Brain Tumor Tissue Research Articles

Guggulsterone inhibits migration and invasion through proteasomal and lysosomal degradation in human glioblastoma cells

Glioblastoma multiforme (GBM) is a deadly brain malignancy, and current therapies offer limited survival benefit. The phytosterol guggulsterone (GS) has been shown to exhibit antitumor efficacy. This study aimed to investigate the effects of GS on migration and invasion and its underlying mechanisms in human GBM cell lines. After GS treatment, the survival rate of GBM cells was reduced, and the migration and invasion abilities of GBM cells were significantly decreased. There was also concomitant decreased expression of focal adhesion complex, matrix metalloproteinase-2 (MMP2), MMP9 and cathepsin B. Furthermore, GS induced ERK phosphorylation and autophagy, with increased p62 and LC3B-II expression. Notably, treatment of in GBM cells with the proteasome inhibitor MG132 or the lysosome inhibitor NH4Cl reversed the GS-mediated inhibition of migration and invasion. In an orthotopic xenograft mouse model, immunohistochemical staining of brain tumor tissues demonstrated that MMP2 and cathepsin B expression was reduced in GS-treated mice. GS treatment inhibited GBM cell migration and invasion via proteasomal and lysosomal degradation, suggesting its therapeutic potential in clinical use in the future.

Diffuse reflectance spectroscopy sensor to differentiate between glial tumor and healthy brain tissue: a proof-of-concept study.

Glial tumors grow diffusely in the brain. Survival is correlated to the extent of tumor removal, but tumor borders are often invisible. Resection beyond the borders as defined by conventional methods may further improve prognosis. In this proof-of-concept study, we evaluate diffuse reflectance spectroscopy (DRS) for discrimination between glial tumors and normal brain ex vivo. DRS spectra and histology were acquired from 22 tumor samples and nine brain tissue samples retrieved from 30 patients. The content of biological chromophores and scattering features were estimated by fitting a model derived from diffusion theory to the DRS spectra. DRS parameters differed significantly between tumor and normal brain tissue. Classification using random forest yielded a sensitivity and specificity for the detection of low-grade gliomas of 82.0% and 82.7%, respectively, and the area under curve (AUC) was 0.91. Applied in a hand-held probe or biopsy needle, DRS has the potential to provide intra-operative tissue analysis.

INNV-28. A PILOT STUDY EVALUATING THE FEASIBILITY OF USING AN ULTRA-FAST DIGITAL CONFOCAL MICROSCOPY SCANNER FOR REAL-TIME INTRA-OPERATIVE BRAIN TUMOUR DIAGNOSIS

Abstract INTRODUCTION Real-time intra-operative brain tumour tissue analysis in the operating room (OR) can shorten turnaround times and facilitate repeated sampling; the latter can improve diagnostic accuracy and guide resection to maximise extent. We evaluated the feasibility of using an ultra-fast confocal microscopy scanner (Histolog®, SamanTree Medical SA) in the OR for real-time intra-operative brain tumour tissue diagnosis. This study is the first use of Histolog® on primary human brain tissue globally. METHODS An observational pilot study with varying brain tumour types was conducted in a tertiary UK brain tumour centre. Multiple, freshly excised tissue samples were stained with Acridine Orange, and images captured within 60 seconds on Histolog® in the OR in parallel to current diagnostic pathways. A Consultant Neuropathologist performed a blinded correspondence assessment between Histolog® and current gold standard histopathology. Qualitative logging of diagnostic features was performed. RESULTS Cases studied (n=12) included glioma, metastasis, meningioma, schwannoma, and pituitary adenoma. Concordance between Histolog® and traditional (frozen section) intra-operative histopathological diagnosis was 83% (10/12 cases). One Histolog® case demonstrated superior diagnostic accuracy to frozen section. Histolog® images demonstrated specific diagnostic features such as clusters of cohesive epithelioid cells (metastatic carcinoma), sheet-like variably cellular and pleomorphic cells (gliomas), diffuse sheet-like monomorphic round nuclei (pituitary adenoma), elongated spindle cells (schwannoma), and nodular architecture and oval nuclei (meningioma). DISCUSSION Ultra-fast confocal microscopy scanning with Histolog® is feasible in the OR and Histolog® image resolution is adequate to identify histopathological features necessary for intra-operative brain tumour diagnosis. Training and clinico-radiological details are important in minimising discordance. A larger study with greater numbers and tumour types will now be performed to define an optimal scanning protocol and determine the strengths and limitations of the technology. Future studies should investigate the effect of real-time margin zone analysis on extending resection.

