Glioblastoma multiforme (GBM) is the most lethal primary brain tumor characterized by high cellular and molecular heterogeneity, hypervascularization, and innate drug resistance. Cellular components and extracellular matrix (ECM) are the two primary sources of heterogeneity in GBM. Here, biomimetic tri‐regional GBM models with tumor regions, acellular ECM regions, and an endothelial region with regional stiffnesses patterned corresponding to the GBM stroma, pathological or normal brain parenchyma, and brain capillaries, are developed. Patient‐derived GBM cells, human endothelial cells, and hyaluronic acid derivatives are used to generate a species‐matched and biochemically relevant microenvironment. This in vitro study demonstrates that biophysical cues are involved in various tumor cell behaviors and angiogenic potentials and promote different molecular subtypes of GBM. The stiff models are enriched in the mesenchymal subtype, exhibit diffuse invasion of tumor cells, and induce protruding angiogenesis and higher drug resistance to temozolomide. Meanwhile, the soft models demonstrate enrichment in the classical subtype and support expansive cell growth. The three‐dimensional bioprinting technology utilized in this study enables rapid, flexible, and reproducible patient‐specific GBM modeling with biophysical heterogeneity that can be employed by future studies as a tunable system to interrogate GBM disease mechanisms and screen drug compounds.
- Award ID(s):
- 1903357
- NSF-PAR ID:
- 10329136
- Date Published:
- Journal Name:
- International Journal of Molecular Sciences
- Volume:
- 22
- Issue:
- 19
- ISSN:
- 1422-0067
- Page Range / eLocation ID:
- 10720
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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ABSTRACT Glioblastoma multiforme (GBM) is the deadliest form of primary brain tumor. GBM tumors are highly heterogeneous, being composed of tumor cells as well as glioblastoma stem cells (GSCs) that contribute to drug resistance and tumor recurrence following treatment. To develop therapeutic strategies, an improved understanding of GSC behavior in their microenvironment is critical. Herein, we have employed three‐dimensional (3D) hyaluronic acid (HA) hydrogels that allow the incorporation of brain microenvironmental cues to investigate GSC behavior. U87 cell line and patient‐derived D456 cells were cultured as suspension cultures (serum‐free) and adherently (in the presence of serum) and were then encapsulated in HA hydrogels. We observed that all the seeded single cells expanded and formed spheres, and the size of the spheres increased with time. Increasing the initial cell seeding density of cells influenced the sphere size distribution. Interestingly, clonal expansion of serum‐free grown tumor cells in HA hydrogels was observed. Also, stemness marker expression of serum and/or serum‐free grown cells was altered when cultured in HA hydrogels. Finally, we demonstrated that HA hydrogels can support long‐term GSC culture (up to 60 days) with retention of stemness markers. Overall, such biomimetic culture systems could further our understanding of the microenvironmental regulation of GSC phenotypes.
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Abstract Glioblastoma (GBM) is the most common and deadly form of brain cancer. Interactions between GBM cells and vasculature in vivo contribute to poor clinical outcomes, with GBM‐induced vessel co‐option, regression, and subsequent angiogenesis strongly influencing GBM invasion. Here, elements of the GBM perivascular niche are incorporated into a methacrylamide‐functionalized gelatin hydrogel as a means to examine GBM–vessel interactions. The complexity of 3D endothelial cell networks formed from human umbilical vein endothelial cells and normal human lung fibroblasts as a function of hydrogel properties and vascular endothelial growth factor (VEGF) presentation is presented. While overall length and branching of the endothelial cell networks decrease with increasing hydrogel stiffness and incorporation of brain‐mimetic hyaluronic acid, it can be separately altered by changing the vascular cell seeding density. It is shown that covalent incorporation of VEGF supports network formation as robustly as continuously available soluble VEGF. The impact of U87‐MG GBM cells on the endothelial cell networks is subsequently investigated. GBM cells localize in proximity to the endothelial cell networks and hasten network regression in vitro. Together, this in vitro platform recapitulates the close association between GBM cells and vessel structures as well as elements of vessel co‐option and regression preceding angiogenesis in vivo.
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Abstract Background Glioblastoma Multiforme (GBM) is a fast-growing and highly aggressive brain tumor that invades the nearby brain tissue and presents secondary nodular lesions across the whole brain but generally does not spread to distant organs. Without treatment, GBM can result in death in about 6 months. The challenges are known to depend on multiple factors: brain localization, resistance to conventional therapy, disrupted tumor blood supply inhibiting effective drug delivery, complications from peritumoral edema, intracranial hypertension, seizures, and neurotoxicity. Main text Imaging techniques are routinely used to obtain accurate detections of lesions that localize brain tumors. Especially magnetic resonance imaging (MRI) delivers multimodal images both before and after the administration of contrast, which results in displaying enhancement and describing physiological features as hemodynamic processes. This review considers one possible extension of the use of radiomics in GBM studies, one that recalibrates the analysis of targeted segmentations to the whole organ scale. After identifying critical areas of research, the focus is on illustrating the potential utility of an integrated approach with multimodal imaging, radiomic data processing and brain atlases as the main components. The templates associated with the outcome of straightforward analyses represent promising inference tools able to spatio-temporally inform on the GBM evolution while being generalizable also to other cancers. Conclusions The focus on novel inference strategies applicable to complex cancer systems and based on building radiomic models from multimodal imaging data can be well supported by machine learning and other computational tools potentially able to translate suitably processed information into more accurate patient stratifications and evaluations of treatment efficacy. Graphical Abstractmore » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/ijms221910720
