The Androgen Receptor and Its Crosstalk With the Src Kinase During Castrate-Resistant Prostate Cancer Progression
Bibliographic record
Abstract
While the androgen receptor (AR) signalling is the mainstay therapeutic target for metastatic prostate cancers, these tumours will inevitably develop therapy resistance to AR pathway inhibitors suggesting that prostate tumour cells possess the capability to develop mechanisms to bypass their dependency on androgens and/or AR to survive and progress. In many studies, protein kinases such as Src are reported to promote prostate tumour progression. Specifically, the pro-oncogene tyrosine Src kinase regulates prostate cancer cell proliferation, adhesion, invasion, and metastasis. Not only can Src be activated under androgen depletion, low androgen, and supraphysiological androgen conditions, but also through crosstalk with other oncogenic pathways. Reciprocal activations between Src and AR proteins had also been reported. These findings rationalize Src inhibitors to be used to treat castrate-resistant prostate tumours. Although several Src inhibitors had advanced to clinical trials, the failure to observe patient benefits from these studies suggests that further evaluation of the roles of Src in prostate tumours is required. Here, we summarize the interplay between Src and AR signalling during castrate-resistant prostate cancer progression to provide insights on possible approaches to treat prostate cancer patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".