of brain tumors is among neuro-oncology’s challenges. Functional MRI (fMRI) studies showed that gliomas are integrated with brain networks’ activity, exerting effects beyond their margins. We investigated longitudinal modifications of fMRI gradients and graph-theory parameters in glioma patients, assessing their role in cognitive prognostication. MATERIAL AND METHODS Diffuse non-enhancing glioma patients underwent fMRI and cognitive assessments (OCS-Bridge) before, within 72h after, 3 and 12 months after surgery. Cognitive data were normalized and averaged into a global score. Differences between the last and preoperative scores were computed (“cognitive difference”). fMRI data underwent preprocessing, cortical parcellation, computation of timeseries, connectivity matrices, gradients and graph-theory measures. To quantify gradient dispersion, we computed a circularity metric (“CM”) of the hull perimeter of the first two gradients’ distribution. T-tests, partial correlations (correcting for volume and surgery radicality) and linear regression (regressors: age, volume, preoperative cognition, surgery radicality) were used to investigate associations with cognitive difference. RESULTS 17 patients (22-56 years, 8 females) were included, 4 had long-term cognitive worsening. Postoperative circularity metric, especially of the healthy intra- and inter-hemispheric connectivity (“HIIHC”), was higher than the preoperative (0.43 vs 0.32, t=2.4, p=0.03). Month3-postoperative and month3-preoperative differences in HIIHC CM were higher in patients with compared to those without cognitive worsening (0.33 vs -0.06, t=6.9, p<0.0001 and 0.27 vs 0.009, t=4.3, p=0.001). Both were negative predictors of the cognitive difference (the first: R=-0.94 with p=0.0002; β=-1.1 with p=0.001 and regression model’s p=0.003; the second: R=-0.76 with p=0.006; β=-1.1 with p=0.02 and model’s p=0.05). Month3-preoperative network modularity difference correlated with the second metric (R=-0.72, p=0.01) and the cognitive difference (R=0.67, p=0.02), while the preoperative modularity with the first metric (R=0.77, p=0.014). Similar results were obtained using a predetermined number of modules (Yeo’s 7 networks). Month12-preoperative difference in healthy intra-hemispheric connectivity CM also correlated with cognitive difference (R=-0.91, p=0.002). CONCLUSION Functional gradients seem dispersed after glioma surgery. Their recovery or worsening at month 3 could predict opposite long-term cognitive outcomes, putatively due to connectivity changes mainly involving the healthy hemisphere. Low preoperative network modularity and its increase at month 3 are associated with such gradient dispersion. Cognitive rehabilitation might be beneficial in the early post-surgical phase.

