Recent Advances in the Management of EGFR-Mutated Advanced Non-Small Cell Lung Cancer—A Narrative Review
Bibliographic record
Abstract
The treatment landscape for EGFR-mutated metastatic non-small cell lung cancer (mNSCLC) has evolved significantly with multiple combination regimens demonstrating superiority over single agent Osimertinib over the past two years. Recent trials such as FLAURA2 and MARIPOSA have explored intensified front-line regimens, with FLAURA2 demonstrating improvement in PFS with the addition of chemotherapy to Osimertinib and MARIPOSA, showing both a PFS and OS benefit with a novel combination regimen of Amivantamab and Lazertinib. However, these regimens are associated with significantly higher toxicity to patients and pose a huge financial and logistical burden to the health care system; therefore, treatment selection must therefore be individualized, considering disease biology, patient fitness, and toxicity burden. Post-progression strategies remain challenging due to resistance mechanisms like EGFR C797S mutations and MET amplification and the lack of data post-progression on novel first-line combinations. Ongoing trials are investigating fourth-generation EGFR TKIs, MET inhibitors, antibody-drug conjugates, and bispecific antibodies in subsequent lines. While regimens like Amivantamab-Lazertinib show promise even in second-line settings, toxicity, cost, and access remain barriers. As therapeutic options expand, biomarker-driven sequencing and personalized care will be critical to optimizing long-term outcomes in EGFR-mutated mNSCLC.
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 | 0.002 | 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) | 0.004 | 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".