Epiregulin gene expression as a biomarker of benefit from cetuximab in the treatment of advanced colorectal cancer
Bibliographic record
Abstract
BACKGROUND: Anti-EGFR antibody, cetuximab, improves overall survival (OS) in K-ras wild-type chemotherapy-refractory colorectal cancer. Epidermal growth factor receptor ligand epiregulin (EREG) gene expression may further predict cetuximab benefit. METHODS: Tumour samples from a phase III clinical trial of cetuximab plus best supportive care (BSC) vs BSC alone (CO.17) were analysed for EREG mRNA gene expression. Predictive effects of high vs low EREG on OS and progression-free survival (PFS) were examined for treatment-biomarker interaction. RESULTS: Both EREG and K-ras status were ascertained in 385 (193 cetuximab, 192 BSC) tumour samples. Within the high EREG and K-ras wild-type status ('co-biomarker')-positive group (n=139, 36%), median PFS was 5.4 vs 1.9 months (hazard ratio (HR) 0.31; P<0.0001), and median OS was 9.8 vs 5.1 months (HR 0.43; P<0.001) for cetuximab vs BSC, respectively. In the rest (n=246, 64%), PFS (HR 0.82; P=0.12) and OS (HR 0.90; P=0.45) were not significantly different. Test for treatment interaction showed a larger cetuximab effect on OS (HR 0.52; P=0.007) and PFS (HR 0.49; P=0.001) in the co-biomarker-positive group. CONCLUSION: In pre-treated K-ras wild-type status colorectal cancer, patients with high EREG gene expression appear to benefit more from cetuximab therapy compared with low expression. Epiregulin as a selective biomarker requires further evaluation.
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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 | 0.000 |
| 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 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".