Global post­marketing safety surveillance of Tumor Treating Fields (TTFields) therapy in over 25,000 patients with CNS malignancies treated between 2011-2022.

BACKGROUND: Tumor Treating Fields (TTFields) are alternating electric fields that disrupt cancer cell processes. TTFields therapy is approved for recurrent glioblastoma (rGBM), and newly-diagnosed (nd) GBM (with concomitant temozolomide for ndGBM; US), and for grade IV glioma (EU). We present an updated global, post-marketing surveillance safety analysis of patients with CNS malignancies treated with TTFields therapy. METHODS: Safety data were collected from routine post-marketing activities for patients in North America, Europe, Israel, and Japan (October 2011-October 2022). Adverse events (AEs) were stratified by age, sex, and diagnosis. RESULTS: Overall, 25,898 patients were included (diagnoses: ndGBM [68%], rGBM [26%], anaplastic astrocytoma/oligodendroglioma [4%], other CNS malignancies [2%]). Median (range) age was 59 (3-103) years; 66% patients were male. Most (69%) patients were 18-65 years; 0.4% were < 18 years; 30% were > 65 years. All-cause and TTFields-related AEs occurred in 18,798 (73%) and 14,599 (56%) patients, respectively. Most common treatment-related AEs were beneath-array skin reactions (43%), electric sensation (tingling; 14%), and heat sensation (warmth; 12%). Treatment-related skin reactions were comparable in pediatric (39%), adult (42%), and elderly (45%) groups, and in males (41%) and females (46%); and similar across diagnostic subgroups (ndGBM, 46%; rGBM, 34%; anaplastic astrocytoma/oligodendroglioma, 42%; other, 40%). No TTFields-related systemic AEs were reported. CONCLUSIONS: This long-term, real-world analysis of > 25,000 patients demonstrated good tolerability of TTFields in patients with CNS malignancies. Most therapy-related AEs were manageable localized, non-serious skin events. The TTFields therapy safety profile remained consistent across subgroups (age, sex, and diagnosis), indicative of its broad applicability.

Multicenter integration analysis of TRP channels revealed potential mechanisms of immunosuppressive microenvironment activation and identified a machine learning-derived signature for improving outcomes in gliomas.

AIM: This study aimed to explore the mechanisms of transient receptor potential (TRP) channels on the immune microenvironment and develop a TRP-related signature for predicting prognosis, immunotherapy response, and drug sensitivity in gliomas. METHODS: Based on the unsupervised clustering algorithm, we identified novel TRP channel clusters and investigated their biological function, immune microenvironment, and genomic heterogeneity. In vitro and in vivo experiments revealed the association between TRPV2 and macrophages. Subsequently, based on 96 machine learning algorithms and six independent glioma cohorts, we constructed a machine learning-based TRP channel signature (MLTS). The performance of the MLTS in predicting prognosis, immunotherapy response, and drug sensitivity was evaluated. RESULTS: Patients with high expression levels of TRP channel genes had worse prognoses, higher tumor mutation burden, and more activated immunosuppressive microenvironment. Meanwhile, TRPV2 was identified as the most essential regulator in TRP channels. TRPV2 activation could promote macrophages migration toward malignant cells and alleviate glioma prognosis. Furthermore, MLTS could work independently of common clinical features and present stable and superior prediction performance. CONCLUSION: This study investigated the comprehensive effect of TRP channel genes in gliomas and provided a promising tool for designing effective, precise treatment strategies.

The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model.

