Generation 2.5 Antisense Oligonucleotides Targeting the Androgen Receptor and Its Splice Variants Suppress Enzalutamide-Resistant Prostate Cancer Cell Growth
Bibliographic record
Abstract
PURPOSE: Enzalutamide (ENZ) is a potent androgen receptor (AR) antagonist with activity in castration-resistant prostate cancer (CRPC); however, progression to ENZ-resistant (ENZ-R) CRPC frequently occurs with rising serum PSA levels, implicating AR full-length (ARFL) or variants (AR-Vs) in disease progression. EXPERIMENTAL DESIGN: To define functional roles of ARFL and AR-Vs in ENZ-R CRPC, we designed 3 antisense oligonucleotides (ASO) targeting exon-1, intron-1, and exon-8 in AR pre-mRNA to knockdown ARFL alone or with AR-Vs, and examined their effects in three CRPC cell lines and patient-derived xenografts. RESULTS: ENZ-R-LNCaP cells express high levels of both ARFL and AR-V7 compared with CRPC-LNCaP; in particular, ARFL levels were approximately 12-fold higher than AR-V7. Both ARFL and AR-V7 are highly expressed in the nuclear fractions of ENZ-R-LNCaP cells even in the absence of exogenous androgens. In ENZ-R-LNCaP cells, knockdown of ARFL alone, or ARFL plus AR-Vs, similarly induced apoptosis, suppressed cell growth and AR-regulated gene expression, and delayed tumor growth in vivo. In 22Rv1 cells that are inherently ENZ-resistant, knockdown of both ARFL and AR-Vs more potently suppressed cell growth, AR transcriptional activity, and AR-regulated gene expression than knockdown of ARFL alone. Exon-1 AR-ASO also inhibited tumor growth of LTL-313BR patient-derived CRPC xenografts. CONCLUSIONS: These data identify the AR as an important driver of ENZ resistance, and while the contributions of ARFL and AR-Vs can vary across cell systems, ARFL is the key driver in the ENZ-R LNCaP model. AR targeting strategies against both ARFL and AR-Vs is a rational approach for AR-dependent 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".