P18.39.A SEIZURE PROPHYLAXIS IN GLIOMA SURGERY (SPRING): A MULTI-CENTRE, UNBLINDED, RANDOMISED TRIAL

Abstract BACKGROUND 50-80% of all glioma patients will suffer from seizures during their lifetime. Over half of these patients will have drug resistant seizures. Seizure risk is increased peri-operatively, at tumour progression, and shortly before death. Current guidelines do not support routine use of prophylactic peri-operative anti-epileptic drugs (AED) in seizure naïve patients; despite this, levetiracetam is frequently prescribed for this indication. The aim of this trial was to determine the clinical and cost effectiveness of prophylactic levetiracetam in seizure naïve patients undergoing surgery for a glioma at reducing the risk of seizures. MATERIAL AND METHODS Seizure naïve patients with newly diagnosed glioma undergoing surgery (biopsy or resection) were randomised (1:1) to receive 12 months prophylactic levetiracetam or no prophylaxis at 14 neurosurgery and neuro-oncology units in the UK. Participants received standard of care neurosurgery and neuro-oncology treatment and were followed until death or for a maximum of one year. The primary outcome was one year risk of first seizure analyzed using a logistic regression model and intention to treat analysis. Outcomes were analyzed by intention-to-treat. The target accrual was 804 participants. This trial was registered with ISRCTN 49474281. RESULTS Between Oct 10, 2019 and Aug 30, 2022, we randomised 96 patients, from 24 to 79 years of age, to levetiracetam (n=49) or no prophylaxis (n=47). Due to slow accrual during the Covid-19 pandemic the trial closed early. In the levetiracetam group there were 43 high grade glioma (HGG) and 6 low grade glioma (LGG), and in the no prophylaxis group there were 43 HGG and 4 LGG. Seventeen (35%) of 49 patients randomised to levetiracetam had a seizure, compared to 15 (32%) of 47 patients randomised to no prophylaxis (OR 1.25, 95%CI 0.49-3.21, p=0.64). Median time to first seizure was 4.8 months in the levetiracetam group and 3.0 months in the no prophylaxis group. In the levetiracetam group 20 (41%) of 49 patients died within 12 months (median overall survival 6.8 months; range 0.2-11.9), and in the no prophylaxis group 15 (32%) of 47 patients died within 12 months (median 4.7 months; range 0.1-12.0). There were no serious adverse events or suspected unexpected serious adverse events related to levetiracetam. CONCLUSION Due to poor accrual caused by the Covid-19 pandemic the SPRING trial closed early and is therefore underpowered. Based on the limited data there was no difference in the 12 month seizure risk in the prophylaxis and no prophylaxis patients undergoing surgery for newly diagnosed glioma. The role of prophylactic anti-epileptic drugs remains undefined.

P10.06.A AGENT-BASED MODELING FOR PATIENT-CENTERED CLINICAL DECISION SUPPORT IN NEURO-ONCOLOGY

Abstract BACKGROUND Complex clinical decision-making in neuro-oncology is a multifaceted process involving numerous specialties influenced by various objective factors and patient preferences. Modeling these multidisciplinary neuro-oncology discussions presents significant challenges, particularly given the multimodal nature of the data. Recent advancements in large language models (LLMs) have enabled the development of LLM “agents”. These agents pave the way for multi-agent systems capable of simulating interacting components, thus capturing the nuances of interdisciplinary clinical discussions. MATERIAL AND METHODS Utilizing GPT-4, we developed conversational agents with an agent-based modeling (ABM) approach; each agent was assigned a specific specialty. We simulated their interactions in various clinical decision-making scenarios. Lastly, we simulated 30 patient-agents and included them in the ABM system. We applied this ABM approach to a dataset of 16 clinical vignettes extracted from the UCSF Clinical Data Warehouse of primary adult glioma patients who underwent tumor board (TB) reviews. The TB decision was used as ground truth for quantitative evaluation. Seven independent clinicians evaluated the final decisions. RESULTS Our quantitative assessment showed that, when blinded to whether ABM or TB generated responses, evaluators preferred ABM recommendations 47.8% of the time, followed by TB recommendations (38%). The highest recommendation concordance was between TB and ABM (50.6%). The ABM was deemed clinically accurate in 76.8% of cases and provided clinically appropriate reasoning in 73.2%. Our qualitative assessment showed that including the patient agent significantly influenced the course of discussions, notably when the patient agents participated in the system by stating their healthcare goals. The most recommended individual treatment was standard-of-care chemotherapy, followed by targeted therapies, clinical trials, immunotherapy, and palliative care. The least frequently recommended management options were radiation, surgery, consultation with other specialties, and devices. CONCLUSION This study underscores the potential of ABM in neuro-oncology, particularly in simulating complex, real-world scenarios. ABM not only empowers patients but also offers clinicians a novel tool for understanding and navigating the multifaceted landscape of patient preferences and treatment options. It provides a platform for clinicians to visualize the potential impacts of various decisions, enhancing their ability to provide personalized, effective care. We envision future work where ABM systems are patient-centric, enabling patients to explore the potential impacts of their individual characteristics on clinical management options.

