<i>TMPRSS2-ERG</i> Status Is Not Prognostic Following Prostate Cancer Radiotherapy: Implications for Fusion Status and DSB Repair
Bibliographic record
Abstract
BACKGROUND: Preclinical data suggest that TMPRSS2-ERG gene fusions, present in about 50% of prostate cancers, may be a surrogate for DNA repair status and therefore a biomarker for DNA-damaging agents. To test this hypothesis, we examined whether TMPRSS2-ERG status was associated with biochemical failure after clinical induction of DNA damage following image-guided radiotherapy (IGRT). METHODS: Pretreatment biopsies from two cohorts of patients with intermediate-risk prostate cancer [T1/T2, Gleason score (GS) < 8, prostate-specific antigen (PSA) < 20 ng/mL; >7 years follow-up] were analyzed: (i) 126 patients [comparative genomic hybridization (CGH) cohort] with DNA samples assayed by array CGH (aCGH) for the TMPRSS2-ERG fusion; and (ii) 118 patients [immunohistochemical (IHC) cohort] whose biopsy samples were scored within a defined tissue microarray (TMA) immunostained for ERG overexpression (known surrogate for TMPRSS2-ERG fusion). Patients were treated with IGRT with a median dose of 76 Gy. The potential role of TMPRSS2-ERG status as a prognostic factor for biochemical relapse-free rate (bRFR; nadir + 2 ng/mL) was evaluated in the context of clinical prognostic factors in multivariate analyses using a Cox proportional hazards model. RESULTS: TMPRSS2-ERG fusion by aCGH was identified in 27 (21%) of the cases in the CGH cohort, and ERG overexpression was found in 59 (50%) patients in the IHC cohort. In both cohorts, TMPRSS2-ERG status was not associated with bRFR on univariate or multivariate analysis. CONCLUSIONS: In two similarly treated IGRT cohorts, TMPRSS2-ERG status was not prognostic for bRFR, in disagreement with the hypothesis that these prostate cancers have DNA repair defects that render them clinically more radiosensitive. TMPRSS2-ERG is therefore unlikely to be a predictive factor for IGRT response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".