Embryonal Brain Tumor Networks

描述(由申请人提供)：我们提出了一种创新的系统生物学方法来发现儿童胚胎肿瘤的新治疗策略。我们的项目是两个独立的整合癌症生物学计划(ICBP)中心的实验室和一个领先的医院转化研究实验室之间的合作，该实验室不在ICBP网络内。胚胎肿瘤是儿童最常见的中枢神经系统恶性肿瘤，迫切需要更好的治疗方法。目前的存活率从30%到80%不等，几乎所有的幸存者都有神经和神经认知功能受损。髓母细胞瘤是最常见的胚胎肿瘤，对其进行了广泛的基因组分析，但未能找到能够解释大多数肿瘤起源或提出新策略的“驱动基因”。然而，这些肿瘤可以归类为少数亚型，它们具有相同的转录模式和临床结果。我们认为，现在是时候采取一种根本性的新方法，寻找致癌的“驱动途径”，而不是“驱动基因”。由于许多不同的基因组变化都可能影响相同的驱动途径，这些途径不能通过寻找反复发生的基因组变化来发现。相反，我们将使用系统生物学方法来识别这些致癌驱动途径。我们将通过测量突变、拷贝数变异、mRNA表达、miRNA表达和表观基因组数据来收集人髓母细胞瘤肿瘤和细胞系的全面数据集。然后，我们将构建网络模型，识别在一个亚型中改变了许多患者的共享路径。最后，我们将对从网络建模中指定的驱动路径进行功能性测试。通过合并从单个患者的肿瘤中收集的这些不同的基因组和转录数据，我们将拥有前所未有的能力来揭示癌症的根本原因，提供新的治疗策略。我们合作的集体专业知识为解决癌症这一关键障碍提供了一个独特的环境，将分析基因组数据、建模信号通路和转录调控网络的优势与胚胎脑瘤的临床专业知识结合在一起。我们将共同生成和合并所有类型的转录、基因组和表观基因组数据，提取与生物相关的网络模型，并在实验上验证新的药物靶点。

DESCRIPTION (provided by applicant): We propose an innovative, systems biology approach to uncover new therapeutic strategies for childhood embryonal tumors. Our project is a collaboration between labs in two separate Integrative Cancer Biology Program (ICBP) centers and a leading hospital-based translational research lab that is not within the ICBP network. Embryonal tumors are the most common central nervous system malignancies in childhood, and there is a pressing need for better therapies. Current survival rates range from 30 - 80%, and nearly all survivors have impaired neurological and neurocognitive function. Extensive genomic analysis of medulloblastomas, the most common embryonal tumors, failed to identify "driver genes" that could explain the origin of most tumors or suggest new strategies. Nevertheless, these tumors can be grouped into a small number of subtypes that share transcriptional patterns and clinical outcomes. We believe that it is time for a fundamentally new approach that seeks oncogenic "driver pathways" rather than "driver genes." As many different genomic changes can all affect the same driver pathway, such pathways cannot be uncovered by looking for recurring genomic changes. Rather, we will use a systems biology approach to identify these oncogenic driver pathways. We will collect comprehensive datasets in human medulloblastoma tumors and cell lines by measuring mutations, copy number variations, mRNA expression, miRNA expression and epigenomic data. We will then construct network models identifying shared pathways altered across many patients within a subtype. Finally, we will functionally test driver pathways nominated from the network modeling. By merging these diverse genomic and transcriptional data collected from tumors of individual patients, we will have an unprecedented ability to uncover the root causes of cancer, providing new therapeutic strategies. The collective expertise of our collaboration provides a unique environment for solving this critical barrier in cancer, by combining strengths in analyzing genomic data, modeling signaling pathways and transcriptional regulatory networks and clinical expertise in embryonal brain tumors. Together, we will generate and merge all types of transcriptional, genomic and epigenomic data, extract biologically-relevant network models and experimentally validate novel drug targets.