Emergence of Enzalutamide Resistance in Prostate Cancer is Associated with BCL-2 and IKKB Dependencies
Bibliographic record
Abstract
Abstract Purpose: Although enzalutamide (ENZ) has been widely used to treat de novo or castration-resistant metastatic prostate cancer, resistance develops and disease progression is ultimately inevitable. There are currently no approved targeted drugs to specifically delay or overcome ENZ resistance. Experimental Design: We selected several ENZ-resistant cell lines that replicated clinical characteristics of the majority of patients with ENZ-resistant disease. A high-throughput pharmacologic screen was utilized to identify compounds with greater cytotoxic effect for ENZ-resistant cell lines, compared with parental ENZ-sensitive cells. We validated the potential hits in vitro and in vivo, and used knockdown and overexpression assays to study the dependencies in ENZ-resistant prostate cancer. Results: ABT199 (BCL-2 inhibitor) and IMD0354 (IKKB inhibitor) were identified as potent and selective inhibitors of cell viability in ENZ-resistant cell lines in vitro and in vivo which were further validated using loss-of-function assays of BCL-2 and IKKB. Notably, we observed that overexpression of BCL-2 and IKKB in ENZ-sensitive cell lines was sufficient for the emergence of ENZ resistance. In addition, we confirmed that BCL-2 or IKKB inhibitors suppressed the development of ENZ resistance in xenografts. However, validation of both BCL-2 and IKKB in matched castration-sensitive/resistant clinical samples showed that, concurrent with the development of ENZ/abiraterone resistance in patients, only the protein levels of IKKB were increased. Conclusions: Our findings identify BCL-2 and IKKB dependencies in clinically relevant ENZ-resistant prostate cancer cells in vitro and in vivo, but indicate that IKKB upregulation appears to have greater relevance to the progression of human castrate-resistant prostate cancer.
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) | 0.000 | 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".