Treatment of extended RAS/ <i>BRAF</i> wild-type metastatic colorectal cancer with anti-EGFR antibody combinations
Bibliographic record
Abstract
Receptor tyrosine kinase pathways are frequently deregulated in cancer. Inhibiting these pathways with small molecule inhibitors or monoclonal antibodies has become a crucial addition to the therapeutic armamentarium in oncology. Since the introduction of drugs that target receptor tyrosine kinase pathways, it has become evident that not all patients respond to treatment. Therefore, biomarkers to predict response and benefit of drugs targeting tyrosine kinases have been sought. Monoclonal antibodies targeting the Epidermal Growth Factor Receptor (EGFR), one of the four receptors of the EGFR family were among the first targeted therapies used in solid tumors. Two drugs of this class, cetuximab and panitumumab, have been used in patients with metastatic colorectal cancer initially without any biomarker requirement. Soon, it became clear that responses were mostly observed in patients without mutations in KRAS oncogene. Currently, additional mutations of the pathway, including non-exon 2 mutations in KRAS, mutations in the homologous GTPase NRAS, in kinase BRAF and PIK3CA and other pathway proteins, have been added in the evaluation for responsiveness prediction to cetuximab and panitumumab. In this review, the predictive biomarker landscape for anti-EGFR monoclonal antibody inhibitors in metastatic colorectal cancers with no extended RAS and BRAF mutations will be examined.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".