Rominiyi, O. orcid.org/0000-0002-9724-0224, McGarrity-Cottrell, C., Myers, K.N. orcid.org/0000-0002-1951-4744 et al. (16 more authors) (2024) Ex-vivo models of post-surgical residual disease in human glioblastoma [version 1; peer review: awaiting peer review]. F1000Research, 13. 1316. ISSN 2046-1402
Abstract
Background
Glioblastoma is a highly infiltrative, currently incurable brain cancer. To date, translation of novel therapies for glioblastoma from the laboratory into clinical trials has relied heavily on in vitro cell culture and murine (subcutaneous and orthotopic) xenograft models using cells derived from the main bulk of patient tumours. However, it is the residual cells left-behind after surgery that are responsible for disease progression and death in the clinic. A lack of substantial improvements in patient survival for decades suggests commonly used murine xenograft models, a key step before clinical trials, do not reflect the biology of residual disease in patients.
Methods
To address this, we have developed the ‘Sheffield Protocol’ to generate ex vivo models that reflect both resected, and post-surgical residual disease from the same patient. The protocol leverages parallel derivation of inherently treatment-resistant glioblastoma stem cells (GSCs) from ‘core’ and distant ‘edge’ regions through careful macrodissection of a large en bloc specimen, such as from a partial lobectomy for tumour, followed by tissue dissociation and propagation in serum-free media. Opportunistic en bloc specimen use can liberate the most distant infiltrative cells feasibly accessible from living patients.
Results
We provide an example illustrating that resected and residual disease models represent spatially divergent tumour subpopulations harbouring distinct transcriptomic and cancer stem cell marker expression profiles. We also introduce the ‘Sheffield Living Biobank’ of glioma models (SLB) that incorporates over 150 GSC lines from 60+ patients, including 44+ resected and residual models, which are available for academic use via MTA.
Conclusions
These models provide a novel tool to reduce animal xenograft usage by improving candidate drug triage in early preclinical studies and directly replacing animal studies for some therapies that are post-Phase 1+ clinical trial for other cancers/conditions to, ultimately, deliver more effective treatments for post-surgical residual disease in glioblastoma.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 Rominiyi O et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Glioblastoma; residual disease; patient-derived models; 3D models; replacement |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health |
Funding Information: | Funder Grant number National Centre for the Replacement, Refinement and Reduction of Animals in Research NC/T001895/1 National Centre for the Replacement Refinement and Reduction of Animals in Research 2488438 NATIONAL CENTRE FOR THE REPLACEMENT, REFINEMENT AND REDUCTION OF ANIMALS IN RESEARCH NC/T001895/1 THE BRAIN TUMOUR CHARITY ET_2019/1_10403 ROYAL COLLEGE OF SURGEONS OF ENGLAND UNSPECIFIED SHEFFIELD HOSPITALS CHARITABLE TRUST UNSPECIFIED BRAIN TUMOUR RESEARCH AND SUPPORT ACROSS YORKSHIRE UNSPECIFIED SHEFFIELD HOSPITALS CHARITY 161724 Yorkshire's Brain Tumour Charity YBTC 18th birthday research funding call ACADEMY OF MEDICAL SCIENCES SGL027\1046 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 05 Nov 2024 16:35 |
Last Modified: | 05 Nov 2024 16:35 |
Status: | Published |
Publisher: | F1000 Research Ltd |
Identification Number: | 10.12688/f1000research.157013.1 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:219268 |
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Filename: GBM models paper v1 (F1000Research, Nov 2024).pdf
Licence: CC-BY 4.0