State of the art of the molecular hyperselection to guide treatment with anti-EGFR antibodies in RAS WT mCRC: implications for clinical practice and future perspectives
Bibliographic record
Abstract
INTRODUCTION: Adding monoclonal antibodies to chemotherapy drastically changed the landscape of advanced colorectal cancer. The prediction of benefit from anti-EGFR therapies is mainly based on the absence of mutations in RAS and BRAF genes, the primary tumor sidedness and microsatellite MSS/MSI status. Molecular hyperselection may optimize the outcome of patients receiving anti-EGFR while detecting additional resistance alterations, both in chemo-naïve and in chemo-refractory settings. AREAS COVERED: Our review focuses on negative molecular hyperselection, both on tissue samples and ctDNA, and the impact of this further patient selection on response rate and survival outcomes. We searched electronic database, selecting relevant English-language publications from 2017 to 2024. EXPERT OPINION: Negative hyperselection beyond RAS and BRAF in advanced colorectal cancer appears to be a powerful tool for predicting outcomes to anti-EGFR therapy and spare patients from unnecessary treatment. This improvement appears in both naïve and pre-treated patients. However, data come mainly from retrospective studies. Therefore, to validate and integrate these findings in the clinical practice, prospective studies should be conducted. It will be interesting to elucidate the role of ctDNA in this setting and the choice of molecular techniques, considering costs and accessibility, to guarantee its implementation in the clinic.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".