Molecular Genetic Epidemiology of Primary Hepatocellular Carcinoma

LPG开发的扩展模型和分析策略将用于深入了解HCC的病因和易感性。HCC是一种典型的复杂特征，具有充分记录的环境风险因素（65-69）和体质遗传易感性差异的证据（70-74）。Buetow实验室通过网络分析推测，一致性将从hcc明显的分子病因异质性中产生。我们验证了HCC通过有限数量的生物过程中的分子改变而产生的假设。更具体地说，将观察到网络的一个子集在肝脏疾病谱系中是常见的，不同的网络成分区分不同的疾病状态。此外，这些共同的过程为不同的环境危险因素HBV、HCV、酗酒和肥胖提供了统一的潜在分子病因学。最后，我们假设t细胞介导的免疫可能代表这些模块之一。主要目标是使用遗传和途径方法识别这些网络组件，实验室将通过分析额外的、前瞻性的、独立的数据集来验证其当前的发现，并将使用基于实验室的网络特征来评估其重要性。该实验室将综合多维分子数据，并与李实验室合作进行表观遗传分析。与Nodality合作，核酸的发现将被翻译成发生在单细胞蛋白水平的改变。利用NIDDK NASH临床研究网络的资源，将评估更广泛的疾病进展定义。该实验室将利用这些发现开发HCC和易感性的预测因子。Buetow实验室已经确定了与HCC发生和进展相关的途径（85）。使用Affymetrix寡核苷酸微阵列U133A平台检测48例HBV+和HCV+肿瘤和肿瘤邻近肝脏样本的基因表达。使用病理学家，我们通过从观察到的基因表达数据中计算通路活性和通路一致性评分，测量了PID中通路中分子相互作用的变化（28）。为了研究SHH信号通路在HCC发生发展中的作用，我们转染了PTCH、SHH、RASGRF1对应的小干扰rna （sirna）和阴性对照。因此，siRNA介导的SHH基因表达沉默导致SNU449细胞系细胞增殖显著降低，表明SHH在促进HCC细胞增殖中起重要作用。除了SHH网络的作用外，4种途径的组合正确区分肿瘤和正常组织，准确率达到92%。有趣的是，四种途径之一是内脏脂肪沉积和代谢综合征。这条途径代表了胰岛素和类固醇、炎症和脂质代谢激活的基因的整合。这一结果表明，以HBV和HCV为主要危险因素的样本可能通过一个共同的病因途径发生作用，肥胖/糖尿病相关途径可能在低发病率地区的HCC中很重要（88）。为了确定HCC的病因和易感位点，Buetow实验室进行了一项关联研究，分析了从外周血中分离的DNA中的单核苷酸多态性（snp）和拷贝数变异（CNV）（89）。本工作采用Affymetrix SNP 6.0微阵列。本研究纳入了在韩国首尔牙山医疗中心就诊的不相关HCC和肝硬化（LC）患者，89%的HCC病例和76%的LC病例慢性感染HBV或HCV。使用了两种对照来源。第一组控制组是来自韩国首尔峨山医疗中心（Asan Medical Center）的无关个体。对照组的病毒感染状况尚未确定。第二个对照来源是来自中国海门市的HBV+中国籍个体。我们使用两阶段发现和复制设计来控制过度拟合并验证观察到的结果。韩国人肝癌386例、韩国人肝硬化86例、韩国对照587例、中国对照100例通过了质量控制评价。来自韩国人群的个体根据其入组顺序被分配到发现（阶段1）或验证（阶段2）组。第一阶段包括271例对照、180例HCC和66例LC；第二阶段包括316名对照组、206名HCC患者和20名肝硬化患者。使用中国对照样本进一步验证了两阶段分析的主要发现。使用随机数发生器逐个随机选择每个板的病例。对于发现阶段的每个病例，通过在控制样本中找到性别和年龄的最佳匹配来选择匹配控制。这种处理每个阶段1病例以及匹配对照的策略旨在最大限度地减少技术变化对实验结果的可能影响。验证阶段的对照临床信息有限，因此随机选择。使用Affymetrix基因分型控制台程序单独分析样本的拷贝数变化，并将得到的拷贝数log2比率数据作为循环二值分割（CBS）算法的R DNA拷贝包的输入。我们发现，与对照组相比，HCC病例中t细胞受体γ和α位点的拷贝数变化与p值（1 × 10-15）密切相关。使用TaqMan实时PCR检测（Applied Biosystems, Foster City， CA）来确认TRA@和TRG@.的SNP6.0 CNV结果TRA@和TRG@的变异似乎源于躯体，反映了肝脏疾病患者和健康个体淋巴细胞中t细胞受体加工的差异，这与慢性肝炎病毒感染无关。T细胞受体变异的模式表明，病例和对照人群中存在不同的T淋巴细胞群，这可能反映了T细胞亚群的成熟或增殖。TRA@的CNV模式表明重排事件产生功能性α链的频率高于δ链。在单个样本中观察到的低拷贝数片段经常包含TCR δ常数区，但很少包括TCR α常数区。为了确定SNP6.0基因型呼叫不是实验伪像，我们使用TaqMan分析对这些标记进行了基因分型。这些TaqMan结果与高通量Affymetrix 6.0平台SNP阵列生成的数据完全一致。所有三个snp都与HCC独立相关，既不表现出加性效应，也不表现出乘法效应。有趣的是，除了与HCC相关外，三个snp中有两个与肝硬化相关（p值分别为0.0052和0.0007）。相比之下，一个SNP仅与肝硬化弱相关（p值为0.0408）。然而，比较HCC和肝硬化患者的SNP等位基因频率，发现了两个区分HCC和肝硬化的变异。其中一个SNP位于TPTE2基因内；第二个位于基因贫乏的2q14.1区域。这两种多态性都不同于HCC病例和对照组比较中发现的多态性。拷贝数变异、个体snp和通路的综合分析表明，HCC易感性是由影响免疫应答和t细胞受体加工差异的种系因子介导的。我们的研究结果提供了基因组证据，表明参与免疫反应的基因在HCC的发展中起着关键作用。

the extended models and analytic strategies developed at LPG will be applied to gain insight into HCC etiology and susceptibility. HCC is a quintessential complex trait with well-documented environmental risk factors (65-69) and evidence of constitutional genetic differences in susceptibility (70-74). The Buetow laboratory hypothesizes that coherence will emerge from HCCs apparent molecular etiologic heterogeneity through analysis of networks. We tested the hypothesis that HCC arises through molecular alterations in a finite number of biologic processes. More specifically, a subset of networks will be observed to be common across the spectrum of liver disease with different network components discriminating the different disease states. Moreover, the common processes provide a unified underlying molecular etiology for the diverse environmental risk factors HBV, HCV, alcoholism, and obesity. Lastly, we hypothesize that T-cell mediated immunity may represent one of these modules.It