Glioblastoma is an aggressive brain tumor that carries a poor prognosis. The tumor’s molecular and cellular landscapes are complex, and their relationships to histologic features routinely used for diagnosis are unclear. A team led by researchers at the Allen Institute for Brain Science present the Ivy Glioblastoma Atlas, an anatomically based transcriptional atlas of human glioblastoma that aligns individual histologic features with genomic alterations and gene expression patterns, thus assigning molecular information to the most important morphologic hallmarks of the tumor. The atlas and its clinical and genomic database are freely accessible online data resources that will serve as a valuable platform for future investigations of glioblastom.
(A) Clinical data were collected for the Ivy cohort of 41 patients. (B) Tissue preparation required en bloc resection and formation of tissue blocks with custom-made L bars. (C) Two studies, anatomic feature–based profiling and cancer stem cell marker–based profiling, provided a framework for the ISH surveys, LMD and RNA-seq experiments, and ISH validations. (D) Informatics included image registration, ontology development, and anatomic feature prediction based on a novel machine learning (ML) analysis of histological data. Search tools enable queries of the data set by tumor, tumor block, and gene expression filtered by anatomic feature, molecular subtype, and clinical information. Searchable manual labels delineating the laser microdissections for 270 RNA-seq samples from the two studies overlay the histology images. The atlas is equipped with image viewers that resolve the histology at 0.5 μm per pixel, a transcriptome browser, an application programming interface, and help documentation. The database has detailed longitudinal clinical information and MRI time courses.
Availability – This free resource is made available as part of the Ivy Glioblastoma Atlas Project (Ivy GAP). (http://glioblastoma.alleninstitute.org/) via the Allen Institute data portal (www.brain-map.org), the Ivy GAP Clinical and Genomic Database (http://ivygap.org/) via the Swedish Neuroscience Institute (www.swedish.org/services/neuroscience-institute), and the Cancer Imaging Archive (https://wiki.cancerimagingarchive.net).