Dataset Information

Non-invasive Analysis of Genomic Copy Number Variation in Patients with Hepatocellular Carcinoma by Next Generation DNA Sequencing.

ABSTRACT: To explore new molecular diagnosis approaches for early detection and differential diagnosis of hepatocellular carcinoma (HCC), we analyzed genomic copy number variations (CNV) using plasma cell-free DNA from patients with HCC by next generation DNA sequencing. Plasma samples from 31 patients with HCC and 8 patients with chronic hepatitis or cirrhosis were analyzed. In HCC group, most samples with large tumor size (tumor dimension greater than 50 mm) showed CNVs that are visually recognizable at chromosome CNV plots, few samples with small tumor and none samples with chronic liver diseases showed CNVs recognizable at CNV plots. CNV Z score analysis showed significant CNVs in samples with HCC and chronic liver diseases although more significant changes were found in HCC group, some are differentially valuable (such as gain in 1q, 7q, and 19q in HCC), while others are less differentially valuable (such as loss in 4q, 13q, gain in 17q, 22q). We proposed a CNV scoring method that generated positive result in 26 of the 31 HCC patients (83.9%) or 11 of the 16 HCC with tumor dimension 50 mm or less (68.8%) or 4 of the 7 HCC with tumor dimension 30 mm or less (57.1%), while all the 8 samples with chronic hepatitis or cirrhosis scored negative. Ten HCC patients had normal or low serum AFP levels, among them, 7 were scored positive by CNV analysis, including 4 with tumor dimension 50 mm or less. Our study suggested that non-invasive genomic CNV analysis using plasma samples could be a valuable tool for early detection and differential diagnosis of HCC. Although CNV analysis itself cannot establish the diagnosis, it can help identify patients at high risk for HCC among patients with chronic liver diseases, which would prompt closer and more frequent surveillance for early tumor detection and intervention.

SUBMITTER: Xu H

PROVIDER: S-EPMC4317760 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Non-invasive Analysis of Genomic Copy Number Variation in Patients with Hepatocellular Carcinoma by Next Generation DNA Sequencing.

Xu Hongtao H Zhu Xia X Xu Zulong Z Hu Yue Y Bo Shiping S Xing Tongjing T Zhu Kuichun K

Journal of Cancer 20150118 3

To explore new molecular diagnosis approaches for early detection and differential diagnosis of hepatocellular carcinoma (HCC), we analyzed genomic copy number variations (CNV) using plasma cell-free DNA from patients with HCC by next generation DNA sequencing. Plasma samples from 31 patients with HCC and 8 patients with chronic hepatitis or cirrhosis were analyzed. In HCC group, most samples with large tumor size (tumor dimension greater than 50 mm) showed CNVs that are visually recognizable at ...[more]

PMID: 25663942

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Project description:BackgroundDetection of copy number variation (CNV) in genes associated with disease is important in genetic diagnostics, and next generation sequencing (NGS) technology provides data that can be used for CNV detection. However, CNV detection based on NGS data is in general not often used in diagnostic labs as the data analysis is challenging, especially with data from targeted gene panels. Wet lab methods like MLPA (MRC Holland) are widely used, but are expensive, time consuming and have gene-specific limitations. Our aim has been to develop a bioinformatic tool for CNV detection from NGS data in medical genetic diagnostic samples.ResultsOur computational pipeline for detection of CNVs in NGS data from targeted gene panels utilizes coverage depth of the captured regions and calculates a copy number ratio score for each region. This is computed by comparing the mean coverage of the sample with the mean coverage of the same region in other samples, defined as a pool. The pipeline selects pools for comparison dynamically from previously sequenced samples, using the pool with an average coverage depth that is nearest to the one of the samples. A sliding window-based approach is used to analyze each region, where length of sliding window and sliding distance can be chosen dynamically to increase or decrease the resolution. This helps in detecting CNVs in small or partial exons. With this pipeline we have correctly identified the CNVs in 36 positive control samples, with sensitivity of 100% and specificity of 91%. We have detected whole gene level deletion/duplication, single/multi exonic level deletion/duplication, partial exonic deletion and mosaic deletion. Since its implementation in mid-2018 it has proven its diagnostic value with more than 45 CNV findings in routine tests.ConclusionsWith this pipeline as part of our diagnostic practices it is now possible to detect partial, single or multi-exonic, and intragenic CNVs in all genes in our target panel. This has helped our diagnostic lab to expand the portfolio of genes where we offer CNV detection, which previously was limited by the availability of MLPA kits.

Dataset Information

Non-invasive Analysis of Genomic Copy Number Variation in Patients with Hepatocellular Carcinoma by Next Generation DNA Sequencing.

Publications

Non-invasive Analysis of Genomic Copy Number Variation in Patients with Hepatocellular Carcinoma by Next Generation DNA Sequencing.

Similar Datasets

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