Modelling Glioma Stem Cell-mediated Tumorigenesis Using Zebrafish PDX Systems
Bibliographic record
Abstract
Summary Glioblastoma is an aggressive brain tumour associated with high post-therapy recurrence and very poor survival rates. One of the factors contributing to the aggressive nature of this disease is the level of heterogeneity seen at the phenotypic and genetic level. Glioma Stem Cells (GSCs) are stem-like cells within the tumour with the ability to self-renew and give rise to different types of cells within the tumour, hence giving rise to the heterogeneity found in glioblastoma. GSCs are often implicated in the resistance of glioma to standard of care radiation and chemotherapy. The physical niche within a tumour mass supports stemness and aggressive characteristics of GSCs, hence, experimental systems providing a relevant tumour microenvironment are critical for adequate assessment of molecular mechanisms regulating GSC populations. Although, mouse models are a staple of an in vivo experimental design, they are neither time-nor cost-efficient. Danio rerio (zebrafish) patient-derived xenografts (PDXs) overcome several of the obstacles of the mammalian systems. Zebrafish constitute a high throughput, easily reproducible experimental platform allowing for life relevant investigation into the aggressiveness of GSC populations. This chapter describes methods required for generation of zebrafish PDXs to study aspects of GSC-mediated tumorigenesis and interactions with the tumour microenvironment. Consistency between labs for these experiments is required to move the discovery of effective treatments for glioblastoma moving forward.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".