A Novel HSP90 Inhibitor Delays Castrate-Resistant Prostate Cancer without Altering Serum PSA Levels and Inhibits Osteoclastogenesis
Bibliographic record
Abstract
PURPOSE: Prostate cancer responds initially to antiandrogen therapies; however, progression to castration-resistant disease frequently occurs. Therefore, there is an urgent need for novel therapeutic agents that can prevent the emergence of castrate-resistant prostate cancer (CRPC). HSP90 is a molecular chaperone involved in the stability of many client proteins including Akt and androgen receptor (AR). 17-Allylamino-17-demethoxy-geldanamycin (17-AAG) has been reported to inhibit tumor growth in various cancers; however, it induces tumor progression in the bone microenvironment. METHODS: Cell growth, apoptosis, and AR transactivation were examined by crystal violet assay, flow cytometric, and luciferase assays, respectively. The consequence of HSP90 therapy in vivo was evaluated in LNCaP xenograft model. The consequence of PF-04928473 therapy on bone metastasis was studied using an osteoclastogenesis in vitro assay. RESULTS: PF-04928473 inhibits cell growth in a panel of prostate cancer cells, induces cell-cycle arrest at sub-G(1), and leads to apoptosis and increased caspase-3 activity. These biological events were accompanied by decreased activation of Akt and Erk as well as decreased expression of Her2, and decreased AR expression and activation in vitro. In contrast to 17-AAG, PF-04928473 abrogates RANKL-induced osteoclast differentiation by affecting NF-κB activation and Src phosphorylation. Finally, PF-04929113 inhibited tumor growth and prolonged survival compared with controls. Surprisingly, PF-04929113 did not reduce serum prostate-specific antigen (PSA) levels in vivo; in parallel, these decrease in tumor volume. CONCLUSION: These data identify significant anticancer activity of PF-04929113 in CRPC but suggest that serum PSA may not prove useful as pharmacodynamic tool for this drug.
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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) | 0.002 | 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".