Efficacy and toxicity of combined inhibition of EGFR and VEGF in patients with advanced non-small-cell lung cancer harboring activating EGFR mutations: A systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Purpose: 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 (NSCLC) harboring EGFR mutations. We conducted a systematic review and meta-analysis to assess the efficacy and toxicity of combined treatment with EGFR tyrosine kinase inhibitors (TKIs) and VEGF blockade.Methods: The electronic databases PubMed, Cochrane Library and EMBASE, were searched for relevant randomized trials between 2000 and 2020. The primary endpoints were overall survival (OS) and progression-free survival (PFS). Secondary endpoints included objective response rate (ORR), disease control rate (DCR), and adverse events (AEs). Pooled hazard ratios for OS and PFS, and odds ratios for ORR, DCR and toxicity were meta-analyzed using the generic inverse variance and the Mantel-Haenszel methods. Subgroup analyses compared PFS by gender, age, smoking status, EGFR mutation, intra-cranial disease and ECOG status.Results: A total of 1,246 patients from 6 trials were evaluated for analyses. Dual treatment decreased the risk of disease progression by 38%, but had no added benefit on OS compared to EGFR inhibition alone. There was no significant difference in ORR or DCR between treatments and more AEs reported in the dual treatment arm. The PFS benefit was consistent across all subgroups.Conclusions: This meta-analysis suggests combined inhibition of EGFR and VEGF pathways significantly improves PFS, with no interim OS benefit, and increases AEs. Mature OS data are needed along with results from trials exploring this strategy with 3rd generation 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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.004 | 0.005 |
| 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".