Exome capture sequencing reveals new insights into hepatitis B virus-induced hepatocellular carcinoma at the early stage of tumorigenesis

  • Authors:
    • Yong Chen
    • Lijuan Wang
    • Hexiang Xu
    • Xingxiang Liu
    • Yingren Zhao
  • View Affiliations

  • Published online on: August 2, 2013     https://doi.org/10.3892/or.2013.2652
  • Pages: 1906-1912
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Abstract

Hepatocellular carcinoma (HCC), the most common type of liver cancer, is the third primary cause of cancer-related mortality worldwide. The molecular mechanisms underlying the initiation and formation of HCC remain obscure. In the present study, we performed exome sequencing using tumor and normal tissues from 3 hepatitis B virus (HBV)-positive BCLC stage A HCC patients. Bioinformatic analysis was performed to find candidate protein-altering somatic mutations. Eighty damaging mutations were validated and 59 genes were reported to be mutated in HBV-related HCCs for the first time here. Further analysis using whole genome sequencing (WGS) data of 88 HBV-related HCC patients from the European Genome-phenome Archive database showed that mutations in 33 of the 59 genes were also detected in other samples. Variants of two newly found genes, ZNF717 and PARP4, were detected in more than 10% of the WGS samples. Several other genes, such as FLNA and CNTN2, are also noteworthy. Thus, the exome sequencing analysis of three BCLC stage A patients provides new insights into the molecular events governing the early steps of HBV-induced HCC tumorigenesis.

Introduction

Hepatocellular carcinoma (HCC), the most common type of liver cancer, is the third primary cause of cancer mortality worldwide (1). The approximately 750,000 new liver cancer cases and 700,000 deaths worldwide each year are a relatively high burden (2). Among primary liver cancer, HCC, which arises from hepatocytes, represents the major histological subtype, accounting for 70–85% of all cases worldwide (3). The common risk factors for HCC include viral hepatitis, alcohol, non-alcoholic fatty liver disease and toxin such as aflatoxin, hemochromatosis, α1-antitrypsin deficiency (4). Chronic hepatitis B virus (HBV) infection accounts for approximately half of all cases of HCC (1), and it is the most common etiology of HCC in Asian countries.

Based on the current understanding of tumorigenesis, cancer is a genetic disease that arises from a single clone of cells expanding in an unregulated manner due to genomic instability and somatically acquired mutations (5,6). The pathogenesis of liver cancer is considered a multistep process. HCC may develop through a multistep process involving multiple genetic events over decades of chronic liver disease, which could facilitate the progressive accumulation of somatically genetic alterations. These newly somatic mutations, which trigger oncogenes and/or inactivate tumor-suppressor genes (TSGs), and preexisting endogenous virus- or chemical-induced mutations may lead to liver cancer, in particular HCC.

Recently, with markedly increased throughput, next-generation sequencing (NGS) technologies provide an efficient tool to identify the somatic mutations at exome and even the whole genome level (7). Several reports involving whole-genome and/or whole-exome sequencing on human HCC samples with viral infection were recently published, and provide a catalog of somatic mutations in the cancer genome, including candidate cancer genes in HCC (811). Although previous studies have revealed that certain genetic alterations, such as TP53 and β-catenin mutations, occur in HCC cells, the molecular mechanisms underlying the initiation and formation of HCC remain obscure.

In the present study, with a whole exome capturing approach, we sequenced the exome of tumor and normal tissues from 3 HBV-induced early stage HCC patients, which was diagnosed to be in stage A of Barcelona Clinic Liver Cancer (BCLC) system. Further analysis using whole genome sequencing data of 88 HBV-related HCC patients from the European Genome-phenome Archive database were carried out to discover recurrently mutated genes. Our investigation here may provide new insights into the molecular mechanism of HBV-related HCC.