EXTH-15. DRUG SCREENING ON PATIENT GBM CELL CULTURES IDENTIFIES OMACETAXINE MEPESUCCINATE AS A POTENT ANTI-GLIOMA AGENT WITH THE ABILITY TO CROSS THE BLOOD-BRAIN-BARRIER

Abstract INTRODUCTION Little progress has been made in the development of effective new therapies for glioblastoma (GBM) the past decades. Fresh patient-derived GBM cell culture models have become the gold standard for GBM drug discovery and development. One of the major obstacles in identifying novel candidate drugs against GBM remains the blood-brain barrier (BBB). Therefore, it is crucial to select drugs with favourable physicochemical properties to cross BBB and reach the tumour tissue in therapeutically effective concentrations. In current drug repurposing approach, we evaluated available anti-cancer agents in our patient-derived drug screening platform against GBM. METHODS The FDA-approved Oncology Drug Set II library was tested on 45 primary GBM cell cultures. We developed a drug shortlisting pipeline combining efficacy data with pharmacodynamic and pharmacokinetic characteristics of each compound. The therapeutic efficacy of the selected agent was assessed in an orthotopic mouse PDX model, while penetration into the CNS by LC/MS/MS. RESULTS Omacetaxine mepesuccinate (OMA) was ranked as one of the most promising candidates applying our drug selection approach. In vitro, OMA revealed anti-tumour activity at IC50 values well-below reported Cmax plasma values in approximately 80% of GBM cultures. NanoString nCounter analysis, revealed DNA damage repair as the main pathway involved in OMA’s anti-tumour effect. Activation of caspase 3/7 activity and decrease of glioma cell invasiveness were also linked to its anti-tumour effect. In vivo, 1mg/kg dose of OMA was found to reach the brain tumour tissue in concentrations similar to the reported IC50 values in vitro. No adverse reactions were noted and a survival benefit was observed in a proportion of the treated mice. CONCLUSIONS At 1 mg/kg, OMA reaches the tumour brain tissue in therapeutically effective concentrations in mice while a moderate therapeutic benefit was observed. Additional in vivo experiments are ongoing investigating higher dosages of OMA and longer exposure.