P10.22.B PREDICTING INTRAOPERATIVE 5-ALA-INDUCED TUMOR FLUORESCENCE VIA MRI AND DEEP LEARNING IN GLIOMAS WITH RADIOGRAPHIC LOWER-GRADE CHARACTERISTICS

Abstract BACKGROUND Lower-grade gliomas typically exhibit 5-aminolevulinic acid (5-ALA)-induced fluorescence in only 20-30% of cases, a rate that can be increased by doubling the administered dose of 5-ALA. Fluorescence can depict anaplastic foci, which can be precisely resected to avoid undergrading. We aimed to analyze whether a deep learning model can predict intraoperative fluorescence based on preoperative magnetic resonance imaging (MRI). Material and Methods: The MRI images of gliomas lacking high-grade characteristics (necrosis, extended contrast-enhancement, a.o.) consisted of T1, T1-post gadolinium, and FLAIR sequences. The preprocessed MRIs were fed into an encoder-decoder convolutional neural network (U-Net), pre-trained for tumor segmentation using those three MRI sequences. We used the outputs of the bottleneck layer of the U-Net in the Variational Autoencoder (VAE) as features for classification. We identified and utilized the most effective features in a Random Forest classifier using the principal component analysis (PCA) and the partial least square discriminant analysis (PLS-DA) algorithms. We evaluated the performance of the classifier using a 10-fold cross-validation procedure. RESULTS We evaluated a cohort of 163 glioma patients categorized as fluorescent (n=83) or non-fluorescent (n=80). Our proposed approach’s performance was evaluated using metrics such as mean balanced accuracy, mean sensitivity, and mean specificity. The optimal results were obtained by employing top-performing features selected by PCA, resulting in a mean balanced accuracy of 80% and mean sensitivity and specificity of 84% and 76%, respectively. CONCLUSION Our findings highlight the potential of a U-Net model, coupled with a random forest classifier, for intraoperative fluorescence prediction. We achieved good accuracy using advanced techniques such as deep learning-based tumor segmentation and Variational Autoencoder for radiomics feature extraction. While the model can still be improved, it has the potential for evaluating when to administer 5-ALA to gliomas lacking typical high-grade radiographic features.

P11.09.B STRUCTURAL REORGANIZATION IN MOTOR-ELOQUENT GLIOMA PATIENTS EXHIBITING MOTOR DEFICITS

Abstract BACKGROUND Recent advancements in navigated transcranial magnetic stimulation (nTMS) have significantly improved the localization of motor functions in human subjects. Although previous studies have demonstrated the occurrence of tumor-induced motor plasticity, the detailed mechanisms of motor function plasticity and reorganization in patients with tumors affecting motor-eloquent regions are not fully elucidated. Our study aims to investigate the functional plasticity of motor functions in patients with gliomas located within motor-eloquent regions, providing deeper insights into the mechanisms of functional plasticity. MATERIAL AND METHODS In this research, we studied 60 glioma patients with tumors located in the motor-eloquent region. These patients were categorized into two groups depending on whether they experienced motor impairments in the upper extremities. We utilized nTMS, including Motor Evoked Potentials (MEP), to outline the control regions of motor function in terms of area, localization, and Center of Gravity (CoG). These identified regions also acted as seeds for tracing the Corticospinal Tract (CST). RESULTS The study unveiled significant variations in cortical and subcortical motor function localizations between the groups. Patients with motor impairments exhibited a broader cortical distribution connected to motor functions and increased volumes of CST, reflecting compensatory adaptations with different fractional anisotropy (FA) values (FA 0.15, p=0.02; FA 0.10, p=0.04). The analysis of the CST also showed overlapping regions, particularly within the hand-knob areas, which is indicative of these areas acting as critical motor output pathways. Despite varying degrees of functional impairment, all patients maintained some degree of motor output capability. CONCLUSION These findings suggest that motor-eloquent gliomas induce significant cortical and subcortical reorganization of motor functions. A persistent network of CST fibers, crucial for preserving motor function, highlights a complex interplay between cortical adaptation and subcortical reorganization. This study underscores the resilience of motor pathways and their pivotal role in functional recovery and plasticity, emphasizing the clinical relevance of nTMS in the therapeutic planning and monitoring of patients with motor-eloquent gliomas.

