Molecular Genetic Epidemiology of Primary Hepatocellular Carcinoma

原发性肝细胞癌的分子遗传学流行病学

• 批准号: 7733711
• 负责人: Kenneth H Buetow
• 金额: $ 157.7万
• 依托单位: DIVISION OF CANCER EPIDEMIOLOGY AND GENETICS
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7733711
• 关键词: 17q11.2; 4q13.3; Aflatoxin B1; Age of Onset; Algorithms; Alleles; COMT gene; Cancer Model; Candidate Disease Gene; Case-Control Studies; Catechol O-Methyltransferase; Chromosome Mapping; Chromosomes; Chromosomes, Human, Pair 22; Chronic; Clinical; Collection; Complex; Copy Number Polymorphism; DNA; Data; Data Correlations; Data Set; Databases; Disease; Disease Association; EPHX1 gene; Environmental Risk Factor; Epidemiologic Studies; Evaluation; Family; GSTM1 gene; GSTT1 gene; GSTT1 protein; Gene Expression; Gene Frequency; Gene Targeting; Genes; Genetic; Genetic Markers; Genetic Polymorphism; Genetic Risk; Genetic Variation; Genome; Genotype; Goals; Haplotypes; Hepatitis B Virus; Hepatitis C virus; Housing; Human; Individual; Investigation; Joints; Laboratories; Liver; Loss of Heterozygosity; Malignant Neoplasms; Malignant neoplasm of liver; Maps; Methods; Microsomal Epoxide Hydrolase; Modeling; Molecular; Molecular Abnormality; Molecular Genetics; Mutation; NQO1 gene; Normal Cell; Numbers; Odds Ratio; Oncogenes; Pathway interactions; Phenotype; Phylogeny; Population; Predisposition; Primary carcinoma of the liver cells; Process; Publishing; Rate; Relative (related person); Research Project Grants; Resources; Risk; Risk Assessment; Role; Sampling; Short Tandem Repeat Polymorphism; Simian B disease; Single Nucleotide Polymorphism; Site; Structure; TP53 gene; Testing; Tissues; Training; Tumor Suppressor Genes; Validation; Variant; base; case control; gene environment interaction; genetic analysis; genetic epidemiology; genetic profiling; glutathione S-transferase M1; interest; knock-down; member; reconstruction; research study; tool; trait; tumor

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). Molecular studies of genetic alterations in tumors have identified p53 as a tumor suppressor gene commonly altered in HCC. Epidemiologic studies have firmly established the role of chronic hepatitis B virus infection (HBV) and aflatoxin B1 (AFB1) exposure as environmental risk factors. However, the majority of individuals exposed to HBV and AFB1 do not develop HCC. Genetic analysis is being used 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 more than 900,000 SNPs and more than 940,000 copy number probes) for genetic analysis. Using Affymetrix SNP6.0 arrays, we generated 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. Currently. We will be performing the following analysises that involves an iterative process of querying the database, storing the results and refining the query based on the analytical results. These analysis include: a) Odds-ratio of case and control to identify disease association SNPs. We will probably include only SNPs with a high call rate (for example greater than or equal to 85% of the samples have genotype calls; minimum allele frequency exceeds 10%; genotype quality exceeds certain threshold) in this query. This query is likely to be performed across all 1.1 billion genotype rows. b) Identify genes underlying the high-association SNPs; obtain genotypes in these genes to construct haplotypes and LD bin for more structured analysis like haplotype clad. c) Identify allelic-interaction across SNPs. This would require evaluation of genetic risk using multiple SNPs as a single-unit for risk assessment. d) Test and validation analysis. The initial association study will be performed using 2/3 of the samples as training data to identify disease association variations. The results will be tested in the remaining 1/3 of samples. The samples serving as the test and validation shall have the comparable genetic profile and clinical features. The test and validation can be performed using single SNP or multi-SNP as a disease predictor. e) For tumor/normal paired liver tissues, we will identify genetic abnormality including loss-heterozygosity and copy-number variation. This data will be compared against the expression data that we have generated to evaluate the correlation between genetic alteration and expression change. Data collected on gene expression, candidate loci, and somatic allele loss will be integrated via hierarchical clustering of expression data, and correlation of the resulting clusters with variation at candidate susceptibility loci. This information will be used to develop, test, and validate laboratory strategies for pathway models of the cancer/normal cell. To validate the pathways model, siRNAs experiments will be carried out to knock down targeted genes involved in these pathways.

