Characterization of <i>TMPRSS2-ERG</i> Fusion High-Grade Prostatic Intraepithelial Neoplasia and Potential Clinical Implications
Bibliographic record
Abstract
PURPOSE: More than 1,300,000 prostate needle biopsies are done annually in the United States with up to 16% incidence of isolated high-grade prostatic intraepithelial neoplasia (HGPIN). HGPIN has low predictive value for identifying prostate cancer on subsequent needle biopsies in prostate-specific antigen-screened populations. In contemporary series, prostate cancer is detected in approximately 20% of repeat biopsies following a diagnosis of HGPIN. Further, discrete histologic subtypes of HGPIN with clinical implication in management have not been characterized. The TMPRSS2-ERG gene fusion that has recently been described in prostate cancer has also been shown to occur in a subset of HGPIN. This may have significant clinical implications given that TMPRSS2-ERG fusion prostate cancer is associated with a more aggressive clinical course. EXPERIMENTAL DESIGN: In this study, we assessed a series of HGPIN lesions and paired prostate cancer for the presence of TMPRSS2-ERG gene fusion. RESULTS: Fusion-positive HGPIN was observed in 16% of the 143 number of lesions, and in all instances, the matching cancer shared the same fusion pattern. Sixty percent of TMPRSS2-ERG fusion prostate cancer had fusion-negative HGPIN. CONCLUSIONS: Given the more aggressive nature of TMPRSS2-ERG prostate cancer, the findings of this study raise the possibility that gene fusion-positive HGPIN lesions are harbingers of more aggressive disease. To date, pathologic, molecular, and clinical variables do not help stratify which men with HGPIN are at increased risk for a cancer diagnosis. Our results suggest that the detection of isolated TMPRSS2-ERG fusion HGPIN would improve the positive predictive value of finding TMPRSS2-ERG fusion prostate cancer in subsequent biopsies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".