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2,292 results for “glioma”
Antineoplaston Therapy in Treating Patients With Brain Stem Glioma
ClinicalTrials.gov study NCT00003459. IPD Sharing: NO. Countries: 1. Publications: 1.
Low-Dose or High-Dose Lenalidomide in Treating Younger Patients With Recurrent, Refractory, or Progressive Pilocytic Astrocytoma or Optic Pathway Glioma
ClinicalTrials.gov study NCT01553149. IPD Sharing: Not stated. Countries: 4. Publications: 1.
ONC201 in Adults With Recurrent H3 K27M-mutant Glioma
ClinicalTrials.gov study NCT03295396. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Randomized Phase II Trial of Vandetanib (ZD6474) in Combination With Carboplatin Versus Carboplatin Alone Followed by Vandetanib Alone in Adults With Recurrent High-Grade Gliomas
ClinicalTrials.gov study NCT00995007. IPD Sharing: Not stated. Countries: 1. Publications: 2.
A Trial of Poly-ICLC in the Management of Recurrent Pediatric Low Grade Gliomas
ClinicalTrials.gov study NCT01188096. IPD Sharing: NO. Countries: 1. Publications: 0.
Long Term Survivors of High-grade Glioma and Their Caregivers
ClinicalTrials.gov study NCT02965144. IPD Sharing: NO. Countries: 1. Publications: 1.
A Study of Bevacizumab (Avastin) in Combination With Temozolomide (TMZ) and Radiotherapy in Paediatric and Adolescent Participants With High-Grade Glioma
ClinicalTrials.gov study NCT01390948. IPD Sharing: Not stated. Countries: 14. Publications: 5.
Return to work following diagnosis of low-grade glioma: A nationwide matched cohort study
Open the record for dataset details and reuse information.
Reactivating PTEN to impair glioma stem cells by inhibiting cytosolic iron-sulfur assembly pathway
Open the record for dataset details and reuse information.
N-cadherin dynamically regulates pediatric glioma cell migration in complex environments
Open the record for dataset details and reuse information.
The temporal response of a glioma cell population to irradiation: modeling the effect of dose and cell density
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somalier files for glioma dataset
<p>somalier files for the glioma dataset used in the paper: run with:</p> <p> </p> <pre><code class="language-bash">somalier relate -g glioma-somalier/groups.txt glioma-somalier/*.somalier </code></pre> <p> </p>
Modeling Glioma Oncostreams In Vitro: Spatiotemporal Dynamics of their Formation, Stability, and Disassembly
<p>Movie 1 of 16 is a supplementary material for the manuscript 'Modeling Glioma Oncostreams In Vitro: Spatiotemporal Dynamics of their Formation, Stability, and Disassembly.' It visually demonstrates the effect of various treatments on the formation and dynamics of oncostreams in high-grade glioma cells. The time-lapse videos provide insightful observations into how various pharmacological drugs influence the initial formation and structural dynamics of oncostreams."</p> <p>It aims to enhance the understanding of the foundational stages of oncostream formation in glioma cells and their response to various pharmacological treatments, supporting the findings discussed in the manuscript.</p> <p>Individual Movie Descriptions:</p> <p><strong>Movie 1: Low Density Oncostream Dynamics: </strong>Demonstrates the formation and dynamics of oncostreams in GFP+ NPA glioma cells at low seeding density (1 × 10^5 cells), over 24 hours using time-lapse confocal imaging.</p> <p><strong>Movie 2: High Density Oncostream Dynamics:</strong> Explores the effect of high cell seeding density (2 × 10^5 cells) on the formation and behavior of oncostreams in GFP+ NPA glioma cells, analyzed over 24 hours.</p> <p><strong>Movie 3: Spatiotemporal Progression of Oncostreams:</strong> Showcases the sequential self-formation and developmental stages of oncostreams over a 24-hour period.</p> <p><strong>Movie 4: Collagenase Impact on Oncostreams:</strong> Description: Illustrates the disassembly of aggressive and malignant oncostreams with 15 U/ml collagenase treatment over 15 hours.</p> <p><strong>Movie 5: TC-I-15 Inhibition of Oncostream Formation:</strong> Details the impact of TC-I-15, an integrin antagonist, on adhesion and oncostream formation in glioma cells, recorded over 20 hours.</p> <p><strong>Movie 6: Cytochalasin D Disruption of Oncostreams:</strong> Captures the effect of Cytochalasin D on oncostream formation by inhibiting actin polymerization, observed over a 1-hour period with rapid imaging intervals.</p> <p><strong>Movie 7: Myosin II Inhibition in Oncostreams with p-nitro Blebbistatin: </strong>Presents the influence of p-nitro Blebbistatin on oncostream formation by inhibiting myosin II, documented over 1 hour with dynamic cellular responses.</p> <p><strong>Movie 8: Control - Oncostream Formation without BAPTA-AM:</strong> Demonstrates oncostream formation and dynamics without BAPTA-AM treatment, monitored over 16 hours.</p> <p><strong>Movie 9: BAPTA-AM (Calcium Modulation) Impact on Oncostream Formation:</strong> Reveals the effects of BAPTA-AM treatment on oncostream dynamics, observed over a 16-hour period.