is the primary goal to identify these network components using genetic and pathway methods, the laboratory will validate its current findings through analysis of additional, prospective, independent data sets and will use laboratory-based characterizations of the networks to assess their significance. The laboratory will synthesize the multidimensional molecular data, and add epigenetic analysis in partnership with the Lee lab. In partnership with Nodality, the nucleic acid findings will be translated into alterations occurring at the single cell protein level. Leveraging the resources of the NIDDK NASH Clinical Research Network, a broader definition of disease progression will be assessed. The laboratory will use the findings to develop predictors of HCC and susceptibility.The Buetow laboratory has identified pathways associated with the development and progression of HCC (85). Gene expression was measured for 48 HBV+ and HCV+ tumor and tumor-adjacent liver samples using the Affymetrix oligonucleotide microarrays U133A platform. Using PathOlogist, we measured alterations in molecular interaction in pathways in the PID (28) by calculating pathway activity and pathway consistency scores from the observed gene expression data. To investigate the role that SHH signaling pathway may play in the development and progression of HCC, SNU449 (human hepatocellular carcinoma cells) were transfected with small interfering RNAs (siRNAs) corresponding to PTCH, SHH, RASGRF1 and a negative control. Thus, siRNA mediated silencing of SHH gene expression resulted in a significant reduction of cell proliferation in the SNU449 cell line indicating that SHH plays a major role in promoting cell proliferation in HCC.Over and above the role of the SHH network, a combination of 4 pathways correctly classified tumor and normal tissue with 92% accuracy. Interestingly, one of the four pathways was visceral fat deposits and the metabolic syndrome. This pathway represents the integration of genes activated by insulin and steroids, inflammation, and lipid metabolism. This result suggests that the samples whose major risk factors are HBV and HCV may operate through a common etiologic pathway obesity/diabetes-associated pathway likely to be important in HCC in low-rate areas (88). To identify etiologic and susceptibility loci for HCC, the Buetow laboratory has conducted an association study analyzing single nucleotide polymorphisms (SNPs) and copy number variations (CNV) in DNA isolated from peripheral blood (89). This work used the Affymetrix SNP 6.0 microarray. This study involved unrelated HCC and liver cirrhosis (LC) patients seen at the Asan Medical Center, Seoul, South Korea. 89% of the HCC cases and 76% of the LC cases were chronically infected with either HBV or HCV. Two sources of controls were used. The first sets of controls were unrelated individuals from the Asan Medical Center, Seoul, South Korea. The viral infection status of controls was not ascertained. A second source of controls was HBV+ individuals of Chinese origin from Haimen City, China. We used a two stage discovery and replication design to control for over-fitting and to validate observed results. A total of 386 Korean HCC cases, 86 Korean cirrhosis cases 587 Korean controls, and 100 Chinese controls passed the quality control evaluations. Individuals from the Korean population set were assigned to