A living biobank of post-surgical residual glioblastoma to replace animal studies

Glioblastoma is the most common cancer arising within the brain and contributes to ~190,000 brain tumour related deaths/year globally. Despite surgery followed by radiotherapy and chemotherapy, survival for patients remains around 1 year. Translation of new therapies from the laboratory into clinical trials has relied heavily on cells derived from the bulk of patient tumours. However, the lack of substantial improvement in survival rates over the last 40 years suggests that the traditional paradigm of 2D in-vitro studies using these cells followed by implantation into animal models poorly predicts the treatment response of glioblastoma cells left behind after surgery in patients. Additionally, recent increases in the number of potentially exploitable therapeutic targets emphasise a need to generate robust and efficient models which faithfully recapitulate post-surgical disease in humans.

This Studentship will develop a living biobank of 3D glioblastoma models which represent post-surgical residual disease in patients and could supersede current animal models in many contexts. In glioblastoma research, animal studies commonly use orthotopic patient-derived xenograft (PDX) models. These involve implantation of tumour cells from patients into the brain of immunocompromised rodents and usually allow symptomatic cancer progression prior to the inevitable death/cull of animals. Based on published studies, we estimate over 140,000 animals per year are used in glioblastoma research globally. Importantly, even rodent orthotopic PDX models of glioblastoma may not accurately reflect the disease and treatment in humans, for example, these models generally do not receive the full spectrum of multi-modal therapy that is standard of care for patients, since surgical resection of tumours in rodent models is technically challenging. Consequently, it is unclear whether survival gains observed in orthotopic PDX models reflect treatment response within the core mass of the tumour, which is typically resected in patients, or the highly infiltrative glioblastoma margin. We believe a living biobank of residual glioblastoma has the potential to address this issue and replace at least 15% of animal usage for glioblastoma research. With widespread adoption, this equates to approximately 21,000 animals per year that would no longer be used.

Using rare en-bloc specimens from patients, we have developed a protocol which generates parallel glioblastoma stem cells from the tumour and adjacent infiltrated brain tissue to model resected and residual (typically left behind) disease within customised 3D organoid and scaffold-based architectures. Our initial experiments confirm that important differences between these models exist, for example, residual disease models express significantly higher levels of stemness markers and DNA damage repair genes including ATM, ATR and BRCA1/2.

The Studentship will build this resource and develop the potential to replace animal studies through multi-omic characterisation and detailed functional assays to ensure our living biobank reflects the molecular features and intratumoural heterogeneity observed in human glioblastoma. Differential responses between resected and residual disease models will be used to establish a rationale for specific targeting of post-surgical residual disease. Whilst correlation between 3D in-vitro and corresponding patient response to standard chemo- & radiotherapy will be used to establish the predictive value of our models. Finally, to confirm utility, the models will be used to evaluate a hypothesis-driven panel of combination therapies.

The living biobank of resected and post-surgical residual glioblastoma will therefore provide a highly-characterised and reliable replacement for animal studies aiming to test new treatment strategies for glioblastoma.

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PhD Studentship

Status:

Active

Principal investigator

Dr Spencer Collis

Institution

University of Sheffield

Co-Investigator

Mr Ola Rominiyi

Grant reference number

NC/T001895/1

Award date:

Oct 2020 - Sep 2023

Grant amount

£90,000