Abstract 2815: Urine TMPRSS2:ERG for prostate cancer risk stratification in men with elevated serum PSA
Bibliographic record
Abstract
Abstract Background: Over 1,000,000 men undergo prostate biopsy each year in the U.S., most for “elevated” serum PSA. Given the lack of sensitivity and specificity, and unclear mortality benefit of PSA testing, methods to individualize management of elevated PSA are needed. We evaluated urine expression of TMPRSS2:ERG, a gene fusion occurring in 50% of prostate cancers, for risk-stratifying men presenting for biopsy. Methods: TMPRSS2:ERG was measured by a clinical grade, transcription-mediated-amplification assay in prospectively collected whole-urine from 1,094 men undergoing biopsy at 10 academic and community clinics. Findings: Urine TMPRSS2:ERG was associated with indicators of clinically significant cancer at biopsy and prostatectomy, including tumor size, high prostatectomy Gleason score and upgrading at prostatectomy. TMPRSS2:ERG in combination with urine PCA3, improved the multivariate PCPT risk calculator performance for predicting cancer on biopsy (AUC in test set, 0.79 vs. 0.64, p<0.001). Using a three-class stratification, men in the highest and lowest TMPRSS2:ERG+PCA3 score groups had markedly different rates of cancer (69% vs. 21%, p<0.001), clinically significant cancer by Epstein criteria (61% vs. 15%, p<0.001) and high grade cancer (40% vs. 7%, p<0.001) on biopsy. Interpretation: Urine TMPRSS2:ERG, in combination with urine PCA3, enhances the utility of serum PSA for predicting prostate cancer and clinically relevant cancer on biopsy. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2815. doi:10.1158/1538-7445.AM2011-2815
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".