This research study explores of the effectiveness of a machine learning image classification model in the accurate identification of various types of brain tumors. The types of tumors under consideration in this study are gliomas, meningiomas, and pituitary tumors. These are some of the most common types of brain tumors and pose significant challenges in terms of accurate diagnosis and treatment. The machine learning model that is the focus of this study is built on the Google Teachable Machine platform (Alphabet Inc., Mountain View, CA). The Google Teachable Machine is a machine learning image classification platform that is built from Tensorflow, a popular open-source platform for machine learning. The Google Teachable Machine model was specifically evaluated for its ability to differentiate between normal brains and the aforementioned types of tumors in MRI images. MRI images are a common tool in the diagnosis of brain tumors, but the challenge lies in the accurate classification of the tumors. This is where the machine learning model comes into play. The model is trained to recognize patterns in the MRI images that correspond to the different types of tumors. The performance of the machine learning model was assessed using several metrics. These include precision, recall, and F1 score. These metrics were generated from a confusion matrix analysis and performance graphs. A confusion matrix is a table that is often used to describe the performance of a classification model. Precision is a measure of the model's ability to correctly identify positive instances among all instances it identified as positive. Recall, on the other hand, measures the model's ability to correctly identify positive instances among all actual positive instances. The F1 score is a measure that combines precision and recall providing a single metric for model performance. The results of the study were promising. The Google Teachable Machine model demonstrated high performance, with accuracy, precision, recall, and F1 scores ranging between 0.84 and 1.00. This suggests that the model is highly effective in accurately classifying the different types of brain tumors. This study provides insights into the potential of machine learning models in the accurate classification of brain tumors. The findings of this study lay the groundwork for further research in this area and have implications for the diagnosis and treatment of brain tumors. The study also highlights the potential of machine learning in enhancing the field of medical imaging and diagnosis. With the increasing complexity and volume of medical data, machine learning models like the one evaluated in this study could play a crucial role in improving the accuracy and efficiency of diagnoses. Furthermore, the study underscores the importance of continued research and development in this field to further refine these models and overcome any potential limitations or challenges. Overall, the study contributes to the field of medical imaging and machine learning and sets the stage for future research and advancements in this area.

Single Nucleus Total RNA Sequencing of Formalin-Fixed Paraffin-Embedded Gliomas.

Gliomas, the predominant form of brain cancer, comprise diverse malignant subtypes with limited curative therapies available. The insufficient understanding of their molecular diversity and evolutionary processes hinders the advancement of new treatments. Technical complexities associated with formalin-fixed paraffin-embedded (FFPE) clinical samples hinder molecular-level analyses of gliomas. Current single-cell RNA sequencing (scRNA-seq) platforms are inadequate for large-scale clinical applications. In this study, automated snRandom-seq is developed, a high-throughput single-nucleus total RNA sequencing platform optimized for archival FFPE samples. This platform integrates automated single-nucleus isolation and droplet barcoding systems with the random primer-based scRNA-seq chemistry, accommodating a broad spectrum of sample types. The automated snRandom-seq is applied to analyze 116 492 single nuclei from 17 FFPE samples of various glioma subtypes, including rare clinical samples and matched primary-recurrent glioblastomas (GBMs). The study provides comprehensive insights into the molecular characteristics of gliomas at the single-cell level. Abundant non-coding RNAs (ncRNAs) with distinct expression profiles across different glioma clusters and uncovered promising recurrence-related targets and pathways in primary-recurrent GBMs are identified. These findings establish automated snRandom-seq as a robust tool for scRNA-seq of FFPE samples, enabling exploration of molecular diversities and tumor evolution. This platform holds significant implications for large-scale integrative and retrospective clinical research.

Trans-temporal trans-choroidal resection of thalamic and thalamopeduncular tumors: how I do it.

BACKGROUND:  Surgical resection is the cornerstone of treatment for low-grade tumors, albeit total excision is beneficial. As the thalamus is surrounded by vital neurovascular system, lesions here present a surgical challenge. METHOD: This article aims to demonstrate the trans-temporal, trans-choroidal fissure approach's effective surgical therapy on patients with thalamic lesions. With this approach, we were able to remove the tumor completely in three patients and almost completely in six more. Here we discuss a few technical details and potential hazards of the procedure with an operative video. CONCLUSION: This approach  provides excellent access to the deep areas of brain.

Enhanced Multimodal Brain Tumor Classification in MR Images using 2D ResNet as backbone with Explicit Tumor Size Information.

