Toward precision medicine with next-generation EGFR inhibitors in non-small-cell lung cancer
Bibliographic record
Abstract
The use of genomics to discover novel targets and biomarkers has placed the field of oncology at the forefront of precision medicine. First-generation epidermal growth factor receptor (EGFR) inhibitors have transformed the therapeutic landscape of EGFR mutant non-small-cell lung carcinoma through the genetic stratification of tumors from patients with this disease. Somatic EGFR mutations in lung adenocarcinoma are now well established as predictive biomarkers of response and resistance to small-molecule EGFR inhibitors. Despite early patient benefit, primary resistance and subsequent tumor progression to first-generation EGFR inhibitors are seen in 10%-30% of patients with EGFR mutant non-small-cell lung carcinoma. Acquired drug resistance is also inevitable, with patients developing disease progression after only 10-13 months of antitumor therapy. This review details strategies pursued in circumventing T790M-mediated drug resistance to EGFR inhibitors, which is the most common mechanism of acquired resistance, and focuses on the clinical development of second-generation EGFR inhibitors, exemplified by afatinib (BIBW2992). We discuss the rationale, mechanism of action, clinical efficacy, and toxicity profile of afatinib, including the LUX-Lung studies. We also discuss the emergence of third-generation irreversible mutant-selective inhibitors of EGFR and envision the future management of EGFR mutant lung adenocarcinoma.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".