Frequency of the <i>TMPRSS2:ERG</i> gene fusion is increased in moderate to poorly differentiated prostate cancers
Bibliographic record
Abstract
BACKGROUND: Recent reports indicate that prostate cancers (CaP) frequently over-express the potential oncogenes, ERG or ETV1. Many cases have chromosomal rearrangements leading to the fusion of the 5' end of the androgen-regulated serine protease TMPRSS2 (21q22.2) to the 3' end of either ERG (21q22.3) or ETV1 (7p21.3). The consequence of these rearrangements is aberrant androgen receptor-driven expression of the potential oncogenes, ETV1 or ERG. AIM: To determine the frequency of rearrangements involving TMPRSS2, ERG, or ETV1 genes in CaP of varying Gleason grades through fluorescence in situ hybridisation (FISH) on CaP tissue microarrays (TMAs). METHODS: Two independent assays, a TMPRSS2 break-apart assay and a three-colour gene fusion FISH assay were applied to TMAs. FISH positive cases were confirmed by reverse transcriptase (RT) PCR and DNA sequence analysis. RESULTS: A total of 106/196 (54.1%) cases were analysed by FISH. None of the five benign prostatic hyperplasia cases analysed exhibited these gene rearrangements. TMPRSS2:ERG fusion was found more frequently in moderate to poorly differentiated tumours (35/86, 40.7%) than in well differentiated tumours (1/15, 6.7%, p = 0.017). TMPRSS2:ETV1 gene fusions were not detected in any of the cases tested. TMPRSS2:ERG fusion product was verified by RT-PCR followed by DNA sequencing in 7/7 randomly selected positive cases analysed. CONCLUSION: This study indicates that TMPRSS2:ERG gene rearrangements in CaP may be used as a diagnostic tool to identify prognostically relevant sub-classifications of these cancers.
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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) | 0.002 | 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".