It's a major public health problem of global concern that malignant gliomas tend to grow rapidly and infiltrate surrounding tissues. Accurate grading of the tumor can determine the degree of malignancy to formulate the best treatment plan, which can eliminate the tumor or limit widespread metastasis of the tumor, saving the patient's life and improving their prognosis. To more accurately predict the grading of gliomas, we proposed a novel method of combining the advantages of 2D and 3D Convolutional Neural Networks for tumor grading by multimodality on Magnetic Resonance Imaging. The core of the innovation lies in our combination of tumor 3D information extracted from multimodal data with those obtained from a 2D ResNet50 architecture. It solves both the lack of temporal-spatial information provided by 3D imaging in 2D convolutional neural networks and avoids more noise from too much information in 3D convolutional neural networks, which causes serious overfitting problems. Incorporating explicit tumor 3D information, such as tumor volume and surface area, enhances the grading model's performance and addresses the limitations of both approaches. By fusing information from multiple modalities, the model achieves a more precise and accurate characterization of tumors. The model I s trained and evaluated using two publicly available brain glioma datasets, achieving an AUC of 0.9684 on the validation set. The model's interpretability is enhanced through heatmaps, which highlight the tumor region. The proposed method holds promise for clinical application in tumor grading and contributes to the field of medical diagnostics for prediction.

Beyond conventional imaging: Advancements in MRI for glioma malignancy prediction and molecular profiling.

This review examines the advancements in magnetic resonance imaging (MRI) techniques and their pivotal role in diagnosing and managing gliomas, the most prevalent primary brain tumors. The paper underscores the importance of integrating modern MRI modalities, such as diffusion-weighted imaging and perfusion MRI, which are essential for assessing glioma malignancy and predicting tumor behavior. Special attention is given to the 2021 WHO Classification of Tumors of the Central Nervous System, emphasizing the integration of molecular diagnostics in glioma classification, significantly impacting treatment decisions. The review also explores radiogenomics, which correlates imaging features with molecular markers to tailor personalized treatment strategies. Despite technological progress, MRI protocol standardization and result interpretation challenges persist, affecting diagnostic consistency across different settings. Furthermore, the review addresses MRI's capacity to distinguish between tumor recurrence and pseudoprogression, which is vital for patient management. The necessity for greater standardization and collaborative research to harness MRI's full potential in glioma diagnosis and personalized therapy is highlighted, advocating for an enhanced understanding of glioma biology and more effective treatment approaches.

The Landscape of Pediatric High-Grade Gliomas: The Virtues and Pitfalls of Pre-Clinical Models.

Pediatric high-grade gliomas (pHGG) are malignant and usually fatal central nervous system (CNS) WHO Grade 4 tumors. The majority of pHGG consist of diffuse midline gliomas (DMG), H3.3 or H3.1 K27 altered, or diffuse hemispheric gliomas (DHG) (H3.3 G34-mutant). Due to diffuse tumor infiltration of eloquent brain areas, especially for DMG, surgery has often been limited and chemotherapy has not been effective, leaving fractionated radiation to the involved field as the current standard of care. pHGG has only been classified as molecularly distinct from adult HGG since 2012 through Next-Generation sequencing approaches, which have shown pHGG to be epigenetically regulated and specific tumor sub-types to be representative of dysregulated differentiating cells. To translate discovery research into novel therapies, improved pre-clinical models that more adequately represent the tumor biology of pHGG are required. This review will summarize the molecular characteristics of different pHGG sub-types, with a specific focus on histone K27M mutations and the dysregulated gene expression profiles arising from these mutations. Current and emerging pre-clinical models for pHGG will be discussed, including commonly used patient-derived cell lines and in vivo modeling techniques, encompassing patient-derived xenograft murine models and genetically engineered mouse models (GEMMs). Lastly, emerging techniques to model CNS tumors within a human brain environment using brain organoids through co-culture will be explored. As models that more reliably represent pHGG continue to be developed, targetable biological and genetic vulnerabilities in the disease will be more rapidly identified, leading to better treatments and improved clinical outcomes.

Microbubble-Enhanced Focused Ultrasound for Infiltrating Gliomas.

