DEVELOPING A NOVEL HUMAN MODEL OF MINIMAL RESIDUAL DISEASE IN GLIOBLASTOMA
Bibliographic record
Abstract
Abstract AIMS Glioblastoma (GBM) is the most common and aggressive primary brain tumour. Recurrence following stan- dard of care is inevitable due to an inability to eradicate all tumour cells with surgery and the stark chemo- radioresistance of the residual population. The underpinning mechanisms remain poorly understood, largely due to the inaccessibility of the residual cells that remain immediately after treatment, known as the minimal residual disease (MRD), in patients. To overcome this, we aim to develop a novel ex-vivo model system of the MRD in human GBM and use it to unravel the molecular basis of resistance. METHODS Primary brain tumour tissue collected from bulk and infiltrative margins of GBM during debulking surgery, was used to generate organotypic tissue slices. To optimise culture conditions, we tested the effect of using different collection and culture mediums, recovery methods and sectioning conditions to preserve different cell types and ensure optimal tissue viability. To determine an appropriate treatment regimen to achieve minimal residual disease, brain slices were then exposed to varying doses of temozolomide and radiotherapy. Tissue viability was analysed using mass cytometry, flow cytometry and TUNEL assays. RESULTS We have determined optimal culture conditions to achieve minimal loss of tissue viability that reflect both, resected and residual tumour populations. We further identified treatment schedules that recapitulate current clinical standard of care to model MRD. Future work will be devoted to optimising mass cytometry panels to analyse cell signalling, cell death and DNA damage response pathways alongside cell fate markers. CONCLUSION Our ex-vivo model of MRD and associated molecular profiling pipeline will enable mechanistic understanding of resistance mechanisms in GBM, ultimately providing a platform for the identification of novel targetable biological vulnerabilities for suppressing recurrence in GBM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".