Human Brain Tumors Research Articles

DDEL-11. PHYSIOLOGICALLY BASED PHARMACOKINETIC MODELING OF SPATIAL HETEROGENEITY OF DRUG PENETRATION AND EXPOSURE IN THE HUMAN BRAIN AND BRAIN TUMORS

Abstract BACKGROUD The difficulty in early, reliable prediction of drug penetration and exposure in the human brain and brain tumors makes development of new drugs and use of existing drugs for treating brain cancer a challenging and often unsuccessful task. We developed a physiologically based pharmacokinetic (PBPK) model platform for prediction of spatial pharmacokinetics in the central nervous system (CNS) following systemic drug administration. METHODS A 9-compartment CNS (9-CNS) PBPK model was developed to predict drug concentrations in brain blood, 2 brain parenchyma compartments (brain tissue adjacent to the CSF tract and deep brain parenchyma), 3 CSF compartments (ventricular CSF, cranial and spinal subarachnoid CSF), and 3 tumor compartments (tumor rim, bulk tumor, and tumor core). The pharmacokinetics in the CNS compartments were driven by plasma concentrations and governed by drug properties and CNS pathophysiology. The 9-CNS model was developed and validated with 4 drugs (abemaciclib, ribociclib, ceritinib, and temuterkib). RESULTS When considering tumor heterogeneity and inter-individual plasma pharmacokinetic variability following clinical standard dosing regimens, the model-predicted steady-state unbound drug tumor concentrations varied from 7.1 to 356 nM for abemaciclib, 74 to 4577 nM for ribociclib, 0.66 to 97 nM for ceritinib, and 0.8 to 288 nM for temuterkib, which were consistent with the heterogeneous drug tumor concentrations observed in glioblastoma patients. Drug penetration into the deep brain parenchyma was significantly less than that into tumors and CSF-adjacent brain tissue, particularly for strong ABCB1/ABCG2 substrate drugs. The ventricular and cranial subarachnoid CSF shared similar pharmacokinetics, while the spinal CSF pharmacokinetics was different. CONCLUSION A novel 9-CNS PBPK model was developed and validated for mechanistic prediction of spatial heterogeneity of drug penetration and exposure in the human brain, brain tumors, and CSF. It provides a valuable computational modeling tool to assist development and design of improved drugs or dosing regimens for brain cancer treatment.

EPCO-33. OMICON: A CLOUD PLATFORM FOR FAIR RESEARCH ON GENE COEXPRESSION NETWORKS IN NORMAL HUMAN BRAIN AND BRAIN TUMORS

Abstract Genome-wide coexpression analysis of bulk human brain samples is a powerful approach for identifying highly reproducible transcriptional signatures of cell types and cell states, since it can survey huge numbers of individuals, cells, and transcripts. However, it is difficult to optimize gene coexpression network construction, compare gene coexpression modules from independent datasets, or even locate gene expression datasets with similar attributes. To address these challenges, we have developed the OMICON platform (theomicon.ucsf.edu) to promote FAIR (Findable, Accessible, Interoperable, Reusable) research practices involving human brain gene coexpression networks. OMICON contains structured gene expression data for >17K bulk human brain samples (~10K normal and ~7K malignant glioma), which were collected from diverse public repositories and consortia (e.g., GEO, TCGA, CGGA, CPTAC, REMBRANDT, IvyGAP, GTEx, NABEC, the Allen Institute). To promote interoperability, we have standardized metadata for hundreds of dataset and sample attributes, including single-nucleotide and copy-number mutations. For each dataset, we have used the FindModules R function to construct dozens of gene coexpression networks by iterating over module detection parameters. These efforts have identified ~100K gene coexpression modules, which have been extensively characterized via enrichment analysis with thousands of curated gene sets. All datasets, metadata, gene coexpression networks, enrichment results, and analysis steps can be browsed with an interactive workflow visualization tool, which promotes accessibility, reusability, and reproducibility by maintaining complete data provenance with unique identifiers. To promote findability, we have built an advanced search engine to identify datasets, samples, modules, and more, by filtering standardized metadata (e.g., find gene coexpression modules in human glioblastoma datasets that are significantly enriched with markers of T-cells). A detailed description of OMICON’s functionality is described on our Help page: theomicon.ucsf.edu/home/help. Through this functionality, OMICON seeks to build community and focus therapeutic efforts around reproducible analyses of transcriptional variation in normal human brain and brain tumors.

