Resistance Mutation Profiles Associated with Current Treatments for Epidermal Growth Factor Receptor-Mutated Non-Small-Cell Lung Cancer in the United States: A Systematic Literature Review
Bibliographic record
Abstract
Treatment resistance due to gene alterations remains a challenge for patients with EGFR-mutated advanced or metastatic non-small-cell lung cancer (a/mNSCLC). A systematic literature review (SLR) was conducted to describe resistance mutation profiles and their impact on clinical outcomes in adults with a/mNSCLC in the United States (US). A comprehensive search of MEDLINE and Embase (2018-August 2022) identified 2986 records. Among 45 included studies, osimertinib was the most commonly reported treatment (osimertinib alone: 15 studies; as one of the treatment options: 18 studies), followed by other tyrosine kinase inhibitors (TKIs; 5 studies) and non-TKIs (1 study). For first-line (1L) and second-line (2L) osimertinib, the most frequent EGFR-dependent resistance mechanisms were T790M loss (1L: 15.4%; 2L: 20.5-49%) and C797X mutation (1L: 2.9-12.5%; 2L: 1.4-22%). EGFR-independent mechanisms included MET amplification (1L: 0.6-66%; 2L: 7.2-19%), TP53 mutation (1L: 29.2-33.3%), and CCNE1 amplification (1L: 7.9%; 2L: 10.3%). For patients receiving osimertinib, EGFR T790M mutation loss, EGFR/MET/HER2 amplification, RET fusion, and PIK3CA mutation were associated with worse progression-free survival. Resistance mechanisms resulting from current NSCLC treatments in the US are complex, underscoring the need to address such heterogeneous resistance profiles and improve outcomes for patients with EGFR-mutated a/mNSCLC.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".