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Preclinical models of glioblastoma: limitations of current models and the promise of new developments

Published online by Cambridge University Press:  02 December 2021

Peng Liu*
Affiliation:
Institute of Medical Sciences, School of Medicine Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
Scott Griffiths
Affiliation:
Institute of Medical Sciences, School of Medicine Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
Damjan Veljanoski
Affiliation:
Aberdeen Royal Infirmary, Foresterhill Health Campus, Aberdeen, UK
Philippa Vaughn-Beaucaire
Affiliation:
School of Applied Sciences, University of Huddersfield, Huddersfield, UK
Valerie Speirs*
Affiliation:
Institute of Medical Sciences, School of Medicine Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
Anke Brüning-Richardson*
Affiliation:
School of Applied Sciences, University of Huddersfield, Huddersfield, UK
*
Author for correspondence: Anke Brüning-Richardson, E-mail: [email protected]Co-corresponding authors: Peng Liu, E-mail: [email protected]; Valerie Speirs, E-mail: [email protected].
Author for correspondence: Anke Brüning-Richardson, E-mail: [email protected]Co-corresponding authors: Peng Liu, E-mail: [email protected]; Valerie Speirs, E-mail: [email protected].
Author for correspondence: Anke Brüning-Richardson, E-mail: [email protected]Co-corresponding authors: Peng Liu, E-mail: [email protected]; Valerie Speirs, E-mail: [email protected].

Abstract

Glioblastoma (GBM) is the most common and aggressive primary brain tumour, yet little progress has been made towards providing better treatment options for patients diagnosed with this devastating condition over the last few decades. The complex nature of the disease, heterogeneity, highly invasive potential of GBM tumours and until recently, reduced investment in research funding compared with other cancer types, are contributing factors to few advancements in disease management. Survival rates remain low with less than 5% of patients surviving 5 years. Another important contributing factor is the use of preclinical models that fail to fully recapitulate GBM pathophysiology, preventing efficient translation from the lab into successful therapies in the clinic. This review critically evaluates current preclinical GBM models, highlighting advantages and disadvantages of using such models, and outlines several emerging techniques in GBM modelling using animal-free approaches. These novel approaches to a highly complex disease such as GBM show evidence of a more truthful recapitulation of GBM pathobiology with high reproducibility. The resulting advancements in this field will offer new biological insights into GBM and its aetiology with potential to contribute towards the development of much needed improved treatments for GBM in future.

Type
Review
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

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Footnotes

*

Co-corresponding authors.

References

Gladson, CL et al. (2010) The pathobiology of glioma tumors. Annual Review of Pathology 5, 3350.CrossRefGoogle ScholarPubMed
Gimple, R et al. (2019a) Glioblastoma stem cells: lessons from the tumor hierarchy in a lethal cancer. Genes & Development 33, 591609.CrossRefGoogle Scholar
Rock, K et al. (2012) A clinical review of treatment outcomes in glioblastoma multiforme – the validation in a non-trial population of the results of a randomised phase III clinical trial: has a more radical approach improved survival? The British Journal of Radiology 85, e729e733.CrossRefGoogle Scholar
Tamimi, AF and Juweid, M (2017) Epidemiology and outcome of glioblastoma. In De Vleeschouwer, S (ed.), Glioblastoma, vol. 1, Brisbane (AU): Codon Publications, pp. 143154.CrossRefGoogle Scholar
McGuire, S (2016) World Cancer Report 2014. Geneva, Switzerland: World Health Organization, International Agency for Research on Cancer, WHO Press, 2015. Advances in Nutrition (Bethesda, Md.), 7(2), 418419.Google Scholar