DDDR-03. DEVELOPMENT OF ANTI-TUMOR AROMA THERAPY FOR GLIOBLASTOMA WITH LEMONGRASS ESSENTIAL OIL

Abstract INTRODUCTION Aroma essential oils apply to cancer therapy because of their immune-enhancing and sedative effects, and their ability to reduce the side effects of chemotherapy. Recently, aroma essential oils have also been reported to have anti-tumor effects. We investigate the anti-tumor effects of aromatic compounds contained in essential oils as a novel treatment for glioblastoma. In this study, we analyzed the antitumor effect of lemongrass essential oil and investigated its antitumor aroma therapy for glioblastoma. methods and RESULTS Human glioblastoma cells (U87, T98G) and mouse brain tumor cells (RSV-M) were used in the study. Tumor cells were cultured in the presence of lemongrass essential oil for 72 hours, and the growth inhibitory effect was observed, suggesting that the evaporated lemongrass essential oil components acted on tumor cells in the vicinity. In addition, a similar effect was observed with citral alone, which is the main component of lemongrass. Analysis of the effects of citral revealed that citral strongly binds to MARK4 and inhibits its enzyme activity, and that citral decreases the phosphorylation of tau protein. Next, a mouse brain tumor model with RSV-M was established, and the anti-tumor effect of lemongrass essential oil was studied. The results showed that the mice that inhaled lemongrass essential oil for 12 h/day had a significantly prolonged survival period compared to the control group. DISCUSSION Citral is the main component of lemongrass essential oil, which contains 70-80% of lemongrass essential oil. This study suggests a mechanism of inhibition of tumor cell proliferation by citral in malignant gliomas. Citral is a volatile monoterpene, and its anti-tumor effect was demonstrated by diffusion through evaporation. CONCLUSION Citral in lemongrass essential oil shows anti-tumor effects and is expected to be applied to a new glioblastoma therapy using “aroma”

Efficient brain tumor grade classification using ensemble deep learning models

Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental to the diagnosis because it contains detailed structural views of the brain, which is vital in identifying any of its abnormalities. The other option of performing an invasive biopsy is very painful and uncomfortable, which is not the case with MRI as it is free from surgically invasive margins and pieces of equipment. This helps patients to feel more at ease and hasten the diagnostic procedure, allowing physicians to formulate and practice action plans quicker. It is very difficult to locate a human brain tumor by manual because MRI scans produce large numbers of three-dimensional images. Complete applicability of pre-written computerized diagnostics, affords high possibilities in providing areas of interest earlier through the application of machine learning techniques and algorithms. The proposed work in the present study was to develop a deep learning model which will classify brain tumor grade images (BTGC), and hence enhance accuracy in diagnosing patients with different grades of brain tumors using MRI. A MobileNetV2 model, was used to extract the features from the images. This model increases the efficiency and generalizability of the model further. In this study, six standard Kaggle brain tumor MRI datasets were used to train and validate the developed and tested model of a brain tumor detection and classification algorithm into several types. This work consists of two key components: (i) brain tumor detection and (ii) classification of the tumor. The tumor classifications are conducted in both three classes (Meningioma, Pituitary, and glioma) and two classes (malignant, benign). The model has been reported to detect brain tumors with 99.85% accuracy, to distinguish benign and malignant tumors with 99.87% accuracy, and to type meningioma, pituitary, and glioma tumors with 99.38% accuracy. The results of this study indicate that the described technique is useful in the detection and classification of brain tumors.

EXPLORING MOLECULAR LINKS IN BRAIN TUMOUR BIOPSY TISSUES AND ALZHEIMER’S DISEASE

Abstract AIMS Generate and identify putative targets in the SORL1-network by mining online databases. Analyse 21 FFPE human brain tumour biopsy tissues through transcriptome sequencing performed by CeGaT (Tübingen, Germany) to explore differentially expressed genes. These biopsy samples are from consenting patients in association with Brain Tumour North West (BTNW) and the Royal Preston Hospital. METHOD Group comparisons are performed with the control and cancerous sample groups (low-grade glioma staying low, low-grade glioma becoming high and high-grade staying high). Further analysis involves comparing section and resection from the same patient for each cancer tissue group. Samples are explored on four different bioinformatic levels and sequenced on the Illumina platform. In Level 1 the data is demultiplexed and trimmed and in Level 2 trimmed reads are additionally mapped. Level 3 provides insights into the cell’s metabolic state and Level 4 performs several group comparisons. RESULTS Gain insights into differentially expressed genes’ presence in biopsy tissues from different stages of brain cancer, expression levels and links to clinical characteristics. Bioinformatic analysis provides data on quality control of the starting material, library preparation, sequencing parameters, and the Q30 value of the sequencing. CONCLUSION The analysis demonstrates molecular links in the SORL1-network in tissue from section and resection from a patient cohort to better understand disease recurrent and progression. Prospective bioinformatic work would explore interactions between significant targets identified to create a curated network that could predict druggable targets in gliomas.