Materials and methods

Sample and DNA preparation

Three male patients aged 48–52 years with chronic hepatitis B infection were diagnosed as BCLC stage A HCC in 2011 at Huai’an Fourth People’s Hospital. There was only 1 tumor nodule in each patient. The tumors were all on the right lobe of liver, with the size of 3×4, 2×4 and 1.5×3 cm2, respectively. The matched normal tissues were obtained ~5 cm from the cancer tissues. Written informed consent from the patient was obtained, and the study was reviewed and approved by the Ethics Committees of Huai’an Fourth People’s Hospital. DNA of these tissues was extracted through traditional phenol chloroform method.

Exome capture and sequencing

Exome sequencing was performed using SureSelect Human All Exon 38Mb kit (Agilent Technologies Inc., Santa Clara, CA, USA) according to the manufacturer’s protocols. The capture region is ~38 Mb and covers 1.22% of the human genome that corresponds to the consensus coding sequence (CCDS) collection of genes. Genomic DNA was randomly fragmented by sonication in to an average size of 500 bp. A pair of adaptors was ligated to each end of DNA fragments. The adaptor-ligated templates were then hybridized to the array to capture fragments in target regions. The captured fragments were amplified, purified and subjected to paired-end sequencing on the Illumina GA IIx platform (Illumina, San Diego, CA, USA). The genomic DNA library preparation, targeted sequence capturing, and massively parallel sequencing were finished by Guangzhou iGenomics Co., Ltd.

Sequence analysis

The raw reads were filtered with Fastx-tools (http://hannonlab.cshl.edu/fastx_toolkit/index.html). Low quality reads were discarded (fractions of N bases ≥10% and fractions of bases with quality less than <50%). Then, software BWA (version 0.5.9) (12) was used to map the paired-end reads to the human reference genome (hg19). The reference genome was downloaded from the UCSC (University of California, Santa Cruz, CA, USA) database (http://genome.ucsc.edu). After the alignment, PCR duplications were removed by the SAMtools software package (version 0.1.16) (13). Candidate somatic variants were identified with the VarScan 2 software (version 2.2.8) and filtered with default parameters (14). Function prediction for all the missense single nucleotide variations (SNVs) were carried out using PROVEAN (15) and SIFT (16).

Validation of variants

Somatic indels, nonsense and missense SNVs which were predicted to be damaging with both prediction methods, were validated by PCR and Sanger sequencing. Primers were designed with Primer Premier 5 (Premier Biosoft International, Palo Alto, CA, USA). PCR amplification was performed with 50 μl reaction using the following procedure: 95°C for 2 min, 35 cycles at 95°C for 15 sec, 60°C for 20 sec and 72°C for 30 sec, followed by 72°C for 2 min. The PCR products were purified with E.Z.N.A.® Gel Extraction kit. Sanger sequencing was performed on an ABI 3730 DNA Analyzer. Sequence trace files were manually analyzed.

Whole genome sequence analysis

In order to examine the prevalence of the mutated genes with validated damaging variants in various stages of HCCs, we further checked their mutation frequency in 88 HBV-related HCC patients using whole genome sequencing (WGA) data downloaded from the European Genome-phenome Archive database (ftp://ftp.sra.ebi.ac.uk/). The data set was not restricted to BCLC stage A patients. Early-to-advanced stage HCCs were included. Detailed information on the patients was provided in a previous study (8) which focused on HBV integration detection. Sequence alignment and somatic variants calling were carried out using the same procedure of exome sequencing reads analysis.

Results

Three patients with chronic HBV infection were diagnosed as BCLC stage A of HCC without metastasis in 2011. To identify the somatic mutations related to the disease, we performed the exome capture with Agilent SureSelect 38Mb kit for the 3 pairs of cancer and normal tissues. The captured samples with insertion size of 300–400 bp were subjected to paired-end sequencing using Illumina GA IIx. For the 3 pairs of samples, 34.93 Gb clean data were obtained (Table I). A total of 20.76 Gb data were mapped to the target region, achieving a minimum mean depth of 40.14 (Table I). The coverage of target region for each sample was all >95%. The capture rate for each sample ranged from 52.44 to 68.66% (Table I).

Table I

Data summary of exome sequencing.