DDDR-22. TRANSLATION OF THE PDGFRA/KIT INHIBITOR AVAPRITINIB FOR PEDIATRIC HIGH-GRADE GLIOMA

Abstract Pediatric high-grade glioma (pHGG) is an incurable disease with a median survival of less than 6 months post-progression and no effective targeted therapy. PDGFRA is commonly altered in pHGG, but targeting PDGFRA in this disease has been unsuccessful, likely due to poor central nervous system (CNS) penetrance. Avapritinib is a novel and CNS-penetrant PDGFRA/KIT inhibitor that is FDA-approved for adults with unresectable or metastatic PDGFRA exon 18-mutant gastrointestinal stromal tumor (GIST) and is being studied in CNS tumors. We performed a pre-clinical and clinical assessment to determine the potential suitability of avapritinib therapy in PDGFRA-driven glioma. A multi-institutional cohort genetic analysis revealed PDGFRA amplification and mutation in 10.2% and 6.1% of pHGG, respectively. Additionally, PDGFRA expression in the absence of genetic events was significantly increased in H3K27-altered diffuse midline glioma (DMG) compared to H3-wildtype pHGG. Avapritinib performed well in: (i) mutant PDGFRA enzyme inhibition and wildtype inhibition at high dose, (ii) minimal off-target kinase inhibition, (iii) brain penetration (peak 10 µM), and (iv) proliferation/pPDGFRA reduction in PDGFRA-amplified and mutant pHGG cell lines. Avapritinib treatment in an aggressive PDX model of pHGG resulted in significant survival benefit. We pursued treatment of eight pediatric and young adult HGG patients with avapritinib across seven institutions. Patients were a mixture of local (N = 4) and metastatic disease (N = 4); all patients were post-initial radiation, with 7/8 having progressed on prior treatment. 7/8 patients had PDGFRA amplifications or mutations, and 7/8 had H3K27M mutations. Therapy was generally well-tolerated. 4/8 patients showed radiographic response to avapritinib, with one patient demonstrating complete response of target lesion and remains on therapy. Avapritinib levels in patients’ CSF and brain tumor tissue reached micromolar levels. These results demonstrate that avapritinib is a potent, selective, and CNS-penetrant PDGFRA/KIT inhibitor that is promising for further study in pHGG with relevant alterations.

The LncRNA MIAT is identified as a regulator of stemness-associated transcript in glioma.

Myocardial infarction-associated transcript (MIAT) is a long non-coding RNA (lncRNA) with altered expression in different diseases and malignancies. In this study, the potential expression and function of lncRNA MIAT in intuition and progression of brain cancer was investigated. At first, TCGA data analysis demonstrated that lncRNA MIAT is significantly upregulated in various malignancies, especially its expression is dramatically elevated in brain tumors. In line with the data, we further evaluated the expression of MIAT in a series of brain tumor tissue, and our results revealed that the expression of MIAT was noticeably overexpressed in glioblastoma (p = < 0.0001). We further found that the expression of MIAT was markedly upregulated in low-grade brain tumors rather than high-grade ones. To further investigate the biological function of MIAT in brain cancer cells, its expression was suppressed by si-RNA-mediated knocking down. Inhibition of MIAT resulted in reduced proliferation of brain tumor cells followed by cell cycle arrest at the G1 phase, and significant induction of apoptosis, and senescence, but limited the migration ability and epithelial-mesenchymal-transition (EMT). Moreover, knocking-down of MIAT reduced the expression of stemness factors, followed by upregulation of their downstream miRNAs (micro RNAs), let-7a-5p, and miR-29b-3p. Altogether, our data demonstrated that lncRNA MIAT could control proliferation, migration, and metastasis of brain cancer cells via regulating the Nanog/ Sox2 / let-7a-5p / miR-29b-3p axis. This data could introduce lncRNA MIAT as a novel oncogene in brain cancer pathogenesis.

An Approach to the Simultaneous Determination of a Panel of Five Biomarkers for the Early Detection of Brain Cancer Using the Stochastic Method

The simultaneous determination of heregulin-α and HER 1–4 plays an important role in brain cancer diagnosis and treatment. To date, only enzyme-linked immunosorbent assay (ELISA) or a semiquantitative colorimetric method have been used for the assay of these biomarkers; these methods are quite expensive and can only determine one biomarker in a run. Four 3D stochastic microsensors based on multi-walled carbon nanotubes enriched with gold nanoparticles and modified with inulin were designed for the simultaneous determination of heregulin-α and HER 1–4 in tumor brain tissue and whole blood samples. For the simultaneous measurement of HRG-α and HER 1–4, all sensors demonstrated low limits of determination (as fg mL−1 magnitude order), high sensitivities, and wide concentration ranges. From biological samples, high recovery values of more than 96% of analytes were obtained. The proposed sensor can detect HER 1–4 and heregulin-α in whole blood and tumor tissue samples at the same time.

Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation

A major obstacle to the learning-based segmentation of healthy and tumorous brain tissue is the requirement of having to create a fully labeled training dataset. Obtaining these data requires tedious and error-prone manual labeling with respect to both tumor and non-tumor areas. To mitigate this problem, we propose a new method to obtain high-quality classifiers from a dataset with only small parts of labeled tumor areas. This is achieved by using positive and unlabeled learning in conjunction with a domain adaptation technique. The proposed approach leverages the tumor volume, and we show that it can be either derived with simple measures or completely automatic with a proposed estimation method. While learning from sparse samples allows reducing the necessary annotation time from 4 h to 5 min, we show that the proposed approach further reduces the necessary annotation by roughly 50% while maintaining comparative accuracies compared to traditionally trained classifiers with this approach.

Deciphering the Biochemical Similarities and Differences Among Human Neuroglial Cells and Glioma Cells Using Fourier Transform Infrared Spectroscopy

The use of Fourier transform infrared spectroscopy to identify the peritumoral tissue of gliomas proves the potential of this technique to distinguish normal brain tissues from glioma tissues. However, due to the heterogeneity of gliomas, it is difficult to characterize the representative spectra of normal brain tissues and glioma tissues. The linear spectra of major cellular components, such as microglia, astrocytes, and glioma cells, were obtained to quantify the biochemical changes between healthy cells and tumor cells, and provide supporting data for the final distinction between tumor and normal brain tissue. Fourier transform infrared was used to measure human astrocytes, microglia (HM1900), and glioma cells (U87, BT325), and the cellular components of the 4 types of cells were analyzed by means of average spectra, second-derivative spectra, principal component analysis, hierarchical cluster analysis, and difference spectra. The proteomics, lipidomics, genomics, and metabolic statuses of the cells were different. The amide I, lipid, and nuclear acid regions of the spectra are the most obvious regions to use for distinguishing the 4 types of cells. We conclude that an improved understanding of both similarities and differences in the cellular components of astrocytes, microglia, and glioma cells can help us better understand the heterogeneity of gliomas. We suggest that targeting cellular metabolism (protein, lipid, and nuclear acids) is helpful to distinguish between normal brain tissue and glioma tissue, which has broad application prospects.

Edge U-Net: Brain tumor segmentation using MRI based on deep U-Net model with boundary information

Blood clots in the brain are frequently caused by brain tumors. Early detection of these clots has the potential to significantly lower morbidity and mortality in cases of brain cancer. It is thus indispensable for a proper brain tumor diagnosis and treatment that tumor tissue magnetic resonance images (MRI) be accurately segmented. Several deep learning approaches to the segmentation of brain tumor MRIs have been proposed, each having been designed to properly map out ‘boundaries’ and thus achieve highly accurate segmentation. This study introduces a deep convolution neural network (DCNN), named the Edge U-Net model, built as an encoder-decoder structure inspired by the U-Net architecture. The Edge U-Net model can more precisely localise tumors by merging boundary-related MRI data with the main data from brain MRIs. In the decoder phase, boundary-related information from original MRIs of different scales is integrated with the appropriate adjacent contextual information. A novel loss function was added to this segmentation model to improve performance. This loss function is enhanced with boundary information, allowing the learning process to produce more precise results. In the conducted experiments, a public dataset with 3064 T1-Weighted Contrast Enhancement (T1-CE) images of three well-known brain tumor types were used. The experiment demonstrated that the proposed framework achieved satisfactory Dice score values compared with state-of-art models, with highly accurate differentiation of brain tissues. It achieved Dice scores of 88.8 % for meningioma, 91.76 % for glioma, and 87.28 % for pituitary tumors. Computations of other performance metrics such as the Jaccard index, sensitivity, and specificity were also conducted. According to the results, the Edge U-Net model is a potential diagnostic tool that can be used to help radiologists more precisely segment brain tumor tissue images.

