TMOD-21. DEFINING THE GROWTH FACTOR NICHE THAT SUPPORTS GBM INITIATION
Bibliographic record
Abstract
It has been challenging to fully understand the initiation and progression of Glioblastoma Multiforme (GBM), as the early events in tumour development cannot be readily discerned. Although previous work has hypothesized that genetic alterations, such as chromosome 7 gain and chromosome 10 loss, are critical and conserved during tumour initiation in all GBMs, the functional roles of these events have not been characterized. We have developed a murine model which allows us to study the early stages of GBM initiation in vitro and interrogate the role of specific molecular events at different stages of tumour development. This model combines overexpression of platelet-derived growth factor-AA (PDGF-AA) with inactivation of the tumour suppressor protein, p53 (TP53); two genetic alterations which are alone sufficient to induce gliomagenesis. This model provides us with an opportunity to explore the roles of growth factor signaling and p53 in regulating brain tumour initiation in a murine system. In this model, cells from the subventricular zone (SVZ) of p53 null mice are cultured in PDGF-AA for several months. After this time the cells transform; becoming growth factor independent and tumorigenic. We have found that p53 null SVZ cells struggle to survive when cultured in PDGF-AA, but eventually adapt and resume proliferating. In contrast, p53 wildtype cells fail to recover and proliferate when cultured in PDGF-AA. However, when p53 wildtype cells are cultured in PDGF-AA with an additional growth factors, such as insulin, FGF, or EGF, the cells begin to proliferate again, suggesting that PDGF-AA alone is not sufficient to promote normal, healthy proliferation in this cell population in our model. This work may help us define the growth factor profiles that are necessary to sustain and promote proliferation of SVZ cells, as well as understand the roles of growth factors that are required for GBM tumorigenesis.
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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 | 0.001 | 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".