We leveraged IDH wild-type glioblastomas, derivative neurospheres, and single-cell gene expression profiles to define three tumor-intrinsic transcriptional subtypes designated as proneural, mesenchymal, and classical. Transcriptomic subtype multiplicity correlated with increased intratumoral heterogeneity and presence of tumor microenvironment. In silico cell sorting identified macrophages/microglia, CD4+ T lymphocytes, and neutrophils in the glioma microenvironment. NF1 deficiency resulted in increased tumor-associated macrophages/microglia infiltration. Longitudinal transcriptome analysis showed that expression subtype is retained in 55% of cases. Gene signature-based tumor microenvironment inference revealed a decrease in invading monocytes and a subtype-dependent increase in macrophages/microglia cells upon disease recurrence. Hypermutation at diagnosis or at recurrence associated with CD8+ T cell enrichment. Frequency of M2 macrophages detection associated with short-term relapse after radiation therapy.

Glioblastoma (GBM) is a prototypical heterogeneous brain tumor refractory to conventional therapy. A small residual population of cells escapes surgery and chemoradiation, resulting in a typically fatal tumor recurrence ∼ 7 mo after diagnosis. Understanding the molecular architecture of this residual population is critical for the development of successful therapies. We used whole-genome sequencing and whole-exome sequencing of multiple sectors from primary and paired recurrent GBM tumors to reconstruct the genomic profile of residual, therapy resistant tumor initiating cells. We found that genetic alteration of the p53 pathway is a primary molecular event predictive of a high number of subclonal mutations in glioblastoma. The genomic road leading to recurrence is highly idiosyncratic but can be broadly classified into linear recurrences that share extensive genetic similarity with the primary tumor and can be directly traced to one of its specific sectors, and divergent recurrences that share few genetic alterations with the primary tumor and originate from cells that branched off early during tumorigenesis. Our study provides mechanistic insights into how genetic alterations in primary tumors impact the ensuing evolution of tumor cells and the emergence of subclonal heterogeneity.

Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human review has limitations that can result in low reproducibility and inter-observer agreement. Computerized image analysis can partially overcome these shortcomings due to its capacity to quantitatively and reproducibly measure histologic structures on a large-scale. In this paper, we present an end-to-end image analysis and data integration pipeline for large-scale morphologic analysis of pathology images and demonstrate the ability to correlate phenotypic groups with molecular data and clinical outcomes. We demonstrate our method in the context of glioblastoma (GBM), with specific focus on the degree of the oligodendroglioma component. Over 200 million nuclei in digitized pathology slides from 117 GBMs in the Cancer Genome Atlas were quantitatively analyzed, followed by multiplatform correlation of nuclear features with molecular and clinical data. For each nucleus, a Nuclear Score (NS) was calculated based on the degree of oligodendroglioma appearance, using a regression model trained from the optimal feature set. Using the frequencies of neoplastic nuclei in low and high NS intervals, we were able to cluster patients into three well-separated disease groups that contained low, medium, or high Oligodendroglioma Component (OC). We showed that machine-based classification of GBMs with high oligodendroglioma component uncovered a set of tumors with strong associations with PDGFRA amplification, proneural transcriptional class, and expression of the oligodendrocyte signature genes MBP, HOXD1, PLP1, MOBP and PDGFRA. Quantitative morphologic features within the GBMs that correlated most strongly with oligodendrocyte gene expression were high nuclear circularity and low eccentricity. These findings highlight the potential of high throughput morphologic analysis to complement and inform human-based pathologic review.