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Ex-vivo drug screening of surgically resected glioma stem cells to replace murine avatars and provide personalise cancer therapy for glioblastoma patients [version 2; peer review: 2 approved]

Authors :
Hannah Gagg
Sophie T. Williams
Samantha Conroy
Katie N. Myers
Connor McGarrity-Cottrell
Callum Jones
Thomas Helleday
Juha Rantala
Ola Rominiyi
Sarah J. Danson
Spencer J. Collis
Greg Wells
Author Affiliations :
<relatesTo>1</relatesTo>Oncology & Metabolism, The University of Sheffield, Sheffield, England, S10 2RX, UK<br /><relatesTo>2</relatesTo>Neurosurgery, Royal Hallamshire Hospital, Sheffield, S10 2JF, UK<br /><relatesTo>3</relatesTo>Urology, Royal Hallamshire Hospital, Sheffield, S10 2JF, UK<br /><relatesTo>4</relatesTo>Karolinska Institut, Solnavägen, Solna, 171 77, Sweden<br /><relatesTo>5</relatesTo>Misvik Biology Ltd, Karjakatu, Turku, FI-20520, Finland<br /><relatesTo>6</relatesTo>Weston Park Hospital, Sheffield, S10 2SJ, UK
Source :
F1000Research. 12:954
Publication Year :
2024
Publisher :
London, UK: F1000 Research Limited, 2024.

Abstract

With diminishing returns and high clinical failure rates from traditional preclinical and animal-based drug discovery strategies, more emphasis is being placed on alternative drug discovery platforms. Ex vivo approaches represent a departure from both more traditional preclinical animal-based models and clinical-based strategies and aim to address intra-tumoural and inter-patient variability at an earlier stage of drug discovery. Additionally, these approaches could also offer precise treatment stratification for patients within a week of tumour resection in order to direct tailored therapy. One tumour group that could significantly benefit from such ex vivo approaches are high-grade gliomas, which exhibit extensive heterogeneity, cellular plasticity and therapy-resistant glioma stem cell (GSC) niches. Historic use of murine-based preclinical models for these tumours has largely failed to generate new therapies, resulting in relatively stagnant and unacceptable survival rates of around 12-15 months post-diagnosis over the last 50 years. The near universal use of DNA damaging chemoradiotherapy after surgical resection within standard-of-care (SoC) therapy regimens provides an opportunity to improve current treatments if we can identify efficient drug combinations in preclinical models that better reflect the complex inter-/intra-tumour heterogeneity, GSC plasticity and inherent DNA damage resistance mechanisms. We have therefore developed and optimised a high-throughput ex vivo drug screening platform; GliExP, which maintains GSC populations using immediately dissociated fresh surgical tissue. As a proof-of-concept for GliExP, we have optimised SoC therapy responses and screened 30+ small molecule therapeutics and preclinical compounds against tumours from 18 different patients, including multi-region spatial heterogeneity sampling from several individual tumours. Our data therefore provides a strong basis to build upon GliExP to incorporate combination-based oncology therapeutics in tandem with SoC therapies as an important preclinical alternative to murine models (reduction and replacement) to triage experimental therapeutics for clinical translation and deliver rapid identification of effective treatment strategies for individual gliomas.

Details

ISSN :
20461402
Volume :
12
Database :
F1000Research
Journal :
F1000Research
Notes :
Revised Amendments from Version 1 The second version supersedes the first version following revision of the manuscript. Changes have been made to figure captions, spelling errors and punctuation. We have included some extra detail into the introduction regarding PDX reproducibility discrepancies due to absence of standardised methodologies. We also amended most dose responses to employ a 4-parameter dose response fitted model (Figures 5 and 7). This was implemented using the DRC package in R, we have detailed this within the RStudio analysis section of the methods., , [version 2; peer review: 2 approved]
Publication Type :
Academic Journal
Accession number :
edsfor.10.12688.f1000research.135809.2
Document Type :
methods-article
Full Text :
https://doi.org/10.12688/f1000research.135809.2