Validation of Companion Diagnostic for Detection of Mutations in Codons 12 and 13 of the KRAS Gene in Patients with Metastatic Colorectal Cancer: Analysis of the NCIC CTG CO.17 Trial
Bibliographic record
Abstract
CONTEXT: The therascreen KRAS RGQ polymerase chain reaction kit is being developed as a companion diagnostic to aid clinicians, through detection of KRAS mutations, in the identification of patients with metastatic colorectal cancer (mCRC) who are more likely to benefit from cetuximab. OBJECTIVES: To assess whether KRAS mutation status, determined by using the therascreen KRAS kit, is a predictive marker of cetuximab efficacy. DESIGN: Tissue samples were obtained from patients with mCRC treated on the National Cancer Institute of Canada Clinical Trials Group (NCIC CTG) CO.17 phase 3 study of cetuximab plus best supportive care (BSC) versus BSC alone. Tumor DNA samples were assessed for the presence of KRAS mutations by using the therascreen KRAS kit. Efficacy and safety were assessed to determine whether mutation status was predictive of outcomes. Results.-Evaluable samples were available from 453 patients (79.2%) enrolled in the NCIC CTG CO.17 trial. The KRAS wild-type subset represented 54.1% (245 of 453) of the evaluated population. Median overall survival of patients with KRAS wild-type tumors was 8.6 months among those who received cetuximab plus BSC and 5.0 months among patients who received BSC alone (hazard ratio [HR], 0.63; P = .002). Among patients with KRAS mutant mCRC, no meaningful difference in overall survival was observed between arms (HR, 0.91; P = .55). These results are consistent with a previous report that analyzed patient tumor samples by using bidirectional sequencing. CONCLUSIONS: These data support the utility of the therascreen KRAS kit as a means of selecting patients who may benefit from cetuximab therapy.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".