Selective targeting of PARP-2 inhibits androgen receptor signaling and prostate cancer growth through disruption of FOXA1 function
Bibliographic record
Abstract
Androgen receptor (AR) is a ligand-activated transcription factor and a key driver of prostate cancer (PCa) growth and progression. Understanding the factors influencing AR-mediated gene expression provides new opportunities for therapeutic intervention. Poly(ADP-ribose) Polymerase (PARP) is a family of enzymes, which posttranslationally modify a range of proteins and regulate many different cellular processes. PARP-1 and PARP-2 are two well-characterized PARP members, whose catalytic activity is induced by DNA-strand breaks and responsible for multiple DNA damage repair pathways. PARP inhibitors are promising therapeutic agents that show synthetic lethality against many types of cancer (including PCa) with homologous recombination (HR) DNA-repair deficiency. Here, we show that, beyond DNA damage repair function, PARP-2, but not PARP-1, is a critical component in AR transcriptional machinery through interacting with the pioneer factor FOXA1 and facilitating AR recruitment to genome-wide prostate-specific enhancer regions. Analyses of PARP-2 expression at both mRNA and protein levels show significantly higher expression of PARP-2 in primary PCa tumors than in benign prostate tissues, and even more so in castration-resistant prostate cancer (CRPC) tumors. Selective targeting of PARP-2 by genetic or pharmacological means blocks interaction between PARP-2 and FOXA1, which in turn attenuates AR-mediated gene expression and inhibits AR-positive PCa growth. Next-generation antiandrogens act through inhibiting androgen synthesis (abiraterone) or blocking ligand binding (enzalutamide). Selective targeting of PARP-2, however, may provide an alternative therapeutic approach for AR inhibition by disruption of FOXA1 function, which may be beneficial to patients, irrespective of their DNA-repair deficiency status.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".