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2,550 results for “Glioblastoma”
TCGA Glioblastoma Multiforme (GBM) Gene Expression
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset contains information about GBM, an aggressive and highly malignant brain tumor that arises from glial cells, characterized by rapid growth and infiltrative behavior. The gene expression profile was measured experimentally using the Affymetrix HT Human Genome U133a microarray platform by the Broad Institute of MIT and Harvard University cancer genomic characterization center. The Sample IDs serve as unique identifiers for each sample.</p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The log2(x) normalization was removed, and z-normalization was performed on the dataset using a Python script.</p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
TCGA Glioblastoma Multiforme (GBM) Clinical Data
<p><strong>Abstract:</strong></p> <p>The Cancer Genome Atlas (TCGA) was a large-scale collaborative project initiated by the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI). It aimed to comprehensively characterize the genomic and molecular landscape of various cancer types. This dataset includes curated survival data from the Pan-cancer Atlas paper titled <a href="http://www.cell.com/cell/fulltext/S0092-8674(18)30229-0">"An Integrated TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR) to drive high quality survival outcome analytics"</a>. The paper highlights four types of carefully curated survival endpoints, and <a href="http://www.cell.com/action/showFullTableImage?isHtml=true&tableId=tbl3&pii=S0092867418302290">recommends the use of the endpoints of OS, PFI, DFI, and DSS for each TCGA cancer type</a>. The dataset also includes phenotypic information about GBM. The Sample IDs are unique identifiers, which can be paired with the gene expression dataset. </p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Instruction:</strong></p> <p>The survival and phenotype data were merged into one file. Empty columns were removed. Columns with the same value for every sample were also removed. </p> <p><strong>Acknowledgments:</strong></p> <p>Goldman, M.J., Craft, B., Hastie, M. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0546-8</p> <p>Liu, Jianfang, Caesar-Johnson, Samantha J. et al. An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell, Volume 173, Issue 2, 400 - 416.e11. <a href="https://doi.org/10.1016/j.cell.2018.02.052">https://doi.org/10.1016/j.cell.2018.02.052</a></p> <p>The Cancer Genome Atlas Research Network., Weinstein, J., Collisson, E. et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 45, 1113–1120 (2013). https://doi.org/10.1038/ng.2764</p> <p><strong>U-BRITE last update: </strong>07/13/2023</p>
Supplementary data to: Detection of Neoantigen-specific T Cells Following a Personalized Vaccine in a Patient with Glioblastoma
<p>Supplemental Data for the patient described in the manuscript: "Detection of Neoantigen-specific T Cells Following a Personalized Vaccine in a Patient with Glioblastoma". Summary of somatic variant calls from DNA whole exome, gene FPKM from RNA sequencing, and neoantigen predictions for high-affinity (ic<sub>50</sub> <500 nM) candidates.</p>
Dataset of single-cell transcriptomic matrix of 10 human glioblastoma tissue
<p>Dataset of single-cell transcriptomic matrix of 10 human glioblastoma tissue. <span>scRNA-seq was performed using the droplet-based 10x Genomics platform</span><span> </span><span>(10x Genomics, Pleasanton, CA, USA)<span>. <span>GBM tissues for single-cell RNA sequencing (<a name="_Hlk147870629"></a>scRNA-seq)</span> were collected from patients admitted to Xiangya Hospital, Central South University.</span></span></p>
Data set: Genome scale CRISPR Cas9a knockout screen reveals genes that controls glioblastoma susceptibility to the alkylating agent temozolomide (TMZ).
<p>Glioblastoma is the deadliest of all primary brain tumor with a very poor survival outcome. Alkylating agent such as temozolomide is used as a mainstay drug used in treating of glioblastoma patients including radiation, tumor treating field and surgery. However, this drug offers little to no benefit for the glioblastoma patients. Hence, the objective of the generation of this data is to understand and unravel genes that controls glioblastoma susceptibility and resistance to temozolomide using an unbiased genome scale CRISPR Cas9a knockout screen</p>
Safety and Efficacy of INC280 and Buparlisib (BKM120) in Patients With Recurrent Glioblastoma
ClinicalTrials.gov study NCT01870726. IPD Sharing: UNDECIDED. Countries: 5. Publications: 1.
A Phase 2 Study of PLX3397 in Patients With Recurrent Glioblastoma
ClinicalTrials.gov study NCT01349036. IPD Sharing: YES. Countries: 1. Publications: 1.
A Phase 1b/2 Study of PLX3397 + Radiation Therapy + Temozolomide in Patients With Newly Diagnosed Glioblastoma
ClinicalTrials.gov study NCT01790503. IPD Sharing: YES. Countries: 1. Publications: 2.