Ohgaki, H and Kleihues, P (2013) The definition of primary and secondary glioblastoma. Clinical Cancer Research 19, 764.CrossRefGoogle ScholarPubMed
Golla H, AAM et al. (2014) Glioblastoma multiforme from diagnosis to death: a prospective, hospital-based, cohort, pilot feasibility study of patient reported symptoms and needs. Supportive Care in Cancer 22, 3341–3335.CrossRefGoogle ScholarPubMed
Nelson, JS et al. (2012) Potential risk factors for incident glioblastoma multiforme: the Honolulu heart program and Honolulu-Asia aging study. Journal of Neuro-Oncology 109, 315321.CrossRefGoogle ScholarPubMed
Nørøxe, DS et al. (2016) Hallmarks of glioblastoma: a systematic review. ESMO Open 1, e000144.CrossRefGoogle ScholarPubMed
Louis, DN et al. (2016) The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta Neuropathologica 131. doi: https://doi.org/10.1007/s00401-016-1545-1.CrossRefGoogle ScholarPubMed
Stupp, R et al. (2014) High-grade glioma: eSMO clinical practice guidelines for diagnosis, treatment and follow-up. Annals of Oncology 25(Suppl 3), iii93ii101.CrossRefGoogle ScholarPubMed
Hegi, ME et al. (2005) MGMT gene silencing and benefit from temozolomide in glioblastoma. New England Journal of Medicine 352, 9971003.CrossRefGoogle ScholarPubMed
Institute, NC (2021) The Cancer Genome Atlas. Available at https://www.cancer.gov/tcga.Google Scholar
Cerami, E et al. (2012) The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discovery 2, 401.CrossRefGoogle ScholarPubMed
Gao, J et al. (2013) Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Science Signaling 6, pl1.CrossRefGoogle ScholarPubMed
Stupp, R et al. (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. New England Journal of Medicine 352, 987996.CrossRefGoogle ScholarPubMed
Tan, AC et al. (2020) Management of glioblastoma: State of the art and future directions. CA: A Cancer Journal for Clinicians 70. doi: https://doi.org/10.3322/caac.21613.Google ScholarPubMed
Crowley, RW et al. (2006) Gamma knife surgery for glioblastoma multiforme. Neurosurgical Focus 20, E17.CrossRefGoogle ScholarPubMed
Yaprak, G et al. (2020) Stereotactic radiotherapy in recurrent glioblastoma: a valid salvage treatment option. Stereotactic and Functional Neurosurgery 98, 167175.CrossRefGoogle ScholarPubMed
Lipani, JD et al. (2008) Survival following CyberKnife radiosurgery and hypofractionated radiotherapy for newly diagnosed glioblastoma multiforme. Technology in Cancer Research & Treatment 7, 249255.CrossRefGoogle ScholarPubMed
Stummer, W et al. (2006) Fluorescence-guided surgery with 5-aminolevulinic acid for resection of malignant glioma: a randomised controlled multicentre phase III trial. The Lancet. Oncology 7, 392401.CrossRefGoogle ScholarPubMed
Valdés, PA et al. (2016) Optical technologies for intraoperative neurosurgical guidance. Neurosurgical Focus 40, E8.CrossRefGoogle ScholarPubMed
Damiani, E et al. (2019) How reliable are in vitro IC50 values? Values vary with cytotoxicity assays in human glioblastoma cells. Toxicology Letters 302, 2834.CrossRefGoogle ScholarPubMed
Lawson, HC et al. (2007) Interstitial chemotherapy for malignant gliomas: the Johns Hopkins experience. Journal of Neuro-Oncology 83, 6170.CrossRefGoogle ScholarPubMed
Lamborn, KR et al. (2004) Prognostic factors for survival of patients with glioblastoma: recursive partitioning analysis. Neuro-Oncology 6, 227235.CrossRefGoogle ScholarPubMed
Girvan, AC et al. (2015) Glioblastoma treatment patterns, survival, and healthcare resource use in real-world clinical practice in the USA. Drugs in Context 4, 212274.CrossRefGoogle ScholarPubMed
Shergalis, A et al. (2018) Current challenges and opportunities in treating glioblastoma. Pharmacological Reviews 70, 412445.CrossRefGoogle ScholarPubMed
Wick, W and Kessler, T (2018) Drug repositioning meets precision in glioblastoma. Clinical Cancer Research 24, 256.CrossRefGoogle ScholarPubMed