</p> <p><strong>Movie 10: Glutamate Influence on Oncostreams:</strong> Shows the effects of glutamate treatment on oncostream formation and dynamics, monitored over 16 hours.</p> <p><strong>Movie 11: Histamine Impact on Oncostream Dynamics:</strong> Demonstrates the influence of histamine on the formation and behavior of oncostreams, captured over a 16-hour period.</p> <p><strong>Movie 12: Oncostream Formation on Non-Laminin Coated Surfaces:</strong> Illustrates the compromised organization of oncostreams on poly-D-lysine coated dishes without laminin, observed over 45 hours.</p> <p><strong>Movie 13: 4-HAP Treatment Effect on Oncostreams:</strong> Shows the impact of 4-HAP treatment on the structure of oncostreams, monitored over 20 hours.</p> <p><strong>Movie 14: Rho-Activator I Untreated Control:</strong> Presents the formation and dynamics of oncostreams without Rho-Activator I treatment, recorded over 16 hours.</p> <p><strong>Movie 15: Rho-Activator I Influence on Oncostreams Dynamics:</strong> Details the effects of Rho-Activator I on oncostream formation and dynamics, observed over a 16-hour period.</p> <p><strong>Movie 16: Rho-Inhibitor Effect on Oncostreams Dynamics:</strong> Demonstrates the impact of Rho-Inhibitor on the formation and behavior of oncostreams, monitored over 16 hours.</p>
Localization of protoporphyrin IX in glioma patients with paired stimulated Raman histology and two-photon 3 excitation fluorescence microscopy
<div> <div> <div> <h1>Spatially resolved transcriptomics</h1> <p>Tissue fixation was performed following the ‘Methanol Fixation, H&E Staining & Imaging for Visium Spatial Protocols’ (CG000160 | Rev C), which included heating the slide and immersing it in pre-chilled methanol. In the tissue staining phase, isopropanol was applied to tissue sections followed by a series of air-drying, hema- toxylin application, washing, bluing buffer application, eosin mix addition, and further washing. The slide was then dried on a heating block. Imaging was conducted using the Evos microscope, with the settings following the previously described protocol. Permeabilization and reverse transcription were undertaken without a preceding tissue optimization on Visium Tissue Optimization Slides, as the optimal permeabiliza- tion time for brain tissue had been established at 12 minutes by a previous researcher. The overall library preparation adhered to the ‘Visium Spatial Gene Expression Reagent Kits – User Guide’ (CG000239 | Rev F). During permeabilization, the Visium slide with stained tissue sections was fitted into a slide cassette and exposed to permeabilization enzyme, followed by a wash with 0.1X SSC buffer. For reverse transcription, an RT master mix was dispensed into each well, followed by a 45-minute incubation period in a thermocycler at 53 ° Celsius. In the second strand synthesis stage, each well received an addition of 75 ul 0.08 M KOH, followed by a brief room-temperature incubation. Subsequently, wells were washed with buffer EB and re- ceived the second strand mix, before undergoing a 15-minute incubation at 65 ° Celsius in a thermocycler. The denaturation process involved washing the wells with buffer EB and adding 35 ul 0.08 M KOH in each well, which were then incubated at room temperature. Afterward, Tris 1 M pH 7.0 was pipetted into four tubes of an 8-tube strip, followed by a transfer of samples from each well into these tubes. The tubes were then vortexed, centrifuged, and placed on ice, with the remaining sample stored for subsequent stages. The experiment initiated with the determination of cycle number wherein a qPCR mix was allocated across five wells of a qPCR plate, with a negative control included. The ensuing qPCR and Cq determination followed the standard protocol used for FFPE methods. Notably, uneven Cq values starting from n.5 were rounded up. In the subsequent cDNA amplification phase, an amplification mix was introduced to each sample tube, followed by thermo-cycling for actual PCR using a specified protocol. The cDNA cleanup process involved adding a SPRIselect reagent to each sample tube, followed by a series of incubation, washing, drying, and buffer addition steps. The cleaned-up samples were then transferred to new tubes. Finally, cDNA quality control and quantification were performed using a Tape Station. The total cDNA yield was calculated, factoring in the library concentration and elution volume. The process of fragmentation, end repair, and A-tailing started with using just a quarter of the purified library, with the remaining portion stored at -20 ° Celsius. The selected volume was mixed with buffer EB and fragmentation mix and incubated in a thermal cycler. Double-sided size selection was performed to discard large fragments and retain fragments within the desired size range. This involved the use of SPRIselect reagent, and resulted in a