Prevalence of Molecular Mutations in Non–Small Cell Lung Cancer and Current Treatment Approaches in the MENA Region: Systematic Review and Expert Opinion
Bibliographic record
Abstract
ABSTRACT Over the past decade, the discovery of immunotherapy and targeted therapy has set new standards for the management of advanced non–small cell lung cancer (NSCLC). This study aims to investigate the prevalence of ALK , EGFR , KRAS , ROS1 , MET , BRAF , and HER2 mutations in patients with NSCLC within the Middle East and North Africa (MENA) region and to assess the current state of molecular testing and targeted treatments in the Gulf Cooperation Council (GCC) region. The systematic literature review was performed using PubMed, Google Scholar, and Google searches to identify studies on the prevalence of ALK , EGFR , KRAS , ROS1 , MET , BRAF , and HER2 mutations in patients with NSCLC in the MENA region. Additionally, 10 experts from the GCC region were interviewed to provide insights into molecular mutation testing, the challenges faced, and the current approaches to targeted therapies. The prevalence of ALK , EGFR , KRAS , ROS1 , MET , and BRAF mutations was 7.9% (95% CI, 6.69–9.03%), 24% (95% CI, 22.05–25.41%), 19.7% (95% CI, 15.29–24.07%), 2.2% (95% CI, 0.77–3.57%), 4.7% (95% CI, 2.29–7.07%) and 3.7% (95% CI, 1.54–5.80%), respectively. HER2 mutation data were unavailable. Treatment generally adhered to international guidelines, with therapy selection based on tumor stage, molecular profile, and drug availability. Expert opinions highlighted significant advancements in molecular diagnostics and targeted therapies but also pointed out the challenges in standardizing and implementing these techniques across the GCC region. This review underscores the importance of personalized and region-specific approaches to NSCLC treatment, given the significant differences in mutation patterns in the MENA region. Further research is needed to gain a more comprehensive understanding of the prevalence and effect of driver mutations across broader MENA countries to inform future treatment strategies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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".