Urinary TMPRSS2:ERG and PCA3 in an Active Surveillance Cohort: Results from a Baseline Analysis in the Canary Prostate Active Surveillance Study
Bibliographic record
Abstract
PURPOSE: Active surveillance is used to manage low-risk prostate cancer. Both PCA3 and TMPRSS2:ERG are promising biomarkers that may be associated with aggressive disease. This study examines the correlation of these biomarkers with higher cancer volume and grade determined at the time of biopsy in an active surveillance cohort. EXPERIMENTAL DESIGN: Urine was collected after digital rectal examination prospectively as part of the multi-institutional Canary Prostate Active Surveillance Study (PASS). PCA3 and TMPRSS2:ERG levels were analyzed in urine collected at study entry. Biomarker scores were correlated to clinical and pathologic variables. RESULTS: In 387 men, both PCA3 and TMPRSS2:ERG scores were significantly associated with higher volume disease. For a negative repeat biopsy, and 1% to 10%, 11% to 33%, 34% or more positive cores, median PCA3, and TMPRSS2:ERG scores increased incrementally (P < 0.005). Both PCA3 and TMPRSS2:ERG scores were also significantly associated with the presence of high-grade disease. For a negative repeat biopsy, Gleason 6 and Gleason ≥7 cancers, the median PCA3, and TMPRSS2:ERG scores also increased incrementally (P = 0.02 and P = 0.001, respectively). Using the marker scores as continuous variables, the ORs for a biopsy in which cancer was detected versus a negative repeat biopsy (ref) on modeling was 1.41 (95% CI: 1.07-1.85), P = 0.01 for PCA3 and 1.28 (95% CI: 1.10-1.49), P = 0.001 for TMPRSS2:ERG. CONCLUSIONS: For men on active surveillance, both PCA3 and TMPRSS2:ERG seem to stratify the risk of having aggressive cancer as defined by tumor volume or Gleason score.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".