Clinical and genomic characterization of low-prostate-specific antigen, high-grade prostate cancer.
Bibliographic record
Abstract
59 Background: The consequences of a low prostate-specific antigen (PSA) in high-grade (Gleason 8-10) prostate cancer are unknown. We sought to evaluate the clinical implications and genomic features of this entity. Methods: Clinical and transcriptomic data from 626,057 patients with N0M0 prostate cancer were collected from two national cohorts and a large transcriptome database. Multivariable Fine-Gray and Cox regressions analyzed prostate-cancer specific mortality (PCSM) and all-cause mortality, respectively. GRID data were used to analyze transcriptomic features. Results: For Gleason 8-10 disease, the distribution of PCSM was U-shaped by PSA (PSA 4.1-10.0 ng/mL = referent), with adjusted hazard ratio (AHR) 2.70 for PSA ≤2.5 ng/mL (P < 0.001) versus 1.97, 1.36, and 2.56 for PSA 2.6-4.0, 10.1-20.0, and > 20.0 ng/mL, respectively. In contrast, distribution of PCSM by PSA was linear for Gleason ≤7 with AHR 0.41 for PSA ≤2.5 ng/mL (P = 0.127) versus 1.38, 2.28, and 4.61 for PSA 2.6-4.0, 10.1-20.0, and > 20.0 ng/mL, respectively (PGleason*PSA interaction< 0.001). Gleason 8-10, PSA ≤2.5 ng/mL disease had a significantly higher PCSM than standard high and very high-risk disease with PSA > 2.5 ng/mL (AHR 2.15, P = 0.009; 47-month PCSM 13.8% versus 4.9%). Among Gleason 8-10 patients treated with definitive radiotherapy, androgen deprivation therapy (ADT) was associated with a survival benefit for PSA > 2.5 ng/mL (AHR 0.87, P < 0.001) but not for ≤2.5ng/mL (AHR 1.36, P = 0.084; PADT*PSA interaction= 0.021). For Gleason 8-10 tumors, PSA ≤2.5 ng/mL was associated with a higher expression of neuroendocrine markers compared to > 2.5 ng/mL (P = 0.046), with no such relationship for Gleason ≤7. Conclusions: Low-PSA, high-grade prostate cancer appears to be a unique entity that has a very high risk for PCSM, potentially responds poorly to ADT, and is associated with neuroendocrine genomic features.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".