Le Rhun, E et al. (2019) Molecular targeted therapy of glioblastoma. Cancer Treatment Reviews 80, 101896.CrossRefGoogle ScholarPubMed
Weller, M et al. (2017) Rindopepimut with temozolomide for patients with newly diagnosed, EGFRvIII-expressing glioblastoma (ACT IV): a randomised, double-blind, international phase 3 trial. The Lancet Oncology 18, 13731385.CrossRefGoogle ScholarPubMed
Chang, SM et al. (2005) Phase II study of CCI-779 in patients with recurrent glioblastoma multiforme. Investigational New Drugs 23, 357361.CrossRefGoogle ScholarPubMed
Ma, DJ et al. (2015) A phase II trial of everolimus, temozolomide, and radiotherapy in patients with newly diagnosed glioblastoma: NCCTG N057K. Neuro-Oncology 17, 12611269.CrossRefGoogle ScholarPubMed
Taylor, JW et al. (2018) Phase-2 trial of palbociclib in adult patients with recurrent RB1-positive glioblastoma. Journal of Neuro-Oncology 140, 477483.CrossRefGoogle ScholarPubMed
Batchelor, TT et al. (2013) Phase III randomized trial comparing the efficacy of cediranib as monotherapy, and in combination with lomustine, versus lomustine alone in patients with recurrent glioblastoma. Journal of Clinical Oncology 31, 32123218.CrossRefGoogle ScholarPubMed
Brandes, AA et al. (2016) A phase II randomized study of galunisertib monotherapy or galunisertib plus lomustine compared with lomustine monotherapy in patients with recurrent glioblastoma. Neuro-Oncology 18, 11461156.CrossRefGoogle ScholarPubMed
Reardon, DA et al. (2017) OS10.3 randomized phase 3 study evaluating the efficacy and safety of nivolumab vs bevacizumab in patients with recurrent glioblastoma: CheckMate 143. Neuro-Oncology 19(suppl_3), iii21iii21.CrossRefGoogle Scholar
Pontén, J and Macintyre, EH (1968) Long term culture of normal and neoplastic human glia. Acta Pathologica Et Microbiologica Scandinavica 74, 465486.CrossRefGoogle ScholarPubMed
Candolfi, M et al. (2007) Intracranial glioblastoma models in preclinical neuro-oncology: neuropathological characterization and tumor progression. Journal of Neuro-Oncology 85, 133148.CrossRefGoogle ScholarPubMed
Houchens, DP et al. (1983) Human brain tumor xenografts in nude mice as a chemotherapy model. European Journal of Cancer & Clinical Oncology 19, 799805.CrossRefGoogle ScholarPubMed
Houchens, DP et al. (1983) Human brain tumor xenografts in nude mice as a chemotherapy model. Eur J Cancer Clin Oncol 19, 799805.CrossRefGoogle ScholarPubMed
Radaelli, E et al. (2009) Immunohistopathological and neuroimaging characterization of murine orthotopic xenograft models of glioblastoma multiforme recapitulating the most salient features of human disease. Histology and Histopathology 24, 879891.Google ScholarPubMed
Roberts, WG et al. (1998) Host microvasculature influence on tumor vascular morphology and endothelial gene expression. The American Journal of Pathology 153, 12391248.CrossRefGoogle ScholarPubMed
Camphausen, K et al. (2005) Influence of in vivo growth on human glioma cell line gene expression: convergent profiles under orthotopic conditions. Proceedings of the National Academy of Sciences of the United States of America 102, 8287.CrossRefGoogle ScholarPubMed
Newcomb, E and Zagzag, D (2009) The murine GL261 glioma experimental model to assess novel brain tumor treatments. In Meir, E (ed.), Cancer Drug Discovery and Development. Totowa, New Jersey, USA: Humana Press, pp. 227241.Google Scholar
Zhu, X et al. (2011) Systemic delivery of neutralizing antibody targeting CCL2 for glioma therapy. Journal of Neuro-Oncology 104, 8392.CrossRefGoogle ScholarPubMed
Voutouri, C et al. (2019) Experimental and computational analyses reveal dynamics of tumor vessel cooption and optimal treatment strategies. Proceedings of the National Academy of Sciences of the United States of America 116, 26622671.CrossRefGoogle ScholarPubMed
Sharifzad, F et al. (2019) Neuropathological and genomic characterization of glioblastoma-induced rat model: How similar is it to humans for targeted therapy. Journal of Cellular Physiology 234, 2249322504.CrossRefGoogle ScholarPubMed