the discovery (Stage 1) or validation (Stage 2) groups based on their order of enrollment in the study. Stage 1 included 271 controls, 180 HCC cases and 66 LC cases; Stage 2 included 316 controls, 206 HCC patients and 20 individuals with cirrhosis. Key findings from the two stage analysis were further validated using the Chinese control samples. Cases were randomly selected for each plate one-by-one using a random-number generator. For each case in the discovery phase, a matching control was selected by finding its best match in sex and age among the control samples. This strategy of processing each Stage 1 case along with a matched control was aimed at minimizing the possible effects of technical variation on experimental results. Controls in validation phase have limited clinical information and therefore were selected randomly. Samples were analyzed separately for copy number variation using the Affymetrix Genotyping Console program with the resulting copy number log2ratio data serving as input for the R DNA copy package for the circular binary segmentation (CBS) algorithm We identified a strong association with copy number variation at the T-cell receptor gamma and alpha loci (p-value <1x10-15) in HCC cases when contrasted to controls. TaqMan real-time PCR assays (Applied Biosystems, Foster City, CA) were used to confirm the SNP6.0 CNV results for TRA@ and TRG@. TRA@ and TRG@ variation appears to be somatic in origin, reflecting differences between T-cell receptor processing in lymphocytes from individuals with liver disease and healthy individuals that is not attributable to chronic hepatitis virus infection.The pattern of T cell receptor variation suggests the presence of different T-lymphocyte populations in case and control populations that potentially reflects maturation or proliferation of a subpopulation of T cells. CNV patterns at TRA@ suggest that rearrangement events generate functional alpha chains more frequently than delta chains. Low copy number segments observed in individual samples frequently encompass the TCR delta constant region, but rarely include the TCR alpha constant regionTo establish that the SNP6.0 genotype calls were not experimental artifacts, we genotyped these markers using TaqMan assays. These TaqMan results were in complete agreement with the high-throughput Affymetrix 6.0 platform SNP array generated data. All three SNPs are independently associated with HCC, showing neither an additive nor multiplicative effect. Interestingly, in addition to their association with HCC, two of the three SNPs were associated with cirrhosis (p values of 0.0052 and 0.0007, respectively). In contrast, one SNP was only weakly associated with cirrhosis (p-value is 0.0408).Comparison of SNP allele frequencies in HCC and cirrhosis patients, however, identified two variants that distinguish HCC from cirrhosis. One SNP, is located within the TPTE2 gene; the second, lies in a gene-poor region of 2q14.1. Both polymorphisms are distinct from those identified in the comparison of HCC cases and controls. Combined analysis of copy number variation, individual SNPs, and pathways suggest that HCC susceptibility is mediated by germline factors affecting the immune response and differences in T-cell receptor processing. Our findings provide genomic evidence that genes involved in the immune response play a critical role in the development of HCC.