Thioredoxin System and miR-21, miR-23a/b and let-7a as Potential Biomarkers for Brain Tumor Progression: Preliminary Case Data

The thioredoxin system and microRNAs (miRNAs) are potential targets for both cancer progression and treatment. However, the role of miRNAs and their relation with the expression profile of thioredoxin system in brain tumor progression remains unclear. In this study, we aimed to determine the expression profiles of redox components Trx-1, TrxR-1 and PRDX-1, and oncogenic miR-21, miR-23a/b and let-7a and oncosuppressor miR-125 in different brain tumor tissues and their association with increasing tumor grade. We studied Trx-1, TrxR-1, and PRDX-1 messenger RNA expression levels by quantitative real-time polymerase chain reaction and protein levels by Western blot and miR-23a, miR-23b, miR-125a, miR-21, and let-7a miRNA expression levels by quantitative real-time polymerase chain reaction in 16 glioma, 15 meningioma, 5 metastatic, and 2 benign tumor samples. We also examined Trx-1, TrxR-1, and PRDX-1 protein levels in serum samples of 36 patients with brain tumor and 37 healthy volunteers by enzyme-linked immunosorbent assay. We found that Trx-1, TrxR-1, and PRDX-1 presented high messenger RNA expression but low protein expression in low-grade brain tumor tissues, whereas they showed higher protein expression in sera of patients with low-grade brain tumors. miR-23b, miR-21, miR-23a, and let-7a were highly expressed in low-grade brain tumor tissues and positively correlated with the increase in thioredoxin system activity. Our findings showed that Trx-1, TrxR-1, miR-21, miR-23a/b, and let-7a might be used for brain tumor diagnosis in the clinic. Further prospective studies including molecular pathway analyses are required to validate the miRNA/Trx system regulatory axis in brain tumor progression.

Simultaneous Molecular Recognition of Interleukins 2 and 4, and of Tumour Necrosis Factor‐α in Biological Samples

AbstractTwo 3D stochastic sensors based on single‐walled carbon nanotubes and multi‐walled carbon nanotubes decorated with gold nanoparticles and modified with 5,10,15,20‐tetrakis (pentafluorophenyl)‐21H,23H‐porphyrin iron (III) chloride were constructed and utilized for the simultaneous molecular recognition of IL‐2, IL‐4, and TNF‐α in biological samples. The proposed sensors present low limits of quantification, high sensitivity, and wide concentration ranges. Recoveries values higher than 98.2 % of analytes from whole blood and brain tumour tissue samples were obtained.

P14.04.A TrackingTERTpromoter andIDH1mutations in liquid biopsies - suitable biomarkers for disease monitoring in glioma patients?

Abstract Background Mutations within the telomerase reverse transcriptase promoter (TERTprom) and isocitrate dehydrogenase (IDH) account for the most common genetic alterations in gliomas. Each of these mutations impact clinicopathologic diagnosis and course of diseases. While TERTprom mutations are frequently detected in glioblastoma, IHD mutations are assigned to astrocytoma of grade 2-4, thus mostly associated with better prognosis. In the era of precision oncology, molecular profiling and continuous monitoring of treatment response or relapse are of increasing importance. Accordingly, this study aims to detect TERTprom and IDH mutations in plasma-derived cell-free (cf)DNA of gliomas. The mutant allele frequencies (MAF) will be compared retrospectively to clinico-pathological parameters including extent of resection and tumor progression. Material and Methods Digital droplet PCR (ddPCR) analyses were performed using the QX200TM Digital Droplet System from BioRad. First, to evaluate probes for ddPCR, genomic DNA of several brain tumor cell models (n=6) and tumor tissue (n=1), as well as cfDNA of plasma (n=3) from samples with known TERTprom and IDH mutation status was investigated. For detection of IDH mutations, the unique assay ID dHSaMDV2010055 (IDH1p.R132H) and for TERTprom mutations the TaqMan dPCR Liquid Biopsy Assays for C228T (Hs000000092) and C250T (Hs000000093) were used. The results of ddPCR were analyzed with QuantaSoftTM software and the MAF was calculated Results To validate the detection method for IDH1R132H, we analyzed the MAF in one tissue and corresponding plasma sample of a confirmed IDH1-mutated astrocytoma. In addition, plasma from one astrocytoma grade 2-3 as well as from an IDH1-mutated glioblastoma was tested. Interestingly, both astrocytoma cases exhibited undetectable or very low MAF ranging from 0.1 to 1% in tissue as well as in plasma samples, while in plasma from the high-grade glioblastoma case, IDH1R132H was detected with a frequency of 1.9%. Due to the high GC content of the TERT promoter region, amplification steps are challenging. Accordingly, we first optimized ddPCR conditions for C228T and C250T probes by adding 7-deaza-2-deoxyguanosine-5-triphosphate (7-ddGTP) in varying concentrations to each ddPCR reaction. When using 4µM of 7-ddGTP per sample, a clear separation between mutant and wild-type droplets was reached, detecting MAF between 36-63% in DNA from cell culture models. Conclusion Within this pilot study we optimized the ddPCR method for the detection of IDH1R132H and TERTprom mutations in plasma and tissue samples. Subsequently, we hypothesize that these mutations are suitable liquid biomarkers correlating with extent of resection and tumor progression in gliomas.

