<i>PIK3CA, BRAF</i>, and PTEN Status and Benefit from Cetuximab in the Treatment of Advanced Colorectal Cancer—Results from NCIC CTG/AGITG CO.17
Bibliographic record
Abstract
PURPOSE: Cetuximab improves survival in patients with K-ras wild-type advanced colorectal cancer. We examined the predictive and prognostic significance of additional biomarkers in this setting, in particular BRAF, PIK3CA, and PTEN. EXPERIMENTAL DESIGN: Available colorectal tumor samples were analyzed from the CO.17 study. BRAF mutations were identified in tumor-derived DNA by direct sequencing and PIK3CA mutations were identified using a high-resolution melting screen with confirmation by sequencing. PTEN expression by immunohistochemistry (IHC) was performed on tissue microarrays. For each biomarker, prognostic and predictive effects were examined using a Cox model with tests for treatment-biomarker interaction. RESULTS: A total of 572 patients with pretreated colorectal cancer were randomly assigned to receive cetuximab or best supportive care (BSC). Of 401 patients assessed for BRAF status, 13 (3.2%) had mutations. Of 407 patients assessed for PIK3CA status, 61 (15%) had mutations. Of 205 patients assessed for PTEN, 148 (72%) were negative for IHC expression. None of BRAF, PIK3CA, or PTEN was prognostic for overall or progression-free survival in the BSC arm. None was predictive of benefit from cetuximab, either in the whole study population or the K-ras wild-type subset. In the K-ras wild-type subgroup, the overall survival adjusted HR according to BRAF mutation status was 1.39 (interaction P = 0.69), PIK3CA mutation status HR = 0.79 (interaction P = 0.63), and PTEN expression HR = 0.75 (interaction P = 0.61). CONCLUSIONS: In chemotherapy-refractory colorectal cancer, neither PIK3CA mutation status nor PTEN expression were prognostic, nor were they predictive of benefit from cetuximab. Evaluation of predictive significance of BRAF mutations requires a larger sample size.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".