TCGA Glioblastoma Data
<p>All data was downloaded from UCSC Xena on 16 August 2016. The compressed folder includes the following files:</p> <p> </p> <p>GBM_clinicalMatrix - tab separated file with 629 samples measured by 139 variables. Refer to the source for more details.</p> <p>SOURCE: https://genome-cancer.soe.ucsc.edu/proj/site/xena/datapages/?dataset=TCGA.GBM.sampleMap/GBM_clinicalMatrix&host=https://tcga.xenahubs.net</p> <p>HT_HG-U133A - tab separated gene expression (Affy U133A microarry) file with 539 samples measured by 12,043 genes. Refer to the source for more details.</p> <p>SOURCE: https://genome-cancer.soe.ucsc.edu/proj/site/xena/datapages/?dataset=TCGA.GBM.sampleMap/HT_HG-U133A&host=https://tcga.xenahubs.net</p>
Data from: Spatiotemporal modeling reveals high-resolution invasion states in glioblastoma
<p>Diffuse invasion of glioblastoma cells through normal brain tissue is a key contributor to tumor aggressiveness, resistance to conventional therapies, and dismal prognosis in patients. A deeper understanding of how components of the tumor microenvironment (TME) contribute to overall tumor organization and to programs of invasion may reveal opportunities for improved therapeutic strategies. Towards this goal, we applied a novel computational workflow to a spatiotemporally profiled GBM xenograft cohort, leveraging the ability to distinguish human tumor from mouse TME to overcome previous limitations in analysis of diffuse invasion. Our analytic approach, based on unsupervised deconvolution, performs reference-free discovery of cell types and cell activities within the complete GBM ecosystem. We present a comprehensive catalogue of 15 tumor cell programs set within the spatiotemporal context of 90 mouse brain and TME cell types, cell activities, and anatomic structures. Distinct tumor programs related to invasion were aligned with routes of perivascular, white matter, and parenchymal invasion. Furthermore, sub-modules of genes serving as program hubs were highly prognostic in GBM patients. The compendium of programs presented here provides a basis for rational targeting of tumor and/or TME components. We anticipate that our approach will facilitate an ecosystem-level understanding of immediate and long-term consequences of such perturbations, including identification of compensatory programs that will inform improved combinatorial therapies.</p>
Data for 'Simulating Photodynamic Therapy for the Treatment of Glioblastoma using Monte Carlo Radiative Transport'
<p>Files added include the data used to make each data plot within the paper.</p> <p>Figure 5:</p> <p>pen_depth_plot.py</p> <p>pen_depth_plot_y.py</p> <p>pen_depth_plot_z.py</p> <p>jmean_run2NB.dat</p> <p>o21_full_run2NB.dat</p> <p>rhokap_run2NB.dat</p> <p>tumour.dat</p> <p> </p> <p>Figure 6:</p> <p>tumour_percentage.py</p> <p>tumkill_full_994.dat</p> <p>tumkill_full_1.dat</p> <p> </p> <p>Figure 7:</p> <p>temp_run2NB.dat</p> <p>max_temp_timeNB.dat</p> <p> </p> <p>Figure 8:</p> <p>power_run2NB.dat</p> <p>s0_slice_run2NB.dat</p> <p>o23_slice_run2NB.dat</p> <p>o21_slice_run2NB.dat</p> <p>temp_slice_run2NB.dat</p> <p> </p> <p>Figure 9:</p> <p>percent_left_run2NB.dat</p> <p>percent_left_run11NB.dat</p> <p>percent_left_run12NB.dat</p> <p>percent_left_run13NB.dat</p> <p> </p> <p>Figure 10:</p> <p>percent_left_run1NB.dat</p> <p>percent_left_run2NB.dat</p> <p>percent_left_run3NB.dat</p> <p>percent_left_run4NB.dat</p> <p> </p> <p>Figure 11:</p> <p>max_temp_time_run1NB.dat</p> <p>max_temp_timeNB.dat</p> <p>percent_left_run1NB.dat</p> <p>percent_left_run2NB.dat</p> <p> </p> <p>Figure 12:</p> <p>percent_left_run2NB.dat</p> <p>percent_left_run10NB.dat</p> <p>max_temp_time_run10NB.dat</p> <p>max_temp_timeNB.dat</p> <p> </p> <p>Figure 13:</p> <p>percent_left_run2NB.dat</p> <p>max_temp_timeNB.dat</p> <p>percent_left_run9NB.dat</p> <p>max_temp_time_run9NB.dat</p> <p> </p> <p>Figure 14:</p> <p>percent_left_run2NB.dat</p> <p>percent_left_run14NB.dat</p> <p>percent_left_run15NB.dat</p> <p> </p> <p>Figure 15:</p> <p>percent_left_run2NB.dat</p> <p>percent_left_run56NB.dat</p> <p>percent_left_run62NB.dat</p> <p>max_temp_time_run56NB.dat</p> <p>max_temp_timeNB.dat</p> <p> </p>
Visium Spatially Resolved Transcriptomics of Glioblastoma Samples
