ArticleBMC cancer2019
Technical advance in targeted NGS analysis enables identification of lung cancer risk-associated low frequency TP53, PIK3CA, and BRAF mutations in airway epithelial cells.
Article in BMC cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 23 citations in OpenAlex.
- Premalignant Progression in the Lung: Knowledge Gaps and Novel Opportunities for Interception of Non-Small Cell Lung Cancer. An Official American Thoracic Society Research Statement.American journal of respiratory and critical care medicine · 2024Review
- Leveraging the fundamentals of heat transfer and fluid mechanics in microscale geometries for automated next-generation sequencing library preparation.Scientific reports · 2024Article
- Internal Standards for Limit Controls and Absolute Abundance Measurement of Oncogenic Fusions and Mutations.The journal of applied laboratory medicine · 2024Article
- Extrinsic and intrinsic preanalytical variables affecting liquid biopsy in cancer.Cell reports. Medicine · 2023Review
- TP53 mutation prevalence in normal airway epithelium as a biomarker for lung cancer risk.BMC cancer · 2023Article
- FDA-led consortium studies advance quality control of targeted next generation sequencing assays for precision oncology.Precision cancer medicine · 2021Article
- Advancing NGS quality control to enable measurement of actionable mutations in circulating tumor DNA.Cell reports methods · 2021Article
- Next-generation sequencing-based identification of EGFR and NOTCH2 complementary mutations in non-small cell lung cancer.Oncology letters · 2021Article
- SequencErr: measuring and suppressing sequencer errors in next-generation sequencing data.Genome biology · 2021Article
- Article
- Screening and identification of biomarkers associated with the diagnosis and prognosis of lung adenocarcinoma.Journal of clinical laboratory analysis · 2020Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStandardized Nucleic Acid Quantification for SEQuencing (SNAQ-SEQ) is a novel method that utilizes synthetic DNA internal standards spiked into each sample prior to next generation sequencing (NGS) library preparation. This method was applied to analysis of normal appearing airway epithelial cells (AEC) obtained by bronchoscopy in an effort to define a somatic mutation field effect associated with lung cancer risk. There is a need for biomarkers that reliably detect those at highest lung cancer risk, thereby enabling more effective screening by annual low dose CT. The purpose of this study was to test the hypothesis that lung cancer risk is characterized by increased prevalence of low variant allele frequency (VAF) somatic mutations in lung cancer driver genes in AEC.
methodsSynthetic DNA internal standards (IS) were prepared for 11 lung cancer driver genes and mixed with each AEC genomic (g) DNA specimen prior to competitive multiplex PCR amplicon NGS library preparation. A custom Perl script was developed to separate IS reads and respective specimen gDNA reads from each target into separate files for parallel variant frequency analysis. This approach identified nucleotide-specific sequencing error and enabled reliable detection of specimen mutations with VAF as low as 5 × 10
resultsTP53 mutations with 0.05-1.0% VAF were more prevalent (p < 0.05) and also enriched for tobacco smoke and age-associated mutation signatures in normal AEC from lung cancer cases compared to non-cancer controls matched for smoking and age. Further, PIK3CA and BRAF mutations in this VAF range were identified in AEC from cases but not controls.
conclusionsApplication of SNAQ-SEQ to measure mutations in the 0.05-1.0% VAF range enabled identification of an AEC somatic mutation field of injury associated with lung cancer risk. A biomarker comprising TP53, PIK3CA, and BRAF somatic mutations may better stratify individuals for optimal lung cancer screening and prevention outcomes.
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