The catalytic subunit of DNA-PK regulates transcription and splicing of AR in advanced prostate cancer
Bibliographic record
Abstract
Aberrant androgen receptor (AR) signaling drives prostate cancer (PC), and it is a key therapeutic target. Although initially effective, the generation of alternatively spliced AR variants (AR-Vs) compromises efficacy of treatments. In contrast to full-length AR (AR-FL), AR-Vs constitutively activate androgenic signaling and are refractory to the current repertoire of AR-targeting therapies, which together drive disease progression. There is an unmet clinical need, therefore, to develop more durable PC therapies that can attenuate AR-V function. Exploiting the requirement of coregulatory proteins for AR-V function has the capacity to furnish tractable routes for attenuating persistent oncogenic AR signaling in advanced PC. DNA-PKcs regulates AR-FL transcriptional activity and is upregulated in both early and advanced PC. We hypothesized that DNA-PKcs is critical for AR-V function. Using a proximity biotinylation approach, we demonstrated that the DNA-PK holoenzyme is part of the AR-V7 interactome and is a key regulator of AR-V-mediated transcription and cell growth in models of advanced PC. Crucially, we provide evidence that DNA-PKcs controls global splicing and, via RBMX, regulates the maturation of AR-V and AR-FL transcripts. Ultimately, our data indicate that targeting DNA-PKcs attenuates AR-V signaling and provide evidence that DNA-PKcs blockade is an effective therapeutic option in advanced AR-V-positive patients with PC.
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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) | 0.001 | 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".