Type Of Brain Tumor Research Articles

A novel nanoplatform-based circCSNK1G3 affects CBX7 protein and promotes glioma cell growth

Bioenvironmental and biological factors have the potential to contribute to the development of glioma, a type of brain tumor. Recent studies have suggested that a unique circular RNA called circCSNK1G3 could play a role in promoting the growth of glioma cells. It does this by stabilizing a specific microRNA called miR-181 and reducing the expression of a tumor-suppressor gene known as chromobox protein homolog 7 (CBX7). To further investigate circCSNK1G3 and its effects on glioma, we utilized a nanoplatform called adeno-associated virus (AAV)-RNAi.To explore the functional implications of circCSNK1G3, we employed siRNA to silence its expression. Along with these effects, the silencing of circCSNK1G3 led to a depletion of miR-181d and an upregulation of CBX7. When we introduced miR-181d mimics, which artificially increase the levels of miR-181d, the anti-glioma cell activity induced by circCSNK1G3 siRNA was almost completely reversed. Conversely, inhibiting miR-181d mimicked the effects of circCSNK1G3 silencing. Moreover, when we overexpressed circCSNK1G3 in glioma cells, we observed an elevation of miR-181d and a depletion of CBX7. We found that the growth of A172 xenografts (tumors) carrying circCSNK1G3 shRNA was significantly inhibited. In these xenograft tissues, we detected a depletion of circCSNK1G3 and miR-181d, as well as an upregulation of CBX7.

A Multi-Center, Multi-Parametric MRI Dataset of Primary and Secondary Brain Tumors

Brain metastases (BMs) and high-grade gliomas (HGGs) are the most common and aggressive types of malignant brain tumors in adults, with often poor prognosis and short survival. As their clinical symptoms and image appearances on conventional magnetic resonance imaging (MRI) can be astonishingly similar, their accurate differentiation based solely on clinical and radiological information can be very challenging, particularly for “cancer of unknown primary”, where no systemic malignancy is known or found. Non-invasive multiparametric MRI and radiomics offer the potential to identify these distinct biological properties, aiding in the characterization and differentiation of HGGs and BMs. However, there is a scarcity of publicly available multi-origin brain tumor imaging data for tumor characterization. In this paper, we introduce a multi-center, multi-origin brain tumor MRI (MOTUM) imaging dataset obtained from 67 patients: 29 with high-grade gliomas, 20 with lung metastases, 10 with breast metastases, 2 with gastric metastasis, 4 with ovarian metastasis, and 2 with melanoma metastasis. This dataset includes anonymized DICOM files alongside processed FLAIR, T1-weighted, contrast-enhanced T1-weighted, T2-weighted sequences images, segmentation masks of two tumor regions, and clinical data. Our data-sharing initiative is to support the benchmarking of automated tumor segmentation, multi-modal machine learning, and disease differentiation of multi-origin brain tumors in a multi-center setting.

MicroRNA-143 overexpression enhances the chemosensitivity of A172 glioblastoma cells to carmustine.

As an aggressive malignancy, glioblastoma multiforme (GBM) is the most common type of brain tumor. The existing treatments have shown limited achievement in increasing the overall survival of patients. Therefore, identifying the key molecules involved in GBM will provide new potential therapeutic targets. Carmustine is an alkylating agent used as a supplementary therapeutic option for GBM. However, the extensive use of carmustine has been limited by uncertainty about its efficacy. MicroRNAs (miRNAs) are essential in post-transcriptional gene regulation. Many aberrantly expressed miRNAs have been detected in various types of human cancer, including GBM. In this study, we evaluated the potential therapeutic effect of miR-143 in combination with carmustine on GBM cells. A172 cells were transfected with miR-143 mimics and then treated with carmustine. To assess the cell viability, apoptosis induction, and cell cycle progression, the MTT assay, Annexin V/PI apoptosis assay, and flow cytometry were used, respectively. Furthermore, qRT-PCR assay was applied to evaluate the expression level of genes related to apoptosis. The obtained results evidenced that miR-143 transfection could promote the sensitivity of A172 cells to carmustine and enhance carmustine-induced apoptosis via modulating the expression levels of Caspase-3, Caspase-9, Bax, and Bcl-2. Also, our results revealed that combination therapy could effectively diminish cell cycle progression in A172 cells. In conclusion, these results confirmed that miR-143 could enhance carmustine-mediated suppression of cell proliferation and improve the chemosensitivity of A172 cells to this chemotherapeutic agent. Therefore, miR-143 combination therapy may be a promising GBM treatment approach.

