Efficacy and Toxicity of Combined Inhibition of EGFR and VEGF in Patients With Advanced Non–small Cell Lung Cancer Harboring Activating EGFR Mutations
Bibliographic record
Abstract
Dual inhibition of epidermal growth factor receptor (EGFR) and vascular endothelial growth factor (VEGF) pathways have demonstrated promising results for treatment of advanced non-small cell lung cancer. We conducted a systematic review and meta-analysis to assess the efficacy and toxicity of the combined treatment with EGFR tyrosine kinase inhibitors (TKIs) and VEGF blockade for patients with advanced non-small cell lung cancer harboring activating EGFR mutations, in comparison to EGFR TKIs alone. The electronic databases were searched for relevant randomized trials between 2000 and 2022. The primary endpoints were overall survival (OS) and progression-free survival. Secondary endpoints included objective response rate (ORR), disease control rate, and grade ≥3 adverse events (AEs). The pooled hazard ratios (HR) and odds ratios were meta-analyzed using the generic inverse variance and the Mantel-Haenszel methods. A total of 1528 patients from 8 trials were evaluated for analyses. The combination treatment decreased the risk of disease progression by 37% (HR=0.63; 95% CI, 0.56 to 0.72) but had no added benefit on OS compared with EGFR inhibition alone (HR=0.90; 95% CI, 0.76 to 1.05). There was no significant difference in objective response rate or disease control rate between treatments. There was a significantly increased number of AEs reported in the dual treatment arm (odds ratio=3.02; 95% CI, 1.71 to 5.31), with proteinuria and hypertension being the most significantly increased AEs. This meta-analysis suggests combined inhibition of EGFR and VEGF pathways significantly improves progression-free survival, with no OS benefit, and increases AEs. Mature OS data are needed along with results from more trials exploring this strategy with third-generation EGFR TKIs to strengthen these results.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.019 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".