P07.07.A Fluorescence lifetime imaging for improved visualization of 5-ALA fluorescence during biopsies of suspected brain tumors

Abstract Background Stereotactic brain tumor biopsies represent a standard neurosurgical technique enabling a precise histopathological tumor diagnosis which is of utmost importance for optimal further treatment allocation. However, non-diagnostic biopsies are not uncommon in routine clinical practice. To overcome this shortcoming, serial biopsies or intraoperative histopathology are typically performed prolonging the operating times and potentially increasing the risk of complications. Recently, the value of 5-aminolevulinic acid (5-ALA) induced protoporphyrin IX (PpIX) fluorescence for visualization of diagnostic tumor tissue was demonstrated in navigation-guided brain tumor biopsies. However, a not negligible proportion of brain tumors lack visible fluorescence. In some of these tumors, a subvisual concentration of PpIX could be detected by spectroscopic analysis. Thus, detection of such subvisual PpIX accumulation in tumor samples in biopsies by the new “Fluorescence Lifetime Imaging” (FLIM) technique might lead to further improvement of this approach. Material and Methods Patients that underwent navigation-guided biopsy of a suspected brain tumor were eligible for inclusion in this study. After administration of 5-ALA, the tissue samples were checked for visible fluorescence during the navigation-guided stereotactic biopsy with a sterile microscope. In each sample the observed fluorescence signal was documented. Additionally, the tissue samples were investigated ex-vivo by FLIM for detection of PpIX accumulation. In each case, a histopathological diagnosis was established according to the WHO classification of CNS tumors. Results A total number of 20 tissue samples could be retrieved in 16 patients with suspected brain tumors. The biopsy with assistance of 5-ALA was feasible in all patients and no relevant side effects occurred. Visible PpIX fluorescence could be observed in 75% of cases. In contrast, 25% of samples did not show visible fluorescence. In one specimen, no fluorescence signal could be evaluated as the sample was covered by too much blood. However, significant accumulation of PpIX could be detected by FLIM in all samples in which evaluation was possible (n=19). A final histopathological diagnosis was feasible in all patients. Conclusion Fluorescence Lifetime Imaging is a promising new technique for improved detection of brain tumor tissue during navigation-guided biopsies. Thus, this new technique might lead in future to reduced operating times and complication rate by minimizing the cases that need serial biopsies or intraoperative histopathology.

Brain Tumor Classification Using Machine Learning Mixed Approach

Abstract: In this paper, we propose an effective method using Machine learning for the classification of brain tumor tissues. For successful treatment correct and early detection of brain tumors is essential. Here proposed system is using Convolutional Neural Network for feature extraction and classification. In feature extraction, we reduce the number of features in the dataset by creating new features from the existing ones. Here we recognize the types of tissues using CNN. The pooling layer is used to reduce the spatial resolution of the feature maps. This layer brings down the number of parameters needed for image processing. This paper is focused on helping the radiologist and physician to have a second opinion on the diagnosis. These systems help specialists to perform tumor detection very easily. This study aims to diagnose brain tumors using MRI images.

