Management of Egfr-Mutated Non-Small-Cell Lung Cancer: Practical Implications from a Clinical and Pathology Perspective
Bibliographic record
Abstract
Starting in the early 2000s, non-small-cell lung cancer (nsclc) subtypes have evolved from being histologically described to molecularly defined. Management of lung adenocarcinomas now generally requires multiple molecular tests at baseline to define the optimal treatment strategy. More recently, second biopsies performed at progression in patients treated with tyrosine kinase inhibitors (tkis) have further defined the continued use of molecularly targeted therapy. In the present article, we focus on one molecular subtype: EGFR-mutated nsclc. For that patient population, multiple lines of tki therapy are now available either clinically or in clinical trials. Each line of treatment is guided by the specific mutations (for example, L858R, T790M, C797S) identified in EGFR. We first describe the various mechanisms of acquired resistance to EGFR tki treatment. We then focus on strategies that clinicians and pathologists can both use during tissue acquisition and handling to optimize patient results. We also discuss future directions for the molecular characterization of lung cancers with driver mutations, including liquid biopsies. Finally, we provide an algorithm to guide treating physicians managing patients with EGFR-mutated nsclc. The same framework can also be applied to other molecularly defined nsclc subgroups as resistance patterns are elucidated and additional lines of treatment are developed.
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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 | 0.001 |
| 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".