Integrating Convolutional Neural Networks with Attention Mechanisms for Magnetic Resonance Imaging-Based Classification of Brain Tumors.

The application of magnetic resonance imaging (MRI) in the classification of brain tumors is constrained by the complex and time-consuming characteristics of traditional diagnostics procedures, mainly because of the need for a thorough assessment across several regions. Nevertheless, advancements in deep learning (DL) have facilitated the development of an automated system that improves the identification and assessment of medical images, effectively addressing these difficulties. Convolutional neural networks (CNNs) have emerged as steadfast tools for image classification and visual perception. This study introduces an innovative approach that combines CNNs with a hybrid attention mechanism to classify primary brain tumors, including glioma, meningioma, pituitary, and no-tumor cases. The proposed algorithm was rigorously tested with benchmark data from well-documented sources in the literature. It was evaluated alongside established pre-trained models such as Xception, ResNet50V2, Densenet201, ResNet101V2, and DenseNet169. The performance metrics of the proposed method were remarkable, demonstrating classification accuracy of 98.33%, precision and recall of 98.30%, and F1-score of 98.20%. The experimental finding highlights the superior performance of the new approach in identifying the most frequent types of brain tumors. Furthermore, the method shows excellent generalization capabilities, making it an invaluable tool for healthcare in diagnosing brain conditions accurately and efficiently.

Biomarkers in Cerebrospinal Fluid for the Diagnosis and Monitoring of Gliomas.

Gliomas are the most common type of malignant brain tumor and are characterized by a plethora of heterogeneous molecular alterations. Current treatments require the emergence of reliable biomarkers that will aid personalized treatment decisions and increase life expectancy. Glioma tissues are not as easily accessible as other solid tumors; therefore, detecting prominent biomarkers in biological fluids is necessary. Cerebrospinal fluid (CSF) circulates adjacent to the cerebral parenchyma and holds promise for discovering useful prognostic, diagnostic, and predictive biomarkers. In this review, we summarize extensive research regarding the role of circulating DNA, tumor cells, proteins, microRNAs, metabolites, and extracellular vesicles as potential CSF biomarkers for glioma diagnosis, prognosis, and monitoring. Future studies should address discrepancies and issues of specificity regarding CSF biomarkers, as well as the validation of candidate biomarkers.

Ensemble XMOB Approach for Brain Tumor Detection Based on Feature Extraction

Brain tumors are a serious health threat in adults. These fast-growing abnormal cell masses disrupt normal brain function. Doctors use various imaging techniques to identify the specific type, size, and location of brain tumors in patients. Accurately identifying and classifying brain tumors is crucial for understanding how they develop and progress. Magnetic Resonance Imaging (MRI), a well-established medical imaging technique, plays a vital role in this process by assisting radiologists in investigating the location of the tumor. Previous models frequently encounter a compromise between accuracy and computational efficiency, lacking an approach that successfully integrates both aspects.This study introduces an innovative ensemble model termed as “XMob Approach” that combines the deep features extraction abilities of Xception with computational efficiency of MobleNet for binary classification of brain Tumor. The Xmob Approach leverages the strengths of both architectures : Xception depthwise seperable convolutions allow for detailed feature extraction whereas MobileNet’s lightweight structure ensures efficient computation making it suitable for real life application. This combination aims to enhance in medical diagnostics, promising enhanced accuracy and efficiency. This study explores the potential of integrating these pre-trained architectures to provide real-time, automated diagnostic assistance, improving the speed and precision of brain tumor detection. In our methodology pre-processed MRI scans undergo feature extraction through Xception model, capturing complicated patterns indicative of tumor presence. Simultaneously MobileNet processed these images emphasizing computational efficiency without compromising on performance.The output of both the modesl are then integrated using ensemble technique to improve overall classification accuracy. By integrating the complementary strengths of Xception and MobileNet , the XMob Approach represent a significant step towards the field of medical diagnostic promising improved outcomes for patients through advanced technology. DOI: https://doi.org/10.52783/tjjpt.v45.i03.7253