Table I

Data summary of exome sequencing.

S1S2S3



SamplesTumorNormalTumorNormalTumorNormal
Raw data (Gb)4.914.327.767.647.627.96
Read length (bp)100100100100100100
Clean data (Gb)3.643.167.016.966.927.24
Uniquely mapped on the genome (Gb)3.172.766.436.236.456.57
Mapped on target region (Gb)1.771.454.374.304.364.51
Capture rate (%)55.9852.4467.9968.9067.6668.66
Mean depth of target region (x)49.0340.1485.0183.5384.8287.69
Coverage of target region (%)95.8296.1799.9099.9099.8099.80

All uniquely mapped sequences (target regions and adjacent regions) were used for subsequent variant detection. Using VarScan 2 with default parameters, 461, 524 and 638 somatic variants were identified for the 3 samples, respectively (Table II). According to the annotation results, a total of 266 variants were found to be protein-altering, including 16 exonic indels, 12 nonsense SNVs, 227 missense SNVs and 12 splicing site mutations (Table II). Among these indels, we found 10 mutations leading to frameshift and 6 mutations causing amino acid residue deletion or insertion (Table II).

Table II

Statistics of the identified somatic variants for each sample.

Table II

Statistics of the identified somatic variants for each sample.

AnnotationS1S2S3
Splicing435
ncRNA_splicing100
Downstream376
UTR53518
ncRNA_exonic61410
Intergenic8793102
ncRNA_UTR5010
Intronic208269290
ncRNA_UTR3001
Exonic11190160
UTR3101426
Upstream279
ncRNA_intronic291215
Frameshift insertion021
Unknown213
Nonframeshift insertion101
Frameshift deletion106
Stopgain SNV237
Nonframeshift deletion220
Nonsynonymous SNV7154102
Synonymous SNV322840

[i] Variants are annotated based on transcripts, i.e., one variant may be assigned as different types according to different transcripts. Therefore, overlap exists in different cells from the same column in this table.

Function prediction results for missense SNVs and splicing sites showed that 82 missense SNVs (29 in S1, 16 in S2 and 37 in S3, respectively) were predicted to be damaging by both prediction methods. These SNVs, together with the indels and nonsense mutations, were further validated with PCR and Sanger sequencing. A total of 80 (74.1%, 80/108) variants in 78 genes were validated. The distribution pattern of these validated variants across the whole genome is shown in Fig. 1. Detailed information on these variants is listed Table III. Of the variants, 85% (68/80) have not been reported in the dbSNP database (version 137) before. Nineteen of the 78 genes were already reported to mutate in HCC by previous studies according to the Catalogue of Somatic Mutations in Cancer (COSMIC) database (version v64). The other 59 genes were first reported to mutate in HCC in this study. All the validated, mutated genes were annotated through the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Thirty genes are involved in various pathways, such as MAPK signaling pathway, Wnt signaling pathway and cell adhesion molecules (CAMs).

Table III

Information on the validated somatic mutations.

Table III

Information on the validated somatic mutations.

