Ultra-deep massively parallel sequencing with unique molecular identifier tagging achieves comparable performance to droplet digital PCR for detection and quantification of circulating tumor DNA from lung cancer patients.

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ID: 71165
2019
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Abstract
The identification and quantification of actionable mutations are of critical importance for effective genotype-directed therapies, prognosis and drug response monitoring in patients with non-small-cell lung cancer (NSCLC). Although tumor tissue biopsy remains the gold standard for diagnosis of NSCLC, the analysis of circulating tumor DNA (ctDNA) in plasma, known as liquid biopsy, has recently emerged as an alternative and noninvasive approach for exploring tumor genetic constitution. In this study, we developed a protocol for liquid biopsy using ultra-deep massively parallel sequencing (MPS) with unique molecular identifier tagging and evaluated its performance for the identification and quantification of tumor-derived mutations from plasma of patients with advanced NSCLC. Paired plasma and tumor tissue samples were used to evaluate mutation profiles detected by ultra-deep MPS, which showed 87.5% concordance. Cross-platform comparison with droplet digital PCR demonstrated comparable detection performance (91.4% concordance, Cohen's kappa coefficient of 0.85 with 95% CI = 0.72-0.97) and great reliability in quantification of mutation allele frequency (Intraclass correlation coefficient of 0.96 with 95% CI = 0.90-0.98). Our results highlight the potential application of liquid biopsy using ultra-deep MPS as a routine assay in clinical practice for both detection and quantification of actionable mutation landscape in NSCLC patients.
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tran2019ultradeepplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tran, Le Son;Pham, Hong-Anh Thi;Tran, Vu-Uyen;Tran, Thanh-Truong;Dang, Anh-Thu Huynh;Le, Dinh-Thong;Nguyen, Son-Lam;Nguyen, Ngoc-Vu;Nguyen, Trieu-Vu;Vo, Binh Thanh;Dao, Hong-Thuy Thi;Nguyen, Nguyen Huu;Tran, Tam Huu;Nguyen, Chu Van;Pham, Phuong Cam;Dang-Mai, Anh Tuan;Dinh-Nguyen, Thien Kim;Phan, Van Hieu;Do, Thanh-Thuy Thi;Truong Dinh, Kiet;Do, Han Ngoc;Phan, Minh-Duy;Giang, Hoa;Nguyen, Hoai-Nghia;
Journal PloS one
Year 2019
DOI
10.1371/journal.pone.0226193
URL
Keywords
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