Evaluation of an Image-based Classification Model to Identify Glioma Subtypes Using Arterial Spin Labeling Perfusion MRI On the Publicly Available UCSF Glioma Dataset.

Glioma is acomplex cancer comprising various subtypes and mutations, which may have different metabolic characteristics that can potentially be investigated and identified using perfusion imaging. Therefore, the aim of this work was to use radiomics and machine learning analysis of arterial spin labeling MRI data to automatically differentiate glioma subtypes and mutations. Atotal of 495 Arterial Spin Labeling (ASL) perfusion imaging datasets from the UCSF Glioma database were used in this study. These datasets were segmented to delineate the tumor volume and classified according to tumor grade, pathological diagnosis, and IDH status. Perfusion image data was obtained from a3T MRI scanner using pseudo-continuous ASL. High level texture features were extracted for each ASL dataset using PyRadiomics after tumor volume segmentation and then analyzed using amachine learning framework consisting of ReliefF feature ranking and logistic model tree classification algorithms. The results of the evaluation revealed balanced accuracies for the three endpoints ranging from 55.76% (SD = 4.28, 95% CI: 53.90-57.65) for the tumor grade using 25.4 ± 37.21 features, 62.53% (SD = 2.86, 95% CI: 61.27-63.78) for the mutation status with 23.3 ± 29.17 picked features, and 80.97% (SD = 1.83, 95% CI: 80.17-81.78) for the pathological diagnosis which used 47.3 ± 32.72 selected features. Radiomics and machine learning analysis of ASL perfusion data in glioma patients hold potential for aiding in the diagnosis and treatment of glioma, mainly for discerning glioblastoma from astrocytoma, while performance for tumor grading and mutation status appears limited.

STYK1 mediates NK cell anti-tumor response through regulating CCR2 and trafficking

The serine/threonine/tyrosine kinase 1 (STYK1) is a receptor protein-tyrosine kinase (RPTK)-like molecule that is detected in several human organs. STYK1 plays an important role in promoting tumorigenesis and metastasis in various cancers. By analyzing the expression of RTKs in immune cells in the database of 2013 Immunological Genome Project, we found that STYK1 was principally expressed in NK cells. In order to investigate the function of STYK1, we used CRISPR/Cas9 technology to generate STYK1-deleted mice, we found STYK1 deletion mice have normal number, development, and function of NK cells in spleen and bone marrow in tumor-free resting state. To examine the tumor surveillance of STYK1 in vivo, we utilized a variety of tumor models, including NK cell-specific target cell (ß2M and RMA-S) clearance experiments in vivo, subcutaneous and intravenous injection of B16F10 melanoma model, and the spontaneous breast cancer model MMTV-PyMT. Surprisingly, we discovered that deletion of the oncogenic STYK1 promoted the four-model tumor progression, and we observed a reduction of NK cell accumulation in the tumor tissues of STYK1 deletion mice compared to WT mice. In order to study the mechanism of STYK1 in NK, RNA sequence of STYK1−/− and WT NK have unveiled a disparity in the signaling pathways linked to migration and adhesion in STYK1−/− NK cells. Further analysis of chemokine receptors associated with NK cell migration revealed that STYK1-deficient NK cells exhibited a significant reduction in CCR2 expression. The STYK1 expression was negatively associated with tumor progression in glioma patients. Overall, our study found the expression of STYK1 in NK cell mediates NK cell anti-tumor response through regulating CCR2 and infiltrating into tumor tissue.

