Real-world application of targeted next-generation sequencing for identifying molecular variants in Asian non-small-cell lung cancer

Abstract

Background: The advent of novel therapeutic agents has advanced biomarker characterization in non-small-cell lung cancer (NSCLC), driving increased adoption of next-generation sequencing (NGS) technologies for molecular testing. However, comprehensive data addressing the clinical utility of different NGS platforms for NSCLC remains limited.

Methods: This retrospective study analyzed real-world data from 478 Taiwanese NSCLC patients over five years, using the Oncomine Focus Assay (OFA) to assess genetic alterations. The evaluation focused on assay accuracy, limit of detection (LoD), sequencing performance, and the genetic landscape of NSCLC.

Results: The OFA achieved an NGS success rate of 80.5% (385/478), with tumor cell percentage, specimen source and FFPE block age identified as key factors affecting success. Quality metrics demonstrated robust sequencing performance, including 97.0 ± 9.6% on-target alignment, 94.7 ± 6.4% uniformity, and ≥ 500 × coverage for 98.0 ± 6.6% of amplicons. Among the 385 patients analyzed, 86.8% (334/385) were found to harbor pathogenic or likely pathogenic variants, of which 78.4% (262/334) were SNVs/Indels, 41.6% (139/334) were CNVs, 2.7% (9/334) were exon skipping alterations, and 10.2% (34/334) were gene fusions. Actionable driver mutations included EGFR mutations (46.2%, 178/385), KRAS mutations (9.4%, 36/385), ERBB2 mutations (6.8%, 26/385), ALK fusions (4.4%, 17/385), MET exon 14 skipping (2.3%, 9/385), BRAF mutations (2.3%, 9/385), ROS1 and RET fusions (1.8%, 7/385 each), and NTRK1 fusions (0.5%, 2/385). Notably, KRAS G12 C mutation was detected in 2.8% (11/385) of cases.

Conclusions: This study demonstrates the robust performance of the OFA in identifying clinically relevant genetic alterations in NSCLC. The findings support its clinical utility in precision oncology and provide valuable insights into the genetic landscape of Asian NSCLC, enhancing personalized treatment strategies for lung cancer patients.

Keywords: Actionable driver mutations; Non-small-cell lung cancer; Real-world application; Targeted next-generation sequencing.

Fig. 1

Average depth of coverage across 269 amplicons in clinical samples analyzed with the OFA. The X-axis represents the different amplicons, which are arranged in ascending order of average coverage (from left to right). The lowest average coverage observed was 1,266×, with approximately 96% of amplicons achieving an average coverage of at least 2,000×. The data are presented as means ± SDs. The amplicon IDs are listed in Supplementary Table 4

Fig. 2

Distribution of clinically relevant variants in NSCLC patients. A Proportion of NSCLC samples with different types of genetic alterations, including single nucleotide variants (SNVs) and insertions and deletions (Indels), copy number variations (CNVs), MET exon skipping alterations, and gene fusions. B Prevalence of actionable driver mutations in NSCLC

Fig.3

Genomic landscape of SNVs and Indels in NSCLC patients. A Oncoprint diagram showing the distribution of SNVs and Indels in the NSCLC patient cohort. The X-axis represents individual patient samples, and the Y-axis lists the mutated genes. Green indicates missense mutations, blue indicates Indels, pink represents complex mutations, and gray denotes no alterations. B Variant allele frequency (VAF) distribution for clinically relevant SNVs and Indels in the patient cohort. The X-axis shows the gene variant names, and the Y-axis represents the allele frequencies, presented as means ± standard deviations (SDs)

Fig. 4

Genomic landscape of CNVs and gene fusions in NSCLC patients. A Oncoprint diagram showing the distribution of copy number variations (CNVs) in the NSCLC patient cohort. The X-axis represents individual patient samples, while the Y-axis lists the affected genes. Red indicates gene amplifications, and gray denotes no alterations. B Distribution of gene fusions in the NSCLC patient cohort. The X-axis represents individual patient samples, and the Y-axis shows the fusion variants. Purple represents structural variants, pink denotes exon skipping, and gray indicates no alterations