Infiltrating gliomas are challenging to treat, as the blood-brain barrier significantly impedes the success of therapeutic interventions. While some clinical trials for high-grade gliomas have shown promise, patient outcomes remain poor. Microbubble-enhanced focused ultrasound (MB-FUS) is a rapidly evolving technology with demonstrated safety and efficacy in opening the blood-brain barrier across various disease models, including infiltrating gliomas. Initially recognized for its role in augmenting drug delivery, the potential of MB-FUS to augment liquid biopsy and immunotherapy is gaining research momentum. In this review, we will highlight recent advancements in preclinical and clinical studies that utilize focused ultrasound to treat gliomas and discuss the potential future uses of image-guided precision therapy using focused ultrasound.

Does 5-ALA Fluorescence Microscopy Improve Complete Resectability in Cerebral/Cerebellar Metastatic Surgery? A Retrospective Data Analysis from a Cranial Center.

(1) Background: In this study, the intraoperative fluorescence behavior of brain metastases after the administration of 5-aminolevulinic acid (5-ALA) was analyzed. The aim was to investigate whether the resection of brain metastases using 5-ALA fluorescence also leads to a more complete resections and thus to a prolongation of survival; (2) Methods: The following variables have been considered: age, sex, number of metastases, localization, involvement of eloquent area, correlation between fluorescence and primary tumor/subtype, resection, and survival time. The influence on the degree of resection was determined with a control MRI within the first three postoperative days; (3) Results: Brain metastases fluoresced in 57.5% of cases. The highest fluorescence rates of 73.3% were found in breast carcinoma metastases and the histologic subtype adenocarcinoma (68.1%). No correlation between fluorescence behavior and localization, primary tumor, or histological subtype was found. Complete resection was detected in 82.5%, of which 56.1% were fluorescence positive. There was a trend towards improved resectability (increase of 12.1%) and a significantly longer survival time (p = 0.009) in the fluorescence-positive group; (4) Conclusions: 5-ALA-assisted extirpation leads to a more complete resection and longer survival and can therefore represent a low-risk addition to modern surgery for brain metastases.

Awake Craniotomy for a Frontal Astrocytoma: A Case Report.

Awake craniotomy is a surgical procedure that has been gaining significance over the past decades. Neuronavigation is an intraoperative technology that locates tumors and monitors the brain cortex during awake craniotomy. The presence of cerebral low-grade gliomas in the frontal lobe creates a risk of affecting vital centers of the brain cortex during surgery. We present a clinical case of a 42-year-old male patient who entered the neurosurgery clinic with a clinical manifestation of headache for two months. MRI showed evidence of the recurrence of a left frontal glioma. Differential diagnoses of frontal gliomas include metastases, abscesses, and cysts. The pathophysiologic background of the disease is the mutation of neuroglial cells, which leads to an abnormal and uncontrollable proliferation. Under sleep-awake anesthesia, operative treatment was performed through left frontal awake craniotomy under neuronavigation. As a result, a total excision was achieved. Motor functions of the right limbs and speech have been preserved. The patient was mobilized on the day after the intervention. Surgery-related complications were not observed. The patient had relief from the symptoms and was discharged on the fifth day. Awake craniotomy combined with neuronavigation was the most efficient and the least harmful method for the excision of the tumor. For low-grade gliomas localized in the frontal area of the encephalon, awake craniotomy is the only secure option for surgery.

Safety and Efficacy of Laser Interstitial Thermal Therapy as Upfront Therapy in Primary Glioblastoma and IDH-Mutant Astrocytoma: A Meta-Analysis.

Although primary studies have reported the safety and efficacy of LITT as a primary treatment in glioma, they are limited by sample sizes and institutional variation in stereotactic parameters such as temperature and laser power. The current literature has yet to provide pooled statistics on outcomes solely for primary brain tumors according to the 2021 WHO Classification of Tumors of the Central Nervous System (WHO CNS5). In the present study, we identify recent articles on primary CNS neoplasms treated with LITT without prior intervention, focusing on relationships with molecular profile, PFS, and OS. This meta-analysis includes the extraction of data from primary sources across four databases using the Covidence systematic review manager. The pooled data suggest LITT may be a safe primary management option with tumor ablation rates of 94.8% and 84.6% in IDH-wildtype glioblastoma multiforme (GBM) and IDH-mutant astrocytoma, respectively. For IDH-wildtype GBM, the pooled PFS and OS were 5.0 and 9.0 months, respectively. Similar to rates reported in the prior literature, the neurologic and non-neurologic complication rates for IDH-wildtype GBM were 10.3% and 4.8%, respectively. The neurologic and non-neurologic complication rates were somewhat higher in the IDH-mutant astrocytoma cohort at 33% and 8.3%, likely due to a smaller cohort size.

