Dataset Information

Detection of fusion gene in cell-free DNA of a gastric synovial sarcoma.

ABSTRACT: Synovial sarcoma (SS) is genetically characterized by chromosomal translocation, which generates SYT-SSX fusion transcripts. Although SS can occur in any body part, primary gastric SS is substantially rare. Here we describe a detection of the fusion gene sequence of gastric SS in plasma cell-free DNA (cfDNA). A gastric submucosal tumor was detected in the stomach of a 27-year-old woman and diagnosed as SS. Candidate intronic primers were designed to detect the intronic fusion breakpoint and this fusion sequence was confirmed in intron 10 of SYT and intron 5 of SSX2 by genomic polymerase chain reaction (PCR) and direct sequencing. A locked nucleic acid (LNA) probe specific to the fusion sequence was designed for detecting the fusion sequence in plasma and the fusion sequence was detected in preoperative plasma cfDNA, while not detected in postoperative plasma cfDNA. This technique will be useful for monitoring translocation-derived diseases such as SS.

SUBMITTER: Ogino S

PROVIDER: S-EPMC5829158 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Detection of fusion gene in cell-free DNA of a gastric synovial sarcoma.

Ogino Shinpei S Konishi Hirotaka H Ichikawa Daisuke D Hamada Junichi J Shoda Katsutoshi K Arita Tomohiro T Komatsu Shuhei S Shiozaki Atsushi A Okamoto Kazuma K Yamazaki Sanae S Yasukawa Satoru S Konishi Eiichi E Otsuji Eigo E

World journal of gastroenterology 20180201 8

Synovial sarcoma (SS) is genetically characterized by chromosomal translocation, which generates <i>SYT-SSX</i> fusion transcripts. Although SS can occur in any body part, primary gastric SS is substantially rare. Here we describe a detection of the fusion gene sequence of gastric SS in plasma cell-free DNA (cfDNA). A gastric submucosal tumor was detected in the stomach of a 27-year-old woman and diagnosed as SS. Candidate intronic primers were designed to detect the intronic fusion breakpoint a ...[more]

PMID: 29491688

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Project description:MOTIVATION:Cancer is a complex disease that involves rapidly evolving cells, often forming multiple distinct clones. In order to effectively understand progression of a patient-specific tumor, one needs to comprehensively sample tumor DNA at multiple time points, ideally obtained through inexpensive and minimally invasive techniques. Current sequencing technologies make the 'liquid biopsy' possible, which involves sampling a patient's blood or urine and sequencing the circulating cell free DNA (cfDNA). A certain percentage of this DNA originates from the tumor, known as circulating tumor DNA (ctDNA). The ratio of ctDNA may be extremely low in the sample, and the ctDNA may originate from multiple tumors or clones. These factors present unique challenges for applying existing tools and workflows to the analysis of ctDNA, especially in the detection of structural variations which rely on sufficient read coverage to be detectable. RESULTS:Here we introduce SViCT?, a structural variation (SV) detection tool designed to handle the challenges associated with cfDNA analysis. SViCT?can detect breakpoints and sequences of various structural variations including deletions, insertions, inversions, duplications and translocations. SViCT?extracts discordant read pairs, one-end anchors and soft-clipped/split reads, assembles them into contigs, and re-maps contig intervals to a reference genome using an efficient k-mer indexing approach. The intervals are then joined using a combination of graph and greedy algorithms to identify specific structural variant signatures. We assessed the performance of SViCT?and compared it to state-of-the-art tools using simulated cfDNA datasets with properties matching those of real cfDNA samples. The positive predictive value and sensitivity of our tool was superior to all the tested tools and reasonable performance was maintained down to the lowest dilution of 0.01% tumor DNA in simulated datasets. Additionally, SViCT?was able to detect all known SVs in two real cfDNA reference datasets (at 0.6-5% ctDNA) and predict a novel structural variant in a prostate cancer cohort. AVAILABILITY:SViCT?is available at https://github.com/vpc-ccg/svict. Contact:faraz.hach@ubc.ca.

Dataset Information

Detection of fusion gene in cell-free DNA of a gastric synovial sarcoma.

Publications

Detection of fusion gene in cell-free DNA of a gastric synovial sarcoma.

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