Low nuclear ErbB3 predicts biochemical recurrence in patients with prostate cancer
Bibliographic record
Abstract
OBJECTIVE: To further evaluate the association between the cytoplasmic or nuclear localization of ErbB3 with biochemical recurrence (BCR) in patients with prostate cancer and positive surgical margins, as there is a greater risk of BCR for such patients after radical prostatectomy (RP). PATIENTS AND METHODS: We recently noted that ErbB3, which is normally associated with the plasma membrane, can translocate to the nucleus, an event which appears to be associated with disease progression. We evaluated ErbB3 expression and localization using immunohistochemistry on tissue samples from 55 patients with positive surgical margins after RP; 30 of these 55 (55%) had BCR after 3 years of follow-up. The relationship between ErbB3 nuclear localization and BCR (prostate-specific antigen, PSA, >0.3 ng/mL) after RP was analysed by Kaplan-Meier survival analysis and Cox regression models. RESULTS: The BCR-free survival probability at 3 years was 0.65 and 0.35 for positive and negative nuclear ErbB3, respectively (Kaplan-Meier, P = 0.029). Patients negative for nuclear ErbB3 had a 2.47-fold increase in BCR frequency in a univariate Cox model (P = 0.008) and it remained an independent prognostic marker when combined with clinical prognostic variables in a multivariate model (P = 0.023). CONCLUSION: Low nuclear localization of ErbB3 is a predictor of BCR in patients with prostate cancer and positive surgical margins after RP.
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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 | 0.002 |
| 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".