Grobben, B et al. (2002) Rat C6 glioma as experimental model system for the study of glioblastoma growth and invasion. Cell and Tissue Research 310, 257270.CrossRefGoogle Scholar
Nagaraja, TN et al. (2017) Reproducibility and relative stability in magnetic resonance imaging indices of tumor vascular physiology over a period of 24 h in a rat 9L gliosarcoma model. Magnetic Resonance Imaging 44, 131139.CrossRefGoogle Scholar
Kielian, T et al. (2002) MCP-1 expression in CNS-1 astrocytoma cells: implications for macrophage infiltration into tumors In vivo. Journal of Neuro-Oncology 56, 112.CrossRefGoogle ScholarPubMed
Ishii, N et al. (1999) Frequent co-alterations of TP53, p16/CDKN2A, p14ARF, PTEN tumor suppressor genes in human glioma cell lines. Brain Pathology 9, 469479.CrossRefGoogle ScholarPubMed
Krakstad, C and Chekenya, M (2010) Survival signalling and apoptosis resistance in glioblastomas: opportunities for targeted therapeutics. Molecular Cancer 9, 135.CrossRefGoogle ScholarPubMed
Allen, M et al. (2016) Origin of the U87MG glioma cell line: good news and bad news. Science Translational Medicine 8, 354re353.CrossRefGoogle ScholarPubMed
Masters, JR (2012) Cell-line authentication: End the scandal of false cell lines. Nature 492. doi: https://doi.org/10.1038/492186a.CrossRefGoogle ScholarPubMed
Arthurs, AL et al. (2020) The suitability of glioblastoma cell lines as models for primary glioblastoma cell metabolism. Cancers 12, 113.CrossRefGoogle ScholarPubMed
Xie, Y et al. (2015) The human glioblastoma cell culture resource: validated cell models representing All molecular subtypes. EBioMedicine 2, 13511363.CrossRefGoogle ScholarPubMed
Sampetrean, O and Saya, H (2013) Characteristics of glioma stem cells. Brain Tumor Pathology 30, 209214.CrossRefGoogle ScholarPubMed
Bao, S et al. (2006) Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature 444, 756760.CrossRefGoogle ScholarPubMed
Hubert, CG et al. (2016) A three-dimensional organoid culture system derived from human glioblastomas recapitulates the hypoxic gradients and cancer stem cell heterogeneity of tumors found in vivo. Cancer Research 76, 24652477.CrossRefGoogle ScholarPubMed
da Hora, CC et al. (2019) Patient-derived glioma models: from patients to dish to animals. Cells 8, 10.CrossRefGoogle ScholarPubMed
Stringer, BW et al. (2019) A reference collection of patient-derived cell line and xenograft models of proneural, classical and mesenchymal glioblastoma. Scientific Reports 9, 4902.CrossRefGoogle ScholarPubMed
Chen, Z et al. (2019) Intravital 2-photon imaging reveals distinct morphology and infiltrative properties of glioblastoma-associated macrophages. Proceedings of the National Academy of Sciences 116, 14254.CrossRefGoogle ScholarPubMed
Seano, G and Jain, RK (2020) Vessel co-option in glioblastoma: emerging insights and opportunities. Angiogenesis 23, 916.CrossRefGoogle ScholarPubMed
Alieva, M et al. (2019) Intravital imaging of glioma border morphology reveals distinctive cellular dynamics and contribution to tumor cell invasion. Scientific Reports 9, 2054.CrossRefGoogle ScholarPubMed
Seligman, AM et al. (1939) Studies in carcinogenesis: VIII. Experimental production of brain tumors in mice with methylcholanthrene. The American Journal of Cancer 37, 364.Google Scholar
Zagzag, D et al. (2000) Expression of hypoxia-inducible factor 1alpha in brain tumors: association with angiogenesis, invasion, and progression. Cancer 88, 26062618.3.0.CO;2-W>CrossRefGoogle Scholar
Hart, MN (2003) Surgical Pathology of the Nervous System and Its Coverings, 4th Edn. London, UK: Churchill Livingstone.Google Scholar
Van Meir, EG et al. (2010) Exciting new advances in neuro-oncology: the avenue to a cure for malignant glioma. CA: A Cancer Journal for Clinicians 60, 166193.Google ScholarPubMed
Jacobs, VL et al. (2011) Current review of in vivo GBM rodent models: emphasis on the CNS-1 tumour model. ASN Neuro 3, e00063e00063.CrossRefGoogle ScholarPubMed
