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Cancers
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Pre-clinical Models in Glioblastoma
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Pre-clinical Models in Glioblastoma

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Clinical Research of Cancer".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 7228

Special Issue Editors

Dr. Chiara Maria Mazzanti

E-Mail Website
Guest Editor
Fondazione Pisana per la Scienza, Via Ferruccio Giovannini 13, 56017 San Giuliano Terme, Italy
Interests: glioblastoma; ex vivo models; personalized medicine
Special Issues, Collections and Topics in MDPI journals
Dr. Diego Ottaviani

E-Mail Website
Guest Editor
Department of Oncology, University College London Hospitals, London NW1 2PG, UK
Interests: molecular oncology; basic research for patient's care; clinical trials; glioblastoma; translational medicine; pre-clinical development of novel therapies; tumor molecular mechanisms

Special Issue Information

Dear Colleagues, 

Glioblastoma multiforme (GBM) is the most malignant primary central nervous system tumor, and the prognosis for patients is often bleak. Currently, there are no curative treatment options for GBM, and despite rigorous therapeutic research, the survival rate of patients diagnosed with GBM remains low. To improve therapeutic options, studies to identify and validate single protein targets are underway. However, in most cases, targeted compounds that perform well in preclinical studies have failed expensive Phase III clinical trials in humans. Ultimately, several major factors are responsible for drug failure, including poor pharmacokinetic properties, the emergence of resistance pathways, complex intratumoral heterogeneity, and suboptimal clinical trial design. Thus, there is a desperate need for an efficient approach to identify and vet potential drugs at the preclinical stage to prevent late stage failure. This Special Issue will focus on new advances in the development of preclinical approaches in glioblastoma to study drug efficacy and mechanisms of action.

Dr. Chiara Maria Mazzanti
Dr. Diego Ottaviani
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • glioblastoma
  • in vitro models
  • drug development
  • primary cell lines
  • organoids
  • explants
  • stem cells
  • blood–brain barrier
  • glioblastoma therapies
  • clinical trials
  • tumor microenvironment
  • multiomics

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Published Papers (3 papers)

