Targeted deep sequencing across broad genomic regions has been used to detect circulating tumor DNA (ctDNA) in pancreatic ductal adenocarcinoma (PDAC) patients. However, since most PDACs harbor a mutation in KRAS, sequencing of broad regions needs to be systemically compared to analyzing only KRAS mutations for PDAC. Using capture-based targeted deep sequencing, we detected somatic tumor mutations in 17 fine needle aspiration biopsy and 69 longitudinal cell-free DNA (cfDNA) samples from 17 PDAC patients. KRAS mutations were detected in 10 out of 17 pretreatment patient plasma samples. Next, interrogation of genetic alterations in matched primary tumor samples detected ctDNA in 12 of 17 pretreatment plasma samples and cfDNA sequencing across the 83 target genes identified ctDNA in 15 of 17 cases (88.2% sensitivity). This improved sensitivity of ctDNA detection resulted in enhanced tumor burden monitoring when we analyzed longitudinal plasma samples. We found that cfDNA sequencing detected the lowest mutant allelic fractions and number of variants when complete response or partial response to chemotherapy was achieved. We demonstrated that ctDNA levels measured by targeted deep sequencing sensitively indicate the presence of cancer and correlate well with clinical responses to therapy and disease progression in PDAC patients.

BackgroundTargeted deep sequencing is increasingly used to detect low-allelic fraction variants; it is therefore essential that errors that constitute baseline noise and impose a practical limit on detection are characterized. In the present study, we systematically evaluate the extent to which errors are incurred during specific steps of the capture-based targeted sequencing process.ResultsWe removed most sequencing artifacts by filtering out low-quality bases and then analyze the remaining background noise. By recognizing that plasma DNA is naturally fragmented to be of a size comparable to that of mono-nucleosomal DNA, we were able to identify and characterize errors that are specifically associated with acoustic shearing. Two-thirds of C:G > A:T errors and one quarter of C:G > G:C errors were attributed to the oxidation of guanine during acoustic shearing, and this was further validated by comparative experiments conducted under different shearing conditions. The acoustic shearing step also causes A > G and A > T substitutions localized to the end bases of sheared DNA fragments, indicating a probable association of these errors with DNA breakage. Finally, the hybrid selection step contributes to one-third of the remaining C:G > A:T and one-fifth of the C > T errors.ConclusionsThe results of this study provide a comprehensive summary of various errors incurred during targeted deep sequencing, and their underlying causes. This information will be invaluable to drive technical improvements in this sequencing method, and may increase the future usage of targeted deep sequencing methods for low-allelic fraction variant detection.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1275-2) contains supplementary material, which is available to authorized users.

Although exome sequencing data are generated primarily to detect single-nucleotide variants and indels, they can also be used to identify a subset of genomic rearrangements whose breakpoints are located in or near exons. Using >4,600 tumor and normal pairs across 15 cancer types, we identified over 9,000 high confidence somatic rearrangements, including a large number of gene fusions. We find that the 5' fusion partners of functional fusions are often housekeeping genes, whereas the 3' fusion partners are enriched in tyrosine kinases. We establish the oncogenic potential of ROR1-DNAJC6 and CEP85L-ROS1 fusions by showing that they can promote cell proliferation in vitro and tumor formation in vivo. Furthermore, we found that ∼4% of the samples have massively rearranged chromosomes, many of which are associated with upregulation of oncogenes such as ERBB2 and TERT. Although the sensitivity of detecting structural alterations from exomes is considerably lower than that from whole genomes, this approach will be fruitful for the multitude of exomes that have been and will be generated, both in cancer and in other diseases.

Targeted capture massively parallel sequencing is increasingly being used in clinical settings, and as costs continue to decline, use of this technology may become routine in health care. However, a limited amount of tissue has often been a challenge in meeting quality requirements. To offer a practical guideline for the minimum amount of input DNA for targeted sequencing, we optimized and evaluated the performance of targeted sequencing depending on the input DNA amount. First, using various amounts of input DNA, we compared commercially available library construction kits and selected Agilent’s SureSelect-XT and KAPA Biosystems’ Hyper Prep kits as the kits most compatible with targeted deep sequencing using Agilent’s SureSelect custom capture. Then, we optimized the adapter ligation conditions of the Hyper Prep kit to improve library construction efficiency and adapted multiplexed hybrid selection to reduce the cost of sequencing. In this study, we systematically evaluated the performance of the optimized protocol depending on the amount of input DNA, ranging from 6.25 to 200 ng, suggesting the minimal input DNA amounts based on coverage depths required for specific applications.