Comment on ‘Clinical significance of BRAF non-V600E mutations on the therapeutic effects of anti-EGFR monoclonal antibody treatment in patients with pretreated metastatic colorectal cancer: the Biomarker Research for anti-EGFR monoclonal Antibodies by Comprehensive Cancer genomics (BREAC) study’
Bibliographic record
Abstract
We read, with great interest, the manuscript published in a recent issue of British Journal of Cancer , entitled “Clinical significance of BRAF non-V600E mutations on the therapeutic effects of anti-EGFR monoclonal antibody treatment in patients with pretreated metastatic colorectal cancer: the Biomarker Research for anti-EGFR monoclonal Antibodies by Comprehensive Cancer genomics (BREAC) study”. 1 In this publication, Shinozaki et al. provide preliminary evidence that patients with BRAF non-V600E mutant metastatic colorectal cancers (mCRC) may be resistant to epidermal growth factor receptor (EGFR) inhibition. The results from the retrospective BREAC study are consistent with the emerging paradigm that any activating MAPK mutation (KRAS, NRAS, BRAF V600E) is sufficient to promote intrinsic resistance to EGFR inhibitors. 2 , 3 , 4 Conversely, these data represent a stark contrast to a recent retrospective analysis of clinical outcomes for mCRC patients with non-V600 BRAF mutations. 5 Jones et al. have demonstrated that non-V600 BRAF mutant mCRC represents a clinically distinct molecular subtype, which is associated with significantly longer overall survival (OS) compared to mCRC patients with BRAF V600E mutations. Herein, we will explore some possible explanations for the discrepancy in findings between these two recent studies.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.053 | 0.035 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".