Targeting the BAG-1 family of co-chaperones in lethal prostate cancer
Bibliographic record
Abstract
Abstract Therapies that abrogate persistent androgen receptor (AR) signaling in castration resistant prostate cancer (CRPC) remain an unmet clinical need. The N-terminal domain (NTD) of the AR drives transcriptional activity in CRPC but is intrinsically disordered and remains a challenging therapeutic target. Therefore, inhibiting critical co-chaperones, such as BAG-1L, is an attractive alternative strategy. We performed druggability analyses demonstrating the BAG domain to be a challenging drug target. Thio-2, a tool compound, has been reported to bind the BAG domain of BAG-1L and inhibit BAG-1L-mediated AR transactivation. However, despite these data, the mechanism of action of Thio-2 is poorly understood and the BAG domain which is present in all BAG-1 isoforms has not been validated as a therapeutic target. Herein, we demonstrate growth inhibiting activity of Thio-2 in CRPC cell lines and patient derived models with decreased AR genomic binding and AR signaling independent of BAG-1 isoform function. Furthermore, genomic abrogation of BAG-1 isoforms did not recapitulate the described Thio-2 phenotype, and NMR studies suggest that Thio-2 may bind the AR NTD, uncovering a potential alternative mechanism of action, although in the context of low compound solubility. Furthermore, BAG-1 isoform knockout mice are viable and fertile, in contrast to previous studies, and when crossed with prostate cancer mouse models, BAG-1 deletion does not significantly impact prostate cancer development and growth. Overall, these data demonstrate that Thio-2 inhibits AR signaling and growth in CRPC independent of BAG-1 isoforms, and unlike previous studies of the activated AR, therapeutic targeting of the BAG domain requires further validation before being considered a therapeutic strategy for the treatment of CRPC.
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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".