Mechanisms of Innate and Acquired Resistance to Anti-EGFR Therapy: A Review of Current Knowledge with a Focus on Rechallenge Therapies
Bibliographic record
Abstract
Abstract Innate and acquired resistance to anti-EGFR therapy (EGFRi) is a major limitation in the treatment of metastatic colorectal cancer (mCRC). Although RAS genes are the most commonly mutated innate and acquired oncogenes in cancer, there are a number of other mechanisms that limit the effectiveness of EGFRi. Patients with innate resistance have been found to contain BRAFV600E mutations, and possibly MET, MEK, PIK3CA, PTEN, and HER2 alterations. Meanwhile, BRAFV600E mutations may also be involved in acquired resistance to EGFRi, in addition to EGFR ectodomain mutations, MET alterations, and possibly HER2 amplification. In addition, paracrine effects and cell-fate mechanisms of resistance are being increasingly described as contributing to acquired resistance. Utilization of circulating tumor DNA has been paramount in monitoring the dynamic nature of acquired resistance and has helped to guide treatment decisions, particularly in the EGFRi rechallenge setting. Herein, we provide an in-depth review of EGFRi-resistance mechanisms and describe the current therapeutic landscape in the hopes of identifying effective rechallenge strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".