Associations Between Genetic Variants and Adverse Effects of Gefitinib in Non-small Cell Lung Cancer: A Systematic Review
Bibliographic record
Abstract
Introduction: Lung cancer remains the leading cause of cancer-related death worldwide, with non–small cell lung cancer (NSCLC) accounting for approximately 85% of cases. Gefitinib is a tyrosine kinase inhibitor frequently used in NSCLC with favorable outcome. However, many patients develop severe adverse effects which might be influenced by genetic variability. Therefore, we aim to systematically review the gene variants and its association with adverse effects of gefitinib in NSCLC patients. Methods: A systematic search was conducted according to PRISMA guidelines across PubMed, Scopus, and Cochrane. Studies investigating the association between genetic variations with adverse effects following gefitinib in NSCLC were included. Extracted data encompassed study and patient characteristics, adverse effects, and identified gene variations. Risk of bias was assessed using the RoB-2 for randomized trials and Newcastle–Ottawa Quality Assessment Scale for cohort and case–control studies. Results: Nineteen studies involving 2.087 patients were included, with Japanese populations being the most studied. Polymorphisms in EGFR and ABCG2 were among the most studied genes. Rash, diarrhea, and hepatotoxicity are the most common adverse effects reported. Poor metabolizers of CYP2D6 and CYP3A53/3, and variations in ABCG2, ABCB1, and EGFR were associated with higher incidence of adverse effects. However, several studies demonstrated no associations between gene variations with adverse effects. Conclusion: Genetic variations in ABCG2, ABCB1, CYP2D6, CYP3A53/3, and EGFR may influence gefitinib-associated adverse effects, highlighting the need of pharmacogenomic testing to guide personalized treatment and improved patient safety. Keywords: Pharmacogenomics, Genetic Variants, Gefitinib, Non-Small Cell Lung Cancer, Adverse Effects
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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".