Expanded RAS and BRAF V600 testing as predictive biomarkers for single agent cetuximab in the randomized phase III CO.17 trial.
Bibliographic record
Abstract
537 Background: KRAS/NRAS ( RAS) testing of exons 2, 3 and 4 is standard prior to anti-EGFR treatment in metastatic colorectal cancer and many consider BRAFV600 ( BRAF) mutations predictive. CO.17 was a randomized phase III trial comparing cetuximab vs best supportive care (BSC) in unselected patients (pts). Re-analysis tested only KRAS exon 2, thus the benefit of cetuximab in RAS/BRAF wild type (WT) pts is unclear. Methods: We retrospectively performed expanded RAS/BRAF testing using a highly sensitive digital PCR method (BEAMing; 1% allele frequency detection limit) on micro-dissected archival tissue from 248 CO.17 pts. Additional pts without available archival tissue, with prior Sanger sequencing or therascreen results were included in analyses if mutations were previously detected (n = 77). Overall survival (OS), progression free survival (PFS), and response rates (RR) were compared by molecular profile. Results: Of 248 sequenced pts, 139 (56%) were RAS mutant, with 112 (45%) exon 2, 11 (4%) exon 3 and 6 (2%) exon 4 KRAS mutant, and 10 (4%) NRAS mutant pts. Seven (3%) BRAF mutant, and 97 (30%) confirmed RAS/BRAF WT pts were identified. Results are summarized below. A test of interaction indicated RAS status was predictive for PFS (p = 0.0001) and OS (p = 0.037) and BRAF status neared significance as a predictive marker for PFS (p = 0.089) but not OS (p = 0.24). Conclusions: These updated results demonstrate an improved PFS (HR 0.25 vs 0.40 previously) and OS (HR 0.51 vs 0.55 previously) for cetuximab in RAS/BRAF WT pts compared to prior analyses that included only KRAS exon 2 mutation status. We provide an estimate of single agent cetuximab efficacy for future anti-EGFR re-challenge studies and demonstrate further support that BRAF mutations may predict lack of benefit from anti-EGFR therapy. Clinical trial information: NCT00079066. [Table: see text]
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".