Abstract B133: Regulation of the androgen receptor function by a metabolic kinase in prostate cancer.
Bibliographic record
Abstract
Abstract In a recent genome-wide study to identify androgen receptor target genes we found that the AR promotes tumor growth by enhancing the expression and activity of enzymes required for metabolic biosynthesis. This raises the possibility for effective therapeutic approaches directed towards AR target genes. Kinases are well-established drug targets. In this study we identify androgen regulated kinome and show that an important androgen regulated metabolic kinase is a drug target in prostate cancer. Our proteome study shows that this kinase interacts with the androgen receptor and regulates AR stability. Inhibition of this kinase significantly reduces AR protein levels and suppresses prostate cancer growth in cell-lines and ex vivo cultures. In clinical prostate cancer this kinase is highly over-expressed in metastatic prostate cancers and inhibition represses metastatic potential of prostate cancer cells. Our study therefore establishes this kinase for the first time as an AR target gene that regualtes AR itself as part of feedback loop and makes a case for further investigation of its inhibitors as novel AR antagonists in both localised and advanced disease. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):B133. Citation Format: Mohammad asim, Charlie Massie, Heather Zecchini, Ajoeb Baridi, Nelma Gomes, Hisham Mohammed, Vincent Zecchini, Basetti Madhu, Arham Qureshi, Roslin Russell, Wiebke Hessenkemper, Rouchelle Sriranjan, Carrie Yang, Andy Lynch, Elena Gregorengko, Rory Stark, Paul Rennie, John Griffiths, Aria Baniahmad, Jasol Carroll, Ian Mills, David Neal. Regulation of the androgen receptor function by a metabolic kinase in prostate cancer. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr B133.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".