Relationship between radiation dose and cerebral microbleed formation in dogs with intracranial tumors.

Cerebral microbleeds (CMBs) are a possible sequela in human brain tumor patients treated with radiation therapy (RT). No such association is reported in dogs. To investigate whether CMBs occur in dogs after radiotherapy, and if there is an association between number and dose, and an increase over time. Thirty-four client-owned dogs irradiated for primary intracranial neoplasia. ≥2 magnetic resonance imaging (MRI) scans including susceptibility-weighted imaging (SWI) were required. Retrospective, observational, single-center study. Cerebral microbleeds identified on 3 T SWI were counted within the entire brain, and within low- (<20 Gy), intermediate- (20-30 Gy), and high- (>30 Gy) dose regions. A generalized linear mixed-effects model was used to analyze the relationship between the CMBs count and the predictor variables (irradiation dose, time after treatment). Median follow-up time was 12.6 months (range, 1.8-37.6 months). Eighty-three MR scans were performed. In 4/15 dogs (27%, 95% CI, 10%-52%) CMBs were present at baseline. ≥1 CMBs after RT were identified in 21/34 dogs (62%, 95% CI, 45%-77%). With each month, the number of CMBs increased by 14% (95% CI, 11%-16%; P < .001). The odds of developing CMBs in the high-dose region are 4.7 times (95% CI, 3.9-5.6; P < .001) greater compared with the low-dose region. RT is 1 possible cause of CMBs formation in dogs. Cerebral microbleeds are most likely to occur in the peritumoral high-dose volume, to be chronic, and to increase in number over time. Their clinical relevance remains unknown.

Rhythms in lipid droplet content driven by a metabolic oscillator are conserved throughout evolution

The biological clock in eukaryotes controls daily rhythms in physiology and behavior. It displays a complex organization that involves the molecular transcriptional clock and the redox oscillator which may coordinately work to control cellular rhythms. The redox oscillator has emerged very early in evolution in adaptation to the environmental changes in O2 levels and has been shown to regulate daily rhythms in glycerolipid (GL) metabolism in different eukaryotic cells. GLs are key components of lipid droplets (LDs), intracellular storage organelles, present in all living organisms, and essential for energy and lipid homeostasis regulation and survival; however, the cell bioenergetics status is not constant across time and depends on energy demands. Thus, the formation and degradation of LDs may reflect a time-dependent process following energy requirements. This work investigated the presence of metabolic rhythms in LD content along evolution by studying prokaryotic and eukaryotic cells and organisms. We found sustained temporal oscillations in LD content in Pseudomonas aeruginosa bacteria and Caenorhabditis elegans synchronized by temperature cycles, in serum-shock synchronized human embryonic kidney cells (HEK 293 cells) and brain tumor cells (T98G and GL26) after a dexamethasone pulse. Moreover, in synchronized T98G cells, LD oscillations were altered by glycogen synthase kinase-3 (GSK-3) inhibition that affects the cytosolic activity of the metabolic oscillator or by knocking down LIPIN-1, a key GL synthesizing enzyme. Overall, our findings reveal the existence of metabolic oscillations in terms of LD content highly conserved across evolutionary scales notwithstanding variations in complexity, regulation, and cell organization.Graphical abstract

Unsupervised clustering of brain tumor leukocytes to reveal atypical lymphoid cell subsets.