GeneChrPositionRefAlterRef_aaAlter_aaTypeSampleCOSMICNum in 88adbSNP
ABCC91221954112GAR*NonsenseS3No2NA
ACTR52037394933TGISMissenseS3Yes0rs34733925
ACVRL11252309919ATEVMissenseS1No0NA
ADAMTS12533576162GTSRMissenseS1Yes2NA
ALPK21856149264CGGRMissenseS3No2NA
ATG2A1164676833TCYCMissenseS3No2rs148849835
ATP2C21684495388TADEMissenseS1No1NA
BCORX39933872GARCMissenseS2No1NA
C9orf40977563010GATIMissenseS2No0NA
CAT1134482880GTRLMissenseS1No0rs140071186
CD5L1157805636GTAEMissenseS3No2NA
CDCA2825319663CAADMissenseS2No0NA
CDHR25176017571AGGAGA.QESRDeletionS2Yes0NA
CDHR25176017568CCT--FrameshiftS2NA
CNTN21205034321CAFLMissenseS3No2NA
CNTNAP3B943849812TGCGMissenseS1No2rs3864761
COL22A18139825238ATYNMissenseS3No4NA
CTNNB1341266125CTTIMissenseS3Yes16rs121913413
CUL9643152422GTRMMissenseS1Yes4NA
DPP102116548913TAYNMissenseS1Yes2NA
EXD11541507990GAQ*NonsenseS3Yes0NA
FAM193A42673948AGHRMissenseS3No4NA
FAM196B5169309669GALFMissenseS3No0NA
FLNAX153594965CAVFMissenseS3No1NA
GPR125422390134TAR*NonsenseS1No1NA
HADHA226426943TGQPMissenseS3No1NA
HAPLN1582940402ATDEMissenseS1Yes1NA
HLA-DRB5632489813GCTRMissenseS3No5rs79606458
HNRNPH15179046361CAGWMissenseS1No1NA
HRNR1152188109TAQLMissenseS2Yes14rs61814940
HSPH11331724120CTEKMissenseS2No1NA
KAT6A841791810GAQ*NonsenseS3No2NA
KIAA12573128706628CTW*NonsenseS2No0NA
KLF51373636078GTRLMissenseS1No0NA
KRT321739619088TCDGMissenseS3No1NA
LILRA51954823629CGGRMissenseS1No1NA
MACC1720193850GAPLMissenseS3No3NA
MAP3K121253876968TGDAMissenseS3Yes2NA
MAP3K46161470088GAEKMissenseS3No1NA
MESP11590294304CCG--FrameshiftS2No0NA
MLL37151945225TCEGMissenseS2Yes23rs77957001
MROH2B541061715CTW*NonsenseS3No0rs1023840
NACAD745124694ACLRMissenseS3No1NA
NEBL1021074726TCTAMissenseS1No2NA
NPAT11108056150GCNKMissenseS3No0NA
NVL1224455814CAVLMissenseS3No1NA
OR13C29107367609CAQHMissenseS1No2NA
OR1I11915198054TAYNMissenseS2No0NA
OR2D3116942894TCVAMissenseS3No0NA
OR2H2629556464TALHMissenseS1No3NA
PARP41325051949TCYCMissenseS1No9NA
PCDHA125140255941TCITMissenseS3Yes0NA
PCDHB125140588823ATQLMissenseS1No0rs199513227
PHLDB111118527435CTRWMissenseS3Yes1NA
PLA2G4C1948601455GAPLMissenseS2No0NA
PLEK268613658TGVGMissenseS1No0NA
PPP1R13B14104224075CARLMissenseS1No1NA
PRAM1198555622CAVFMissenseS2No0NA
PRPH1249690017GARHMissenseS3No1NA
RARG1253606940CTRQMissenseS2No0NA
RNPEP1201970804CAFLMissenseS3No0NA
SEMA3F350225285CTRWMissenseS3No0NA
SEMA7A1574707184CGGRMissenseS3No0NA
SIGLEC71951645658GTWLMissenseS3No2NA
SKIV2L2554640368ATTSMissenseS1No0NA
SNCG1088719421CASRMissenseS3No0NA
SORL111121429524CAPHMissenseS2Yes2NA
TAMM41311885549TCYCMissenseS3No0rs149208464
TBL3162028215CTQ*NonsenseS3No0NA
TNKS21093579726ATNYMissenseS1No0NA
TNN1175048774GAGSMissenseS2Yes1NA
TP53177577534CARSMissenseS1;S3Yes34rs28934571
TRAPPC81829444630CARLMissenseS1Yes0NA
TTN2179402119ATLHMissenseS2Yes26NA
2179581852CCT--FrameshiftS3NA
UNC13C1554793167ACRSMissenseS3No4NA
ZCCHC11152961155TGTPMissenseS3No0NA
ZFYVE16579768637GARQMissenseS3Yes0NA
ZNF60512133503218TCKEMissenseS1No1NA
ZNF717375786430CTGRMissenseS3No47NA

{ label (or @symbol) needed for fn[@id='tfn2-or-30-04-1906'] } All missense SNVs were predicted to be damaging or deleterious by the 2 methods. Chr, chromosome;

a Number of individuals with mutated corresponding genes in the 88 HBV-related HCC patients; SNVs, single nucleotide variations.

