Inhibition of HER-2/neu Kinase Impairs Androgen Receptor Recruitment to the Androgen Responsive Enhancer
Bibliographic record
Abstract
Advanced prostate cancer invariably recurs despite androgen deprivation therapy. The androgen receptor (AR) likely plays a key role in this progression and in the continued survival and proliferation of prostate cancer cells in the low androgen environment. Cross-talk with growth factor receptors, such as epidermal growth factor receptor (EGFR) family, has been postulated as a potential mechanism to activate AR in recurrent prostate cancer. We have investigated the role of HER-2/neu (ErbB-2) tyrosine kinase in AR function by characterizing the effect of inhibiting endogenous HER-2 activity in LNCaP cells. We used two independent methods, expression of intracellular single-chain antibody against HER-2 and treatment with a novel dual EGFR/HER-2 kinase inhibitor GW572016 (lapatinib). Expression of intracellular HER-2 antibody scFv-5R and treatment with GW572016 inhibited HER-2 signaling. This HER-2 inhibition led to impairment of AR-mediated functions, such as androgen-stimulated growth and the induction of endogenous prostate-specific antigen (PSA) mRNA and protein. Androgen-stimulated recruitment of AR and histone acetylation at the androgen responsive enhancer of the PSA gene, detected by chromatin immunoprecipitation analysis, were impaired by HER-2 inhibition. GW572016 was more potent in its ability to inhibit PSA expression and AR recruitment and histone acetylation than the EGFR-selective kinase inhibitor ZD1839 (gefitinib), consistent with the HER-2 kinase playing the major role in AR regulation. These results show that HER-2 signaling is required for optimal transcriptional activity of AR in prostate cancer cells and suggest that HER-2 inhibition may provide a novel strategy to disrupt AR function in prostate cancer.
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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 | 0.000 |
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".