PO.ET06.05 · 实验与分子治疗
基于扩增子的DNA与RNA NGS联合检测相较于杂交捕获DNA-NGS在中国NSCLC驱动基因SNV/INDEL分析中的性能比较
Comparative performance of amplicon-based DNA and RNA NGS co-detection versus hybrid-capture DNA-NGS for SNV/INDEL profiling in Chinese NSCLC driver genes
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摘要 Abstract
中文摘要
背景：尽管在非小细胞肺癌（NSCLC）中，基于扩增子的DNA+RNA NGS联合检测在识别基因融合方面已显示出相较于杂交捕获DNA-NGS的明显优势，但单核苷酸变异（SNV）和小片段插入/缺失（INDEL）在NSCLC中的临床相关性对于精准肿瘤学同样至关重要。然而，基于扩增子的DNA+RNA NGS联合检测在检测NSCLC驱动基因SNV/INDEL方面相较于杂交捕获DNA-NGS是否提供相当或更优的性能，尚未得到系统评估。
方法：我们对19个临床相关NSCLC驱动基因的SNV和INDEL检测进行了比较分析，这些基因包括AKT1、ALK、ARAF、BRAF、EGFR、ERBB2、FGFR1/2、HRAS、IDH1/2、KIT、KRAS、MAP2K1、MET、MTOR、NRAS、NTRK、PDGFRA、PIK3CA和RET。将一项35基因、基于扩增子的DNA+RNA NGS联合检测（n = 2,242）所产生的检出率与使用杂交捕获DNA-NGS（n = 2,504）所获得的检出率进行比较。
结果：与杂交捕获DNA-NGS相比，基于扩增子的DNA+RNA NGS联合检测对EGFR（50.68% vs 45.73%，P < 0.05）、KRAS（12.25% vs 10.78%，P < 0.05）和KIT（0.40% vs 0.08%，P < 0.05）表现出显著更高的检出率。EGFR检出率的增加主要由EGFR L858R识别的改善所驱动（23.39% vs 20.01%，P < 0.05）。对于KRAS，差异主要由KRAS G12C（4.12% vs 3.75%）和G12D（2.43% vs 1.80%）贡献，尽管这些差异未达到统计学意义。相比之下，杂交捕获DNA-NGS对PIK3CA显示出显著更高的检出率（6.11% vs 4.51%，P < 0.05）。对于所有其余基因，包括AKT1（0.32% vs 0.28%）、ALK（0.11% vs 0.12%）、ARAF（0.12% vs 0.12%）、BRAF（2.89% vs 2.6%）、ERBB2（2.98% vs 3%）、FGFR2（0.08% vs 0.08%）、FGFR3（0.21% vs 0.16%）、HRAS（0.08% vs 0.16%）、IDH1（0.28% vs 0.28%）、IDH2（0.12% vs 0.12%）、MAP2K1（0.31% vs 0.48%）、MET（0.52% vs 0.52%）、MTOR（0.26% vs 0.32%）、NRAS（0.41% vs 0.52%）、NTRK（0.01% vs 0.04%）、PDGFRA（0.12% vs 0.16%）、RET（0.1% vs 0.12%），两个平台之间的突变检出率均无显著差异。
结论：这项大队列比较表明，基于扩增子的DNA+RNA NGS联合检测在大多数NSCLC驱动基因SNV/INDEL改变方面的表现与杂交捕获DNA-NGS相当，同时对EGFR、KRAS和KIT中关键突变提供显著更高的敏感性。这些发现提示，整合DNA和RNA的联合检测流程可能提高关键可干预NSCLC生物标志物的突变检测效率，支持其在常规临床基因组分析中的更广泛采用。
查看英文原文 English abstract
Background: Although amplicon-based DNA+RNA NGS co-detection has demonstrated clear advantages over hybrid-capture DNA-NGS for identifying gene fusions in non-small cell lung cancer (NSCLC), the clinical relevance of single-nucleotide variants (SNVs) and small insertions/deletions (INDELs) in NSCLC remains equally critical for precision oncology. However, whether amplicon-based DNA+RNA NGS co-detection provides comparable or improved performance for detecting NSCLC driver-gene SNVs/INDELs relative to hybrid-capture DNA-NGS has not been systematically evaluated.
Methods: We performed a comparative analysis of SNV and INDEL detection across 19 clinically relevant NSCLC driver genes, including AKT1, ALK, ARAF, BRAF, EGFR, ERBB2, FGFR1/2, HRAS, IDH1/2, KIT, KRAS, MAP2K1, MET, MTOR, NRAS, NTRK, PDGFRA, PIK3CA, and RET. Detection rates generated by a 35-gene amplicon-based DNA+RNA NGS co-detection assay (n = 2,242) were compared with those obtained using hybrid-capture DNA-NGS (n = 2,504).
Results: Amplicon-based DNA+RNA NGS co-detection demonstrated significantly higher detection rates for EGFR (50.68% vs 45.73%, P < 0.05), KRAS (12.25% vs 10.78%, P < 0.05), and KIT (0.40% vs 0.08%, P < 0.05) compared with hybrid-capture DNA-NGS. The increase in EGFR detection was primarily driven by improved identification of EGFR L858R (23.39% vs 20.01%, P < 0.05). For KRAS, differences were mainly contributed by KRAS G12C (4.12% vs 3.75%) and G12D (2.43% vs 1.80%), though these did not reach statistical significance. In contrast, hybrid-capture DNA-NGS showed a significantly higher detection rate for PIK3CA (6.11% vs 4.51%, P < 0.05). For all remaining genes, including AKT1(0.32% vs 0.28%), ALK(0.11% vs 0.12%), ARAF(0.12% vs 0.12%), BRAF(2.89% vs 2.6%), ERBB2(2.98% vs 3%), FGFR2(0.08% vs 0.08%), FGFR3(0.21% vs 0.16%), HRAS(0.08% vs 0.16%), IDH1(0.28% vs 0.28%), IDH2(0.12% vs 0.12%), MAP2K1(0.31% vs 0.48%), MET(0.52% vs 0.52%), MTOR(0.26% vs 0.32%), NRAS(0.41% vs 0.52%), NTRK(0.01% vs 0.04%), PDGFRA(0.12% vs 0.16%), RET(0.1% vs 0.12%), the mutation detection rates showed no significant difference between the two platforms.
Conclusion: This large-cohort comparison demonstrates that amplicon-based DNA+RNA NGS co-detection performs comparably to hybrid-capture DNA-NGS for most NSCLC driver-gene SNV/INDEL alterations, while offering significantly higher sensitivity for critical mutations in EGFR, KRAS, and KIT. These findings indicate that co-detection workflows integrating both DNA and RNA may improve mutation detection efficiency for key actionable NSCLC biomarkers, supporting their broader adoption in routine clinical genomic profiling.
利益披露 Disclosure
Z. Xie, None..
Y. Zhao, None..
H. Li, None..
Y. Li, None..
Y. Chen, None..
M. Liu, None..
X. Li, None.