Efficacy of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) combined with bevacizumab for advanced non-squamous non-small-cell lung cancer patients with gradual progression on EGFR-TKI treatment
Bibliographic record
Abstract
ABSTRACT: Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) significantly improve outcomes of patients with EGFR-mutated non-small-cell lung cancer (NSCLC). However, acquired resistance inevitably emerges and remains a major challenge. The present study aimed to evaluate the efficacy of EGFR-TKIs plus bevacizumab in advanced non-squamous NSCLC patients with gradual progression on EGFR-TKIs.Advanced non-squamous EGFR-mutated NSCLC patients with gradual progression on EGFR-TKIs were administered bevacizumab while EGFR-TKIs were continued until disease progression occurred. Tumor lesions were assessed, and blood samples were collected at the start of the combination treatment and every 6 weeks until disease progression.Among the 15 included patients, there were no grade 3 or higher adverse events (AEs). Partial response (PR) and stable disease (SD) were achieved in 1 and 13 patients, respectively, with an objective response rate (ORR) of 6.7% and a disease control rate (DCR) of 93.3%. The median progression-free survival 2 (PFS2), defined as the time from the initiation of combination treatment to disease progression, was 5.0 (95% confidence interval [CI]: 4.0-6.0) months. Additionally, Spearman correlation analysis revealed that PFS2 was positively correlated with the serum vascular endothelial growth factor (VEGF) level at baseline (r = 0.7212, P = .0234). Patients with high baseline serum VEGF levels showed a better median PFS2 than those with low baseline serum VEGF levels (5.5 months vs 3.6 months, P = .0333).EGFR-TKIs plus bevacizumab led to a durable prolongation of PFS in non-squamous NSCLC patients with gradual progression on EGFR-TKIs. This therapeutic regimen was well tolerated and could be a promising strategy for these patients. Serum VEGF could be a potential biomarker to predict a subset of patients who are likely to benefit from EGFR-TKIs combined with bevacizumab.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".