25 Background: The brain tumor immune microenvironment (TiME) is characterised by suppression of tumor-infiltrating leukocytes. The lymphocyte niche activity is particularly downregulated within this TiME milieu. Methods: Here, using data-driven approaches in flow cytometry and RNA sequencing analysis, we aimed at dissecting leukocyte cell types infiltrating human brain tumor TiME. Results: Unsupervised clustering provided T lymphocyte subsets including double-negative (CD3+CD4- CD8-) T cells (DNT). Our DNT phenotype was validated with an independent dataset, suggesting DNT to be compatible with γδ T cell phenotype. In our cohort, myeloid immune cell frequencies were associated with malignancy type as previously reported, while a gliosarcoma notably presented unexpectedly high lymphocyte frequencies. Consistently, double-positive (CD4+ CD8+) T cells (DPT) had higher frequency in the gliosarcoma than other tumors. The flow cytometry results were supported by transcriptome patterns and the activity of immune-related genes in these tumors. Conclusions: Our findings feature the benefit of data-driven approaches for brain TiME lymphocyte analysis, and show that primary brain tumors can entail atypical lymphocyte subsets like DNTs and DPT cells. Unbiased lymphocyte characterisation can potentially outline novel targets for immune checkpoint blockade therapy.

Peptide-based immuno-PET/CT monitoring of dynamic PD-L1 expression during glioblastoma radiotherapy

Real-time, noninvasive programmed death-ligand 1 (PD-L1) testing using molecular imaging has enhanced our understanding of the immune environments of neoplasms and has served as a guide for immunotherapy. However, the utilization of radiotracers in the imaging of human brain tumors using positron emission tomography/computed tomography (PET/CT) remains limited. This investigation involved the synthesis of [18F]AlF-NOTA-PCP2, which is a novel peptide-based radiolabeled tracer that targets PD-L1, and evaluated its imaging capabilities in orthotopic glioblastoma (GBM) models. Using this tracer, we could noninvasively monitor radiation-induced PD-L1 changes in GBM. [18F]AlF-NOTA-PCP2 exhibited high radiochemical purity (>95%) and stability up to 4 hours after synthesis. It demonstrated specific, high-affinity binding to PD-L1 in vitro and in vivo, with a dissociation constant of 0.24 nM. PET/CT imaging, integrated with contrast-enhanced magnetic resonance imaging, revealed significant accumulation of [18F]AlF-NOTA-PCP2 in orthotopic tumors, correlating with blood–brain barrier disruption. After radiotherapy (15 Gy), [18F]AlF-NOTA-PCP2 uptake in tumors increased from 9.51% ± 0.73% to 12.04% ± 1.43%, indicating enhanced PD-L1 expression consistent with immunohistochemistry findings. Fractionated radiation (5 Gy × 3) further amplified PD-L1 upregulation (13.9% ± 1.54% ID/cc) compared with a single dose (11.48% ± 1.05% ID/cc). Taken together, [18F]AlF-NOTA-PCP2 may be a valuable tool for noninvasively monitoring PD-L1 expression in brain tumors after radiotherapy.

Neuro-Oncologic Veterinary Trial for the Clinical Transfer of Microbeam Radiation Therapy: Acute to Subacute Radiotolerance after Brain Tumor Irradiation in Pet Dogs.

Synchrotron Microbeam Radiation Therapy (MRT) has repeatedly proven its superiority compared with conventional radiotherapy for glioma control in preclinical research. The clinical transfer phase of MRT has recently gained momentum; seven dogs with suspected glioma were treated under clinical conditions to determine the feasibility and safety of MRT. We administered a single fraction of 3D-conformal, image-guided MRT. Ultra-high-dose rate synchrotron X-ray microbeams (50 µm-wide, 400 µm-spaced) were delivered through five conformal irradiation ports. The PTV received ~25 Gy peak dose (within microbeams) per port, corresponding to a minimal cumulated valley dose (diffusing between microbeams) of 2.8 Gy. The dogs underwent clinical and MRI follow-up, and owner evaluations. One dog was lost to follow-up. Clinical exams of the remaining six dogs during the first 3 months did not indicate radiotoxicity induced by MRT. Quality of life improved from 7.3/10 [±0.7] to 8.9/10 [±0.3]. Tumor-induced seizure activity decreased significantly. A significant tumor volume reduction of 69% [±6%] was reached 3 months after MRT. Our study is the first neuro-oncologic veterinary trial of 3D-conformal Synchrotron MRT and reveals that MRT does not induce acute to subacute radiotoxicity in normal brain tissues. MRT improves quality of life and leads to remarkable tumor volume reduction despite low valley dose delivery. This trial is an essential step towards the forthcoming clinical application of MRT against deep-seated human brain tumors.