Role of Advanced Magnetic Resonance Imaging in Differentiating among Glioma Subtypes and Predicting Tumor-Proliferative Behavior

Abstract Objective Gliomas are a devastating and heterogeneous group of primary brain tumors. Previously, the source of glioma was undetermined. Recent literature indicates that neural stem cells, or progenitors, are proposed to be the source of glioma. The prognosis of different types of gliomas differs due to their various biological tissue types. Besides the histological grade, the two useful immunohistochemistry markers that show the tumor's biological behavior are isocitrate dehydrogenase (IDH) labeling and the Ki-67 labeling index. We sought to determine the magnetic resonance imaging (MRI) characteristics associated with IDH mutational status and ascertain whether MRI combined with IDH mutational status, can better predict the clinical outcomes of gliomas. Materials and Methods This period study was conducted in the Department of Radiology, Sri Ramachandra Institute of Higher Education and Research, Chennai, Tamil Nadu, India for 5 years (May 2016–May 2021). The study cohort included 30 patients diagnosed with gliomas who underwent preoperative MRI followed by surgical resection and histopathological examination. Preoperative MRI images were done to assess qualitative tumor characteristics such as location, margin of tumor, extent, cortical involvement, cystic component, mineralization or hemorrhage, and contrast enhancement. Discussion Differences in MRI features between IDH-mutant (MT) and IDH-wild-type (WT) groups were analyzed using the chi-square test for categorical variables and the Mann–Whitney U test for continuous variables. Statistical analysis was conducted using SPSS software. Results Among the 30 patients evaluated, 18 had IDH-WT and 12 had IDH-MT type gliomas. Male predominance (73.33%) was noted in our study. Brainstem location, indistinct borders (83.33%), less cortical involvement (72.22%), less cystic changes (88.89%), more area of necrotic component (44.44%), significantly increased choline/creatine (Cho/Cr) ratio, and choline/N-acetyl aspartate (Cho/NAA) ratio favors IDH-WT tumors. Positive T2-fluid-attenuated inversion recovery mismatch sign is more frequently seen in IDH-MT (7/12; 58.33%) tumors than in IDH-WT (4/18; 22.22%) tumors. Whereas well-defined contours (66.67%), more cortical involvement (83.33%), more cystic changes (58.33%), and less area of necrotic component favor IDH-MT type tumors. Conclusion MRI is a very promising and valuable tool for differentiating among glioma subtypes and predicting tumor-proliferative behavior in glioma cases. The combination of MRI characteristics with IDH mutation status enhances the predictive accuracy for clinical outcomes in glioma patients. This approach could potentially guide treatment planning and improve prognostic assessments.

MGMT METHYLATION AND ITS PROGNOSTIC SIGNIfiCANCE IN INOPERABLE IDH WILDTYPE GLIOBLASTOMA: A SIX-YEAR REVIEW AT A TERTIARY NEURO-ONCOLOGY CENTER

Abstract AIMS IDH wild-type glioblastoma has the worst prognosis among glioma patients. The methylation of the O6- Methylguanine-DNA Methyltransferase (MGMT) gene is a valid biomarker for predicting response to therapy with alkylating agents and, independently, prognosis. Patients undergoing biopsy only without subsequent treatment have poor prognosis. We aimed to study the survival impact of MGMT methylation in this subset of patients. METHOD We collected six-year retrospective (2017-2023) data at a tertiary neuro-oncology center for patients undergoing biopsy as the only surgical procedure for an unresectable IDH wildtype glioblastoma. Data was collected from patient records including neuro-oncology MDT documentation. Patients were grouped into four categories according to different types of treatment received after biopsy (biopsy only, Biopsy + chemotherapy(CT), biopsy + radiotherapy(RT), biopsy + complete/incomplete STUPP, biopsy + STUPP + 2nd Line). Analysis was performed in python and R statistical software. Survival analysis was performed with Kaplan-Meier Analysis in python. RESULTS 166 glioblastoma IDH wildtype patients were included in the study with mean age of 62.5 years (M: F = 1.5: 1). 50% (n=83) patients had unmethylated MGMT status (0-5%), 29.5% (n=49) patients had methylated MGMT status (>15%) and 19.2% (n=32) patients had borderline methylated MGMT status (5-15%). 21%(n=35) patients did not receive any treatment post biopsy,, 6.6% (n=11) received CT only, 15% (n=25) RT only, 27% (n= 45) complete/incomplete STUPP therapy, whereas 13.2% (n=22) received STUPP therapy along with second line treatment. In biopsy only group, there was no notable difference in survival outcomes among the different methylation statuses. For biopsy and any-other-form-of-treatment groups showed a distinct trend of better survival compared to the borderline or unmethylated groups. Overall, methylated patients had better survival as compared to unmethylated or borderline groups. CONCLUSION Methylated MGMT status is a predictor for better overall survival in unresectable IDH wildtype glioblastoma patients undergoing biopsy and treatment.

