PM-08 * INVESTIGATING THE DETERMINANTS OF GBM SUBTYPES USING NOVEL MOUSE MODELS
Bibliographic record
Abstract
Molecular characterization of glioblastoma multiforme (gbm) has shown adult gbms to comprise a number of subtypes based on their gene expression signatures. While these subtypes are somewhat associated with distinct genetic alterations, the relative contributions of oncogenic driver, cell of origin and developmental stage are not fully understood. Using a novel and highly versatile modeling platform based on in vivo electroporation mediated transfer of transposon based plasmids to ventricular neural progenitors, we have generated high grade gliomas in mice using PDGFA, EGFRVIII or RasV12 expression in combination with p53 loss. These tumours show complete penetrance by 5 weeks post electroporation and bear key features of gbm such as necrosis and prevalent vascularisation. Gene expression analysis by Nanostring and subsequent unsupervised clustering shows each oncogenic driver to result in tumours of distinct gene expression profiles, with the PDGFA and RasV12 driven tumours showing similarity to the proneural and mesenchymal subclasses respectively. Modification of our plasmid system to incorporate selective promoters, floxed stop cassettes and tamoxifen inducible cre recombinase grants us precise spatiotemporal control over induction of gliomagenesis and allows us to probe the influence of developmental stage, cell of origin, brain region and gene expression level to the resulting tumour gene expression profiles.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".