Androgen receptor genomic alterations and treatment resistance in metastatic prostate cancer
Bibliographic record
Abstract
BACKGROUND: Genomic alterations to the androgen receptor (AR) are common in metastatic castration-resistant prostate cancer (mCRPC). AR copy number amplifications, ligand-binding domain missense mutations, and intronic structural rearrangements can all drive resistance to approved AR pathway inhibitors and their detection via tissue or liquid biopsy is linked to clinical outcomes. With an increasingly crowded treatment landscape, there is hope that AR genomic alterations can act as prognostic and/or predictive biomarkers to guide patient management. METHODS: In this review, we evaluate the current evidence for AR genomic alterations as clinical biomarkers in mCRPC, focusing on correlative studies that have used plasma circulating tumor DNA to characterize AR genotype. RESULTS: We highlight data that demonstrates the complexity of AR genotype within individual patients, and suggest that future studies should account for cancer clonal heterogeneity and variable tumor content in liquid biopsy samples. Given the potential for cooccurrence of multiple AR genomic alterations in the same or competing subclones of a patient, it is distinctly challenging to attribute blanket clinical significance to any individual alteration. This challenge is further complicated by the varied treatment exposures in contemporary patients, and the fact that AR genotype continues to evolve in the mCRPC setting across sequential lines of systemic therapy. CONCLUSIONS: As treatment access and liquid biopsy technology continues to improve, we posit that real-time measures of AR biology are likely to play a key role in emerging precision oncology strategies for metastatic 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".