Duplex Proximity Sequencing (Pro-Seq): A method to improve DNA sequencing accuracy without the cost of molecular barcoding redundancy

Strategies for improving detection of circulating tumor DNA using next generation sequencing

Cancer has become a global health issue and liquid biopsy has emerged as a non-invasive tool for various applications. In cancer, circulating tumor DNA (ctDNA) can be detected from cell-free DNA (cfDNA) obtained from plasma and has potential for early diagnosis, treatment, resistance, minimal residual disease detection, and tumoral heterogeneity identification. However, the low frequency of ctDNA requires techniques for accurate analysis. Multitarget assay such as Next Generation Sequencing (NGS) need improvement to achieve limits of detection that can identify the low frequency variants present in the cfDNA. In this review, we provide a general overview of the use of cfDNA and ctDNA in cancer, and discuss techniques developed to optimize NGS as a tool for ctDNA detection. We also summarize the results obtained using NGS strategies in both investigational and clinical contexts.

Single duplex DNA sequencing with CODEC detects mutations with high sensitivity

Detecting mutations from single DNA molecules is crucial in many fields but challenging. Next-generation sequencing (NGS) affords tremendous throughput but cannot directly sequence double-stranded DNA molecules ('single duplexes') to discern the true mutations on both strands. Here we present Concatenating Original Duplex for Error Correction (CODEC), which confers single duplex resolution to NGS. CODEC affords 1,000-fold higher accuracy than NGS, using up to 100-fold fewer reads than duplex sequencing. CODEC revealed mutation frequencies of 2.72 × 10-8 in sperm of a 39-year-old individual, and somatic mutations acquired with age in blood cells. CODEC detected genome-wide, clonal hematopoiesis mutations from single DNA molecules, single mutated duplexes from tumor genomes and liquid biopsies, microsatellite instability with 10-fold greater sensitivity and mutational signatures, and specific tumor mutations with up to 100-fold fewer reads. CODEC enables more precise genetic testing and reveals biologically significant mutations, which are commonly obscured by NGS errors.

A quantification method of somatic mutations in normal tissues and their accumulation in pediatric patients with chemotherapy

Somatic mutations are accumulated in normal human tissues with aging and exposure to carcinogens. If we can accurately count any passenger mutations in any single DNA molecule, since their quantity is much larger than driver mutations, we can sensitively detect mutation accumulation in polyclonal normal tissues. Duplex sequencing, which tags both DNA strands in one DNA molecule, enables accurate count of such mutations, but requires a very large number of sequencing reads for each single sample of human-genome size. Here, we reduced the genome size to 1/90 using the BamHI restriction enzyme and established a cost-effective pipeline. The enzymatically cleaved and optimal sequencing (EcoSeq) method was able to count somatic mutations in a single DNA molecule with a sensitivity of as low as 3 × 10^-8 per base pair (bp), as assessed by measuring artificially prepared mutations. Taking advantages of EcoSeq, we analyzed normal peripheral blood cells of pediatric sarcoma patients who received chemotherapy (n = 10) and those who did not (n = 10). The former had a mutation frequency of 31.2 ± 13.4 × 10^-8 per base pair while the latter had 9.0 ± 4.5 × 10^-8 per base pair (P < 0.001). The increase in mutation frequency was confirmed by analysis of the same patients before and after chemotherapy, and increased mutation frequencies persisted 46 to 64 mo after chemotherapy, indicating that the mutation accumulation constitutes a risk of secondary leukemia. EcoSeq has the potential to reveal accumulation of somatic mutations and exposure to environmental factors in any DNA samples and will contribute to cancer risk estimation.

A Streamlined and High-Throughput Error-Corrected Next-Generation Sequencing Method for Low Variant Allele Frequency Quantitation

