Src as a therapeutic target in men with prostate cancer and bone metastases
Bibliographic record
Abstract
While responsive to androgen ablation in its early stages, prostate cancer eventually becomes castration-resistant and metastasizes preferentially to bone. Once this happens, the disease carries considerable morbidity and is incurable. The process of bone metastasis involves a complex interplay between tumour and bone tissue. The eventual characteristic clinical presentation of disorganized osteoblastic bone lesions is preceded by a facilitatory osteoblastic phase; an osteoblastic component then continues to underlie the process. Increasing evidence has shown a ubiquitous role for Src (a proto-oncogene tyrosine-protein kinase) in multiple tumour and bone-signalling processes involved in prostate tumour progression, driving proliferation, survival, migration and transition to androgen-independent growth. It is also intimately involved in positively regulating osteoclast physiology. As such, this molecule represents an attractive target for managing progressing prostate cancer. Encouraging results have been obtained in preclinical and clinical studies using Src inhibitors like AZD0530 and dasatinib. Both compounds reduced markers of bone resorption, in patients with cancer and those with advanced castration-resistant prostate cancer, respectively. Moreover, because Src is central to many mechanisms thought to be responsible for the development of castration resistance, adding Src inhibitors to a treatment regimen might reverse this phenomenon. As a result, many Src inhibitors are in preclinical development. This review explores Src inhibition as a strategy for managing bone metastasis in prostate cancer, with a particular focus on targeting the critical osteoclastic response. Other emerging and novel approaches are also considered.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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".