library with reduced total volume and a smaller range of fragment sizes. Adaptor ligation involved mixing adaptor ligation mix with each sample and incubating in a thermocycler. Post-ligation cleanup followed the cleanup steps post-cDNA amplification, with minor adjustments to the quantities of SPRIselect reagent and buffer EB. Sample index PCR was then performed, with an amp mix and dual index TT set A added to each sample, followed by a specific PCR protocol. The total number of cycles was determined based on the cDNA yield. Another round of double-sided size selection was performed, this time with varied substance quantities, to ensure another cleanup stage. The process concluded with a post-library construction quality control, ensuring the success of the library construction. While no exact concentration calculations were necessary, the fragment size in base pairs was of interest. A Fragment Analyzer was used due to its availability and accuracy in fragment size calculation. Sequencing was performed on a NextSeq 550.</p> <h1>Postprocessing and analysis pipeline</h1> <p>The data analysis and quality control for this research was conducted using the 10X Genomics’ space ranger pipeline and the SPATA2 (version 2.0) framework for spatial data analysis. The SPATA2 object was initiated through the ‘SPATA2::initiateSpataObject_10X’ function. This import procedure involved several stages using the Seurat version 4.0 package. Firstly, gene expression normalization was performed by dividing each spot’s values by the estimated total number of transcripts. These normalized values were then multiplied by 10,000 and underwent a natural logarithm transformation to improve interpretability and comparability across genes. Next, a regression model was applied to remove batch effects and scale the data. This model factored in sample batch and the expression percentages of ribosomal and mitochondrial genes, helping to control for potential sources of unwanted variation in the data. For a more detailed understanding of this process, you can refer to the guide provided at this link: https://themilolab.github.io/SPATA2/. This guide provides comprehensive information about the SPATA2 package and its application in spatial transcriptomics analysis.</p> </div> </div> </div> <div> <div> <div> <h1>Postprocessing and imaging analysis</h1> <p>The H&E images along with the PpIX and SRH images were aligned using afine transformation as described recently. For classification of the PpIX patterns we extracted 160x160 sized patches from each barcode spot and predicted the pattern using the pretrained ResNet architecture.</p> </div> </div> </div>
The raw data of Spatial omics of glioma
<p>Our files include the raw data from single-cell RNA sequencing, spatial transcriptomics sequencing conducted in our study. The raw data of the paper "Deciphering radial glial stem-like cells based on spatial multi-omics guides safe therapy in glioma".</p>
Processed Seurat Object of scRNAseq data from wildtype and CaMKK2 KO immune infiltrate of CT2a preclinical murine glioma
<p>This repository contains the processed Seurat objects generated from the raw data deposited at the Gene Expression Omnibus (GEO) under GSE197879.</p> <p>Details about the experiment and sequencing are available under GSE197879.</p> <p>Information on how the Seurat objects were created can be found in this GitHub repository https://github.com/wht10/CT2A_scRNAseq_CaMKK2KOvWT .</p> <p>Notable metadata within each Seurat object:</p> <p>1. Processed_CD45_Live_Fig2b.rds</p> <ul> <li>Genotype - whether the cell is from a WT or CaMKK2 KO mouse</li> <li>HTO_maxID - The biological replicate that the cell came from (4 biological replicates per genotype)</li> <li>MouseID - A concatenation between the genotype and HTO_maxID, providing a unique identifier for each biological replicate</li> <li>Cell.Type - The cell type annotations for each cell. Can be assigned to "Idents()" to change the name of the cell identities.</li> <li>Geno.Ident - A concatenation between Genotype and Cell.Type. By re-assigning this to "Idents()" "FindMarkers()" can be used to investigate differentially expressed genes within a cell-type between genotypes. </li> </ul> <p>2. Reclustered_TILs_Fig3a.rds</p> <ul> <li>Genotype - whether the cell is from a WT or CaMKK2 KO mouse</li> <li>HTO_maxID - The biological replicate that the cell came from (4 biological replicates per genotype)</li> <li>MouseID - A concatenation between the genotype and HTO_maxID, providing a unique identifier for each biological replicate</li> <li>Celltype - The cell type annotations for each cell. Can be assigned to "Idents()" to change the name of the cell identities.</li> <li>Geno_Ident - A concatenation between Genotype and cell-type. By re-assigning this to "Idents()" "FindMarkers()" can be used to investigate differentially expressed genes within a cell-type between genotypes. </li> </ul>
