Assessment of Androgen Receptor splice variant-7 as a biomarker of clinical response in castration-sensitive prostate cancer
Bibliographic record
Abstract
Abstract Background Therapies targeting the androgen receptor (AR) have improved the outcome for patients with castration-sensitive prostate cancer (CSPC). Expression of the constitutively active AR splice variant-7 (AR-V7) has shown clinical utility as a predictive biomarker of AR-targeted therapy resistance in castration-resistant prostate cancer (CRPC), but its importance as predictive biomarker in CSPC remains understudied. Methods We explored multiple approaches to quantify AR-V7 mRNA and protein in prostate cancer cell lines and patient-derived xenograft (PDX) models, in both publicly available and independent institutional clinical cohorts, to identify reliable approaches for detecting AR-V7 mRNA and protein, and its association with clinical outcome. Results In publicly available benign prostate, CSPC and CRPC cohorts, AR-V7 mRNA was much less abundant when detected using reads across splice boundaries than when considering isoform-specific exonic reads. The RM7 AR-V7 antibody had increased sensitivity and specificity for AR-V7 protein detection by immunohistochemistry (IHC) in CRPC cohorts and identified AR-V7 protein reactivity very rarely in CSPC cohorts, when compared to the EPR15656 AR-V7 antibody. Using multiple CRPC PDX models, we demonstrated that AR-V7 expression was exquisitely sensitive to hormonal manipulation. In CSPC cohorts, AR-V7 protein quantification by either assay did not associate with time to development of castration-resistance or overall survival, and intense neoadjuvant androgen-deprivation therapy did not lead to significant detectable AR-V7 mRNA or staining following treatment, and neither pre- nor post-treatment AR-V7 levels associated with the volume of residual disease after therapy. Conclusion This study demonstrates that further analytical validation and clinical qualification is required before AR-V7 can be considered for clinical use in CSPC as a predictive biomarker.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".