Dataset Information

A Next-Generation Sequencing-Based Platform for Quantitative Detection of Hepatitis B Virus Pre-S Mutants in Plasma of Hepatocellular Carcinoma Patients.

ABSTRACT: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. Early diagnosis and treatment of HCC remain a key goal for improving patient survival. Chronic hepatitis B virus (HBV) infection is a major risk factor for HCC development. Pre-S mutants harboring deletions in HBV large surface antigen have been well demonstrated as HBV oncoproteins that dysregulate multiple signaling pathways in hepatocytes, leading to HCC formation. The presence of pre-S mutants in plasma represents important predictive and prognostic markers for HCC in patients with chronic HBV infection. However, the method to detect pre-S mutants remains to be optimized. In this study, we developed a platform, based on the next-generation sequencing (NGS) technology, for detection of pre-S mutants in plasma of HBV-related HCC patients. Compared to the current TA cloning-based analysis, the NGS-based analysis could detect pre-S deletion quantitatively, and the detection rate was significantly more sensitive in 49 plasma analyzed (McNemar's paired proportion test, P value?

SUBMITTER: Teng CF

PROVIDER: S-EPMC6172208 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Next-Generation Sequencing-Based Platform for Quantitative Detection of Hepatitis B Virus Pre-S Mutants in Plasma of Hepatocellular Carcinoma Patients.

Teng Chiao-Fang CF Huang Hsi-Yuan HY Li Tsai-Chung TC Shyu Woei-Cherng WC Wu Han-Chieh HC Lin Chien-Yu CY Su Ih-Jen IJ Jeng Long-Bin LB

Scientific reports 20181004 1

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. Early diagnosis and treatment of HCC remain a key goal for improving patient survival. Chronic hepatitis B virus (HBV) infection is a major risk factor for HCC development. Pre-S mutants harboring deletions in HBV large surface antigen have been well demonstrated as HBV oncoproteins that dysregulate multiple signaling pathways in hepatocytes, leading to HCC formation. The presence of pre-S mutants in plasma repr ...[more]

PMID: 30287845

Similar Datasets

Project description:Recently, oncology has moved to a new clinical practice, more personalized, called Predictive Oncology (PO). PO comes from our knowledge about tumor heterogeneity that implies that each disease, thus each patient, is unique. PO’s goal is to identify and administrate the right treatment to the right patient. For this, PO requires to go through 3 majors steps: 1. A good characterization of the tumor to identify candidates, 2. A well-established panel of drugs targeting the identified candidates, 3. A relevant model to functionally test these candidates. The first point could easily be addressed with recent technologies that now allow the Next Generation Sequencing (NGS) and/or the simultaneous analysis of transcriptomic profiles from thousands of patients. The last two points have not been efficiently achieved so far, which prevents PO to be really efficient. Indeed, even if NGS allows the identification of potential targets, the presence of a molecular candidate does not necessary means obligatory functional response. The number of drugs approved by the Food and Drug Administration remains limited and most frequent targets in solid tumors (for ex. RAS, P53, MYC, RB1 ...) still do not have specific drugs approved in clinic. Finally, available pre-clinical models still present many major inconvenient: * Chimiogrammes on 2D cultures are not sufficiently relevant to be really predictive of the in vivo situation; * Patient derived xenograft (PDX) are not adapted for clinical use because not all tumors graft and the time to develop a PDX is too long (several months), thus incompatible with the history of the disease (especially for most severe patients). Furthermore the host (NOD-SCID mouse) is immuno-depressed, preventing to objectively test antibodies-mediated drugs. Recently, the 3D cell culture technology has proven its superiority to predict drug response over classical 2D chimiogrammes. It consists in growing "mini-tissues", or organoid-derived from tumor/healthy tissues, thanks to the amplification of stem cells contained within the sample. The generated organoids are personalized and biologically relevant (organoids are expend form the patient’s stem cells which self-organized according to the architecture of the tissue they are originating from), they are genetically stable, their growth is compatible with patient’s disease history (organoids grow in few weeks), easy and convenient to achieve, even from small biological material quantities (0.5< x < 1cm3), and they can be amplified, frozen and thawed on demand. Moreover, organoids can be made more complex with the addition of other cell types (fibroblasts, immune cells ...). None of the actual available pre-clinical model regroups all these characteristics. The constitution of a "next generation" biobank of liver samples (Metastases to the liver and Hepato Cellular Adenocarcinoma) will be very useful in the context of predictive oncology. For this, a biopsy needs to be dissociated and grown in Matrigel, in presence of a well-defined list of growth factors. Once the culture is established, organoids can be frozen then defrost on demand. Our main objective is to evaluate the feasibility for building a biobank of liver-derived organoids, from liver metastases of colorectal cancers, hepatocellular adenoma and adenocarcinoma (waste tissues). Applications related to organoids derived from tumors are quasi indefinite, from drug screening assays, tests for novel therapies or original drug combinations, to patients’ stratifications or fundamental research. In our case, we are interested in building this a biobank in the prospect of using it to build the "next generation of model for predictive oncology" to study liver-related cancers and related drugs testing. Briefly, we want to implement these organoids with cells from the microenvironment in order to makes the global model more pertinent for drug testing. If successful, the generation of such biobank, including both tumor-derived organoids and healthy counterpart, could be really helpful for the scientific and medical community.

