Retreatment with EGFR inhibitor in non-small cell lung cancer patients previously exposed to EGFR-TKI: A systematic review and meta-analysis.
Bibliographic record
Abstract
e21159 Background: Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI) are the standard therapy for EGFR-mutated non-small cell lung cancer (NSCLC) patients. Unfortunately, patients eventually develop resistance to EGFR-TKI and disease progression. Re-exposure after a drug-free interval may be an alternative to overcome tumor resistance. We performed a systematic review and meta-analysis to assess EGFR-TKI retreatment’s efficacy in advanced NSCLC. Methods: We systematically searched PubMed, Embase, and Cochrane databases for clinical trials and observational cohort studies evaluating EGFR-TKI retreatment in advanced NSCLC patients. We aimed to assess the objective response rate (ORR), disease control rate (DCR), and survival outcomes. Subgroup analyses were performed according to the type of EGFRmutation and the TKI drug used in retreatment. We further stratified studies to assess the efficacy of rechallenging with the same drug used initially or a different one. Heterogeneity was assessed using the Cochran Q test, and I2 statistics and random effects models were fitted. Results: We included 16 studies (7 prospective clinical trials, and 9 retrospective cohorts) with 806 patients. Most of them had adenocarcinoma (70.8%), were females (58.1%), and were non-smokers (74.5%). The most frequently TKI given as the initial treatment was gefitinib (58.1%), whereas in the rechallenge it was erlotinib (36.6%) followed by gefitinib (31.6%). In a pooled analysis of patients who were retreated with TKI, the median PFS was 4.1 months (95%CI 3.0 - 4.4), and OS was 12.6 months (95%CI 10.2 - 12.6). ORR was 16% (95%CI 10 - 22%) and DCR was documented in 63% (95%CI 0.54 - 0.72%). Patients harboring a sensitive EGFR mutation had a significantly higher DCR, compared to patients with EGFRT790M mutation, and those with unknown mutational status, (DCR: 70%, 62%, and 48%, respectively, p = 0.02). Patients rechallenged with gefitinib, erlotinib, or afatinib had a similar DCR of 60%, while patients re-exposed to osimertinib had a greater DCR of 70% (95%CI 59 - 80%), (p < 0.01). Regarding ORR, no significant difference was observed amongst the groups defined by the type of EGFR mutation (p = 0.74) or type of TKI used (p = 0.05). Rechallenge using the same versus a different TKI resulted in similar ORR and DCR. In a subgroup analysis of 102 patients who had disease control with the first TKI, 62% (95%CI 45 – 79%) achieved disease control with TKI rechallenge. Conclusions: Our meta-analysis suggests that a subgroup of advanced EGFR-mutated NSCLC patients who failed TKI treatment benefit from rechallenge with an EGFR-TKI after a TKI-free interval. Re-exposure with either the same or a different TKI was shown to be equally effective. Patients treated with osimertinib and those with EGFR-sensitive mutations have better responses to the treatment.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| 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".