These 80 somatic mutations would cause protein changes. Genes with these variants were defined as candidate HCC-related genes and were selected for further analysis. Whole genome sequence analysis was carried out to analyze the prevalence of the 78 mutated genes in 88 HBV-related HCC patients. For these patients, a minimum 751.49 million reads were mapped to the genome for each sample, yielding a minimum mean depth of 23.3 and a minimum coverage of 99.03% of the whole genome. Forty-seven genes were found to be mutated in at least one patient. Thirty-three of them have not previously been reported in the COSMIC database.

Genes mutated in at least 9 patients of the 88 samples (>10%) are indicated in Fig. 1. Two of these seven genes, ZNF717 and PARP4 have not been reported in the COSMIC database before. ZNF717 showed the highest frequency as variants of this gene were found in 47 of the 88 samples. Furthermore, the missense SNV in this gene was not reported in the dbSNP database before. This SNV was also detected in another patient of the 88 HCC samples. The variant in PARP4 was also reported for the first time. According to the KEGG database, the protein encoded by PARP4 is involved in the base excision repair pathway. The remaining 5 genes, HRNR, CTNNB1, MLL3, TTN and TP53 have previously been reported in the COSMIC database. However, the variants identified in TTN were reported for the first time here.

Discussion

We performed exome sequencing of HCCs and corresponding normal tissues from 3 Chinese male BCLC stage A HCC patients with chronic HBV infection to identify key genetic lesions contributing to the initial stage of the disease. Further whole genome sequence analysis in 88 patients at various stages (across early to advanced stage) was also carried out to check the prevalence of the mutated genes.

Eighty protein-altering somatic mutations, including exonic indels, nonsense SNVs and missense SNVs which were predicted to be damaging by 2 function prediction methods were detected and validated in this study. Fifty-nine genes were first reported to mutate in HCC in the present study (Table III).

For the 78 genes with these variants, 47 of them were also mutated at least once in the 88 WGS samples. Among them, 7 genes, ZNF717 and PARP4, HRNR, CTNNB1, MLL3, TTN and TP53 were mutated in at least 9 patients (over 10%, Fig. 1). HRNR, CTNNB1, MLL3, TTN and TP53 were reported to be related to HCC according to the COSMIC database.

TP53 is a famous tumor suppressor gene. The missense SNV identified on TP53 here was also called R249S, and it is frequently found in 10–61% of HCC cases (17,18). Consistently, we identified the mutation in sample S1 and S3 and further WGS analysis also detected this mutation in 10 samples. In HCC, TP53 mutations also vary in different geographic areas, presumably reflecting differences in both etiological agents and host susceptibility factors (19). In some areas, such as sub-Saharan Africa and China, aflatoxin B1 exposure and chronic viral hepatitis are responsible for a very high incidence of HCC (with up to 100/100,000 cases/year), where a high proportion of an R249S point mutation was found (2022). Furthermore, it was shown that adduction of AFB1 metabolites in the third base of codon 249 in the TP53 leading to the R249S mutation (23), indicating that this is an early mutational event in hepatocarcinogenesis. Recently, it was found that TP53 with mutation R249S can interact with HBx, which may contribute to cell proliferation and survival of HBx-expressing HCC cells (24). Thus, this mutation may play an important role in the cancer development of HBV-induced HCC patients.

The missense SNV of ZNF717 was detected in sample S3. The mutation was also detected in other WGS samples. Protein-altering variants of ZNF717 were detected in 47 WGS samples at various stages. This suggested that this gene may be related to various stages of the disease. Protein encoded by this gene belongs to the zinc-finger family, which is known to play key roles in regulating expression of genes important for cell growth, proliferation, differentiation and apoptosis (25,26). However, the function of most zinc-finger proteins in tumor occurrence and development is currently unknown. As a zinc-finger protein containing 8 conserved C2H2-type zinc-finger motifs, ZNF717 may function in the transcriptional regulation. Further investigations should be carried out to confirm its roles.