MAPPING GLIOMAS: A RADIOGRAPHIC AND SPATIAL ANALYSIS ACCORDING TO THE 2021 WHO CLASSIfiCATION

Abstract AIMS The spatial positioning of gliomas within the brain significantly influences prognosis, potentially mirroring the genetic landscape of their precursor cells. Furthermore, the association between glioma subtypes and their spatial distribution underscores a complex interplay that may illuminate underlying pathogenetic mechanisms. This study explores the intricate relationship between the location of gliomas and their classification according to the 2021 WHO guidelines, aiming to unravel the spatial and genetic nuances that define glioma heterogeneity. METHOD In this study, preoperative anatomical MRI data from 242 glioma patients were meticulously analyzed to elucidate spatial distribution patterns in relation to tumor genetics. The imaging data underwent registration to the MNI-152 standard brain space, facilitating uniform analysis. Subsequent tumor segmentation and division of the brain into 84 distinct subregions allowed for a detailed examination of tumor locations. A statistical analysis was conducted to explore the stereospecific frequency of glioma occurrence across these subregions, correlating findings with tumor grade and pivotal genetic markers, including MGMT promoter methylation, IDH1 mutation, 1p/19q co-deletion, and CDKN2A/B homozygous deletion. RESULTS We developed 21 distinct tumor frequency atlases based on subtypes (Glioblastoma, Oligodendroglioma, Astrocytoma), biomarker status (IDH, 1p19q, MGMT, CDKN2A/B), and grade (2, 3, 4). Employing probability vectors for statistical analysis revealed a significant correlation among different subtypes, locations and overall survival. Furthermore, we used a Visualization tool to build a 3D heatmap for each tumor subtype, more intuitively illustrating spatial correlations. CONCLUSION Our study underscores the prognostic value of tumor location and its correlation with glioma subtypes and patient outcomes, suggesting a potential pathway to personalized treatments. Future efforts will focus on incorporating multi-omics data into spatial radiographic analyses.

HAS THE INITIATION OF A FORMAL PATHWAY FOR TUMOUR TISSUE COLLECTION IN NEURO ONCOLOGY PATIENTS LED TO AN INCREASE IN PATIENT’S HAVING WHOLE GENOME SEQUENCING (WGS), AND HOW WILL THIS IMPACT ONCOLOGY MANAGEMENT IN THE FUTURE?

Abstract AIMS To change the standard of care for collection of glial tumour tissue at a London teaching hospital. This would enable WGS on the tumour tissue, and give patients access to further clinical trials. METHOD The previous standard of care was for brain tumour pathology samples to be stored in paraffin. This was not suitable for WGS or for many clinical trials; the tumour samples needed to be sent to the lab as fresh tissue. The introduction of a genomics clinical nurse specialist meant that relationships were built between the surgical team, pathology teams and ward teams. There was a teaching by one of the neurosurgical team to the theatre staff. Genomics CNS led training for the nursing team. Adults started having WGS in May 2023 but many patient’s still had tissue incorrectly stored and could not have the sequencing performed. There was an introduction of a formal pathway in theatre created by one of the neurosurgeons in Dec 2023. RESULTS From May 2023 - November 2023, 58% adult glioma patients had WGS. Since December 2023 - current (March 2023) and the introduction of the formal pathways, 71% had WGS. CONCLUSION A formal pathway has now changed the standard of care for collecting tissue. There is still not 100% compliance, and this remains multifactorial. However the introduction of a formal pathway has now increased the percentage of eligible patients receiving WGS. This positive outcome has now led to discussions about freezing the fresh glioma tissue to allow glioma patient’s wider access to clinical trials which they would previously not have had access to.

