Beyond Conventional Models: Recreating the Initiation, Evolution, and Genome of GBM
Bibliographic record
Abstract
ABSTRACT Background Imagining ways to prevent or treat glioblastoma (GBM) have been hindered by a lack of understanding of its pathogenesis. Although PDGF-AA overexpression may be an early event, critical details of the core biology are lacking. Existing PDGF-driven models replicate its microscopic appearance but not the genomic architecture characteristic of the human disease. Here we report a new model of GBM that overcomes this barrier to authenticity. Methods Using a method developed to study neural stem cells, we investigated the effects of PDGF-AA on subventricular zone (SVZ) cells, the putative cell of origin of GBM. We micro-dissected SVZ tissue from p53-null and wild-type adult mice, established primary cultures in media supplemented with PDGF-AA, and assessed cell viability, proliferation, genome stability, and tumour forming potential. Results Counterintuitive to its canonical role as a growth factor, we observed immediate and massive death of SVZ cells in PDGF-AA. Wild-type cells did not survive in PDGF-AA. However, a small fraction of null cells evaded apoptosis, displayed attenuated proliferation, gradually accumulated whole chromosome gains and losses, and, signalled by sudden rapid proliferation and growth factor independence, became tumorigenic in immune-competent syngeneic mice. Transformed cells had an OPC-like profile, were resistant to PDGFR-α inhibition, and harboured highly abnormal karyotypes similar to those seen in human GBMs. Conclusion This model associates genome instability in SVZ cells with chronic exposure to PDGF-AA; it is the first model to replicate the genomic landscape of GBM and first in which the earliest phases of GBM can be directly observed. IMPORTANCE OF STUDY We have developed a mouse model in which the initiation, evolution and genomic landscape of GBM can be thoroughly studied thus paving the way for ideas about how this deadly brain cancer might be prevented, interrupted at an occult stage, or treated with very different therapies.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".