Uncommon EGFR Compound Mutations in Non-Small Cell Lung Cancer (NSCLC): A Systematic Review of Available Evidence
Bibliographic record
Abstract
Compound epidermal growth factor receptor (EGFR) mutations represent a heterogeneous subgroup of non-small cell lung cancer (NSCLC) patients with uncommon EGFR mutations. We conducted a systematic review to investigate the available data on this patients’ subgroup. Overall, we found a high heterogeneity in the incidence of compound mutations (4–26% of total EGFR mutant cases), which is dependent on the different testing methods adopted and the specific mutations considered. In addition, the relative incidence of distinct compound subclasses identified is reported with extreme variability in different studies. Preclinical and clinical data, excluding de novoEGFR exon 20 p.T790M compound mutations, show good responses with EGFR tyrosine kinase inhibitors (TKIs) (combined common mutations: response rate (RR) ≥ 75% with either first- or second-generation TKIs; combined common plus uncommon: RR 40–80% and 100% with first-generation TKIs and afatinib, respectively; combined uncommon: RR 20–70%, ~80% and ~75% with first-generation TKIs, afatinib and osimertinib, respectively). Overall, data are consistent in supporting the use of EGFR TKIs in treating compound EGFR mutations, taking into account different sensitivity profile of accompanying EGFR mutations for selecting the most adequate EGFR TKI for individual patients.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| 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 | 0.000 |
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".