INTEGRATING TRANSCRIPTOMICS WITH SPATIAL RADIOGRAPHIC ATLASES FOR GLIOMA ANALYSIS

Abstract AIMS The localization of gliomas in the brain is a critical prognostic factor, reflecting the genetic makeup of their originating cells. Variations in transcriptomic profiles across different regions and subtypes may further shed light on the mechanisms driving tumor development. Our research delves into the complex interplay between glioma locations and their transcriptomic signatures, seeking to dissect the spatial and genetic intricacies contributing to glioma diversity. METHOD In this study, we conducted a comprehensive analysis of preoperative anatomical MRI and transcriptomic data from 242 glioma patients to investigate the relationship between spatial distribution and tumor transcriptomic profiles. The MRI data were standardized by registration to the MNI-152 brain space. Tumors were segmented, and the brain was partitioned into 84 discrete subregions for detailed examination. Transcriptomic data underwent preprocessing, with principal component analysis employed to reduce dimensionality. Statistical analyses were used to assess the relationship between glioma description across different subtypes and PCA results. Additionally, we identify differentially expressed genes and common genes across regions for various glioma subtypes. Comparative analyses with the Allen Brain Atlas (divided into 84 subregions with expression data) were also performed to discern transcriptional differences between healthy individuals and glioma patients, further enhancing our understanding of glioma biology at the molecular level. RESULTS We generated 21 frequency maps detailing regional glioma occurrences, categorized by biomarkers, subtypes, and grades. Notably, some of these maps showed significant correlations with expression principal component analyses (PCAs). Our findings include a set of genes consistently expressed across subregions, alongside region specific differentially expressed genes. Additionally, we identified distinct gene expression patterns when comparing glioma patients to healthy controls in the Allen Brain Atlas, highlighting potential molecular underpinnings of glioma heterogeneity. CONCLUSION Our analysis revealed notable correlations between glioma subtype locations and transcriptomic profiles, underscoring the role of transcriptomic signatures in explaining regional variations in glioma incidence.

Procollagen C-protease enhancer protein promotes glioma growth by activating ERK signaling.

Procollagen C-proteinase enhancer (PCOLCE) promotes tumor progression in multiple cancers. However, the specific role of PCOLCE in gliomas remains enigmatic. In this study, we focused on analyzing PCOLCE expression and its correlation with clinicopathological parameters in glioma specimens; moreover, we explored the effects of PCOLCE in glioma proliferation in vitro and in vivo. A tissue microarray containing 159 human glioma specimens was pressed to explore the correlation between PCOLCE expression and the survival of glioma patients. Cell Counting Kit-8 (CCK8), colony formation, and immunoblot assays were used to detect the role of PCOLCE on cell proliferation in glioma. Furthermore, the in vivo role of PCOLCE was investigated using a subcutaneous glioma xenograft model. The expression of PCOLCE was higher in grade III and IV gliomas than in grade I and II gliomas. High PCOLCE expression was related to a remarkably worse prognosis in glioma patients. Additionally, PCOLCE downregulation impeded glioma cell proliferation. Furthermore, PCOLCE knockdown markedly abrogated p-ERK expression in glioma cells, whereas, it negligibly influenced p38 and JNK signaling. These results indicate that PCOLCE is a feasible prognostic biomarker for glioma, and PCOLCE-induced activation of ERK signaling may be a pro-growth mechanism in glioma cells.