Nature versus nurture in glioblastoma: microenvironment and genetics can both drive mesenchymal transcriptional signature

Bioengineered Models to Study Microenvironmental Regulation of Glioblastoma Metabolism

Despite extensive research and aggressive therapies, glioblastoma (GBM) remains a central nervous system malignancy with poor prognosis. The varied histopathology of GBM suggests a landscape of differing microenvironments and clonal expansions, which may influence metabolism, driving tumor progression. Indeed, GBM metabolic plasticity in response to differing nutrient supply within these microenvironments has emerged as a key driver of aggressiveness. Additionally, emergent biophysical and biochemical interactions in the tumor microenvironment (TME) are offering new perspectives on GBM metabolism. Perivascular and hypoxic niches exert crucial roles in tumor maintenance and progression, facilitating metabolic relationships between stromal and tumor cells. Alterations in extracellular matrix and its biophysical characteristics, such as rigidity and topography, regulate GBM metabolism through mechanotransductive mechanisms. This review highlights insights gained from deployment of bioengineering models, including engineered cell culture and mathematical models, to study the microenvironmental regulation of GBM metabolism. Bioengineered approaches building upon histopathology measurements may uncover potential therapeutic strategies that target both TME-dependent mechanotransductive and biomolecular drivers of metabolism to tackle this challenging disease. Longer term, a concerted effort integrating in vitro and in silico models predictive of patient therapy response may offer a powerful advance toward tailoring of treatment to patient-specific GBM characteristics.

Neurofibromin knockdown in glioma cell lines is associated with changes in cytokine and chemokine secretion in vitro

The neurofibromin-1 tumor suppressor gene (NF1) is altered in approximately 20% of sporadic glioblastoma (GBM) cases. NF1 deficient GBM frequently shows a mesenchymal gene expression signature, suggesting a relationship between NF1 status and the tumor microenvironment. To identify changes in the production of secreted cytokines/chemokines in NF1 deficient glioma, we applied cytokine arrays to conditioned media from a panel of three GBM cell lines after siRNA-mediated NF1 knockdown. We identified increased secretion of platelet-derived growth factor AA (PDGF-AA), chitinase-3-like protein 1 (CHI3L1), interleukin-8 (IL-8), and endoglin (ENG) in different subsets of these cell lines. Secretion was associated with induction of the corresponding messenger RNA, suggesting a mechanism involving transcriptional upregulation. By contrast, in non-transformed immortalized normal human astrocytes, PDGF-AA secretion was increased upon NF1 knockdown, while secreted CHI3L1, ENG, and IL-8 were reduced or unchanged. Analysis of The Cancer Genome Atlas confirmed a relationship between glioma NF1 status and ENG and CHI3L1 in tumor samples. Overall, this study identifies candidate changes in secreted proteins from NF1 deficient glioma cells that could influence the tumor microenvironment, and suggests a direct link between NF1 loss and increased tumor cell production of CHI3L1 and endoglin, two factors implicated in mesenchymal identity in glioblastoma.

Multi-omics analysis of primary glioblastoma cell lines shows recapitulation of pivotal molecular features of parental tumors

Background

Glioblastoma (GBM) is the deadliest primary brain cancer in adults. Emerging innovative therapies hold promise for personalized cancer treatment. Improving therapeutic options depends on research relying on relevant preclinical models. In this line we have established in the setting of the GlioTex project (GBM and Experimental Therapeutics) a GBM patient-derived cell line (GBM-PDCL) library. A multi-omic approach was used to determine the molecular landscape of PDCL and the extent to which they represent GBM tumors.

Methods

Single nucleotide polymorphism array, expression arrays, exome sequencing, and RNA sequencing were used to measure and compare the molecular landscapes of 20 samples representing 10 human GBM tumors and paired GBM-PDCLs.

Results

Copy number variations were similar for a median of 85% of the genome and for 59% of the major focal events. Somatic point mutations were similar in a median of 41%. Mutations in GBM driver and "druggable" genes were maintained in 67% of events. Mutations that were not conserved in the PDCL were mainly low allelic fraction and/or non-driver mutations. Based on RNA expression profiling, PDCLs cluster closely to their parental tumor with overexpression of pathways associated with cancer progression in PDCL.

Conclusions

Overall, PDCLs recapitulate pivotal molecular alterations of paired-parental tumors supporting their use as a preclinical model of GBM. However, some driver aberrations are lost or gained in the passage from tumor to PDCL. Our results support using PDCL as a relevant preclinical model of GBM. Further investigations of changes between PDCLs and their parental tumor may provide insights into GBM biology.

The Long Non-coding RNA HIF1A-AS2 Facilitates the Maintenance of Mesenchymal Glioblastoma Stem-like Cells in Hypoxic Niches

Long non-coding RNAs (lncRNAs) have an undefined role in the pathobiology of glioblastoma multiforme (GBM). These tumors are genetically and phenotypically heterogeneous with transcriptome subtype-specific GBM stem-like cells (GSCs) that adapt to the brain tumor microenvironment, including hypoxic niches. We identified hypoxia-inducible factor 1 alpha-antisense RNA 2 (HIF1A-AS2) as a subtype-specific hypoxia-inducible lncRNA, upregulated in mesenchymal GSCs. Its deregulation affects GSC growth, self-renewal, and hypoxia-dependent molecular reprogramming. Among the HIF1A-AS2 interactome, IGF2BP2 and DHX9 were identified as direct partners. This association was needed for maintenance of expression of their target gene, HMGA1. Downregulation of HIF1A-AS2 led to delayed growth of mesenchymal GSC tumors, survival benefits, and impaired expression of HMGA1 in vivo. Our data demonstrate that HIF1A-AS2 contributes to GSCs' speciation and adaptation to hypoxia within the tumor microenvironment, acting directly through its interactome and targets and indirectly by modulating responses to hypoxic stress depending on the subtype-specific genetic context.

Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data

BACKGROUND

The integration and visualization of multimodal datasets is a common challenge in biomedical informatics. Several recent studies of The Cancer Genome Atlas (TCGA) data have illustrated important relationships between morphology observed in whole-slide images, outcome, and genetic events. The pairing of genomics and rich clinical descriptions with whole-slide imaging provided by TCGA presents a unique opportunity to perform these correlative studies. However, better tools are needed to integrate the vast and disparate data types.

OBJECTIVE

To build an integrated web-based platform supporting whole-slide pathology image visualization and data integration.

MATERIALS AND METHODS

All images and genomic data were directly obtained from the TCGA and National Cancer Institute (NCI) websites.

RESULTS

The Cancer Digital Slide Archive (CDSA) produced is accessible to the public (http://cancer.digitalslidearchive.net) and currently hosts more than 20,000 whole-slide images from 22 cancer types.

DISCUSSION

The capabilities of CDSA are demonstrated using TCGA datasets to integrate pathology imaging with associated clinical, genomic and MRI measurements in glioblastomas and can be extended to other tumor types. CDSA also allows URL-based sharing of whole-slide images, and has preliminary support for directly sharing regions of interest and other annotations. Images can also be selected on the basis of other metadata, such as mutational profile, patient age, and other relevant characteristics.

CONCLUSIONS

With the increasing availability of whole-slide scanners, analysis of digitized pathology images will become increasingly important in linking morphologic observations with genomic and clinical endpoints.