Unveiling the therapeutic prospects of IFNW1 and IFNA21: insights into glioma pathogenesis and clinical significance.

Glioma, a type of brain tumor, poses significant challenges due to its heterogeneous nature and limited treatment options. Interferon-related genes (IRGs) have emerged as potential players in glioma pathogenesis, yet their expression patterns and clinical implications remain to be fully elucidated. We conducted a comprehensive analysis to investigate the expression patterns and functional enrichment of IRGs in glioma. This involved constructing protein-protein interaction networks, heatmap analysis, survival curve plotting, diagnostic and prognostic assessments, differential expression analysis across glioma subgroups, GSVA, immune infiltration analysis, and drug sensitivity analysis. Our analysis revealed distinct expression patterns and functional enrichment of IRGs in glioma. Notably, IFNW1 and IFNA21 were markedly downregulated in glioma tissues compared to normal tissues, and higher expression levels were associated with improved overall survival and disease-specific survival. Furthermore, these genes showed diagnostic capabilities in distinguishing glioma tissues from normal tissues and were significantly downregulated in higher-grade and more aggressive gliomas. Differential expression analysis across glioma subgroups highlighted the association of IFNW1 and IFNA21 expression with key pathways and biological processes, including metabolic reprogramming and immune regulation. Immune infiltration analysis revealed their influence on immune cell composition in the tumor microenvironment. Additionally, elevated expression levels were associated with increased resistance to chemotherapeutic agents. Our findings underscore the potential of IFNW1 and IFNA21 as diagnostic biomarkers and prognostic indicators in glioma. Their roles in modulating glioma progression, immune response, and drug sensitivity highlight their significance as potential therapeutic targets. These results contribute to a deeper understanding of glioma biology and may inform the development of personalized treatment strategies for glioma patients.

Size-Dependent Glioblastoma Targeting by Polymeric Nanoruler with Prolonged Blood Circulation.

Currently, there is no effective treatment for glioblastoma multiforme (GBM), the most frequent and malignant type of brain tumor. The blood-brain (tumor) barrier (BB(T)B), which is composed of tightly connected endothelial cells and pericytes (with partial vasculature collapse), hampers nanomedicine accumulation in tumor tissues. We aimed to explore the effect of nanomedicine size on passive targeting of GBM. A series of size-tunable poly(ethylene glycol) (PEG)-grafted copolymers (gPEGs) were constructed with hydrodynamic diameters of 8-30 nm. Biodistribution studies using orthotopic brain tumor-bearing mice revealed that gPEG brain tumor accumulation was maximized at 10 nm with ∼14 dose %/g of tumor, which was 19 times higher than that in the normal brain region and 4.2 times higher than that of 30-nm gPEG. Notably, 10-nm gPEG exhibited substantially higher brain tumor accumulation than 11-nm linear PEG owing to the prolonged blood circulation property of gPEGs, which is derived from a densely PEG-packed structure. 10 nm gPEG exhibited deeper penetration into the brain tumor tissue than the larger gPEGs did (>10 nm). This study demonstrates, for the first time, the great potential of a nanomedicine downsizing strategy for passive GBM targeting.

