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INTERNATIONAL CONGRESS OF

CancerGenetics

• Comprehensive Review and Meta-Analysis of Next-Generation Sequencing Applications in Lung Cancer: Focus on Mutation Identification

• Farzaneh Kashef,¹ Tina Abdollahi,² Hanieh-Sadat Mostafavi-Fini,³ Mohammad Fazel Mollaverdi,⁴ Zahra Zakhmi-Alishah ,⁵ Shahla Mohammad Ganji,^6,*
  1. National Institute of Genetic Engineering and Biotechnology (NIGEB)
  2. National Institute of Genetic Engineering and Biotechnology (NIGEB)
  3. National Institute of Genetic Engineering and Biotechnology (NIGEB)
  4. National Institute of Genetic Engineering and Biotechnology (NIGEB)
  5. National Institute of Genetic Engineering and Biotechnology (NIGEB)
  6. National Institute of Genetic Engineering and Biotechnology (NIGEB)
  
  
  
• Introduction: Lung cancer is one of the most common causes of cancer-related mortality in the world.Despite extensive research into molecular diagnostic methods, there are barriers such as the lack of sufficient tissue samples for genetic profiling. The main purpose of this study was to analyze the efficiency of next generation sequencing technology(NGS) in lung cancer, to identify patterns of genetic mutations in a variety of clinical samples and to investigate the relationship between them and clinical outcomes.
• Methods: A systematic review was conducted using 36 relevant articles from credible databases, including Google Scholar, PubMed, and ScienceDirect, published between 2015 and 2025. Search terms encompassed "NGS","NextSeq","Lung Cancer","Diagnosis","Detection" and "Mutation."
• Results: Key genes—TP53, EGFR, KRAS, PIK3CA, and ALK—were identified as the most frequently mutated in lung cancer. Mutation frequencies revealed TP53 with a range of 25%-93.2% (average 54.33%), EGFR ranging from 8%-95.7% (average 41.42%), KRAS spanning 3%-30% (average 15.49%), PIK3CA between 1.3%-59% (average 5.70%), and ALK varying from 1%-83.3% (average 9.94%). Furthermore, the Sel-CapTM digital enrichment panel demonstrated remarkable sensitivity for EGFR mutations in circulating free DNA (cfDNA), predicting disease progression approximately five months earlier than conventional diagnostic approaches.
• Conclusion: Lung cancer is among the cancers that widely benefit from NGS due to its high mutation burden and high number of mutations. NGS is superior to methods such as qRT-PCR because it provides information about mutation sequences and allele frequencies in addition to detecting mutations outside of hotspot regions. This technology plays a key role by developing various assays using liquid biopsy samples for initial diagnosis, treatment selection, minimal disease diagnosis, treatment effectiveness monitoring, and tumor burden assessment in lung cancer. This review article pays special attention to the importance of the role of NGS in the management and accurate identification of lung cancer.
• Keywords: NGS, lung cancer, mutation, detection, TP53