Abstract 5729: Dynamic phase separation of the androgen receptor and its coactivators to regulate gene expression
Bibliographic record
Abstract
Abstract Numerous cancers, including prostate cancer (PCa), have been shown to be addicted to transcription programs driven by specific genomic sites known as superenhancers (SEs). Recently, it has been proposed that the robust transcription of genes at such SEs is enabled by the formation of phase-separated condensates by transcription factors and coactivators with intrinsically disordered regions. The androgen receptor (AR), the main oncogenic driver in PCa, contains large disordered regions and is co-recruited with the transcriptional coactivator MED1 to SEs in androgen-dependent prostate cancer cells, thereby promoting oncogenic transcriptional programs. In this work, we show that AR-rich, liquid-like foci form in prostate cancer models upon androgen stimulation. We reveal that foci formation correlates with AR transcriptional activity, which can be modulated by changing cellular foci content genetically or chemically. We also demonstrate that the transcriptional coactivator MED1 plays an essential role in the formation of transcriptionally active AR-rich foci and that AR antagonists that block cofactor recruitment or DNA binding hinder foci formation and thus AR transcriptional activity. The liquid like properties of the condensates were also validated in-vitro by using recombinant AR protein. Using clinical specimens, we detected the interaction between AR and MED1 in advanced forms of prostate cancer and not in benign tissues. These results suggest that enhanced compartmentalization of AR and coactivators at SEs may play an important role in the activation of oncogenic transcription programs in androgen-dependent PCa. A better understanding of the assembly and the regulation of these AR-rich compartments may provide novel therapeutic options for PCa by targeting downstream events of androgen activation and DNA binding of AR. Citation Format: Fan Zhang, Maitree Biswas, Joseph Lee, Shreyas Lingadahalli, Samantha Wong, Christopher Wells, Neetu Saxena, Bei Sun, Ana K. Parra-Nuñez, Christophe Sanchez, Jane Foo, Novia Chan, Lauren Ung, Nabeel Khan, Umut Berkay Altıntaş, Jennifer M. Bui, Yuzhuo Wang, Ladan Fazli, Paul S. Rennie, Nathan Lack, Artem Cherkasov, Martin E. Gleave, Joerg Gsponer, Nada Lallous. Dynamic phase separation of the androgen receptor and its coactivators to regulate gene expression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5729.
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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) | 0.004 | 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".