Project description:Next-generation sequencing (NGS) is a powerful and high-throughput method for the detection of viral mutations. This article provides a brief overview about optimization of NGS analysis for hepatocellular carcinoma (HCC)-associated hepatitis B virus (HBV) mutations, and hepatocarcinogenesis of relevant mutations.For the application of NGS analysis in the genome of HBV, four noteworthy steps were discovered in testing. First, a sample-specific reference sequence was the most effective mapping reference for NGS. Second, elongating the end of reference sequence improved mapping performance at the end of the genome. Third, resetting the origin of mapping reference sequence could probed deletion mutations and variants at a certain location with common mutations. Fourth, using a platform-specific cut-off value to distinguish authentic minority variants from technical artifacts was found to be highly effective. One hundred and sixty-seven HBV single nucleotide variants (SNVs) were found to be studied previously through a systematic literature review, and 12 SNVs were determined to be associated with HCC by meta-analysis. From comprehensive research using a HBV genome-wide NGS analysis, 60 NGS-defined HCC-associated SNVs with their pathogenic frequencies were identified, with 19 reported previously. All the 12 HCC-associated SNVs proved by meta-analysis were confirmed by NGS analysis, except for C1766T and T1768A which were mainly expressed in genotypes A and D, but including the subgroup analysis of A1762T. In the 41 novel NGS-defined HCC-associated SNVs, 31.7% (13/41) had cut-off values of SNV frequency lower than 20%. This showed that NGS could be used to detect HCC-associated SNVs with low SNV frequency. Most SNV II (the minor strains in the majority of non-HCC patients) had either low (<?20%) or high (>?80%) SNV frequencies in HCC patients, a characteristic U-shaped distribution pattern. The cut-off values of SNV frequency for HCC-associated SNVs represent their pathogenic frequencies. The pathogenic frequencies of HCC-associated SNV II also showed a U-shaped distribution. Hepatocarcinogenesis induced by HBV mutated proteins through cellular pathways was reviewed.NGS analysis is useful to discover novel HCC-associated HBV SNVs, especially those with low SNV frequency. The hepatocarcinogenetic mechanisms of novel HCC-associated HBV SNVs defined by NGS analysis deserve further investigation.

Dataset Information

A Next-Generation Sequencing-Based Platform for Quantitative Detection of Hepatitis B Virus Pre-S Mutants in Plasma of Hepatocellular Carcinoma Patients.

Publications

A Next-Generation Sequencing-Based Platform for Quantitative Detection of Hepatitis B Virus Pre-S Mutants in Plasma of Hepatocellular Carcinoma Patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets