EGFR-mutated advanced non-small cell lung cancer: new era of systemic therapy towards new standards of care
Bibliographic record
Abstract
This review summarizes current systemic treatment strategies for advanced EGFR-mutated non-small cell lung cancer (NSCLC), with a focus on novel first-line regimens and emerging concepts beyond progression. Evidence from pivotal phase III trials and recent regulatory developments was analyzed, emphasizing efficacy outcomes, safety profiles, and patient selection factors relevant for routine clinical practice. Osimertinib has long been the standard first-line therapy, yet resistance inevitably emerges. The two novel combination strategies, osimertinib plus platinum–pemetrexed chemotherapy (FLAURA2) and amivantamab plus lazertinib (MARIPOSA), have shown superior progression-free and overall survival compared with osimertinib monotherapy. These regimens are associated with higher toxicity, treatment burden, and costs, requiring careful patient selection based on comorbidities, risk features, and preferences. After progression, treatment options remain limited. Current postprogression data are almost exclusively derived from patients previously treated with first-line osimertinib monotherapy, and it remains unclear how outcomes will evolve following upfront combination regimens. Platinum-based chemotherapy remains the historical backbone, while continuation of EGFR inhibition with targeted agents or chemotherapy (e.g., MARIPOSA‑2, COMPEL) and the development of antibody–drug conjugates such as datopotamab deruxtecan represent promising strategies, especially in view of the rapidly changing first-line landscape. Upfront combination regimens have redefined the standard of care in EGFR-mutated advanced NSCLC, but their survival benefit must be weighed against added toxicity, logistical complexity, and financial impact. Expanding therapeutic options make individualized shared decision-making essential to balance efficacy, tolerability, logistics, and patient priorities, aiming to maintain quality of life while extending survival.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".