Ivy Glioblastoma Atlas Project (SNP)
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PROVIDER: PRJNA420741 | ENA |
REPOSITORIES: ENA
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Project description:The Ivy Glioblastoma Atlas Project (Ivy GAP) is a detailed anatomically based transcriptomic atlas of human glioblastoma tumors. As collaborators, the Ivy Foundation funded the Allen Institute and the Swedish Neuroscience Institute to design and create the atlas. The Paul G. Allen Family Foundation also supported the project. This resource consists of a viewer interface that resolves the manually- and machine-annotated histologic images (H&E and RNA in situ hybridization) at 0.5 µm/pixel, a transcriptome browser to view and mine the anatomically-based RNA-Seq samples, an application programming interface, help documentation that describes the methods and how to use the resource, as well as SNP array data and the supporting longitudinal clinical information and MRI time course data. The resource is made available to the public without charge as part of the Ivy GAP (http://glioblastoma.alleninstitute.org/) via the Allen Institute data portal (http://www.brain-map.org), the Ivy GAP Clinical and Genomic Database (http://ivygap.org/) via the Swedish Neuroscience Institute (http://www.swedish.org/services/neuroscience-institute), and The Cancer Imaging Archive (https://wiki.cancerimagingarchive.net/display/Public/Ivy+GAP). The Ivy GAP processed data at GEO includes normalized RNA-Seq FPKM files used for analysis in "An anatomic transcriptional atlas of glioblastoma,” which is under review. Other processed data files as well as sample and donor meta-data and QC metrics are available at http://glioblastoma.alleninstitute.org/static/download.html. The raw RNA-Seq and SNP array data will be submitted to dbGaP.
2017-12-01 | GSE107558 | GEO
Project description:The Ivy Glioblastoma Atlas Project (Ivy GAP) is a detailed anatomically based transcriptomic atlas of human glioblastoma tumors. As collaborators, the Ivy Foundation funded the Allen Institute and the Swedish Neuroscience Institute to design and create the atlas. The Paul G. Allen Family Foundation also supported the project. This resource consists of a viewer interface that resolves the manually- and machine-annotated histologic images (H&E and RNA in situ hybridization) at 0.5 µm/pixel, a transcriptome browser to view and mine the anatomically-based RNA-Seq samples, an application programming interface, help documentation that describes the methods and how to use the resource, as well as SNP array data and the supporting longitudinal clinical information and MRI time course data. The resource is made available to the public without charge as part of the Ivy GAP (http://glioblastoma.alleninstitute.org/) via the Allen Institute data portal (http://www.brain-map.org), the Ivy GAP Clinical and Genomic Database (http://ivygap.org/) via the Swedish Neuroscience Institute (http://www.swedish.org/services/neuroscience-institute), and The Cancer Imaging Archive (https://wiki.cancerimagingarchive.net/display/Public/Ivy+GAP). The Ivy GAP processed data at GEO includes normalized RNA-Seq FPKM files used for analysis in "An anatomic transcriptional atlas of glioblastoma,” which is under review. Other processed data files as well as sample and donor meta-data and QC metrics are available at http://glioblastoma.alleninstitute.org/static/download.html. The raw RNA-Seq and SNP array data will be submitted to dbGaP.
2017-12-01 | GSE107559 | GEO
Project description:Ivy Glioblastoma Atlas Project
| PRJNA866255 | ENA
Project description:Ivy Glioblastoma Atlas Project
| PRJNA420563 | ENA
Project description:Ivy Glioblastoma Atlas Project (RNA-Seq)
| PRJNA420740 | ENA
Project description:This SuperSeries is composed of the SubSeries listed below.
2017-12-01 | GSE107560 | GEO
Project description:Glioblastoma Multiforme (GBM) is the most frequent and lethal primary brain cancer. Due to its therapeutic resistance and aggressiveness, its clinical management is challenging. Platelet-derived Growth Factor (PDGF) genes have been enrolled as drivers of this tumour progression as well as potential therapeutic targets. As detailed understanding of the expression pattern of PDGF system in the context of GBM intra- and intertumoral heterogeneity is lacking in the literature, this study aims at characterising PDGF expression in different histologically-defined GBM regions as well as investigating correlation of these genes expression with parameters related to poor prognosis. Z-score normalised expression values of PDGF subunits from multiple slices of 36 GBMs, alongside with clinical and genomic data on those GBMs patients, were compiled from Ivy Glioblastoma Atlas Project - Allen Institute for Brain Science data sets. PDGF subunits show differential expression over distinct regions of GBM and PDGF family is heterogeneously expressed among different brain lobes affected by GBM. Further, PDGF family expression correlates with bad prognosis factors: age at GBM diagnosis, Phosphatase and Tensin Homolog deletion and Isocitrate Dehydrogenase 1 mutation. These findings may aid on clinical management of GBM and development of targeted curative therapies against this devastating tumour.
| S-EPMC5681588 | biostudies-literature
Project description:PurposeThe availability of radiographic magnetic resonance imaging (MRI) scans for the Ivy Glioblastoma Atlas Project (Ivy GAP) has opened up opportunities for development of radiomic markers for prognostic/predictive applications in glioblastoma (GBM). In this work, we address two critical challenges with regard to developing robust radiomic approaches: (a) the lack of availability of reliable segmentation labels for glioblastoma tumor sub-compartments (i.e., enhancing tumor, non-enhancing tumor core, peritumoral edematous/infiltrated tissue) and (b) identifying "reproducible" radiomic features that are robust to segmentation variability across readers/sites.Acquisition and validation methodsFrom TCIA's Ivy GAP cohort, we obtained a paired set (n = 31) of expert annotations approved by two board-certified neuroradiologists at the Hospital of the University of Pennsylvania (UPenn) and at Case Western Reserve University (CWRU). For these studies, we performed a reproducibility study that assessed the variability in (a) segmentation labels and (b) radiomic features, between these paired annotations. The radiomic variability was assessed on a comprehensive panel of 11 700 radiomic features including intensity, volumetric, morphologic, histogram-based, and textural parameters, extracted for each of the paired sets of annotations. Our results demonstrated (a) a high level of inter-rater agreement (median value of DICE ≥0.8 for all sub-compartments), and (b) ≈24% of the extracted radiomic features being highly correlated (based on Spearman's rank correlation coefficient) to annotation variations. These robust features largely belonged to morphology (describing shape characteristics), intensity (capturing intensity profile statistics), and COLLAGE (capturing heterogeneity in gradient orientations) feature families.Data format and usage notesWe make publicly available on TCIA's Analysis Results Directory (https://doi.org/10.7937/9j41-7d44), the complete set of (a) multi-institutional expert annotations for the tumor sub-compartments, (b) 11 700 radiomic features, and (c) the associated reproducibility meta-analysis.Potential applicationsThe annotations and the associated meta-data for Ivy GAP are released with the purpose of enabling researchers toward developing image-based biomarkers for prognostic/predictive applications in GBM.
| S-EPMC8382093 | biostudies-literature
Project description:Mouse Atlas of Gene Expression Project A Quantitative and Comprehensive Atlas of Gene Expression in Mouse Development. Also available at CGAP: http://cgap.nci.nih.gov/ Keywords: LongSAGE, SAGE
2006-04-27 | GSE4726 | GEO