Effect of Jardiance on glucose uptake into astrocytomas

PurposeSGLT2, the sodium glucose cotransporter two, is expressed in human pancreatic, prostate and brain tumors, and in a mouse cancer model SGLT2 inhibitors reduce tumor glucose uptake and growth. In this study we have measured the effect of a specific SGLT2 inhibitor, Jardiance® (Empagliflozin), on glucose uptake into astrocytomas in patients.MethodsWe have used a specific SGLT glucose tracer, α-methyl-4-[18F]fluoro-4-deoxy-α-D-glucopyranoside (Me4FDG), and Positron Emission Tomography (PET) to measure glucose uptake. Four of five patients enrolled had WHO grade IV glioblastomas, and one had a low grade WHO Grade II astrocytoma. Two dynamic brain PET scans were conducted on each patient, one before and one after treatment with a single oral dose of Jardiance, a specific SGLT2 inhibitor. As a control, we also determined the effect of oral Jardiance on renal SGLT2 activity.ResultsIn all five patients an oral dose (25 or 100 mg) of Jardiance reduced Me4FDG tumor accumulation, highly significant inhibition in four, and inhibited SGLT2 activity in the kidney.ConclusionsThese initial experiments show that SGLT2 is a functional glucose transporter in astocytomas, and Jardiance inhibited glucose uptake, a drug approved by the FDA to treat type 2 diabetes mellitus (T2DM), heart failure, and renal failure. We suggest that clinical trials be initiated to determine whether Jardiance reduces astrocytoma growth in patients.

In vivo mouse models for adult brain tumors: Exploring tumorigenesis and advancing immunotherapy development.

Brain tumors, particularly glioblastoma (GBM), are devastating and challenging to treat, with a low 5-year survival rate of only 6.6%. Mouse models are established to understand tumorigenesis and develop new therapeutic strategies. Large-scale genomic studies have facilitated the identification of genetic alterations driving human brain tumor development and progression. Genetically engineered mouse models (GEMMs) with clinically relevant genetic alterations are widely used to investigate tumor origin. Additionally, syngeneic implantation models, utilizing cell lines derived from GEMMs or other sources, are popular for their consistent and relatively short latency period, addressing various brain cancer research questions. In recent years, the success of immunotherapy in specific cancer types has led to a surge in cancer immunology-related research which specifically necessitates the utilization of immunocompetent mouse models. In this review, we provide a comprehensive summary of GEMMs and syngeneic mouse models for adult brain tumors, emphasizing key features such as model origin, genetic alteration background, oncogenic mechanisms, and immune-related characteristics. Our review serves as a valuable resource for the brain tumor research community, aiding in the selection of appropriate models to study cancer immunology.

Brain tumor classification in VIT-B/16 based on relative position encoding and residual MLP.

Brain tumors pose a significant threat to health, and their early detection and classification are crucial. Currently, the diagnosis heavily relies on pathologists conducting time-consuming morphological examinations of brain images, leading to subjective outcomes and potential misdiagnoses. In response to these challenges, this study proposes an improved Vision Transformer-based algorithm for human brain tumor classification. To overcome the limitations of small existing datasets, Homomorphic Filtering, Channels Contrast Limited Adaptive Histogram Equalization, and Unsharp Masking techniques are applied to enrich dataset images, enhancing information and improving model generalization. Addressing the limitation of the Vision Transformer's self-attention structure in capturing input token sequences, a novel relative position encoding method is employed to enhance the overall predictive capabilities of the model. Furthermore, the introduction of residual structures in the Multi-Layer Perceptron tackles convergence degradation during training, leading to faster convergence and enhanced algorithm accuracy. Finally, this study comprehensively analyzes the network model's performance on validation sets in terms of accuracy, precision, and recall. Experimental results demonstrate that the proposed model achieves a classification accuracy of 91.36% on an augmented open-source brain tumor dataset, surpassing the original VIT-B/16 accuracy by 5.54%. This validates the effectiveness of the proposed approach in brain tumor classification, offering potential reference for clinical diagnoses by medical practitioners.