Olaparib in recurrent isocitrate dehydrogenase mutant high-grade glioma: A phase 2 multicenter study of the POLA Network.

Background: Based on preclinical studies showing that IDH-mutant (IDHm) gliomas could be vulnerable to PARP inhibition we launched a multicenter phase 2 study to test the efficacy of olaparib monotherapy in this population. Methods: Adults with recurrent IDHm high-grade gliomas (HGGs) after radiotherapy and at least one line of alkylating chemotherapy were enrolled. The primary endpoint was a 6-month progression-free survival rate (PFS-6) according to response assessment in neuro-oncology criteria. Pre-defined threshold for study success was a PFS-6 of at least 50%. Results: Thirty-five patients with recurrent IDHm HGGs were enrolled, 77% at ≥ 2nd recurrence. Median time since diagnosis and radiotherapy were 7.5 years and 33 months, respectively. PFS-6 was 31.4% (95% CI [16.9; 49.3%]). Two patients (6%) had an objective response and 14 patients (40%) had a stable disease as their best response. Median PFS and median overall survival were 2.05 and 15.9 months, respectively. Oligodendrogliomas (1p/19q codeleted) had a higher PFS-6 (53.4% vs. 15.7%, P = .05) than astrocytomas while an initial diagnosis of grade 4 astrocytoma tended to be associated with a lower PFS-6 compared to grade 2/3 gliomas (0% vs 31.4%, P = .16). A grade 2 or 3 treatment-related adverse event was observed in 15 patients (43%) and 5 patients (14%), respectively. No patient definitively discontinued treatment due to side effects. Conclusions: Although it did not meet its primary endpoint, the present study shows that in this heavily pretreated population, olaparib monotherapy was well tolerated and resulted in some activity, supporting further PARP inhibitors evaluation in IDHm HGGs, especially in oligodendrogliomas.

Pan-cancer analysis of the disulfidptosis-related gene RPN1 and its potential biological function and prognostic significance in gliomas.

Background: Numerous studies have shown a strong correlation between disulfidptosis and various cancers. However, the expression and function of RPN1, a crucial gene in disulfidptosis, remain unclear in the context of cancer. Methods: Gene expression and clinical information on lung adenocarcinoma were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. RPN1 expression was analyzed using the Timer2.0 and the Human Protein Atlas (HPA) databases. Prognostic significance was assessed using Cox regression analysis and Kaplan-Meier curves. Genetic mutations and methylation levels were examined using the cBioPortal and UALCAN platforms, respectively. The relationship between RPN1 and tumor mutation burden (TMB) and microsatellite instability (MSI) across different cancer types was analyzed using the Spearman correlation coefficient. The relationship between RPN1 and immune cell infiltration was analyzed using the Timer2.0 database, whereas variations in drug sensitivity were explored using the CellMiner database. Receiver operating characteristic curves validated RPN1's diagnostic potential in glioma, and its correlation with immune checkpoint inhibitors (ICIs) was assessed using Spearman's correlation coefficient. Single-sample gene set enrichment analysis elucidated a link between RPN1 and immune cells and pathways. In addition, a nomogram based on RPN1 was developed to predict patient prognosis. The functional impact of RPN1 on glioma cells was confirmed using scratch and Transwell assays. Result: RPN1 was aberrantly expressed in various cancers and affected patient prognosis. The main mutation type of RPN1 in the cancer was amplified. RPN1 exhibited a positive correlation with myeloid-derived suppressor cells, neutrophils, and macrophages, and a negative correlation with CD8+ T cells and hematopoietic stem cells. RPN1 expression was associated with TMB and MSI in various cancers. The expression of RPN1 affected drug sensitivity in cancer cells. RPN1 was positively correlated with multiple ICIs in gliomas. RPN1 also affected immune cell infiltration into the tumor microenvironment. RPN1 was an independent prognostic factor for gliomas, and the nomogram demonstrated excellent predictive performance. Interference with RPN1 expression reduces the migratory and invasive ability of glioma cells. Conclusion: RPN1 exerts multifaceted effects on different stages of cancer, including immune infiltration, prognosis, and treatment outcomes. RPN1 expression affects the prognosis and immune microenvironment infiltration in patients with glioma, making RPN1 a potential target for the treatment of glioma.