Quantifying mutant or variable allele frequencies (VAFs) of ≤10-3 using next-generation sequencing (NGS) has utility in both clinical and nonclinical settings. Two common approaches for quantifying VAFs using NGS are tagged single-strand sequencing and duplex sequencing. While duplex sequencing is reported to have sensitivity up to 10-8 VAF, it is not a quick, easy, or inexpensive method. We report a method for quantifying VAFs that are ≥10-4 that is as easy and quick for processing samples as standard sequencing kits, yet less expensive than the kits. The method was developed using PCR fragment-based VAFs of Kras codon 12 in log10 increments from 10-5 to 10-1, then applied and tested on native genomic DNA. For both sources of DNA, there is a proportional increase in the observed VAF to input VAF from 10-4 to 100% mutant samples. Variability of quantitation was evaluated within experimental replicates and shown to be consistent across sample preparations. The error at each successive base read was evaluated to determine if there is a limit of read length for quantitation of ≥10-4, and it was determined that read lengths up to 70 bases are reliable for quantitation. The method described here is adaptable to various oncogene or tumor suppressor gene targets, with the potential to implement multiplexing at the initial tagging step. While easy to perform manually, it is also suited for robotic handling and batch processing of samples, facilitating detection and quantitation of genetic carcinogenic biomarkers before tumor formation or in normal-appearing tissue.

Reducing noise and stutter in short tandem repeat loci with unique molecular identifiers

Unique molecular identifiers (UMIs) are a promising approach to contend with errors generated during PCR and massively parallel sequencing (MPS). With UMI technology, random molecular barcodes are ligated to template DNA molecules prior to PCR, allowing PCR and sequencing error to be tracked and corrected bioinformatically. UMIs have the potential to be particularly informative for the interpretation of short tandem repeats (STRs). Traditional MPS approaches may simply lead to the observation of alleles that are consistent with the hypotheses of stutter, while with UMIs stutter products bioinformatically may be re-associated with their parental alleles and subsequently removed. Herein, a bioinformatics pipeline named strumi is described that is designed for the analysis of STRs that are tagged with UMIs. Unlike other tools, strumi is an alignment-free machine learning driven algorithm that clusters individual MPS reads into UMI families, infers consensus super-reads that represent each family and provides an estimate the resulting haplotype's accuracy. Super-reads, in turn, approximate independent measurements not of the PCR products, but of the original template molecules, both in terms of quantity and sequence identity. Provisional assessments show that naïve threshold-based approaches generate super-reads that are accurate (∼97 % haplotype accuracy, compared to ∼78 % when UMIs are not used), and the application of a more nuanced machine learning approach increases the accuracy to ∼99.5 % depending on the level of certainty desired. With these features, UMIs may greatly simplify probabilistic genotyping systems and reduce uncertainty. However, the ability to interpret alleles at trace levels also permits the interpretation, characterization and quantification of contamination as well as somatic variation (including somatic stutter), which may present newfound challenges.

Next-Generation Sequencing in High-Sensitive Detection of Mutations in Tumors: Challenges, Advances, and Applications

Next-generation sequencing (NGS) technologies have come of age as preferred technologies for screening of genomic variants of pathologic and therapeutic potential. Because of their capability for high-throughput and massively parallel sequencing, they can screen for a variety of genomic changes in multiple samples simultaneously. This has made them platforms of choice for clinical testing of solid tumors and hematological malignancies. Consequently, they are increasingly replacing conventional technologies, such as Sanger sequencing and pyrosequencing, expression arrays, real-time PCR, and fluorescence in situ hybridization methods, for routine molecular testing of tumors. However, one limitation of routinely used NGS technologies is the inability to detect low-level genomic variants with high accuracy. This can be attributed to the frequent occurrence of low-level sequencing errors and artifacts in NGS workflow that need specialized approaches to be identified and eliminated. This review focuses on the origins and nature of these artifacts and recent improvements in the NGS technologies to overcome them to facilitate accurate high-sensitive detection of low-level mutations. Potential applications of high-sensitive NGS in oncology and comparisons with non-NGS technologies of similar capabilities are also summarized.

SinoDuplex: An Improved Duplex Sequencing Approach to Detect Low-frequency Variants in Plasma cfDNA Samples

Accurate detection of low frequency mutations from plasma cell-free DNA in blood using targeted next generation sequencing technology has shown promising benefits in clinical settings. Duplex sequencing technology is the most commonly used approach in liquid biopsies. Unique molecular identifiers are attached to each double-stranded DNA template, followed by production of low-error consensus sequences to detect low frequency variants. However, high sequencing costs have hindered application of this approach in clinical practice. Here, we have developed an improved duplex sequencing approach called SinoDuplex, which utilizes a pool of adapters containing pre-defined barcode sequences to generate far fewer barcode combinations than with random sequences, and implemented a novel computational analysis algorithm to generate duplex consensus sequences more precisely. SinoDuplex increased the output of duplex sequencing technology, making it more cost-effective. We evaluated our approach using reference standard samples and cell-free DNA samples from lung cancer patients. Our results showed that SinoDuplex has high sensitivity and specificity in detecting very low allele frequency mutations. The source code for SinoDuplex is freely available at https://github.com/SinOncology/sinoduplex.

