TMOD-01. EGFR AMPLIFICATION ACCOMPANIES ADAPTATION TO MITOGEN-INDUCED DEFECTIVE MITOSIS IN PDGFA
Bibliographic record
Abstract
Abstract Using a newly described in vitro murine model of GBM [Bohm et al., Neuro-Oncol 2020 and Omairi et al., Neuro-Oncol 2023] in which P53 null neural progenitor cells (NPCs) divide abnormally and evolve a GBM-like genome during exposure to Platelet-Derived Growth Factor-AA (PDGFA), we asked how a brain-abundant mitogen could transform NPCs. By analyzing gene and protein expression over time, we found that PDGFA fails to induce the transcription of kinetochore and spindle assembly checkpoint genes, while simultaneously driving NPCs to enter mitosis. These dual effects caused chromosome miss-segregation in continuously dividing NPCs; moreover, they occurred in both WT and null NPCs, although only null cells survived defective mitosis. These surviving cells gradually expanded in PDGFA accumulating both random and clonal chromosomal re-arrangements. Transcriptome analysis of NPCs in PDGFA revealed significant under-expression of Foxm1, the major regulator of kinetochore transcription, together with over-expression and phosphorylation of the immediate early response gene, FOS. Analysis of signalling downstream of PDGFRα identified the Ras-MAPK pathway, especially ERK, as responsible for FOS activation. As surviving null cells gradually expanded, they accumulated random and recurrent chromosomal rearrangements. Expansion and subsequent PDGFA-independent proliferation and tumorigenicity were associated with re-expression of Foxm1 and kinetochore proteins, and accompanied by over-expression of Egfr, an RTK-signature that defines human GBM. By stimulating proliferation without setting the stage for error-free mitosis, exposure to PDGFA transforms p53 null NPCs and generates Egfr amplified GBM-like cancer cells.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".