Prostate cancer genes associated with TMPRSS2–ERG gene fusion and prognostic of biochemical recurrence in multiple cohorts
Bibliographic record
Abstract
BACKGROUND: Recent studies have indicated that prostate cancer patients with the TMPRSS2-ERG gene fusion have a higher risk of recurrence. To identify markers associated with TMPRSS2-ERG fusion and prognostic of biochemical recurrence, we analysed a cohort of 139 men with prostate cancer for 502 molecular markers. METHODS: RNA from radical prostatectomy tumour specimens was analysed using cDNA-mediated, annealing, selection, extension and ligation (DASL) to determine mRNAs associated with TMPRSS2-ERG T1/E4 fusion and prognostic of biochemical recurrence. Differentially expressed mRNAs in T1/E4-positive tumours were determined using significance analysis of microarrays (false discovery rate (FDR) <5%). Univariate and multivariate Cox regression determined genes, gene signatures and clinical factors prognostic of recurrence (P-value <0.05, log-rank test). Analysis of two prostate microarray studies (GSE1065 and GSE8402) validated the findings. RESULTS: In the 139 patients from this study and from a 455-patient Swedish cohort, 15 genes in common were differentially regulated in T1/E4 fusion-positive tumours (FDR <0.05). The most significant mRNAs in both cohorts coded ERG. Nine genes were found prognostic of recurrence in this study and in a 596-patient Minnesota cohort. A molecular recurrence score was significant in prognosticating recurrence (P-value 0.000167) and remained significant in multivariate analysis of a mixed clinical model considering Gleason score and TMPRSS2-ERG fusion status. CONCLUSIONS: TMPRSS2-ERG T1/E4 fusion-positive tumours had differentially regulated mRNAs observed in multiple studies, the most significant one coded for ERG. Several mRNAs were consistently associated with biochemical recurrence and have potential clinical utility but will require further validation for successful translation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".