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Multiplex Digital PCR Assay to Detect Multiple KRAS and GNAS Mutations Associated with Pancreatic Carcinogenesis from Minimal Specimen Amounts

GNAS复合轨迹 克拉斯 数字聚合酶链反应 放大器 多路复用 胰腺癌 癌变 分子生物学 生物 突变 底漆(化妆品) 遗传学 癌症研究 病理 癌症 聚合酶链反应 医学 基因 化学 有机化学
作者
Chiho Maeda,Yoshio Ono,Akira Hayashi,Kenji Takahashi,Kenzui Taniue,Rika Kakisaka,Masayuki Mori,Takahiro Ishii,Hiroki Sato,Tetsuhiro Okada,Hidemasa Kawabata,Takuma Goto,Nobue Tamamura,Yuko Omori,Kuniyuki Takahashi,Akio Katanuma,Hidenori Karasaki,Andrew S. Liss,Yusuke Mizukami
出处
期刊:The Journal of Molecular Diagnostics [Elsevier BV]
卷期号:25 (6): 367-377 被引量:3
标识
DOI:10.1016/j.jmoldx.2023.02.007
摘要

Digital PCR (dPCR) allows for highly sensitive quantification of low-frequency mutations and facilitates early detection of cancer. However, low-throughput targeting of single hotspots in dPCR hinders variant specification when multiple probes are used. We developed a dPCR method to simultaneously identify major variants related to pancreatic carcinogenesis. Using a two-dimensional plot of droplet fluorescence under the optimized concentration of two fluorescent probe pools, the absolute quantification of different KRAS and GNAS variants was determined. Successful detection of the multiple driver mutations was verified in 24 surgically resected tumor samples from 19 patients and 22 fine-needle aspiration samples from patients with pancreatic ductal adenocarcinoma. Precise quantification of the variant allele frequency was optimized by using template DNA at a concentration as low as 1 to 10 ng. Furthermore, amplicons targeting multiple hotspots were successfully enriched with fewer false-positive findings using high-fidelity polymerase, allowing for the detection of various KRAS and GNAS mutations with high probability in small amount of cell/tissue specimens. Using this target enrichment, mutations at a rate of 90% in small residual tissues, such as the fine-needle aspiration needle flush and microscopic lesions in resected specimens, were successfully identified. The proposed method allows for low-cost, accurate detection of driver mutations to diagnose cancers, even with minimal tissue collection.

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