Efficacy and safety of crizotinib in the treatment of advanced non-small cell lung cancer with ROS1 gene fusion: a systematic literature review and meta-analysis of real-world evidence
Bibliographic record
Abstract
Background: Crizotinib was approved to treat patients with advanced non-small cell lung cancer (aNSCLC) with ROS proto-oncogene 1 (ROS1) gene fusion in 2016.We conducted a systematic literature review to identify realworld evidence (RWE) studies and estimated the efficacy and safety of crizotinib using meta-analyses (MA) for objective response rate (ORR), real-world progression-free survival (PFS), and overall survival (OS).Methods: We searched MEDLINE®, Embase, and Cochrane CENTRAL from January 2016 to March 2023 using Ovid® for published single-arm or comparative RWE studies evaluating patients (N ≥ 20) receiving crizotinib monotherapy for aNSCLC with ROS1 gene fusion.Pooled estimates for ORR and grade 3/4 adverse events (AEs) were derived using the metafor package in R while pooled estimates for median real-world PFS (rwPFS) and OS were derived using reconstructed individual patient data from published Kaplan-Meier curves.The primary analysis included all studies regardless of crizotinib line of therapy; a subgroup analysis (SA) was conducted using studies evaluating patients receiving first-line crizotinib.Results: Fourteen studies met the eligibility criteria and were considered feasible for MA.For the primary analysis, the pooled ORR (N = 9 studies) was 70.6 % (95 % confidence interval [CI]: 57.0, 81.3), median rwPFS was 14.5 months (N = 11 studies), and OS was 40.2 months (N = 9 studies).In the SA, the pooled ORR (N = 4 studies) was 81.1 % (95 % CI: 76.1, 85.2) and the median rwPFS (N = 4 studies) and OS (N = 2 studies) were 18.1 and 60 months, respectively.All MAs were associated with significant heterogeneity (I 2 > 25 %).Grade 3/4 AEs occurred in 18.7 % of patients (pooled estimate).Conclusion: The results from this study are consistent with clinical trial data and, taken collectively, supports crizotinib as a safe and effective treatment across different lines of therapy in patients with ROS1 aNSCLC in the real-world 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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".