Ralaniten Sensitizes Enzalutamide-Resistant Prostate Cancer to Ionizing Radiation in Prostate Cancer Cells that Express Androgen Receptor Splice Variants
Bibliographic record
Abstract
Blocking androgen receptor (AR) transcriptional activity by androgen deprivation therapy (ADT) improves the response to radiotherapy for intermediate and high risk prostate cancer. Unfortunately, ADT, antiandrogens, and abiraterone increase expression of constitutively active splice variants of AR (AR-Vs) which regulate DNA damage repair leading to resistance to radiotherapy. Here we investigate whether blocking the transcriptional activities of full-length AR and AR-Vs with ralaniten leads to enhanced sensitivity to radiotherapy. Combination therapies using ralaniten with ionizing radiation were evaluated for effects on proliferation, colony formation, cell cycle, DNA damage, and Western blot analyses in human prostate cancer cells that express both full-length AR and AR-Vs. Ralaniten and a potent next-generation analog (EPI-7170) decreased expression of DNA repair genes whereas enzalutamide had no effect. FACS analysis revealed a dose-dependent decrease of BrdU incorporation with increased accumulation of γH2AX with a combination of ionizing radiation with ralaniten. An additive inhibitory effect on proliferation of enzalutamide-resistant cells was achieved with a combination of ralaniten compounds with ionizing radiation. Ralaniten and EPI-7170 sensitized prostate cancer cells that express full-length AR and AR-Vs to radiotherapy whereas enzalutamide had no added benefit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".