Phase II Trial of Trametinib and Panitumumab in RAS/RAF Wild Type Metastatic Colorectal Cancer
Bibliographic record
Abstract
INTRODUCTION: MEK inhibition may overcome resistance to EGFR inhibition in patients with RAS wildtype (wt) metastatic colorectal cancer (mCRC). We evaluated antitumor activity of trametinib (MEK1/2 inhibitor) with panitumumab (EGFR monoclonal antibody) in a phase II trial. METHODS: Patients with KRAS, NRAS, and BRAF wt mCRC with prior 5-FU, irinotecan, oxaliplatin, +/- bevacizumab and no prior anti-EGFR therapy were treated with trametinib 1.5 mg oral daily and panitumumab 4.8 mg/kg IV every 2 weeks. Primary endpoint was clinical benefit rate (CB; CR, PR, or SD ≥24 weeks) by RECIST v1.1. A 2-stage minimax design was used. Serial plasma circulating free DNA (cfDNA) was collected and profiled using Oncomine Lung cfDNA assay. RESULTS: Fourteen patients were enrolled from November 2015 to April 2019. CB rate was 38% (5/13) and median progression free survival (PFS) was 4.4 months (95% CI, 2.9-7.1). Confirmed overall response rate was 38% (5/13). Treatment-related AE (trAE) included acneiform rash (85%), diarrhea (62%), maculopapular rash (54%), mucositis (46%), and others. Dose modifications and interruptions of trametinib occurred in 69% and panitumumab in 54% of patients. The trial did not progress to stage II accrual due to tolerability and short duration of response. RAS or BRAF mutations cfDNA were detected in 3/13 patients (23%) before radiographic disease progression. CONCLUSION: The addition of trametinib to panitumumab led to a high rate of tumor shrinkage in RAS/RAF wt metastatic colorectal cancer, with poor tolerability due to a high incidence of skin toxicity. Median PFS was similar to panitumumab alone in historical control data.
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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 | 0.001 |
| 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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".