Induction of senescence in primary glioblastoma cells by serum and TGFβ
Bibliographic record
Abstract
Glioblastoma is the most common type of adult brain tumour and has a median survival after diagnosis of a little more than a year. Glioblastomas have a high frequency of mutations in the TERT promoter and CDKN2A locus that are expected to render them resistant to both replicative and oncogene-induced senescence. However, exposure of PriGO8A primary glioblastoma cells to media with 10% serum induced a senescence-like phenotype characterized by increased senescence-associated β galactosidase activity, PML bodies and p21 and morphological changes typical of senescence. Microarray expression analysis showed that 24 h serum exposure increased the expression of genes associated with the TGFβ pathway. Treatment of PriGO8A cells with TGFβ was sufficient to induce senescence in these cells. The response of PriGO8A cells to serum was dependent on basal expression of the TGFβ activator protein thrombospondin. Primary glioblastoma cells from three additional patients showed a variable ability to undergo senescence in response to serum. However all were able to undergo senescence in response to TGFβ, although for cells from one patient this required concomitant inhibition of Ras pathway signalling. Primary glioblastoma cells therefore retain a functional senescence program that is inducible by acute activation of the TGFβ signalling pathway.
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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) | 0.001 | 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 | 0.001 |
| 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".