An anatomic transcriptional atlas of human glioblastoma

Abstract

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. We 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 glioblastoma pathogenesis, diagnosis, and treatment.

Fig. 1.. Data generation, analysis, and presentation pipeline for the Ivy Glioblastoma Atlas Project.

(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 L bars. (C) Two studies, Anatomic Feature Based Profiling and Cancer Stem Cell Marker Based Profiling, provided a framework for the ISH surveys, LMD/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 support 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 0.5μm/pixel, a transcriptome browser, an application programming interface, and help documentation. The database has detailed longitudinal clinical information and MRI time courses (table S1). (E) 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 (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).

Fig. 2.. Gene expression in anatomic features.

(A) Differential expression matrix based on genes identified in the 122 anatomic feature RNA-Seq samples isolated in triplicate from 8–10 tumors. Values are numbers of genes, of the total 3627, whose expression is enriched in the row feature relative to the column feature (FDR<0.01, fold change >2; P<0.1, BH corrected). Values on diagonal are numbers of genes with higher expression in one feature compared with all other features (i.e. top marker genes). (B) Multidimensional scaling of all genes reflects anatomic specificity. (C) Gene ontology enrichment analysis. LE and CT were enriched for gene ontology terms related to neuronal systems and glial cell differentiation, respectively, whereas PAN was associated with stress, hypoxia, and immune responses, and MVP was related to angiogenesis, immune regulation, and response to wounding. (D) Mean Euclidean distance within and between tumors based on hierarchical clustering of all genes in all 122 anatomic feature RNA-Seq samples grouped by anatomic feature (fig. S1; fig. S2). Cross Feature measures variance between anatomic features. (E-I) Representative marker genes showing RNA-Seq expression levels for features isolated by LMD, representative ISH, ML annotations for ISH and H&E, and H&E adjacent to ISH. LE (blue), IT (purple), CT (green), PNZ (light blue), PAN (turquoise), HBV (orange), MVP (red/magenta).