Therapeutic potential of boron-based nanoparticles for enhanced glioblastoma treatment

Glioblastoma multiforme (GBM) is the most common type of brain tumor and its treatment is difficult due to its infiltrative nature into normal brain tissue, heterogeneity, and presence of the blood-brain barrier (BBB), which restricts the penetration of chemotherapy drugs into the brain tissue and also the tumor site. Nanoparticles have emerged as a potential solution to improve the stability and delivery of the therapeutic agents directly to the tumor site. In this paper, we developed boron-based nanocarrier systems for providing the anticancer drug doxorubicin (DOX) to T98G human glioblastoma cells. Three types of nanoparticles, calcium tetraborate (CBO), magnesium tetraborate (MBO), and strontium tetraborate (SBO) were prepared with sizes ranging approximately from 200 to 350 nm. DOX was effectively incorporated into the nanoparticles with ∼95 % loading capacity. As a result of cytotoxicity studies, the IC50 of free DOX was determined as ∼25 μg/mL (0.046 mM) for the glioblastoma cells. In contrast to blank borate nanoparticles, which had no harmful effect on the cells except SBO at high concentrations, DOX-containing borate nanoparticles increased toxicity in a way that was dependent on both time and concentration. Also, the nanoparticles were successively taken by the glioblastoma cells and therefore exhibited more cytotoxic effect than free DOX. As a result, these DOX-loaded boron-based nanoparticles are promising carrier systems for further research in glioblastoma treatment such as combined therapy with boron neutron capture therapy (BNCT).

Epidemiology of malignant gliomas in Iran: first report of Iranian National Population-based Cancer Registry, 2009-2017.

Malignant gliomas constitute the most common type of primary malignant brain tumors. Most previous studies have evaluated the epidemiology of malignant gliomas in developed countries. Hence, there is a lack of evidence in this regard from developing countries. This study is the first epidemiological report on the status of malignant glioma in Iran between 2009 and 2017. Data from the Iranian National Population-based Cancer Registry (covering 98% of the Iranian population) on CNS tumors recorded from 2009 to 2017 were used for analysis. Age-adjusted incidence rates were calculated by sex, tumor histology, tumor site, and year of diagnosis. Trend analysis of incidence rates was also performed. Survival data were recorded and the Cox proportional hazards model was used to evaluate underlying risk factors. A total of 8484 patients were diagnosed with malignant glioma between 2009 and 2017 in Iran. The overall age-adjusted incidence rate of malignant gliomas over the 9-year period was 1.71 per 100,000 persons. The most common histology of malignant gliomas was glioblastoma (81.4%). A significant increase in the incidence of malignant gliomas was found between 2009 and 2012. The median overall survival was 13.0 (95% CI 12.6-13.5) months over the study period. Older age groups, higher tumor grade, male sex, the first half of the study period, and receiving no treatment were significantly associated with worse prognoses. This study is the latest epidemiological report on the status of malignant gliomas in Iran. Although the overall incidence rate was lower than the rates in developed countries, several findings were consistent with those in prior reports.

Study of the stress in adults diagnosed with meningioma: Insights from a tertiary neurosurgical hospital.

Meningiomas are the most common type of primary brain tumor, originating from the meninges - the protective membranes that surround the brain and spinal cord. Several well-studied risk factors for meningiomas include gender, age, radiation exposure, genetic factors, and hormonal factors. Moreover, the influence of a person's psycho-emotional stateon their overall health and mental well-being, specifically stress, iscurrently a significant and relevant topic of discussion. This case-control study aimed to study the association between perceived stress, chronic stress, and meningioma in adult patients. The study included cases, which comprised adult patients with histologically confirmed meningioma, and controls, consisting of adult patients with no history of brain cancer. Data collection involved the use of three types of questionnaires. The first questionnaire focused on patients' personal information, geographic factors, and lifestyle habits. Two additional questionnaires "The Perceived Stress Scale" and "The Chronic Stress Scale" were employed to assess perceived stress and chronic stress. The questioning was conducted by a neurologist. Microsoft Excel and Stata 14 were used for the data analysis. Overall, 148 questionnaires were completed and included in the analyses. The average age of participants was 45.60 ± 13.90 years. Females outnumbered males in both groups. Patients with meningioma diagnosis had a higher level of perceived high stress compared to those without meningioma (p = .045). Respondents without a diagnosis of meningioma have reported having more chronic stress in general and ambient problems (p = .004), financial issues (p = .006), work (p < .001), non-employment (p = .008), love and marriage (p < .001), isolation (p < .001), and residence (p < .001). Patients with meningioma, however, had less chronic stress compared to meningioma-free patients. This study revealed no discernible connection between stress and meningioma within our study sample. Further research with matched case-control methodology with a larger sample size is warranted to thoroughly evaluate the potential role of stress in patients with meningioma.

