Dataset Information

Noninvasive detection of tumor-associated mutations from circulating cell-free DNA in hepatocellular carcinoma patients by targeted deep sequencing.

ABSTRACT:

Background

Detection of circulating cell-free DNA (cfDNA) has potential clinical value for assessing tumor biology in patients with hepatocellular carcinoma (HCC), yet many traditional assays lack robustness. This study was the first to apply a high-throughput sequencing platform to detect tumor-associated mutations in HCC from circulating tumor-derived DNA (ctDNA) and to evaluate the utility and feasibility of this approach.

Methods

Using the MiSeq™ system, plasma and matched tumor DNA samples were analyzed for hotspot mutations in the TERT, CTNNB1, and TP53 genes that had been verified as the most prevalent mutations in HCC. We compared tumor and plasma data and prospectively investigated the association between significant mutations detected in ctDNA and the patients' clinical outcomes.

Results

In 41 patients, we detected tumor-associated mutations for HCC in 8 (19.5%) plasma samples. Among them, one showed a tumor-associated mutation in ctDNA but not in the tumor tissue which we used to detect. We also found that ctDNA with mutations could be detected more easily in patients who suffered vascular invasion (P=0.041) and predicted a shorter recurrence-free survival time (P<0.001). There was no relationship between detectable mutations and concentration of cfDNA (P=0.818).

Conclusions

The results of our study suggest that tumor-associated mutations detected in plasma are associated with vascular invasion and might be used to predict a shorter recurrence-free survival time for HCC patients. This kind of biomarker can overcome the limitations of tumor heterogeneity. Moreover, the diagnostic performance is improved if multiple mutations in different genes are combined.

SUBMITTER: Liao W

PROVIDER: S-EPMC5130021 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Publications

Noninvasive detection of tumor-associated mutations from circulating cell-free DNA in hepatocellular carcinoma patients by targeted deep sequencing.

Liao Wenjun W Yang Huayu H Xu Haifeng H Wang Yanan Y Ge Penglei P Ren Jinjun J Xu Wei W Lu Xin X Sang Xinting X Zhong Shouxian S Zhang Hongbing H Mao Yilei Y

Oncotarget 20160601 26

<h4>Background</h4>Detection of circulating cell-free DNA (cfDNA) has potential clinical value for assessing tumor biology in patients with hepatocellular carcinoma (HCC), yet many traditional assays lack robustness. This study was the first to apply a high-throughput sequencing platform to detect tumor-associated mutations in HCC from circulating tumor-derived DNA (ctDNA) and to evaluate the utility and feasibility of this approach.<h4>Methods</h4>Using the MiSeq™ system, plasma and matched tum ...[more]

PMID: 27248174

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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

Noninvasive detection of tumor-associated mutations from circulating cell-free DNA in hepatocellular carcinoma patients by targeted deep sequencing.

Background

Methods

Results

Conclusions

Publications

Noninvasive detection of tumor-associated mutations from circulating cell-free DNA in hepatocellular carcinoma patients by targeted deep sequencing.

Similar Datasets

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