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。用于研究人类复杂癌症表型的理想范式是原发性肝细胞癌（HCC）。肿瘤中遗传改变的分子研究已将p53鉴定为通常在HCC中改变的肿瘤抑制基因。流行病学研究已经牢固确定了慢性丙型肝炎病毒感染（HBV）和黄曲霉毒素B1（AFB1）的作用，作为环境风险因素。但是，暴露于HBV和AFB1的大多数人不会发展HCC。 遗传分析用于评估基因在确定原发性肝细胞癌（HCC）中的良好描述途径中的作用。这种方法通过将途径的所有成员包括在内，将基因映射和候选基因座研究融合在一起。每个感兴趣的基因在其附近或附近都被“标记”，以确定调节暴露于AFB1的种群中HCC的风险的遗传因素。 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 人口。基因座GSTM1，GSTP，GSTT1，EPHX1与HCC风险显示显着相关，而EPHX2基因座与发病年龄有关。当结果按病例的HBV状态进行分层时，GSTM1和GSTT1仅在HBV（+）情况下关联，而GSTP在HBV（ - ）情况下是相关的。这些结果表明，这些基因是用于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（n = 550例和550例对照）中检查了15个候选癌症敏感性基因座的候选基因变异。复杂性状分析中的遗传信息可以从癌症的遗传性变异和体细胞（肿瘤）变异的联合研究中获得。使用全基因组简单串联重复多态性（STRP）标记，候选基因座和1,300个单核苷酸多态性（SNP）的集合来检查HCC肿瘤/正常对。分析了该数据以识别杂合性丧失（LOH）的区域，并与使用含有12,000个特征基因的Affymetrix HG-U95A芯片相关，与从相同样品中收集的基因表达数据相关。在22个染色体上产生了超过16个HCC的LOH特征。我们发现，相对于非LOH区域，LOH区域中癌症基因的数量（肿瘤基因和肿瘤抑制基因）的数量明显更高。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着相关。并确定了两个LOH签名，即4Q13.3和17Q11.2，这对于生成HCC LOH签名可能很重要。 现已扩展了这项研究，以使用Affymetrix HG-U133芯片（45,000个探针集）和SNP数据包括表达数据，并使用Affymetrix映射10K阵列（10,000 SNP）来完善LOH的区域。还生成了数据来研究使用内部算法和Affymetrix CCNT工具的染色体拷贝数和杂合性损失的关系。使用最新的Affymetrix SNP6.0阵列进行其他实验。每个SNP阵列6.0的遗传变异总数超过180万个标记（包括超过900,000个SNP和940,000多个拷贝数探针），用于遗传分析。使用Affymetrix SNP6.0阵列，我们从550例和550个对照中生成了基因分型数据。此外，在同一平台上分析了20对肿瘤/正常肝组织。估计的基因型总数为11亿。现在。我们将执行以下分析，该分析涉及迭代过程，以查询数据库，存储结果并根据分析结果完善查询。这些分析包括：a）病例和控制的赔率，以鉴定疾病关联SNP。在此查询中，我们可能仅包括具有较高呼叫率的SNP（例如大于或等于85％的样品具有基因型调用；最小等位基因频率超过10％；基因型质量超过某些阈值）。此查询可能会在所有11亿个基因型行中执行。 b）识别高缔合SNP的基因；在这些基因中获得基因型以构建单倍型和LD垃圾箱，以进行更结构化的分析，例如单倍型。 c）确定跨SNP的等位基因相互作用。这将需要使用多个SNP作为风险评估的单单元来评估遗传风险。 d）测试和验证分析。最初的关联研究将使用2/3样品作为训练数据进行，以识别疾病关联的变化。结果将在其余1/3的样品中进行测试。用作测试和验证的样品应具有可比的遗传特征和临床特征。可以使用单个SNP或多SNP作为疾病预测因子进行测试和验证。 e）对于肿瘤/正常配对肝组织，我们将确定遗传异常，包括损失杂合性和拷贝数变化。将将这些数据与我们生成的表达数据进行比较，以评估遗传改变与表达变化之间的相关性。收集的有关基因表达，候选基因座和体细胞等位基因损失的数据将通过表达数据的层次聚类进行集成，以及在候选敏感性基因座时与所得簇的相关性。该信息将用于开发，测试和验证癌症/正常细胞途径模型的实验室策略。为了验证途径模型，将进行siRNA实验，以击倒这些途径中涉及的靶向基因。

项目成果

Genetic variations at loci involved in the immune response are risk factors for hepatocellular carcinoma.

• DOI: 10.1002/hep.23943
• 发表时间: 2010-12
• 期刊: Hepatology (Baltimore, Md.)
• 作者: Clifford RJ;Zhang J;Meerzaman DM;Lyu MS;Hu Y;Cultraro CM;Finney RP;Kelley JM;Efroni S;Greenblum SI;Nguyen CV;Rowe WL;Sharma S;Wu G;Yan C;Zhang H;Chung YH;Kim JA;Park NH;Song IH;Buetow KH
• 通讯作者: Buetow KH