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Androgen receptor activity in biopsy specimens at initial diagnosis of prostate cancer and correlation with outcomes and treatment response.

2025· article· en· W4407701227 on OpenAlexaff
Nicole Handa, Mohammed Alshalalfa, Yangyang Hao, Hyunnam Ryu, J. Proudfoot, Elai Davicioni, Matthew R. Cooperberg, Alejandro Berlín, Paul L. Nguyen, Daniel E. Spratt, Ridwan Alam, Ashley E. Ross, Edward M. Schaeffer

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
FundersVeracyte
KeywordsMedicineProstate cancerAndrogen receptorBiopsyOncologyProstateInternal medicineCorrelationAndrogenCancerPathologyHormone

Abstract

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409 Background: Androgen receptor activity (AR-A) has been described after radical prostatectomy (RP) and metastatic castration-sensitive prostate cancer (mCSPC). In RP specimens low AR-A is associated with basal subtypes, decreased DNA repair and increased immune activity. In mCSPC, low AR-A is associated with poor overall survival (OS) and time to progression to CRPC. However, AR-A has not been well characterized in localized disease at initial diagnosis. Here we sought to assess AR-A signatures in biopsy samples from patients across the prostate cancer risk continuum and assess correlations between AR-A and outcomes. Methods: We analyzed 150,162 biopsy samples tested (2016-2024) with the Decipher prostate genomic classifier (Veracyte, Inc. San Diego, CA). Transcriptome-wide expression data and clinical factors were retrieved from the Decipher GRID (NCT02609269). Patients with low and high AR-A expression as defined by Spratt et al 2019 were compared using Chi-square tests. Clinical and pathologic outcomes for specific cohorts in the overall sample population were analyzed using Cox regression. Results: Overall, 11,752 (7.8%) patients had low AR-A expression. 9.8% of patients > 70 years of age had low AR-A compared to 7.6% of patients <70 (p<0.0001). Low AR-A was enriched in samples with poor prognostic clinical factors such as very high Decipher (p<0.0001), Grade Group (GG) 5 (p<0.0001) and very high NCCN risk (p<0.0001). The same differences were present when looking only at patients with a PSA <4 ng/mL. Like in RP samples, AR-A expression in biopsy samples positively correlated with intact DNA repair and negatively correlated with basal subtypes, PORTOS and immune infiltration scores (all p<0.001). Low AR-A was prognostic of poor clinical outcomes across 4 independent retrospective cohorts. In cohort 1, intermediate risk disease (n=647), low AR-A correlated with adverse pathology at time of RP (p <0.05). In cohort 2, intermediate risk disease treated with radiation therapy (RT) (n=121), low AR-A correlated with biochemical failure (p<0.05). In cohort 3, high risk disease (n=405), low AR-A correlated with decreased OS (p<0.05) in all patients and metastasis (p<0.05) after RT and androgen deprivation therapy (ADT). Finally, in cohort 4, high risk disease treated with RT+ADT (n=100), low AR-A correlated with metastasis (p<0.01). Conclusions: Overall, in a large cohort of biopsy specimens, low AR-A was associated with increased age, very high Decipher score, very high NCCN risk, and GG5 disease. Subpopulation analyses suggest that low AR-A portends a poor prognosis. Given that patients with low AR-A had decreased DNA repair activity and increased PORTOS scores, clinicians should consider post-operative RT and novel clinical trials with PARP inhibitors for these patients.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.114
GPT teacher head0.497
Teacher spread0.384 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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