Benda, P et al. (1968) Differentiated rat glial cell strain in tissue culture. Science 161, 370371.CrossRefGoogle ScholarPubMed
Whittle, IR et al. (1998) Can experimental models of rodent implantation glioma be improved? A study of pure and mixed glioma cell line tumours. Journal of Neuro-Oncology 36, 231242.CrossRefGoogle ScholarPubMed
Chicoine, MR and Silbergeld, DL (1995) Invading C6 glioma cells maintaining tumorigenicity. Journal of Neurosurgery 83, 665. doi: 10.3171/jns.1995.83.4.0665.CrossRefGoogle ScholarPubMed
Furnari, FB et al. (2007) Malignant astrocytic glioma: genetics, biology, and paths to treatment. Genes & Development 21, 26832710.CrossRefGoogle ScholarPubMed
Parsa, AT et al. (2000) Limitations of the C6/Wistar rat intracerebral glioma model: implications for evaluating immunotherapy. Neurosurgery 47, 993999. discussion 999–1000.CrossRefGoogle ScholarPubMed
San-Galli, F et al. (1989) Assessment of the experimental model of transplanted C6 glioblastoma in Wistar rats. Journal of Neuro-Oncology 7, 299304.CrossRefGoogle ScholarPubMed
Kruse, CA et al. (1994) A rat glioma model, CNS-1, with invasive characteristics similar to those of human gliomas: a comparison to 9L gliosarcoma. Journal of Neuro-Oncology 22, 191200.CrossRefGoogle ScholarPubMed
Dumeule, MRA et al. (2004) Brain endothelial cells as pharmacological targets in brain tumors. Molecular Neurobiology 30, 157183.CrossRefGoogle Scholar
Vargas-Patron, LA et al. (2019) Xenotransplantation of human glioblastoma in zebrafish larvae: in vivo imaging and proliferation assessment. Biology Open 8, bio043257.CrossRefGoogle ScholarPubMed
Kimmel, CB et al. (1995) Stages of embryonic development of the zebrafish. Developmental Dynamics 203, 253310.CrossRefGoogle ScholarPubMed
Gamble, JT et al. (2018) Quantification of glioblastoma progression in zebrafish xenografts: adhesion to laminin alpha 5 promotes glioblastoma microtumor formation and inhibits cell invasion. Biochemical and Biophysical Research Communications 506, 833839.CrossRefGoogle ScholarPubMed
Yang, N et al. (2014) A co-culture model with brain tumor-specific bioluminescence demonstrates astrocyte-induced drug resistance in glioblastoma. Journal of Translational Medicine 12, 278.CrossRefGoogle ScholarPubMed
Zeng, A et al. (2017) Identify a blood-brain barrier penetrating drug-TNB using zebrafish orthotopic glioblastoma xenograft model. Scientific Reports 7, 14372.CrossRefGoogle ScholarPubMed
Hamilton, L et al. (2016) A zebrafish live imaging model reveals differential responses of microglia toward glioblastoma cells in vivo. Zebrafish 13, 523534.CrossRefGoogle ScholarPubMed
Pudelko, L et al. (2018) An orthotopic glioblastoma animal model suitable for high-throughput screenings. Neuro-Oncology 20, 14751484.CrossRefGoogle ScholarPubMed
Vittori, M et al. (2016) Imaging of human glioblastoma cells and their interactions with mesenchymal stem cells in the zebrafish (Danio rerio) embryonic brain. Radiology and Oncology 50, 159167.CrossRefGoogle ScholarPubMed
Chen, A and Read, R (2019) Drosophila melanogaster as a model system for human glioblastomas. Advances in Experimental Medicine and Biology 1167, 207224.CrossRefGoogle ScholarPubMed
Caragher, S et al. (2019) Glioblastoma's next top model: novel culture systems for brain cancer radiotherapy research. Cancers (Basel) 11, 44.CrossRefGoogle ScholarPubMed
Bjerkvig, R et al. (1990) Multicellular tumor spheroids from human gliomas maintained in organ culture. Journal of Neurosurgery 72, 463475.CrossRefGoogle ScholarPubMed
Coniglio, S et al. (2016) Coculture assays to study macrophage and microglia stimulation of glioblastoma invasion. Journal of Visualized Experiments: JoVE 116, e53990. doi: 10.3791/53990.Google Scholar
Avci NG, FY et al. (2015) Investigating the influence of HUVECs in the formation of glioblastoma spheroids in high-throughput three-dimensional microwells. IEEE Transactions on Nanobioscience 7, 790796.CrossRefGoogle Scholar
