Preoperative Plasma HER2 and Epidermal Growth Factor Receptor for Staging and Prognostication in Patients with Clinically Localized Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Human epidermal growth factor receptor-2 (HER2) and epidermal growth factor receptor (EGFR) expression have been associated with disease progression in patients with prostate cancer. We tested the hypothesis that plasma levels of HER2 and/or EGFR are associated with prostate cancer stage and prognosis in patients with clinically localized disease. EXPERIMENTAL DESIGN: We measured preoperative plasma HER2 and EGFR levels using commercially available ELISAs on banked plasma from 227 patients treated with radical prostatectomy and bilateral lymphadenectomy for clinically localized prostate adenocarcinoma. RESULTS: Median preoperative plasma EGFR and HER2 levels were 31.4 ng/mL (interquartile range, 19.2 ng/mL) and 10.0 ng/mL (interquartile range, 2.7 ng/mL), respectively. HER2 was elevated in patients with seminal vesicle invasion (P = 0.033). In separate multivariate analyses that adjusted for the effects of standard preoperative predictors, lower EGFR, higher HER2, and higher HER2/EGFR ratio were associated with prostate-specific antigen (PSA) progression (P = 0.003, P < 0.001, and P < 0.001, respectively). In separate multivariate analyses that adjusted for the effects of standard postoperative predictors, lower EGFR and higher HER2/EGFR ratio were associated with PSA progression (P = 0.027 and P < 0.001, respectively). Among the patients who experienced PSA progression, HER2 was significantly higher (P = 0.023) and EGFR was lower (P = 0.04) in those with features of aggressive disease (i.e., development of metastasis, PSA doubling time <10 months, and/or failure to respond to local salvage radiation therapy). CONCLUSION: Preoperative plasma HER2 and EGFR were associated with prostate cancer progression after radical prostatectomy. Plasma HER2 and EGFR may provide a tool for predicting long-term recurrence-free survival and early metastasis.
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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) | 0.001 | 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".