BRAF, PIK3CA, and PTEN status and benefit from cetuximab (CET) in the treatment of advanced colorectal cancer (CRC): Results from NCIC CTG/AGITG CO.17.
Bibliographic record
Abstract
3515 Background: CET, a monoclonal antibody targeting the epidermal growth factor receptor, improves overall survival (OS) and progression free survival (PFS) in patients (pts) with KRAS wild-type (WT) chemotherapy refractory CRC. BRAF and PIK3CA mutation status, and PTEN expression levels may further predict benefit from CET therapy. Methods: Available colorectal tumour samples were analyzed from a phase III trial of CET plus best supportive care (BSC) vs BSC alone (NEJM 2007; 357(20)). BRAF and PIK3CA mutations (MUT) identified in tumour-derived DNA using a high resolution melting analysis to identify amplicons with mutations were confirmed by sequencing. PTEN expression by immunohistochemistry (IHC) was performed on tissue microarrays constructed from available tumour blocks. For each biomarker, prognostic (treatment independent) effects were assessed in patients on the BSC alone arm. Predictive effects (benefit from CET) on OS and PFS among all patients and those in the KRAS wild-type subset were examined using a Cox model with tests for treatment-biomarker interaction, adjusting for covariates. Results: Of 401 pts assessed for BRAF status (70% of CO17 population), 13(3%) had mutations. Of 407 pts assessed for PIK3CA status (71% of CO17 population), 61(15%) had mutations. Of 205 pts assessed for PTEN (36% of CO17 population), 148(72%) were negative for IHC expression. No biomarker was prognostic for OS or PFS, and none were predictive of benefit from CET, either in the whole study population or the KRAS WT subset. Conclusions: In chemotherapy-refractory CRC, neither PIK3CA mutation status nor PTEN expression were predictive of benefit from CET therapy. BRAF mutations are uncommon in this setting. Larger sample sizes would be required to determine if BRAF status is predictive for CET benefit. [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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".