Data from Emergence of Enzalutamide Resistance in Prostate Cancer is Associated with BCL-2 and IKKB Dependencies
Bibliographic record
Abstract
AbstractPurpose: 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.
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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 | 0.001 | 0.001 |
| 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) | 0.009 | 0.002 |
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".