Mitogen-induced defective mitosis transforms neural progenitor cells
Bibliographic record
Abstract
BACKGROUND: Chromosome instability (CIN) with recurrent copy number alterations is a feature of many solid tumors, including glioblastoma (GBM), yet the genes that regulate cell division are rarely mutated in cancers. Here, we show that the brain-abundant mitogen, platelet-derived growth factor-A (PDGFA) fails to induce the expression of kinetochore and spindle assembly checkpoint genes leading to defective mitosis in neural progenitor cells (NPCs). METHODS: Using a recently reported in vitro model of the initiation of high-grade gliomas from murine NPCs, we investigated the immediate effects of PDGFA exposure on the nuclear and mitotic phenotypes and patterns of gene and protein expression in NPCs, a putative GBM cell of origin. RESULTS: NPCs divided abnormally in defined media containing PDGFA with P53-dependent effects. In wild-type cells, defective mitosis was associated with P53 activation and cell death, but in some null cells, defective mitosis was tolerated. Surviving cells had unstable genomes and proliferated in the presence of PDGFA accumulating random and clonal chromosomal rearrangements. The outcome of this process was a population of tumorigenic NPCs with recurrent gains and losses of chromosomal regions that were syntenic to those recurrently gained and lost in human GBM. By stimulating proliferation without setting the stage for successful mitosis, PDGFA-transformed NPCs lacking P53 function. CONCLUSIONS: Our work describes a mechanism of transformation of NPCs by a brain-associated mitogen, raising the possibility that the unique genomic architecture of GBM is an adaptation to defective mitosis that ensures the survival of affected 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 | 0.000 | 0.000 |
| 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".