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Record W4413927588 · doi:10.1093/neuonc/noaf185.063

DEVELOPING A NOVEL HUMAN MODEL OF MINIMAL RESIDUAL DISEASE IN GLIOBLASTOMA

2025· article· en· W4413927588 on OpenAlexaff
Dipal Patel, Lucy Brooks, Ciaran Scott Hill, Simona Parrinello

Bibliographic record

VenueNeuro-Oncology · 2025
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsInstitute of Cancer Research
Fundersnot available
KeywordsGlioblastomaMinimal residual diseaseResidualCancer researchComputer scienceComputational biologyBiologyMedicineInternal medicineLeukemiaAlgorithm

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.053
GPT teacher head0.366
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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