Mechanisms of resistance to EGFR tyrosine kinase inhibitors: implications for patient selection and drug combination strategies
Bibliographic record
Abstract
The receptor for epidermal growth factor (EGFR, ErbB1, HER1) supports the growth and maintenance of a broad range of human tumor types, and EGFR-targeting drugs are approved for the treatment of several advanced stage cancers, including non-small cell lung cancer (NSCLC), pancreatic cancer, squamous cell cancer of the head and neck (SCCHN), and colorectal cancer. Recent years have witnessed significant advances in our understanding of dysregulated signal transduction in cancer cells resulting from changes in the expression and/or mutational status of key signaling molecules that modulate sensitivity to drugs targeting EGFR. Based on this knowledge, we have an exciting opportunity to maximize the benefit provided to cancer patients by EGFR inhibitors. In this review article, we describe molecular determinants of sensitivity or resistance to EGFR-targeted agents, with specific emphasis on EGFR tyrosine kinase inhibitors (TKIs). The impact of these findings on our ability to evaluate candidate predictive biomarkers and to design robust mechanism-based combination strategies is also discussed.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".