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

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.

Conflict of interest statement

This research is funded by The Vietnam National Foundation for Science and Technology Development (NAFOSTED, https://nafosted.gov.vn/) under grant number 108.01-2017.306 (to HNN) and Ho Chi Minh city Department of Science and Technology under grant number 257/2017/HD-SKHCN (to HNN) and Gene Solutions (GS-003). We confirm that the funder Gene Solutions provided support in the form of salaries for authors LS Tran, HAT Pham, VU Tran, TT Tran, BT Vo, HTT Dao, NH Nguyen, HN Do, M-D Phan and H Giang but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. We also confirm that this does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1. Schematic diagram of the sample handling procedure.

Fig 2. Comparing mutation allele frequency quantified from plasma by ultra-deep MPS and ddPCR.

(A) Linear regression and Pearson’s correlation coefficients of VAFs in plasma samples as determined by ddPCR and ultra-deep MPS with unique molecular identifier tagging. VAFs of del19, L858R and T790M mutations in EGFR were analysed separately (blue, yellow and grey, respectively) and combined (red) to show that MPS achieved significant correlation with ddPCR. (B) Bland-Altman plots demonstrating the agreement between ultra-deep MPS and ddPCR in quantifying VAFs of the three mutation types in EGFR from plasma samples.