AI Article Synopsis

  • Digital PCR (dPCR) improves the detection of low-frequency mutations and can help in early cancer diagnosis, particularly by focusing on major variants associated with pancreatic cancer.
  • A new dPCR method was developed to identify KRAS and GNAS mutations simultaneously using a dual-probe approach, which led to precise quantification of variants even from small DNA amounts (1 to 10 ng).
  • This method demonstrated success in identifying driver mutations in patient samples, including those with minimal tissue, indicating a cost-effective and accurate tool for cancer diagnosis.

Article Abstract

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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Source
http://dx.doi.org/10.1016/j.jmoldx.2023.02.007DOI Listing

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