BACKGROUND: Glioblastoma represents an archetypal example of a heterogeneous malignancy. To understand the diverse molecular consequences of this complex tumor ecology, we analyzed RNA-seq data generated from commonly identified intratumoral structures in glioblastoma enriched using laser capture microdissection. METHODS: Raw gene-level values of fragments per kilobase of transcript per million reads mapped and the associated clinical data were acquired from the publicly available Ivy Glioblastoma Atlas Project database and analyzed using MetaboAnalyst (v3.0). The database includes gene expression data generated from multiple structural features commonly identified in glioblastoma enriched by laser capture microdissection. RESULTS: We uncovered a relationship between subtype heterogeneity in glioblastoma and its unique tumor microenvironment, with infiltrating cells harboring a proneural signature while the mesenchymal subtype was enriched in perinecrotic regions. When evaluating the tumors' transcriptional profiles in the context of their derived structural regions, there was a relatively small amount of intertumoral heterogeneity in glioblastoma, with individual regions from different tumors clustering tightly together. Analyzing the transcriptional profiles in the context of evolutionary progression identified unique cellular programs associated with specific phases of gliomagenesis. Mediators of cell signaling and cell cycle progression appear to be critical events driving proliferation in the tumor core, while in addition to a multiplex strategy for promoting angiogenesis and/or an immune-tolerant environment, transformation to perinecrotic zones involved global metabolic alterations. CONCLUSION: These findings suggest that intratumoral heterogeneity in glioblastoma is a conserved, predictable consequence to its complex microenvironment, and combinatorial approaches designed to target these unequivocally present tumor biomes may lead to therapeutic gains.
BACKGROUND: Glioblastoma represents an archetypal example of a heterogeneous malignancy. To understand the diverse molecular consequences of this complex tumor ecology, we analyzed RNA-seq data generated from commonly identified intratumoral structures in glioblastoma enriched using laser capture microdissection. METHODS: Raw gene-level values of fragments per kilobase of transcript per million reads mapped and the associated clinical data were acquired from the publicly available Ivy Glioblastoma Atlas Project database and analyzed using MetaboAnalyst (v3.0). The database includes gene expression data generated from multiple structural features commonly identified in glioblastoma enriched by laser capture microdissection. RESULTS: We uncovered a relationship between subtype heterogeneity in glioblastoma and its unique tumor microenvironment, with infiltrating cells harboring a proneural signature while the mesenchymal subtype was enriched in perinecrotic regions. When evaluating the tumors' transcriptional profiles in the context of their derived structural regions, there was a relatively small amount of intertumoral heterogeneity in glioblastoma, with individual regions from different tumors clustering tightly together. Analyzing the transcriptional profiles in the context of evolutionary progression identified unique cellular programs associated with specific phases of gliomagenesis. Mediators of cell signaling and cell cycle progression appear to be critical events driving proliferation in the tumor core, while in addition to a multiplex strategy for promoting angiogenesis and/or an immune-tolerant environment, transformation to perinecrotic zones involved global metabolic alterations. CONCLUSION: These findings suggest that intratumoral heterogeneity in glioblastoma is a conserved, predictable consequence to its complex microenvironment, and combinatorial approaches designed to target these unequivocally present tumor biomes may lead to therapeutic gains.
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