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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.

SUBMITTER: Tran LS

PROVIDER: S-EPMC6913927 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Although digital detection of nucleic acids has been achieved by amplification of single templates in uniform microfluidic droplets and widely used for genetic analysis, droplet-based digital detection of proteins has rarely been reported, largely due to the lack of an efficient target amplification method for protein in droplets. Here, we report a key step towards digital detection of proteins using a highly parallel microfluidic droplet approach for single enzyme molecule detection in picoliter droplets via enzyme catalyzed signal amplification. An integrated microfluidic chip was designed for high throughput uniform droplet generation, monolayer droplet collection, incubation, detection, and release. Single β-galatosidase (β-Gal) molecules and the fluorogenic substrate fluorescein di-β-D-galactopyranoside were injected from two separated inlets to form uniform 20 μm droplets in fluorinated oil at a frequency of 6.6 kHz. About 200 000 droplets were captured as a monolayer in a capture well on-chip for subsequent imaging detection. A series of β-Gal solutions at different concentrations were analyzed at the single-molecule level. With no enzyme present, no droplets were found to fluoresce, while brightly fluorescent droplets were observed under single-enzyme molecule conditions. Droplet fluorescence intensity distribution analysis showed that the distribution of enzyme molecules under single-molecule conditions matched well with theoretical prediction, further proving the feasibility of detecting single enzyme molecules in emulsion droplets. Moreover, the population of fluorescent droplets increased as the β-Gal concentration increased. Based on a digital counting method, the measured concentrations of the enzyme were found to match well with input enzyme concentration, establishing the accuracy of the digital detection method for the quantification of β-Gal enzyme molecules. The capability of highly parallel detection of single enzyme molecules in uniform picoliter droplets paves the way to microdroplet based digital detection of proteins.

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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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