Continuous response despite reduced dose of trametinib as single agent in an adolescent with a relapsed disseminated pediatric low-grade glioma KIAA1549-BRAF fusion positive: a case report and review of the literature.

Dissemination in pediatric low-grade glioma may occur in about 4%-10% of patients according to retrospective cohort studies. Due to its low incidence, there is no consensus on treatment for these patients. According to the constitutional activation of the MAPK/ERK pathway in these tumors, MEK inhibitors such as trametinib have been used successfully in the relapsed setting. Skin toxicity is frequent in patients receiving trametinib, normally mild to moderate, but sometimes severe, needing to discontinue the drug, limiting the efficacy in the tumor. There is not much information in the literature regarding whether reducing the dose of trametinib is able to maintain efficacy while, at the same time, decreasing toxicity. Here, we present an adolescent, with severe skin toxicity, whose trametinib dose was reduced by 50% and efficacy on the tumor continued while skin toxicity significantly decreased.

[Frontal aslant tract: Anatomy and implications in brain glioma neurosurgery]. / Tracto oblicuo frontal: anatomía e implicancia en neurocirugía de gliomas cerebrales.

The frontal aslant tract (FAT) connects the supplementary motor area (SMA) with the pars opercularis. Its role in language and its implications in glioma surgery remain under discussion. We present an anatomosurgical study of three cases with surgical resolution. Three patients with gliomas in the left frontal lobe were operated on using an awake patient protocol with cortical and subcortical mapping techniques, conducting motor and language evaluations. Tractography was performed using DSI Studio software. All three patients showed intraoperative language inhibition through subcortical stimulation of the FAT. Resection involving the FAT correlated with language deficits in all cases and movement initiation deficits in two cases. All patients recovered from their deficits at six months postoperatively. In conclusion, the tract has been successfully reconstructed, showing both anatomical and functional complexity, supporting the idea of its mapping and preservation in glioma surgery. Future interdisciplinary studies are necessary to determine the transient or permanent nature of the deficits.

El tracto oblicuo frontal (TOF) conecta el área motora suplementaria (AMS) con la pars opercularis. Su rol en el lenguaje y su implicancia en la cirugía de gliomas siguen en discusión. Presentamos un estudio anatomoquirúrgico de tres casos con resolución quirúrgica. Se operaron tres pacientes con gliomas en el lóbulo frontal izquierdo utilizando protocolo de paciente despierto con técnicas de mapeo cortical y subcortical realizando evaluación motora y del lenguaje. Las tractografías fueron realizadas con el software DSI Studio. Los tres pacientes presentaron inhibición intraoperatoria del lenguaje mediante la estimulación subcortical de TOF. La resección en contacto con el TOF se correlacionó con déficits del lenguaje en todos los casos y en dos casos déficits en la iniciación del movimiento. Todos los pacientes recuperaron su déficit a los seis meses postoperatorios. En conclusión, se ha logrado reconstruir al tracto. Éste presenta una complejidad anatómica y funcional, que apoya la idea de su mapeo y preservación en la cirugía de gliomas. Futuros estudios interdisciplinarios son necesarios para determinar el carácter transitorio o permanente de los déficits.

Dissecting the prognostic signature of patients with astrocytoma isocitrate dehydrogenase-mutant grade 4: a large multicenter, retrospective study.