Eisemann, T et al. (2018) An advanced glioma cell invasion assay based on organotypic brain slice cultures. BMC Cancer 18, 103.CrossRefGoogle ScholarPubMed
Jiguet Jiglaire, C et al. (2014) Ex vivo cultures of glioblastoma in three-dimensional hydrogel maintain the original tumor growth behavior and are suitable for preclinical drug and radiation sensitivity screening. Experimental Cell Research 321, 99108.CrossRefGoogle ScholarPubMed
Lv, D et al. (2016) A three-dimensional collagen scaffold cell culture system for screening anti-glioma therapeutics. Oncotarget 7, 5690456914.CrossRefGoogle ScholarPubMed
Wang, C et al. (2017) Effect of matrix metalloproteinase-mediated matrix degradation on glioblastoma cell behavior in 3D PEG-based hydrogels. Journal of Biomedical Materials Research. Part A 105, 770778.CrossRefGoogle ScholarPubMed
Gomez-Roman, N et al. (2017) A novel 3D human glioblastoma cell culture system for modeling drug and radiation responses. Neuro-Oncology 19, 229241.Google ScholarPubMed
Wang, J et al. (2009) A reproducible brain tumour model established from human glioblastoma biopsies. BMC Cancer 9, 465.CrossRefGoogle ScholarPubMed
William, D et al. (2017) Optimized creation of glioblastoma patient derived xenografts for use in preclinical studies. Journal of Translational Medicine 15, 27.CrossRefGoogle ScholarPubMed
Golebiewska, A et al. (2020) Patient-derived organoids and orthotopic xenografts of primary and recurrent gliomas represent relevant patient avatars for precision oncology. Acta Neuropathologica 140, 919949.CrossRefGoogle ScholarPubMed
Gao, H et al. (2015) High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nature Medicine 21, 13181325.CrossRefGoogle ScholarPubMed
da Silva, B et al. (2018) Spontaneous glioblastoma spheroid infiltration of early-stage cerebral organoids models brain tumor invasion. SLAS Discovery 23, 862868.Google ScholarPubMed
Bian, S et al. (2018) Genetically engineered cerebral organoids model brain tumor formation. Nat. Methods 15, 631639.CrossRefGoogle ScholarPubMed
Ogawa, J et al. (2018) Glioblastoma model using human cerebral organoids. Cell Reports 23, 12201229.CrossRefGoogle ScholarPubMed
Bergmann, S et al. (2018) Blood-brain-barrier organoids for investigating the permeability of CNS therapeutics. Nature Protocols 13, 28272843.CrossRefGoogle ScholarPubMed
Ben-David, U et al. (2017) Patient-derived xenografts undergo mouse-specific tumor evolution. Nature Genetics 49, 15671575.CrossRefGoogle ScholarPubMed
Pine, AR et al. (2020) Tumor microenvironment is critical for the maintenance of cellular states found in primary glioblastomas. Cancer Discovery 10, 964979.CrossRefGoogle ScholarPubMed
Rominiyi, O et al. (2019) The ‘ins and outs’ of early preclinical models for brain tumor research: Are they valuable and have we been doing it wrong?. Cancers 11. doi: https://doi.org/10.3390/cancers11030426.CrossRefGoogle ScholarPubMed
Qutaish, MQ et al. (2012) Cryo-image analysis of tumor cell migration, invasion, and dispersal in a mouse xenograft model of human glioblastoma multiforme. Molecular Imaging and Biology 14, 572583.CrossRefGoogle Scholar
Randall, EC et al. (2020) Localized metabolomic gradients in patient-derived Xenograft models of glioblastoma. Cancer Research 80, 12581267.CrossRefGoogle ScholarPubMed
Heffernan, JM et al. (2015) Bioengineered scaffolds for 3D analysis of glioblastoma proliferation and invasion. Annals of Biomedical Engineering 43, 19651977.CrossRefGoogle ScholarPubMed
Quereda, V et al. (2018) A cytotoxic three-dimensional-spheroid, high-throughput assay using patient-derived glioma stem cells. SLAS Discovery 23, 842849.Google ScholarPubMed
Kessel, S et al. (2017) Real-time apoptosis and viability high-throughput screening of 3D multicellular tumor spheroids using the celigo image cytometer. SLAS DISCOVERY: Advancing the Science of Drug Discovery 23, 202210.Google ScholarPubMed