Brain tumor MRI images identification and classification based on the recurrent convolutional neural network

Brain tumor detection and analysis are necessary for any indicative system and have testified that exhaustive research and procedural development over time. This work needs to implement an effective automated system to improve the accuracy of tumor detection. Various segmentation algorithms have been developed to achieve and enhance the accuracy of brain tumor classification. Brain image segmentation has been recognized as a complex and challenging area in medical image processing. This paper proposes a novel automated scheme for detection and classification. The proposed method is divided into various categories: MRI image preprocessing, image segmentation, feature extraction, and image classification. The image preprocessing step is performed using an adaptive filter to remove the noise of the MRI image. Image segmentation is performed using the improved K-means clustering (IKMC) algorithm, and the gray level co-occurrence matrix (GLCM) is used for feature extraction to extract features. After extracting features from MRI images, we used a deep learning model to classify the types of images such as gliomas, meningiomas, non-tumors, and pituitary tumors. The classification process was performed using recurrent convolutional neural networks (RCNN). The proposed method provides better results for classifying brain images from a given input dataset. The experiments were conducted on the Kaggle dataset with 394 testing sets and 2870 training set MRI images. The results illustrate that the proposed method achieves a higher performance than previous methods. Finally, the proposed RCNN method is compared with the current classification methods of BP, U-Net, and RCNN. The proposed classifier obtained 95.17% accuracy in classifying brain tumor tissues from MRI images.

Detection of perillyl alcohol and its metabolite perillic acid in postsurgical glioblastoma tissue after intranasal administration of NEO100: illustrative case.

Intranasal delivery of NEO100, a pharmaceutical-grade version of the natural monoterpene perillyl alcohol (POH), is undergoing clinical phase IIa testing as a treatment for glioblastoma (GBM). However, so far there is no evidence that intranasal delivery of NEO100 indeed results in POH reaching intracranial malignancies in a patient. After surgical removal of her recurrent GBM tumor, a patient received daily intranasal NEO100 therapy for more than 3 years before a second recurrence emerged. At that time, a final dose of NEO100 was given shortly before the tumor tissue was surgically removed, and the tissue was processed for high-performance liquid chromatography analysis of POH and its primary metabolite, perillic acid (PA). Both molecules could readily be detected in the tumor tissue. This is the first demonstration of POH and PA in brain tumor tissue from any patient. It reveals that intranasal administration of NEO100 is a valid approach to achieve delivery of this agent to a brain tumor. In view of the noninvasive and safe nature of this method, along with tentative indications of activity, our findings add confidence to the notion that intranasal administration of NEO100 holds potential as a new treatment option for brain-localized malignancies.

Development and validation of a liquid chromatography coupled with tandem mass spectrometry method for determining total and unbound pamiparib in human plasma and brain tumors.

Pamiparib (BGB-290) is an orally bioavailable, small molecule inhibitor of poly (ADP-ribose) polymerase 1 (PARP1) and PARP2. A reversed-phase LC with tandem mass spectrometry method was developed and fully validated for determining total and unbound pamiparib concentrations in human plasma and brain tumor tissue. Plasma and tissue homogenate samples were prepared by methanol protein precipitation. Pamiparib and the internal standard [13 C2 ,15 N2 ]pamiparib were separated on a Waters BEH C18 (50 × 2.1 mm, 1.7 μm) column, with a gradient elution consisting of mobile phases A (0.1% formic acid in water) and B (0.1% formic acid in acetonitrile) at a flow rate of 0.25 mL/min. The analytes were monitored with multiple reaction monitoring mode under positive electrospray ionization. The method was fully validated for specificity, linearity, accuracy and precision, matrix effect and recovery, and short- and long-term stability. The lower limit of quantitation was 0.5 nM of pamiparib in plasma or tissue homogenate. The calibration curve was linear over the pamiparib concentration range of 0.5-1000 nM in plasma. The intra- and inter-day precision and accuracy were within the generally accepted criteria for bioanalytical method. Pamiparib was stable in plasma at -80°C for at least 6 months. The method was successfully applied to assess the plasma and tumor pharmacokinetics of total and unbound pamiparib in patients with glioma.

