Safety and Efficacy of Osimertinib in Patients With Non–Small-Cell Lung Cancer and Uncommon Tumoral Epidermal Growth Factor Receptor Mutations: A Systematic Review and Single-Arm Meta-Analysis
Bibliographic record
Abstract
PURPOSE The activity of osimertinib is not fully characterized in non–small-cell lung cancer (NSCLC) with uncommon epidermal growth factor receptor ( EGFR ) mutations. Therefore, we conducted a systematic review and meta-analysis to assess the safety and efficacy of osimertinib in patients with NSCLC harboring uncommon somatic EGFR mutations. METHODS PubMed, Embase, and the Cochrane Library were searched for eligible studies reporting the efficacy and safety of osimertinib in NSCLC with uncommon EGFR mutations defined as any mutations other than exon 19 deletion, L858R and T790M mutations, and exon 20 insertion, except when in compound. Then, we performed a meta-analysis to pool survival outcomes and antitumoral activity, including intracranial (ic) response and adverse events. RESULTS Fifteen studies comprising 594 patients were included. The most frequently observed uncommon solitary mutations were G719X in 25% (81/327) of patients and L861Q in 21% (69/327). The most common compound mutations were G719X with T790M in 12% (23/192) of patients and G719X with S768I in 11% (22/192). Pooled analysis showed an objective response rate (ORR) of 51.30% (95% CI, 45.80 to 56.81), a disease control rate (DCR) of 90.11% (95% CI, 86.27 to 92.96), a median progression-free survival of 9.71 months (95% CI, 7.96 to 11.86), and a median overall survival of 16.79 months (95% CI, 9.93 to 28.39). icORR was 45.96% (95% CI, 30.18 to 62.17), and icDCR was 95.76% (95% CI, 69.84 to 100). Osimertinib was well tolerated with a frequency of grade 3 or more adverse events of 21.77% (95% CI, 6.24 to 43.33). CONCLUSION Osimertinib demonstrated robust response in NSCLC harboring uncommon EGFR mutations, without unanticipated safety concerns.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".