Transcriptomic Heterogeneity of Androgen Receptor Activity Defines a <i>de novo</i> low AR-Active Subclass in Treatment Naïve Primary Prostate Cancer
Bibliographic record
Abstract
Abstract Purpose: The heterogeneity of androgen receptor (AR)-activity (AR-A) is well-characterized in heavily treated metastatic castration-resistant prostate cancer (mCRPC). However, the diversity and clinical implications of AR-A in treatment-naïve primary prostate cancer is largely unknown. We sought to characterize AR-A in localized prostate cancer and understand its molecular and clinical implications. Experimental Design: Genome-wide expression profiles from prostatectomy or biopsy samples from 19,470 patients were used, all with independent pathology review. This was comprised of prospective discovery (n = 5,239) and validation (n = 12,728) cohorts, six retrospective institutional cohorts with long-term clinical outcomes data (n = 1,170), and The Cancer Genome Atlas (n = 333). Results: A low AR-active subclass was identified, which comprised 9%–11% of each cohort, and was characterized by increased immune signaling, neuroendocrine expression, and decreased DNA repair. These tumors were predominantly ERG and basal subtype. Low AR-active tumors had significantly more rapid development of recurrence or metastatic disease across cohorts, which was maintained on multivariable analysis [HR, 2.61; 95% confidence interval (CI), 1.22–5.60; P = 0.014]. Low AR-active tumors were predicted to be more sensitive to PARP inhibition, platinum chemotherapy, and radiotherapy, and less sensitive to docetaxel and androgen-deprivation therapy. This was validated clinically, in that low AR-active tumors were less sensitive to androgen-deprivation therapy (OR, 0.41; 95% CI, 0.21–0.80; P = 0.008). Conclusions: Leveraging large-scale transcriptomic data allowed the identification of an aggressive subtype of treatment-naïve primary prostate cancer that harbors molecular features more analogous to mCRPC. This suggests that a preexisting subgroup of patients may have tumors that are predisposed to fail multiple current standard-of-care therapies and warrant dedicated therapeutic investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".