Classification of brain tumor types through MRIs using parallel CNNs and firefly optimization

Image segmentation is a critical and challenging endeavor in the field of medicine. A magnetic resonance imaging (MRI) scan is a helpful method for locating any abnormal brain tissue these days. It is a difficult undertaking for radiologists to diagnose and classify the tumor from several pictures. This work develops an intelligent method for accurately identifying brain tumors. This research investigates the identification of brain tumor types from MRI data using convolutional neural networks and optimization strategies. Two novel approaches are presented: the first is a novel segmentation technique based on firefly optimization (FFO) that assesses segmentation quality based on many parameters, and the other is a combination of two types of convolutional neural networks to categorize tumor traits and identify the kind of tumor. These upgrades are intended to raise the general efficacy of the MRI scan technique and increase identification accuracy. Using MRI scans from BBRATS2018, the testing is carried out, and the suggested approach has shown improved performance with an average accuracy of 98.6%.

GraphMriNet: a few-shot brain tumor MRI image classification model based on Prewitt operator and graph isomorphic network

Brain tumors are regarded as one of the most lethal forms of cancer, primarily due to their heterogeneity and low survival rates. To tackle the challenge posed by brain tumor diagnostic models, which typically require extensive data for training and are often confined to a single dataset, we propose a diagnostic model based on the Prewitt operator and a graph isomorphic network. Firstly, during the graph construction stage, edge information is extracted from MRI (magnetic resonance imaging) images using the Prewitt filtering algorithm. Pixel points with a gray value intensity greater than 128 are designated as graph nodes, while the remaining pixel points are treated as edges of the graph. Secondly, the graph data is inputted into the GIN model for training, with model parameters optimized to enhance performance. Compared with existing work using small sample sizes, the GraphMriNet model has achieved classification accuracies of 100%, 100%, 100%, and 99.68% on the BMIBTD, CE-MRI, BTC-MRI, and FSB open datasets, respectively. The diagnostic accuracy has improved by 0.8% to 5.3% compared to existing research. In a few-shot scenario, GraphMriNet can accurately diagnose various types of brain tumors, providing crucial clinical guidance to assist doctors in making correct medical decisions. Additionally, the source code is available at this link: https://github.com/keepgoingzhx/GraphMriNet.

Brain Metastases from Thyroid Carcinoma: Prognostic Factors and Outcomes.

