Integrative genomic analysis in hepatocellular carcinoma

描述(申请人提供)：肝细胞癌(HCC)是全球第三大癌症相关死亡原因，美国和西欧的癌症发病率上升最快。这种疾病是一个重大的公共卫生问题，目前还没有分子诊断方法，有效的治疗选择也有限。该项目的假设是，了解肝细胞癌的基因表达谱和体细胞遗传变化将有助于识别参与疾病发病机制的肿瘤抑制基因或癌基因，并将提供早期发现的标记和新的治疗靶点。识别预测临床结果的基因特征将使对肝细胞癌的分子分类成为可能。为此，我们将测试230个样本[包括20个发育不良结节和100对与丙型肝炎病毒相关的肝细胞癌/非肿瘤性肝硬变]。我们已经从三个转诊的肝细胞癌单位收集了214个样本：纽约西奈山；巴塞罗那医院诊所(合作者#2)；米兰国家癌症研究所(协作者#3)。具体目的：目的1：研究丙型肝炎病毒感染患者中异型增生病变和肝细胞癌的基因表达谱特征。1A.通过全基因组基因表达分析，确定与肝癌发生有关的基因和途径。通过使用38,500个基因的全基因组微阵列技术(Affymetrix，GeneChip Human Genome-U133 Plus 2.0)，我们在初步的73个样本中识别了对发育不良结节、早期肿瘤和晚期癌症进行分类的分子特征。我们将把分析范围扩大到230个样本。1B.确定预测肝细胞癌复发和存活的分子特征。对85名已有完整临床资料记录的接受肝细胞癌切除术的患者进行分析，得出能够区分好和差结果的基因特征。多变量方法将确保临床和分子数据的适当整合。目的#2：使用询问500,000个基因组座位的高密度单核苷酸多态阵列来确定肝细胞癌基因组结构改变的程度。将新出现的遗传图谱与配对表达数据相结合，以确定候选的肿瘤抑制基因和癌基因。我们将与哈佛大学Dana-Farber研究所的William Sellers/Matthew Meyerson博士团队(第一合作者)合作，将高密度500K SNP阵列应用于肝癌结构性体细胞遗传变化的分析。通过使用分子图谱和遗传干扰的综合基因组分析，我们将识别新的肿瘤抑制基因或癌基因，这可能成为化学预防和治疗策略的新靶点。目的#3：寻找诊断早期肝细胞癌的分子生物标志物。#3a：生物标志物的鉴定。我们已经通过Real-Time RT-PCR鉴定了3个基因(glypcan-3、Survivin、Lyve-1)的基因特征，能够区分异常增生性病变和早期癌症，准确率为94%。通过微阵列技术，我们获得了一组93个潜在候选基因的初步数据，用于识别新的生物标记物。#3b：验证分子标记对正在接受筛查的肝硬变患者早期肝细胞癌的诊断价值。确定的基因签名将在临床实践中得到验证，测试集的80个样本来自0.5-2厘米之间的结节，这些样本来自丙型肝炎-肝硬变患者的预期队列(合作者#2，巴塞罗那大学)。

DESCRIPTION (provided by applicant): Hepatocellular carcinoma (HCC) is the third cause of cancer-related death worldwide with the most rapidly rising cancer incidence in the US and Western Europe. The disease is a major public health problem, for which there are no molecular diagnostics and limited effective treatment options. The hypothesis of the project is that knowledge of the gene expression profiles and the somatic genetic alterations of HCC will enable the identification of tumor suppressor genes or oncogenes involved in the pathogenesis of the disease, and will provide markers of early detection and new treatment targets. The identification of gene signatures predictive of clinical outcomes will enable a molecular classification of HCC. For that purpose we will test 230 samples [including 20 dysplastic nodules and 100 paired HCV- related HCC/non-tumoral cirrhotic liver]. We already have collected 214 samples from patients in 3 referral HCC Units: Mount Sinai, New York; Hospital Clinic Barcelona (Collaborator #2); National Cancer Institute, Milan (Collaborator #3). Specific aims: Aim #1: Characterize the gene expression profiles of dysplastic lesions and hepatocellular carcinoma in HCV infected patients. 1a. Determine the genes and pathways involved in hepatocarcinogenesis, by genome- wide gene expression analysis. By using genome-wide microarray technology profiling of 38,500 genes, (Affymetrix, Genechip Human Genome-U133 Plus 2.0), we have identified molecular signatures that classify dysplastic nodules, early tumors and advanced cancers in a preliminary set of 73 samples. We will expand the analysis to 230 samples. 1b. Identify a molecular signature predictive of HCC recurrence and survival. A gene signature able to discriminate good and poor outcomes will be analyzed in 85 patients undergoing resection for HCC in which complete clinical data has been recorded. Multivariate approaches will ensure a proper integration of the clinical and molecular data. Aim #2: To define the extent of structural alterations in the HCC genome using high-density single nucleotide polymorphisms arrays interrogating 500,000 genomic loci. To integrate emerging genetic maps with paired expression data to identify candidate tumor suppressors and oncogenes. We will apply high-density 500K SNP arrays to the analysis of structural somatic genetic alterations in HCC, in collaboration with the Dr. William Sellers/Matthew Meyerson's group in Dana-Farber Institute, Harvard (Collaborator #1). By using integrative genomic analysis of the molecular profiling and genetic disturbances we'll identify novel tumor suppressors or oncogenes, which may constitute new targets for chemopreventive and therapeutic strategies. Aim #3: Identify molecular biomarkers for the diagnosis of early HCC. #3a: Identification of biomarkers. We have identified by Real-Time RT-PCR a gene signature of 3 genes (Glypican-3, Survivin, LYVE-1) able to discriminate between dysplastic lesions from early cancer with an accuracy of 94%. By microarray technology, we have preliminary data with a set of 93 potential candidate genes for the identification of new biomarkers. #3b: To validate the molecular signature for the diagnosis of early HCC in cirrhotic patients undergoing screening. The gene- signature identified will be validated in the clinical practice in a testing set of 80 samples from nodules between 0.5-2cm obtained from a prospective cohort of HCV-cirrhotic patients (Collaborator #2, University of Barcelona).