Systematic comparison of somatic variant calling performance among different sequencing depth and mutation frequency

In the past decade, treatments for tumors have made remarkable progress, such as the successful clinical application of targeted therapies. Nowadays, targeted therapies are based primarily on the detection of mutations, and next-generation sequencing (NGS) plays an important role in relevant clinical research. The mutation frequency is a major problem in tumor mutation detection and increasing sequencing depth is a widely used method to improve mutation calling performance. Therefore, it is necessary to evaluate the effect of different sequencing depth and mutation frequency as well as mutation calling tools. In this study, Strelka2 and Mutect2 tools were used in detecting the performance of 30 combinations of sequencing depth and mutation frequency. Results showed that the precision rate kept greater than 95% in most of the samples. Generally, for higher mutation frequency (≥20%), sequencing depth ≥200X is sufficient for calling 95% mutations; for lower mutation frequency (≤10%), we recommend improving experimental method rather than increasing sequencing depth. Besides, according to our results, although Strelka2 and Mutect2 performed similarly, the former performed slightly better than the latter one at higher mutation frequency (≥20%), while Mutect2 performed better when the mutation frequency was lower than 10%. Besides, Strelka2 was 17 to 22 times faster than Mutect2 on average. Our research will provide a useful and comprehensive guideline for clinical genomic researches on somatic mutation identification through systematic performance comparison among different sequencing depths and mutation frequency.

Detection of RAS mutations in circulating tumor DNA: a new weapon in an old war against colorectal cancer. A systematic review of literature and meta-analysis

Background:

Tissue evaluation for RAS (KRAS or NRAS) gene status in metastatic colorectal cancer (mCRC) patients represent the standard of care to establish the optimal therapeutic strategy. Unfortunately, tissue biopsy is hampered by several critical limitations due to its invasiveness, difficulty to access to disease site, patient’s compliance and, more recently, neoplastic tissue spatial and temporal heterogeneity.

Methods:

The authors performed a systematic literature review to identify available trials with paired matched tissue and ctDNA RAS gene status evaluation. The authors searched EMBASE, MEDLINE, Cochrane, www.ClinicalTrials.gov, and abstracts from international meetings. In total, 19 trials comparing standard tissue RAS mutational status matched paired ctDNA evaluated through polymerase chain reaction (PCR), next generation sequencing (NGS) or beads, emulsions, amplification and magnetics (BEAMing) were identified.

Results:

The pooled sensitivity and specificity of ctDNA were 0.83 (95% CI: 0.80–0.85) and 0.91 (95% CI: 0.89–0.93) respectively. The pooled positive predictive value (PPV) and negative predictive value (NPV) of the ctDNA were 0.87 (95% CI: 0.81–0.92) and 0.87 (95% CI: 0.82–0.92), respectively. Positive likelihood ratio (PLR) was 8.20 (95% CI: 5.16–13.02) and the negative likelihood ratio (NLR) was 0.22 (95% CI: 0.16–0.30). The pooled diagnostic odds ratio (DOR) was 50.86 (95% CI: 26.15–98.76), and the area under the curve (AUC) of the summary receiver operational characteristics (sROC) curve was 0.94.

Conclusion:

The authors’ meta-analysis produced a complete and updated overview of ctDNA diagnostic accuracy to test RAS mutation in mCRC. Results provide a strong rationale to include the RAS ctDNA test into randomized clinical trials to validate it prospectively.

Enhancing the accuracy of next-generation sequencing for detecting rare and subclonal mutations

Mutations, the fuel of evolution, are first manifested as rare DNA changes within a population of cells. Although next-generation sequencing (NGS) technologies have revolutionized the study of genomic variation between species and individual organisms, most have limited ability to accurately detect and quantify rare variants among the different genome copies in heterogeneous mixtures of cells or molecules. We describe the technical challenges in characterizing subclonal variants using conventional NGS protocols and the recent development of error correction strategies, both computational and experimental, including consensus sequencing of single DNA molecules. We also highlight major applications for low-frequency mutation detection in science and medicine, describe emerging methodologies and provide our vision for the future of DNA sequencing.