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
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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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".