Modulating the unfolded protein response: Impacts of radiation on the response of prostate cancer cells to ONC201
Bibliographic record
Abstract
Abstract Prostate cancer (PCa) is the most common non-cutaneous cancer in men and a notable cause of cancer mortality when it metastasises. Localised disease is mostly treated with surgery or radiotherapy. As PCa develops and treatment resistance emerges, the unfolded protein response (UPR) arises as an important adaptive biology co-amplifying with key cancer drivers [1]. The UPR can be cytoprotective but when acutely activated can lead to cell death. In this study we sought to enhance the acute activation of the UPR using radiation and ONC201, previously reported to be an UPR activator [2]. We found that treating PCa cells with ONC201 quickly increases the expression of components in all arms of the UPR – ATF4, ATF6 and IRE1-XBP1 – culminating in the subsequent cell death. During this time window between UPR activation and cell death we tested the priming effect of short-term administration of ONC201 on radiation responses. Pre-treatment with ONC201 for 24 hours prior to irradiation led to enhanced cytotoxicity compared to radiation alone assessed by cell viability and clonogenic assays. With priming, RNA-Seq analysis showed a sustained suppression of transcripts encoding cell cycle regulators as well as components of the DNA damage response pathways. Phenotypically this was reflected in enhanced cell cycle arrest and induction of necrosis and apoptosis. Furthermore, we demonstrated that short-term administration of inhibitors of cell cycle regulators (Dinaciclib and BI2536), could replicate this priming effect. Thus, we propose future studies to assess the impact of the short-term administration of drugs targeting the UPR and cell cycle regulation to enhance radiotherapy response.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".