Ivy Glioblastoma Atlas project

This is the online help for the Ivy Glioblastoma Atlas Project web application.

The Ivy Glioblastoma Atlas Project is a foundational resource for exploring the anatomic and genetic basis of glioblastoma at the cellular and molecular levels. . The project is organized into six studies that were designed to identify genes enriched in glioblastoma, three of those studies were based on the anatomic structures or features of glioblastoma and three were focused on putative cancer stem cells.


In Situ Hybridization (ISH): Cellular resolution gene expression image data with adjacent hematoxylin and eosin (H&E)-stained sections that have been manually annotated for anatomic structures.

RNA-Seq: RNA sequencing data from anatomic structures and putative cancer stem cell clusters

Specimen Data: Partner database with de-identified clinical data for each patient and tumor, and search portal for each tumor block’s ISH and annotated H&E image data. For detailed clinical and sequence data requiring user registration to gain access, see Ivy GAP Clinical and Genomic Database

Related Resources
Ivy GAP Clinical and Genomic Database: Partner database with additional clinical, genomic, and transcriptomic data for Ivy GAP.
The Cancer Genome Atlas (TCGA): NIH-funded initiative for genomic mapping of cancers, including glioblastoma.

Moved from Documentation


The 6 studies generated several data modalities, which are listed below.

• De-identified clinical data for 41 patients including age, gender, initial KPS, neurosurgery (resection number), hemisphere, chemotherapy, radiation therapy, recurrence by 6 months, and multifocality
• MRI anonymized scans (42)
• Resected tumor images (42) with tissue block subdivisions and orientation of tumor in brain
• ISH (22,000) acquired with 20x objective for 0.50μm/pixel resolution of 15K x 18K pixel images
• H&E (11,500) acquired with 20x objective for 0.50μm/pixel resolution of 15K x 18K pixel images
• Annotated anatomic structures in H&E images (11,500)
• Nuclear counts for anatomic structures in H&E images (11,500 data sets)

• RNA-Seq (270 laser microdissection samples) with depth of 15 million mapped reads
• RNA-Seq (27 bulk tumor samples) with 100-150 million mapped reads

• Affymetrix SNP array 6.0
• Fusion genes
• Mutations: EGFR amplification, EGFRvIII deletion, PTEN deletion, IDH1 point mutation

• MGMT methylation status

Tumor derived cell lines and xenografts