P.042 Raloxifene sensitizes glioblastoma cells to hypoxia-induced death through inhibition of stress granule dissolution
Bibliographic record
Abstract
Background: Glioblastoma (GBM) is the most common primary malignant brain tumour. Despite aggressive therapy, median survival is only 14 months. Death typically results from treatment failure and local recurrence. The GBM microenvironment is highly hypoxic, which correlates with treatment resistance. Cytoplasmic RNA stress granules (SGs) form in response to hypoxic stress and act as sights of mRNA triage, allowing preferential translation of pro-survival mRNA during stress. We hypothesize that SGs may play a role in hypoxia-induced resistance to therapy, and may be targetable by chemotherapeutics to improve outcomes. Methods: We screened 1280 approved compounds to identify drugs that inhibited formation or dissolution of SGs in U251 glioma cells. Raloxifene inhibited SG dissolution in a dose dependent manner. We treated cells with raloxifene and incubated them in hypoxia, and then measured rates of cell death using cell counting and Presto blue. Results: Cell death rates were synergistically higher in cells treated with the combination of raloxifene and hypoxia compared to either treatment alone. Conclusions: Raloxifene inhibits the dissolution of SGs in glioma cells, and combination treatment results in synergistic tumour cell death. Taken together, this provides evidence that inhibition of SG dissolution may be a viable target for future GBM chemotherapeutics.
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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) | 0.005 | 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".