TMOD-02. A MODEL OF THE INITIATION AND GENOMIC LANDSCAPE OF GLIOBLASTOMA (GBM)
Bibliographic record
Abstract
Abstract IDH-wildtype GBM is the most common variant of this cancer and occurs in older adults. Unfortunately patients’ tumors are either inherently resistant to standard treatment, which includes radio- and chemo-therapy, or acquire resistance during the therapeutic process. Additionally, although effective in other cancers, targeted therapies have yielded disappointing results in GBM, perhaps because the fully developed disease has significant cellular and molecular heterogeneity, allowing the tumour to adapt to treatments. Better insight into managing GBM might result from a detailed knowledge of its initiating events, which have not yet been elucidated. With this in mind, we developed a mouse model of GBM in which the earliest stages can be studied. This ex vivo model recreates GBM by culturing subventricular zone (SVZ) cells, the putative ‘cell of origin’ of GBM in platelet-derived growth factor A (PDGFA). Under this condition SVZ cells from p53 null mice transform, becoming exogenous growth factor independent and tumorigenic in immune-competent mice. In contrast, wildtype SVZ cells do not proliferate in PDGFA and null cells in EGF/FGF do not transform. To discover why p53 null SVZ cells uniquely transform in PDGFA, we performed array comparative genomic hybridization (aCGH) on cells before and after transformation in PDGFA and whole genome sequencing (WGS) on transformed cells and tumours generated from PDGFA-transformed cells. aCGH and WGS revealed that the genomic landscape of transformed cells displayed a striking similarity to that observed in primary human GBM. Specifically, these studies showed that chromosomal alterations are a hallmark of culturing SVZ cells in PDGFA, an intriguing finding considering GBM is also characterized by a specific landscape of copy number alterations. This model may resemble the pathogenesis of human GBM and be leveraged to investigate the early stages of tumorigenesis, further leading to the development of preventative strategies and novel therapeutics.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".