<p>This repository contains samples (Visium Spatially resolved Transcriptomics) of the project entitled: <strong>Epigenetic neural glioblastoma integrates into neuron-to-glioma-networks and predicts therapeutic vulnerability</strong></p>
BRAT1 - a new therapeutic target for glioblastoma
<p>Proteomic and phosphoproteomic raw data files (Excel) used for further analysis of the research on BRAT1 as a new therapeutic target for glioblastoma.</p>
Tracking Glioblastoma-astrocytoma cells imaged in brightfield with TrackMate-Cellpose
<p>Glioblastoma-astrocytoma U373 cells migrating on a polyacrylamide gel.</p> <p>This dataset is used in a tutorial on using TrackMate and its cellpose integration to track such cells in brightfield, using a custom cellpose model (included in the dataset).</p> <p>See here for details: <a href="https://imagej.net/plugins/trackmate/trackmate-cellpose">https://imagej.net/plugins/trackmate/trackmate-cellpose</a> </p>
Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma
<p><span>Glioblastomas are malignant tumors of the central nervous system hallmarked by subclonal diversity and dynamic adaptation amid developmental hierarchies </span><span>(Couturier et al., 2020; Neftel et al., 2019; Richards et al., 2021)</span><span>. The source of the dynamic reorganization within the spatial context of these tumors remains elusive. Here, we characterized glioblastomas in-depth by spatially resolved transcriptomics, metabolomics, and proteomics. By </span><span>deciphering regionally shared transcriptional programs across patients, </span><span>we infer that glioblastoma is organized by spatial segregation of lineage states and adapt to inflammatory and/or metabolic stimuli, </span><span>reminiscent </span><span>of the reactive transformation in</span> <span>mature astrocytes. Integration of metabolic imaging and imaging mass cytometry uncovered locoregional tumor-host interdependence, resulting in spatially exclusive adaptive transcriptional programs. Inferring copy-number alterations emphasizes a spatially cohesive organization of subclones associated with reactive transcriptional programs, confirming that environmental stress gives rise to selection pressure. A model of glioblastoma stem cells implanted into human and rodent neocortical tissue mimicking various environments confirmed that transcriptional states originate from dynamic adaptation to various environments.</span></p>
RBBP4 regulates the expression of Mre11-Rad50-NBS1(MRN)complex and promotes DNA double-strand breaks repair to mediate glioblastoma Chemoradiotherapy resistance
<p>Article <a>related</a> SUPPLEMENTARY data.</p> <p> </p>
Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma
<p>This repository contains signalling to metabolic pathways interconnecting (S-M) protein-protein interaction (PPI) paths. The repository consists of two file:</p> <p><a href="https://zenodo.org/api/files/61dbb365-104e-4c41-ad0a-79f4c657bbee/S-M_paths_z-score_greater_equal_1.txt">S-M_paths_z-score_greater_equal_1.txt </a>: It contains all the S-M paths having z-score >= 1.</p> <p><a href="https://zenodo.org/api/files/61dbb365-104e-4c41-ad0a-79f4c657bbee/S-M_paths_z-score_greater_equal_3.txt">S-M_paths_z-score_greater_equal_3.txt </a>: It contains all the S-M paths having z-score >=3</p>
Data from: Antineoplastic Nature of WWOX in Glioblastoma Is Mainly a Consequence of Reduced Cell Viability and Invasion
<p>Supporting data for the article "Antineoplastic Nature of WWOX in Glioblastoma Is Mainly a Consequence of Reduced Cell Viability and Invasion ", published in Biology (DOI: <span>10.3390/biology12030465</span>).</p>
Data from: Molecular landscapes of glioblastoma cell lines revealed a group of patients that do not benefit from WWOX tumor suppressor expression
<p>Supporting data for the article "Molecular landscapes of glioblastoma cell lines revealed a group of patients that do not benefit from WWOX tumor suppressor expression", published in Frontiers in Neuroscience (DOI: 10.3389/fnins.2023.1260409).</p>
Characterizing and targeting glioblastoma neuron-tumor networks with retrograde tracing
<h2>Dataset</h2> <p>Space ranger output (Visium platform) of two human slice culture samples (S1 & S2) injected with GBstarter cells (<span><span>Tetzlaff et al., 2024</span></span>). </p>
ScienceDex guides
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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.