Bioinformatics: Characterizing Brain Tumor Date

生物信息学：表征脑肿瘤日期

• 批准号: 9556747
• 负责人: Mark Gilbert
• 金额: $ 10.81万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9556747
• 关键词: Acceleration; Amino Acid Sequence; Animals; Area; Belief; Big Data; Biochemical Pathway; Bioinformatics; Biological; Biological Markers; Biological Process; Biology; Biomedical Research; Brain Neoplasms; Cancer Genome Anatomy Project; Cancer Research Project; Cell Line; Cells; Central Nervous System Neoplasms; Chordoma; Classification; Clinical Data; Clinical Trials; Collaborations; Complex; Computer Analysis; DNA; DNA Sequence; DNA biosynthesis; Data; Data Analyses; Data Sources; Databases; Decision Making; Development; Disease; Enrollment; Ependymoma; Evolution; Formalin; Freezing; Functional disorder; Funding; Gene Expression Profile; Gene Expression Profiling; Gene Proteins; Generations; Genes; Genetic Translation; Genome; Genomic Data Commons; Genomics; Glioma; Human; Human Genome Project; Hybrids; Impact evaluation; In Vitro; Information Storage; Intramural Research Program; Laboratories; Link; Mainstreaming; Malignant Neoplasms; Maps; Methodology; MicroRNAs; Microarray Analysis; Mining; Modality; Molecular; Molecular Analysis; Molecular Genetics; Molecular Medicine; Monitor; Morphology; Nature; Nucleic Acids; Oligodendroglioma-Astrocytoma; Paraffin Embedding; Patients; Pattern; Pharmacotherapy; Phenotype; Process; Proteins; Proteomics; RNA; RNA chemical synthesis; Research; Research Personnel; Resolution; Resources; Sampling; Science; Signal Transduction Pathway; Standardization; Stem cells; Stream; System; Techniques; Technology; The Cancer Genome Atlas; Therapeutic; Time; Tissue Sample; Tissues; Translating; Treatment Efficacy; Tumor Biology; Variant; Work; analytical tool; anticancer research; base; cDNA Arrays; cell behavior; clinical care; clinical practice; companion diagnostics; data acquisition; design; differential expression; driving force; drug development; genome wide methylation; genome-wide; genomic profiles; histone methylation; improved; in vivo; laboratory experiment; metabolomics; methylation pattern; molecular diagnostics; molecular targeted therapies; neuro-oncology; novel; novel therapeutics; older patient; oncology; outcome forecast; patient stratification; precision medicine; programs; prospective; protein distribution; protein metabolite; protein structure; sample collection; small molecule; therapeutic target; three dimensional structure; tool; transcriptomics; treatment response; tumor; user-friendly

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 been collecting and characterizing nearly one thousand gliomas of every grade and histopathological class (oligodendrogliomas, astrocytomas, ependymomas) under a multi-institutional study conceived of. This study is broadly termed the Glioma Molecular Diagnostic Initiative (GMDI), and was designed to obtain a large amount of molecular data on DNA and RNA of freshly collected tumor samples that were collected, processed and analyzed in a standardized fashion to allow for large-scale cross sample analysis. 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. The resultant public database (REMBRANDT) is nearly unprecedented, not only in oncology, but in all of molecular medicine and is a testament to what is possible within the intramural program and how it can impact on the overall national cancer research enterprise. On the other hand, realizing that a majority of patients who come to us do not have obtainable fresh frozen tissue we spent over a year developing and optimizing a methodology that allows us to extract high quality DNA from FFPE enabling genomic analyses of these patients. 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 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.

作为一门将生物数据与信息存储、分发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，生物信息学一直受到生物学数据生成过程的极大加速的推动。在过去的几年里，NOB 在一项多机构研究的框架下，收集并描述了近千个各个等级和组织病理学类别的神经胶质瘤（少突胶质细胞瘤、星形细胞瘤、室管膜瘤）。这项研究被广泛地称为神经胶质瘤分子诊断计划 (GMDI)，旨在获得大量新鲜采集的肿瘤样本 DNA 和 RNA 的分子数据，这些样本以标准化方式收集、处理和分析，以便进行大量的研究。规模交叉样本分析。此外，样本收集还伴随着仔细和前瞻性的临床数据采集，从而获得了前所未有的丰富的匹配分子和临床数据，从而可以进行广泛的分析。由此产生的公共数据库（REMBRANDT）几乎是前所未有的，不仅在肿瘤学领域，而且在所有分子医学领域，都证明了校内项目的可能性以及它如何影响整个国家癌症研究事业。另一方面，意识到大多数来找我们的患者没有可获得的新鲜冷冻组织，我们花了一年多的时间开发和优化一种方法，使我们能够从 FFPE 中提取高质量的 DNA，从而对这些患者进行基因组分析。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，并提高了我们监测治疗反应和对患者进行分层以提高治疗效果的能力。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 年推出，为 NCI 倡议和癌症研究项目（包括 TCGA 和 TARGET）的数据提供单一来源，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 数据有效地使我们必须使用的 GBM 数量增加一倍，并为我们提供了基于一个数据库制定计算得出的假设的优势，并且能够在完全不同的数据库上验证这些假设。例如，NOB 生物信息学在数据库上花费了大量时间，试图了解更具侵袭性的表型的生物学基础，从而与年轻患者相比，老年患者的 GBM 的生存期更短。迄今为止，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区分老年和年轻 GBM 的特定染色体数量变异。我们正在利用其中一些发现进行湿实验室实验，以更好地诠释这些发现的重要性。此外，我们与 NCI ClinOmics 项目合作，分析并创建了脊索瘤等罕见中枢神经系统肿瘤的基因组分析数据，并剖析了信号转导途径，并协助设计新型疗法。除了对入组患者的样本进行表征之外，NOB 生物信息学还对实验室产生的许多人类神经胶质瘤起始细胞/神经胶质瘤干细胞 (GIC/GSC) 系进行了基因组规模分析。这种表征既在原代细胞水平（包括传代进化），又评估不同处理（分化、动物传代、药物处理等）对细胞生物学行为的影响。