Combined analysis of VEGF and EGFR predicts complete tumour response in rectal cancer treated with preoperative radiotherapy
Bibliographic record
Abstract
The ability to predict complete pathologic response or sensitivity to radiation before treatment would have a significant impact on the selection of patients for preoperative radiotherapy or chemo-radiation therapy schedules. The aim of this study was to determine the value of epidermal growth factor receptor (EGFR), vascular endothelial growth factor (VEGF), p53, Bcl-2 and apoptosis protease-activating factor-1 (APAF-1) as predictors of complete pathologic tumour regression in patients undergoing preoperative radiotherapy for advanced rectal cancer. Pretreatment tumour biopsies from predominantly cT3 patients undergoing a preoperative high-dose-rate brachytherapy protocol were immunostained for EGFR, VEGF, p53, Bcl-2 and APAF-1. Immunoreactivity was evaluated by three pathologists. Cut-off scores for tumour marker positivity were obtained by receiver-operating characteristic (ROC) curve analysis. The association of marker expression with complete pathologic response was analysed in univariate and multivariable analysis. Multi-marker phenotypes of the independent protein markers were evaluated. In multivariable analysis, loss of VEGF (P-value=0.009; odds ratio (OR) (95% CI)=0.24 (0.08-0.69)) and positive EGFR (P-value=0.01; OR (95% CI)=3.82 (1.37-10.6)) both demonstrated independent predictive value for complete pathologic response. The odds of complete response were 12.8 for the multi-marker combination of VEGF-negative and EGFR-positive tumours. Of the 34 EGFR-negative- and VEGF-positive cases, 32 (94.1%) had no complete pathologic response. The combined analysis of VEGF and EGFR is predictive of complete pathologic response in patients undergoing preoperative radiotherapy. In addition, the findings of this study have identified a subgroup of simultaneous EGFR-negative and VEGF-positive patients who are highly resistant to radiotherapy and should perhaps be considered candidates for innovative neoadjuvant combined modalities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".