PARP4 was also reported for the first time here. The missense SNV is detected in sample S1. Other protein-altering mutations of this gene were also detected in 9 other early-stage WGS samples. The protein encoded by this gene is involved in the base excision repair pathway (hsa03410). A previous study showed that inhibition of PARP4 interfered with DNA base excision repair but can sensitize cells to apoptotic stimuli, resulting in increased tumor cell apoptosis in vivo. Interruption of this gene may prevent tumor formation through this mechanism (27). Thus, mutations of this gene at early-stage of the disease may serve as a self-compensation.

Several other newly discovered genes are also noteworthy. For example, the protein encoded by FLNA is an actin-binding protein that crosslinks actin filaments and links actin filaments to membrane glycoprotein. It is involved in several important pathways, such as the MAPK signaling pathway (hsa04010) and proteoglycans in cancer (hsa05205). Although mutation of this gene in HCCs has not previously been reported, comparative proteomics analysis showed that FLNA protein was significantly differentially expressed in HCC cell lines and may influence the metastasis of HCC cells (28). Mutation of this gene was also detected in another early-stage HBV-induced HCC patient of the WGS samples, indicating that interruption of this gene at early stage may contribute to the initial formation of the disease. Moreover, FLNA protein is also involved in the focal adhesion (hsa04510) pathway. Another newly reported gene here, CNTN2, is also related to cell adhesion as it is a member of the cell adhesion molecules pathway. The cell adhesion pathway is important for cancer cell invasion and metastasis. Abnormality of the cell adhesion pathway is considered a characteristic of the advanced stage of cancer. Our findings here thus support the hypothesis that the cancer cells may have acquired the capacity for metastasis at the early stage of development (29). Four genes involved in olfactory transduction including OR13C2, OR1I1, OR2D3 and OR2H2 were also found for the first time in this study to be mutated in HBV-induced HCCs, suggesting these genes warrant further investigation.

In summary, exome sequencing of HCCs and adjacent normal tissues from 3 BCLC stage A HCC patients was carried out to identify key genetic lesions contributing to the initial stage of the disease. Eighty damaging mutations were validated and 59 genes were first reported to be mutated in HBV-related HCCs here. Further WGS analysis showed that mutations in 33 of the 59 genes were also detected in other samples. Variants of 2 newly found genes, ZNF717 and PARP4, were detected in more than 10% of the WGS samples. Several other genes, such as FLNA and CNTN2, are also noteworthy. Thus, the exome sequencing analysis of 3 BCLC stage A patients provided new insights into the molecular events governing the early step of HBV-induced HCC tumorigenesis.

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October 2013
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Chen Y, Wang L, Xu H, Liu X and Zhao Y: Exome capture sequencing reveals new insights into hepatitis B virus-induced hepatocellular carcinoma at the early stage of tumorigenesis. Oncol Rep 30: 1906-1912, 2013
APA
Chen, Y., Wang, L., Xu, H., Liu, X., & Zhao, Y. (2013). Exome capture sequencing reveals new insights into hepatitis B virus-induced hepatocellular carcinoma at the early stage of tumorigenesis. Oncology Reports, 30, 1906-1912. https://doi.org/10.3892/or.2013.2652
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Chen, Y., Wang, L., Xu, H., Liu, X., Zhao, Y."Exome capture sequencing reveals new insights into hepatitis B virus-induced hepatocellular carcinoma at the early stage of tumorigenesis". Oncology Reports 30.4 (2013): 1906-1912.
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Chen, Y., Wang, L., Xu, H., Liu, X., Zhao, Y."Exome capture sequencing reveals new insights into hepatitis B virus-induced hepatocellular carcinoma at the early stage of tumorigenesis". Oncology Reports 30, no. 4 (2013): 1906-1912. https://doi.org/10.3892/or.2013.2652