Data from: Genome-wide Polygenic Risk Scores Predict Risk of Glioma and Molecular Subtypes
<div> <div> <div> <p><strong>Background</strong>: Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to efficiently capture genetic risk using available data.</p> <p><strong>Methods</strong>: We applied a method based on continuous shrinkage priors (PRS-CS) to model the joint effects of over 1 million common variants on disease risk and compared this to an approach (PRS-CT) that only selects a limited set of independent variants that reach genome-wide significance (P<5×10-8). PRS models were trained using GWAS stratified by histological (10,346 cases, 14,687 controls) and molecular subtype (2,632 cases, 2,445 controls), and validated in two independent cohorts.</p> <p><strong>Results</strong>: PRS-CS was generally more predictive than PRS-CT with a median increase in explained variance (R2) of 24% (interquartile range=11-30%) across glioma subtypes. Improvements were pronounced for glioblastoma (GBM), with PRS-CS yielding larger odds ratios (OR) per standard deviation (OR=1.93, P=2.0×10-54 vs. OR=1.83, P=9.4×10-50) and higher explained variance (R2=2.82% vs. R2=2.56%). Individuals in the 80th percentile of the PRS- CS distribution had significantly higher risk of GBM (0.107%) at age 60 compared to those with average PRS (0.046%, P=2.4×10-12). Lifetime absolute risk reached 1.18% for glioma and 0.76% for IDH wildtype tumors for individuals in the 95th PRS percentile. PRS-CS augmented the classification of IDH mutation status in cases when added to demographic factors (AUC=0.839 vs. AUC=0.895, P=6.8×10-9).</p> <p><strong>Conclusions</strong>: Genome-wide PRS has potential to enhance the detection of high-risk individuals and help distinguish between prognostic glioma subtypes.</p> <p><strong>Citation</strong>: Nakase T, Guerra GA, Ostrom QT, et al. Genome-wide Polygenic Risk Scores Predict Risk of Glioma and Molecular Subtypes. <em>Neuro-Oncology</em>. Published online June 25, 2024:noae112. doi:10.1093/neuonc/noae112</p> </div> </div> </div>
Data and Codes for Publication: "Sexually Dimorphic Computational Histopathological Signatures Prognostic of Overall Survival in High-Grade Gliomas via Deep Learning"
<p><strong>Data</strong></p> <p>The patches and the associated tumor segmentation labels (expert-vetted) from our analysis are available in Patches.pytable file.<br><br><strong>Codes<br><br></strong>The codes for training tumor segmentation models and conducting survival analysis are available in the following files</p> <ul> <li>ResNet-train: Code to train Resnet18 model for Tumor Segmentation</li> <li>Tumor_Segmentation: Code to segment tumor regions from WSI using ResNet18 model</li> <li>ResNet_Cox_train: Code to train ResNet-Cox model in 5 folds cross-validation setting</li> <li>Evaluate_ResNetCox: Code to evaluate ResNet-Cox model</li> </ul>
Publication analysis on gliomas
<p>Supplementary material 1: Original data of Gliomas publication</p> <p>Supplementary material 2: Python code for analysis.</p>
DICOM converted Slide Microscopy images for the ICDC-Glioma collection
<p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=icdc_glioma">ICDC-Glioma</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <strong>Download instructions</strong> below.</p> <h3>Collection description</h3> <p><strong> <a href="https://doi.org/10.7937/TCIA.SVQT-Q016"> ICDC-Glioma <em> </em> </a> </strong> contains treatment-naïve naturally-occurring <strong> canine glioma </strong> participants from the <a href="https://caninecommons.cancer.gov/#/study/GLIOMA01"> Integrated Canine Data Commons </a> . Brain radiology (57/81 participant animals) and H&E-stained biopsy or necropsy pathology (76/81 participants) are classified by veterinary and physician neuropathologists. <br><br>Please see the wiki <a href="https://doi.org/10.7937/TCIA.SVQT-Q016"> <strong> ICDC-Glioma </strong> <em> </em> </a> to learn more about the images and to obtain any supporting metadata for this collection.</p> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>collection_id-idc_v8-aws.s5cmd</code> corresponds to the contents of the <code>collection_id</code> collection introduced in IDC data release v8. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <ol> <li><code>icdc_glioma-idc_v15-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>icdc_glioma-idc_v15-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>icdc_glioma-idc_v15-dcf.dcf</code>: Gen3 manifest (for details see <a href="Gen3 manifest documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/ImagingDataCommons/idc-index">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code>.</li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> <p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. <em>National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence</em>. RadioGraphics (2023). <a href="https://doi.org/10.1148/rg.230180">https://doi.org/10.1148/rg.230180</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.