GENOMIC ANALYSIS OF MEDULLOBLASTOMAS

描述(申请人提供)：最近，DNA寡核苷酸微阵列基因表达分析已被用于确定髓母细胞瘤的分子图谱，髓母细胞瘤是儿童最常见的恶性脑肿瘤(Pomeroy等人，2002年)。髓母细胞瘤在分子水平上有别于其他中枢神经系统胚胎性肿瘤，其组织亚型具有独特的和功能显著的基因表达模式。基因表达谱高度预测治疗反应，在回顾分析中预测生存比目前的临床分期标准或单标记基因结果预测要准确得多。现在必须对这些结果进行前瞻性验证，然后分子标记才能用于未来临床试验的风险分层。这项建议的实验是儿童肿瘤组(COG)第三阶段髓母细胞瘤治疗试验的相关生物学研究，旨在优化和验证预后分子标志物。在具体目标1中，基于未来5年在COG髓母细胞瘤试验中接受治疗的500名儿童的肿瘤的基因表达谱(Affymetrix Human Genome U133阵列)的多分子结果预测指标将被优化、验证和开发，以便在未来的临床试验中进行“实时”分析。在具体目标2中，基因表达数据库将用于发展分子分类学，识别由促进肿瘤发生的分子信号通路的表达谱定义的髓母细胞瘤的亚类。基因表达将与特定目标3的致癌基因组突变联系在一起，通过结合表达谱分析和从基于DNA BAC克隆(光谱基因组学)微阵列的比较基因组杂交获得的全基因组突变分析。为了实现这些目标，我们将创建一个全面的髓母细胞瘤基因表达和突变数据库，该数据库与一大批具有良好特征的儿童的临床结果相联系。这些数据将托管在NCI/NINDS神经肿瘤学分部的Howard Fine和NIH生物信息学小组正在开发的NCI/NINDS胶质瘤分子诊断倡议的网站上，以便世界各地的研究人员可以免费访问该项目产生的分子和临床数据库。

DESCRIPTION (provided by applicant): Recently, DNA oligonucleotide microarray gene expression analysis has been used to define molecular profiles of medulloblastomas, the most common malignant brain tumors of childhood (Pomeroy et al., 2002). Medulloblastomas were found to be molecularly distinct from other CNS embryonal tumors, and their histological subtypes had unique and functionally significant gene expression patterns. Gene expression profiles were highly predictive of response to therapy, predicting survival in a retrospective analysis with much greater accuracy than current clinical staging criteria or single marker gene outcome predictors. These results must now be prospectively validated before molecular markers can be used for risk stratification in future clinical trials. The experiments of this proposal are correlative biological studies of Phase III medulloblastoma therapy trials of the Children's Oncology Group (COG), designed to optimize and validate prognostic molecular markers. In Specific Aim 1, a multi-molecular outcome predictor, based on gene expression profiles (Affymetrix Human Genome U133 arrays) of tumors from 500 children treated in COG medulloblastoma trials over the next 5 years, will be optimized, validated and developed for "real time" analysis in the context of future clinical trials. In Specific Aim 2, the gene expression database will be used to develop a molecular taxonomy, identifying subclasses of medulloblastomas defined by the expression profiles of molecular signaling pathways that promote tumorigenesis. Gene expression will be linked to oncogenic genomic mutations in Specific Aim 3, by combining expression profiling with genome-wide mutation analysis obtained from DNA BAC clone (Spectral Genomics) microarray-based comparative genomic hybridization. In achieving these Aims, we will create a comprehensive medulloblastoma gene expression and mutation database that is linked to clinical outcome of a large and well-characterized cohort of children. The data will hosted on the website of the NCI/NINDS Glioma Molecular Diagnostic Initiative that is being developed by Howard Fine of the NCI/NINDS Neuro-Oncology Branch and the NIH Bioinformatics group, so that investigators throughout the world can have free access to the molecular and clinical database generated by this project.