Interrogation of <i> <scp>ERG</scp> </i> gene rearrangements in prostate cancer identifies a prognostic 10‐gene signature with relevant implication to patients' clinical outcome
Bibliographic record
Abstract
OBJECTIVES: ERG-gene rearrangement defines a distinct molecular subtype of PCA with potential biological and clinical implications. To identify a molecular signature reflective of the downstream effects of ERG-mediated transcriptional regulation with prognostic implication in patients with prostate cancer (PCA). MATERIAL AND METHODS: We used a singular value decomposition (SVD) bioinformatics approach to re-analyse gene expression data previously generated from 46 prostate tumours, and identified an ERG-like gene signature. The signature was validated on several patient cohorts and individual genes were correlated to ERG expression and PCA progression. RESULTS: An ERG-like 10-gene signature was identified and validated in PCA cohorts of the physician health study (p115) (n = 110) in addition to three independent public datasets, and was significantly associated with disease progression, biochemical recurrence and PCA-specific mortality. Patients with the ERG-like signature were significantly associated with disease recurrence on univariate (hazard ratio [HR] 2.6; 95% confidence interval [CI]:1.3-5.2; P = 0.004) and multivariate analysis (HR 2.3; 95% CI:1.1-4.6, P = 0.016) compared with patients without this signature. Within the group of patients with Gleason score (GS) 6 and 7 PCA, the signature added prognostic value beyond GS and identified patients at higher risk of cancer deaths more accurately than GS alone or in combination with ERG status. Protein expression of the 10 genes were significantly associated with ERG and disease progression regardless of ERG status. CONCLUSION: The characterized ERG-like signature was reflective of aggressive features of ERG-mediated transcription and was prognostically robust. The combination of this signature with clinicopathological variables should be validated prospectively to explore its clinical utility in stratifying patients with PCA and in identifying those at higher risk of metastatic and lethal disease.
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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 | 0.001 |
| 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".