Prevalence of highly actionable mutations among Indian patients with advanced non-small cell lung cancer: A systematic review and meta-analysis
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Updated: Sep 21, 2025

Next Generation Sequencing for the Detection of Actionable Mutations in Solid and Liquid Tumors
Published on: September 20, 2016
Ruchir Raman1,2, Varun Ramamohan3, Anurag Rathore1,2
1Centre of Excellence for Biopharmaceutical Technology, Indian Institute of Technology, Delhi, India.
Background:
Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality in India. To clarify rates of actionable mutations, and thereby identify opportunities to improve the delivery of best available care for a large volume of patients, a comprehensive review of available data is warranted.
Methods:
Studies that reported prevalence of any actionable gene variant among adult Indian patients with advanced NSCLC were selected from three databases (PubMed, EMBASE, and Cochrane Library). Ranges in actionable variant prevalence were reported. Meta-analysis of proportions was completed among studies specifically evaluating mutational prevalence within ALK or EGFR. Sensitivity analyses were undertaken among populations sharing high heterogeneity.
Results:
Twenty-six studies were selected. Ranges in actionable mutational prevalence among NSCLC patients were as follows: ALK: 4.1-21.4%, BRAF: 1.5-3.5%, EGFR: 11.9-51.8%, HER2: 0-1.5%, KRAS: 4.5-6.4%, NTRK: 0-.7%, and ROS-1: 3.5-4.1%. Following sensitivity analysis, pooled ALK mutational prevalence rates were 8.3% (95% CIs: 6.6-10.4%) and 4.01% (95% CIs: 2.3-7.0) for adenocarcinoma and NSCLC patients, respectively. Pooled EGFR mutational prevalence rates were 28.7% (95% CIs: 23.5-34.6%) and 24.2% (95% CIs: 19.9-29.1%) for adenocarcinoma and NSCLC patients, respectively.
Conclusions:
Nearly 40% of Indian patients with advanced adenocarcinoma and 30% with NSCLC share an actionable mutation in ALK or EGFR. Approximately one-half of adenocarcinoma patients have an actionable variant. Efforts should be directed toward efficiently identifying candidates for targeted agents and delivering such treatments.
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