Drug Repurposing, a Fast-Track Approach to Develop Effective Treatments for Glioblastoma.

Simple SummaryIntroducing novel and effective treatments against glioblastoma (GBM) remains an arduous journey as reflected in the negative outcome of most clinical trials. The blood–brain barrier and the tremendous heterogeneity of the disease comprise major obstacles in this process. Drug repurposing is a drug discovery approach that can accelerate the drug development timeline and identify promising candidates for GBM treatment. Obtaining insights already at preclinical stage into drug sensitivity and physicochemical properties for central nervous system (CNS) penetration of these candidates could shift research outcomes to more effective drugs for clinical investigation against GBM.Glioblastoma (GBM) remains one of the most difficult tumors to treat. The mean overall survival rate of 15 months and the 5-year survival rate of 5% have not significantly changed for almost 2 decades. Despite progress in understanding the pathophysiology of the disease, no new effective treatments to combine with radiation therapy after surgical tumor debulking have become available since the introduction of temozolomide in 1999. One of the main reasons for this is the scarcity of compounds that cross the blood–brain barrier (BBB) and reach the brain tumor tissue in therapeutically effective concentrations. In this review, we focus on the role of the BBB and its importance in developing brain tumor treatments. Moreover, we discuss drug repurposing, a drug discovery approach to identify potential effective candidates with optimal pharmacokinetic profiles for central nervous system (CNS) penetration and that allows rapid implementation in clinical trials. Additionally, we provide an overview of repurposed candidate drug currently being investigated in GBM at the preclinical and clinical levels. Finally, we highlight the importance of phase 0 trials to confirm tumor drug exposure and we discuss emerging drug delivery technologies as an alternative route to maximize therapeutic efficacy of repurposed candidate drug.

The first-in-class ERK inhibitor ulixertinib shows promising activity in mitogen-activated protein kinase (MAPK)-driven pediatric low-grade glioma models.

Pediatric low-grade gliomas (pLGG) are the most common pediatric central nervous system tumors, with driving alterations typically occurring in the MAPK pathway. The ERK1/2 inhibitor ulixertinib (BVD-523) has shown promising responses in adult patients with mitogen-activated protein kinase (MAPK)-driven solid tumors. We investigated the antitumoral activity of ulixertinib monotherapy as well as in combination with MEK inhibitors (MEKi), BH3-mimetics, or chemotherapy in pLGG. Patient-derived pLGG models reflecting the two most common alterations in the disease, KIAA1549:BRAF-fusion and BRAFV600E mutation (DKFZ-BT66 and BT40, respectively) were used for in vitro and in vivo (zebrafish embryos and mice) efficacy testing. Ulixertinib inhibited MAPK pathway activity in both models, and reduced cell viability in BT40 with clinically achievable concentrations in the low nanomolar range. Combination treatment of ulixertinib with MEKi or BH3-mimetics showed strong evidence of antiproliferative synergy in vitro. Ulixertinib showed on-target activity in all tested combinations. In vivo, sufficient penetrance of the drug into brain tumor tissue in concentrations above the in vitro IC50 and reduction of MAPK pathway activity was achieved. In a preclinical mouse trial, ulixertinib mono- and combined therapies slowed tumor growth and increased survival. These data indicate a high clinical potential of ulixertinib for the treatment of pLGG and strongly support its first clinical evaluation in pLGG as single agent and in combination therapy in a currently planned international phase I/II umbrella trial.