Intracranial metastases from thyroid cancer are rare. Although the prognosis of thyroid cancer patients is generally favorable, the prognosis of patients with intracranial metastases from thyroid cancer has been considered unfavorable owing to lower survival rates among such patients compared to those without intracranial involvement. Many questions about their management remain unclear. The aim of the present study was to analyze the characteristics, treatment modalities, and outcomes of patients with brain metastases from thyroid cancer. Among 4320 patients with thyroid cancer recorded in our institutional database over a 30-year period, the data of 20 patients with brain metastasis were retrospectively collected and analyzed. The clinical characteristics, histological type of primary cancer and metastatic brain tumor, additional previous distant metastasis, treatment modalities, locations and characteristics on radiologic findings, time interval between the first diagnosis of primary thyroid cancer and brain metastasis, and survival were analyzed. Among our patient cohort, the mean age at initial diagnosis was 59.3 ± 14.1 years, and at the manifestation of diagnosis of cerebral metastasis, the mean age was found to be 64.8 ± 14.9 years. The histological types of primary thyroid cancer were identified as papillary in ten patients, follicular in seven, and poorly differentiated carcinoma in three. The average interval between the diagnosis of thyroid cancer and brain metastasis was 63.4 ± 58.4 months (range: 0-180 months). Ten patients were identified as having a single intracranial lesion, and ten patients were found to have multiple lesions. Surgical resection was primarily performed in fifteen patients, and whole-brain radiotherapy, radiotherapy, or tyrosine kinase inhibitors were applied in the remaining five patients. The overall median survival time was 15 months after the diagnosis of BMs from TC (range: 1-252 months). Patients with thyroid cancer can develop brain metastasis even many years after the diagnosis of the primary tumor. The results of our study demonstrate increased overall survival in patients younger than 60 years of age at the time of diagnosis of brain metastasis. There was no difference in survival between patients with brain metastasis from papillary carcinoma and those with follicular thyroid carcinoma.

Variances in the Expression Profile of Circadian Clock-Related Genes in Astrocytic Brain Tumors.

This study explores the role of circadian clock genes in the progression of astrocytic tumors, a prevalent type of brain tumor. The aim was to assess the expression patterns of these genes in relation to the tumor grade. Using microarray analysis, qRT-PCR, and methylation-specific PCR, we examined gene expression, DNA methylation patterns, and microRNA interactions in tumor samples from 60 patients. Our results indicate that the expression of key circadian clock genes, such as clock circadian regulator (CLOCK), protein kinase AMP-activated catalytic subunit alpha 1 (PRKAA1), protein kinase AMP-activated catalytic subunit alpha 2 (PRKAA2), protein kinase AMP-activated non-catalytic subunit beta 1 (PRKAB1), protein kinase AMP-activated non-catalytic subunit beta 2 (PRKAB2), period circadian regulator 1 (PER1), period circadian regulator 2 (PER2) and period circadian regulator 3 (PER3), varies significantly with the tumor grade. Notably, increased CLOCK gene expression and protein levels were observed in higher-grade tumors. DNA methylation analysis revealed that the promoter regions of PER1-3 genes were consistently methylated, suggesting a mechanism for their reduced expression. Our findings also underscore the complex regulatory mechanisms involving miRNAs, such as hsa-miR-106-5p, hsa-miR-20b-5p, and hsa-miR-30d-3p, which impact the expression of circadian clock-related genes. This underscores the importance of circadian clock genes in astrocytic tumor progression and highlights their potential as biomarkers and therapeutic targets. Further research is needed to validate these results and explore their clinical implications.

An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique.

Neuroscience is a swiftly progressing discipline that aims to unravel the intricate workings of the human brain and mind. Brain tumors, ranging from non-cancerous to malignant forms, pose a significant diagnostic challenge due to the presence of more than 100 distinct types. Effective treatment hinges on the precise detection and segmentation of these tumors early. We introduce a cutting-edge deep-learning approach employing a binary convolutional neural network (BCNN) to address this. This method is employed to segment the 10 most prevalent brain tumor types and is a significant improvement over current models restricted to only segmenting four types. Our methodology begins with acquiring MRI images, followed by a detailed preprocessing stage where images undergo binary conversion using an adaptive thresholding method and morphological operations. This prepares the data for the next step, which is segmentation. The segmentation identifies the tumor type and classifies it according to its grade (Grade I to Grade IV) and differentiates it from healthy brain tissue. We also curated a unique dataset comprising 6,600 brain MRI images specifically for this study. The overall performance achieved by our proposed model is 99.36%. The effectiveness of our model is underscored by its remarkable performance metrics, achieving 99.40% accuracy, 99.32% precision, 99.45% recall, and a 99.28% F-Measure in segmentation tasks.

