TMOD-14CHARACTERIZING SIGNALING PATHWAYS IN A MOUSE MODEL OF GLIOBLASTOMA
Bibliographic record
Abstract
INTRODUCTION: Glioblastoma (GBM) is a fatal brain cancer, characteristically ‘driven’ by mutations in multiple tyrosine kinase and tumour suppressor pathways. We have developed a new murine model of GBM in which the sequence of molecular events and signaling pathways that underlie human GBM can be explored. In our model, cells from the subventricular zone (SVZ) of p53-/- mice become growth factor independent when cultured in serum-free media supplemented with platelet-derived growth factor-AA (PDGF-AA). When these cells are implanted into the brains of immune-competent p53 wild-type mice they form tumours that closely resemble human GBM. Here, we explored the mechanism of sustained cell proliferation in the transformed state. METHODS: Phospho-receptor tyrosine kinase (RTK) arrays were used to evaluate RTK phosphorylation, non-RTK arrays to examine pathway activation, and PDGFR-α inhibitors to assess the role of PDGFR-α in sustaining cell viability in pre-transformed and transformed cells. RESULTS: PDGFR-α was phosphorylated in pre-transformed SVZ cells and remained phosphorylated in transformed cells proliferating in the absence of exogenous PDGF-AA. An inhibitor of PDGFR-α that blocked PDGF-AA binding decreased the viability of pre-transformed SVZ cells but had no effect on transformed cells. In contrast, Imatinib, an inhibitor of PDGFR-α phosphorylation, blocked the proliferation of both. Downstream proteins, ERK1/2 and WNK1, and the transcription factor, CREB, were activated in transformed cells. CONCLUSION: In a PDGF-AA initiated mouse model of GBM, exogenous growth factor independent proliferation of p53-null SVZ cells and tumorigenicity are associated with persistent phosphorylation (i.e., activation) of PDGFR-α. The finding that transformed cells are insensitive to inhibition by a PDGFR-α blocking antibody, but respond to Imatinib, is consistent with receptor autophosphorylation. These observations may be applicable to human GBM where over-expression of PDGF-AA appears to be an early event in the pathogenesis of proneural and other subtypes of GBM.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".