Combination of immune checkpoint inhibitors with multi-targeted tyrosine kinase inhibitors for second- or later-line therapy of non-small cell lung cancer: a systematic review and meta-analysis
Why this work is in the frame
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Bibliographic record
Abstract
Background: Second- or later-line therapy for patients with advanced non-small cell lung cancer (NSCLC) is highly individualized. Combining immune checkpoint inhibitors (ICIs) with multi-targeted tyrosine kinase inhibitors (multi-TKIs) has emerged as a chemotherapy-free option for these patients. We aim to provide a comprehensive overview of the efficacy and safety of the treatment. Methods: We systematically searched four databases for studies evaluating ICIs combined with multi-TKIs in second- or later-line therapy for NSCLC. Data were extracted and study quality was assessed using the Canadian Institute of Health Economics tool for case series. A systematic review and meta-analysis were conducted for efficacy outcomes. Results: Twenty studies (10 prospective and 10 retrospective) were included from 155 retrieved articles. Nineteen studies were conducted in China, with programmed death receptor 1 (PD-1) antibodies and anlotinib as the most frequently used combination. The single-arm meta-analysis showed that the pooled median progression-free survival (mPFS) was 5.74 months [95% confidence interval (CI): 4.65-6.84], and the median overall survival was 15.41 months (95% CI: 13.40-17.41). The objective response rate was 26.35% (95% CI: 19.52-33.18%), and the disease control rate was about 80.73% (95% CI: 75.59-85.86%). For patients with EGFR/ALK/ROS1 mutations, the mPFS was 3.17 months (95% CI: 2.54-3.79). The most commonly reported severe adverse events across the included studies were hypertension, fatigue, hepatic dysfunction, urinary abnormalities, and hand-foot syndrome. Conclusions: The combination of ICIs and multi-TKIs offers an alternative chemotherapy-free treatment option for patients with advanced NSCLC in the second- or later-line setting.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it