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Glioblastoma Atlas Offers Transcriptional Insights Linked to Anatomical Features

NEW YORK (GenomeWeb) – A team led by the Allen Institute for Brain Science and the Swedish Neuroscience Institute has established a resource called the Ivy Glioblastoma Atlas, an anatomy-based transcriptional atlas for human glioblastoma (GBM) that links to related genomic and clinical insights.

The resource is "a comprehensive molecular pathology map of glioblastoma in which we have assigned key genomic alterations and gene expression profiles to the tumor's anatomic features," cocorresponding authors Greg Foltz and Michael Hawrylycz and their colleagues wrote in Science. "The atlas will facilitate accurate deconvolution of anatomic features in new samples of glioblastoma, providing unique information for the comprehensive diagnostic characterization of the tumor's heterogeneity."

Hawrylycz is a researcher at the Allen Institute. Foltz, who is now deceased, was director of the Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment at the Swedish Neuroscience Institute. 

As part of the Ivy Glioblastoma Atlas Project, or Ivy GAP, the researchers collected longitudinal clinical data for 41 GBM patients, using a combination of in situ hybridization-based anatomical profiling, laser microdissection, and RNA sequencing to profile 42 tumors from these individuals. The team profiled multiple samples per tumor for a subset of those cases, providing insight into the gene sets with enhanced expression in parts of the tumor classified as the "leading edge," "infiltrating tumor," "cellular tumor," "pseudopalisading cells around necrosis" (PAN), and "microvascular proliferation" (MVP).

The researchers went on to assemble the online Ivy Glioblastoma Atlas by combining the anatomically informed gene set enrichment profiles, tumor heterogeneity clues, and other features from that analysis with genomic and molecular subtyping insights gleaned from GBM analyses by the Cancer Genome Atlas Project.

In addition to digging into some of the potential functional and cellular differences that were predicted by the differentially expressed genes in anatomically distinct GBM samples, the team came up with a 293-gene signature to computationally tease out leading edge, cellular tumor, PAN, and MVP anatomical features from bulk tumor data — an approach the group used to assess RNA sequence data for 167 GBM samples from TCGA.

"This atlas and the associated database for clinical and genomic data will serve as a useful platform for developing and testing new hypotheses related to the pathogenesis, diagnosis, and treatment of glioblastoma," the authors wrote, noting that other research and clinical teams have already started applying the Ivy GAP dataset to GBM research and preclinical studies.