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22 pages, 3671 KiB  
Article
Optimization, Characterization, and Comparison of Two Luciferase-Expressing Mouse Glioblastoma Models
by Louis T. Rodgers, Julia A. Schulz Pauly, Bryan J. Maloney, Anika M. S. Hartz and Björn Bauer
Cancers 2024, 16(11), 1997; https://doi.org/10.3390/cancers16111997 - 24 May 2024
Viewed by 1243
Abstract
Glioblastoma (GBM) is the most aggressive brain cancer. To model GBM in research, orthotopic brain tumor models, including syngeneic models like GL261 and genetically engineered mouse models like TRP, are used. In longitudinal studies, tumor growth and the treatment response are typically tracked [...] Read more.
Glioblastoma (GBM) is the most aggressive brain cancer. To model GBM in research, orthotopic brain tumor models, including syngeneic models like GL261 and genetically engineered mouse models like TRP, are used. In longitudinal studies, tumor growth and the treatment response are typically tracked with in vivo imaging, including bioluminescence imaging (BLI), which is quick, cost-effective, and easily quantifiable. However, BLI requires luciferase-tagged cells, and recent studies indicate that the luciferase gene can elicit an immune response, leading to tumor rejection and experimental variation. We sought to optimize the engraftment of two luciferase-expressing GBM models, GL261 Red-FLuc and TRP-mCherry-FLuc, showing differences in tumor take, with GL261 Red-FLuc cells requiring immunocompromised mice for 100% engraftment. Immunohistochemistry and MRI revealed distinct tumor characteristics: GL261 Red-FLuc tumors were well-demarcated with densely packed cells, high mitotic activity, and vascularization. In contrast, TRP-mCherry-FLuc tumors were large, invasive, and necrotic, with perivascular invasion. Quantifying the tumor volume using the HALO® AI analysis platform yielded results comparable to manual measurements, providing a standardized and efficient approach for the reliable, high-throughput analysis of luciferase-expressing tumors. Our study highlights the importance of considering tumor engraftment when using luciferase-expressing GBM models, providing insights for preclinical research design. Full article
(This article belongs to the Special Issue Pre-clinical Models in Glioblastoma)
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14 pages, 7633 KiB  
Article
Mismatch between Bioluminescence Imaging (BLI) and MRI When Evaluating Glioblastoma Growth: Lessons from a Study Where BLI Suggested “Regression” while MRI Showed “Progression”
by Mathilde Bausart, Elia Bozzato, Nicolas Joudiou, Xanthippi Koutsoumpou, Bella Manshian, Véronique Préat and Bernard Gallez
Cancers 2023, 15(6), 1919; https://doi.org/10.3390/cancers15061919 - 22 Mar 2023
Cited by 9 | Viewed by 2699
Abstract
Orthotopic glioblastoma xenografts are paramount for evaluating the effect of innovative anti-cancer treatments. In longitudinal studies, tumor growth (or regression) of glioblastoma can only be monitored by noninvasive imaging. For this purpose, bioluminescence imaging (BLI) has gained popularity because of its low cost [...] Read more.
Orthotopic glioblastoma xenografts are paramount for evaluating the effect of innovative anti-cancer treatments. In longitudinal studies, tumor growth (or regression) of glioblastoma can only be monitored by noninvasive imaging. For this purpose, bioluminescence imaging (BLI) has gained popularity because of its low cost and easy access. In the context of the development of new nanomedicines for treating glioblastoma, we were using luciferase-expressing GL261 cell lines. Incidentally, using BLI in a specific GL261 glioblastoma model with cells expressing both luciferase and the green fluorescent protein (GL261-luc-GFP), we observed an apparent spontaneous regression. By contrast, the magnetic resonance imaging (MRI) analysis revealed that the tumors were actually growing over time. For other models (GL261 expressing only luciferase and U87 expressing both luciferase and GFP), data from BLI and MRI correlated well. We found that the divergence in results coming from different imaging modalities was not due to the tumor localization nor the penetration depth of light but was rather linked to the instability in luciferase expression in the viral construct used for the GL261-luc-GFP model. In conclusion, the use of multi-modality imaging prevents possible errors in tumor growth evaluation, and checking the stability of luciferase expression is mandatory when using BLI as the sole imaging modality. Full article
(This article belongs to the Special Issue Pre-clinical Models in Glioblastoma)
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16 pages, 2642 KiB  
Article
Glioblastoma-Derived Three-Dimensional Ex Vivo Models to Evaluate Effects and Efficacy of Tumor Treating Fields (TTFields)
by Vera Nickl, Ellina Schulz, Ellaine Salvador, Laureen Trautmann, Leopold Diener, Almuth F. Kessler, Camelia M. Monoranu, Faramarz Dehghani, Ralf-Ingo Ernestus, Mario Löhr and Carsten Hagemann
Cancers 2022, 14(21), 5177; https://doi.org/10.3390/cancers14215177 - 22 Oct 2022
Cited by 10 | Viewed by 2475
Abstract
Glioblastoma (GBM) displays a wide range of inter- and intra-tumoral heterogeneity contributing to therapeutic resistance and relapse. Although Tumor Treating Fields (TTFields) are effective for the treatment of GBM, there is a lack of ex vivo models to evaluate effects on patients’ tumor [...] Read more.
Glioblastoma (GBM) displays a wide range of inter- and intra-tumoral heterogeneity contributing to therapeutic resistance and relapse. Although Tumor Treating Fields (TTFields) are effective for the treatment of GBM, there is a lack of ex vivo models to evaluate effects on patients’ tumor biology or to screen patients for treatment efficacy. Thus, we adapted patient-derived three-dimensional tissue culture models to be compatible with TTFields application to tissue culture. Patient-derived primary cells (PDPC) were seeded onto murine organotypic hippocampal slice cultures (OHSC), and microtumor development with and without TTFields at 200 kHz was observed. In addition, organoids were generated from acute material cultured on OHSC and treated with TTFields. Lastly, the effect of TTFields on expression of the Ki67 proliferation marker was evaluated on cultured GBM slices. Microtumors exhibited increased sensitivity towards TTFields compared to monolayer cell cultures. TTFields affected tumor growth and viability, as the size of microtumors and the percentage of Ki67-positive cells decreased after treatment. Nevertheless, variability in the extent of the response was preserved between different patient samples. Therefore, these pre-clinical GBM models could provide snapshots of the tumor to simulate patient treatment response and to investigate molecular mechanisms of response and resistance. Full article
(This article belongs to the Special Issue Pre-clinical Models in Glioblastoma)
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