Molecular Genetic Epidemiology of Primary Hepatocellular Carcinoma

大多数癌症表现为复杂的表型，并通过基因-基因和/或基因-环境相互作用表现出来。研究人类复杂癌症表型的理想范例是原发性肝细胞癌（HCC）。原发性肝癌是癌症相关死亡的第三大常见原因，在西方国家发病率不断上升。HCC的发展与几个主要危险因素有关，包括慢性乙型和丙型肝炎感染、黄曲霉毒素暴露和肝硬化（LC）。在相同的环境暴露和HCC的家族聚集性之后，结果的可变性表明遗传易感性。我们之前的研究发现EPHX和GSTM1是中国人群中HCC的易感位点。这两个基因都参与肝细胞黄曲霉毒素的解毒。这个项目的目的是检查遗传分析，以评估基因在确定原发性肝细胞癌（HCC）的良好描述途径中的作用。这种方法结合了基因定位和候选位点研究，包括候选通路的所有成员。每个感兴趣的基因在其内部或附近被多个多态性位点“标记”，以确定在暴露于AFB1的人群中调节发生HCC风险的遗传因素。每个家族的个体成员（GSTA1、GSTM1、GSTM3、GSTP、GSTT1、GST12、EPHX1、EPHX2、GSTA4、GSTT2、GSTZ1、STP、COMT、ESD、DTD、CYP、MGST1）都被标记上了新的或已发表的多态性，并在巢式病例对照人群中研究了它们在HCC风险中的作用。基因座GSTM1、GSTP、GSTT1、EPHX1与HCC风险显著相关，而EPHX2位点与发病年龄相关。当根据病例的HBV状态对结果进行分层时，GSTM1和GSTT1仅在HBV（+）病例中相关，而GSTP在HBV（-）病例中相关。这些结果表明，这些基因是更详细的功能和遗传分析的候选者。目前正在一项大型病例对照研究（550例和550例对照）中检查15个候选癌症易感位点的候选基因变异。复杂性状分析中的遗传信息可以从癌症的遗传变异和体细胞（肿瘤）变异的联合研究中获得。使用Affymetrix HuSNP芯片上的全基因组简单串联重复多态性（STRP）标记、候选位点和1,300个单核苷酸多态性（snp）对HCC肿瘤/正常配对进行检测。对这些数据进行分析，以确定杂合性缺失（LOH）区域，并与使用含有12,000个特征基因的Affymetrix HG-U95A芯片从相同样品中收集的基因表达数据进行关联。在22条染色体上产生了16个以上的HCC LOH特征。我们发现，相对于非LOH区域，LOH区域的肿瘤基因（肿瘤基因和肿瘤抑制基因）数量显著高于非LOH区域。此外，通过系统发育重建研究，我们证明了这些LOH特征与基因表达结果显著相关；并鉴定了两个LOH特征，4q13.3和17q11.2，它们可能对产生HCC LOH特征很重要。该研究现已扩展到包括使用Affymetrix hd - u133芯片（45,000个探针集）的表达数据和使用Affymetrix Mapping 10K阵列（10,000个SNP）改进LOH区域的SNP数据。使用内部算法和Affymetrix CCNT工具也生成了数据来研究染色体拷贝数和杂合性损失的关系。正在使用最新的Affymetrix SNP6.0阵列进行其他实验。每个SNP Array 6.0有超过180万个遗传变异标记（包括&gt；900,000个SNP和&gt；940,000个拷贝数探针）用于遗传分析。使用Affymetrix SNP6.0阵列，我们获得了550例病例和550例对照的全基因组基因分型数据。此外，在同一平台上分析了20对肿瘤/正常肝组织。基因型的估计总数为11亿。使用Affymetrix Power Tools生成（HCC）样本的基因型呼叫。对照组中不符合Hardy-Weinberg平衡的snp （p <0.001）被排除在进一步分析之外。使用PLINK进行单核苷酸多态性（SNP）关联分析，采用物流模型；p值经Bonferroni校正校正。对于通路分析，我们从之前的分析中选择了单标记关联分析中最显著的1000个snp。通过鉴定这些标记在连锁不平衡中的snp来定义显著区域。使用fisher超几何密度函数评估nci调控通路中显著区域的基因富集程度。使用Affymetrix基因分型控制台CNAT程序（默认参数）和HapMap270参考模式以及循环二进制分割（CBS）算法对样本进行分析。在第一阶段样本中鉴定出422,062个不重叠的基因组片段。与HCC相关的CNV片段通过2x3 fisher精确检验确定。在第二阶段样本中测试p值低于1x10-4的段；p值经Bonferroni校正校正。TaqMan real-time PCR法对所需基因SNP6.0 CNV结果进行验证。拷贝数的测定采用绝对定量标准曲线法，以白蛋白为内参。对于肿瘤/正常配对的肝脏组织，我们发现了遗传异常，包括缺失杂合性和拷贝数变异。收集到的基因表达、候选基因座和体细胞等位基因丢失数据通过表达数据的分层聚类以及结果聚类与候选易感基因变异的相关性进行整合。通过上述数据分析，我们发现与对照相比，HCC病例与拷贝数变异和参与免疫反应的两个基因有很强的相关性（p值&lt；1x10-15）。这种变异似乎源于体细胞，反映了这种免疫反应基因与癌症患者和健康个体之间的差异。SNP分析确定了其他易感位点，包括与HCC相关的几个免疫相关基因（p值= 1x10-11）。我们的拷贝数变异分析、单标记SNP分析结果和多SNP通路分析表明，影响免疫应答的体细胞事件和种系因素在HCC易感性中起重要作用。

The majority of cancer presents as a complex phenotype and is manifest through gene-gene, and/or gene-environment interactions. An ideal paradigm for the investigation of complex cancer phenotypes in humans is primary hepatocellular carcinoma (HCC). Primary liver cancer is the third most common cause of cancer related deaths with a rising incidence in western countries The development of HCC is associated with several major risk factors including chronic hepatitis B and C infection, exposure to aflatoxin and liver cirrhosis (LC). The variability in outcome following the same environmental exposure and the familial clustering of HCC suggest genetic susceptibility. Our previous study identified EPHX and GSTM1 as HCC susceptibility loci in a Chinese population. Both genes are involved in detoxification of aflatoxin in hepatocytes. The goal of this project is to examine genetic analysis to assess the role of genes in well-described pathways in determining primary hepatocellular carcinoma (HCC). This approach merges gene mapping and candidate locus studies by including as candidates all the members of a pathway. Each gene of interest is "tagged" with multiple polymorphic sites, in or near it, to identify genetic factors modulating the risk of developing HCC among populations exposed to AFB1. The individual members of each family (GSTA1, GSTM1, GSTM3, GSTP, GSTT1, GST12, EPHX1, EPHX2, GSTA4, GSTT2, GSTZ1, STP, COMT, ESD, DTD, CYP, MGST1) have been tagged with new or published polymorphisms, and their role in HCC risk examined, in a nested case-control population. The loci GSTM1, GSTP, GSTT1, EPHX1 showed significant association with HCC risk while the EPHX2 locus was associated with age of onset. When results were stratified by the HBV status of the case, GSTM1 and GSTT1 were associated only in the HBV(+) cases, while GSTP was associated in the HBV(-) cases. These results indicate that these genes are candidates for more detailed functional and genetic analysis. Candidate gene variation at the 15 candidate cancer susceptibility loci are currently being examined in a large case-control study (n=550 cases and 550 controls). Genetic information in complex trait