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，以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综合癌症生物学计划（ICBP）中心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。胚胎肿瘤是儿童时期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。目前的存活率为30 - 80%，几乎所有的幸存者都有神经和神经认知功能受损。对髓母细胞瘤（最常见的胚胎性肿瘤）的广泛基因组分析未能识别出可以解释大多数肿瘤起源或提出新策略的“驱动基因”。然而，这些肿瘤可以分为少数亚型，这些亚型具有相同的转录模式和临床结果。我们相信，现在是寻找致癌“驱动途径”而不是“驱动基因”的新方法的时候了。“由于许多不同的基因组变化都可以影响相同的驱动途径，因此无法通过寻找重复的基因组变化来发现这些途径。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类髓母细胞瘤肿瘤和细胞系的综合数据集。然后，我们将构建网络模型，识别亚型内许多患者之间改变的共享途径。最后，我们将功能测试从网络建模中提名的驱动程序路径。通过合并从个体患者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中的这一关键障碍提供了独特的环境，结合了基因组数据分析，信号通路建模和转录调控网络以及胚胎脑肿瘤临床专业知识的优势。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，提取生物相关的网络模型，并通过实验验证新的药物靶点。

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.