<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 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 | 0.000 | 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 | 0.000 |
| 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".