GENOMIC ANALYSIS OF MEDULLOBLASTOMAS

描述（由申请人提供）：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谱，髓母细胞瘤是儿童最常见的恶性脑肿瘤（Pomeroy et al., 2002）。髓母细胞瘤在分子上不同于其他中枢神经系统胚胎性肿瘤，其组织学亚型具有独特且功能显著的基因表达模式。基因表达谱可以高度预测对治疗的反应，在回顾性分析中预测生存比目前的临床分期标准或单一标记基因结果预测指标要准确得多。在分子标记物用于未来临床试验的风险分层之前，这些结果必须进行前瞻性验证。本提案的实验是儿童肿瘤组（COG） III期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记。在Specific Aim 1中，一种基于基因表达谱（Affymetrix人类基因组U133阵列）的多分子预后预测器将在未来5年内对500名接受COG髓母细胞瘤治疗的儿童肿瘤进行优化、验证和开发，以便在未来的临床试验中进行“实时”分析。在Specific Aim 2中，基因表达数据库将用于开发分子分类学，通过促进肿瘤发生的分子信号通路的表达谱来确定髓母细胞瘤的亚类。通过将表达谱与DNA BAC克隆（光谱基因组学）微阵列比较基因组杂交获得的全基因组突变分析相结合，将基因表达与特异性Aim 3中的致癌基因组突变联系起来。为了实现这些目标，我们将创建一个全面的成神经管细胞瘤基因表达和突变数据库，该数据库与大量具有良好特征的儿童队列的临床结果相关联。这些数据将托管在NCI/NINDS神经肿瘤学分部的Howard Fine和NIH生物信息学小组共同开发的NCI/NINDS神经胶质瘤分子诊断计划的网站上，这样全世界的研究人员都可以免费访问该项目生成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.