Bioinformatics: Characterizing Brain Tumor Data

生物信息学作为一门将生物数据与信息存储、分布和分析技术联系起来，以支持包括生物医学在内的多个科学研究领域的混合科学，受到生物学中数据生成过程的极大加速的推动。在过去的几年中，NOB在自然史研究中招募了800多名患者，该研究要求收集肿瘤组织和外周血样本以获取生殖细胞DNA。这包括前所未有数量的罕见中枢神经系统癌症患者。此外，样品收集伴随着仔细和前瞻性的临床数据采集，允许前所未有的丰富匹配的分子和临床数据允许各种各样的分析。基因、蛋白质和代谢物分子分析的进步极大地提高了我们对生物过程和疾病的理解，提高了我们监测治疗反应和对患者进行分层以提高治疗疗效的能力。伴随诊断促进的精准医疗是加速药物开发过程和改善治疗管理的驱动力之一。NCI基因组数据共享（GDC）于2016年启动，为来自NCI倡议和癌症研究项目（包括TCGA和TARGET）的数据提供了单一来源，以及挖掘这些数据所需的分析工具。新启动的NOB生物信息学扩展了我们的生物信息学和计算分析工作，以利用GDC数据库。例如，使用TCGA数据有效地将我们必须处理的gbm数量增加了一倍，并为我们提供了基于一个数据库制定计算衍生假设的优势，并且能够在完全不同的数据库中验证这些假设。例如，NOB生物信息学在数据库上花费了大量时间，试图了解老年GBMs患者更具侵袭性表型的生物学基础，从而与年轻GBMs患者相比，老年GBMs患者的生存期更短。到目前为止，我们已经发现了一组非常有趣的差异表达基因和mirna，以及特定的全基因组甲基化模式和特定的染色体数量变异，这些变异可以区分老年和年轻的GBMs。我们正在使用其中的一些发现进行湿实验室实验，以更好地说明这些发现的意义。此外，在与NCI ClinOmics（现为COMPASS）项目的合作中，我们分析并创建了来自罕见中枢神经系统肿瘤（如脊索瘤）的基因组图谱数据，并解剖了信号转导途径，并帮助设计了新的治疗方法。除了对入组患者的样本进行表征外，NOB生物信息学还对实验室产生的许多人类胶质瘤起始细胞/胶质瘤干细胞（GIC/GSC）系进行了基因组规模的分析。这种表征既在原代细胞水平上（包括通过传代的进化），也评估不同处理（分化、动物传代、药物处理等）对细胞生物学行为的影响。

As a hybrid science that links biological data with techniques for information storage, distribution, and analysis to support multiple areas of scientific research, including biomedicine, Bioinformatics has been driven by the great acceleration in data-generation processes in biology. The NOB for the past years has enrolled over 800 patients on the Natural History study which mandates the collection of tumor tissue as well as peripheral blood samples for germline DNA. This includes an unprecedented number of patients with rare CNS cancers. Moreover, the sample collection is accompanied by careful and prospective clinical data acquisition, allowing an unprecedented wealth of matched molecular and clinical data permitting a wide variety of analyses. Advances in the molecular analysis of genes, proteins and metabolites have greatly improved our understanding of biological processes and disease and have increased our ability to monitor treatment response and stratify patients to improve treatment efficacy. Precision medicine facilitated by companion diagnostics is one of the driving forces accelerating the drug development process and improving therapeutic management. Launched in 2016, the NCI Genomic Data Commons (GDC) provides a single source for data from NCI initiatives and cancer research projects, including TCGA and TARGET, and the analytical tools needed to mine them. The new initiated NOB Bioinformatics has extended our bioinformatics and computational analyses efforts to utilize the GDC databases. For example, using the TCGA data effectively doubles the number of GBMs we have to work with and affords us the advantage of formulating computationally derived hypothesis based on one database with the ability to validate those hypotheses on a totally different database. For example, a significant amount of time has been spent by NOB Bioinformatics on the databases to try and understand the biologically basis for the more aggressive phenotype and thus shorter survival of GBMs from older patients compared to those of younger GBMs. To date we have found a very interesting set of differentially expressed genes and miRNAs as well as specific genome wide methylation patterns and specific chromosomal number variants that differentiate older versus younger GBMs. We are in the process of using some of these findings to perform wet lab experiments to better annotate the significance of these findings. Furthermore, in collaborated with NCI ClinOmics (now COMPASS) program we analyzed and created genomic profiling data from the rare CNS tumors, such as chordomas, and dissected signal transduction pathways, and aided in the design of novel therapeutics. In addition to characterizing the samples from patients enrolled, the NOB Bioinformatics has generated genomic-scale analyses of the many human glioma initiating cells/glioma stem cells (GIC/GSC) lines produced in laboratory. This characterization is both at the primary cell level (including evolution through passages) as well as evaluation of the impact of different treatments (differentiation, animal passages, drug treatment, etc) on the biological behavior of the cells.