The promise of heat shock protein inhibitors in the treatment of castration resistant prostate cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: To present the recent advances in novel agents that target heat shock proteins (Hsps) to treat or delay the development of castration resistant prostate cancer (CRPC). RECENT FINDINGS: Multiple preclinical studies have shown that silencing Hsp27, Hsp90, or clusterin sensitizes prostate cancer cells to modern chemotherapy and radiation treatments; and overexpression of these chaperones confers resistance to these therapies. Antisense oligonucleotides targeting Hsp27 and clusterin have shown good biological activity in human phase II trials and phase III studies are ongoing. Despite promising preclinical efficacy, a number of phase I/II human trials with various Hsp90 inhibitors have been disappointing with negligible anticancer activity and dose-limiting toxicity profiles. Newer Hsp90 inhibitors with better toxicity profiles, and inhibitors that target Hsp90 cofactors, such as FKBP52, are currently being investigated in human studies. SUMMARY: Many Hsp chaperone client proteins are key components of alternative growth factor pathways upregulated in CRPC and are involved in key resistance pathways to current chemotherapy and radiotherapy regimes. New treatments that inhibit Hsps are attractive anticancer strategies as they have the ability to simultaneously target multiple pathways involved in CRPC.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".