Wang, X et al. (2018b) Coaxial extrusion bioprinted shell-core hydrogel microfibers mimic glioma microenvironment and enhance the drug resistance of cancer cells. Colloids and Surfaces. B, Biointerfaces 171, 291299.CrossRefGoogle Scholar
Maloney, E et al. (2020) Immersion bioprinting of tumor organoids in multi-well plates for increasing chemotherapy screening throughput. Micromachines 11, 208.CrossRefGoogle ScholarPubMed
Yi, HG et al. (2019) A bioprinted human-glioblastoma-on-a-chip for the identification of patient-specific responses to chemoradiotherapy. Nature Biomedical Engineering 3, 509519.CrossRefGoogle ScholarPubMed
Heinrich, MA et al. (2019) 3D-Bioprinted mini-brain: a glioblastoma model to study cellular interactions and therapeutics. Advanced Materials 31, 1806590.CrossRefGoogle ScholarPubMed
Hermida, MA et al. (2020) Three dimensional in vitro models of cancer: bioprinting multilineage glioblastoma models. Advances in Biological Regulation 75, 100658. doi: 10.1016/j.jbior.2019.100658.CrossRefGoogle ScholarPubMed
Ayuso, JM et al. (2017) Glioblastoma on a microfluidic chip: generating pseudopalisades and enhancing aggressiveness through blood vessel obstruction events. Neuro-Oncology 19, 503513.Google ScholarPubMed
Huang, Y et al. (2011) Evaluation of cancer stem cell migration using compartmentalizing microfluidic devices and live cell imaging. Journal of Visualized Experiments: JoVE 58, e3297.Google Scholar
Sullivan, JP et al. (2014) Brain tumor cells in circulation are enriched for mesenchymal gene expression. Cancer Discovery 4, 12991309.CrossRefGoogle ScholarPubMed
Reátegui, E et al. (2018) Engineered nanointerfaces for microfluidic isolation and molecular profiling of tumor-specific extracellular vesicles. Nature Communications 9, 175.CrossRefGoogle ScholarPubMed
Ma, J et al. (2018) Engineered 3D tumour model for study of glioblastoma aggressiveness and drug evaluation on a detachably assembled microfluidic device. Biomedical Microdevices 20, 80.CrossRefGoogle ScholarPubMed
Lee, KH et al. (2014) Integration of microfluidic chip with biomimetic hydrogel for 3D controlling and monitoring of cell alignment and migration. Journal of Biomedical Materials Research. Part A 102, 11641172.CrossRefGoogle ScholarPubMed
Pedron, S et al. (2017) Spatially graded hydrogels for preclinical testing of glioblastoma anticancer therapeutics. MRS Communications 7, 442449.CrossRefGoogle ScholarPubMed
Logun, MT et al. (2016) Glioma cell invasion is significantly enhanced in composite hydrogel matrices composed of chondroitin 4- and 4,6-sulfated glycosaminoglycans. Journal of Materials Chemistry B 4, 60526064.CrossRefGoogle ScholarPubMed
Park, JH et al. (2020) Isolinderalactone suppresses human glioblastoma growth and angiogenic activity in 3D microfluidic chip and in vivo mouse models. Cancer Letters 478, 7181.CrossRefGoogle ScholarPubMed
Liu, W et al. (2015) Controllable organization and high throughput production of recoverable 3D tumors using pneumatic microfluidics. Lab on a Chip 15, 11951204.CrossRefGoogle ScholarPubMed
Fan, Y et al. (2016) Engineering a brain cancer chip for high-throughput drug screening. Scientific Reports 6, 25062.CrossRefGoogle ScholarPubMed
Akay, M et al. (2018) Drug screening of human GBM spheroids in brain cancer chip. Scientific Reports 8, 15423.CrossRefGoogle ScholarPubMed
Truong, D et al. (2019) A three-dimensional (3D) organotypic microfluidic model for glioma stem cells–vascular interactions. Biomaterials 198, 6377.CrossRefGoogle ScholarPubMed
Cui, X et al. (2018) Hacking macrophage-associated immunosuppression for regulating glioblastoma angiogenesis. Biomaterials 161, 164178.CrossRefGoogle ScholarPubMed
Olubajo, F et al. (2020) Development of a microfluidic culture paradigm for ex vivo maintenance of human glioblastoma tissue: a new glioblastoma model? Translational Oncology 13, 110.CrossRefGoogle ScholarPubMed
Aldape, K et al. (2019) Challenges to curing primary brain tumours. Nature Reviews Clinical Oncology 16. doi: https://doi.org/10.1038/s41571-019-0177-5.CrossRefGoogle ScholarPubMed