analysis may be accessible from the joint study of heritable variation and somatic (tumor) variation in cancer. HCC tumor/normal pairs were examined using a collection of genome-wide simple tandem repeat polymorphism (STRP) markers, candidate loci, and the 1,300 single nucleotide polymorphisms (SNPs) present on the Affymetrix HuSNP chip. This data was analyzed to identify regions of loss of heterozygosity (LOH), and was correlated with gene expression data collected from the same samples using Affymetrix HG-U95A chips containing 12,000 characterized genes. More than 16 LOH signatures of HCC were generated across 22 chromosomes. We found that the number of cancer genes (tumor genes and tumor suppressor genes) was significantly higher in regions of LOH relative to regions of non-LOH. In addition, through phylogeny reconstruction studies we demonstrated that these LOH signatures correlate significantly with gene expression results; and identified two LOH signatures, 4q13.3 and 17q11.2 that may be important in generating the HCC LOH signature. This study has now been expanded to include expression data using the Affymetrix HG-U133 chips ( 45,000 probe sets) and SNP data for refining the regions of LOH using the Affymetrix Mapping 10K Array (10,000 SNPS). Data has also been generated to investigate the relationship of chromosome copy number and loss of heterozygosity using in-house algorithms and the Affymetrix CCNT tool. Additional experiments are being carried out using the latest Affymetrix SNP6.0 arrays. Each single SNP Array 6.0 has over 1.8 million total markers for genetic variation (including >900,000 SNPs and >940,000 copy number probes) for genetic analysis. Using Affymetrix SNP6.0 arrays, we generated genome wide genotyping data from 550 cases and 550 controls. In addition, there are 20 pairs of tumor/normal liver tissues analyzed on the same platform. The estimated total number of genotypes is 1.1 billion. Genotype calls for (HCC) samples were generated with the Affymetrix Power Tools. SNPs not in Hardy-Weinberg Equilibrium in controls (p <0.001) were excluded from further analysis. Single Nucleotide Polymorphism (SNP) association analysis was performed with PLINK, using a logistical model; p-values were adjusted by Bonferroni correction. For pathway analysis, the 1,000 most significant SNPs from single marker association analysis were selected from our previous analysis. Regions of significance were defined by identifying SNPs in linkage disequilibrium with these markers. Genes in regions of significance were evaluated for enrichment in NCI-curated pathways using a Fishers hypergeometric density function. Samples were analyzed using the Affymetrix Genotyping Console CNAT program with default parameters and the HapMap270 reference mode as well as the circular binary segmentation (CBS) algorithm. 422,062 non-overlapping genomic segments were identified in Stage 1 samples. CNV segments associated with HCC were identified using a 2x3 Fishers Exact test. Ssegments with p-values below 1x10-4 were tested in the Stage 2 samples; p-values were adjusted by Bonferroni correction. TaqMan real-time PCR assays were used to confirm the SNP6.0 CNV results for the desirable genes. Determination of copy number was performed using the standard curve method of absolute quantitation with normalization to albumin as an internal reference for copy number. For tumor/normal paired liver tissues, we identified genetic abnormality including loss-heterozygosity and copy-number variation. Data collected on gene expression, candidate loci, and somatic allele loss were integrated via hierarchical clustering of expression data, and correlation of the resulting clusters with variation at candidate susceptibility loci. Using the data analysis described above We have identified a strong association with copy number variation and two genes involved in the immune response with a (p-value <1x10-15) in HCC cases when contrasted to controls. This variation appears to be somatic in origin, reflecting differences between this immune response gene and from cancer patients and healthy individuals. SNP analysis identifies other susceptibility loci including several immune related genes that is associated with HCC (pvalue = 1x10-11). Our copy number variation analysis, single marker SNP analysis results, and multi-SNP pathway analysis reveal that somatic events and germline factors affecting immune response are important in HCC susceptibility.