BACKGROUND: The World Health Organization (WHO) 2021 classification of central nervous system (CNS) tumors classified astrocytoma isocitrate dehydrogenase-mutant (A IDHm) with either microvascular proliferation and/or necrosis or homozygous deletion of CDKN2A/B as CNS grade 4 (CNS WHO G4), introducing a distinct entity and posing new challenges to physicians for appropriate management and prognostication. PATIENTS AND METHODS: We retrospectively collected information about patients diagnosed with A IDHm CNS WHO G4 at three reference neuro-oncological Italian centers and correlated them with survival. RESULTS: A total of 133 patients were included. Patients were young (median age 41 years) and most received post-operative treatment including chemo-radiation (n = 101) and/or temozolomide maintenance (n = 112). With a median follow-up of 51 months, the median overall survival (mOS) was 31.2 months, with a 5-year survival probability of 26%. In the univariate analysis, complete resection (mOS: 40.2 versus 26.3 months, P = 0.03), methyl-guaninemethyltransferase (MGMT) promoter methylation (mOS: 40.7 versus 18 months, P = 0.0136), and absence of telomerase reverse transcriptase (TERT) promoter mutation (mOS: 40.7 versus 18 months, P = 0.0003) correlated with better prognosis. In the multivariate models, lack of TERT promoter mutation [hazard ratio (HR) 0.23, 95% confidence interval (CI) 0.07-0.82, P = 0.024] and MGMT methylation (HR 0.40, 95% CI 0.20-0.81, P = 0.01) remained associated with improved survival. CONCLUSIONS: This is the largest experience in Western countries exploring the prognostic signature of patients with A IDHm CNS G4. Our results show that MGMT promoter methylation and TERT promoter mutation may impact clinical outcomes. This may support physicians in prognostication, clinical management, and design of future studies of this distinct diagnostic entity.

High expression of BTN3A1 is associated with clinical and immunological characteristics and predicts a poor prognosis in advanced human gliomas.

Introduction: Gliomas represent the most prevalent and aggressive tumors within the central nervous system. Despite the current standard treatments, the median survival time for glioblastoma patients remains dismal, hovering around 14 months. While attempts have been made to inhibit the PD-1/PD-L1 and CTLA-4/CD80-CD86 axes through immunotherapy, the outcomes have yet to demonstrate significant efficacy. The immune checkpoint Butyrophilin 3A1 (BTN3A1) can either be involved in advantageous or detrimental function depending on the cancer type. Methods: In our study, we utilized a Moroccan cohort to delve into the role of BTN3A1 in gliomas. A transcriptomic analysis was conducted on 34 patients, which was then corroborated through a protein analysis in 27 patients and validated using the TCGA database (n = 667). Results: Our results revealed an elevated expression of BTN3A1 in glioblastoma (grade 4), as evidenced in both the TCGA database and our cohort of Moroccan glioma patients. Within the TCGA cohort, BTN3A1 expression was notably higher in patients with wild-type IDH. We observed a positive correlation between BTN3A1 expression and immune infiltration of B cells, CD8+ T cells, naive CD4+ T cells, and M2 macrophages. Patients exhibiting increased BTN3A1 expression also presented elevated levels of TGF-ß, IL-10, and TIM-3 compared to those with reduced BTN3A1 expression. Notably, patients with high BTN3A1 expression were associated with a poorer prognosis than their counterparts with lower expression. Conclussion: Our findings suggest that BTN3A1 might promote the establishment of an immunosuppressive microenvironment. Consequently, targeting BTN3A1 could offer novel therapeutic avenues for the management of advanced gliomas.

Deep CNNs for glioma grading on conventional MRIs: Performance analysis, challenges, and future directions.

The increasing global incidence of glioma tumors has raised significant healthcare concerns due to their high mortality rates. Traditionally, tumor diagnosis relies on visual analysis of medical imaging and invasive biopsies for precise grading. As an alternative, computer-assisted methods, particularly deep convolutional neural networks (DCNNs), have gained traction. This research paper explores the recent advancements in DCNNs for glioma grading using brain magnetic resonance images (MRIs) from 2015 to 2023. The study evaluated various DCNN architectures and their performance, revealing remarkable results with models such as hybrid and ensemble based DCNNs achieving accuracy levels of up to 98.91%. However, challenges persisted in the form of limited datasets, lack of external validation, and variations in grading formulations across diverse literature sources. Addressing these challenges through expanding datasets, conducting external validation, and standardizing grading formulations can enhance the performance and reliability of DCNNs in glioma grading, thereby advancing brain tumor classification and extending its applications to other neurological disorders.