Delivering Effector T Lymphocytes into Mice Carrying Human Brain Tumors

Delivering Effector T Lymphocytes into Mice Carrying Human Brain Tumors

Effect of luteolin on glioblastoma's immune microenvironment and tumor growth suppression

BackgroundGlioblastoma is the most malignant and prevalent primary human brain tumor, and the immunological microenvironment controlled by glioma stem cells is one of the essential elements contributing to its malignancy. The use of medications to ameliorate the tumor microenvironment may give a new approach for glioma treatment. MethodsGlioma stem cells were separated from clinical patient-derived glioma samples for molecular research. Other studies, including CCK8, EdU, Transwell, and others, supported luteolin's ability to treat glioma progenitor cells. Network pharmacology and molecular docking models were used to study the drug target, and qRT-PCR, WB, and IF were used to evaluate the molecular mechanism. Intracranial xenografts were examined using HE and IHC, while macrophage polarization was examined using FC. ResultsWe originally discovered that luteolin inhibits glioma stem cells. IL6 released by glioma stem cells is blocked during medication action and inhibits glioma stem cell proliferation and invasion via the IL6/STAT3 signaling pathway. Additionally, luteolin inhibits the secretion of TGFβ1, affects the polarization function of macrophages in the microenvironment, inhibits the polarization of M2 macrophages in TAM, and further inhibits various functions of glioma stem cells by affecting the IL6/STAT3 signaling pathway, luteolin crosstalk TGFβ1/SMAD3 signaling pathway, and so on. ConclusionsThrough the suppression of the immunological microenvironment and inhibition of the IL6/STAT3 signaling pathway, our study determined the inhibitory effect of luteolin on glioma stem cells. This medication's dual inhibitory action, which has a significant negative impact on the glioma stem cells' malignant process, makes it both a viable anti-glioma medication and a candidate for targeted glioma microenvironment therapy.

Upregulation of HAS2 promotes glioma cell proliferation and chemoresistance via c-myc

Glioblastoma multiforme (GBM) is the most common and aggressive primary malignant human brain tumor. Although comprehensive therapies, including chemotherapy and radiotherapy following surgery, have shown promise in prolonging survival, the prognosis for GBM patients remains poor, with an overall survival rate of only 14.6 months. Chemoresistance is a major obstacle to successful treatment and contributes to relapse and poor survival rates in glioma patients. Therefore, there is an urgent need for novel strategies to overcome chemoresistance and improve treatment outcomes for human glioma patients. Recent studies have shown that the tumor microenvironment plays a key role in chemoresistance. Our study demonstrates that upregulation of HAS2 and subsequent hyaluronan secretion promotes glioma cell proliferation, invasion, and chemoresistance in vitro and in vivo through the c-myc pathway. Targeting HAS2 sensitizes glioma cells to chemotherapeutic agents. Additionally, we found that hypoxia-inducible factor HIF1α regulates HAS2 expression. Together, our findings provide insights into the dysregulation of HAS2 and its role in chemoresistance and suggest potential therapeutic strategies for GBM.

Investigating neuroinflammation in the human brain tumor microenvironment using the new RNAscopeTM Multiomic assay

Abstract Glioblastomas are one of the most aggressive forms of brain tumors characterized by distinct genetic and molecular signatures. Compared to other solid tumors, role of immune cells in progression, invasion and prognosis is not well studies for CNS tumors. Here, we demonstrate a novel RNAscope Multiomic Assay that enables detection of multiple RNA and protein markers simultaneously. With this novel TSA amplification-based co-detection assay we visualized a few combinations of 3 RNA and 3 protein marker panels on human FFPE normal brain and brain tumor tissues. Antibodies targeting key immune cell markers and morphology markers such as CD8, IBA1 ,CD68, S100B and GFAP were used. RNA probes targeting chemokines and cytokines such as CXCL10, IFNG, TNFA, CXCL2. IL-6 were also used in the panels. Immune cells infiltrating the brain tumor tissues were characterized by studying co-expression of key RNA and protein markers. The tumors demonstrated infiltration of immune cells such as T cells, microglia and macrophages. Expression of key cytokines indicated that some populations within these cells were activated. Distinct differences in neuroinflammation signatures were also observed between the normal and tumor brain tissues. The multiomic assay offers a powerful technique for visualizing target RNA biomarkers in specific cell-types identified by cell-marker protein expression.

Microscope-integrated optical coherence tomography for in vivo human brain tumor detection with artificial intelligence.

