Role of <i>KRAS</i> and <i>EGFR</i> As Biomarkers of Response to Erlotinib in National Cancer Institute of Canada Clinical Trials Group Study BR.21
Bibliographic record
Abstract
PURPOSE: To evaluate the effect of KRAS and epidermal growth factor receptor (EGFR) genotype on the response to erlotinib treatment in the BR.21, placebo-controlled trial. PATIENTS AND METHODS: We analyzed 206 tumors for KRAS mutation, 204 tumors for EGFR mutation, and 159 tumors for EGFR gene copy by fluorescent in situ hybridization (FISH). We reanalyzed EGFR deletion/mutation using two highly sensitive techniques that detect abnormalities in samples with 5% to 10% tumor cellularity. KRAS mutation was analyzed by direct sequencing. RESULTS: Thirty patients (15%) had KRAS mutations, 34 (17%) had EGFR exon 19 deletion or exon 21 L858R mutations, and 61 (38%) had high EGFR gene copy (FISH positive). Response rates were 10% for wild-type and 5% for mutant KRAS (P = .69), 7% for wild-type and 27% for mutant EGFR (P = .03), and 5% for EGFR FISH-negative and 21% for FISH-positive patients (P = .02). Significant survival benefit from erlotinib therapy was observed for patients with wild-type KRAS (hazard ratio [HR] = 0.69, P = .03) and EGFR FISH positivity (HR = 0.43, P = .004) but not for patients with mutant KRAS (HR = 1.67, P = .31), wild-type EGFR (HR = 0.74, P = .09), mutant EGFR (HR = 0.55, P = .12), and EGFR FISH negativity (HR = 0.80, P = .35). In multivariate analysis, only EGFR FISH-positive status was prognostic for poorer survival (P = .025) and predictive of differential survival benefit from erlotinib (P = .005). CONCLUSION: EGFR mutations and high copy number are predictive of response to erlotinib. EGFR FISH is the strongest prognostic marker and a significant predictive marker of differential survival benefit from erlotinib.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".