Developing a classification system for brain tumors using the ResNet152V2 CNN model architecture

According to The American Cancer Society, in 2021 there were 24,530 cases of brain and nervous system tumors. The National Cancer Institute reports that there are approximately 4.4 new cases of brain tumors per 100,000 men and women per year. Brain tumors can be detected using magnetic resonance imaging (MRI), a scanning tool that uses a magnetic field and a computer to record brain images and is able to provide clear visualization of differences in soft tissue such as white matter and gray matter. However, this cannot be done optimally because it still relies on manual analysis, so it cannot classify brain tumor types on larger datasets with the potential for error and a low level of accuracy. To accurately determine the type of brain tumor, a better classification method is needed. The aim of this study is to determine the accuracy of brain tumor calcification using the deep learning model. In this study, the classification of brain tumor types was carried out using the ResNet152V2 convolutional neural network (CNN) model which has a depth of 152 layers. The dataset used in this study was 7,023 MRI images of brain tumors consisting of 1,645 meningiomas, 1,621 gliomas, 1,757 pituitary and 2,000 normal. Research results show an accuracy value of 94.44%, so it can be concluded that the ResNet152V2 model performs well in classifying brain tumor images and can be used as a medium for physicians to more accurately diagnose brain tumor patients more accurately.

ATRT-12. EXPLORING FGFR INHIBITION AND COMBINATION THERAPIES FOR ATYPICAL TERATOID/RHABDOID TUMORS

Abstract BACKGROUND Atypical teratoid/rhabdoid tumors (ATRTs) are aggressive pediatric brain neoplasms characterized by an inactivation of the SWI/SNF chromatin remodeling complex members SMARCB1 or SMARCA4. Based on epigenetic profiling ATRTs can be divided into four different molecular subgroups. Despite improvements in treatment strategies, the prognosis for ATRT patients remains dismal. The FGF/FGFR signaling pathway plays a crucial role in many physiological processes, including embryonic development, tissue repair, angiogenesis. Dysregulation of the signaling axis is driving tumorigenesis. Targeted therapeutic approaches, particularly FGFR inhibitors, have demonstrated potential in enhancing treatment outcomes for various tumors, including pediatric brain tumors. METHODS We used bulk and single-cell RNA sequencing (scRNA-seq) data to examine the expression patterns of FGF family members in ATRTs. Subsequently, we conducted in vitro analysis on a panel of FGFR inhibitors and combination treatments. RESULTS Transcriptomic analysis of more than 130 ATRT tissues samples, revealed a widespread expression of FGFR1 and FGFR2 across all ATRT subtypes. Notably, FGFR1 displayed distinctly elevated expression in ATRT-SHH, surpassing levels seen in other ATRT subtypes and brain tumor types. Utilizing scRNA-seq data of murine ATRT models, we elucidated an autocrine FGFR signaling loop. To assess the therapeutic feasibility of targeting FGFR, we evaluated a panel of FGFR inhibitors, currently in clinical development, across 10 in house established patient-derived cell models (6 ATRT-MYC, 3 ATRT-SHH, 1 ATRT-TYR), revealing sensitivity in a nanomolar to a low-micromolar range. We further explored combination therapies, demonstrating that Erdafitinib, the most effective inhibitor when combined with Abemaciclib and radiation, exhibited enhanced synergistic effects in 2D as well as in 3D tumoroid models. Investigations towards testing these combinations in an organoid co-culture system, along with in vivo models are ongoing. CONCLUSIONS This study emphasizes the prospect of combining FGFR inhibition with radiation and CDK4/6 inhibition as a therapeutic target for ATRTs.