It has been shown that optical coherence tomography (OCT) can identify brain tumor tissue and potentially be used for intraoperative margin diagnostics. However, there is limited evidence on its use in human in vivo settings, particularly in terms of its applicability and accuracy of residual brain tumor detection (RTD). For this reason, a microscope-integrated OCT system was examined to determine in vivo feasibility of RTD after resection with automated scan analysis. Healthy and diseased brain was 3D scanned at the resection edge in 18 brain tumor patients and investigated for its informative value in regard to intraoperative tissue classification. Biopsies were taken at these locations and labeled by a neuropathologist for further analysis as ground truth. Optical OCT properties were obtained, compared, and used for separation with machine learning. In addition, two artificial intelligence-assisted methods were utilized for scan classification, and all approaches were examined for RTD accuracy and compared to standard techniques. In vivo OCT tissue scanning was feasible and easily integrable into the surgical workflow. Measured backscattered light signal intensity, signal attenuation, and signal homogeneity were significantly distinctive in the comparison of scanned white matter to increasing levels of scanned tumor infiltration (p < 0.001) and achieved high values of accuracy (85%) for the detection of diseased brain in the tumor margin with support vector machine separation. A neuronal network approach achieved 82% accuracy and an autoencoder approach 85% accuracy in the detection of diseased brain in the tumor margin. Differentiating cortical gray matter from tumor tissue was not technically feasible in vivo. In vivo OCT scanning of the human brain has been shown to contain significant value for intraoperative RTD, supporting what has previously been discussed for ex vivo OCT brain tumor scanning, with the perspective of complementing current intraoperative methods for this purpose, especially when deciding to withdraw from further resection toward the end of the surgery.

Direct Implantation of Patient Brain Tumor Cells into Matching Locations in Mouse Brains for Patient-Derived Orthotopic Xenograft Model Development.

Background: Despite multimodality therapies, the prognosis of patients with malignant brain tumors remains extremely poor. One of the major obstacles that hinders development of effective therapies is the limited availability of clinically relevant and biologically accurate (CRBA) mouse models. Methods: We have developed a freehand surgical technique that allows for rapid and safe injection of fresh human brain tumor specimens directly into the matching locations (cerebrum, cerebellum, or brainstem) in the brains of SCID mice. Results: Using this technique, we successfully developed 188 PDOX models from 408 brain tumor patient samples (both high-and low-grade) with a success rate of 72.3% in high-grade glioma, 64.2% in medulloblastoma, 50% in ATRT, 33.8% in ependymoma, and 11.6% in low-grade gliomas. Detailed characterization confirmed their replication of the histopathological and genetic abnormalities of the original patient tumors. Conclusions: The protocol is easy to follow, without a sterotactic frame, in order to generate large cohorts of tumor-bearing mice to meet the needs of biological studies and preclinical drug testing.

Chitosan based niosomal hydrogel with polyethylene glycol and halloysite nanotubes for curcumin delivery

The use of curcumin (Cur) as an anticancer drug is restricted due to its poor solubility and bioavailability. In this study, a pH-sensitive niosomal hydrogel composed of halloysite nanotubes (HNTs), polyethylene glycol (PEG), and chitosan (Cs) was developed for Cur delivery. Fourier transform infrared (FT-IR) spectroscopy and X-ray diffraction (XRD) analyses confirmed the successful synthesis of the nanocomposite and provided insights into the interactions between its components and its crystalline structure. Zeta potential analysis corroborated its colloidal stability, and scanning electron microscopy (SEM) images revealed its spherical shape and size in the range of 514–554 nm. The homogeneous size distribution was corroborated by dynamic light scattering (DLS) experiments. Cs/PEG/HNTs nanocarrier showed high Cur loading and encapsulation efficiencies (44.2 % and 84.0 %, respectively), as well as a smart, pH-sensitive release mechanism over a 96-hour period, highlighting its potential for sustained and targeted drug delivery. Cur release data were well-fitted to the Korsmeyer-Peppas and Zero-Order models at pH 5.4 and 7.4, respectively. The reduced survival of U-87 MG human brain tumor cells in the presence of Cs/PEG/HNTs@Cur samples compared to Cs, Cs/PEG, Cs/PEG/HNTs, and free Cur corroborates the hydrogel anti-cancer properties. The percentage of late apoptotic cells in Cs/PEG/HNTs@Cur was higher than in Cs/PEG/HNTs and free Cur, which is attributed to the sustained and smart release of Cur from Cs/PEG/HNTs@Cur. The biocompatibility, stability, biodegradability, ability to effectively encapsulate Cur and regulate its release to specifically target tumors makes the designed hydrogel a highly versatile and promising nanocarrier for Cur-based cancer therapies.