MDB-52. MYC-DRIVEN PEDIATRIC BRAIN TUMORS SHARE A COMMON PHOTORECEPTOR IDENTITY

Abstract Mouse models serve as invaluable tools for improving the understanding and treatment of pediatric brain tumors. We recently published a highly aggressive MYC-driven brain tumor model (GMYC), which exhibits approximately 70% penetrance. Our transcription profiling indicates that GMYC tumors accurately resemble human Group 3 medulloblastoma (MB-G3). Interestingly, a strong photoreceptor program was activated in the tumor model. This is commonly upregulated in MB-G3 but also in pineoblastoma (PB), another malignant pediatric brain tumor. CRX, a transcription factor critical for pineal cell development is also a master regulator for MB-G3 maintenance. By studying early tumor initiation using Crx-lineage tracing and scRNA-seq, we confirmed that GMYC tumors originate from early pinealocytes. Exome sequencing of GMYC tumors further confirmed mutations of epigenetic regulators commonly reported in either PB or MB. To investigate tumor development from photoreceptor-positive progenitors in the developing brain, we performed Crx-lineage tracing with tamoxifen injections, confirming that Crx-positive cells exist in pineal gland progenitors. However, Crx-traced cells also marked granule neurons in the flocculonodular lobe of the cerebellum, which is the suggested site of MB-G3 origin arising from the developing rhombic lip. To investigate if the Myc oncogene can generate different types of pediatric brain tumors in Crx-positive cells, an XMYCT58A-Tomato mouse model was established by crossing Crx-CreERT2; LSL-MycT58A and Rosa26LSL-tdTomato strains. Here, MycT58A was turned on with tamoxifen injection after birth and tumor development followed using the tdTomato reporter. XMYCT58A-Tomato mice developed brain tumors from both the pineal gland and cerebellum after 2-6 months. Histologically, tumors were non-glial (GFAP-, Olig2-), showed neuronal activity (Neurod1+), and stained positive for photoreceptor identity markers (CRX+, OTX2+) similar to both MB-G3 and PB-MYC. Collectively, our data suggest that both MYC-driven MB-G3 and PB-MYC originate from photoreceptor-positive cells, which has implications for developing novel treatments targeting these devastating childhood malignancies.

BIOM-03. IMMUNO-ONCOLOGIC PROFILING OF PEDIATRIC BRAIN TUMORS REVEALS MAJOR CLINICAL SIGNIFICANCE OF THE TUMOR IMMUNE MICROENVIRONMENT

Abstract With the success of immunotherapy in cancer, understanding the tumor immune microenvironment (TIME) has become increasingly important; however in pediatric brain tumors this remains poorly characterized. Accordingly, we developed a clinical immune-oncology gene expression assay, including the 18-gene tumor inflammation signature (TIS) as a marker of overall immune activation, and validated this with immunohistochemistry for key cell-type markers. We used this assay, in conjunction with public data from the Pediatric Brain Tumor Atlas (PBTA), to profile a diverse range of 1382 samples in total with detailed clinical and molecular annotation. Overall among the main types of pediatric brain tumors, low-grade gliomas (LGG) had the highest inflammation levels (regardless of genetic driver alteration) and medulloblastomas the lowest. In LGG we identified three distinct patterns of immune activation with prognostic significance in BRAF V600E-mutant tumors, specifically that higher inflammation predicted worse outcomes. In high-grade gliomas (HGG), we observed immune activation and T-cell infiltrates in tumors that have historically been considered exclusively immune cold. There were genomic correlates of inflammation levels, with BRAF-V600E-mutant HGG having the highest inflammation levels and suggesting these may be candidates for combination MEK inhibition and immunotherapy. In mismatch repair deficient HGG, we found that high TIS was a significant predictor of response to immune checkpoint inhibition, and demonstrated the potential for multimodal biomarkers (TIS plus tumor mutation burden) to improve treatment stratification. Importantly, while overall patterns of immune activation were observed for histologically and genetically defined tumor types, there was significant variability within each entity, indicating that the TIME must be evaluated as an independent feature from diagnosis. In sum, in addition to the histology and molecular profile, this work underscores the importance of reporting on the TIME as an essential axis of cancer diagnosis in the era of personalized medicine.